diff --git a/abses/_bases/datacollector.py b/abses/_bases/datacollector.py
index ea820f5..0fca577 100644
--- a/abses/_bases/datacollector.py
+++ b/abses/_bases/datacollector.py
@@ -23,8 +23,7 @@
 
 import numpy as np
 import pandas as pd
-
-from abses._bases.errors import ABSESpyError
+from loguru import logger
 
 if TYPE_CHECKING:
     from abses.actor import Actor
@@ -88,7 +87,6 @@ def __init__(self, reports: Dict[ReportType, Dict[str, Reporter]]):
 
         self._agent_records: Dict[str, List[pd.DataFrame]] = {}
         self.model_vars: Dict[str, List[Any]] = {}
-        self.final_vars: Dict[str, List[Any]] = {}
 
         self.add_reporters("model", reports.get("model", {}))
         self.add_reporters("agents", reports.get("agents", {}))
@@ -109,7 +107,7 @@ def add_reporters(
             return
         if item == "final":
             for name, reporter in reporters.items():
-                self._new_final_reporter(name, reporter)
+                self.final_reporters[name] = clean_to_reporter(reporter)
             return
         if item == "agents":
             for breed, tmp_reporters in reporters.items():
@@ -133,10 +131,6 @@ def _new_model_reporter(self, name: str, reporter: Reporter) -> None:
         self.model_reporters[name] = clean_to_reporter(reporter)
         self.model_vars[name] = []
 
-    def _new_final_reporter(self, name: str, reporter: Reporter) -> None:
-        self.final_reporters[name] = clean_to_reporter(reporter)
-        self.final_vars[name] = []
-
     def _record_a_breed_of_agents(
         self, time: TimeDriver, breed: str, agents: ActorsList[Actor]
     ) -> None:
@@ -178,8 +172,9 @@ def get_model_vars_dataframe(self):
         """
         # Check if self.model_reporters dictionary is empty, if so raise warning
         if not self.model_reporters:
-            raise UserWarning(
-                "No model reporters have been defined in the DataCollector, returning empty DataFrame."
+            logger.warning(
+                "No model reporters have been defined"
+                "returning empty DataFrame."
             )
 
         return pd.DataFrame(self.model_vars)
@@ -194,13 +189,22 @@ def get_agent_vars_dataframe(
                 for breed in self.agent_reporters
             }
         if not self.agent_reporters:
-            raise ABSESpyError(
+            logger.warning(
                 "No agent reporters have been defined in the DataCollector."
             )
         if results := self._agent_records.get(breed):
             return pd.concat([pd.DataFrame(res) for res in results])
         return pd.DataFrame()
 
+    def get_final_vars_report(self, model: MainModel) -> pd.DataFrame:
+        """Report at the end of this model."""
+        if not self.final_reporters:
+            logger.warning(
+                "No final reporters have been defined"
+                "returning empty DataFrame."
+            )
+        return {var: func(model) for var, func in self.final_reporters.items()}
+
     def collect(self, model: MainModel):
         """Collect all the data for the given model object."""
 
@@ -208,9 +212,5 @@ def collect(self, model: MainModel):
             for var, func in self.model_reporters.items():
                 self.model_vars[var].append(func(model))
 
-        if self.final_reporters:
-            for var, func in self.model_reporters.items():
-                self.final_vars[var].append(func(model))
-
         if self.agent_reporters:
             self._record_agents(model)
diff --git a/abses/experiment.py b/abses/experiment.py
index 8ca3430..a12e992 100644
--- a/abses/experiment.py
+++ b/abses/experiment.py
@@ -241,15 +241,22 @@ def _get_logging_mode(self, repeat_id: Optional[int] = None) -> str | bool:
             logging = False
         return logging
 
+    def _update_log_config(self, config, repeat_id: Optional[int] = None):
+        """Update the log configuration."""
+        if isinstance(config, dict):
+            config = DictConfig(config)
+        log_name = self._get_logging_mode(repeat_id=repeat_id)
+        OmegaConf.set_struct(config, False)
+        logging_cfg = OmegaConf.create({"log": {"name": log_name}})
+        config = OmegaConf.merge(config, logging_cfg)
+        return config
+
     def run(
         self, cfg: DictConfig, repeat_id: int, outpath: Optional[Path] = None
     ) -> Tuple[Dict[str, Any], pd.DataFrame]:
         """运行模型一次"""
+        self._update_log_config(cfg, repeat_id)
         # 获取日志
-        log_name = self._get_logging_mode(repeat_id=repeat_id)
-        OmegaConf.set_struct(cfg, False)
-        logging_cfg = OmegaConf.create({"log": {"name": log_name}})
-        cfg = OmegaConf.merge(cfg, logging_cfg)
         model = self.model(
             parameters=cfg,
             run_id=repeat_id,
@@ -263,7 +270,8 @@ def run(
             df = model.datacollector.get_model_vars_dataframe()
         else:
             df = pd.DataFrame()
-        return model.final_report(), df
+        final_report = model.datacollector.get_final_vars_report(model)
+        return final_report, df
 
     def _update_result(
         self,
diff --git a/abses/main.py b/abses/main.py
index eaff43e..d11b76a 100644
--- a/abses/main.py
+++ b/abses/main.py
@@ -14,7 +14,6 @@
 import functools
 import json
 import os
-import types
 from datetime import datetime
 from pathlib import Path
 from typing import (
@@ -41,7 +40,7 @@
 
 from mesa import Model
 from mesa.time import BaseScheduler
-from omegaconf import DictConfig, OmegaConf
+from omegaconf import DictConfig
 
 from abses import __version__
 from abses._bases.logging import (
@@ -378,29 +377,16 @@ def _step(self) -> None:
     def _end(self) -> None:
         self._do_each("end", order=("nature", "human", "model"))
         self._do_each("set_state", code=3)
-        # result = self.final_report()
-        # msg = (
-        #     "The model is ended.\n"
-        #     f"Total ticks: {self.time.tick}\n"
-        #     f"Final result: {json.dumps(result, indent=4)}\n"
-        # )
-        # log_session(title="Ending Run", msg=msg)
+        result = self.datacollector.get_final_vars_report(self)
+        msg = (
+            "The model is ended.\n"
+            f"Total ticks: {self.time.tick}\n"
+            f"Final result: {json.dumps(result, indent=4)}\n"
+        )
+        log_session(title="Ending Run", msg=msg)
         logger.bind(no_format=True).info(f"{datetime.now()}\n\n\n")
         logger.remove()
 
