model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "81fbfec6-d57f-41ea-ad20-53ae0731b564",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(1, './imports')\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pickle\n",
    "from exploratory_analysis import *\n",
    "from preprocessing import *\n",
    "from model_selection import *\n",
    "from model_end_to_end import *\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "pd.options.display.max_rows = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d47c3272-5715-4e0e-ad9d-334e86dac859",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = pd.read_csv(\"fire_data_processed.csv\")\n",
    "y = fires.loc[:,\"area\"]\n",
    "drop_cols = ['Unnamed: 0', 'area', 'area_bool']\n",
    "X = fires.drop(drop_cols, axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=3558)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "d74ed6e2-484b-4c47-a7a2-61d5f200fd00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# n_estimators = np.arange(1, 1000, 50)\n",
    "# losses = loss_for_n_estimators(n_estimators, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "c742cd43-097f-44c6-8624-cce268b44696",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fig = plt.figure(figsize=(10, 10))\n",
    "# estimators_loss_series = pd.Series(losses, index=n_estimators)\n",
    "# g = estimators_loss_series.plot()\n",
    "# g.set_title(\"Squared loss in function of the number of estimators for the regression forest\", fontsize=14)\n",
    "# g.set_xlabel(\"Number of estimators\")\n",
    "# g.set_ylabel(\"Average squared loss\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "id": "a6ab4509-14e9-48ff-b5f7-2fb2d876af36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2799312662809328"
      ]
     },
     "execution_count": 309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators = 150)\n",
    "rfr.fit(X_train, y_train)\n",
    "y_pred = rfr.predict(X_test)\n",
    "average_absolute_loss(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "734c7071-5972-4163-a0a0-32d386be6093",
   "metadata": {},
   "source": [
    "This will be the reference model, the model has an average loss in hectareas of 1.27, quite a lot. Probably the huge number of hot-encoded variables is problematic for the classifier. The attempts on improving the model will focus on:\n",
    "- Reduction of the correlation for the continuous variables.\n",
    "- Reconsider how the spatial data is preprocessed\n",
    "- Use of a classifier to discern between the variables with 0 output and different from 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed0aee35-bdc1-4635-8795-b9a50237d5aa",
   "metadata": {},
   "source": [
    "## After PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8665856b-f968-4f6d-aac7-4cd62550632a",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = pd.read_csv(\"fire_data_processed_after_pca.csv\")\n",
    "y = fires.loc[:,\"area\"]\n",
    "drop_cols = ['Unnamed: 0', 'area_bool']\n",
    "X = fires.drop(drop_cols, axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=3558)\n",
    "X_test_with_area = X_test.copy()\n",
    "X_test = X_test.drop(\"area\", axis=1)\n",
    "X_with_area = X.copy()\n",
    "X = X.drop(\"area\", axis=1)\n",
    "X_train_with_area = X_train.copy()\n",
    "X_train = X_train.drop(\"area\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "id": "bafd899a-4041-4467-ba69-f53c777238ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2360495491575492"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr = RandomForestRegressor(n_estimators = 150)\n",
    "rfr.fit(X_train, y_train)\n",
    "y_pred = rfr.predict(X_test)\n",
    "average_absolute_loss(y_pred,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b342af-8abd-4c6e-a16b-65cbcac7eb5b",
   "metadata": {},
   "source": [
    "The improvement is very small and probably due to chance"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c333db0-feff-47b4-96eb-68339b7300ca",
   "metadata": {},
   "source": [
    "## Model with previous binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "0d628e78-f2b3-4648-a113-9334f575f9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = fires.loc[:,\"area_bool\"]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=3558)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b67b121-7434-43ea-859b-86e57fa93fcc",
   "metadata": {},
   "source": [
    "### Using a Random forest classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "87871eb8-f42b-46c5-aedf-d9e76814d684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5906432748538012"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train, y_train)\n",
    "y_pred = rfc.predict(X_test)\n",
    "sum(y_pred == y_test)/len(y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3451d4a-6b3e-4c25-8787-361cfd5b553c",
   "metadata": {},
   "source": [
    "### Using a Gradient Boosting classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "fdebf152-f29b-4ba0-995d-986c1e294b30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5906432748538012"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbc = GradientBoostingClassifier()\n",
    "gbc.fit(X_train, y_train)\n",
    "y_pred = gbc.predict(X_test)\n",
    "sum(y_pred == y_test)/len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "484fb204-9211-4a30-8d96-7c229a9f1f7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "importances = rfc.feature_importances_\n",
    "imp_series = pd.Series(data=importances, index=X_train.columns)\n",
    "imp_series.sort_values(ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "21f8a653-3cf7-43c4-a6b6-fdaf4cd69b88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "g = sns.barplot(y=imp_series.index, x=imp_series, orient=\"h\")\n",
    "g.set_title(\"Feature importance using a Random Forest classifier\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c17c9fd-ac68-4584-8afa-bbb00ddfa28f",
   "metadata": {},
   "source": [
    "A lot of features have low importance, it's possible that the model is overfitting based on the features of low importance, so the features of low importance will be greedily removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6a35694-d587-4323-b3a4-07d7dc1bb21e",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies_mf = greedy_feature_removal(X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32671c3a-c85c-42d1-bb27-0557fe4cbab7",
   "metadata": {},
   "source": [
    "There's some improvement but it's not big"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a26c04c-be4e-4d53-b5bc-74b3a7ff7e74",
   "metadata": {},
   "source": [
    "## Using the dataset with less features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "530e8c39-95dd-4417-bcd3-81baa2027091",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires2 = pd.read_csv(\"fire_data_processed_after_pca2.csv\")\n",
    "y2 = fires2.loc[:,\"area_bool\"]\n",
    "drop_cols = ['Unnamed: 0', 'area_bool']\n",
    "X2 = fires2.drop(drop_cols, axis=1)\n",
    "X_train2, X_test2, y_train2, y_test2 = train_test_split(X2, y2, test_size=0.33, random_state=3558)\n",
    "X_test_with_area2 = X_test2.copy()\n",
    "X_test2 = X_test2.drop(\"area\", axis=1)\n",
    "X_with_area2 = X2.copy()\n",
    "X2 = X2.drop(\"area\", axis=1)\n",
    "X_train_with_area2 = X_train2.copy()\n",
    "X_train2 = X_train2.drop(\"area\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2c1a7a84-c3d3-4b72-8fb5-127ce75c91a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6023391812865497"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc2 = RandomForestClassifier()\n",
    "rfc2.fit(X_train2, y_train2)\n",
    "y_pred2 = rfc2.predict(X_test2)\n",
    "sum(y_pred2 == y_test2)/len(y_pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8b9e9df9-8726-44b3-8fe3-5559344b6d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracies_lf = greedy_feature_removal(X_train2, y_train2, X_test2, y_test2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "12fa4c18-8451-44f2-9934-2fcf3825e5b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6081871345029239"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(accuracies_mf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd9d9eb1-01cc-42f3-b04b-6d5ca873fe2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6081871345029239"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(accuracies_lf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0792c39-e677-4cfe-b175-2e354fd8a7cf",
   "metadata": {},
   "source": [
    "There doesn't seem to be a huge difference, probably the method used for evaluation is pessimistic and doesn't reflect the behaviour of the model, let's change to other evaluation method.\n",
    "Random forest are ensemble methods performing a process that resembles K-fold cross validation, and present a evaluation score called out of bag accuracy, that I'm going to use while using the whole dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b7c40851-cb89-43a7-b13c-2cf3fe191606",
   "metadata": {},
   "outputs": [],
   "source": [
    "gfr_low_feature_number = greedy_feature_removal_oob(X2, y)\n",
    "gfr_high_feature_number =greedy_feature_removal_oob(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4ff1dade-b959-482b-bd40-e5b0ffb7cd05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5813953488372093"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(gfr_low_feature_number.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3d4cb43a-03a1-46b6-becd-acc646af76bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5852713178294574"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(gfr_high_feature_number.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "104e25aa-389e-428a-afb5-0b54c361f310",
   "metadata": {},
   "outputs": [],
   "source": [
    "gfr_low_feature_number, gfr_high_feature_number = average_oob(X, X2, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bc3bb160-d60b-4a6f-94b9-3d92119831ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize = (20, 10))\n",
    "pd.Series(gfr_high_feature_number).plot(ax=axes[0])\n",
    "axes[0].set_ylim([0.5, 0.6])\n",
    "fig.suptitle(\"Evaluation metrics for different hyperparameters values for the dataset from january to may\")\n",
    "axes[0].set_title(\"Dataset with higher feature number\", fontsize=14)\n",
    "axes[0].set_xlabel(\"Features removed\")\n",
    "axes[0].set_ylabel(\"OOB\")\n",
    "axes[1].set_xlabel(\"Features removed\")\n",
    "axes[1].set_title(\"Dataset with lower feature number\", fontsize=14)\n",
    "axes[1].set_ylim([0.5, 0.6])\n",
    "pd.Series(gfr_low_feature_number).plot(ax=axes[1])\n",
    "fig.suptitle(\"OOB deleting greedily variables of lower importance\", fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b7a3d65-7472-4f99-8154-e669069c288a",
   "metadata": {},
   "source": [
    "Using oob we reach the same ressult. The performance it's not radically different. And there's no easy way to classify the fires between 0 and non-zero. Two observations:\n",
    "\n",
    "- There are a lot of fires with burnt area close to 0, if the fires predicted as 0 are almost 0, it shouldn't be very problematic.\n",
    "- Maybe there are some instances easier to classify as 0, predicting those as 0 should be bennefitial for the model.\n",
    "\n",
    "This leads to two possible strategies: \n",
    "\n",
    "- Try to find a model where missclassification is weighted differentially depending on the area of the missclassified instance.\n",
    "- Try to find a model where false negatives have a much higher weight. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dad9e3d5-3f2a-499a-87ba-6b764eccbc21",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_important_features = imp_series[0:-25].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bc02e6bb-65fc-4ae1-97f2-bcc5df645fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train[most_important_features], y_train)\n",
    "y_pred = rfc.predict(X_test[most_important_features])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aeede2c0-2011-4b9f-9421-7cfd7439c9a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_negatives = (y_pred == 0) & (y_test == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "93492f56-a9b5-41b8-b70b-5f6c76164bfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "483      27.43\n",
