Merge pull request #186 from jakobrunge/developer

removed ValueError for constants in arrays

setup.py

@@ -59,7 +59,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.0.0.5",
+    version="5.0.0.6",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",

tigramite/independence_tests/cmiknn.py

@@ -162,12 +162,16 @@ def _get_nearest_neighbors(self, array, xyz, knn):
             # Standardize
             array = array.astype(np.float64)
             array -= array.mean(axis=1).reshape(dim, 1)
-            array /= array.std(axis=1).reshape(dim, 1)
+            std = array.std(axis=1)
+            for i in range(dim):
+                if std[i] != 0.:
+                    array[i] /= std[i]
+            # array /= array.std(axis=1).reshape(dim, 1)
             # FIXME: If the time series is constant, return nan rather than
             # raising Exception
-            if np.isnan(array).sum() != 0:
-                raise ValueError("nans after standardizing, "
-                                 "possibly constant array!")
+            # if np.isnan(array).sum() != 0:
+            #     raise ValueError("nans after standardizing, "
+            #                      "possibly constant array!")
         elif self.transform == 'uniform':
             array = self._trafo2uniform(array)
         elif self.transform == 'ranks':
@@ -376,12 +380,16 @@ def get_conditional_entropy(self, array, xyz):
             # Standardize
             array = array.astype(np.float64)
             array -= array.mean(axis=1).reshape(dim, 1)
-            array /= array.std(axis=1).reshape(dim, 1)
+            std = array.std(axis=1)
+            for i in range(dim):
+                if std[i] != 0.:
+                    array[i] /= std[i]
+            # array /= array.std(axis=1).reshape(dim, 1)
             # FIXME: If the time series is constant, return nan rather than
             # raising Exception
-            if np.isnan(array).sum() != 0:
-                raise ValueError("nans after standardizing, "
-                                 "possibly constant array!")
+            # if np.isnan(array).sum() != 0:
+            #     raise ValueError("nans after standardizing, "
+            #                      "possibly constant array!")
         elif self.transform == 'uniform':
             array = self._trafo2uniform(array)
         elif self.transform == 'ranks':

tigramite/independence_tests/gpdc.py

@@ -212,10 +212,14 @@ def _get_single_residuals(self, array, target_var,
         # Standardize
         if standardize:
             array -= array.mean(axis=1).reshape(dim, 1)
-            array /= array.std(axis=1).reshape(dim, 1)
-            if np.isnan(array).sum() != 0:
-                raise ValueError("nans after standardizing, "
-                                 "possibly constant array!")
+            std = array.std(axis=1)
+            for i in range(dim):
+                if std[i] != 0.:
+                    array[i] /= std[i]
+            # array /= array.std(axis=1).reshape(dim, 1)
+            # if np.isnan(array).sum() != 0:
+            #     raise ValueError("nans after standardizing, "
+            #                      "possibly constant array!")
 
         target_series = array[target_var, :]
         z = np.fastCopyAndTranspose(array[2:])

tigramite/independence_tests/gpdc_torch.py

@@ -217,10 +217,14 @@ def _get_single_residuals(self, array, target_var,
         # Standardize
         if standardize:
             array -= array.mean(axis=1).reshape(dim, 1)
-            array /= array.std(axis=1).reshape(dim, 1)
-            if np.isnan(array).any():
-                raise ValueError("Nans after standardizing, "
-                                 "possibly constant array!")
+            std = array.std(axis=1)
+            for i in range(dim):
+                if std[i] != 0.:
+                    array[i] /= std[i]
+            # array /= array.std(axis=1).reshape(dim, 1)
+            # if np.isnan(array).any():
+            #     raise ValueError("Nans after standardizing, "
+            #                      "possibly constant array!")
 
         target_series = array[target_var, :]
         z = np.fastCopyAndTranspose(array[2:])

tigramite/independence_tests/parcorr.py

@@ -91,10 +91,14 @@ def _get_single_residuals(self, array, target_var,
         # Standardize
         if standardize:
             array -= array.mean(axis=1).reshape(dim, 1)
-            array /= array.std(axis=1).reshape(dim, 1)
-            if np.isnan(array).sum() != 0:
-                raise ValueError("nans after standardizing, "
-                                 "possibly constant array!")
+            std = array.std(axis=1)
+            for i in range(dim):
+                if std[i] != 0.:
+                    array[i] /= std[i]
+            # array /= array.std(axis=1).reshape(dim, 1)
+            # if np.isnan(array).sum() != 0:
+            #     raise ValueError("nans after standardizing, "
+            #                      "possibly constant array!")
 
         y = array[target_var, :]