-    # def final_report(self) -> Dict[str, Any]:
-    #     """Report at the end of this model."""
-    #     result = {}
-    #     for k, reporter in self._reports["final"].items():
-    #         if isinstance(reporter, str):
-    #             value = getattr(self, reporter)
-    #         elif isinstance(reporter, types.FunctionType):
-    #             value = reporter(self)
-    #         else:
-    #             raise TypeError(f"Invalid final reporter {type(reporter)}.")
-    #         result[k] = value
-    #     return result
-
     def summary(self, verbose: bool = False) -> pd.DataFrame:
         """Report the state of the model."""
         print(f"Using ABSESpy version: {self.version}")
diff --git a/docs/tutorial/beginner/manage_parameters.ipynb b/docs/tutorial/beginner/manage_parameters.ipynb
index 8dc69b8..a599ff1 100644
--- a/docs/tutorial/beginner/manage_parameters.ipynb
+++ b/docs/tutorial/beginner/manage_parameters.ipynb
@@ -38,6 +38,8 @@
     "\n",
     "        Farmer:\n",
     "            var2: 'test'\n",
+    "    final:\n",
+    "        var1: 'test'\n",
     "```"
    ]
   },
@@ -113,7 +115,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'time': {'end': 3}, 'reports': {'model': {'var1': 'test', 'var2': 'var2'}, 'agents': {'Actor': {'var1': 'test'}, 'Farmer': {'var2': 'test'}}}}\n"
+      "{'time': {'end': 3}, 'reports': {'model': {'var1': 'test', 'var2': 'var2'}, 'agents': {'Actor': {'var1': 'test'}, 'Farmer': {'var2': 'test'}}, 'final': {'var1': 'test'}}}\n"
      ]
     },
     {
@@ -149,7 +151,7 @@
     {
      "data": {
       "text/plain": [
-       "{'time': {'end': 3}, 'reports': {'model': {'var1': 'test', 'var2': 'var2'}, 'agents': {'Actor': {'var1': 'test'}, 'Farmer': {'var2': 'test'}}}}"
+       "{'time': {'end': 3}, 'reports': {'model': {'var1': 'test', 'var2': 'var2'}, 'agents': {'Actor': {'var1': 'test'}, 'Farmer': {'var2': 'test'}}, 'final': {'var1': 'test'}}}"
       ]
      },
      "execution_count": 3,
@@ -318,6 +320,7 @@
     "from abses import Actor\n",
     "\n",
     "model = MainModel(parameters=cfg)\n",
+    "model.test = \"Final result\"\n",
     "\n",
     "actors = model.agents.new(Actor, num=2)\n",
     "actors[0].test = \"testing\"\n",
@@ -368,58 +371,58 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Actor[1]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>Hello World</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
        "      <td>Actor[0]</td>\n",
        "      <td>1</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
        "      <td>testing</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>1</th>\n",
        "      <td>Actor[1]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
        "      <td>Hello World</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>0</th>\n",
        "      <td>Actor[0]</td>\n",
        "      <td>2</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
        "      <td>testing</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>1</th>\n",
        "      <td>Actor[1]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
        "      <td>Hello World</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
+       "      <th>0</th>\n",
        "      <td>Actor[0]</td>\n",
        "      <td>3</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
        "      <td>testing</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Actor[1]</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2024-06-04 13:29:43.786840</td>\n",
+       "      <td>Hello World</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    AgentID  Step                       Time         var1\n",
-       "0  Actor[1]     1 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     1 2024-06-04 12:14:36.153999      testing\n",
-       "0  Actor[1]     2 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     2 2024-06-04 12:14:36.153999      testing\n",
-       "0  Actor[1]     3 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     3 2024-06-04 12:14:36.153999      testing"
+       "0  Actor[0]     1 2024-06-04 13:29:43.786840      testing\n",
+       "1  Actor[1]     1 2024-06-04 13:29:43.786840  Hello World\n",
+       "0  Actor[0]     2 2024-06-04 13:29:43.786840      testing\n",
+       "1  Actor[1]     2 2024-06-04 13:29:43.786840  Hello World\n",
+       "0  Actor[0]     3 2024-06-04 13:29:43.786840      testing\n",
+       "1  Actor[1]     3 2024-06-04 13:29:43.786840  Hello World"
       ]
      },
      "execution_count": 8,
@@ -432,122 +435,30 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['testing', 'Hello World'], dtype='<U11')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "actors.array(\"test\")"
+    "Final result."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AgentID</th>\n",
-       "      <th>Step</th>\n",
-       "      <th>Time</th>\n",
-       "      <th>var1</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Actor[1]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>Hello World</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Actor[0]</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>testing</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Actor[1]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>Hello World</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Actor[0]</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>testing</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Actor[1]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>Hello World</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Actor[0]</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2024-06-04 12:14:36.153999</td>\n",
-       "      <td>testing</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "    AgentID  Step                       Time         var1\n",
-       "0  Actor[1]     1 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     1 2024-06-04 12:14:36.153999      testing\n",
-       "0  Actor[1]     2 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     2 2024-06-04 12:14:36.153999      testing\n",
-       "0  Actor[1]     3 2024-06-04 12:14:36.153999  Hello World\n",
-       "1  Actor[0]     3 2024-06-04 12:14:36.153999      testing"
+       "{'var1': 'Final result'}"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result = model.datacollector.get_agent_vars_dataframe(\"Actor\")\n",
-    "result"
+    "model.datacollector.get_final_vars_report(model)"
    ]
   },
   {
diff --git a/docs/tutorial/completing/fire_tutorial.ipynb b/docs/tutorial/completing/fire_tutorial.ipynb
index 979e149..f2aed4b 100644
--- a/docs/tutorial/completing/fire_tutorial.ipynb
+++ b/docs/tutorial/completing/fire_tutorial.ipynb
@@ -217,9 +217,16 @@
    "execution_count": 4,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[13:39:04][WARNING][datacollector] No final reporters have been definedreturning empty DataFrame.\n"
+     ]