       "223      38.71\n",
       "196      12.06\n",
       "361       1.52\n",
       "295       1.90\n",
       "161       2.94\n",
       "209      25.23\n",
       "329       7.58\n",
       "212      28.35\n",
       "262       2.56\n",
       "479       3.75\n",
       "137       1.36\n",
       "508       3.17\n",
       "423       5.96\n",
       "145       1.95\n",
       "229      89.49\n",
       "187       8.40\n",
       "492      44.32\n",
       "365       2.75\n",
       "467       7.61\n",
       "237    1091.84\n",
       "178       6.23\n",
       "407       8.80\n",
       "234     197.48\n",
       "457       4.32\n",
       "210      27.00\n",
       "477       3.21\n",
       "498      11.82\n",
       "236     213.88\n",
       "345      29.19\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(X_test_with_area.loc[false_negatives][\"area\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a53e1d0b-1499-49ca-85c6-e92df465f1e6",
   "metadata": {},
   "source": [
    "False negatives have high burnt areas too.\n",
    "Trying to preclassify the data didn't work. \n",
    "There are several clues that suggest that the data could have two different generative patterns, like for example:\n",
    "\n",
    "- Different types of vegetation on the area, that could change over time.\n",
    "- If the fire was unintentional or not\n",
    "- Different effects of the weather depending on the time of year."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4cce9d6-3bcd-4e56-be54-08f7b0ee7788",
   "metadata": {},
   "source": [
    "### Exploring data clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1ea998c0-07f1-464d-aa9d-b33a85c8b379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "clustering = AgglomerativeClustering()\n",
    "clustering.fit(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "600ab70a-3814-4123-8e53-2488275010cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_c1 = X2[clustering.labels_ == 0]\n",
    "X2_c2 = X2[clustering.labels_ == 1]\n",
    "months = ['apr_bool', 'aug_bool', 'dec_bool', 'feb_bool','jan_bool', 'jul_bool', 'jun_bool', 'mar_bool', 'may_bool', 'nov_bool','oct_bool', 'sep_bool']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "737f7cd5-dd11-4973-845d-7f0e6e65b0bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apr_bool</th>\n",
       "      <th>aug_bool</th>\n",
       "      <th>dec_bool</th>\n",
       "      <th>feb_bool</th>\n",
       "      <th>jan_bool</th>\n",
       "      <th>jul_bool</th>\n",
       "      <th>jun_bool</th>\n",
       "      <th>mar_bool</th>\n",
       "      <th>may_bool</th>\n",
       "      <th>nov_bool</th>\n",
       "      <th>oct_bool</th>\n",
       "      <th>sep_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>430.0</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.0</td>\n",
       "      <td>430.0</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.0</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>430.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.427907</td>\n",
       "      <td>0.020930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.074419</td>\n",
       "      <td>0.037209</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002326</td>\n",
       "      <td>0.002326</td>\n",
       "      <td>0.034884</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.495352</td>\n",
       "      <td>0.143318</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.262757</td>\n",
       "      <td>0.189495</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.048224</td>\n",
       "      <td>0.048224</td>\n",
       "      <td>0.183699</td>\n",
       "      <td>0.490469</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       apr_bool    aug_bool    dec_bool  feb_bool  jan_bool    jul_bool  \\\n",
       "count     430.0  430.000000  430.000000     430.0     430.0  430.000000   \n",
       "mean        0.0    0.427907    0.020930       0.0       0.0    0.074419   \n",
       "std         0.0    0.495352    0.143318       0.0       0.0    0.262757   \n",
       "\n",
       "         jun_bool  mar_bool    may_bool    nov_bool    oct_bool    sep_bool  \n",
       "count  430.000000     430.0  430.000000  430.000000  430.000000  430.000000  \n",
       "mean     0.037209       0.0    0.002326    0.002326    0.034884    0.400000  \n",
       "std      0.189495       0.0    0.048224    0.048224    0.183699    0.490469  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires[clustering.labels_ == 0][months].describe().head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fdd46752-0c05-4d2e-bd87-9a5aa3dbe5c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>apr_bool</th>\n",
       "      <th>aug_bool</th>\n",
       "      <th>dec_bool</th>\n",
       "      <th>feb_bool</th>\n",
       "      <th>jan_bool</th>\n",
       "      <th>jul_bool</th>\n",
       "      <th>jun_bool</th>\n",
       "      <th>mar_bool</th>\n",
       "      <th>may_bool</th>\n",
       "      <th>nov_bool</th>\n",
       "      <th>oct_bool</th>\n",
       "      <th>sep_bool</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.104651</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.232558</td>\n",
       "      <td>0.023256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.011628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.307899</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.424941</td>\n",
       "      <td>0.151599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.486198</td>\n",
       "      <td>0.107833</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        apr_bool  aug_bool  dec_bool   feb_bool   jan_bool  jul_bool  \\\n",
       "count  86.000000      86.0      86.0  86.000000  86.000000      86.0   \n",
       "mean    0.104651       0.0       0.0   0.232558   0.023256       0.0   \n",
       "std     0.307899       0.0       0.0   0.424941   0.151599       0.0   \n",
       "\n",
       "       jun_bool   mar_bool   may_bool  nov_bool  oct_bool  sep_bool  \n",
       "count      86.0  86.000000  86.000000      86.0      86.0      86.0  \n",
       "mean        0.0   0.627907   0.011628       0.0       0.0       0.0  \n",
       "std         0.0   0.486198   0.107833       0.0       0.0       0.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fires[clustering.labels_ == 1][months].describe().head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8db84ce-3a32-49b8-a993-29e379c36e0a",
   "metadata": {},
   "source": [
    "The clustering seems to split the data by months almost perfectly: All entries in January, february, March and April are in one cluster, and may has elements in both clusters. May has most entries in one of the clusters, so the data and modelling will be done indepently for months 1-5, and 6-12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "9940421c-30c1-4f0e-95ea-7926658c26f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering()"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering = AgglomerativeClustering()\n",
    "clustering.fit(X2_c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "94942b82-b7d5-49ff-8412-2c997cb02e75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X  Y\n",
       "3  4    33\n",
       "4  4    29\n",
       "2  4    25\n",
       "4  3    20\n",
       "2  2    20\n",
       "   5    20\n",
       "1  2    19\n",
       "5  4    18\n",
       "4  5    18\n",
       "1  4    15\n",
       "   3     9\n",
       "4  6     5\n",
       "3  6     4\n",
       "   5     4\n",
       "1  5     4\n",
       "5  6     2\n",
       "6  5     2\n",
       "3  3     1\n",
       "2  3     1\n",
       "5  5     1\n",
       "6  3     1\n",
       "8  6     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_c1[clustering.labels_ == 0].loc[:,[\"X\", \"Y\"]].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "4260d505-eb8e-4675-90a4-01def85d35f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "X  Y\n",
       "8  6    47\n",
       "7  4    39\n",
       "6  5    32\n",
       "   3    19\n",
       "7  5     9\n",
       "6  4     5\n",
       "9  9     4\n",
       "   4     4\n",
       "8  5     4\n",
       "   3     3\n",
       "6  6     3\n",
       "7  6     2\n",
       "9  5     2\n",
       "8  4     1\n",
       "7  3     1\n",
       "8  8     1\n",
       "9  6     1\n",
       "5  6     1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2_c1[clustering.labels_ == 1].loc[:,[\"X\", \"Y\"]].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c70d57ad-7e96-49a1-8e56-7fd900819791",
   "metadata": {},
   "source": [
    "Apparently there could be a second split on the data depending on the x value, first the split by month will be tested"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27ba42dd-1ddc-4306-9e11-1ba4ccfde191",
   "metadata": {},
   "source": [
    "## With splitted model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "c9c24e92-636d-4e56-99bc-7de19ef915cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires_jan = pd.read_csv(\"fires_jan_may.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a6cbebf-caab-4459-826a-35ba6e7f27fb",
   "metadata": {},
   "source": [
    "### First halfth (january to may): Binary classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "580e1121-1118-4b49-b26e-9810db7ab61b",
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['Unnamed: 0', 'area_bool']\n",
    "X_jan = fires_jan.drop(drop_cols, axis=1)\n",
    "y_jan = fires_jan.loc[:,\"area_bool\"]\n",
    "X_train_jan, X_test_jan, y_train_jan, y_test_jan = train_test_split(X_jan, y_jan, test_size=0.33, random_state=3558)\n",
    "X_test_with_area_jan = X_test_jan.copy()\n",
    "X_test_jan = X_test_jan.drop(\"area\", axis=1)\n",
    "X_with_area_jan = X_jan.copy()\n",
    "X_jan = X_jan.drop(\"area\", axis=1)\n",
    "X_train_with_area_jan = X_train.copy()\n",
    "X_train_jan = X_train_jan.drop(\"area\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "8b1b5584-216d-423e-97e0-bb29ac76b600",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5517241379310345"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_temp_jan = SVC(class_weight={0:1, 1:1})\n",
    "rfc_temp_jan.fit(X_train_jan, y_train_jan)\n",
    "y_pred_jan = rfc_temp_jan.predict(X_test_jan)\n",
    "accuracy_score(y_pred_jan, y_test_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "8927d9a9-d7a1-472c-ac70-87e821211586",
   "metadata": {},
   "outputs": [],
   "source": [
    "false_negatives = (y_pred_jan == 0) & (y_test_jan == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "0f595d39-5e46-4fd1-b14b-bcd71f5bcd9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44     7.38\n",
       "53    28.35\n",
       "57    37.85\n",
       "43     5.61\n",
       "76     4.35\n",
       "38     1.95\n",
       "82     6.55\n",
       "49    11.93\n",
       "86    39.48\n",
       "71    52.78\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(X_test_with_area_jan.loc[false_negatives][\"area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cb1d0e70-a731-47cd-a5ad-17a9cbb2c914",
   "metadata": {},
   "outputs": [],
   "source": [
    "dropped_variables = np.arange(0, 20, 1)\n",
    "gammas = np.linspace(1, 10, 10)\n",
    "cs = np.linspace(0, 1000, 20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "f58084e7-435d-4ec3-8785-ac2c9f7b31c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear\n",
      "rbf\n",
      "poly\n"
     ]
    }
   ],
   "source": [