+    },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy80BEi2AAAACXBIWXMAAA9hAAAPYQGoP6dpAABVWklEQVR4nO3deXQUVfo38G9YsrCkESUJgQSiILusAgFHcIhGFscI48JBQVTcgkPAF0dmBFoUAjIoOiqIC+DCoDCCyjgixgF+SthFYVRAZYmYBB1NQlgCJvX+gbTpSrpvnr5V3Z3y+zmnz/Tt+9znqXTicE/VrboRhmEYICIiInKIOqE+ACIiIiIrcXJDREREjsLJDRERETkKJzdERETkKJzcEBERkaNwckNERESOwskNEREROQonN0REROQonNwQERGRo3ByQ1SLud1uREREBDR24MCBGDhwoLUHVAOlpaW44447kJCQgIiICGRlZQX9GIjI2Ti5IQpzJ06cgNvtxvr160N9KJaYNWsWlixZgnvuuQevvPIKbrnllqDV/uKLL3D11VejUaNGaNq0KW655RZ8//33QatPRMERwb2liMLbDz/8gGbNmmH69Olwu91efT///DN+/vlnREdHi/OeO2sT7ElT3759Ua9ePXz00UdBrfvtt9+ie/fucLlc+NOf/oTS0lL87W9/Q3JyMrZu3YrIyMigHg8R2adeqA+AiAJXr1491KsXuv+MDcPAqVOnEBMTU+MxR48eRceOHW08qurNmjULx48fx44dO5CcnAwA6N27N6688kosWbIEd955Z9CPiYjswctSRALHjh1DVlYWWrdujaioKMTFxeHKK6/Ezp07PTEDBw5E586dsWPHDvTr1w8xMTFISUnBwoULvXKdPn0a06ZNQ8+ePeFyudCwYUP87ne/w3/+8x9PzMGDB9GsWTMAwMMPP4yIiAhERER4zuBUt+Zm8eLF+P3vf4+4uDhERUWhY8eOWLBggSU/f+vWrTFs2DCsXbsWvXr1QkxMDJ577jkAQFFREbKyspCUlISoqCi0adMGc+bMQUVFBYCzZ4giIiJw4MAB/Otf//L8LAcPHrTk2FT++c9/YtiwYZ6JDQCkpaXh4osvxhtvvBGUYyCi4OCZGyKBu+++GytXrsT48ePRsWNH/O9//8NHH32EL774Aj169PDE/fTTTxgyZAhuuOEGjBw5Em+88QbuueceREZG4rbbbgMAlJSU4IUXXsDIkSMxbtw4HDt2DC+++CLS09OxdetWdOvWDc2aNcOCBQtwzz334LrrrsPw4cMBAJdcconPY1ywYAE6deqEP/zhD6hXrx7eeecd3HvvvaioqEBmZqb2d7B3716MHDkSd911F8aNG4d27drhxIkTGDBgAI4cOYK77roLycnJ2LRpE6ZMmYL8/HzMnz8fHTp0wCuvvIKJEyeiZcuWuP/++wHAM3mrTnFxMc6cOaM8pujoaDRq1Mhn/5EjR3D06FH06tWrSl/v3r3x7rvv1uAnJ6JawyCiGnO5XEZmZqbfmAEDBhgAjHnz5nk+KysrM7p162bExcUZp0+fNgzDMH7++WejrKzMa+xPP/1kxMfHG7fddpvns++//94AYEyfPr1KrenTpxvm/4xPnDhRJS49Pd248MILqxzngAED/P4sZq1atTIAGO+9957X54888ojRsGFDY9++fV6fP/jgg0bdunWNw4cPe+UYOnRojeqd+y5VrzFjxvjNs23bNgOA8fLLL1fpmzx5sgHAOHXqVI2OiYjCH8/cEAk0adIEW7ZswXfffYfExESfcfXq1cNdd93laUdGRuKuu+7CPffcgx07dqBv376oW7cu6tatCwCoqKhAUVERKioq0KtXL6/LXFKV17+cO/MxYMAArF27FsXFxXC5XAHnBoCUlBSkp6d7fbZixQr87ne/w3nnnYcffvjB83laWhpmz56NjRs3YtSoUeJa8+bNw08//aSM8/e7AICTJ08CAKKioqr0nVuMffLkyWr7iaj24eSGSOCxxx7DmDFjkJSUhJ49e2LIkCEYPXo0LrzwQq+4xMRENGzY0Ouziy++GMDZdTR9+/YFACxduhTz5s3Dl19+6XX5JSUlJeBj/PjjjzF9+nTk5ubixIkTXn1WTW7M9u/fj88++8znJaajR48GVKtnz54BjTM7N+ErKyur0nfq1CmvGCKq/Ti5IRK44YYb8Lvf/Q6rVq3C+++/j7lz52LOnDl48803MXjwYFGuV199FbfeeisyMjIwefJkxMXFoW7dusjOzsbXX38d0PF9/fXXGDRoENq3b4/HH38cSUlJiIyMxLvvvosnnnjCs7hXR3WTgIqKClx55ZV44IEHqh1zbmIn9eOPP+L06dM1OiZ/k7bmzZsDAPLz86v05efno2nTpjxrQ+QgnNwQCTVv3hz33nsv7r33Xhw9ehQ9evTAzJkzvSY33333HY4fP+519mbfvn0Azt5xBAArV67EhRdeiDfffNPrjqfp06d71ZM8gfidd95BWVkZ3n77ba+7girfgWWHiy66CKWlpUhLS7M07/Dhw7FhwwZl3JgxY7BkyRKf/S1atECzZs2wffv2Kn3nFm8TkXNwckNUQ+Xl5SgtLfU6QxAXF4fExMQqlzt+/vlnPPfcc5g0aRKAs7d9P/fcc2jWrJnnUsu59TaGYXgmMFu2bEFubq7XxKRBgwYAzt5qrVI55znFxcVYvHix9McVueGGG+B2u7F27doq63GKiorQqFGjgJ7HY9WaGwAYMWIEli5diry8PCQlJQEAcnJysG/fPkycOFF8bEQUvji5IaqhY8eOoWXLlvjjH/+Irl27olGjRvjggw+wbds2zJs3zys2MTERc+bMwcGDB3HxxRfj9ddfx65du7Bo0SLUr18fADBs2DC8+eabuO666zB06FAcOHAACxcuRMeOHVFaWurJFRMTg44dO+L111/HxRdfjKZNm6Jz587o3LlzlWO86qqrEBkZiWuuuQZ33XUXSktL8fzzzyMuLq7aSzJWmTx5Mt5++20MGzYMt956K3r27Injx49j9+7dWLlyJQ4ePIgLLrhAnNeqNTcA8Je//AUrVqzAFVdcgQkTJqC0tBRz585Fly5dMHbsWMvqEFEYCPXtWkS1RVlZmTF58mSja9euRuPGjY2GDRsaXbt2NZ599lmvuAEDBhidOnUytm/fbqSmphrR0dFGq1atjKefftorrqKiwpg1a5bRqlUrIyoqyujevbuxZs0aY8yYMUarVq28Yjdt2mT07NnTiIyM9LotvLpbwd9++23jkksuMaKjo43WrVsbc+bMMV566SUDgHHgwAGv4wzkVnBft3EfO3bMmDJlitGmTRsjMjLSuOCCC4x+/foZf/vb3zy3v6ty2G3Pnj3GVVddZTRo0MBo0qSJMWrUKKOgoCAkx0JE9uHeUkQWGzhwIH744Qfs2bMn1IdCRPSbxO0XiIiIyFG45oaI8P3336O8vNxnf2RkJJo2bRrEIyIiChwnN0SESy+9FIcOHfLZP2DAAKxfvz54B0REpIFrbogIH3/8sWeLguqcd955lt65REThacGCBViwYAEOHjwIAOjUqROmTZvm9yGlK1aswNSpU3Hw4EG0bdsWc+bMwZAhQ4J0xNXj5IaIiIgAnH0QaN26ddG2bVsYhoGlS5di7ty5+OSTT9CpU6cq8Zs2bcLll1+O7OxsDBs2DMuWLcOcOXOwc+fOah9XESwhXVC8ceNGXHPNNUhMTERERARWr17t1W8YBqZNm4bmzZsjJiYGaWlp2L9/v1fMjz/+iFGjRiE2NhZNmjTB7bff7vWMECIiIqqZa665BkOGDEHbtm1x8cUXY+bMmWjUqBE2b95cbfyTTz6Jq6++GpMnT0aHDh3wyCOPoEePHnj66aeDfOTeQrrm5vjx4+jatStuu+02DB8+vEr/Y489hqeeegpLly5FSkoKpk6divT0dHz++eeenXxHjRqF/Px8rFu3DmfOnMHYsWNx5513YtmyZTU+joqKCnz33Xdo3Lix6FH3RET022IYBo4dO4bExETUqWPf+YFTp07VaF+1mjAqPQX9nKioKOV+auXl5VixYgWOHz+O1NTUamNyc3M9T2I/Jz09vcrJiqAL1QN2zAAYq1at8rQrKiqMhIQEY+7cuZ7PioqKjKioKOMf//iHYRiG8fnnnxsAjG3btnli/v3vfxsRERHGkSNHalw7Ly/PAMAXX3zxxRdfNXrl5eXp/8Pnw8mTJ40EC4+1UaNGVT479yDQ6nz22WdGw4YNjbp16xoul8v417/+5TO2fv36xrJly7w+e+aZZ4y4uDirvo6AhO3dUgcOHEBBQYHXRnwulwt9+vRBbm4ubrrpJuTm5qJJkybo1auXJyYtLQ116tTBli1bcN1111Wbu6yszGsvIOOXZUd5AGLP1TIPetDUnu2n319fdczxqtoqqvqqeirS4zELt/qS360qn9W/Kyndvz3p30qwfxfS+k7+XVud3+rxZtLv3u7vK1BlAJ4AGjdubFuJ06dPowDe/yYFqgRAUmkp8vLyEBv7azZ/Z23atWuHXbt2obi4GCtXrsSYMWOwYcMGdOzYUfNogidsJzcFBQUAgPj4eK/P4+PjPX0FBQWIi4vz6q9Xrx6aNm3qialOdnY2Hn744Sqfx8LPH1K04oD99avGqoR6vN35Ql1f53erE2vHeN18dseHur6Tf9dW5w+3/85C/f87JsFYwuD33yRprthYr8mNP5GRkWjTpg2As/u7bdu2DU8++SSee+65KrEJCQkoLCz0+qywsBAJCQn6B63hN/mE4ilTpqC4uNjzysvLC/UhERERhaWKigqvqx2VpaamIicnx+uzdevW+VyjEyxhe+bm3KyvsLAQzZs393xeWFiIbt26eWKOHj3qNe7nn3/Gjz/+6HfWWJOFVEruAPvCIV7alpLmU8VbPV6aT4c0lzled7yUarw0vzTe6vFWc/t4X5O2Ti0r+nXj7a4nJa1HNTJlyhQMHjwYycnJOHbsGJYtW4b169dj7dq1AIDRo0ejRYsWyM7OBgBMmDABAwYMwLx58zB06FAsX74c27dvx6JFi0L5Y4TvmZuUlBQkJCR4zQhLSkqwZcsWz4wwNTUVRUVF2LFjhyfmww8/REVFBfr06RP0YyYiIqrNjh49itGjR6Ndu3YYNGgQtm3bhrVr1+LKK68EABw+fBj5+fme+H79+mHZsmVYtGgRunbtipUrV2L16tUhfcYNEOIzN6Wlpfjqq6887QMHDmDXrl1o2rQpkpOTkZWVhUcffRRt27b13AqemJiIjIwMAECHDh1w9dVXY9y4cVi4cCHOnDmD8ePH46abbkJiYmKIfioiIqLa6cUXX/TbX902LNdffz2uv/56m44oMCGd3Gzfvh1XXHGFp33uXvkxY8ZgyZIleOCBB3D8+HHceeedKCoqwmWXXYb33nvP84wbAHjttdcwfvx4DBo0CHXq1MGIESPw1FNPBf1nISIiovDA7Rdw9nKXy+VCMXyvTBeti3cL+1XxUqr8qnq68VYz57e7La1vJ93a0nhpPlV+3frS/Lr1dPKraqtyqcZLSfNL69udT9XWrSepX/n9KQCzgeLi4hrffSRVk3+TapwLZx9tYufxhqOwXXNDREREFAhOboiIiMhReFkKv54CrMz8pVS5LOX2k9Dc5y82EKp8qvqqtjSfLmk+c7y0bTW3j/e6uWqSTxovHS9tS+tJ+6X5dds6VLmktVX9Kqp60vzSeKvz6cZL2tXU4mWp8MYzN0REROQonNwQERGRo3ByQ0RERI4SttsvhJsqa3Dcpg/cPt5X19alm888XtWW1g92fmm/Lkl+c6xqrLTf6ny6pPnM8brjg9lv7pPm0h2vYnV+3fqqft380rYkX+X3v9wKTuGNZ26IiIjIUTi5ISIiIkfh5IaIiIgchWtufDA/10brYUBuncHVjFe1g01V39yvipeOl+azmr/65j5/sTWJV41XkY5X1Ze2Vfml9aX5pPn9tXXGVtdWsTu/tL7d8dLxuvn8xavGUtjhmRsiIiJyFE5uiIiIyFG4/QKsedS1313D3QEm9TVe2tbNL6VbX7dft75OPWntUHNrtnXzhxu3oF8Vq8otbUvzW11f2lbVt5q0vrRtwu0XwhvP3BAREZGjcHJDREREjsLJDRERETkKbwX3we8amuq4fbyvri3JFQhVfVV+3XhpPmlbRRWvm8/feEmsFeN166mo8uvWk+azu54kvyQ2kFq6+XXp5lONN/fr1tPN76+/ch+3X6gVeOaGiIiIHIWTGyIiInIUTm6IiIjIUbjmphKXv063IJEkNpB4KXN+3Xqq8br1VOOlbVV+3fpW0s2tGm/uV7V1qfJJ60nz6eaXjrcyl3S8br+UOZ9ufVU+aT1pfd14Cis8c0NERESOwskNEREROQq3X8Cvj7r24g7JoVRfW9VWjdetJ6XKJ62nG6+iyi/JJx2rU8uKeqHuV8VL81s93hzvb7zVua1uq+qr+nXrSetbPV4Vb+73Ff/LreDcfiG88cwNEREROQonN0REROQonNwQERGRo/BW8EC5Fe2a9llRS5pfOt7qeKvr2U1V3+3jfU1yqdq6VPmk/ap4q8fr5pfWU8VL89mZ2xwvbevWV7G6njne6vzBykVBwTM3RERE5Cic3BAREZGjcHJDREREjsLn3KD6ZwpE2FnQLWxLx0vrS/t1mfPrtlX5g8lc2+62ijQ+1NyKtt31VP1uP33SWtK2NJ9uvNX1pPlU46X5rMLn3NQKPHNDREREjsLJDRERETkKbwWvIdW1O6/LWG7NYqrxuvmlzPV066vyqfLr1lfRyW8eq5OrJvmsrqcirSeNV40Pp3jVWGm/Kj7Y46X5pfWtHm91freP91Qr8MwNEREROQonN0REROQonNwQERGRo/BWcNTsVnDzlyS6Vdwd5H7deKvH251PlV+XKr/bx/tAclkdr2pLxwc7PtzG69TSHa9qB7u+1flU+XXjpfl8jeet4LUCz9wQERGRo3ByQ0RERI7CyQ0RERE5Cp9z44NqIZLfNThuYTFzvG5blV9aX5rP6njVeGl+6Xid/NKx5njdtiq/1cz57a4nrS9tq/JLxqridcfXtvxSuvlV43XzU1jhmRsiIiJyFE5uiIiIyFE4uSEiIiJH4ZobH8TPuXH7eF9dWyXY43X7VazOL42XjtfNr8NcW9WW5pPWszpelzSfbn3zeEk+VaxO7kDq6+bXHS/NZ+6X1leNl+ST1qaQ45kbIiIiQnZ2Ni699FI0btwYcXFxyMjIwN69e/2OWbJkCSIiIrxe0dHRQTpi3zi5ISIiImzYsAGZmZnYvHkz1q1bhzNnzuCqq67C8ePH/Y6LjY1Ffn6+53Xo0KEgHbFvvCxViavSe/GeFG6NWFXbbtJ60njVeGlbt540XtXWIa2lqi3ttzq/tF6o80vHS1hdSxWvmz/Y9aXxUtJ8/uIr9/2y/YITvffee17tJUuWIC4uDjt27MDll1/uc1xERAQSEhLsPjwRnrkhIiKiKoqLiwEATZs29RtXWlqKVq1aISkpCddeey3++9//BuPw/OLkhoiIyOFKSkq8XmVlZX7jKyoqkJWVhf79+6Nz584+49q1a4eXXnoJb731Fl599VVUVFSgX79++Pbbb63+EUQ4uSEiInK4pKQkuFwuzys7O9tvfGZmJvbs2YPly5f7jUtNTcXo0aPRrVs3DBgwAG+++SaaNWuG5557zsrDF+Oam0ok28v7W5NT5TZxKbdmvype2pbW182nW183vw5VbnO/Kt7q/NJ6uvWtzq+KV423st/uWirm8aq21fVVzPl123YLdr0gy8vLQ2zsr//CRUVF+YwdP3481qxZg40bN6Jly5aiOvXr10f37t3x1VdfBXysVuCZGyIiIoeLjY31elU3uTEMA+PHj8eqVavw4YcfIiUlRVynvLwcu3fvRvPmza047IDxzA0REREhMzMTy5Ytw1tvvYXGjRujoKAAAOByuRATEwMAGD16NFq0aOG5rDVjxgz07dsXbdq0QVFREebOnYtDhw7hjjvuCNnPAXByQ0RERAAWLFgAABg4cKDX54sXL8att94KADh8+DDq1Pn1os9PP/2EcePGoaCgAOeddx569uyJTZs2oWPHjsE67GpFGIYhfqSL05SUlMDlcnl9pvpS/K6rcSvaKqrx0nzSelaPl+Y3x6va0nrSfmlbJ7fV8Sqq/NJ8VlPVN/er2tLxEtJaVjPnV9WTxuvWV+WX1pfm18lXTe7i4mKvNSxWOvdvkmQdqM9cOPsMNzuPNxxxzQ0RERE5Cic3RERE5Chcc+OD+HZud4B9VrA6vzSfOV7V1qVbT3e8Dmltc1s3n4o03mrm+qq21fkkbelYM2m808cHu55qvNvHewdvv+AkPHNDREREjhLWk5vy8nJMnToVKSkpiImJwUUXXYRHHnkElddAG4aBadOmoXnz5oiJiUFaWhr2798fwqMmIiKiUArryc2cOXOwYMECPP300/jiiy8wZ84cPPbYY/j73//uiXnsscfw1FNPYeHChdiyZQsaNmyI9PR0nDp1KoRHTkRERKES1mtuNm3ahGuvvRZDhw4FALRu3Rr/+Mc/sHXrVgBnz9rMnz8fDz30EK699loAwMsvv4z4+HisXr0aN910k6iev9vuqqzBcYtSy8bq5A4kf6jjVYJdT5rf6noS5trmtipemk81XpduftV4c7+V8apc0tyqfLr1rM4nza9bTxovre/28Z5qhbA+c9OvXz/k5ORg3759AIBPP/0UH330EQYPHgwAOHDgAAoKCpCWluYZ43K50KdPH+Tm5vrMW1ZWVmWHVCIiInKGsD5z8+CDD6KkpATt27dH3bp1UV5ejpkzZ2LUqFEA4Hk0dHx8vNe4+Ph4T191srOz8fDDD9t34ERERBQyYX3m5o033sBrr72GZcuWYefOnVi6dCn+9re/YenSpVp5p0yZguLiYs8rLy/PoiMmIiKiUAvrMzeTJ0/Ggw8+6Fk706VLFxw6dAjZ2dkYM2YMEhISAACFhYVeO5AWFhaiW7duPvNGRUVVuyNq5Q0YzNsvmNsRbj8Hbu6zui0V7PxW11f1281cX9WuaZ8dzPWkbVU+Kd3x0vzStk5+u2vZHa8ab3W8tK0iHa9Tr3Isn3NTK4T1mZsTJ054bdAFAHXr1kVFRQUAICUlBQkJCcjJyfH0l5SUYMuWLUhNTQ3qsRIREVF4COszN9dccw1mzpyJ5ORkdOrUCZ988gkef/xx3HbbbQCAiIgIZGVl4dFHH0Xbtm2RkpKCqVOnIjExERkZGaE9eCIiIgqJsN4V/NixY5g6dSpWrVqFo0ePIjExESNHjsS0adMQGRkJ4Ozt4NOnT8eiRYtQVFSEyy67DM8++ywuvvjiGtexfFdwM7eiLR1vdbzd9VVt3XyhjpfQGRtIPmk9c7xuW5Vft18Vrztekl8SG0g+VX5VvDSfitX5pfG6+cz9wvrcFTy8hfWZm8aNG2P+/PmYP3++z5iIiAjMmDEDM2bMCN6BERERUdgK6zU3RERERFKc3BAREZGjhPVlqWCTXN+scmu4v2C3Ipm5X9VWkcZbTVpfFW/ul+aX5rO6nj/S3Kp4c7+0rZtPRbe+bj4pnXzmsdJcqnhVfqvbqvoqwY63qx5vBa8VeOaGiIiIHIWTGyIiInIUTm6IiIjIUbjmxiKV1+BUWX/j1myrBDu/tF8VLx2vm19aX9VvZS1pfmm/bj1pWze/3aT1Vf2BxtZkvKod7PrS8dL8VtfXySetTSHHMzdERETkKJzcEBERkaNwckNERESOwjU3FvFaZ+M2daraKrr5pOOl+aX1pOODzVzf3PYXr4rVpcpvdb+0raKbTzpeml9VTxJrd22780up8pn7ddu6+VXjw8HzABpo5jgBYJwFx1LL8MwNEREROQonN0REROQovCxViavSe/P2Cmai7RbMbRVpvK5g17O6vnm8qi3NJ60XTOba5rYqXtqvYh6vauvmU8VLSeqpaqn6pcz5VG3VeF3SenbXV+VXxftrq3JT2OGZGyIiInIUTm6IiIjIUTi5ISIiIkfhmptKigHEWpDHvF4nwi1MYI6Xjreaub65bXe8dLw0vzReNV5CN7fV43WZ80nzh9t4ab+/WFVbd7wqnzS/dLzV8VYz1ze3VfFUq/DMDRERETkKJzdERETkKJzcEBERkaNwzU0lkufcqPr9xVZ5Ro5bkCwQ5vzSeqp4ab9ufavbuvXsPBYV3XzBjtdt6+bXZWU+cy4rc9tRXxUvbUtJ86niJW1VLQo7PHNDREREjsLJDRERETkKL0v5YL50JLkMJc0lvlXczDxelU+3npS5ntVtaX3dfn/xqly6pPnN8dJ2sOtLx0vrS/kbr8otrW2OV7Wl+aT1wo3b4rYkf+X3pwDMVuSikOOZGyIiInIUTm6IiIjIUTi5ISIiIkfhmptKdLZfqHJ7dyW6t5X7y10td5D7rW7r0s2nM948VieXHfmk9ayOV41XtaX1Vf06pLnN8dJ2sOtbTZVfWt8cr2pL67l9vKdagWduiIiIyFE4uSEiIiJH4eSGiIiIHIVrbipxqUN8qrxuxrxGxspn5gCocv1Xe82OW9i2mm49Vbxuv068OVY1VpI7kHq69VX9Klbnl8arxuvkU+WS5tbNJ60nZc6vatud3876urko6HjmhoiIiByFkxsiIiJylAjDMLSvktR2JSUlcLm8L0rZ+aWoLhNJLzNZflnKbuZ60vqqeN1+K9ldy5w/2PVU/brxqvEq0vGqeLeP97q5gkFVz9wvjdfNF+zxqnxuH+9/UVxcjNjYQB8e4t+5f5OKnwdiG2jmOgG4xtl7vOGIZ26IiIgI2dnZuPTSS9G4cWPExcUhIyMDe/fuVY5bsWIF2rdvj+joaHTp0gXvvvtuEI7WP05uiIiICBs2bEBmZiY2b96MdevW4cyZM7jqqqtw/Phxn2M2bdqEkSNH4vbbb8cnn3yCjIwMZGRkYM+ePUE88qp4txQRERHhvffe82ovWbIEcXFx2LFjBy6//PJqxzz55JO4+uqrMXnyZADAI488gnXr1uHpp5/GwoULbT9mXzi5qaTy9gviLQ8qsXq7BXO87Yuk3MK27nhpPmm/inS8Od7tp08nlxVt3Xqq8Sp2x6vGq/JJ+/3FS2urSPPp1pfG280tbKvG66ic6xSA2RbmDpKSkhKvdlRUFKKiovyOKS4uBgA0bdrUZ0xubi4mTZrk9Vl6ejpWr14d2IFahJeliIiIHC4pKQkul8vzys7O9htfUVGBrKws9O/fH507d/YZV1BQgPj4eK/P4uPjUVBQYMlxB4pnboiIiBwuLy/P624p1VmbzMxM7NmzBx999JHdh2YLTm6IiIgcLjY2tsa3go8fPx5r1qzBxo0b0bJlS7+xCQkJKCws9PqssLAQCQkJAR+rFTi58UHn2TG62y1Y/Zwbc794PZFb0daNV42X9ludX9qW1NJlzq9qhzqfbj3d+nbW080V6njVeFU+ab80v6otrS/Jr8rlEIZh4L777sOqVauwfv16pKSkKMekpqYiJycHWVlZns/WrVuH1NRUG49UjZMbIiIiQmZmJpYtW4a33noLjRs39qybcblciImJAQCMHj0aLVq08KzZmTBhAgYMGIB58+Zh6NChWL58ObZv345FixaF7OcAuKCYiIiIACxYsADFxcUYOHAgmjdv7nm9/vrrnpjDhw8jPz/f0+7Xrx+WLVuGRYsWoWvXrli5ciVWr17tdxFyMHD7BVS//YKKv0s9qstA0n4p2y87KSgvg6nym/ulbRU76+uMrcmx6PZL29J8KlaPl+aX1vfXrxqritcdr1tP2pbSHW91PvN4afucX24F5/YL4Y1nboiIiMhROLkhIiIiR+HkhoiIiByFa25Q6fomft1+wUrSNTDB/oVYveZHlV/JrWjrjrc6v85YVVuX1fWk+ezOL81nZbw5VlpLWtvu/MGuJ+1Xte2sX/k919zUCjxzQ0RERI7CyQ0RERE5Cic3RERE5Ch8QnENSZ5VY/V2CKp+6RoZ6RoY8ZoZKfdveLx5rE6umuSTtqX5pVT57K4n7bdyrE6t6sbrtu2up8onra/qt7Ktqk1hh2duiIiIyFE4uSEiIiJH4eSGiIiIHIXPuUH1z7mx+9kvEtJj0V0jU+uec6OKV41XkdRX1Va1pcciHS9ldT3VeGk9abwut4/3VrT91QqEtF64cQe5X9U+h8+5qRV45oaIiIgchZMbIiIichTeCu6D9NKPv/jafllJxfZLeG5hvyo+2PkluVS5dWpbwVxft61bP9jx/vqltaRU+c39qrbV9VXxqvHSeNV4K9mZm2zBMzdERETkKGE/uTly5AhuvvlmnH/++YiJiUGXLl2wfft2T79hGJg2bRqaN2+OmJgYpKWlYf/+/SE8YiIiIgqlsJ7c/PTTT+jfvz/q16+Pf//73/j8888xb948nHfeeZ6Yxx57DE899RQWLlyILVu2oGHDhkhPT8epU6dCeOREREQUKmG95mbOnDlISkrC4sWLPZ+lpKR43huGgfnz5+Ohhx7CtddeCwB4+eWXER8fj9WrV+Omm24S1XNVeq+zhYK5T7oGJeS3YptYvZ1EFW6L29J60n4d5ty6tVT5pPXs7rebqr65X9rWyW33eGl+q+Ol+VRt1XhdknyVY3+5FZzCW1ifuXn77bfRq1cvXH/99YiLi0P37t3x/PPPe/oPHDiAgoICpKWleT5zuVzo06cPcnNzfeYtKytDSUmJ14uIiIicIawnN9988w0WLFiAtm3bYu3atbjnnnvwpz/9CUuXLgUAFBQUAADi4+O9xsXHx3v6qpOdnQ2Xy+V5JSUl2fdDEBERUVCF9eSmoqICPXr0wKxZs9C9e3fceeedGDduHBYuXKiVd8qUKSguLva88vLyLDpiIiIiCrWwXnPTvHlzdOzY0euzDh064J///CcAICEhAQBQWFiI5s2be2IKCwvRrVs3n3mjoqIQFRVV5fPK2y/YSfVcGOmaFdV46RoeVX1VfuXP41a0deODPd5dw76a1NYdr5tPlV8ar6LKZ3d+6Xg7c+u2VfmldMer8qnaqvF213f76aOwF9Znbvr374+9e/d6fbZv3z60atUKwNnFxQkJCcjJyfH0l5SUYMuWLUhNTQ3qsRIREVF4COszNxMnTkS/fv0wa9Ys3HDDDdi6dSsWLVqERYsWAQAiIiKQlZWFRx99FG3btkVKSgqmTp2KxMREZGRkhPbgiYiIKCTCflfwNWvWYMqUKdi/fz9SUlIwadIkjBs3ztNvGAamT5+ORYsWoaioCJdddhmeffZZXHzxxTWucW4H1sp0LuVILzNJLytZvd2B7mUw3fxW56tVp5DdFver2tL84c4tbKvGS/JLa6tqSenmV8XbnV+XOb+0nmq828d77gpeK4T1mRsAGDZsGIYNG+azPyIiAjNmzMCMGTOCeFREREQUrsJ6zQ0RERGRFCc3RERE5Chhf1kqmCS3gvtb9xHs7Q5UpGt0rF6EFex8EW6LC5qZ87v99EnG1iTe6n5VvNVtaT1VvC5VfX/1JLE1GR/s/NJ60ny6pPXM8dLxuvUprPDMDRERETkKJzdERETkKJzcEBERkaNwzY1FKq8Dka650X0OjN1rfFT17H5Qkni7CrewgCpep181VhUvbYc6ny5VflU9q+P9jdetJRXs8brxVo/Xza+qR47CMzdERETkKJzcEBERkaPwspQNwm07BN3LVlJWXyaz/DKUdLy5X1JPNVYndzDqS9sqqvhg15Ny+3hfk7YktxXxuvl0x0upxuvm18lndW2yHc/cEBERkaNwckNERESOwskNEREROQrX3FTi8tOnWgcSSqpbpVVrVoK+PYL5A7ep39RWHp9b2NZlZT5VLnO/qi3Nr0u3vm6/1fGq8ZL8qtzStpTV+eyuL40Pt/GkpaioCCtXrsTXX3+NyZMno2nTpti5cyfi4+PRokULcT5OboiIiChkPvvsM6SlpcHlcuHgwYMYN24cmjZtijfffBOHDx/Gyy+/LM7Jy1JEREQUMpMmTcKtt96K/fv3Izo62vP5kCFDsHHjxoBycnJDREREIbNt2zbcddddVT5v0aIFCgoKAsrJy1KVFAOI9dEn2QIg3LYzsHo7COkaGaufS6Nc06PIL10TpPx5KlPUVjKPl+azOt7cr2rr1pMy51PlV8Wrxuvk0qkVyHjd/NJ4q9u69aTj/cWrYklLVFQUSkpKqny+b98+NGvWLKCcPHNDREREIfOHP/wBM2bMwJkzZwAAEREROHz4MP785z9jxIgRAeXk5IaIiIhCZt68eSgtLUVcXBxOnjyJAQMGoE2bNmjcuDFmzpwZUE5eliIiIqKQcblcWLduHT7++GN8+umnKC0tRY8ePZCWlhZwTk5ufAjmfkx2P4dGe+8pt7X1q6yJ0cwvrW91vF9ui8er2lbnV8VbXU83v931Vf12jbWinqq+ql+aP9T5VPkl8ZXfnwIwW/NYyMvLL7+MG2+8Ef3790f//v09n58+fRrLly/H6NGjxTl5WYqIiIhCZuzYsSguLq7y+bFjxzB27NiAcnJyQ0RERCFjGAYiIqpeQ/j222/hcvnbO8A3XpaqpPJXqHu7sCRWehlKdRlJ+7KWW9HWpLpMFezLcNqXBd2qgABjq4uXtqXszqfLnE/VluazMl6aWzeftN/qeNV4ab/d+ahaGzduxNy5c7Fjxw7k5+dj1apVyMjI8Bm/fv16XHHFFVU+z8/PR0JCgt9a3bt3R0REBCIiIjBo0CDUq/frlKS8vBwHDhzA1VdfHdDPIZ7cjBkzBrfffjsuv/zygAoSERFReDp+/Di6du2K2267DcOHD6/xuL179yI29tcnxcXFxSnHnJs07dq1C+np6WjUqJGnLzIyEq1btw74VnDx5Ka4uBhpaWlo1aoVxo4dizFjxgS0qRURERGFl8GDB2Pw4MHicXFxcWjSpIlozPTp0wEArVu3xo033ui19YIu8Zqb1atX48iRI7jnnnvw+uuvo3Xr1hg8eDBWrlzpeQAPERERhY+SkhKvV1lZmaX5u3XrhubNm+PKK6/Exx9/LBo7ZswYSyc2QIBrbpo1a4ZJkyZh0qRJ2LlzJxYvXoxbbrkFjRo1ws0334x7770Xbdu2tfRAg8Hf9gtmknUhqlirb1U2rxnRXkNibqvqC+OrjNcbLt7+Qfd4beVWtKXx0nzS/FK6+VTxuvmtrK2bX1pPWj/Y9ezOZ2V+SayFXF8D0P03/