    "areas_burnt_kernels_jan, test_accuracies_kernels_jan = select_kernel_kcross(X_jan, X_with_area_jan, y_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "051b4bda-2df7-41cd-b057-96ac0289d82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_burnt_gammas_jan, test_accuracies_gammas_jan = select_gamma_kcross(X_jan, X_with_area_jan, y_jan, -10, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6f16791b-8a9c-45ae-beab-e3ae7dae2a41",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_burnt_gammas_refinement_jan, test_accuracies_gammas_refinement_jan = select_gamma_kcross(X_jan, X_with_area_jan, y_jan, -1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "d4542767-36b4-425a-beb1-60aad6622b70",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_burnt_c_jan, test_accuracies_c_jan = select_c_kcross(X_jan, X_with_area_jan, y_jan, gamma=0.3727)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d2acddba-6b06-4a2d-b561-e47d571219a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "areas_burnt_c_refinement_jan, test_accuracies_c_refinement_jan = select_c_kcross_refinement(X_jan, X_with_area_jan, y_jan, min_c=2, max_c=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "315976c4-cd4e-4113-82d0-ce8a50e6d922",
   "metadata": {},
   "outputs": [],
   "source": [
    "series_abg_jan = pd.Series(areas_burnt_gammas_jan)\n",
    "series_abgr_jan = pd.Series(areas_burnt_gammas_refinement_jan)\n",
    "series_abc_jan = pd.Series(areas_burnt_c_jan)\n",
    "series_abcr_jan = pd.Series(areas_burnt_c_refinement_jan)\n",
    "series_ag_jan = pd.Series(test_accuracies_gammas_jan)\n",
    "series_agr_jan = pd.Series(test_accuracies_gammas_refinement_jan)\n",
    "series_ac_jan = pd.Series(test_accuracies_c_jan)\n",
    "series_acr_jan = pd.Series(test_accuracies_c_refinement_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "72f7a49a-291e-421e-865d-b74390503aeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9DAAAUoPkUAMUiySHAAAAFqNUcg2NTmh8spj0UAAASA2SQw1QdFeB5BAAAMCCDecLKrk0PknlEAAAzUJyqAGYVgYAALA4R0cnJUn5ApVDAAA0C8mhBiiVXIUSR7sAAAAWanB0QhKVQwAANBPJocA8nlJG5RAAAMDCDY2VK4dIDgEA0CwkhwIr54ToOQQAALBwQ3HlUJ6G1AAANA3JocDKFUNUDgEAACzcUNxzaJyeQwAANA3JocDKSaFCkeQQAADAQpV7Dk0WmaYPAECzkBwKrNyImmAGAABg4cqVQxIrlgEA0CwkhwIrL1LGamUAAAALV+45JLFiGQAAzUJyKDAqhwAAABavvFqZROUQAADNQnIosKmeQySHAAAAFmywYloZlUMAADQHyaHAis5qZQAAAIt1dHRCndkoRKVyCACA5iA5FFh5lTIqhwAAABZucHRSm1Z3SaJyCACAZiE5FFi5YojKIQAAgIUpllzHxid10upuSdL4JJVDAAA0A8mhwMrTylitDAAAYGGOjU3KXdq8JkoO5QvEUwAANAPJocCmKoeKVA4BAAAsRHmlMiqHAABoLpJDgbFaGQAAwOIMjk5IIjkEAECzkRwKjJ5DAAAAi3M0Xsb+JKaVAQDQVCSHAitQOQQAALAoQ2NR5dDmuHIoT+UQAABNQXIoMCqHAAAAFmdsIqoUWt/TIYml7AEAaBaSQ4FN9xwimAEAAFiIco+h1Sui5FC+QOUQAADNQHIosHJSiMohAACAhRmPk0G9XR0yo3IIAIBmITkUWLlgiJ5DAAAAC5OPk0FduYy6c1kqhwAAaBKSQ4GVK4fcpRIJIgAAgLqNF4rqzGWUyZi6OzJUDgEA0CQkhwKrnE5G9RAAAED98pMldeWi8LQrl53qQQQAABqL5FBglckh+g4BAADUL18oqrsjK0nq7sgoX6ByCACAZiA5FNjMyiECGgAAgHqNT5bU3RGFp90dVA4BANAsJIcCK1A5BAAAsCjjk0V15aLKoa5cRuNUDgEA0BQkhwIrOT2HAAAAFiNfmK4c6urIKk/lEAAATUFyKLBCkcohAACAxRifLKo7V+45lKVyCACAJiE5FBirlQEAACzO+GRRXeXKoVyGyiEAAJqE5FBgxYppZcUiySEAAIB65QulGZVDrFYGAEBzkBwKrMBqZQAAAIsyPjm9lH1XLsNqZQAANAnJocBKrFYGAACwKOOTpalpZd0dGSqHAABoEpJDgRXoOQQAALAo+UJpain77lyWyiEAAJqE5FBgxYqpZFQOAQAA1C8/WaxYyj6aVuZOPAUAQKORHAqsWFH9TOUQAABA/cYL0z2HunNZlZx4CgCAZqgrOWRm55nZPWZ2v5m9tcb17zezPfHfvWY2VHHdpWZ2X/x3acCxt6SZlUPMkwcAAKhHseSaLLq6cuWeQ1GSiKllAAA0Xm6+G5hZVtI/SnqppH2Svm9mN7j7j8u3cfc3Vdz+9ySdE59eL+mdknZKckm3xvcdDPosWsiMnkMsZQ8AAFCXfCFKAk2tVhZPL8sXSupNbFQAAKRDPZVDz5J0v7v/xN0nJH1G0sVz3P41kj4dn365pK+5+5E4IfQ1SectZcCtjtXKAAAAFm58Mqq47i5XDuWoHAIAoFnqSQ5tlbS34vy++LITmNkpkh4v6eaF3NfMrjCz3Wa2e2BgoJ5xtyxWKwMAAFi4cuVQV1XlUDlpBAAAGid0Q+pLJH3e3Rd0iMfdr3H3ne6+s6+vL/CQmqtI5RAAAMCCTVUOlVcriyuHykkjAADQOPUkh/ZL2l5xflt8WS2XaHpK2ULv2xaKVA4BAAAsWHn6WHk6WTeVQwAANE09yaHvSzrNzB5vZp2KEkA3VN/IzM6QtE7Sdyou/k9JLzOzdWa2TtLL4svaVmFG5RDBDAAAQD3yhShu6qquHKLnEAAADTfvamXuXjCz31WU1MlK+qi732lm75a0293LiaJLJH3G3b3ivkfM7M8UJZgk6d3ufiTsU2gtVA4BAAAs3GyVQ+WkEQAAaJx5k0OS5O43Sbqp6rJ3VJ1/1yz3/aikjy5yfMtO0ek5BAAAsFDl5FC5IXV5SXtWKwMAoPFCN6ROvWKxonKoSHIIAACgHlPTynKZGf+O05AaAICGIzkUGJVDAAAACzc1reyEyiGmlQEA0GgkhwIrllyd8ZEueg4BAADUJ1+1lH1PV9T94Hi+kNiYAABIC5JDgRVKPlUGzWplAAAA9cnH08fKq5T1duVkJh0bJzkEAECjkRwKrFTyqaCGyiEAAID6jFdVDmUyplVdOR0bm0xyWAAApALJocAKpVJF5RDJIQAAsHhmdp6Z3WNm95vZW2tc/34z2xP/3WtmQxXXXWpm98V/lzZ14ItQ3XNIklZ3d+jYOMkhAAAara6l7FG/YsW0MiqHAADAYplZVtI/SnqppH2Svm9mN7j7j8u3cfc3Vdz+9ySdE59eL+mdknZKckm3xvcdbOJTWJB8oaSMSbmMTV3W253TsTGmlQEA0GhUDgVW2ZCayiEAALAEz5J0v7v/xN0nJH1G0sVz3P41kj4dn365pK+5+5E4IfQ1Sec1dLRLND5ZVHdHVmbTyaHVKzo0TOUQAAANR3IosMqG1IUiySEAALBoWyXtrTi/L77sBGZ2iqTHS7p5Ifc1syvMbLeZ7R4YGAgy6MUaLxRnTCmTytPKqBwCAKDRSA4FViy5OrKsVgYAAJrqEkmfd/fiQu7k7te4+05339nX19egodUnPzndt7Fs9QoaUgMA0AwkhwIrllzZjCmXMXoOAQCApdgvaXvF+W3xZbVcoukpZQu9b0sYL5RmqRwiOQQAQKORHAqsWHLlsqZsxug5BAAAluL7kk4zs8ebWaeiBNAN1TcyszMkrZP0nYqL/1PSy8xsnZmtk/Sy+LKWNT5ZPLFyqDunkXxBJWIqAAAaiuRQYIWSK5vJUDkEAACWxN0Lkn5XUVLnLkmfc/c7zezdZnZRxU0vkfQZd/eK+x6R9GeKEkzfl/Tu+LKWlS+U1FVdObSiQ+7SyAR9hwAAaCSWsg+s5K6sicohAACwZO5+k6Sbqi57R9X5d81y349K+mjDBhfY+GRR3SdUDnVIko6NTU6dBgAA4VE5FFihGFcOZTMq0JAaAACgLvnJGquVrYiOYx4bo3IIAIBGIjkUWMlduQw9hwAAABZieLygVd0zi9qnKodoSg0AQEORHAqsULlaWZHkEAAAQD2OjRdOmDrWWzGtDPj/27v36Ljv+s7/r/fMaHSXL/LdjuNLbCdOYkJujZMCISXEISlpl20KS5fS04V22+yWUthC97fsLl26vW13u7vZdqFlu71QSoGybi52Qgi3kEAuEF9jx5dcZEuyLN90v8y8f3/MjDxSJOv2+c53pHk+zvGJNBppPhqw9PVr3hcAQHQIhwIrrLKncggAAGDqLvQPqWls5VC+rayrn7YyAACiRDgUWKa4cohwCAAAYFIDwxkNDmfVSFsZAACxIBwKjMohAACA6SlUBjXVjm0rYyA1AAClQDgU2HA2q1TClEqwrQwAAGAqCjOFxlYOpZIJ1aWTVA4BABAxwqHAMlkpQeUQAADAlBUqhxqrq97wsaaaKgZSAwAQMcKhwDKFyqEkM4cAAACmYqK2stxtKQZSAwAQMcKhwIaZOQQAADAtXf3jt5VJ+coh2soAAIgU4VBg2awrafltZRnCIQAAgMlcuFQ4VEs4BABA1AiHAhvOupJJKocAAACm6lJtZY01KbaVAQAQMcKhwDJZZ1sZAADANFzoH5aZ1JAev62si8ohAAAiRTgUWMZzbWVUDgEAAEzNhb4hNaRTSiTsDR9rqk3pQv+w3LmuAgAgKoRDAWWzLncpmUjkZg4RDgEAAEyqq3943JYyKVc5lMm6egczJT4VAACVg3AooEz+Fa1kQlQOAQAATFFX/9C4w6ili3OIGEoNAEB0CIcCKoRByURCqSSVQwAAAFNx4RLhUOF2hlIDABAdwqGACmFQKmFKJhJUDgEAAExBV/+wmmombivL3YfKIQAAokI4FFAhDEokLD9ziG1lAAAAk+nqH56wcqghf3th3T0AAAiPcCigzKjKIRPZEAAAwORyM4fGrxyqTuUuVweGubACACAqhEMBFSqFklQOAQAATIm760L/sJpqx68cKoRDgxmuqwAAiMr4v4UxI4UsKJmvHGLmEAAAwMS6+of0O48cVCbrl6gcSkqSBoZYZQ8AQFSoHArojZVDhEMAAAAT+f6xM/rbH7yutYvrdNO6RePeJ03lEAAAkaNyKKCRVfaW31aWIRwCAACYSNuFfknS3/3SLVq5oHbc+4zMHBoiHAIAICpUDgU0MpA6aUolqRwCAAC4lPYL/UqYtLShesL7UDkEAED0CIcCGqkcYuYQAADApNrO92tJQ7VSyYkvSdNJKocAAIga4VBAw0VtZWwrAwAAuLS2C/1asaDmkvdJJRNKJkyDGQZSAwAQFcKhgMZWDmVdylI9BAAAMK72C/1a3nTpcEjKVQ8NDvOiGwAAUSEcCmjUzKGE5W5zwiEAAIDxtJ3v14ophEPVVQkNEA4BABAZwqGACm1lify2MknMHQIAABhH32BGF/qHJ20rk6gcAgAgaoRDAWXzVUKpRGKkcoiNZQAAAG9UWGM/lbYyKocAAIgW4VBAw5nRM4ckKZMhHAIAACjWN5jRZx4+KElTaiujcggAgGgRDgVUqBxKJkypZKFyiAsZAACAYk8fO62vH2yXJG1e3jDp/atTSQ0Ms60MAICoEA4FNJwdp3KItjIAAIBR+gZzL57t+shbtGwqlUMp2soAAIgS4VBAmXyVUDJhzBwCAACYQKEKqCaVnNL9qwmHAACIFOFQQJn8NUsqwbYyAACAiRTmB6VTU7sUTaeYOQQAQJQIhwKicggAAGByhSqg6imGQ9WEQwAARIpwKKDxZw5xIQMAAFBsupVDDKQGACBahEMBZYrCISqHAAAAxjeYKVQOTW3mUDqVGPkcAAAQHuFQQIVwKFVUOTScIRwCAAAoNjCUqwKqStqU7l+dSmhgiHAIAICoEA4FVKgSSpgplWSVPQAAwHgGMlmlUwmZTS0conIIAIBoEQ4FlC1UDiUvbiujrQwAAGC0gaHslIdRS1QOAQAQNcKhgEYGUtvFmUNUDgEAAIw2mJleOETlEAAA0SIcCmgkBjJdnDnEtjIAAIBRcpVDUxtGLeUGV2eyrmECIgAAIkE4FAETlUMAAAATGczPHJqqwn2pHgIAIBqEQyH5xSAoySp7AACAcQ0MZabXVpbMh0PDhEMAAESBcCigQgxkJqXyA6kzrLIHAAAYZbqVQ9VVufsOEA4BABAJwqEImKgcAgAAmMjg8DQHUlM5BABApAiHAirqKlMqycwhAACA8QwMT7dyKJn/vExURwIAoKIRDgXk+XTIzNhWBgAAMIFc5dDUt5UVKodoKwMAIBqEQwGNzByS2FYGAAAwgYHhzEjgMxXMHAIAIFqEQxEwY+YQAADARAan21bGzCEAACJFOBTQqJlDhW1lhEMAAACjDExzIDWVQwAARItwKKCLbWVG5RAAAMAEpls5lE4mRz4PAACERzgUBSuaOZThIgYAAKDYwHQHUqdoKwMAIEqEQwF5UV9ZMknlEAAAwHimPXMoVWgrY5U9AABRIByKgBnbygAAAMaTzboGM9ObOUTlEAAA0SIcioCJbWUAAADjGcy33M+scohwCACAKBAOBcS2MgAAgEsrhENUDgEAUD4IhwLy/L4yM1O+cIjKIQAAgCIDQ9MPhwrDq5k5BABANKb0W9nMdpjZITM7YmafmOA+95vZATPbb2ZfKLr99/O3HTSz/25mFurw5aZQOWTKBUSphCmT5RUuAACAgouVQ1PfVlaVX/RB5RAAANFITXYHM0tKelDSnZJaJD1rZjvd/UDRfTZJ+qSk29z9rJkty99+q6TbJG3L3/W7kt4m6Zshv4lyU4i/kgmjcggAAKDIwFCu+mc6M4fMTNWpBDOHAACIyFR+K98s6Yi7H3P3QUlflHTfmPt8SNKD7n5Wktz9VP52l1QjKS2pWlKVpPYQBy9HY2OgVMKUyRAOAQAAFMxkIHXh/oRDAABEYyq/lVdLer3o/Zb8bcU2S9psZk+Z2TNmtkOS3P1pSU9Kas3/2e3uB8c+gJl92MyeM7PnOjo6ZvJ9lIWLbWW50iEqhwAAAEYrtIalk9MLh6pTiZFgCQAAhBVqIHVK0iZJt0t6n6TPmdlCM7tC0lWS1igXKN1hZm8Z+8nu/ll3v9Hdb1y6dGmgI8Wn0FaWSibYVgYAAFBkaIaVQ9Wp5MgwawAAENZUfiufkHRZ0ftr8rcVa5G0092H3P24pMPKhUU/LekZd+92925Jj0raPvtjlycf01hG5RAAAMBohdawqmlWDqWpHAIAIDJT+a38rKRNZrbezNKS3itp55j7fE25qiGZ2RLl2syOSXpN0tvMLGVmVcoNo35DW9l84WNyILaVAQAAjDaUn8c4/cqhxMgwawAAENakv5XdfVjSA5J2KxfsfMnd95vZp83s3fm77ZbUaWYHlJsx9HF375T0ZUlHJe2V9KKkF939HyP4PsoK28oAAADGN9OZQ1QOAQAQnUlX2UuSuz8i6ZExt32q6G2X9NH8n+L7ZCT90uyPObcUBlLnKocIhwAAAApmPnMowcwhAAAiEmogNSS5M3MIAADgUgZHZg7ZtD6PyiEAAKJDOBTQyCr7wrayREKZDOEQAABAweBstpUNM3MIAIAoEA5FoPA6GJVDAAAAo8145lAyMfK5AAAgLMKhgMbGQKkk28oAAACKzXTmUDpFOAQAQFQIhwK62FaWqx2icggAAGC0izOHZjCQmnAIAIBIEA5FoNBWxrYyAACA0agcAgCg/BAOBeRiWxkAAMClFAKeVGJ628pyA6kJhwAAiALhUEDjbisjHAIAABgxmHGlU4mRNvyponIIAIDoEA4FVIiBmDkEAAAwvsHhrKqnOW9Iys0cGsxkleXaCgCA4AiHIpSbOcQrXAAAAAVDmayqpjlvSLo4o2gww7UVAAChEQ6F5OPMHMrw6hYAAEDB4HBW6RlWDkli7hAAABEgHArIdXHekCSlkmwrAwAAKJarHJrevCHpYjjE3CEAAMIjHAqs+FInyUBqAACAUQYyM6scoq0MAIDoEA4FNKarTCkGUgMAgFkwsx1mdsjMjpjZJya4z/1mdsDM9pvZF4pu//38bQfN7L/bdNeDRWRoOKuqGbWVJSVJA0OZ0EcCAKDipeI+wHzi8lFrWZMJ2soAAMDMmFlS0oOS7pTUIulZM9vp7geK7rNJ0icl3ebuZ81sWf72WyXdJmlb/q7flfQ2Sd8s3XcwvsFMdqRFbDqoHAIAIDpUDgXkPrqtLFc5xAUMAACYkZslHXH3Y+4+KOmLku4bc58PSXrQ3c9Kkrufyt/ukmokpSVVS6qS1F6SU09iKDPTyqH8QOohrq0AAAiNcChCVA4BAIBZWC3p9aL3W/K3FdssabOZPWVmz5jZDkly96clPSmpNf9nt7sfLMGZJzU4nB2pApoOKocAAIgObWUBvWFbGTOHAABAtFKSNkm6XdIaSd82s2slLZF0Vf42SXrczN7i7t8p/mQz+7CkD0vS2rVrS3LgwYyrLj2bmUOEQwAAhEblUEC5trLimUMJZTKEQwAAYEZOSLqs6P01+duKtUja6e5D7n5c0mHlwqKflvSMu3e7e7ekRyVtH/sA7v5Zd7/R3W9cunRpJN/EWLOvHGIgNQAAoREOhVZcOZSkcggAAMzYs5I2mdl6M0tLeq+knWPu8zXlqoZkZkuUazM7Juk1SW8zs5SZVSk3jLos2sqGZrjKvjBzaHCYyiEAAEIjHArINToIYuYQAACYKXcflvSApN3KBTtfcvf9ZvZpM3t3/m67JXWa2QHlZgx93N07JX1Z0lFJeyW9KOlFd//Hkn8T45hp5VAhHOqnrQwAgOCYORQS28oAAEBA7v6IpEfG3Papordd0kfzf4rvk5H0S6U443TltpXZ5Hccoy6du2ztHaStDACA0KgcCqx4IHUyYcq6lKV6CAAAQNLMK4dq07mB1L2Dw6GPBABAxSMcCmhsBJRK5JKijBMOAQAASLlV9FUzmDlUNxIOUTkEAEBohEMBufsbtpVJYu4QAABA3kwrh6qSCaWTCcIhAAAiQDgUkPvotrJC5RAbywAAAHKGMllVJWZ2CVpXnaStDACACBAOBVY8XjFZaCvLEA4BAAC4u7J+8RppuuqqklQOAQAQAcKhgN4wcyhZqBxiYxkAAEChmHqm4VBtmsohAACiQDgUUK6trHjmUL5yiLYyAACAkWuimYZD9dUpKocAAIgA4VBgxZc6zBwCAAC4KJvf4JqwGVYO0VYGAEAkCIcC8jGNZWwrAwAAuOhi5dDMPj9XOURbGQAAoREOBeSuUaVDVA4BAABclJlt5VCayiEAAKJAOBTYuNvKGEgNAACg7GxnDqWT6h0gHAIAIDTCoQglqRwCAAAYMduB1HVp2soAAIgC4VBA7j7utrLhDOEQAADAbNvK6mgrAwAgEoRDAbkkG2fmEAOpAQAApEKn/cwrh5IazroGh2nZBwAgJMKhwMabOURbGQAAwMXKoeSMB1KnJEl9VA8BABAU4VBAPiYDSrHKHgAAYERhIHViFgOpJamHuUMAAARFOBSQa4KZQ2wrAwAAKBpIPbPPr82HQ8wdAgAgLMKhwIpfB0slmTkEAABQUGi1n+lA6vp8WxkbywAACItwKKCxbWXMHAIAALgo67NdZU/lEAAAUSAcCmjCbWWssgcAALjYVjbTVfbVVA4BABAFwqGAci+GjTdziHAIAAAgM8uB1FQOAQAQDcKhwEZXDrGtDAAAoCA7y1X2hEMAAESDcCio0SEQ28oAAAAuuritbKbhUL6tbIC2MgAAQiIcCsh99Lay6lTu6R0cJhwCAAAoVA7Nuq1siMohAABCIhwKrLhKuj4/NLGHV7cAAACUyb9eNtO2supUQgmTegcIhwAACIlwKKCxq+zrq3OvbnUTDgEAABQNpJ7Z55uZ6tIpZg4BABAY4VBALpcVNZZVp5JKpxLqIhwCAACY9UBqKddaxip7AADCIhwKbOy1TkN1irYyAAAAzX4gtVQIh6gcAgAgJMKhgMa2lUm5cKi7n3AIAAAgM8uB1JLybWVcWwEAEBLhUECu0dvKpHw4ROUQAACAstlQbWVUDgEAEBLhUEDuuUGJxRpqUuqicggAACBMW1k1A6kBAAiNcChiDdUp9VD6DAAAMDKQOjGbyqEqBlIDABAa4VBArjcOHWLmEAAAQE4mm/svA6kBACgvhEMh+TjbymqYOQQAACBdHEidnMUVaF014RAAAKERDgU23ip7wiEAAICLA6ln1VbGtjIAAIIjHAponE32aqhOqX8oq6FCHTUAAECFCjKQOp1U/1B25GsBAIDZIxwKyN1lY5bZN1SnJEk9VA8BAIAKlwkxkDqdlCT1DdFaBgBAKIRDAbnGnzkkiXX2AACg4mWDVA7lrq1oLQMAIBzCocDGXuqMVA5xAQMAACrcxYHUs68c6h2gcggAgFAIhwLycVrfC+EQ6+wBAEClCzWQWhIbywAACIhwKKBcW9mYmUOFtjJmDgEAgAoXaiC1RFsZAAAhEQ4FNmFbGeEQAACocJl8lXUywEBqKocAAAiHcCggH6evjLYyAACAnJG2sllcgTKQGgCA8AiHAnLpDaVDhbaybiqHAABAhQs6kJrKIQAAgiEcCmzspU59mlX2AAAA0sWZQ7MaSF1NOAQAQGiEQyGNs60smTDVpZPMHAIAABUvG2QgNW1lAACERjgUkMvfsK1Mys0doq0MAABUupG2sllUDtVWUTkEAEBohEMBub+xrUzKzR1ilT0AAKh0FwdSzzwcSiZMNVUJwiEAAAJKxX2A+Wa8F8IaqlO0lQEAgIqXcZ9VS1lBfTqlJw6262zPoBprqvTemy/T5uWNAU4IAEBlonIooHE22UvKt5UxkBoAAFS4THZ2LWUFb79ymfoGM3rqyGn9zfdf1Tv/67f1i3/xrA62XghwSgAAKg+VQwG5XDZOY1lDdUqv9fTGcCIAAIDykXVXIsBLk3/4M28aeftsz6D+8ulX9fmnjuvX/+5H2vWRt87+AQAAqDCEQ4GN21ZWk2KVPQAAqHiZrAepHCq2qD6tX3vHJp3uHtA/7jkZ9GsDAFAppvTajZntMLNDZnbEzD4xwX3uN7MDZrbfzL5QdPtaM3vMzA7mP74u0NnLzqXaynpYtwoAACpcJuuzGkZ9KYvqqnS+b2hk6DUAAJi6SSuHzCwp6UFJd0pqkfSsme109wNF99kk6ZOSbnP3s2a2rOhL/KWkz7j742bWICkb9DsoIxNdihRmDrmPv+oeAACgEmQDDaQez4K6tNylC/1DWliXjuQxAACYr6ZSOXSzpCPufszdByV9UdJ9Y+7zIUkPuvtZSXL3U5JkZlslpdz98fzt3e4+b4fvuGvc8KehJqXhrGtgeN7mYgAAAJOKoq2sYGFtlSTpXO9QJF8fAID5bCrh0GpJrxe935K/rdhmSZvN7Ckze8bMdhTdfs7MvmpmPzSzP8hXIs1b413uNFTnCrSYOwQAACpZlJVDi+rz4VAf4RAAANMVapV9StImSbdLep+kz5nZwvztb5H0MUk3Sdog6YNjP9nMPmxmz5nZcx0dHYGOFIfxG8sK4VDPAOEQAACoXJlshG1ltblWsrO9g5F8fQAA5rOphEMnJF1W9P6a/G3FWiTtdPchdz8u6bByYVGLpB/lW9KGJX1N0vVjH8DdP+vuN7r7jUuXLp3Bt1Eecm1lb7y9EA51Ew4BAIAKlslKiYjayhbV5SqHztNWBgDAtE0lHHpW0iYzW29maUnvlbRzzH2+plzVkMxsiXLtZMfyn7vQzAqJzx2SDmgem2iVvURbGQAAqGxRtpUVhlCfo3IIAIBpmzQcylf8PCBpt6SDkr7k7vvN7NNm9u783XZL6jSzA5KelPRxd+9094xyLWVPmNle5UbyfC6Kb6QcXGpbmURbGQAAqGxRtpU15V+MO0vlEAAA0zbpKntJcvdHJD0y5rZPFb3tkj6a/zP2cx+XtG12x5wb3F02zkhq2soAAACkjLsiyoaUSibUVJPSeQZSAwAwbaEGUiPvkm1lhEMAAKCCZTLRVQ5JudYyBlIDADB9hEMBTdZW1s3MIQAAUMFylUNRhkNVOkdbGQAA00Y4FJC7xmkqk2qrkkoYM4cAAEBly0Y4c0jKVQ6do60MAIBpIxwKyKVx+8rMTA3VKWYOAQCAinaqayDacKi2im1lAADMAOFQYBNd7jRUp1hlDwAAKtbXD7Rr74nzE14rhbCItjIAAGaEcCig3NK28TXUpGgrAwAAFau9q1+S9G92XBnZYyyoS+tC/5Ay2YmvyQAAwBsRDgU20YxF2soAAEAly+YDm83LGyN7jIW1VXKXLjB3CACAaSEcCmzCtrKaKlbZAwCAilUo5oly5tCi+ipJYig1AADTRDgU0CW6ytRQnVR3PxcqAACgMhVavSLMhrSwNi1JOstQagAApoVwKCCXyyboK2uoTqlnIFPiEwEAAJSHbP5VtESE6dCCulzl0HmGUgMAMC2EQwG5X2pbWRUzhwAAQMUaCYcmGtAYwKK6XOXQuT4qhwAAmA7CoRJpqE6qe2B4ZBgjAABAJRmZORRhOLSwNlc5dLaHyiEAAKaDcCgg90tsK6tJSZJ6h2gtAwAAlacwcyjCbEhNtVUyYyA1AADTRTgUkMtlEzSWNVTnXsnq7qe1DAAAVB7Pt5VFua0smTA11VTpHAOpAQCYFsKh0CapHOoe4JUsAABQeTLZ3H+jnDkkSQvrqnSOgdQAAEwL4VBAk62yl6QuKocAAEAFujiQOtrHWViXpq0MAIBpIhwKyHXpbWWSWGcPAAAqUtZdZpJFXTlUS1sZAADTRTgU2IQDqatpKwMAAJUrk/VIN5UV0FYGAMD0EQ6FdMm2slw4RFsZAACoRFmPft6QJC2qS+sslUMAAEwL4VBAl9xWlh9I3TNAOAQAACpP1l2JElx5LqitUlf/sIYLE7ABAMCkCIcCcp+4raw+P5C6m3AIAABUoGwJ28ok6QLV2gAATFkq7gPMNxNd81SnkkqnEuoiHAIAABUo416ytjJJOts7qMX16cgfbzq+d/S0OroGZvS5C+vSetvmpYFPBABADuFQQJcYOSQpN3eom1exAADAFJnZDkl/LCkp6c/c/XfHuc/9kv6DcpciL7r7P8vfvlbSn0m6LP+xd7n7K6U5+Ru5S4mo99hLWpCvHCq3odSvnO7RP/vc92f1Nf7hV27Vm9cuCnQiAAAuIhwKyH3imUNSLhxi5hAAAJgKM0tKelDSnZJaJD1rZjvd/UDRfTZJ+qSk29z9rJktK/oSfynpM+7+uJk1SIp1CE8m6ypBNjRSOXS+r7yGUj92oE2S9Pe/vF3N06xoGsxk9VMPPqUvP99COAQAiAThUGCXqpZuqE4xcwgAAEzVzZKOuPsxSTKzL0q6T9KBovt8SNKD7n5Wktz9VP6+WyWl3P3x/O3dpTz4eLLuSpYgHVpYm6scOttTXpVDu/e36+pVTbpp3eIZff5dV6/QP754Up/6ya2qTiUDnw4AUOkYSB3QVNrKWGUPAACmaLWk14veb8nfVmyzpM1m9pSZPZNvQyvcfs7MvmpmPzSzP8hXIo1iZh82s+fM7LmOjo5IvomCrLushAOpz/WVTzh0qqtfL7x2Vu/cumLGX+M916/Rhf5hPXHwVMCTAQCQQzgUkE+SDjXUpNQzSDgEAACCSUnaJOl2Se+T9DkzW5i//S2SPibpJkkbJH1w7Ce7+2fd/UZ3v3Hp0miHHWezKsm2sqaaKplJ53rLp63siYOn5C7ddc3yGX+N265YohVNNfrK8y0BTwYAQA7hUGCXekWMgdQAAGAaTig3TLpgTf62Yi2Sdrr7kLsfl3RYubCoRdKP3P2Yuw9L+pqk66M/8sRy28qif5xEwrSgtqqsBlLv3t+mtYvrtGV544y/RjJh+qk3r9Y3D3fMeOMZAAATIRwKaNK2shpmDgEAgCl7VtImM1tvZmlJ75W0c8x9vqZc1ZDMbIly7WTH8p+70MwK5UB3aPSsopLLZr0k28qk3Nyhcmkr6+of0veOdOquq5fPuq3uPdevVibr+n8/GpsRAgAwO4RDIblfYlcZM4cAAMDU5St+HpC0W9JBSV9y9/1m9mkze3f+brsldZrZAUlPSvq4u3e6e0a5lrInzGyvJJP0udJ/Fxdl3ZUoQVuZJC2sS5dNW9k3D3VoMJPVO6+e+byhgk3LG7VtzQJ99QXCIQBAWGwrC8g1+baygeGshjJZVSUrI5frZcYSAAAz5u6PSHpkzG2fKnrbJX00/2fs5z4uaVvUZ5yqjKsk28qk3FDqzu7yCIceO9Cu5vq0rg+0gv4916/Rv9+5XwdbL+iqlU1BviYAAIRDgU1WOSRJPQPDWliXLs2BYvS7j76kP/3W0biPAQAAykC2RDOHJGlBbZWOdfSU5sEuYWA4oydfOqV7t60MFoz95JtW6T89fEBfeb5F/9+9W4N8TQAACIcCmnRbWT4c6uqf/+FQJuv68vMtuu6yhbr7mtmXUQMA5pdf/r24T4BSy2ZL11ZWl06pdzBTkse6lKePdqp7YFjvvHrmW8rGWlyf1tu3LNPXfnRSn7j7SqUqpBodABAtwqGAXH7pbWU1+cqhCmi1euG1szrdPaB/d+9Vuu+61XEfBwBQZn457gOg5LLuJWsrq08ny6K1/bED7apPJ3XrxiVBv+57blijxw606zsvn9bbr1wW9GsDACoTLzUENpW2skpYZ//o3jalkwndwQULAACQlMlq1tu6pqounVTvYEbZ7GS7ZKOTzboeP9Cu27csU01VMujXfvuWZVpUV6Uvv9AS9OsCACoX4VBAk7aV5SuHuub5Ont31+79bXrLpiVqrKmK+zgAAKAMuLtK1QFVl39Brn84vtayH75+Th1dA0FbygrSqYTuu261Hj/QrvO9Q8G/PgCg8hAOBeQ++bYyaf5XDu09cV4nzvVpB7OGAABAXqaEq+zr07lKnZ6B+MKhx/a3qSppkbV9vef6NRoczuqhvScj+foAgMpCOBRQrnDoEjOHiraVzWeP7mtTMmF6x1XhXykDAABzU9ZV0oHUkmKbO1Soor5lQ7OaIqqivmZ1kzYta9BXnqe1DAAwe4RDgV2ycijfVtY9j8Mhd9eufW3avqFZi+rn90Y2AAAwdbltZaV5rLp85VBcG8tePtWtVzp7ddfV0VVRm5nec8MavfDaOR0/3RPZ4wAAKgPhUEA+ydCh+vTFVfbz1aH2Lh0/3UNLGQAAGCWTLd22ssLMobgqhx7b3yZJunNrtFXUP/3m1UqY9FUGUwMAZolwKLBLXfIkE6a6dHJet5Xt2tcmM0UyfBEAAMxdWfeSbSuLe+bQYwfa9ea1C7W8qSbSx1neVKMf37RUX33hRKyb2QAAcx/hUGCTXfM0VKfmdVvZrn1tuvHyRVrWGO3FEAAAmFuy7kqWKByqHWkrK/0118lzfdrTcl7v3FqaKur3XL9aJ8716ZnjnSV5PADA/EQ4FNBkq+ylXDg0X1fZHz/do5faurTjmpVxHwUAAJSZrKtkbWX1IwOpS1859I2XTkmKvqWs4J1bV6ihOqWvvnCiJI8HAJifCIcCcrnsko1luaHU83WV/a59uf565g0BAICxMlmftMI6lLrqfFtZDOHQy+1daqhOaePS+pI8Xm06qXuuXalH97bGNmMJADD3EQ4FNpW2svk6c2jXvlZtW7NAqxfWxn0UAABQZtxLOJC6UDkUwzXXK529WrekrmTzlSTpPTesUc9gZuSFOgAApotwKKCptpXNx5lDJ8716cWW81QNAQCAcWXclSjVzKGq+FbZv9LZo8ubS1M1VHDTukVa3lStbx3uKOnjAgDmD8KhgFxTqxyaj6vsC69U3c28IQAAMI5sViULh5IJU21VsuRtVkOZrFrO9ml9icMhM9PWlU061NZV0scFAMwfhEMBuU9t5lDPPOwH372vTVeuaNT6JaW9GAIAAHND1l0l6iqTJNVXJ0s+c6jlbJ8yWde6GK6Htqxo0tGObg1lsiV/bADA3Ec4FNpUVtn3D8un0oM2R5zq6tezr57RXVfTUgYAAMaXLeHMISk3qLmvxOHQK6d7JEnrmutK+riSdOWKRg1lXMfzZwAAYDoIhwKaStxTX53ScNY1MDx/XtV5bH+73KW7ryUcAgAA48tkSzdzSMqtsy/1EpBXOvPhUAyVQ5uXN0qSXqK1DAAwA4RDIfmkhUNqrMltz5hPc4d272/T+iX12pK/KAEAABjLXUqUsHKoLp0s+UDqV073qKE6peb6dEkfV5I2LqtXMmE6TDgEAJgBwqHAJltb2lCdC4fmyzr7c72Devpop+66ekVJV7YCAIC5JVPimUN16VTJB1Ifj2GNfUF1Kqn1S+qpHAIAzAjhUEBTaSsrhEPzZZ394wfaNZx13c0KewAAcAmZrCtZwtAkjsqhVzt7tK7Em8qKbVnRqMPthEMAgOkjHAoot63s0grh0HxpK9u1r02rF9Zq25oFcR8FAACUsVK3ldVXl3ZDbGGNfZzh0JXLG/Xamd55U6EOACgdwqGAXNJkL4g11MyftrLugWF95+XTtJQBAIBJ5QZSl+7xSr2tLM419gWbV+TmP1I9BACYLsKhwKZaOTQf2sq+8dIpDWay2kFLGQAAmESpV9nXp5PqGShdOBTnGvuCKwmHAAAzRDgUkE9h6FAhHDp+ukc+lU8oY7v2tWpJQ7VuuHxR3EcBAABlLute0krjunRKfUMZZbOlud46fjq+NfYFly2qU21VkqHUAIBpIxwKyDX5Rc+i+rQ2L2/QHz/xsn7mT5/Wd17umJMhUf9QRk++1KG7rl5e0lcBAQDA3JR1lXwgtST1DZWmeujVzh41xrTGviCRMG1e3kDlEABg2giHApvskqcqmdDOB35cn77vap0416d//uc/0D/5k+/pm4dOzamQ6FuHO9Q3lNHd16yM+ygAAGAOKPXMobp8tXaphlIf7+zV5TGtsS+2ZUWjDlE5BACYJsKhgKaa7dRUJfWB7ev0zY/frv/0U9fo1IUBffD/PKufevApPXGwfU6ERLv2tWlBbZV+bMPiuI8CAADmgKx7abeV5SuHeks0dyjuNfYFm5c36nT3oE53D8R9FADAHEI4FJC7Ji8dKlKdSurnbrlcT37sdv3nf3KtOnsG9Yv/9zn95P/8rh7b31a2IdHgcFZfP9iuO7cuV1WS/wsBAIDJZbOuRIlnDklSbwk2lpXDGvuCK1c0SZIOUz0EAJgG/mUfmE0nHcpLpxJ6381r9eTHbtfvv2ebLvQN68N/9bw+8nc/Cn/AAL539LS6+od1N1vKAADAFGVdJZ1TWJg51FuCtrLXz/TGvsa+YEt+YxlDqQEA00E4VEaqkgndf9Nl+sZvvE0/c8MaPbK3tSQXNNO1a1+b6tNJ3XbFkriPAgAA5oiMu0o5jqe+OhcO9ZSgcujVzl5J0vol8a2xL1jSkNbi+jRzhwAA00I4FJAHuuhJJRO6Z9tKDWVcz796dvZfMKDhTFaPHWjXHVctV01VMu7jAACAOSKb9RJvK8u3lQ1E/0JbYY395WXQVmZm2rK8UYfYWAYAmIZU3AeYT6Y5cuiSbly3WMmE6emjnXrLpqWBvursPfvKWZ3pGaSlDAAATEvWPaa2slJUDsW/xr7YlhWN+tJzr+fmPJVyRRxQRk6e61Nn92DcxwBmZNPyhpIXYxAOBRbqBbGG6pS2rVmgZ451hvmCgeza16rqVEK3bymfwAoAAJS/rId7EW0qLg6kLkHlUJmssS/YsqJRvYMZnTjXp8sWx9/qBpTa+d4h/cR/+Zb6hkqzrRAI7Zsfu73kc+wIhwIKvVxs+4Zmffbbx9QzMKz66vj/p8pmXbv2t+ltm5eOXHABAABMprCBtaSr7Es4c+iV0z3atmZB5I8zVcVDqQmHUIl2H2hT31BGv33f1Vq5oDbu4wDTtqypuuSPyb/wA3L5jLaVTeSWDc36X988qmdfOaPbtywL9nVn6oevn1P7hQHdfS0tZQAAYOqy+RfQSrnKviZVmrayweGsWs726r7rVkX6ONOxeXkuHDrUdkF3bl0e82mA0nt4T6vWLKrVz91yedlU9AHljoHUgYX82XPjukWqSpqeOXYm3Bedhd3721SVNN1xJRcZAABg6rL5yqFS/hMtkTDVpZORD6RuOdurrJfHMOqChuqU1iyq1aH27riPApTc2Z5BPXXktO7ZtpJgCJgGwqGAQreV1aVTetOahXq6DOYOubse3deq265YogW1VXEfBwAAzCGFa6RSD0euS6cibyt7pTO3qawc1tgX27K8UYfaLsR9DKDkdu9v03DW9ZPbyqeaD5gLCIcCcoWtHJKk7Rubte/EeXX1D4X9wtO0/+QFvX6mTzuupqUMAABMz0jlUIlfxK+vTqov4oHUr5zulVRelUNSbu7QsY4eDQ5n4z4KUFIP723V5c11unpVU9xHAeYUwqGActc9Ya96btnQrEzW9dwrZ4N+3enavb9NCRN96wAAYNoKlUMhZzNORW1VsiSVQ+W0xr5gy4pGDWddx07TWobK0dk9oO8d7dQ919JSBkwX4VBgoX8G3XD5IqWTidhbyx7d16YfW9+s5obST00HAABzmyu/razklUOpyFfZv9LZq3VL6svuH6KFjWWH2rpiPglQOrv2tymTdd1LSxkwbYRDQQUeOiSppiqp69Yu1NNH4wuHjpzq0pFT3dpxDS1lAABg+uLYViYpN5A66sqh0z26vLm85g1J0oYlDUoljHAIFeXhPa3asKReV61sjPsowJxDOBSQezRbOG7Z0Kz9J8/rfF88c4ce3dsmSbqLeUMAAGAG4po5lNtWFl04VFhjv35Jec0bkqR0KqGNSxsIh1AxOroG9MyxTraUATM0pXDIzHaY2SEzO2Jmn5jgPveb2QEz229mXxjzsSYzazGz/xni0OUsip9D2zc0K+vSs8fjWWm/a3+brl+7UCsW1MTy+AAAYG7z/EzkUv+DrT6dUk+EbWXluMa+2OYVjXqJcAgVYte+VmVdtJQBMzRpOGRmSUkPSrpb0lZJ7zOzrWPus0nSJyXd5u5XS/rImC/z25K+HeLA5Sx8U1nOm9cuVDoVz9yh1zp7tf/kBd19zcqSPzYAAJgf4po5VFedVF+EbWXlusa+4MoVjTpxri/2rbdAKTy0p1VXLGvQ5uUNcR8FmJOmUjl0s6Qj7n7M3QclfVHSfWPu8yFJD7r7WUly91OFD5jZDZKWS3oszJHLl7tHsoWjpiqp62OaO7Rrf6skMW8IAADMWHwzh6KtHCrXNfYFm5fn5q4cbmdjGea39gv9+sErZ3QvLWXAjE0lHFot6fWi91vytxXbLGmzmT1lZs+Y2Q5JMrOEpP8i6WOXegAz+7CZPWdmz3V0dEz99GUoqp9F2zcs0cG2CzrXOxjNA0zg0X1tunpVky5bXJ6viAEAgPIX58yh/qGsMtlo6rvLdY19wZUrCuEQrWWY3x7d2yp36Z5r6XYAZirUQOqUpE2Sbpf0PkmfM7OFkn5F0iPu3nKpT3b3z7r7je5+49KlSwMdqfSiaiuTpO0bm+Uufb+Ec4fazvfrh6+d091UDQEAgFnIZ0OxzBySpL6haFrLjp/uKcs19gWrF9aqPp1kKDXmvYf3tmrL8kZtWs6WMmCmphIOnZB0WdH7a/K3FWuRtNPdh9z9uKTDyoVF2yU9YGavSPpDSR8ws9+d9anLVFTbyiTpTZctUE1VoqStZbv357aU7WDeEAAAmAX3eGYO1aaTkqTegWhay17t7C3LNfYFiYRp0/JGvdR2Ie6jAJFpPd+nZ185q3u38W8WYDamEg49K2mTma03s7Sk90raOeY+X1OuakhmtkS5NrNj7v5+d1/r7uuUay37S3cfd9vZfODukb1yVJ1K6obLF+mZEg6lfnRfbqjbFcsY6gYAAGau0NUVxWzGS6mvzoVDPREMpS7nNfbFrlzRqENtXSMBHTDfPLI394L2uwiHgFmZNBxy92FJD0jaLemgpC+5+34z+7SZvTt/t92SOs3sgKQnJX3c3Us/PXme276hWS+1delMT/Rzhzq7B/SD42doKQMAALMW27ayfFtZbwRDqct9jX3B5uWNOts7pI7ugbiPAkTioT0nddXKJm1cygvawGykpnInd39E0iNjbvtU0dsu6aP5PxN9jb+Q9BczOeRcEfXrMds3NkuSvn+sU3dHPGzt8QPtyjpbygAAwOzFta2sfiQcmnrlkLvrQt+wFtRVXfJ+B1tzc3zKvcK6MJT6UFuXljXWxHwaIKyWs7364Wvn9PG7tsR9FGDOCzWQGpLk0W7huHb1QtVWJUvSWvbovjZdtrhWW1c2Rf5YAABgfste7CsrqcLMoZ5pzBz6xkundONnHtfrZ3oveb+njp5WQ3VK16wq72ulLUXhEDDfPJpvKWPeEDB7hEOBRdlLn04ldOO6RXo64nDofN+Qvnf0tO6+ZmXZbt8AAABzT8krh/Izh6ZTOfS9o50ayri+/XLHJe/39NFO3bx+sVLJ8r6cbm6o1pKGNOEQ5qWH9pzUtasXlH17JzAXlPdvszmmFGP+tm9s1uH2bp2OsG/8Gy+1ayjjtJQBAIAgsjFtK5tJW9nelvOSdMkNsSfP9en46R7dmm/5L3dbVjTqcDvhEOaX18/06sWW87qHqiEgCMKhgHLbyqJ9jFs2FOYOnYnsMR7d26YVTTW6bs3CyB4DAABUjrhmDo2ssp/iQOpM1rXvZC4ceubYmQk3fH0vHxzdunFJgFNGb/PyRh1u777Y3gfMAw/vbZUk3RPxLFagUhAOBeSKvpX+2tULVJ9O6uljpyP5+j0Dw/rW4Q7ddfVyJUr98h4AAJiXCpVDpe5WL1QO9QxMrXLoWEe3egczunn9Yp3uHtDRju5x7/e9I6e1uD49Muy53F25olF9Qxm9NskcJWAueWjPSb3psoW6bHFd3EcB5gXCoTmmKpnQTesXX7LUeTa+dbhDA8NZ7biGBB4AAIRRKMAp9SzDmqqEzKS+KVYO7cm3lP3SWzdIGr+1zN31vaOd2r6xec68kLZlRW5o9iFayzBPvHK6R/tOXNC9VA0BwRAOBeQRbysruGVDs4529OjUhf7gX/vRfW1qrk/r5vWLg39tAABQmTymmUNmprqqpHqmOHNo74nzqksndfuWZVq1oGbcJSDHTveo7UL/nJk3JEmbljVIYmMZ5o9CS9m7mDcEBEM4FJDLS/KK2Pb83KFnjoedO9Q/lNE3Drbrzq3LlZwjr4QBAIDyd3GTfemvL+qqU1OeObSn5ZyuWbVAyYTplo3N484d+t6RXGv/bXNk3pAk1VentHZxHZVDmDce2tOq69cu1OqFtXEfBZg3CIcCK8Ulz9WrmtRYnQreWvbdl0+rZzDDljIAABCUK57KIUmqTyentK1sOJPV/pMXdO2aBZJyL8ad6RnU4fbRc4e+d7RTqxfW6vLmuTXnZMuKRiqHMC8c7ejWwdYLumfbqriPAswrhEMBTbDQIrhUMqGb1y/WM+OUOs/Grv1taqxJzZnNGwAAYG7IZnP/LfXMIUmqS6emNJD65VPdGhjOals+HCpsiH366MUlINms6+ljuXlDcXwvs7FleaOOn+7RwPDUWuyAcvXwnnxL2bW8oA2ERDgUkEulKR1S7oLl+OketZ0PM3doKJPV4wfadedVy5VO8X8LAAAQTlzbyiSpLp2cUlvZ3hO5YdTXrs6FQ5ctrtOaRbWj5g4daL2gc71Duu2KuTNvqGDLikZlsq6jp3riPgowKw/vadVN6xZp5QJayoCQSAECK1Uv/fb8EMRQ1UPPHOvU+b4h3UVLGQAAiEgijsqh6tSU2sr2tpxXQ3VK65rrR27bvqFZ3z9+Rtn80KTv5auI5mKV9ZYVjZKkQ+0XYj4JMHMvt3fpUHuX7mFLGRAc4VBIJWork6SrVjapqSbc3KFd+9pUl07qbZuXBvl6AAAABdmYtpVJhZlDk1cO7TlxXtesbhq1nn77xmad6x3SS/lZPU8d6dTGpfVa3lQT2Xmjsn5JvaqSNvK9AHPRQ3taZSa9i3AICI5wKKDctrLSPFYyYbp5fbOeOT77cCiTde3e3663b1mmmqpkgNMBAABcVNhWFkflUG06OenMocHhrA62XtC2NQtH3T4yd+hYpwaHs3r2lTO67Yq5VzUkSVXJhDYubdBhwiHMUe6uh/e26uZ1i7VsDga0QLkjHArIvWQjhyTlXs16tbNXJ8/1zerrPP/qWZ3uHmBLGQAAiEShciiGTfaqT6fUN3TpcOhwe5cGh7Mj84YKVuW3kj19tFMvtpxT72BGt26ce/OGCrasaKRyCHPWofYuHTnVrXu3UTUERIFwKLBSviC2fWSLxuyqhx7d16p0KqG3X7ksxLEAAABG8Rgrh+qqk+oZuHRbWWEYdWFTWbHc3KFOfefl0zK7WE00F21d2aTW8/062zMY91GAaXt4T6sSJu24hnAIiALhUEAlHDkkSbpyRaMW1lXNaii1u2v3vja9ddMSNVSnAp4OAAAgx2OcOVRXldLAcFaZ7MRXantazqupJqW1i+ve8LFbNjSrq39YX/j+q7pm1QItrEtHedxIbV3VJEk62MpQaswt7q6H97Tqlg3NWtpYHfdxgHmJcCggdy/ZtjJJSiRMP7Z+8agVq9O1p+W8Tp7vJ4EHAACRyY50lZU+Haqvzs1TvNRQ6r0nzmnbmoWycSqbChtiT3cPzumWMim30ESSDhAOYY450HpBx0736B5ayoDIEA4FVupq6e0bmtVytk+vn+md0ec/uq9NqYTpHVfRUgYAAKIRa+VQOlcZPdE6+/6hjA61denacVrKJGl5U402LMmtt791jg6jLljSUK1ljdWEQ5hzHt7TqmTCtONqZqQCUSEcCqjUbWWStH1j7iJlJtVD7q5d+1q1fWPznC6RBgAA5W2kciiGmUMNNblw6HT3wLgfP9TWpaGMa9vq8cMhSbr1imalUwndtG5RJGcspa2rmnTgJOEQ5o7ClrJbNzaruYGWMiAqhEMBlXpbmSRtWtagxfXpGc0dOtTepVc6e9lSBgAAIlWoHIohGxoJdL51uGPcj+/JD6OeqHJIkn7jzi36yi/fOlKFNJdtXdmkI6e6NTB86Q1uQLnYd+KCXu3sZUsZEDHCodBKfNWTSJhu2bBYzxztHLnwmqpH97bJTHrnVsIhAAAQncIVShzbylYuqNWbLluo3fvbx/34ntfPaXF9WqsX1k74NRbVpy8ZHs0lW1c1aTjrerm9O+6jAFPy0N6TSiWMf7MAESMcmge2b2jWyfP9em2ac4d27WvTTZcvZuI/AACIVDbGmUOSdNfVy/Xi6+fUer5v1O19gxl9/WC7btmwOJaWtzhsZSg15pDClrLbrliiRfWMwQCiRDgUyEi5dAyPfcuG3OaM6bSWHevo1qH2LlrKAABA5C7OHIrn8e/KD7F9bEz10FdeaNHZ3iF98Nb1cRwrFpc316sunWTuEOaEF1vOq+VsHy1lQAkQDgXiMV70XLGsQUsaqvX00amHQ7v2t0kS4RAAAIjcxZlD8aRDG5c2aNOyBu3OX/9IUjbr+vx3j+tNaxbMi0HTU5VMmLasaNRBKocwBzy856SqkrSUAaVAOBSYxVA7ZJabO/T0sanPHdq1r01vumyhVl2ivx4AACCEwuVJHDOHCu66eoW+f/yMzvYMSpKeeOmUjp3u0S++ZUPFtJQVbF3ZpAOtF6Y9rxIopWw211L2lk1LtaCuKu7jAPMe4VAgcf9qvWVDs9ovDOiVzsnnDp0416c9Lee142oSeAAAEL24Zw5JuXAok3V9/WCutexz3zmm1Qtr9a4KrKLeuqpJXf3DajnbN/mdgZj88PVzOnm+n5YyoEQIhwKJc0WrJG3fmJs7NJXWsl37ciXVd1fgxRAAACi9kZlDsUxnzLlmdZNWL6zV7v3t2tNyTj84fka/cNs6pZKVdznMUGrMBQ/vaVU6mdA7ti6P+yhARai834YRi+uSZ8OSei1rrNbTUxhKvWtfq65c0ah1S+pLcDIAAFDp4n4RLffYpndevVzffrlD//2Jl9VYndLP3nRZfAeK0ZUrmpQwMZQaZSubdT2yt1Vv27JUTTW0lAGlQDgUSNxtZbm5Q816ZpK5Q6e6+vXcq2cZRA0AwBxgZjvM7JCZHTGzT0xwn/vN7ICZ7TezL4z5WJOZtZjZ/yzNiceXLYOZQ1KutWxwOKuvHzyl9958mRor9B+dtemk1i+pp3IIZev5186q7QItZUApEQ4FEue2soLtG5vV0TWgox09E97nsf3tcpfuvoYftAAAlDMzS0p6UNLdkrZKep+ZbR1zn02SPinpNne/WtJHxnyZ35b07ehPe2nlUDkkSTetW6zm+rSSCdMHb6uc9fXj2bpqAZVDKFsP72lVdSqhn7iKljKgVAiHAnHFu6JVkrZvyM8dukRr2a59bdqwpF6blzeU6lgAAGBmbpZ0xN2PufugpC9Kum/MfT4k6UF3PytJ7n6q8AEzu0HSckmPlei8EyrUNMddOZRMmH7tHZv06+/YpNUVvrH1qpWNOnGuT+d7h+I+CjBKJut6eG+r3r5lmRqqU3EfB6gYhEPzyOXNdVq5oEbPTDCU+mzPoJ4+1qkd16youJWtAADMQaslvV70fkv+tmKbJW02s6fM7Bkz2yFJZpaQ9F8kfexSD2BmHzaz58zsuY6OjoBHH60ctpUVfGD7Oj1wx6a4jxG7wlDqg21UD6G8PPvKGXV0DegeWsqAkiIcCuQSY35KZrK5Q18/2K5M1pk3BADA/JGStEnS7ZLeJ+lzZrZQ0q9IesTdWy71ye7+WXe/0d1vXLp0aWSHzJZB+z1G27oqv7GM1jKUmYf2nFRNVUJ3XLks7qMAFYVwKLC4L3q2b2hWZ8+gXj7V/YaP7drXptULa3Xt6gUxnAwAAEzTCUnF67TW5G8r1iJpp7sPuftxSYeVC4u2S3rAzF6R9IeSPmBmvxv9kcd3ceYQ6VC5WNZYoyUN1QylRlkZzmS1a1+bfuLK5aqnpQwoKcKhwCy2ZfY52zfm5w6NaS3r6h/Sd14+TUsZAABzx7OSNpnZejNLS3qvpJ1j7vM15aqGZGZLlGszO+bu73f3te6+TrnWsr9093G3nZWCl8m2Moy2dVUTlUMoKz84fkanuwdpKQNiQDgUSDm0lUnSmkW1Wr2wVs+MGUr9jZdOaTCT1d20lAEAMCe4+7CkByTtlnRQ0pfcfb+ZfdrM3p2/225JnWZ2QNKTkj7u7hNvpohJYeYQ0VB52bqySS+f6tLgcDbuowCSpH/c06q6dFJv30JLGVBq1OoFcnFbWbznKMwd+sZL7cpmXYn85Mfd+9u0tLFa169dFO8BAQDAlLn7I5IeGXPbp4redkkfzf+Z6Gv8haS/iOaEU5OlcqgsbV3VpKGM68ip7pEZREBcci1lrfqJq5arNp2M+zhAxaFyKLByuOTZvrFZZ3uHdKi9S5LUN5jRky916K6rl4+ERQAAAKVyceZQzAfBKIWNZcwdQjl4+linzvYO6Z5raSkD4kA4FEi5tJVJ0i0bFkvSSGvZtw53qG8oo7uv4QctAAAovZGZQ7xIVVbWL6lXTVVCBwmHUAYeerFV9emkbt8S3eZEABMjHAqkkA2VwytiaxbV6bLFtSNDqXfvb9PCuirdvH5xzCcDAACViJlD5SmZMG1ZwVBqxG8ok9Wu/W26c+ty1VTRUgbEgXAokJFy6TK57Nm+oVnfP35G/UMZff1gu+68armqkvzPDQAASq/wIhozh8rP1pVNOtB6YeRaFojDd4+c1vm+Id2zbVXcRwEqFmlBYOVyzbN9Y7PO9w3pz797XF39w7r7WraUAQCAeBQqh+gqKz9bVzXpfN+QTp7vj/soqGAP72lVY3VKb928JO6jABWLcCiQcnut5ZYNzZKkB588oobqlG67gh+0AAAgHtmR/vtYj4FxjAylprUMMRkczmr3/jbdefVyVadoKQPiQjgUSLlV4q5cUKt1zXXqHczojiuX8YMWAADEZ6RyiHSo3Fy5olFmhEOIz3de7lBX/7Du3cbyHCBOhEOBWRld9GzfmKseuvsaWsoAAEB8CpVDhEPlp746pfXN9TrQej7uo6BCPbynVU01Kf34FWwpA+KUivsA80aZVQ5J0j+5fo1e7ezV7VuWxX0UAABQwdhWVt6uWtmkvScIh1B6/UMZPX6gXTuuWaF0iroFIE78DQzEVX4XPTetW6wvfOgW1aZpKQMAAPFxKofK2tZVTXrtTK8u9A/FfRRUmG8f7lDXwLDufRNbyoC4EQ4FUrjo4ZoHAABgtJHKIa48y1JhKPVLrV0xnwSV5uG9rVpYV6Vb8+MwAMSHX9GBkQ0BAACMRuVQedu6qrCxjNYylE7/UEZfP9CuHVevUFWSf5YCceNvYSBlOHIIAACgLDBzqLwta6xWc31aB1rZWIbS+eahU+oZzOjebbSUAeWAcCgQL1z08IoYAADAKIUX0agcKk9mpq2rmgiHUFIP7WnV4vq0btmwOO6jABDhUHBc8wAAAIw2UjnEdVLZ2rqySYfbujWUycZ9FFSA3sFhPXHwlHZcs0IpWsqAssDfxEBoKwMAABgfizvK31UrmzSYyepYR0/cR0EFePKlDvUNZXTvtpVxHwVAHuFQICMXPfEeAwAAoOwU2u9pKytfI0OpWxlKjeg9vPekljSk9WPr2VIGlAvCodC46AEAABgly7aysrdhSb3SqYQOnGTuEKLVMzCsb7x0Sndfs1LJBD8TgHJBOBSI01gGAAAwLraVlb9UMqErVzQylBqRe+KlU+ofytJSBpQZwqFQaCsDAAAYFzOH5oatK5t04OSFkTZAIAoPvXhSyxqrdeM6tpQB5YRwKJDCr1AuegAAAEZzd5nlVqajfG1d1aSzvUNqu9Af91EwT3V0Deibhzv0rmtpKQPKDeFQYEbtEAAAwChZp7p6Lti6Mj+UmrlDiMBwJqt//bc/lEl6/4+tjfs4AMYgHAqE6lsAAIDxZd0ZRj0HXJkPhw4ydwgR+IPHDunpY536zE9fq03LG+M+DoAxCIcCKQyk5roHAABgNBebyuaChuqULm+uYyg1gnt0b6v+97eO6f0/tlb/9IY1cR8HwDgIhwLjsgcAAGC0bH7mEMpfYSg1EMrRjm59/Mt79KbLFupTP7k17uMAmADhUCC0lQEAAIzPnerquWLryia90tmr7oHhuI+CeaBnYFi//FfPK51K6E/ef72qU8m4jwRgAoRDgbCtDAAAYHzOzKE5Y+uq3Nyhl2gtwyy5u37zK3t0tKNb/+N9b9aqhbVxHwnAJRAOBeL50iG2lQEAAIzGtrK5oxAOMXcIs/X5p17RQ3ta9bG7tui2K5bEfRwAkyAcCo0rHwAAgFHYVjZ3rGiq0aK6KuYOYVZ+cPyMfueRg3rn1uX6l2/bGPdxAEwB4VAgzBwCAAAYHzOH5g4z01Urm6gcwoydutCvX/3CC1q7uE5/eP+bZPzlB+YEwqHA+NEHAAAwmrsrkeAqaa7YurJJh9q6NJTJxn0UzDFDmax+9QsvqLt/WH/6czeoqaYq7iMBmCLCocBIxgEAAEZj5tDcctsVSzQwnNXfP9cS91Ewx/znR17Ss6+c1e++51ptWdEY93EATAPhUCC0lQEAAIzPxcyhueT2LUt107pF+qPHD7PSHlO288WT+vxTx/XBW9fpvutWx30cANNEOBSIq7CtDAAAAMWyTnX1XGJm+q13XaXT3QP67LePxX0czAGH27v0m1/eoxsvX6TfetdVcR8HwAwQDgXGdQ8AAMBo7s410hzz5rWLdM+2lfrct4+p/UJ/3MdBGevqH9Iv/9Xzqq9O6cH3X690in9iAnMRf3MDoa0MAABgfO4S86jnnt+860oNZ7P6o8cOx30UlCl318f+/kW9eqZXD/6zN2t5U03cRwIwQ1MKh8xsh5kdMrMjZvaJCe5zv5kdMLP9ZvaF/G3XmdnT+dv2mNnPhjx8OSlkQ7wqBgAAMFrWmTk0F61trtMHtq/T3z//ug61dcV9HJSh//3tY9q9v12fvPtK/diG5riPA2AWJg2HzCwp6UFJd0vaKul9ZrZ1zH02SfqkpNvc/WpJH8l/qFfSB/K37ZD038xsYbDTlxH3wswhLnwAAACKsa1s7vpXd1yhhuqU/vOjB+M+CsrM946c1u/vekn3bFupX/zx9XEfB8AsTaVy6GZJR9z9mLsPSvqipPvG3OdDkh5097OS5O6n8v897O4v598+KemUpKWhDl+OeFEMAABgNGcg9Zy1sC6tB+64Qt881KHvvnw67uOgTLSe79O/+tsfasPSBv3ee7bx9xuYB6YSDq2W9HrR+y3524ptlrTZzJ4ys2fMbMfYL2JmN0tKSzo6zsc+bGbPmdlzHR0dUz99GWHkEAAAwPjcXQkmXc5ZH9i+TmsW1ep3HjmobJar3ko3MJzRv/zrF9Q/lNGf/twNaqhOxX0kAAGE+jWdkrRJ0u2S3ifpc8XtY2a2UtJfSfoFd8+O/WR3/6y73+juNy5dOjcLixhIDQAAML6sO633c1hNVVIfv2uLDrRe0D/88ETcx0HM/tNDB/Wj18/pD3/mTbpiWUPcxwEQyFTCoROSLit6f03+tmItkna6+5C7H5d0WLmwSGbWJOlhSf/W3Z+Z/ZHLGyWVAAAAo7nYVjbX/eS2Vdq2ZoH+8LFD6h/KxH0cxOQrz7for555Vb/01g26+9qVcR8HQEBTCYeelbTJzNabWVrSeyXtHHOfrylXNSQzW6Jcm9mx/P3/QdJfuvuXQx26PFE6BAAAMJ6si21lc1wiYfqtd12l1vP9+vPvHo/7OIjB/pPn9Vv/sFe3bFisj9+1Je7jAAhs0nDI3YclPSBpt6SDkr7k7vvN7NNm9u783XZL6jSzA5KelPRxd++UdL+kt0r6oJn9KP/nuii+kbgV2sq47AEAABgt66wrmw9u2dCsd1y1XH/yzaPq7B6I+zgoofO9Q/qXf/2CFtZV6X+873qlkgwRA+abKf2tdvdH3H2zu29098/kb/uUu+/Mv+3u/lF33+ru17r7F/O3/7W7V7n7dUV/fhTZd1MGeFEMAABgDCqH5o1P3H2l+oYy+uMnXo77KCiRbNb10S/9SK3n+/S/3n+DljZWx30kABEg8g2EpjIAAIDxZd2ZOTRPXLGsQe+96TJ94fuv6VhHd9zHQQk8+OQRPfHSKf27e7fqhssXxX0cABEhHArkYlsZVz4AAADF2FY2v3zkHZtVnUro93a9FPdRELFvHe7QH339sH76zav1z2+5PO7jAIgQ4VAgnq8domIaAABgNHeukeaTpY3V+uW3bdTu/e36wfEzcR8HEXn9TK9+7Ys/1Jbljfqdn76WrczAPEc4FBg/MgEAAEZjW9n88y/eskHLm6r1O48clDsDFuab/qGMfuVvXlAm6/rTn7tBtelk3EcCEDHCoUD4nQgAADA+d6dyaJ6pTSf1G3du0Y9eP6eH97bGfRwE9h927tfeE+f1R/dfp3VL6uM+DoASSMV9gPliZOYQFz4AAACjuKgcmo/ec8Maff6p4/q9XS/xv+888lJbl7747Ot64O1X6M6ty+M+DoASIRwKjl+MAAAAxdhWNj8lE6Z/e89V+uD/eVa/8jcvxH0cBPTWzUv163dujvsYAEqIcCgQZ5k9AADAuLIuyqvnqbdsWqqnfvMOne8bivsoCMRM2ri0QUkSXaCiEA4FQlsZAADA+JzKoXltxYIarVhQE/cxAACzwEDqwLjuAQAAGM3ZVgYAQFkjHArMuPABAAAYJevOC2gAAJQxwqFAWGUPAAAwPiqHAAAob4RDgRQGUnPZAwAAMFrWnbmMAACUMcKhwLjwAQAAGM2dayQAAMoZ4VAgtJUBAACMz+W0lQEAUMYIhwIpZENc9wAAAIyWZeYQAABljXAoMGPqEAAAwCjMHAIAoLwRDgXi9JUBAACMKzdziHQIAIByRTgUyEg0xHUPAADAKO6uBNdIAACULcKhQAqFQ1z3AAAAjJZ1rpEAAChnhEPB5NIhhi0CAACMxrYyAADKG+FQIJls7r9c+AAAAIyWzTJzCACAckY4FEg231eW4BkFAAAYhW1lAACUN6KMQEbCIa58AAAA3oCB1AAAlC/CoUCytJUBAACMK+vMHAIAoJwRDgVSqBxK8owCAACMknXRVgYAQBkjygikEA4xbBEAAGA0d+caCQCAMkY4FAgzhwAAAMbnzjUSAADljHAokMLMoSQXPgAAAKNk3cUVEgAA5YtwKJDMSFtZzAcBAAAoMy62lQEAUM4IhwLxkYHUXPkAAAAUY1sZAADljXAokGwuG+LCBwAAYIxsVqKvDACA8kU4FEgmWxhIHfNBAAAAyhAvoAEAUL4IhwIZ2VZGOgQAADBKrq0s7lMAAICJEA4Fwip7AACA8eW2lXGNBABAuSIcCoRV9gAAAONzlxJcdQIAULb4NR3AqQv9+oPdhySxyh4AAGCsrEvGRRIAAGWLcCiA092DarvQL4mZQwAAIBwz22Fmh8zsiJl9YoL73G9mB8xsv5l9IX/bdWb2dP62PWb2s6U9+WjuTlMZAABlLBX3AeaDrauaRt6mrQwAAIRgZklJD0q6U1KLpGfNbKe7Hyi6zyZJn5R0m7ufNbNl+Q/1SvqAu79sZqskPW9mu939XGm/ixwXcxkBAChnVA4FRuEQAAAI5GZJR9z9mLsPSvqipPvG3OdDkh5097OS5O6n8v897O4v598+KemUpKUlO/kYbCsDAKC8EQ4FRj89AAAIZLWk14veb8nfVmyzpM1m9pSZPWNmO8Z+ETO7WVJa0tHITjqJbNa5RgIAoIzRVhZYkpfFAABA6aQkbZJ0u6Q1kr5tZtcW2sfMbKWkv5L08+6eHfvJZvZhSR+WpLVr10Z2SBdLOwAAKGdUDgV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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(20, 20))\n",
    "g1 = series_abg_jan.plot(ax=axes[0][0])\n",
    "g1.set_title(\"Average area burnt for each false negative in function of gamma\")\n",
    "#g1.set_ylim([0, 20])\n",
    "g1.set_xlim([-5, 50])\n",
    "g2 = series_abgr_jan.plot(ax=axes[0][1])\n",
    "g2.set_title(\"Average area burnt for each false negative  burnt in function of g (smaller range)\")\n",
    "#g2.set_ylim([0,20])\n",
    "g3 = series_ag_jan.plot(ax=axes[1][0])\n",
    "g3.set_xlim([-5, 50])\n",
    "g3.set_title(\"Accuracy in function of gamma\")\n",
    "g4 = series_agr_jan.plot(ax=axes[1][1])\n",
    "g4.set_title(\"Accuracy in function of gamma (smaller range)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "2cb84206-55fb-46e5-adda-e38b28b80899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j9T110r6taq1dM9XjHMZN6b58HjLhcU5srY1/yR7utZ50/2F6d5IzWjdx8Z9N2JeZPN9vTnJB/9r9tnTHZnz/DvfczaXdOfzr/nCuyvSTFB7ueNyU7pfYia/3ya/1mfpWkjMmTSp4NI/1j0l+4Bi2C0uC9sY9Hl97Y2aPqb1xZEu5vTGx1jPTfZ/flEntierO+Lnpnqse1Wv1aF/Xx2sQ7aHk3u/HM3P3Z8XR+FaSk6pqYmBztO2h59TMJ6C/LN28zofCp9ba11prz08X2v9+krfVNGc6O4L/lO64Paz/DPnRHP7zdtxV6XriTHx9r22t/d6EZWbyujqa763DuTbdczLxdTX5e+1I9T4o3VxEx0w4tDQ8O90vFQ9O9yvdueleHP8vyQv7Zd6UbtzjC/rL4/4iycv6Xx2qqtZW1dMnfVhMtDbdm+DGJKmql6RLRMe9NcnLq2pLVW1I1wUuSdJauzbdhGz/tapOqKplVbWtqr57mm2tTzfW8o6qemC6cZmHc7h9OVLdUzm/qp7b/wr18+kamp/o77s0yY9U1fKqekq68erH4/ocXQPovUnuX1U/UlUjVfXD6Z7/98xw/U8lub2qfrWqVvf78dCqelR//+GO/XuSnFZVP19VK6tqfVV921HUPp0V6cYL35jkQFU9Nd0443upqgdU1RP6Bt5d6RpV471B/izJ7/Qf0KmqTVX1rKMtpv/19i+S/GFVbe4fa0tVPblf5K1JXlJVD+o/yP+/SQ+xPt0vAHdV1aPT/Ro37sa+3mmf89ad6vOmJH+Z5AOttVv7u4703M2lS5M8rapOqqr7pHtfzNRfpTte39u/97f0r63kMK//Cf/Z/J3+tXZWujHVbzyG+j+Z7j9av1JVo1X1+CTPTPer6Uz8RpLvqKo/6Pc/VbW9qt7Yf97BUvfsaG8caV+0N+5Je+MIlnh740er6sF93a9O8rb+e/2r6Xp6Pb2qRtOdaGLlUTzuZEfcz1l2aZLvqqozq+rEdEPFZurvkjyxqn6of0+dXFXn9vcd6f355iT/sX+tnZJuPqijbg+11q5KN1/R71bVqqp6eLqePDN9rP+W5IQkb5jw+t9SVf+tf6zJ29uXLlA69PlVVT9aVZv61/+t/c1jk9edgfXpJs6+rbrw6ZdnuN4bkzyzqp7cv7ZXVdXjq2rrEdec3rF8/idJWmvfSDdB96uqakVVPSZdG/Vo6v3uJO87jvqFQ0vEi9KN//1ma+268b90Z3x4QVWNtNY+mS7lPj0TXjSttYvSjU/843Rd/i5LNx5ySq21L6U7O8jH032APSzdnALj/iJdg+xz6c6c8N504ycP9ve/MN2X8pf67b0t03dL/KV0X3C394/794c7CIfblxnUPZV3pWvg3pJuzP1zW2v7+/tenu4Ne2u6BvA7j/BYR/K76T7sb62qXzrSwq21m9OdKeUX03Xt/JUkz2h3d/M/0voH+/XPTTfx5Xij4MR+kWmPff8r8Pel2//r0o07/p6ZbPcINd2e5N+lawTd0m//3dMsvjLJ7/V1X5fuV4fxL+b/3q/3waq6PV0D+1gbk7+a7nX0ieq6qv5juskp01p7X5L/kW4YxWW5uyG/t//3Z5K8uq/h1/v9Gt/XPenPgNA/598+zfbflG6egzdNWPdIz91c+tt0v0hcme59ftj35EStO8XmS9JNunlbkv+bu3/9+u/pfrW8par+xxSr/9t0n19XpDvLw5vSzcFwVPrGyTOTPDXdcfvTdPOhfGWG61+ebh6Is5N8sbru+29P92V++2FWhaVCe+MI+6K9ca/1tTdmZqm2N/423Tw616WbXPjf9Y99W1/3X6brrbI73XDKY3IU+zkrWmsfSvda/Vy6SbtnGpamtfbNdMMYfzHdmcIuTTchdtL9kPbgfh/eOcXqv52uzfG5dGcLu6S/7Vg8P1175lvphv/+RmvtH2e4DzvTncVwf5JP9q+9/5OufXfZNKv9ebrPt3FPSdeWuiPde+l5rbU7j2E/fjPJef22/yHJO2a4D1elO4vuK9OFOVelC5aOOR85xs//iV6Qrp15c7rn9e/Tv8+PVG8f2t7Rt7eP2fjM2DAn+l9j/qy1NrlLMiwpVfWgdF1HV7Z7jvcGYI5pbzAstDdYrKrqY0l+ru+txhFU1d8n+Upr7TdmsOzbk/xVa+29x7NNPYeYVX2306f13SS3pBuGceGR1oPFqKqe03d135huvPT/1lADmHvaGwwT7Q2WgtbaYwVD06uqR1U3BHpZP4z4WZlhb9HW2g8cbzCUCIeYfZWue98t6bp5fzldF1dYiv5NutOEXp5uKMOR5qkAYHZobzBMtDdg6btPko+km0PpfyT56fkO0wwrAwAAABhieg4BAAAADDHhEAAAAMAQGxl0AZOdcsop7eyzzx50GQDAHLr44otvaq1tGnQd3E0bDACWtsO1vxZcOHT22WfnoosuGnQZAMAcqqpvDLoG7kkbDACWtsO1vwwrAwAAABhiwiEAAACAISYcAgAAABhiwiEAAACAISYcAgAAABhiwiEAAACAISYcAgAAABhiwiEAgEWsqq6sqs9X1aVVddGk+36xqlpVnTKo+gCAhW9k0AUAAHDcvqe1dtPEG6rqjCRPSvLNwZQEACwWeg4BACxNf5jkV5K0QRcCACxswiEAgMWtJflgVV1cVS9Nkqp6VpJrWmufHWxpAMBiYFgZAMDi9rjW2jVVtTnJh6rqK0lemW5I2WH1YdJLk+TMM8+c2yoBgAVLzyEAgEWstXZN/+8NSS5M8t1J7pvks1V1ZZKtSS6pqvtMse5rW2s7Wms7Nm3aNI9VAwALiXAIAGCRqqq1VbV+/HK63kKfbq1tbq2d3Vo7O8nVSc5rrV03wFIBgAXMsDIAgMXr1CQXVlXSteve1Fp7/2BLAgAWG+EQAMAi1Vq7IskjjrDM2fNTDQCwWBlWBgAAADDEhEMAAAAAQ0w4BAAAADDEFtycQ9+4eU9+6m8uOuwy61eO5NXPfmjWrVxw5QMAAAAsKgsuXdl3cCxX33LntPffue9Arrx5Ty44f2u+Y/sp81gZAAAAwNKz4MKhczavy/te/p3T3v/la3flqf/9/+WWPfvnsSoAAACApWnRzTm0cc2KJMkte/YNuBIAAACAxW/RhUMb1owmSW4VDgEAAAAct0UXDq0aXZ41K5YbVgYAAAAwCxZdOJR0Q8sMKwMAAAA4fosyHNqwZjS36jkEAAAAcNwWZTi0cc2K7Nyt5xAAAADA8VqU4VDXc0g4BAAAAHC8FmU4dNLaFSakBgAAAJgFizIc2rBmRXbdtT8Hx9qgSwEAAABY1BZlOLRxzWhaS267U+8hAAAAgOOxSMOhFUnidPYAAAAAx2lRhkMb1owmSW5xxjIAAACA47Iow6GT1o73HDKsDAAAAOB4LMpwyLAyAAAAgNmxKMOh8WFltwqHAAAAAI7LogyH1q0cyciyMqwMAAAA4DgtynCoqrJhzQo9hwAAAACO06IMh5Jk45rR7HS2MgAAAIDjsnjDobUrDCsDAAAAOE6LNxxaM2pYGQAAAMBxWsThkJ5DAAAAAMdrZDYfrKquTHJ7koNJDrTWdlTVSUn+PsnZSa5M8kOttVuOd1vjE1K31lJVx/twAAAAAENpLnoOfU9r7dzW2o7++q8l+T+ttXOS/J/++nHbuGY0+w+27N53cDYeDgAAAGAozcewsmcleUN/+Q1Jnj0bD7pxzYokyS3OWAYAAABwzGY7HGpJPlhVF1fVS/vbTm2tXdtfvi7JqbOxoY1r+3DIpNQAAAAAx2xW5xxK8rjW2jVVtTnJh6rqKxPvbK21qmqTV+qDpJcmyZlnnjmjDW1cM5okJqUGAAAAOA6z2nOotXZN/+8NSS5M8ugk11fVaUnS/3vDFOu9trW2o7W2Y9OmTTPa1oZ+WJnT2QMAAAAcu1kLh6pqbVWtH7+c5ElJvpDk3Ule1C/2oiTvmo3tHeo5ZM4hAAAAgGM2m8PKTk1yYX9a+ZEkb2qtvb+qPp3krVX1E0m+keSHZmNjJ642rAwAAADgeM1aONRauyLJI6a4/eYk3ztb2xk3snxZTlg1YkJqAAAAgOMwH6eynzMnrV2h5xAAAADAcVjU4dCGNStMSA0AAABwHBZ1OLRxzahhZQAAAADHYZGHQytyy