9TZ/0lKSvL6ePr06XC73ZrJgebNm2PhwoXo1asXysrK8MILL2DgwIHYsmULevToUaMc5eXleOKJJ/DGG2/g8OHDOH36tFf/jz/+KD4urQXF+fn5WLduHdatW4e6detiyJAh2L17Nzp27IjHHnsMEydO1ElPREREFsjLy/NaExMVFWVJ3nbt2qFdu3aedr9+/fD111/jiSeewCuvvFKjHA8//DBeeOEF3H///XjooYfw17/+FQcPHsTq1asxbdq0gI5LfFnqzJkz+Oc//4lhw4ahVatWWLFiBbKysvDdd99h6dKl+OCDD/DGG29gxowZAR0QERERWSs2NtbrZdXkpjq9e/fGV199VeP41157Dc8//zzuv/9+1KtXDyNHjsQLL7yAadOmYfPmzQEdg/jMTfPmzVFRUYGRI0di69at6NatW5WYK664QrywiIiIiGq/Xbt2oXnz5jWOLygoQJcuXQAAjRo18jzQb9iwYZg6dWpAxyCe3DzxxBO4/vrr/S7+adKkCQ4cOBDQAYUr1ToQyboZK3MFMl57uwVzv6od5PziNTTSfP7iLa4d8ufi6Oazu740XjVelc/KWtJ4Fd36dver4qWk+XXjz3H49gulpaVeZ10OHDiAXbt2oWnTpkhOTsaUKVNw5MgRvPzyywCA+fPnIyUlBZ06dcKpU6fwwgsv4MMPP8T7779f45otW7ZEfn4+kpOTcdFFF+H9999Hjx49sG3btoDPMIknN7fccktAhYiIiCi8bd++3euhfJMmTQJw9o6mJUuWID8/H4cPH/b0nz59Gvfffz+OHDmCBg0a4JJLLsEHH3xQ7YP9fLnuuuuQk5ODPn364L777sPNN9+MF198EYcPHw547S6fUExEREQAgIEDB8IwfJ//X7JkiVf7gQcewAMPPKBVc/bsX0+F3XjjjWjVqhU2bdqEtm3b4pprrgkoZ4Th76f4jSgpKQl4/4pASB/3r7udgvb2Dm7FAN1+q5nrSduqfBK6tYPNrdmvmy/c80vGh7K2Ffml9aTjdetLx0vzKRQXF3vdfWQlz79JD8KaW8Fn23u8ujZu3Ih+/fp5bb8AAD///DM2bdoU0I4I4ruliIiIiKxyxRVXVPssm+LiYtHlrco4uSEiIqKQ8bUr+P/+9z80bNgwoJxcc0NERERBd25jzoiICNx6661ed0aVl5fjs88+Q79+/QLKzclNJZW3X7D7dm1/dLcfsHyNjbmtooqX5tfNF8z8qly6taWk43XrqfKp8lvdb3c9ndpSqvzS+uHWr4pXkY5Xxbt9vCfLnFvrahgGGjdujJiYGE9fZGQk+vbti3HjxgWUm5MbIiIiCrrFixcDOLtfpdvtRoMGDQDAs/VChw4dcMEFFwSUm2tuiIiIKGQ++eQTz0MBi4qK0LdvX8ybNw8ZGRlYsGBBQDk5uSEiIqKQ+eSTT/C73/0OALBy5UrEx8fj0KFDePnll/HUU08FlJOXpSqx6kk30jUzuut3dNfoVHmujlszoe54aT7dfilzPncN+2rQVv4uzG1d0nzmeNV4abxufVV+3fGS3Lq1Vfmk9VXxdrdVVPHSfKrxuvnIMidOnEDjxo0BAO+//z6GDx+OOnXqoG/fvjh06FBAOXnmhoiIiEKmTZs2WL16NfLy8rB27VpcddVVAICjR48G/OBBTm6IiIgoZKZNm4b/9//+H1q3bo0+ffogNTUVwNmzON27dw8oJy9LVeLvVnArt0Sw+jKU3dsxqH72oD82XZVfxep6lfqNtt5dqu9K/N0LjqVGbZVQx5v7rc4vrecvn7SWdLw0vzle1ZbWszq/Kl41Xpc5n9vHe4fvCh4Kf/zjH3HZZZchPz8fXbt29Xw+aNAgXHfddQHl5OSGiIiIQiohIQEJCQlen/Xu3TvgfLwsRURERI7CyQ0RERE5Ci9L+RDM7RVCvQbHzPZ1ICrmeN38qny6+f2w/DZ/t2KAtN/UNl5TjDfRfmxAuHFbOFbaluazm7SeOV53vG5+K+NVYyns8MwNEREROQonN0REROQonNwQERGRo3DNTSWVt1/QWSth9Xodq59ro1tPmc+taEvH2xyv+j51t7fwl0v8t+LWq696Dk/EKFO8tIBbM146Xpo/mPWlua2sHUh+c79uWze/aryKbj2q1XjmhoiIiByFkxsiIiJyFF6WqqTy9gtmOpcmrL5VW5XfTFpPfBlKxS1sC8dXuX3ZdGnFzOpd1P3ltvp3rf27UVx20t3RPui3hqvqmftVbd16VtaS5rO6LWV3fru5FW2qVXjmhoiIiByFkxsiIiJyFE5uiIiIyFEiDMOwc6eBWqGkpAQul8tvjM6XJF2HYfUaGlV+y9fYmLmF/cJ46fdpdbxVY4Mx3u6tP6pwCxOa46Xj7c6nU0tVW3e8Kp/d8arxqnhpfVV+u5wCMBsoLi5GbKyvVZp6PP8mPQggWjNZEI43HNWqMzezZ89GREQEsrKyPJ+dOnUKmZmZOP/889GoUSOMGDEChYWFoTtIIiIiCqlaM7nZtm0bnnvuOVxyySVen0+cOBHvvPMOVqxYgQ0bNuC7777D8OHDQ3SUREREFGq1YnJTWlqKUaNG4fnnn8d5553n+by4uBgvvvgiHn/8cfz+979Hz549sXjxYmzatAmbN28O4RETERFRqNSK59xkZmZi6NChSEtLw6OPPur5fMeOHThz5gzS0tI8n7Vv3x7JycnIzc1F3759RXX8PedGpfLaA+3tEdyKtjCf5c+5cQsTqmjmU/280nUnOmtygr3GRpft9d2Ktio+2PUk9aW5pflUbd180vG6VPnM/dJ4XdL6FNbCfnKzfPly7Ny5E9u2bavSV1BQgMjISDRp0sTr8/j4eBQUFPjMWVZWhrKyMk+7pKTEsuMlIiKi0Arry1J5eXmYMGECXnvtNURH6y4Z/1V2djZcLpfnlZSUZFluIiIiCq2wntzs2LEDR48eRY8ePVCvXj3Uq1cPGzZswFNPPYV69eohPj4ep0+fRlFRkde4wsJCJCQk+Mw7ZcoUFBcXe155eXk2/yREREQULGF9WWrQoEHYvXu312djx45F+/bt8ec//xlJSUmoX78+cnJyMGLECADA3r17cfjwYaSmpvrMGxUVhaioqCqfV37STTDXPqj259F9Lo6qnjKfWzFA1W8x3d+Nnc9+sfoZRVY/tybke125FW0pq/NJ6+nESvulbWk+3XgpaT7deOl4qtXCenLTuHFjdO7c2euzhg0b4vzzz/d8fvvtt2PSpElo2rQpYmNjcd999yE1NVW8mJiIiIicIawnNzXxxBNPoE6dOhgxYgTKysqQnp6OZ599NtSHRURERCHC7Rdg//YLZsHcDqAmQv4IfWF+3e0i7Lw0FOrLTmG3/YKZW7Og1fkU44223u2IUUE8Fqvb0vrSfl1215eO9xXP7RdqhbBeUExEREQkxckNEREROQonN0REROQoXHODX69vSrZfsPP2Yek6Cd1jkeavwi0sKGXKr729hYmd/wEE+3cb9PVZbmFbNV5KOt4cb2qb19jAtMbG6+dX1ba533hNMV7B7/qhQLgtzietJ62vGq/IxzU34Y1nboiIiMhROLkhIiIiR+HkhoiIiByFa25gzZqbyqRrVux+donV/cF+nob2GiLT2gTVWoNgbs8Q6ufQaP8tSLl1EwjzS+uZ4v19/9pbTZjbJqr1P7qCvs2KNL9uW0WSr5rcXHMT3njmhoiIiByFkxsiIiJylFq/t1RtFOzbfS3fKdot7FfFm1S5xdXf7biowfFrns7X+X3ZvTWE1Syv71a07aaqb27bWUsRr/q7t1qVv2u36QNz20wVL82nIh1vZXzlvl8u81B445kbIiIichROboiIiAgAsHHjRlxzzTVITExEREQEVq9erRyzfv169OjRA1FRUWjTpg2WLFli+3GqcHJDREREAIDjx4+ja9eueOaZZ2oUf+DAAQwdOhRXXHEFdu3ahaysLNxxxx1Yu3atzUfqH9fcWMX961vzteugr4kxsX37AbewX9VWsPr7kq5xEvW7FW2hYN9Kbia+Ld/tf7zuYwZ0f3eW5nMr2qrcwkcUWE25BkeXKp9uPTvH6+auRQYPHozBgwfXOH7hwoVISUnBvHnzAAAdOnTARx99hCeeeALp6el2HaYSz9wQERFRQHJzc5GWlub1WXp6OnJzc0N0RGfxzA0REZHDlZSUeLWjoqIQFRWlnbegoADx8fFen8XHx6OkpAQnT55ETEyMdo1A8MwNERGRwyUlJcHlcnle2dnZoT4kW/HMjQ/aj5m3ULg9sl+b239bt772dg2a+bxyu63LVRNW/+6D/twdt7X1VetIpP8tiZ5p5K4myA+r/251f5eWb71h5la07c5ndf0wl5eX57X9ghVnbQAgISEBhYWFXp8VFhYiNjY2ZGdtAE5uiIiIHC82NtaWvaVSU1Px7rvven22bt06pKamWl5LgpeliIiICABQWlqKXbt2YdeuXQDO3uq9a9cuHD58GAAwZcoUjB492hN/991345tvvsEDDzyAL7/8Es8++yzeeOMNTJw4MRSH78EzNz7o3LJq9fYJVrP9Efu641X5FP2qSw+6p9slO8Lb/bdg9W3+Vu9Qr1tPt7709yH5+a28fGkHyy9RmraHUN26XoVb0ZbGq8ZL+ctvda0wtn37dlxxxRWe9qRJkwAAY8aMwZIlS5Cfn++Z6ABASkoK/vWvf2HixIl48skn0bJlS7zwwgshvQ0c4OSGiIiIfjFw4EAYhu+pcXVPHx44cCA++eQTG49KjpeliIiIyFE4uSEiIiJH4WWpGpLeUuoVK6xl9bqFYG9HYPktpG69eKsfI2/n2grld+X237Z7DYtUsNfw6OaX1A/22jmtbUAg/27Na2xgWmOj/btya7Z186m4fbw/BWC2MBcFHc/cEBERkaNwckNERESOwskNEREROQrX3NSQ5Pq69DHs0kfCB3udhO2PcXcr2qp4Yb+d6zp01ytV4fbfDvYzlYK9Jsdqqr89O7fLsPwZQG7/be1n+kjX2JiPR9qvoptf2q+Kp7DGMzdERETkKJzcEBERkaNwckNERESOwjU3NWTpWgO3vbV1133Y/fwO7Wv3qnhT28r9hGoSbym3Xm27937SXR9mZvv6LjO3/7a/ZyRpr69S1RbGq+pbvQ+XklszXtWWjteJV+WisMMzN0REROQonNwQERGRo0QY/rb//I0oKSmBy+VCMYDYGo7xe8rZrWibhPoXoHv6O9iPnde9VV33MfX++nW3P7D61m+VUP/upOPN7L5kK3nkg+2XW1X9ivigb4ui2291fWk9X/G/bL9QXFyM2Nia/oshc+7fJDwIIFozWRCONxzxzA0RERE5Cic3RERE5Cic3BAREZGj8FZwH8RrBdw+3tdkrGZty2+fdSv6hfV114EE+1ZuK38e7dvI3aZ8prbud6NL93ZiK7e+sGK85Puy/VZpc7/V8VKq/Kp6Vo+3ul83nsIKz9wQERGRo3ByQ0RERI7CyQ0RERE5Ctfc+KC7NqIy3WdvSPOp4nXXaeiu6THTfWS/bn1pfG3afsEs1N+tNJ/2s1g06wX7ePxy68VX2UpClS/Y8bWl/5fnxlB445kbIiIichROboiIiMhReFmqElel91qPUjf3+YuF/52Hq61tortzszRed/sDVT0zqy+lWL1Fgc72C1W4A69dE8HersDqnaftviwnyu8W9qva0vFSuvXsHq8izWdlvGqsXXj5K2A8c0NERESOwskNEREROQonN0REROQoEYZhBPXO1nDk2V6+Eq21Dm7FYFO/3dsHqNi9jiHU60KkdL5v3fVIdq+BUQn2YwuCvd5Kyl997dvC3Yq2Kl6aTze/tJ40Xjo+VH65Fby4uBixsbG2lKju3yRddh5vOOKZGyIiInIUTm6IiIjIUTi5ISIiIkfhc24qKQZw7oqkpY9Zd3s37X62h9XPqbH6OTsqVj/i3up1KP6ejaK7xibY64eC/Qyh2lbf1ufe6Par2tL8KnaPt7ufflN45oaIiIgchZMbIiIichRelqpE68Y7t4/3sP/2XxWrL8NI40O6kzKsvzXdzMrtF+zeykJaX1ov2LuE2/237Xe8WxGs6pcy55PWt7qtYne8arxuPqrVeOaGiIiIHIWTGyIiInIUTm6IiIjIUbjmppLKt4JLRbh/fW/1mhPd8bq3auveLmv37b9myu/LLWtHmNo660yCvWYk1Gt4rM4f9L8VM7eP99W17Rbq+maq+qp+q/NL4/31q2Ip7IT1mZvs7GxceumlaNy4MeLi4pCRkYG9e/d6xZw6dQqZmZk4//zz0ahRI4wYMQKFhYUhOmIiIiIKtbCe3GzYsAGZmZnYvHkz1q1bhzNnzuCqq67C8ePHPTETJ07EO++8gxUrVmDDhg347rvvMHz48BAeNREREYVSWF+Weu+997zaS5YsQVxcHHbs2IHLL78cxcXFePHFF7Fs2TL8/ve/BwAsXrwYHTp0wObNm9G3b99QHDYRERGFUFhPbsyKi4sBAE2bNgUA7NixA2fOnEFaWponpn379khOTkZubq7W5MbKdSHBXnNiZvWzS6xex2H1c2eqcOu1ddbUhJrud6Vab2Q36d+a8m/LrUig6tcZq5PbDm5F2+p4u0nrmeNVbapVas3kpqKiAllZWejfvz86d+4MACgoKEBkZCSaNGniFRsfH4+CggKfucrKylBWVuZpl5SU2HLMREREFHxhveamsszMTOzZswfLly/XzpWdnQ2Xy+V5JSUlWXCEREREFA5qxZmb8ePHY82aNdi4cSNatmzp+TwhIQGnT59GUVGR19mbwsJCJCQk+Mw3ZcoUTJo0ydMuKSmpMsGx8xZbux/Rr7sdgt2XVnRvZ1b2uxUDzP2mtu7x+GP1VhqWb93htrZesHd4V/4tmNrGa4rx/vKZcytqG21NtUb5jxdTjZf2q+J1x1ud3+76VKuE9ZkbwzAwfvx4rFq1Ch9++CFSUlK8+nv27In69esjJyfH89nevXtx+PBhpKam+swbFRWF2NhYrxcRERE5Q1ifucnMzMSyZcvw1ltvoXHjxp51NC6XCzExMXC5XLj99tsxadIkNG3aFLGxsbjvvvuQmprKO6WIiIh+o8J6crNgwQIAwMCBA70+X7x4MW699VYAwBNPPIE6depgxIgRKCsrQ3p6Op599tkgHykRERGFiwjDMIJ9d2fYKSkpgcvl0tp+oTK718RYvWZHl9W3dkvXTSjZHV+J3eupgh2vS7weysS8TgWmdSrSrTZU+SR0/zsVbxMijVe1g51Pml8Vr9svqV+57xSA2WcfTWLXkoZz/yZZyc7jDUdhveaGiIiIguuZZ55B69atER0djT59+mDr1q0+Y5csWYKIiAivV3R0dBCPtnqc3BAREREA4PXXX8ekSZMwffp07Ny5E127dkV6ejqOHj3qc0xsbCzy8/M9r0OHDgXxiKvHyQ0REREBAB5//HGMGzcOY8eORceOHbFw4UI0aNAAL730ks8xERERSEhI8Lzi4+ODeMTVC+sFxcFW+QqnzpYD0kfEq/qlz3mxe3sDu9cEVTl+t2ZCM3M+YdvOdSp2r9kJ9e+2Crcpv+m5M6o1MeL/Vkz5rNxWRUW8lkzVVpHG6+Yz90vbuvVUVPWl+WoZ85P4o6KiEBUV5fXZ6dOnsWPHDkyZMsXzWZ06dZCWlobc3FyfuUtLS9GqVStUVFSgR48emDVrFjp16mTtDyDEMzdEREQOl5SU5PVk/uzs7CoxP/zwA8rLy6ucefG3pVG7du3w0ksv4a233sKrr76KiooK9OvXD99++60tP0dN8cwNERGRw+Xl5XndLWU+axOo1NRUr4fm9uvXDx06dMBzzz2HRx55xJIageDkhoiIyOFq8jT+Cy64AHXr1kVhYaHX56otjSqrX78+unfvjq+++irgY7UCJzeV+HvOjWQNjt3PEtHNr/ssFKv3G9Jeg+RWJJD2m9qSdSBBf0aRW9HWpLuuRLl+ytSO2C87HnE9TZXr2b3+SHu8tK3KZ3e/bj4V3fG/AZGRkejZsydycnKQkZEBAKioqEBOTg7Gjx9foxzl5eXYvXs3hgwZYuORqnFyQ0RERACASZMmYcyYMejVqxd69+6N+fPn4/jx4xg7diwAYPTo0WjRooVnzc6MGTPQt29ftGnTBkVFRZg7dy4OHTqEO+64I5Q/Bic3REREdNaNN96I77//HtOmTUNBQQG6deuG9957z7PI+PDhw6hT59d7kX766SeMGzcOBQUFOO+889CzZ09s2rQJHTt2DNWPAIDbLwAI/fYLZuG2XYNZsB/hr6L7iP8qTOOltydLWH0bv4rVt4Lrbv8gvTRh9WUyVXwVbh/vq8lt+d+l1dzCflXb7vrSfNLxktzg9gvhjreCExERkaNwckNERESOwskNEREROQoXFNvA6jUowV7TovsIf2l+FdtvuVW1FSS3B1u9hkZ8rIr4YD9WwM5btaurp6L8edyVGm4/fTVhd7xuPnO/qi3NH+x60vxuH+9PAZitqEUhxzM3RERE5Cic3BAREZGjcHJDREREjsI1N5VUfqqAlc+aka5ZkT5rRHfdgyq/lO6aHcvXpWiSbgngNVYQWyNui9sm5nUjVv+tSvulrP7bEh2fW5jc7vGqtu54Vb5QcyvakvHSsRRyPHNDREREjsLJDRERETkKL0tZxN+u4CrSR9IHe/sD6SU5u7d70OYWti0k/lncFseb+4X5dX8Xwd41PdiPLfDi1uwPdT1zvLRtdT5Vft16qvFUq/DMDRERETkKJzdERETkKJzcEBERkaNwzU0lxQB8bQgvufVbm9t/7WBT/uxuU7/bHGAxaX5zvKqtW68S6Xdn+zoMzfzSv0Wr1+jo/rdg6fG7Az+OgJjrqerr9qvojpfmC3Y9qtV45oaIiIgchZMbIiIichROboiIiMhRuOamhiRrB6TX9e1+Lkyw10lU4bY6obCeqr6030+88rsT5AoJt3dTtX4q2NsrqNi9XYTf35e5T9qW1LIjXtXWzaeK182vW0/VT7UKz9wQERGRo3ByQ0RERI7Cy1IWkWy/EOxHwlv+yH9pv6qtIh0v7Re2/f3+LH9kgOJYLN9uQVhP+rdu9Y73UuLLVm4/weY+f7E16dclzW+Ol46X5pPml8bbOV43FwUdz9wQERGRo3ByQ0RERI7CyQ0RERE5CtfcVOKq9N7Oa/+Wr8OQ9tsdb+6v5fFGW1P/KN9DxbeCS/tV8aq2NJ9mv3S7iZCvV3ML21bm0q0t7VfFq+iOV+VTta0er8pHtQrP3BAREZGjcHJDREREjsLJDRERETlKhGEYwX5CetgpKSmBy+VCMYDYXz7T3cKgMum6A8uvxUtJ86nizf3StrSebn0Ff38LQV3TUZN4q+tJSfOr4hX9yufqmMer2laS5jbHq9pW15Myj1e1pfXtzieMLS4uRmxsbNUOC5z7N8lKdh5vOOKZGyIiInIUTm6IiIjIUTi5ISIiIkfhmhtUf31Td08cf0T719SEary53+p46Xhp/mDXV9SzdP1VqLnDLJ/V46VtST7VWFUuSa1A8od6vNXxqvG6+WrqFIDZXHMT7njmhoiIiByFkxsiIiJyFG6/UEnlW8HNrLx2V+WSl1uYQBUv7be7vjRet74qXjreRPKYAPFlKLfN/br1rR5vjleN142X9vuLN4+1upZqvLS+1fV0x+vm1xXq+mQrnrkhIiIiR+HkhoiIiByFkxsiIiJyFK65qSFb11mouC1uq/LrsjqfKr+qrRqv2S/6fVtc2/J+KWk9abzVpPnN8ZLx0lpS5vyqtmq8tF8Vr2pL66n6pST1rK5NtuOZGyIiInIUTm6IiIjIUTi5ISIiIkfhmptKJA+79rcGR7l1g1tQyAqqeuZ+VbyuYNdT1Zf268b7G6uTqyb5VPml/ap4aX5pPavz2ZnLytqB1NPNJ2XOJ21bTaeeJJbCAs/cEBERkaNwckNERESOwstSlVTefkF167e53+/twG5hW0UVr9uvG2/1eFU+VVuaT7e+DlVu3dq68dLx0nzSeqp+abykvpW5AqHKr6qn6pfSzWcer8qnW0/FV/5fdgWn8MYzN0REROQonNwQERGRo3ByQ0RERB7PPPMMWrdujejoaPTp0wdbt271G79ixQq0b98e0dHR6NKlC959990gHalvXHPjg7/tFWrSX1mE2/SBua2iipfml8arxlvdr0u3vrRfFR9KbmG/Kj7Y3Iq2dLy0X4c5t7QtzaeKV7E6vyqfblvK6nwO9frrr2PSpElYuHAh+vTpg/nz5yM9PR179+5FXFxclfhNmzZh5MiRyM7OxrBhw7Bs2TJkZGRg586d6Ny5cwh+grN45oaIiIgAAI8//jjGjRuHsWPHomPHjli4cCEaNGiAl156qdr4J598EldffTUmT56MDh064JFHHkGPHj3w9NNPB/nIvXFyQ0RERDh9+jR27NiBtLQ0z2d16tRBWloacnNzqx2Tm5vrFQ8A6enpPuODhZelABjG2YtMJcEqeCrE+UIdH+qfP9zGh1PtcP/d2R1v9Xgrc4f770Z3fLB/Pk3n/t2oLUpKvP+Fi4qKQlRUlNdnP/zwA8rLyxEfH+/1eXx8PL788stq8xYUFFQbX1BQYMFRazDIyMvLM3B2GQ1ffPHFF198KV95eXm2/Zt08uRJIyEhwbJjbdSoUZXPpk+fXqXukSNHDADGpk2bvD6fPHmy0bt372qPtX79+sayZcu8PnvmmWeMuLg4y76PQPDMDYDExETk5eXBMAwkJycjLy8PsbGx6oHkUVJSgqSkJH53AeB3p4ffX+D43ckZhoFjx44hMTHRthrR0dE4cOAATp8+bUk+wzAQEeH9qFnzWRsAuOCCC1C3bl0UFhZ6fV5YWIiEhIRqcyckJIjig4WTG5y9ptiyZUvPabvY2Fj+hx4gfneB43enh99f4PjdybhcLttrREdHIzo62vY6lUVGRqJnz57IyclBRkYGAKCiogI5OTkYP358tWNSU1ORk5ODrKwsz2fr1q1DampqEI7YN05uiIiICAAwadIkjBkzBr169ULv3r0xf/58HD9+HGPHjgUAjB49Gi1atEB2djYAYMKECRgwYADmzZuHoUOHYvny5di+fTsWLVoUyh+DkxsiIiI668Ybb8T333+PadOmoaCgAN26dcN7773nWTR8+PBh1Knz643W/fr1w7Jly/DQQw/hL3/5C9q2bYvVq1eH9Bk3ACc3XqKiojB9+vRqr0WSf/zuAsfvTg+/v8Dxu6PqjB8/3udlqPXr11f57Prrr8f1119v81HJRBhGLbufjYiIiMgPPsSPiIiIHIWTGyIiInIUTm6IiIjIUTi5ISIiIkfh5OYXzzzzDFq3bo3o6Gj06dMHW7duDfUhhZ3s7GxceumlaNy4MeLi4pCRkYG9e/d6xZw6dQqZmZk4//zz0ahRI4wYMaLK0ysJmD17NiIiIrwefMXvzr8jR47g5ptvxvnnn4+YmBh06dIF27dv9/QbhoFp06ahefPmiImJQVpaGvbv3x/CIw4P5eXlmDp1KlJSUhATE4OLLroIjzzyiNfeSPzuyHFCt/ND+Fi+fLkRGRlpvPTSS8Z///tfY9y4cUaTJk2MwsLCUB9aWElPTzcWL15s7Nmzx9i1a5cxZMgQIzk52SgtLfXE3H333UZSUpKRk5NjbN++3ejbt6/Rr1+/EB51+Nm6davRunVr45JLLjEmTJjg+ZzfnW8//vij0apVK+PWW281tmzZYnzzzTfG2rVrja+++soTM3v2bMPlchmrV682Pv30U+MPf/iDkZKSYpw8eTKERx56M2fONM4//3xjzZo1xoEDB4wVK1YYjRo1Mp588klPDL87chpObgzD6N27t5GZmelpl5eXG4mJiUZ2dnYIjyr8HT161ABgbNiwwTAMwygqKjLq169vrFixwhPzxRdfGACM3NzcUB1mWDl27JjRtm1bY926dcaAAQM8kxt+d/79+c9/Ni677DKf/RUVFUZCQoIxd+5cz2dFRUVGVFSU8Y9//CMYhxi2hg4datx2221enw0fPtwYNWqUYRj87siZfvOXpU6fPo0dO3YgLS3N81mdOnWQlpaG3NzcEB5Z+CsuLgYANG3aFACwY8cOnDlzxuu7bN++PZKTk/ld/iIzMxNDhw71+o4Afncqb7/9Nnr16oXrr78ecXFx6N69O55//nlP/4EDB1BQUOD1/blcLvTp0+c3//3169cPOTk52LdvHwDg008/xUcffYTBgwcD4HdHzvSbf0LxDz/8gPLycs+jpc+Jj4/Hl19+GaKjCn8VFRXIyspC//79PY/ZLigoQGRkJJo0aeIVGx8fj4KCghAcZXhZvnw5du7ciW3btlXp43fn3zfffIMFCxZg0qRJ+Mtf/oJt27bhT3/6EyIjIzFmzBjPd1Tdf8e/9e/vwQcfRElJCdq3b4+6deuivLwcM2fOxKhRowCA3x050m9+ckOByczMxJ49e/DRRx+F+lBqhby8PEyYMAHr1q0L+k6/TlBRUYFevXph1qxZAIDu3btjz549WLhwIcaMGRPiowtvb7zxBl577TUsW7YMnTp1wq5du5CVlYXExER+d+RYv/nLUhdccAHq1q1b5a6UwsJCJCQkhOiowtv48eOxZs0a/Oc//0HLli09nyckJOD06dMoKiryiud3efay09GjR9GjRw/Uq1cP9erVw4YNG/DUU0+hXr16iI+P53fnR/PmzdGxY0evzzp06IDDhw8DgOc74n/HVU2ePBkPPvggbrrpJnTp0gW33HILJk6c6NnVmd8dOdFvfnITGRmJnj17Iicnx/NZRUUFcnJykJqaGsIjCz+GYWD8+PFYtWoVPvzwQ6SkpHj19+zZE/Xr1/f6Lvfu3YvDhw//5r/LQYMGYffu3di1a5fn1atXL4waNcrznt+db/3796/y2IF9+/ahVatWAICUlBQkJCR4fX8lJSXYsmXLb/77O3HihNcuzgBQt25dVFRUAOB3Rw4V6hXN4WD58uVGVFSUsWTJEuPzzz837rzzTqNJkyZGQUFBqA8trNxzzz2Gy+Uy1q9fb+Tn53teJ06c8MTcfffdRnJysvHhhx8a27dvN1JTU43U1NQQHnX4qny3lGHwu/Nn69atRr169YyZM2ca+/fvN1577TWjQYMGxquvvuqJmT17ttGkSRPjrbfeMj777DPj2muv5e3MhmGMGTPGaNGihedW8DfffNO44IILjAceeMATw++OnIaTm1/8/e9/N5KTk43IyEijd+/exubNm0N9SGEHQLWvxYsXe2JOnjxp3HvvvcZ5551nNGjQwLjuuuuM/Pz80B10GDNPbvjd+ffOO+8YnTt3NqKiooz27dsbixYt8uqvqKgwpk6dasTHxxtRUVHGoEGDjL1794boaMNHSUmJMWHCBCM5OdmIjo42LrzwQuOvf/2rUVZW5onhd0dOE2EYlR5TSURERFTL/ebX3BAREZGzcHJDREREjsLJDRERETkKJzdERETkKJzcEBERkaNwckNERESOwskNEREROQonN0REROQonNwQERGRo3ByQ0RERI7CyQ0ReXz//fdISEjArFmzPJ9t2rQJkZGRXrtGExGFM+4tRURe3n33XWRkZGDTpk1o164dunXrhmuvvRaPP/54qA+NiKhGOLkhoioyMzPxwQcfoFevXti9eze2bduGqKioUB8WEVGNcHJDRFWcPHkSnTt3Rl5eHnbs2IEuXbqE+pCIiGqMa26IqIqvv/4a3333HSoqKnDw4MFQHw4RkQjP3BCRl9OnT6N3797o1q0b2rVrh/nz52P37t2Ii4sL9aEREdUIJzdE5GXy5MlYuXIlPv30UzRq1AgDBgyAy+XCmjVrQn1oREQ1wstSROSxfv16zJ8/H6+88gpiY2NRp04dvPLKK/i///s/LFiwINSHR0RUIzxzQ0RERI7CMzdERETkKJzcEBERkaNwckNERESOwskNEREROQonN0REROQonNwQERGRo3ByQ0RERI7CyQ0RERE5Cic3RERE5Cic3BAREZGjcHJDREREjsLJDRERETnK/wfZ6DAgxOmn9wAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -256,7 +263,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -321,7 +328,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "51fb0b455e2e437fa608efd3297f2ca7",
+       "model_id": "5583f38112a54483938b2b6b7ae9f41c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -353,7 +360,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47c197eef7f6464fa2bc2ad620db52eb",
+       "model_id": "bb90eb1453f349a1a7117f0f5cd55d6b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -367,7 +374,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "17ea6459362b4d4b9d80c06dc8b10ab9",
+       "model_id": "f8c65ca4bdf046b3a33050018e045877",
        "version_major": 2,
        "version_minor": 0
       },
@@ -381,7 +388,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "6f5c3f6b4dc8410d911943260b615e02",
+       "model_id": "9187f19197a24d1c9f27f40b81c63512",
        "version_major": 2,
        "version_minor": 0
       },
@@ -395,7 +402,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "83bf692078c548da909b7dd9ba4178d3",
+       "model_id": "37a748702b474d369f5596299fb9021e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -409,7 +416,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ee8cb186052e4c41a06af013608bda4c",
+       "model_id": "27a7592d03094d27be6d6763618f847f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -423,7 +430,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d0c3ba2007454fdda6ef8f639e053d50",
+       "model_id": "d9661a24b26f40c69ee5e1bfa114cfa9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -437,7 +444,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1371d06595e74914b0476fca856d3cd1",
+       "model_id": "56d61ced73714894b883125d9e98b785",
        "version_major": 2,
        "version_minor": 0
       },
@@ -451,7 +458,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8ab676a43f4845e1a8af48c79b3a2a18",
+       "model_id": "145912919c0e4a7eba97730c79d0d7b8",
        "version_major": 2,
        "version_minor": 0
       },
@@ -465,7 +472,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7f1f25e7d2a34db8bbb2af995e69d0e8",
+       "model_id": "351a065ac14646fd87c4baa6c5b6d31e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -479,7 +486,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "e59eaf0bafb040d8a07201c8a68ab9f1",
+       "model_id": "94ff0e496544478c92ef5814c52d04e4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -535,28 +542,28 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.005</td>\n",
+       "      <td>0.009</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.009</td>\n",
+       "      <td>0.010</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.015</td>\n",
+       "      <td>0.014</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.013</td>\n",
+       "      <td>0.016</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "      <td>0.1</td>\n",
@@ -617,10 +624,10 @@
       ],
       "text/plain": [
        "    burned  job_id  repeat_id  model.density\n",
-       "0    0.005       0          1            0.1\n",
-       "1    0.009       0          2            0.1\n",
-       "2    0.015       0          3            0.1\n",
-       "3    0.013       0          4            0.1\n",
+       "0    0.009       0          1            0.1\n",
+       "1    0.010       0          2            0.1\n",
+       "2    0.014       0          3            0.1\n",
+       "3    0.016       0          4            0.1\n",
        "4    0.013       0          5            0.1\n",
        "..     ...     ...        ...            ...\n",
        "95   1.000       9          6            1.0\n",
@@ -648,7 +655,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -724,7 +731,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "147f24aeb68c42ec8f1928bd64c0d24a",
+       "model_id": "271b4f98bb6548749819eb28def7fdda",
        "version_major": 2,
        "version_minor": 0
       },
@@ -764,31 +771,31 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.04825</td>\n",
+       "      <td>0.01775</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.03300</td>\n",
+       "      <td>0.03050</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.02225</td>\n",
+       "      <td>0.04400</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.02525</td>\n",
+       "      <td>0.02825</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.04175</td>\n",
+       "      <td>0.02800</td>\n",
        "      <td>0</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
@@ -800,31 +807,31 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95</th>\n",
-       "      <td>0.02375</td>\n",
+       "      <td>0.03225</td>\n",
        "      <td>0</td>\n",
        "      <td>96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96</th>\n",
-       "      <td>0.02800</td>\n",
+       "      <td>0.02225</td>\n",
        "      <td>0</td>\n",
        "      <td>97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
-       "      <td>0.03225</td>\n",
+       "      <td>0.02300</td>\n",
        "      <td>0</td>\n",
        "      <td>98</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
-       "      <td>0.02825</td>\n",
+       "      <td>0.04300</td>\n",
        "      <td>0</td>\n",
        "      <td>99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
-       "      <td>0.02025</td>\n",
+       "      <td>0.01775</td>\n",
        "      <td>0</td>\n",
        "      <td>100</td>\n",
        "    </tr>\n",
@@ -835,17 +842,17 @@
       ],
       "text/plain": [
        "     burned  job_id  repeat_id\n",
-       "0   0.04825       0          1\n",
-       "1   0.03300       0          2\n",
-       "2   0.02225       0          3\n",
-       "3   0.02525       0          4\n",
-       "4   0.04175       0          5\n",
+       "0   0.01775       0          1\n",
+       "1   0.03050       0          2\n",
+       "2   0.04400       0          3\n",
+       "3   0.02825       0          4\n",
+       "4   0.02800       0          5\n",
        "..      ...     ...        ...\n",
-       "95  0.02375       0         96\n",
-       "96  0.02800       0         97\n",
-       "97  0.03225       0         98\n",
-       "98  0.02825       0         99\n",
-       "99  0.02025       0        100\n",
+       "95  0.03225       0         96\n",
+       "96  0.02225       0         97\n",
+       "97  0.02300       0         98\n",
+       "98  0.04300       0         99\n",
+       "99  0.01775       0        100\n",
        "\n",
        "[100 rows x 3 columns]"
       ]
@@ -899,7 +906,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2757bf3e440b4108b99c6f5e9361fa47",
+       "model_id": "bb59d2b8e8fb4f24a985415938690eef",
        "version_major": 2,
        "version_minor": 0
       },
@@ -939,61 +946,61 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.04825</td>\n",
+       "      <td>0.03150</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.02400</td>\n",
+       "      <td>0.02000</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.04300</td>\n",
+       "      <td>0.02675</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.02450</td>\n",
+       "      <td>0.03275</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.01975</td>\n",
+       "      <td>0.03225</td>\n",
        "      <td>0</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.01775</td>\n",
+       "      <td>0.02925</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>0.03950</td>\n",
+       "      <td>0.02275</td>\n",
        "      <td>0</td>\n",
        "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>0.02125</td>\n",
+       "      <td>0.01775</td>\n",
        "      <td>0</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>0.04300</td>\n",
+       "      <td>0.03375</td>\n",
        "      <td>0</td>\n",
        "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>0.01950</td>\n",
+       "      <td>0.02800</td>\n",
        "      <td>0</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