27DygAAAACO1aIOhwwrAwAAADg+izocOmntaHbvO5i9Bw4OuhQAAACARWlRh0Mb1qxIktzqjGUAAAAAx2RRh0Mb+3DIpNQAAAAAx2aRh0OjSWJSagAAAIBjtKjDobuHlek5BAAAAHAsFnU4tHFt33PInEMAAAAAx2Rxh0PmHAIAAAA4Los6HFo1ujyrR5fnlt3CIQAAAIBjsajDoaSblNqwMgAAAIBjs+jDoQ1rVpiQGgAAAOAYLfpwaOPaUXMOAQAAAByjRR8OdT2HDCsDAAAAOBaLPhw6ac2K7NRzCAAAAOCYLPpwaOOa0dx25/4cHGuDLgUAAABg0Vn04dCGNSvSWrLrTkPLAAAAAI7Wog+HNq4dTRKTUgMAAAAcg0UfDm1YsyJJcotJqQEAAACO2qIPhzb24dCteg4BAAAAHLVFHw6d1IdDO3cLhwAAAACO1qIPhzb0cw7dalgZAAAAwFFb9OHQ+pUjGVlWJqQGAAAAOAaLPhyqqmxYM2pCagAAAIBjsOjDoaQ7Y5kJqQEAAACO3pIIhzauGTWsDAAAAOAYLJFwaEVu2W1YGQAAAMDRWjrhkJ5DAAAAAEdtSYRDG9aO5tY9+9NaG3QpAAAAAIvKkgiHNq5ZkX0Hx7Jn38FBlwIAAACwqCyRcGg0SQwtAwAAADhKSyQcWpEkuXWPSakBAAAAjsbSCIfWduHQzt16DgEAAAAcjaURDhlWBgAAAHBMlkQ4tMGwMgAAAIBjsjTCodV6DgEAAAAciyURDo0sX5b1q0b0HAIAAAA4SksiHEqSk9auMCE1AAAAwFFaMuHQhjUrDCsDAAAAOEpLJhzauGbUsDIAAACAozQy6AJmy8Y1K3LZDXcMugwAgHlVVVcmuT3JwSQHWms7quq3kjwryViSG5K8uLX2rcFVCQAsZEum59AGPYcAgOH1Pa21c1trO/rrf9Bae3hr7dwk70ny64MrDQBY6JZMOLRxzYrcsfdA9h0YG3QpAAAD1VrbNeHq2iRtULUAAAvf0gmH1q5IktxqUmoAYLi0JB+sqour6qXjN1bV71TVVUlekGl6DlXVS6vqoqq66MYbb5yncgGAhWbphENrRpMktxhaBgAMl8e11s5L8tQkP1tV35UkrbX/0Fo7I8nfJfm5qVZsrb22tbajtbZj06ZN81cxALCgLKFwqOs55HT2AMAwaa1d0/97Q5ILkzx60iJ/l+QH5rsuAGDxWDLh0Ia+55BhZQDAsKiqtVW1fvxykicl+UJVnTNhsWcl+cog6gMAFocldSr7xLAyAGConJrkwqpKunbdm1pr76+qt1fVA9Kdyv4bSV42wBoBgAVuCYZDeg4BAMOhtXZFkkdMcbthZADAjC2ZYWWrVyzPqtFluWW3cAgAAABgppZMOJR0vYcMKwMAAACYuSUVDm1Ys8KE1AAAAABHYUmFQxvXjOo5BAAAAHAUllg4tMKE1AAAAABHYWmFQ2tHc6ueQwAAAAAztrTCoX7OobGxNuhSAAAAABaFJRUOnbh6NGMtuf2uA4MuBQAAAGBRWHLhUJLcdqehZQAAAAAzIRwCAAAAGGLCIQAAAIAhtrTCoTXCIQAAAICjsbTCIT2HAAAAAI6KcAgAAABgiC2pcGj16PKMLi/hEAAAAMAMLalwqKpy4upR4RAAAADADC2pcChJTlg9ml3CIQAAAIAZWXLhkJ5DAAAAADMnHAIAAAAYYsIhAAAAgCEmHAIAAAAYYksyHNp11/6MjbVBlwIAAACw4C3JcKi15Pa9BwZdCgAAAMCCt+TCoRNWjyaJ09kDAAAAzMCSC4dO7MMh8w4BAAAAHNmSDYf0HAIAAAA4siUbDuk5BAAAAHBkwiEAAACAISYcAgAAABhiSy4cWrNieUaWlXAIAAAAYAaWXDhUVTlx9ahwCAAAAGAGllw4lCQnCIcAAAAAZkQ4BAAAADDElmQ4dOLq0ewSDgEAAAAc0ZINh/QcAgAAADiyJRoOjQiHAAAAAGZgiYZDo9l114G01gZdCgAAAMCCtmTDoYNjLXfsPTDoUgAAAAAWtCUbDiUxtAwAAADgCIRDAAAAAENsVsOhqlpeVZ+pqvf0119fVV+vqkv7v3Nnc3vTOUE4BAAAADAjI7P8eC9P8uUkJ0y47Zdba2+b5e0c1njPoV3CIQAAAIDDmrWeQ1W1NcnTk/zlbD3msTKsDAAAAGBmZnNY2R8l+ZUkY5Nu/52q+lxV/WFVrZzF7U1LOAQAAAAwM7MSDlXVM5Lc0Fq7eNJdr0jywCSPSnJSkl+dZv2XVtVFVXXRjTfeeNz1rFs5kuXLSjgEAAAAcASz1XPosUm+v6quTPKWJE+oqje21q5tnb1JXpfk0VOt3Fp7bWttR2ttx6ZNm467mKrKCatGhEMAAAAARzAr4VBr7RWtta2ttbOTPC/Jh1trP1pVpyVJVVWSZyf5wmxsbyZOXD2aXXcemK/NAQAAACxKs322ssn+rqo2JakklyZ52Rxv75ATV4/qOQQAAABwBLMeDrXWPpLkI/3lJ8z248/UCcIhAAAAgCOazbOVLSjdsDLhEAAAAMDhLOlwSM8hAAAAgMNb8uFQa23QpQAAAAAsWEs6HDow1rJn38FBlwIAAACwYC3pcCiJoWUAAAAAh7Fkw6EThEMAAAAAR7RkwyE9hwAAAACOTDgEAAAAMMSEQwAAAABDbMmGQ+NzDu0SDgEAAABMa8mGQ+tXjqRKzyEAAACAw1my4dCyZZUTVo0KhwAAAAAOY8mGQ0k375BwCAAAAGB6wiEAAACAISYcAgAAABhiwiEAAACAIbakw6ETVo86lT0AAADAYSzpcGi851BrbdClAAAAACxISz4c2n+w5c79BwddCgAAAMCCtOTDoSTZdeeBAVcCAAAAsDANRThkUmoAAACAqQmHAAAAAIaYcAgAAABgiAmHAAAAAIaYcAgAAABgiC3pcGj9qpFUCYcAAAAAprOkw6FlyyrrV45kl3AIAAAAYEpLOhxKkhPXjOo5BAAAADCNpR8OrRYOAQAAAExnZNAFzLUTVgmHAIClq6quTHJ7koNJDrTWdlTVHyR5ZpJ9SS5P8pLW2q0DKxIAWND0HAIAWPy+p7V2bmttR3/9Q0ke2lp7eJKvJnnF4EoDABY64RAAwBLTWvtga+1Af/UTSbYOsh4AYGETDgEALG4tyQer6uKqeukU9/94kvfNc00AwCKy9OccWj2afQfGctf+g1k1unzQ5QAAzLbHtdauqarNST5UVV9prX00SarqPyQ5kOTvplqxD5NemiRnnnnmfNULACwwQ9FzKIneQwDAktRau6b/94YkFyZ5dJJU1YuTPCPJC1prbZp1X9ta29Fa27Fp06Z5qhgAWGiEQwAAi1RVra2q9eOXkzwpyReq6ilJfiXJ97fW9gyyRgBg4Vvyw8qEQwDAEnZqkgurKunadW9qrb2/qi5LsjLdMLMk+URr7WWDKxMAWMiGJxzaIxwCAJaW1toVSR4xxe3bB1AOALBIGVYGAAAAMMSEQwAAAABDbMmHQycIhwAAAACmteTDoeXLKutXjmTXXcIhAAAAgMmWfDiUdL2H9BwCAAAAuLehCIdOXD2aXcIhAAAAgHsZmnBIzyEAAACAexMOAQAAAAwx4RAAAADAEBuOcGiNcAgAAABgKsMRDq0ezV37x7L3wMFBlwIAAACwoAxFOHTC6tEk0XsIAAAAYJKhCIdO7MMhp7MHAAAAuKehCof0HAIAAAC4J+EQAAAAwBAbinDohFUjSYRDAAAAAJMNRTh0qOfQHuEQAAAAwERDEQ7dfbayAwOuBAAAAGBhGYpwaHT5sqxdsdywMgAAAIBJhiIcSrqhZcIhAAAAgHsamnDoBOEQAAAAwL0MTTh04urR7BIOAQAAANzDUIVDeg4BAAAA3JNwCAAAAGCICYcAAAAAhthQhUN37j+YfQfGBl0KAAAAwIIxPOHQmtEkya679B4CAAAAGDc84dDqLhwytAwAAADgbkMTDp0gHAIAAAC4l6EJh/QcAgAAALi3oQuHdgmHAAAAAA4ZunBIzyEAAACAuw1fOLRHOAQAAAAwbmjCodHly7JmxXI9hwAAAAAmGJpwKOl6DwmHAAAAAO4mHAIAAAAYYkMVDp0gHAIAAAC4h6EKh/QcAgAAALinoQqHTlg1ml3CIQAAAIBDhioc0nMIAAAA4J6GLhzave9g9h8cG3QpAAAAAAvCkIVDI0liaBkAAABAb7jCoTWjSWJoGQAAAEBvuMKh1cIhAAAAgImEQwAAAABDTDgEAAAAMMSGKhw6oQ+HTEgNAAAA0BmqcGi859Cuuw4MuBIAAACAhWGowqGVI8uzanSZYWUAAAAAvaEKh5Ku99Bte4RDAAAAAMmwhkN6DgEAAAAkEQ4BAAAADDXhEAAAAMAQGxl0AfPthNWj+fK1tw+6DACAofCb//uL+dK3dg26DABYFB58+gn5jWc+ZN63O6s9h6pqeVV9pqre01+/b1V9sqouq6q/r6oVs7m9Y3Hi6tHs0nMIAAAAIMns9xx6eZIvJzmhv/77Sf6wtfaWqvqzJD+R5DWzvM2jcuLq0dy+90AOjrUsX1aDLAUAYMkbxK+fAMDRmbWeQ1W1NcnTk/xlf72SPCHJ2/pF3pDk2bO1vWN14urRJNF7CAAAACCzO6zsj5L8SpKx/vrJSW5trR3or1+dZMtUK1bVS6vqoqq66MYbb5zFku5tPBwyKTUAAADALIVDVfWMJDe01i4+lvVba69tre1ore3YtGnTbJQ0LeEQAAAAwN1ma86hxyb5/qp6WpJV6eYc+u9JNlTVSN97aGuSa2Zpe8dMOAQAAABwt1npOdRae0VrbWtr7ewkz0vy4dbaC5L8U5IL+sVelORds7G94yEcAgAAALjbrJ7Kfgq/muQXquqydHMQ/dUcb++IhEMAAAAAd5vtU9mntfaRJB/pL1+R5NGzvY3jcYJwCAAAAOCQue45tOCsGl2eFSPLnMoeAAAAIEMYDiXd0DI9hwAAAACEQwAAAABDTTgEAAAAMMSEQwAAAABDTDgEAAAAMMSGNhxytjIAAACAIQ2HTlg9mtv3HsjYWBt0KQAAAAADNZTh0ImrR9NacvtdBwZdCgAAAMBADW04lMS8QwAAAMDQEw4BAAAADDHhEAAAAMAQEw4BAAAADDHhEAAAAMAQEw4BAAAADLGhDIdWjS7LiuXLhEMAAADA0BvKcKiqcsLqUeEQALDoVdWVVfX5qrq0qi7qb/vBqvpiVY1V1Y5B1wgALGwjgy5gUE5cPZJdwiEAYGn4ntbaTROufyHJc5P8+YDqAQAWkSEOh/QcAgCWptbal5OutzQAwJEM5bCyRDgEACwZLckHq+riqnrp0axYVS+tqouq6qIbb7xxjsoDABY64RAAwOL2uNbaeUmemuRnq+q7Zrpia+21rbUdrbUdmzZtmrsKAYAFTTgEALCItdau6f+9IcmFSR492IoAgMVmqMOhXXftz9hYG3QpAADHpKrWVtX68ctJnpRuMmoAgBkb2nDohNWjaS25fe+BQZcCAHCsTk3yz1X12SSfSvIPrbX3V9VzqurqJI9J8g9V9YGBVgkALGhDe7ayE1aPJkl23bk/J/aXAQAWk9baFUkeMcXtF6YbYgYAcERD23NoPBAy7xAAAAAwzIRDwiEAAABgiAmHhEMAAADAEBMOCYcAAACAITb04dAu4RAAAAAwxIY2HFqzYnlGlpWeQwAAAMBQG9pwqKpy4upR4RAAAAAw1IY2HEoiHAIAAACG3lCHQycIhwAAAIAhN9Th0ImrR01IDQAAAAy1oQ+H9BwCAAAAhplwSDgEAAAADLGhD4d23XUgrbVBlwIAAAAwEEMdDm1YM5qDYy277jww6FIAAAAABmKow6GtG9ckSa68efeAKwEAAAAYjKEOh7ZvXpckufzGOwZcCQAAAMBgDHU4dNbJazKyrHLZDcIhAAAAYDgNdTg0unxZzjp5jZ5DAAAAwNAa6nAoSbZtWqfnEAAAADC0hj4c2r55Xb5x857sPzg26FIAAAAA5t3Qh0PbNq3LgbGWb9y8Z9ClAAAAAMy7oQ+HnLEMAAAAGGZDHw7db9PaJDHvEAAAADCUhj4cWr9qNPc5YZWeQwAAAMBQGvpwKEm2bV6by/UcAgAAAIaQcCjJ9k3rcvmNu9NaG3QpAAAAAPNKOJRk2+Z1uWPvgVy/a++gSwEAAACYV8KhdD2HEmcsAwAAAIaPcChdz6HEGcsAAACA4SMcSrJ5/cqsXzmi5xAAAAAwdIRDSaoq99u8Ts8hAAAAYOgIh3rdGcuEQwAAAMBwEQ71tm1em+t37c2uu/YPuhQAAACAeSMc6o2fseyKG3cPuBIAAACA+SMc6jljGQAAADCMhEO9M09ak9HlZd4hAAAAYKgIh3qjy5flrJPX6jkEAAAADBXh0ATOWAYAAAAMG+HQBNs2r803bt6TfQfGBl0KAAAAwLwQDk2wffO6HBxr+eZOZywDAAAAhoNwaIJtm5yxDAAAABguwqEJxsOhy2/UcwgAAAAYDsKhCdauHMlpJ67ScwgAAAAYGsKhSbZvdsYyAAAAYHgIhybZtmldLr/hjrTWBl0KAAAAwJwTDk2ybfO67N53MNftumvQpQAAAADMOeHQJNs2rU2SXH6DSakBAACApU84NMn2zeOns799wJUAAAAAzD3h0CSb1q3M+lUjTmcPAAAADAXh0CRVle2b1zmdPQAAADAUhENT2LbJ6ewBAACA4SAcmsL2zetyw+17s+uu/YMuBQAAAGBOCYemsG1TNyn15YaWAQAAAEuccGgKd5+xTDgEAAAALG3CoSmcsXF1Vixf5oxlAAAAwJInHJrCyPJlOfuUNXoOAQAAAEuecGga2zatyxXOWAYAAAAsccKhaWzfvC7f2Lkn+w6MDboUAAAAgDkjHJrGtk3rcnCs5Rs3m3cIAAAAWLqEQ9NwxjIAAABgGAiHpnHfU9YmSS437xAAAACwhAmHprF25UhOP3GVnkMAAADAkiYcOoxtm9fl8hvNOQQAAAAsXcKhw9i2aV0uv/GOjI21QZcCAAAAMCeEQ4exffO67Nl3MNftumvQpQAAAADMCeHQYWzb5IxlAAAAwNImHDqM8dPZO2MZAAAAsFTNWjhUVauq6lNV9dmq+mJV/WZ/++ur6utVdWn/d+5sbXOunbJuRU5YNaLnEAAAALBkjcziY+1N8oTW2h1VNZrkn6vqff19v9xae9ssbmteVFW2b16n5xAAAACwZM1az6HWGU9RRvu/RX+ar22b1uWyG5zOHgAAAFiaZnXOoapaXlWXJrkhyYdaa5/s7/qdqvpcVf1hVa2cYr2XVtVFVXXRjTfeOJslHbftm9flpjv25rY9+wddCgAAAMCsm9VwqLV2sLV2bpKtSR5dVQ9N8ookD0zyqCQnJfnVKdZ7bWttR2ttx6ZNm2azpON26IxlhpYBAAAAS9CcnK2stXZrkn9K8pTW2rX9kLO9SV6X5NFzsc254oxlAAAAwFI2m2cr21RVG/rLq5N8X5KvVNVp/W2V5NlJvjBb25wPWzeuzorly4RDAAAAwJI0m2crOy3JG6pqebrQ6a2ttfdU1YeralOSSnJpkpfN4jbn3MjyZbnvKWtzudPZAwAAAEvQrIVDrbXPJXnkFLc/Yba2MSjbNq/Nl6+9fdBlAAAAAMy6OZlzaKnZvmldvnHz7uw9cHDQpQAAAADMKuHQDGzbvC5jLfnGzXsGXQoAAADArBIOzcCh09mbdwgAWGCq6sqq+nxVXVpVF/W3nVRVH6qqr/X/bhx0nQDAwiUcmoH7bVqbJCalBgAWqu9prZ3bWtvRX/+1JP+ntXZOkv/TXwcAmJJwaAbWrBjJlg2rc5nT2QMAi8Ozkryhv/yGJM8eXCkAwEInHJqhbZvX5XLhEACw8LQkH6yqi6vqpf1tp7bWru0vX5fk1MGUBgAsBrN2KvulbvumdXnz13dmbKxl2bIadDkAAOMe11q7pqo2J/lQVX1l4p2ttVZVbaoV+zDppUly5plnzn2lAMCCpOfQDG3bvDZ37j+Ya3fdNehSAAAOaa1d0/97Q5ILkzw6yfVVdVqS9P/eMM26r22t7Wit7di0adN8lQwALDDCoRna7oxlAMACU1Vrq2r9+OUkT0ryhSTvTvKifrEXJXnXYCoEABYD4dAMbdvchUPOWAYALCCnJvnnqvpskk8l+YfW2vuT/F6S76uqryV5Yn8dAGBK5hyaoZPXrsiGNaPOWAYALBittSuSPGKK229O8r3zXxEAsBjpOTRDVZVtm9bpOQQAAAAsKcKho7B9k9PZAwAAAEuLcOgobNu8NjfdsS+37tk36FIAAAAAZoVw6ChsH5+UWu8hAAAAYIkQDh2FbZvGz1i2e8CVAAAAAMwO4dBR2LpxTVaMLHPGMgAAAGDJEA4dheXLKvc7Za0zlgEAAABLhnDoKG3bvE7PIQAAAGDJEA4dpW2b1uWqnXty1/6Dgy4FAAAA4LgJh47S9s3rMtaSK282KTUAAACw+AmHjtK2TWuTOGMZAAAAsDQIh47S/U5Zl6rkMpNSAwAAAEuAcOgorV6xPFs2rM7lJqUGAAAAlgDh0DHYvnmdcAgAAABYEoRDx2Dbpi4cGhtrgy4FAAAA4LgIh47B9s3rctf+sXzrtjsHXQoAAADAcREOHYNtm9YlMSk1AAAAsPgJh47B9s1dOHT5jU5nDwAAACxuwqFjcNLaFdm4ZlTPIQAAAGDREw4dI2csAwAAAJYC4dAx2rZpXS7XcwgAAABY5IRDx2j75nW5efe+3LJ736BLAQAAADhmwqFjNH7GMkPLAAAAgMVMOHSM7j5jmXAIAAAAWLyEQ8fo9A2rs3JkmTOWAQAAAIuacOgYLV9Wue8pa3P5jbsHXQoAAADAMRMOHYftm9fpOQQAAAAsasKh47Bt07pcdcue3LX/4KBLAQAAADgmwqHjsH3zurSWfP0mQ8sAAACAxUk4dByczh4AAABY7IRDx+F+m9amKuYdAgAAABYt4dBxWDW6PFs3rnbGMgAAAGDREg4dp+2bnLEMAAAAWLyEQ8dp26Z1ueLGOzI21gZdCgAAAMBREw4dp+2b12XvgbFcc+udgy4FAAAA4KgJh47Tts3dGcsuc8YyAAAAYBESDh2n7eOnszfvEAAAALAICYeO08a1K3LS2hW5XM8hAAAAYBESDs0CZywDAAAAFivh0CzYtnltLr9x96DLAAAAADhqwqFZsG3TuuzcvS87d+8bdCkAAAAAR0U4NAvGz1hm3iEAAABgsREOzQJnLAMAAAAWK+HQLNiyYXVWjS4zKTUAAACw6AiHZsGyZZX7nbLOsDIAAABg0REOzZJtm9flMuEQAAAAsMgIh2bJ9k3rcvUtd+au/QcHXQoAAADAjAmHZsm2zWvTWnLFjbsHXQoAAADAjAmHZsl2p7MHAAAAFiHh0Cw5++S1WVZxxjIAAABgUREOzZJVo8tzxklr9BwCAAAAFhXh0CzatmmdnkMAAADAoiIcmkXbN6/L12/anYNjbdClAAAAAMyIcGgWbdu0NnsPjOWaW+4cdCkAAAAAMyIcmkXOWAYAAAAsNsKhWbRtUxcOmXcIAAAAWCyEQ7Now5oVOWXdCj2HAAAAgEVDODTL7ueMZQAAAMAiIhyaZds3r9NzCAAAAFg0hEOzbNumdbllz/5cv+uuQZcCAAAAcETCoVn22O0nZ3R55d///aXZd2Bs0OUAAAAAHJZwaJY98D4n5Pee+/D8y+U355UXfj6ttUGXBAAAADCtkUEXsBT9wPlbc9Ute/JH//i1nLFxTV7+xHMGXRIAAADAlIRDc+Tl33tOrtp5Z/7wH7+arRtX5wfO3zrokgAAAADuRTg0R6oqv/vch+Xa2+7Mr73jczltw6p8x7ZTBl0WAMy5sbGWPfsPZs++A7lz38Hs6f+6ywdy5/6Dgy4RAIAJhENzaMXIsrzmR8/PBa/5l/ybv7047/jp78g5p64fdFkAcNgAZ/c9busv77873Nk9MeiZuG7/eHftd0IGAIDFRDg0x05cPZrXveRRec6f/kte/LpP58Kf/Y5sXr9q0GUBsMgdHGu5Zc++3HTH3tx8xz3/3bl7X27fe2DaAGf33gPZe5Rn1BxdXlk9ujxrVoxkzcrlWbNiedaMjmTDmhU5fcPyrF7R37ZipP93eVavGMma0fHLd9+3esXybP/9OTowAAAcNeHQPNi6cU3+6kU78sN//on85Bsuylte+u1Zs8KhB+Ce9uw7kJtu35ebdndBz8137M1Nd+zNTXfsy827775+8x37snPPvkx1QsyRZZWT1q7IulUjUwY4aycENIcLcMaDnvHlRpc7wSkAwFIloZgnD9+6If/z+Y/MS//2ovy7N1+aP/+x87N8WQ26LADm0OF699x8x77cvHtvbuxDoJvv2DftXDzrV47klPUrc/LaFbnvKWuz4+yTcsq6lTll3YqcvLb/t79+wqrRLPP9AgDAURAOzaMnPvjUvOr7H5Jff9cX81vv+VJ+45kPTpUGPMBiMpu9e05ZtzInr1uR+52y9lDAc3J/+/h9J61dkVWjy+d/RwEAGBrCoXn2wsecnW/evCd/+c9fzxknrclPPO6+gy4JYKjNV++ek9etOHRd7x4AABYS4dAAvPJpD8rVt9yZ3/6HL2XLhtV5ykPvM+iSAJaUuerdc/LaFYcCIL17AABYKoRDA7BsWeWPnndunv8Xn8jP//1n8uYTvj2PPHPjoMsCWLBms3fPeA+eQ717DgU+evcAADCchEMDsmp0ef7ihTvy3D/9l/zkGy7KhT/z2Jx58ppBlwUwb2ajd8/yZZWT1949GfNUvXvGAx+9ewAAYGrCoQE6Zd3KvO4lj8pz//Rf8uLXfyrv+OnvyIY1KwZdFsAxmd/ePd39evcAAMDxEw4N2LZN6/IXL9yRH/3LT+alf3tx/vYnHp2VI37ZBhaGuezdM35d7x4AABgs4dAC8Oj7npQ/+MGH5+VvuTS/8rbP5Y9++FynuAfmxEx699w04fqRevecrHcPAAAsesKhBeJZ527J1bfcmT/4wL/mjI1r8ktPfsCgSwIWiT37DuTmO/blxjtmr3fPfafp3XPyum4uH717AABg6RAOLSA/8/htuWrnnvzxP12WM05anR9+1JmDLgkYgLnu3XPyhFOwn7JuRU5euzInrta7BwAAhpVwaAGpqvzWsx+ab912V1554Rdy2omr81333zTosoBZMNe9e05etyKb9O6BoVVVy5NclOSa1tozquoJSf5LkhVJLk7yE621A4OsEQBYuIRDC8zo8mX5kx95ZH7wzz6en/m7S/K/XvaYPOi0EwZdFjDJXPTuOftkvXuAY/byJF9OckJVLUvyhiTf21r7alW9OsmLkvzVIAsEABauWQuHqmpVko8mWdk/7ttaa79RVfdN8pYkJ6f75erHWmv7Zmu7S9H6VaN53UselWf/ycfy46//dC78mcfmPieuGnRZsOTNRe+es09e04c8evcAc6OqtiZ5epLfSfIL6dpc+1prX+0X+VCSV0Q4BABMYzZ7Du1N8oTW2h1VNZrkn6vqfekaKX/YWntLVf1Zkp9I8ppZ3O6SdNqJq/PXL35UfujPPp4ff/2n89aXPSbrVuroBUdjtnr3rFs50vXemaJ3z/gp2PXuAQboj5L8SpL1/fWbkoxU1Y7W2kVJLkhyxlQrVtVLk7w0Sc4801yHADCsZi1taK21JHf0V0f7v5bkCUl+pL/9DUleFeHQjDzk9BPzJy84Lz/xhovyc2+6JH/5wh0ZWb5s0GXBQB04OJZrb7vrHr17bt69LzfevnfWevfcfXYuvXuAha2qnpHkhtbaxVX1+KRrk1XV85L8YVWtTPLBJFOm36211yZ5bZLs2LFjik9MAGAYzGpXlH4yxIuTbE/yJ0kuT3LrhAkQr06yZYr1/Go1jcc/YHN+61kPzSsv/Hx+/d1fzO88+6Gp0iuBpW/vgYP5+k2787Xr78hlN3R/X7vh9nz9pt3Zf/De/3+Z3Lvn/LNOyqZ1evcAS95jk3x/VT0tyap0cw69sbX2o0m+M0mq6klJ7j/AGgGABW5Ww6HW2sEk51bVhiQXJnngDNfzq9Vh/Mi3nZmrbtmT13zk8px50pq87Lu3DbokmDV79h3I5TfszmU33p6vXX9HvtYHQd+4eXfG+k+DquTMk9bknM3r8