@@ -1009,16 +1016,16 @@
       ],
       "text/plain": [
        "     burned  job_id  repeat_id\n",
-       "0   0.04825       0          1\n",
-       "1   0.02400       0          2\n",
-       "2   0.04300       0          3\n",
-       "3   0.02450       0          4\n",
-       "4   0.01975       0          5\n",
-       "5   0.01775       0          6\n",
-       "6   0.03950       0          7\n",
-       "7   0.02125       0          8\n",
-       "8   0.04300       0          9\n",
-       "9   0.01950       0         10\n",
+       "0   0.03150       0          1\n",
+       "1   0.02000       0          2\n",
+       "2   0.02675       0          3\n",
+       "3   0.03275       0          4\n",
+       "4   0.03225       0          5\n",
+       "5   0.02925       0          6\n",
+       "6   0.02275       0          7\n",
+       "7   0.01775       0          8\n",
+       "8   0.03375       0          9\n",
+       "9   0.02800       0         10\n",
        "10  0.03325       0         11"
       ]
      },
@@ -1073,61 +1080,61 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.04825</td>\n",
+       "      <td>0.03150</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.02400</td>\n",
+       "      <td>0.02000</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.04300</td>\n",
+       "      <td>0.02675</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.02450</td>\n",
+       "      <td>0.03275</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.01975</td>\n",
+       "      <td>0.03225</td>\n",
        "      <td>0</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.01775</td>\n",
+       "      <td>0.02925</td>\n",
        "      <td>0</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>0.03950</td>\n",
+       "      <td>0.02275</td>\n",
        "      <td>0</td>\n",
        "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>0.02125</td>\n",
+       "      <td>0.01775</td>\n",
        "      <td>0</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>0.04300</td>\n",
+       "      <td>0.03375</td>\n",
        "      <td>0</td>\n",
        "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>0.01950</td>\n",
+       "      <td>0.02800</td>\n",
        "      <td>0</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
@@ -1143,16 +1150,16 @@
       ],
       "text/plain": [
        "     burned  job_id  repeat_id\n",
-       "0   0.04825       0          1\n",
-       "1   0.02400       0          2\n",
-       "2   0.04300       0          3\n",
-       "3   0.02450       0          4\n",
-       "4   0.01975       0          5\n",
-       "5   0.01775       0          6\n",
-       "6   0.03950       0          7\n",
-       "7   0.02125       0          8\n",
-       "8   0.04300       0          9\n",
-       "9   0.01950       0         10\n",
+       "0   0.03150       0          1\n",
+       "1   0.02000       0          2\n",
+       "2   0.02675       0          3\n",
+       "3   0.03275       0          4\n",
+       "4   0.03225       0          5\n",
+       "5   0.02925       0          6\n",
+       "6   0.02275       0          7\n",
+       "7   0.01775       0          8\n",
+       "8   0.03375       0          9\n",
+       "9   0.02800       0         10\n",
        "10  0.03325       0         11"
       ]
      },
@@ -1182,7 +1189,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "06682c6e1bab4290b3b4a40b0012a09c",
+       "model_id": "0db7f0c3a9be4d15a5ddeab22edc8f1c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1196,7 +1203,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd597b7ccc40431099dd48df51fb1d9a",
+       "model_id": "8c43cbf33456426aa74e797cde994840",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1210,7 +1217,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5d5ded9a7008422b9834a651cfb1a1c9",
+       "model_id": "1fcfa6db38a14d07a45e1caedac7551d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1224,7 +1231,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3088978bf9554c938c04cafe8725d281",
+       "model_id": "057a22dd4c53414293ef155751225b3b",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1238,7 +1245,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "03f3110c828a4dada41c3268c9ea089b",
+       "model_id": "26dd867578e54901919756d9a60b1dae",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1252,7 +1259,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7bb7aa46ccdb48b5ae676fc8dd17f963",
+       "model_id": "fc88f5436901437d98a530660fd4e19a",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1266,7 +1273,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b64cdd77d7945e4929373194dc41e1d",
+       "model_id": "1fa11aa749ca4bc8bccd754d9fae4af4",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1280,7 +1287,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "23231c8b05db4e5398e004a9bdbd343b",
+       "model_id": "2ea8596d95b24a808c1bcba1bf09a05f",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1294,7 +1301,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d08eaed3e7dd47ca9b59d49def21ec59",
+       "model_id": "8ed9e3a875004e7ab89c4e73d3de3dfa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1308,7 +1315,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "838aca035b0245ba8c3ef5c7af809506",
+       "model_id": "39cbcb12f51245f1a0f175670b52c44e",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1349,35 +1356,35 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.007</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.013</td>\n",
+       "      <td>0.014</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.009</td>\n",
+       "      <td>0.014</td>\n",
        "      <td>0</td>\n",
        "      <td>3</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.007</td>\n",
+       "      <td>0.006</td>\n",
        "      <td>0</td>\n",
        "      <td>4</td>\n",
        "      <td>0.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.013</td>\n",
+       "      <td>0.008</td>\n",
        "      <td>0</td>\n",
        "      <td>5</td>\n",
        "      <td>0.1</td>\n",
@@ -1431,11 +1438,11 @@
       ],
       "text/plain": [
        "     burned  job_id  repeat_id  model.density\n",
-       "0     0.007       0          1            0.1\n",
-       "1     0.013       0          2            0.1\n",
-       "2     0.009       0          3            0.1\n",
-       "3     0.007       0          4            0.1\n",
-       "4     0.013       0          5            0.1\n",
+       "0     0.006       0          1            0.1\n",
+       "1     0.014       0          2            0.1\n",
+       "2     0.014       0          3            0.1\n",
+       "3     0.006       0          4            0.1\n",
+       "4     0.008       0          5            0.1\n",
        "..      ...     ...        ...            ...\n",
        "105   1.000       9          7            1.0\n",
        "106   1.000       9          8            1.0\n",
@@ -1467,7 +1474,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
diff --git a/tests/config/test_config.yaml b/tests/config/test_config.yaml
index 4b15bfe..4072b87 100644
--- a/tests/config/test_config.yaml
+++ b/tests/config/test_config.yaml
@@ -11,3 +11,5 @@ reports:
 
         Farmer:
             var2: 'test'
+    final:
+        var1: 'test'