oQHnpr7nrImm9av1LsHGHqttVekm08ofc+hX2qt/WhVbW6t3dD3HPrVdPMRAQBMaU4msWmt3VpV/5TkMUk2VNVI33toa5Jr5mKbS90vP+kBuWrnnvze+76SrRtX5xkPP33QJcFR2XXX/q4H0PVdD6CuJ9AdufqWOw8tM7KscvYpa/PA+6zPMx5+WrZvXpdzNq/P/TatFf4AHJ1f7oecLUvymtbahwddEACwcM3m2co2JdnfB0Ork3xfkt9P8k/pJkJ8S7rTqL5rtrY5TJYtq/yXH3xErt91V37hrZ/NfU5YlR1nnzTosuBedu7el69df/uhHkDjw8Gu37X30DIrRpZl26Z1eeSZG/NDO87IOZvXZfvmdTnr5LVZMWJeLYBj0Vr7SJKP9Jd/OckvD7IeAGDxmM2eQ6cleUM/79CyJG9trb2nqr6U5C1V9dtJPhOnUT1mq0aX57U/tiPPfc2/5Kf+5qK842cem/uesnbQZTGEWmu54fa9XfDTB0Ffu+GOXH7DHbl5975Dy61ZsTzbN6/LY7efknM2r+97Aq3LGSetyXJz/gAAACwIs3m2ss8leeQUt1+R5NGztZ1ht3HtirzuxY/Kc1/zL3nx6z6Vd/z0d+TkdSsHXRZL1NhYy7duu7PrBTRpONjtdx04tNwJq0Zyzqnr830PPjXb+15A55y6PqedsMrEzwAAAAvcnMw5xNw6+5S1+YsX7sjz/+IT+am/uShv+qlvNx8Lx+XgWMs3d+7J166/PZfdOB4EdUPC7tx/99mPT1m3Its3r8uzzj0952xef2g42Kb1K51FDwAAYJESDi1S55+1MX/0w+fmZ990SX7xrZ/N/3z+I/XQ4Ij2HRjLlTfv7oeD3d0T6IqbdmffgbFDy5124qps37wuz3v0GYeGg23fvC4nrV0xwOoBAACYC8KhRexpDzstr3zqg/I77/1ytm5cnVc87UGDLokF4q79B3P5jXccCoHGJ4W+8uY9OdifH74q2bpxdc7ZvD7fff9N2dbPB7Rt87qcsGp0wHsAAADAfBEOLXI/+Z33zTd37smff/SKbD1pTX7s288adEnMozv2Hjg0KfTE4WBX3bInrcuAsnxZ5ayT1+SczevylIfe51BPoG2b1mX1CsMRAQAAhp1waJGrqvzGMx+ca269M7/xri9k64bV+Z4Hbh50WcyyW/fsOzQR9PhwsMtvuCPfuu2uQ8usWL4s99u0Ng/bemKee96Wbk6gU9flrJPXZOWIEAgAAICpCYeWgJHly/I/n//I/PBrP56ffdMleeu/eUweuuXEQZfFUWqt5aY79t19RrBDw8HuyE137D203OrR5dm2eW2+7X4nHzo1/PbN63LmSWsysnzZAPcAAACAxUg4tESsXTmSv37Ro/LsP/lYfvz1n86FP/vYbNmwetBlMYXWWq697a5DZwO77Ibb+95Ad+S2O/cfWm79ypFsP3VdnvDATX0I1A0H27JhtcnHAQAAmDXCoSVk8wmr8rqXPDoXvOZf8uOv+3T+108/xsTCA3RwrOXqW/bcYzjYZX2voN377j49/MY1oznn1PV5+sNPyzl9CHTOqeuy2enhAQAAmAfCoSXmAfdZn9f86Pl58es+lZ954yV53UselVFDjebU/oNj+cbNew71ALrsxi4IuvzGO7J3wunhTz1hZbZvXpcf3HHGPYaDnbxu5QCrBwAAYNgJh5agx51zSn73uQ/LL7/tc3nlOz6f/3zBw/VAmQV37T+Yr9+0+17Dwa68eXf2H2yHltuyYXXOOXVdHrv95JyzeX229SHQiav14gIAAGDhEQ4tUT+444xcdcud+R//52s586Q1+bffe86gS1o09uw7kMtv2J2v3XD7oeFgl994R75x8+6M9RnQskrOOnlttm9el+978KmH5gTatnlt1qzwtgIAAGDx8L/YJezfP/GcXL1zT/7rh76arSetznMeuXXQJS0ot925/1APoInzAl1z652HlhldXrnvKWvz4NNOyDMfcXo3J9Cp63L2yWuzatTp4QEAAFj8hENLWFXl937g4fnWbXfmF9/62fz2e76cqsqySqqSZVVZVjXhciZdn3B5WX9f/7jLDrfMPe6/e/lKt0z16917e9OvP5NlDt2/rFu+cs9tJMnVt9zZ9Qi6/o7ccPvdp4dfObIs2zevy46zN+b5m8/I9v7MYGedvMacTQAAACxpwqElbsXIsvz5j+7In3/08uy6a3/GWncq9bGxZKy1u6/3l8daS5v07+GWGb/twNhY2sHJyx9+/XaYx7vH+mP98pl+mdaOeCiSJGtXLM/2U9fnu+6/6dCE0OdsXp8tG1dnudPDAwAAMISEQ0PgxDWj+ZWnPHDQZcypNinMGuvTosmB1AmrRkzODQAAABMIh1gSDg09i+AHAAAAjobJVAAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAAAAYIhVa23QNdxDVd2e5F8HXccAnZLkpkEXMSDDvO/JcO+/fR9ew7z/w7zvSfKA1tr6QRfB3arqxiTfGHQdC8ywv08HwTGfX473/HK855fjfW9ntdY2TXXHyHxXMgP/2lrbMegiBqWqLhrW/R/mfU+Ge//t+3DuezLc+z/M+550+z/oGrin6RqLw2zY36eD4JjPL8d7fjne88vxPjqGlQEAAAAMMeEQAAAAwBBbiOHQawddwIAN8/4P874nw73/9n14DfP+D/O+J/afxcHrdP455vPL8Z5fjvf8cryPwoKbkBoAAACA+bMQew4BAAAAME8GFg5V1VOq6l+r6rKq+rUp7l9ZVX/f3//Jqjp7AGXOiao6o6r+qaq+VFVfrKqXT7HM46vqtqq6tP/79UHUOheq6sqq+ny/X/c6W011/kf/3H+uqs4bRJ2zraoeMOH5vLSqdlXVz09aZkk971X111V1Q1V9YcJtJ1XVh6rqa/2/G6dZ90X9Ml+rqhfNX9WzY5p9/4Oq+kr/ur6wqjZMs+5h3yOLwTT7/6qqumbC6/tp06x72O+HhW6aff/7Cft9ZVVdOs26i/q5n+77bVje9yxOU71np1jm8f378otV9X/ns76l6EjHvKpOrKr/XVWf7Y/5S+a7xqVkhv/3WJLt70GY4fF+QX+cP19V/1JVjxhErUvBTI73hGUfVVUHquqC+axx0WitzftfkuVJLk9yvyQrknw2yYMnLfMzSf6sv/y8JH8/iFrnaP9PS3Jef3l9kq9Osf+PT/KeQdc6R/t/ZZJTDnP/05K8L0kl+fYknxx0zXNwDJYnuS7JWUv5eU/yXUnOS/KFCbf95yS/1l/+tSS/P8V6JyW5ov93Y39546D3Zxb2/UlJRvrLvz/Vvvf3HfY9shj+ptn/VyX5pSOsd8Tvh4X+N9W+T7r/vyb59aX43E/3/TYs73t/i/NvBu/ZDUm+lOTM/vrmQde82P9mcMxfOf45kWRTkp1JVgy67sX6N8P/eyz59vcCO97fMf4dl+SpjvfcHu/+vuVJPpzkvUkuGHTdC/FvUD2HHp3kstbaFa21fUnekuRZk5Z5VpI39JffluR7q6rmscY501q7trV2SX/59iRfTrJlsFUtKM9K8jet84kkG6rqtEEXNcu+N8nlrbVvDLqQudRa+2i6Bt1EE9/bb0jy7ClWfXKSD7XWdrbWbknyoSRPmas658JU+95a+2Br7UB/9RNJts57YfNkmud+Jmby/bCgHW7f+++xH0ry5nktap4c5vttKN73LE4z+Lz6kSTvaK19s1/+hnkpbAmbwTFvSdb3n5nr+mUPHGZ5DmOG//cYhvb3vJjJ8W6t/Uv/XZcs8TbhXDuK/1v/2yRvT+IzfBqDCoe2JLlqwvWrc+8n8NAy/X+mbkty8rxUN4+qGy73yCSfnOLux/Tdad9XVQ+Z38rmVEvywaq6uKpeOsX9M3l9LHbPy/T/OVyqz/u4U1tr1/aXr0ty6hTLDMNr4MfT/UI3lSO9Rxazn+u7Uf/1NEOLlvpz/51Jrm+tfW2a+5fMcz/p+837nsXs/kk2VtVH+vfmCwdd0BD44yQPSvKtJJ9P8vLW2thgS1oaDvN/D5/Bc+AI/9cb9xOZvk3IUZjueFfVliTPSfKaAZS1aIwMuoBhVlXr0qWXP99a2zXp7kvSDTm6o5+X451JzpnnEufK41pr11TV5iQfqqqv9L8gDYWqWpHk+5O8Yoq7l/Lzfi+ttVZVQ3fKxKr6D+l+Af27aRZZqu+R1yT5rXQByG+lG1714wOtaP49P4fvNbQknvvJ328TO/4O6/ueRW0kyfnpev2uTvLxqvpEa+2rgy1rSXtykkuTPCHJtnSfh/9vivYyR+EI//dgls3keFfV96QLhx43n7UtRUc43n+U5Fdba2NLZDDSnBhUz6Frkpwx4frW/rYpl6mqkSQnJrl5XqqbB1U1mu7F+3ettXdMvr+1tqu1dkd/+b1JRqvqlHkuc0601q7p/70hyYXphpFMNJPXx2L21CSXtNaun3zHUn7eJ7h+vJty/+9UXTuX7Gugql6c5BlJXtBam/I/yDN4jyxKrbXrW2sH+19//yJT79dSfu5Hkjw3yd9Pt8xSeO6n+X4b6vc9i97VST7QWtvdWrspyUeTmDx2br0k3VC+1lq7LMnXkzxwwDUtakf6v0d8Bs+qGRzvVNXDk/xlkme11pbM/3MHYQbHe0eSt1TVlUkuSPKnVfXs+atwcRhUOPTpJOdU1X37XhTPS/LuScu8O8n4mUouSPLh6f4jtdj046f/KsmXW2v/bZpl7jM+x1JVPTrdc7XoPzSqam1VrR+/nG6C3slnqnh3khf2Z0349iS3TRiOsBRM23NgqT7vk0x8b78oybumWOYDSZ5UVRv7oUdP6m9b1KrqKUl+Jcn3t9b2TLPMTN4ji9KkuQuek6n3aybfD4vVE5N8pbV29VR3LoXn/jDfb0P7vmdJeFeSx1XVSFWtSfJt6ea0YO58M11PrVTVqUkekG6Seo7BTP7vkaXf/p43M/y/3plJ3pHkx/RCPD4zOd6ttfu21s5urZ2dbj7jn2mtvXP+qlwcBjKsrLV2oKp+Ll2jb3mSv26tfbGqXp3kotbau9M9wX9bVZelm4TueYOodY48NsmPJfl83X0641cmOTNJWmt/li4Q++mqOpDkziTPWyLh2KlJLuzzj5Ekb2qtvb+qXpYc2vf3pjtjwmVJ9qT79WhJ6P/D931J/s2E2ybu+5J63qvqzenOwHZKVV2d5DeS/F6St1bVTyT5RrrJeVNVO5K8rLX2k621nVX1W+mCgiR5dWvtWCY3Hphp9v0VSVam6x6fJJ9orb2sqk5P8pettadlmvfIAHbhuEyz/4+vqnPTDSu7Mv37YOL+T/f9MP97cOym2vfW2l9lirnGluBzP93321C871mcpvm8Gk267+bW2per6v1JPpdkLN17dlEFtwvNkY55uqHHr6+qz6c7e9av9r22ODYz+b/Hkm1/D8BMjvevp5tP90/77/0DrbUd81/qkjCT480M1CL+fycAAAAAx2lQw8oAAAAAWACEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQwAAAABDTDgEAAAAMMSEQ8Bxq6rvrKp/PY71f7uqbqqq62azrhls98+q6v+bz2322/3pqrq+qu6oqpPne/sAgPbLMWx31tsvVfW7VfXzs/FYR7HN11fVb/eXH19VV8/n9udLVf3bqvr9QdfB4lGttUHXAEteVX0kySOS3Ke1tnfA5SwoVXVmkn9NclZr7YY53M6Lk/xka+1xc7WNGdYxmmRXkm9vrX12mmVWJHllkhckOT3JjUk+nOTVrbUr56lUAIac9sv0tF+mXOao2i9VtSnJpUm2t9bunJvKp6zz9Umubq39x6p6fJI3tta2ztf250tVrUpyWZLz5vI1ytKh5xDMsao6O8l3JmlJvn+etz0yn9s7RmcmuXmIvrROTbIqyRcPs8zb0r1WfiTJieka5hcn+d45rw4Aov0yA9ov93a07ZcXJ3nvfAZDs2kmr9OqWj4ftUyltXZXkvcleeGgamBxEQ7B3Hthkk8keX2SF028o6rOqKp3VNWNVXVzVf3xhPt+qqq+XFW3V9WXquq8/vZWVdsnLHevrrFV9at9F+fXVdXGqnpPv41b+stbJ6x/UlW9rqq+1d//zv72L1TVMycsN9p3nX7k5B2c3CW3qq6sql+qqs9V1W1V9ff9rxeT13tikg8lOb3vovz6qbr39o/3xP7yq6rqrVX1N/2x+WJV7TjcMa2qByX5sySP6bdz6+RjN+GYX1ZVO6vq3VV1+oT7WlW9rKq+VlW3VtWfVFVN3qd+2ZVV9Uf9Mf1Wf3llVd0/3a+MSXJrVX14mmPyfUme1Vr7dGvtQGvtttban7TW/mqq7QHAHNB+0X6Z6/bLU5P83wmPcUr/PN/a78v/q6plE47lL/fPze6q+quqOrWq3tcfz3+sqo0THut/VdV1/fP40ap6yDQ1TN6P06vq7f3z8PWq+ncT7ntVVb2tqt5YVbvShVuT1399Vb2mqt5bVbuTfE9VPb2qPlNVu6rqqqp61YTlz+6foxdV1Tf71+p/mHD/6qp6Q/8a/3JV/cqk1+y09fY+kuTpM9l3EA7B3Hthkr/r/55cVacmh35JeE+SbyQ5O8mWJG/p7/vBJK/q1z0h3a8wN89we/dJclKSs5K8NN37/HX99TOT3Jnkjycs/7dJ1iR5SJLNSf6wv/1vkvzohOWeluTa1tpnZljHDyV5SpL7Jnl4pvgCba39Y7qGwbdaa+taa/daZhrfn+5YbUjy7vT7M90xba19OcnLkny8386GyQ9YVU9I8rt93af1j/GWSYs9I8mj+v35oSRPnqa+/5Dk25Ocm+5Xs0cn+Y+tta+mO85JsqG19oQp1n1ikk+11q46zP4DwFzTftF+mev2y8Nyd+iUJL+Y5Ookm9L1VHplup5r434gXQB1/yTPTNcr5pX98suSTAxG3pfknHSvjUvSvY4Pqw+i/neSz6Z7Dr43yc9X1cTj9ax0PaQ2HOYxfyTJ7yRZn+Sfk+xO957YkC6o+emqevakdR6X5AH9Nn+9DwaT5DfSvSbu1+/7odf2DOv9crrnEo5IOARzqKoel65R89bW2sVJLk/3hZF0X7inJ/nl1tru1tpdrbV/7u/7yST/uf/lpbXWLmutfWOGmx1L8huttb2ttTtbaze31t7eWtvTWrs93ZfVd/f1nZaucfOy1totrbX9rbXxX3DemORpVXVCf/3H0jXEZup/tNa+1Vrbme6L69yjWPdI/rm19t7W2sG+pvEvvcMd0yN5QZK/bq1d0s+r8Ip0v9SdPWGZ32ut3dpa+2aSf8r0+/SCdOPrb2it3ZjkN9Mdv5k4Ocm1M1wWAGad9ov2yzy1XzYkuX3C9f3pAq6z+uf0/7V7TpD7P1tr17fWrkny/5J8srX2mX741IVJDvUOa639dWvt9v6YvCrJI6rqxCPU86gkm1prr26t7WutXZHkL5I8b8IyH2+tvbO1NnaY4XDvaq19rF/mrtbaR1prn++vfy7Jm9O/lif4zf51/9l0Yc/4a+OHkvyn/nV+dZL/cZT13p5uiB8ckXAI5taLknywtXZTf/1Nubtr9hlJvtFaOzDFemeka4gdixv7L8kkSVWtqao/r6pv9F1gP5pkQ/8r1RlJdrbWbpn8IK21byX5WJIfqKoN6RphR/zVZYKJZ+7Yk2Td0e/KjB97VXXjvg93TI/k9HS/tiVJWmt3pPu1c8thtjvdPt3jsfrLp0+z7GQ3p2sYAcCgaL90tF/mtv1yS7reNeP+IN0Eyh+sqiuq6tcmLX/9hMt3TnF9XdL1xKqq36uqy/vXzpX9MqccoZ6z0g0VvHX8L13PpFMnLDOTnlH3WKaqvq2q/qkf+nVbut5gk2uZ7jk6fdLjTbw8k3rXJ7ltBjVDFsNkb7AoVdXqdGn/8rr7FKcr0zVsHpHuw/3MqhqZojFwVZJt0zz0nnTdqMfdJ10X3HGTT0H4i+m6qX5ba+26qjo3yWeSVL+dk6pqQ2vt1im29YZ0vwKOpPul5Jrp9ncW7c6E/esbgZtmuO7hjumRTs34rXRfsuPbXZvuV7Bj2efxxxqftPHM/raZ+MckL6+qrf0vRAAwb7Rfjpn2y9G3Xz6XbojYp5Ok7yH2i0l+saoemuTDVfXp1tr/mekO9H4k3fCvJ6YLhk5MF0RNOdfSBFcl+Xpr7ZzDLDOTU31PXuZN6YYQPrW1dldV/VGOHFSNuzbJ1iRf6q+fMeG+mdT7oHQ9keCI9ByCufPsJAeTPDhd991z031A/790444/le4D//eqam1Vraqqx/br/mWSX6qq86uzvarGv/gvTfIj/a8iT8m9u6VOtj7drym3VtVJ6cYuJ0laa9emG5P9p9VN/DhaVd81Yd13JjkvycvTjeGfD19N90va06s7bep/TNconYnDHdPrk2yt7jSrU3lzkpdU1blVtTLJf0rXXfnKY9iHNyf5j1W1qapOSfLr6bq5H1Hr5jH4UJIL++d/pKrWVzeZ5I8fQy0AcDSeHe2XY6H9cvTtl/dmwuugqp7Rv2YqXW+Xg+mGGx6t9Un2puvNtCbdMZmJTyW5vbqJ0Vf3r9WHVtWjjqGGyfXs7IOhR+fuIZoz8dYkr+hf51uS/NxR1vvd6d4rcETCIZg7L0ryutbaN1tr143/pfvl4AXpfr14ZpLtSb6Z7tezH06S1tr/Sje2/k3pxgq/M90kjUnX0Hlmklv7x3nnEer4oySrk9yU7qwj7590/4+lG+P9lSQ3JPn58Tv6sdRvTzcp4ztmvOfHobV2W5KfSdfAvCbdL3Ez+gWqH8M/5TFN8uF0v4RdV1U3TbHuPyb5/9Lt77Xpfvl83uTlZui3k1yU7hexz6ebCPG3D7vGPV2QrsH09+kaR19IsiPdr3IAMJe0X46B9kuSo2+//E26+aFW99fP6Ze9I8nHk/xpa+2fjnIfxh/3G+mehy+le/0cUf88PCNdIPr1dK+9v8zxz9nzM0leXVW3pwvc3noU67463evh6+mOzdvSBV9HrLe6M+09LV1POjiiuuccXwD3VFW/nuT+rbUfPeLCAAALgPbL4lBV/ynJDa21Pxp0LYtBVf10kue11o7U8y5V9W+TnNFa+5W5r4ylQDgETKvvxv2ZJD/WWvvooOsBADgS7ReWiurOzHe/dD2pzknyD0n+WJjGXDCsDJhSVf1Uuonu3qdhBQAsBtovLDErkvx5umGaH07yriR/OtCKWLL0HAIAAAAYYnoOAQAAAAwx4RAAAADAEBsZdAGTnXLKKe3ss88edBkAwBy6+OKLb2qtbRp0HdxNGwwAlrbDtb8WXDh09tln56KLLhp0GQDAHKqqbwy6Bu5JGwwAlrbDtb8MKwMAAAAYYsIhAAAAgCEmHAIAAAAYYsIhAAAAgCE2o3Coqp5SVf9aVZdV1a9Ncf8fVtWl/d9Xq+rW/vZzq+rjVfXFqvpcVf3wLNcPAAAAwHE44tnKqmp5kj9J8n1Jrk7y6ap6d2vtS+PLtNb+/YTl/22SR/ZX9yR5YWvta1V1epKLq+oDrbVbZ3EfAAAAADhGM+k59Ogkl7XWrmit7UvyliTPOszyz0/y5iRprX21tfa1/vK3ktyQZNPxlQwAAADAbJlJOLQlyVUTrl/d33YvVXVWkvsm+fAU9z06yYoklx99mQAAAADMhdmekPp5Sd7WWjs48caqOi3J3yZ5SWttbPJKVfXSqrqoqi668cYbZ7kkAAAAAKYzk3DomiRnTLi+tb9tKs9LP6RsXFWdkOQfkvyH1tonplqptfba1tqO1tqOTZuMOgMAAACYLzMJhz6d5Jyqum9VrUgXAL178kJV9cAkG5N8fMJtK5JcmORvWmtvm52SAQAAAJgtRwyHWmsHkvxckg8k+XKSt7bWvlhVr66q75+w6POSvKW11ibc9kNJvivJiyec6v7c2SsfAAAAgONxxFPZJ0lr7b1J3jvptl+fdP1VU6z3xiRvPI76AAAAAJhDsz0hNQAAAACLiHAIAAAAYIgJhwAAAACGmHAIAAAAYIgJhwAAAACGmHAIAGCBqqqnVNW/VtVlVfVr0yzzQ1X1par6YlW9adJ9J1TV1VX1x/NTMQCwGA11OPT1m3bnv37wX3NwrA26FACAe6iq5Un+JMlTkzw4yfOr6sGTljknySuSPLa19pAkPz/pYX4ryUfnvloAYDEb6nDoQ1+6Lv/zw5fl45ffPOhSAAAme3SSy1prV7TW9iV5S5JnTVrmp5L8SWvtliRprd0wfkdVnZ/k1CQfnKd6AYBFaqjDob37x5Ik7/jM1QOuBADgXrYkuWrC9av72ya6f5L7V9XHquoTVfWUJKmqZUn+a5JfmpdKAYBFbajDoX0Hu3Do/V+4Lnv2HRhwNQAAR20kyTlJHp/k+Un+oqo2JPmZJO9trR32F7CqemlVXVRVF914441zXSsAsEANdTi090AXDu3ZdzAf/OL1A64GAOAerklyxoTrW/vbJro6ybtba/tba19P8tV0YdFjkvxcVV2Z5L8keWFV/d7kDbTWXtta29Fa27Fp06a52AcAYBEY7nBo/8GsXzWSLRtW5x2fmdzWAgAYqE8nOaeq7ltVK5I8L8m7Jy3zznS9hlJVp6QbZnZFa+0FrbUzW2tnpxta9jettSnPdgYAMNTh0L6DY1k9ujzPeeSW/PPXbswNu+4adEkAAEmS1tqBJD+X5ANJvpzkra21L1bVq6vq+/vFPpDk5qr6UpJ/SvLLrTVn2gAAjspQh0N7949lxciyPOe8LRlrybsu/dagSwIAOKS19t7W2v1ba9taa7/T3/brrbV395dba+0XWmsPbq09rLX2like4/WttZ+b79oBgMVjuMOhA2NZObIs2zatyyO2nmhoGQAAADB0hEMjy5Mkz3nklnz52l35ynW7BlwVAAAAwPwZ8nDoYFaMdIfgmY84PSPLKhdeovcQAAAAMDyGPBzqhpUlycnrVua7778p77z0mhwcawOuDAAAAGB+DHU4tO/AWFaOLj90/bnnbc31u/bmXy6/aYBVAQAAAMyfoQ6H9h4Yy4rldx+C733Q5qxfNWJoGQAAADA0hjwcOpiVo3cfglWjy/P0h52W93/xuuzZd2CAlQEAAADMj6EOh/ZNmHNo3HMeuSV79h3MB7543YCqAgAAAJg/Qx0O7Z0iHHrU2Sdly4bVeYehZQAAAMAQGO5waP/BrBxZfo/bli2rPOeRW/Kxy27KDbvuGlBlAAAAAPNjqMOhfQfv3XMoSZ5z3paMteRdl35rAFUBAAAAzJ+hDYdaa93ZyqYIh7ZtWpdHnLEhb7/k6gFUBgAAADB/hjYc2n+wpbVM2XMoSZ77yC35ynW358vX7prnygAAAADmz9CGQ/sOjiXJveYcGvfMR5yekWWVCz9jYmoAAABg6RracGjv/oNJMuWwsiQ5ae2KPP4Bm/KuS6/JwbE2n6UBAAAAzJvhDYcOjPccmv4QPOeRW3P9rr35l8tvmq+yAAAAAObV0IZD+8bDodHpD8H3Pmhz1q8ayYWXGFoGAAAALE1DGw6N9xxasXzqOYeSZNXo8jz9Yafl/V+8Lrv3Hpiv0gAAAADmzdCGQ/tmMKwsSZ573tbs2XcwH/jidfNRFgAAAMC8GtpwaO+BbkLqww0rS5IdZ23M1o2rnbUMAAAAWJKGOBwaH1Z2+EOwbFnlOY/cko9ddlOu33XXfJQGAAAAMG+GNhy6e0Lq6eccGvecR27JWEvedaneQwAAAMDSMrTh0PiwsiP1HEqS+21al0ecsSHvcNYyAAAAYIkZ4nDoyKeyn+i5j9ySr1x3e7587a65LAsAAABgXgmHjnC2snHPfMTpGVlWJqYGAAAAlpShD4dWzDAcOmntijz+AZvzzs9ck4NjbS5LAwAAAJg3wxsO7e9PZT9y5Ampxz33vC254fa9+dhlN81VWQAAAADzamjDoX0Hj25YWZI84YGbs37ViKFlAAAAwJIxtOHQ3v39sLIZnK1s3KrR5XnGw0/L+79wXXbvPTBXpQEAAADMm+ENhw6MZcXyZVm2rI5qvec8cmvu3H8wH/jidXNUGQAAAMD8GdpwaN+BsaMaUjZux1kbs3XjakPLAAAAgCVhaMOhvQcOzvhMZRMtW1Z5ziO35J8vuynX3XbXHFQGAAAAMH+GOBw6tp5DSfKcR25Ja8m7LtV7CAAAAFjchjYc2ndgLCtHZ34a+4nut2ldzj1jg6FlAAAAwKI3tOHQ3gMHj+pMZZM997wt+cp1t+dL39o1i1UBAAAAzK8hDofGsnL02Hf/GQ8/PSPLKhd+5upZrAoAAABgfg1tOHSsZysbd9LaFXn8AzbnXZd+KwfH2ixWBgAAADB/hjYc2ntg7JjOVjbRc8/bkhtu35uPXXbTLFUFAAAAML+GOBw6mJUjxzYh9bgnPHBz1q8ayTsuMbQMAAAAWJyGNhw63mFlSbJqdHme8fDT84EvXp/dew/MUmUAAAAA82dow6HZGFaWdEPL7tx/MO//wnWzUBUAAADA/BrecGj/8fccSpIdZ23MGSetzoWfuWYWqgIAAACYX0MbDu07OHbccw4lSVXlOeduyccuvynX3XbXLFQGAAAAMH+GNhzau//grAwrS5LnnLc1rSXvulTvIQAAAGBxGd5waBYmpB5331PW5twzNuQdl1yT1tqsPCYAAADAfBjKcOjgWMuBsTYrw8rGPfe8LfnX62/Pl67dNWuPCQAAADDXhjIc2ndgLElmbVhZkjzj4adndHnlwksMLQMAAAAWj6EMh/YeOJgkszasLElOWrsij3/A5rzrs9/KgYNjs/a4AAAAAHNpKMOh8Z5DK0dnd/ef+8gtufH2vfnY5TfP6uMCAAAAzJWhDIf2jg8rWz67u/+EB23OCatGcuElV8/q4wIAAADMlSENh/phZaOzNyF1kqwcWZ6nP/z0fOCL1+eOvQdm9bEBAAAA5sKQhkP9sLJZnHNo3A+ctyV37j+Y933+2ll/bAAAAIDZNtTh0GyerWzc+WdtzNknr8nbLja0DAAAAFj4hjIc2jeHPYeqKhecvzWf/PrOfPPmPbP++AAAAACzaSjDobkcVpYkzzlva6qSt5uYGgAAAFjghjMc2t9PSD0yuxNSj9uyYXUeu+2UvP2SqzM21uZkGwAAAACzYSjDoX0H57bnUJJccP7WXH3Lnfnk13fO2TYAAAAAjtdQhkN798/dhNTjnvyQ+2TdyhETUwMAAAAL2nCGQ4fmHJqbYWVJsnrF8jzj4aflfV+4Nrv3Hpiz7QAAAAAcjxmFQ1X1lKr616q6rKp+bYr7/7CqLu3/vlpVt06470VV9bX+70WzWPsx23dgfM6huc3GLjh/a/bsO5j3fv7aOd0OAAAAwLE6YjpSVcuT/EmSpyZ5cJLnV9WDJy7TWvv3rbVzW2vnJvmfSd7Rr3tSkt9I8m1JHp3kN6pq46zuwTEY7zk0l8PKkuT8szbm7JPXGFoGAAAALFgzSUceneSy1toVrbV9Sd6S5FmHWf75Sd7cX35ykg+11na21m5J8qEkTzmegmfDXJ/KflxV5YLzt+aTX9+Zb968Z063BQAAAHAsZpKObEly1YTrV/e33UtVnZXkvkk+fDTrVtVLq+qiqrroxhtvnEndx2XfgbEsX1YZWT73Uy4957ytqUrefoneQwAAAMDCM9vpyPOSvK21dvBoVmqtvba1tqO1tmPTpk2zXNK97T1wMCvmIRhKki0bVuex207J2y+5OmNjbV62CQAAADBTM0lIrklyxoTrW/vbpvK83D2k7GjXnTd7D4xl5ej8najtgvO35upb7swnv75z3rYJAAAAMBMzSUg+neScqrpvVa1IFwC9e/JCVfXAJBuTfHzCzR9I8qSq2thPRP2k/raB2ndgbM7nG5royQ+5T9atHDExNQBwVI50xth+mR+qqi9V1Rer6k39bWdV1SX9mWS/WFUvm9/KAYDF5IgJSWvtQJKfSxfqfDnJW1trX6yqV1fV909Y9HlJ3tJaaxPW3Znkt9IFTJ9O8ur+toHae2Bszs9UNtHqFcvzjIeflvd94drs3ntg3rYLACxeMzljbFWdk+QVSR7bWntIkp/v77o2yWP6M8l+W5Jfq6rT56l0AGCRGZnJQq219yZ576Tbfn3S9VdNs+5fJ/nrY6xvTuw9cDArR5bP6zYvOH9r3vLpq/Lez1+bH9xxxpFXAACG3aEzxiZJVY2fMfZLE5b5qSR/0p8VNq21G/p/901YZmVmf55JAGAJGcqGwnwPK0uS88/amLNPXmNoGQAwUzM56+v9k9y/qj5WVZ+oqqeM31FVZ1TV5/rH+P3W2rcmb2C+zxgLACxMQxkOzfewsiSpqlxw/tZ88us7c9XOPfO6bQBgyRpJck6Sxyd5fpK/qKoNSdJau6q19vAk25O8qKpOnbzyfJ8xFgBYmIYzHNo//z2HkuQ5521NVfL2S/QeAgCOaCZnfb06ybtba/tba19P8tV0YdEhfY+hLyT5zjmsFQBYxIYzHDo4Nu9zDiXJlg2r89htp+Ttl1ydsbF25BUAgGE2kzPGvjNdr6FU1SnphpldUVVbq2p1f/vGJI9L8q/zVDcAsMgMZzi0/+C8Dysbd8H5W3PVzjvzqSsHftI2AGABm+EZYz+Q5Oaq+lKSf0ryy621m5M8KMknq+qzSf5vkv/SWvv8/O8FALAYzOhsZUvNICakHvfkh9wn61aO5G0XX51vv9/JA6kBAFgcjnTG2NZaS/IL/d/EZT6U5OHzUSMAsPgNZ8+hA4MZVpYkq1cszzMeflre+/lrs3vvgYHUAAAAADBuaMOhQQ0rS7qhZXv2Hcz7vnDdwGoAAAAASIY2HDo4sGFlSXL+WRtz9slr8raLrxpYDQAAAADJkIZD+w6MZeXo4Ha9qvID523NJ67Ymat27hlYHQAAAABDFw611ro5h5YPdtefe/7WVCVvv+TqgdYBAAAADLehC4f2HRxLkqwcHcyE1OO2bFid79h2ct5+ydUZG2sDrQUAAAAYXsMXDh3ow6EBzjk07oLzt+aqnXfmU1fuHHQpAAAAwJAafEIyz/b24dAgz1Y27skPuU/WrRzJ2y42tAwAAAAYjMEnJPNs7wLqObRmxUie/rDT8t7PX5vdew8MuhwAAABgCA0+IZlndw8rG+ycQ+Mu2LE1e/YdzPu+cN2gSwEAAACG0NCFQ3sPHEyyMIaVJcmOszbmrJPX5G0XXzXoUgAAAIAhtDASknm0d//CGVaWJFWVC87bmk9csTNX7dwz6HIAAACAIbMwEpJ5NH4q+4XScyhJnnv+1lQlb7/ExNQAAADA/Fo4Cck8ubvn0MKYcyhJtmxYne/YdnLefsnVGRtrgy4HAAAAGCJDFw7tO9jNObRQhpWNu+D8rblq55351JU7B10KAAAAMEQWVkIyD8Z7Di2kYWVJ8uSH3CfrVo7kbRcbWgYAAADMn4WVkMyDvQcW1oTU49asGMnTH3Za3vv5a7N774FBlwMAAAAMiYWVkMyDfePh0OjCmXNo3AU7tmbPvoN53xeuG3QpAAAAwJAYunBo74FuzqEVyxferu84a2POOnlN3nbxVYMuBQAAABgSCy8hmWOHhpWNLrxdr6pccN7WfOKKnblq555BlwMAAAAMgYWXkMyxhTrn0Ljnnr81VcnbLzExNQAAADD3FmZCMofGw6GFOKwsSbZsWJ3v2HZy3n7J1Rkba4MuBwAAAFjiFmZCMof2HjiYFSPLUlWDLmVaF5y/NVftvDOfunLnoEsBAAAAlrihC4f2HRhbsEPKxj35IffJupUjedvFhpYBAAAAc2thpyRzYO8iCIfWrBjJ0x92Wt77+Wuze++BQZcDAAAALGELOyWZA3v3j2XlyPJBl3FEF+zYmj37DuZ9X7hu0KUAAAAAS9jQhUP7Di78nkNJsuOsjTnr5DV528VXDboUAAAAYAlb+CnJLNu7v5uQeqGrqlxw3tZ84oqduWrnnkGXAwAAACxRCz8lmWWLYc6hcc89f2uqkrdfYmJqAAAAYG4sjpRkFnVnK1v4cw4lyZYNq/Md207O2y+5OmNjbdDlAAAAAEvQ0IVDew8sjmFl4y44f2uu2nlnPnXlzkGXAgAAACxBiyclmSWLaVhZkjz5IffJupUjefvFhpYBAAAAs2/xpCSzZN+BsawcXTy7vWbFSJ7+sNPyD5+/Nrv3Hhh0OQAAAMASs3hSklmy98BYVixfXLt9wY6t2bPvYN7/hesGXQoAAACwxCyulGQW7D1wcNFMSD1ux1kbc9bJa/I2Q8sAAACAWTZ04dBiG1aWJFWVC87bmo9fcXOu2rln0OUAAAAAS8jiSklmwWIcVpYkzz1/a6qSd1xyzaBLAQAAAJaQxZeSHKe9i7DnUJJs2bA637Ht5LztkqsyNtYGXQ4AAACwRCy+lOQ4HDg4loNjbdHNOTTugvO35qqdd+bTV+4cdCkAAADAEjFU4dC+g2NJkhUji3O3n/yQ+2TdyhETUwMAAACzZnGmJMdo7/4uHFq5SMOhNStG8vSHnZZ/+Py12b33wKDLAQAAAJaAxZmSHKPxnkOLdVhZkvzA+VuzZ9/BvP8L1w26FAAAAGAJGKpwaLzn0GIdVpYkjzp7Y848aY2hZQAAAMCsWLwpyTHYe+BgksU7rCxJqioXnL81H7/i5ly1c8+gywEAAAAWucWbkhyDvQcWf8+hJHnueVuSJO+45JoBVwIAAPz/7d15mJTVmffx790bm+z7KqggiwLKqmhUcCGbZjGKZjRC1EwSZ0tionkny5tMoknmzTZjFndj4hbHGDMxGqPGRAQFcUdElF0FRFzRbqrrvH90gR0C0mB3V9dT38919UXXU0/Bfaqo7qpfnfscSSp1pZ2S7Kat4VApzxwCGNS9I4fu25MbF60mn0/FLkeSJEmSJJWw0k5JdtPbbWWluyD1VidOGMTql95kwYqXil2KJEmSJEkqYWUVDtVlpK0MYOYB/ehUU+nC1JIkSZIk6V0p/ZRkN2SlrQygY00V7x/bn98/9jxv1OaKXY4kSZIkSSpRpZ+S7Iat4VD76mwM+8QJg9lcV89tj79Q7FIkSZIkSVKJykZK0kTb2soqS3/NIYBJQ7szpEdHW8skSZIkSdIeK6twaNuC1BmZORQRnDhhEPOe3cjqlzYXuxxJkiRJklSCspGSNFFdhtYc2uojBw8E4KZFa4tciSRJkiRJKkXZSUmaoDZDu5VtNah7Rw7dtyc3LlpNPp+KXY4kSZIkSSox2UlJmqB2y9Y1h7I17BMnDGL1S2+yYMVLxS5FkiRJkiSVmGylJLtQV19PVUVQlbFwaOYB/ehUU+nC1LuQkjOrJEmSJEnaXrZSkl2o3ZLPVEvZVh1rqnj/2P78/rHneaM2V+xy2pSVG9/girnLOe2y+xnztdv5j/9dbEgkSZIkSVIjVcUuoDXV5vKZWoy6sRMnDOaGhWu47fEX+OiEQcUup2hy9XkWrtzE3UvWc+eS9Sxb/zoA+/buxJRhPbj03uW8lavnG8cfQEVFFLlaSZIkSZKKr6zCobpcnnZVlcUuo0VMGtqdIT06cuODa8ouHNr0Rh33LN3AnUvWc89T63n1rRzVlcGUYT05dfIQpo/sw9BenUgp8Z3bnuJn9zxDfT7xrQ8daEAkSZIkSSp7ZRUO1ebqM9lWBhARnDhhEN+/YymrX9rM4B4di11Si0kp8fT617nzyfXctWQdD67cRD5Bz041HDumHzNG9uGw4b3o3L76b24XEXxp5v5UVwb/ddcycvWJCz86lkoDIkmSJElSGSuzcCi7bWUAHzl4IN+/Yyk3LVrLvxw9vNjlNKu3ttRz//KXuOvJddy5ZD1rNr0JwJgBXfjsUfsxfWQfxg3qtsuZQBHB54/dn8qK4Id/epr6fOJ7HxtnQCRJkiRJKltlFQ7V5fK0q85uODSoe0cO3bcnNy5azT9N36/kW6bWv/oWdz+1njufXM+9y15kc1097asrOGy/XnzmyP04amRv+nftsEd/978ePYKqiuA//7iUXD7x/ZPGZW4XO0mSJEmSmqKswqHaXJ6ajAcAJ04YxOdueIQFK15iyj49i13ObsnnE48/9wp3LVnPXUvW8+iaVwAY0LU9Hzl4IDNG9uWQfXvSvrp51o06Z/pwKisq+M5tS6jPJ344azzVGf//IUmSJEnS9sosHKrP7ILUW808oB9fuflxbnxwTUmEQ2/U5rh32Yvc9eR67npqPRteqyUCDhrcjXOP25/pI/swsl9nIlpmFtSnj9yX6srgP37/JPX5xI9POSiz61JJkiRJkrQjZRUO1eXydOqU7SF3rKni/WP787+PPs/Xjx9Dp3Ztb7yrX9rMXYWt5uc/s5G6+jyd21Xxnv17M2NkH44Y0Zuee7VrtXrOPHwfKiuC//u7xXzmV4u46OMHZT5ElCRJkiRpq7aXHLSgcmgrAzhxwmBuWLiG2x5/oU1sa5+rz/PQ6pe37S62dN3rAOzTqxOnH7I300f1YdLQHkVt6Zo9bRhVFcFXfvsEn/7lIn7y8YObrX1NkqQ9FREzgR8BlcClKaULd3DOScDXgQQ8klI6NSLGAz8FugD1wLdSSte3Vt2SJKm0lF041K4M3vBPGtqdIT06cuODa4oWDr2yeQt/XtqwdtCfn9rAK29uoaoimDysBydNHMz0kX3Yp/deRaltZ047ZCiVFRV8+TeP8amrH+Tnp00wIJIkFU1EVAIXAccAa4AFEXFLSmlxo3OGA+cD01JKmyKiT+GqzcDpKaWnI2IA8GBE3J5Serl1RyFJkkpBk8KhPf3UqnD8u8D7gQrgDuBfUkqpWarfTXUZ38p+q4jgxAmD+P4dS1mzaTODunds8X8zpcQzG17nzicb2sUeXLmJ+nyiR6cajh7Vlxmj+nDY8F50aV/d4rW8G6dOGUJVRfClmx7lzKsWcsnpE+lQY0AkSSqKycCylNKzABFxHXACsLjROWcBF6WUNgGklNYX/ly69YSU0nMRsR7oDbzcOqVLkqRSsstw6N18ahURhwLTgLGFU+8FjgD+3JyDaKraXH3ZLDb8kYMH8v07lnLTorX884zhLfJv1ObqeWD5S4V2sfWsemkzAKP6d+HTR+zL9FF9GDeoG5UVLbOYdEs5adJgKiuCL9z4CHOuXMBlZ0ykY01ZTbKTJLUNA4HVjS6vAaZsd84IgIiYS8OHeF9PKd3W+ISImAzUAM9s/w9ExNnA2QBDhgxptsIlSVJpaco73j3+1IqGWUTtaXhBEkA1sK55St99tVvKY+YQwKDuHTl0357c+OAa/mn6fs2229eG12q5+6n13PXkev769AbeqKunXVUF0/brxdnv2YfpI/swoFuHZvm3iumjEwZRVRn82/UPc8YVC7j8jEns1QYX95Yklb0qYDhwJDAI+EtEHLi1fSwi+gNXA59IKeW3v3FK6WLgYoCJEycWZWa3JEkqvqa8293jT61SSvMi4m7geRrCof9OKT25/T/QWp9a1dbny2oXqhMnDOJzNzzCghWbmDysxx79HSklnnju1W2LST+y5hUA+ndtz4cOGsiMUX04ZJ9emWy9OmH8QCoi+NfrH+aMyx/gitmT6NzG2+IkSZmyFhjc6PKgwrHG1gD3p5S2AMsjYikNYdGCiOgC/B74Pyml+a1RsCRJKk3NNRVih59aAb2AUYVjAHdExOEppb82vnFrfGqVUqIuly+btjKAmQf04ys3P86ND67erXBoc12Oucs2cteSddy1ZD3rXq0lAsYP7sYXjh3B9JF9GdW/c7PNRmrLPjhuAFUVwT9d+xCnX/4AV82Z3ObXTZIkZcYCYHhEDKMhFJoFnLrdOTcDpwBXREQvGj6wezYiaoDfAL9IKd3YeiVLkqRS1JRw6N18anUkMD+l9DpARPwBOAT4K62sNtcwk7pc2soAOtZU8f6x/fn9o8/z9ePHvOO6OWs2bebuJQ2LSd/3zEbqcnn2alfFe0b0YvrIvhy5f2967dWuFatvO957YH9+UhF89ppFnHbp/fxizhS6djQgkiS1rJRSLiLOAW6nYWb25SmlJyLiG8DClNItheuOjYjFNGxZf25KaWNE/APwHqBnRJxR+CvPSCk93OoDkSRJbV5TwqE9/tQK2Ac4KyIuoKGt7Ajgh81S+W6qqy+/cAjgxAmDuWHhGm57/AU+cvDb29rX5xMPr960bTHpJS+8BsDQnh35hyl7M2NUHyYN7VFWM63eybFj+vGzf5jAp3+5iI9fNp+r50yhe6eaYpclScq4lNKtwK3bHftqo+8T8LnCV+Nzfgn8sjVqlCRJpW+X4dC7/NTqRmA68BgNi1PfllL6XUsN5p3UbinPcGjS0O4M6dGRGx9cw4xRffnL0g3cvWQ9dz+1nk2bt1BVEUwa2oN/f/8opo/swz699yp2yW3WjFF9+fnpE/jU1Q9y6qX386szp9DDgEiSJEmSVOKatObQu/jUqh741Lsv892rzdUDlNWC1AARwYkTBvH9O5Zy8DfvoD6f6N6xmqP278P0UX04fHhvunawRaqpjtq/D5eePpGzfrGQUy+Zzy/PnFK27XaSJEmSpGwom7256wprDpVjm9SsyYN5aNUmRvXvwoxRfRg/uDuVFdlfTLqlvGdEb644YxJzrlrAKRfP51dnTaFP5/bFLkuSJEmSpD1SNklJOS5IvVWfzu25YvZkvjhzJBP27mEw1AwO3a8XV86ezNqX32TWxfNZ9+pbxS5JkiRJkqQ9UjZJybZwqLpshqwWNnWfnlw1ZzLrXnmLWRfP5/lX3ix2SZIkSZIk7baySUq2tZVVlteaQ2pZk4b24BefnMKLr9Vy8s/ns/ZlAyJJkiRJUmkpm3Bo24LUzhxSM5uwd3euPnMKmzbXcfLP57H6pc3FLkmSJEmSpCYrm6SkXLeyV+sYP7gb15w5ldfeyjHr4vms3PhGsUuSJEmSJKlJyiYpqasv393K1DoOHNSVa86awua6hoBo+YsGRJIkSZKktq9skpJtbWVVrjmkljNmQFeuOWsqtbk8J/98Hs9seL3YJUmSJEmS9I7KJxyyrUytZFT/Llx39lTyKXHyz+fz9LrXil2SJEmSJEk7VTZJiW1lak0j+nbmurOnEgGzLp7PUy8YEEmSJEmS2qaySUqcOaTWtl+fzlx/9lSqKoNTLpnP4udeLXZJkiRJkiT9nbJJSrbOHHLNIbWmfXrvxfVnH0L7qgpOvXQ+j699pdglSZIkSZL0N8omHKrd0rAgdXVlFLkSlZuhvTpx/acOoVNNFadeMp9H17xc7JIkSZIkSdqmfMKhXJ52VRVEGA6p9Q3u0ZHrPzWVrh2r+fil9/PQqk3FLkmSJEmSJKAMwyGpWAZ178j1Zx9Cj041nHbZAzy48qVilyRJkiRJUnmFQzWuN6QiG9CtA9effQh9Orfj9Mse4IHlBkSSJEmSpOIqo3Co3plDahP6dW3PdWdPpV/X9nzi8geY98zGYpckSZIkSSpjZZOW1OXytKsum+GqjevTpT3XnX0Ig3t0YPaVD3Dv0y8WuyRJkiRJUpkqm7SkNpenprJshqsS0LtzO649aypDe3bik1ct4J6lG4pdkiRJkiSpDJVNWlKby9Ou2jWH1Lb03Ksd15w1lX1778VZVy3k7iXri12SJEmSJKnMlE04VOeaQ2qjenSq4ZqzprB/v86cffVC7li8rtglSZIkSZLKSNmkJW5lr7asW8cafnnmFEYP6Mqnf/kgtz3+QrFLkiRJkiSVibJJS2q3GA6pbevaoZqrPzmZsYO68tlrFvH7R58vdkmSJEmSpDJQNmlJXX2edlWuOaS2rUv7an7xySkcPKQb/3zdQ/z24bXFLkmSJEmSlHFlEw7V5uqpceaQSsBe7aq4cvZkJu7dnX+7/mF+89CaYpckSZIkScqwsklLbCtTKenUroorZk9i6j49+dwNj/DrhauLXZIkSZIkKaPKJi1paCsrm+EqAzrWVHH5GZM4bL9efPF/HuW6B1YVuyRJkiRJUgaVTVpSuyVvW5lKTvvqSi45fSJHjOjNeTc9xi/nryx2SZIkSZKkjCmbtKQ2V++C1CpJ7asr+flpE5gxsg//fvPjXHXfimKXJEmSJEnKkLIIh3L1efIJ28pUstpVVfLTf5jAsaP78rVbnuDSvz5b7JIkSZIkSRlRFmlJbS4PYFuZSlpNVQUXffxg3ntAP/7j90/y83ueKXZJkiRJkqQMKIu0ZGs45Mwhlbrqygp+fMpBfGBsfy74wxIuuntZsUuSJEmSJJW4qmIX0Brqts0ccs0hlb7qygp+ePJ4qiqC793+FLn6xL8cPbzYZUmSJEmSSlRZhEO1uXrAmUPKjqrKCv7fSeOprKjgB39aSn0+z78dM4KIKHZpkiRJkqQSUybhUKGtrNpwSNlRWRF878SxVFUEP75rGbl84tzj9jcgkiRJkiTtlrIIh7a1lVUaDilbKiqCCz5yIJWVwU/+/Az1+cR57x1pQCRJkiRJarKyCIe2tZVVu+aQsqeiIvjWhw6gqiL4+V+eZUt94isfGGVAJEmSJElqkvIIh7a4W5myLSL4v8ePobIiuHzucurzeb5+/BgDIkmSJEnSLpVHOFS/dbcywyFlV0Tw1Q+MpqoiuOSvy8nlE9884QAqKgyIJEmSJEk7Vx7hkDOHVCYigi+/bxRVlRX8tLAG0bc/fKABkSRJkiRpp8ojHNq2lb1rDin7IoIvHrc/VRXBfxV2MfvOR8dSaUAkSZIkSdqBsgiHtu5W5swhlYuI4PPH7k9VRQU/+NNS6vOJ//zYOAMiSZIkSdLfKYtwqNZwSGXqX44eTmUF/Ocfl5LLJ35w0jiqKn0eSJIkSZLeVmbhkG1lKj/nTB9OVWUFF/5hCfl84oezxlNtQCRJkiRJKiiLcGhrW5m7lalc/eMR+1JVEfzH758kl8/zX6cc7PNBkiRJkgRAWbw73LogtW+GVc7OPHwfvvbB0dz+xDo+86sHtz0vJEmSJEnlrSzSkrpcnurKcDFelb3Z04bxzRPG8Kcn1/OPVz/IW1sMiCRJkiSp3JVFOFSby1PjGisSAKcdMpRvf/hA7n5qA2cbEEmSJElS2SuLxKQ2V0+7ahejlrY6dcoQvvvRsfz16Q2cedVC3qwzIJIkSZKkclUW4VBdLu829tJ2Tpo0mP88cRz3PfMis698gM11uWKXJEmSJEkqgrJITGpzeRejlnbgoxMG8YOTx/PA8pc44/IFvF5rQCRJkiRJ5aYsEpPaLc4cknbmhPED+fEpB/Hgqk184vIHeO2tLcUuSZIkSZLUisoiMamrz9OuyjWHpJ35wNgB/PcpB/HI6pc57bIHeOVNAyJJkiRJKhdlEQ7V5uptK5N24b0H9ucnHz+YJ557hdMuu59XNhsQSZIkSVI5KIvExLYyqWmOHdOPn/3DBJY8/xqnXjqfTW/UFbskSZIkSVILK4vEpKGtrCyGKr1rM0b15eLTJ/D0+tc55ZL5bHy9ttglSZIkSZJaUFkkJrVb3K1M2h1H7t+Hyz4xkeUvvsGpl9zPiwZEkiRJkpRZZZGY1ObqXZBa2k2HD+/NFWdMYtVLm5l18XzWv/ZWsUuSJEmSJLWAsgiH6nK2lUl74tD9enHl7Ek89/KbzLp4PuteNSCSJEmSpKwpi8SkNmdbmbSnpuzTk1/Mmcy6V97i5J/P4/lX3ix2SZIkSZKkZlQWiUltLm9bmfQuTBzag6vPnMLG1+s4+efzWbNpc7FLkiRJkiQ1k7IIh+pyedpVl8VQpRZz8JDu/PLMKby8uSEgWv2SAZEkSZIkZUHmE5N8PlFXn6emMvNDlVrcuMHduOasqbxem+Pkn89j5cY3il2SJGVaRMyMiKciYllEnLeTc06KiMUR8UREXNPo+G0R8XJE/G/rVSxJkkpR5hOTuvo8gDOHpGZywMCuXHPWFN7cUs/JP5/P8hcNiCSpJUREJXAR8F5gNHBKRIze7pzhwPnAtJTSGOBfG139PeC01qlWkiSVsswnJrW5hnDImUNS8xkzoCvXnj2VLfV5Tv75PJatf73YJUlSFk0GlqWUnk0p1QHXASdsd85ZwEUppU0AKaX1W69IKd0JvNZaxUqSpNKV+cSkNlcPQLtqF6SWmtPIfl247uyp5BPMung+T6/z/YckNbOBwOpGl9cUjjU2AhgREXMjYn5EzGy16iRJUmZkPxzaUmgrcyt7qdkN79uZ686eSkU0BERLXni12CVJUrmpAoYDRwKnAJdERLem3jgizo6IhRGxcMOGDS1ToSRJavOqil1AS9u25pDhkNQi9uuzF9d/6hBOuXg+p1w8n1+dOZXRA7oUuyxJyoK1wOBGlwcVjjW2Brg/pbQFWB4RS2kIixY05R9IKV0MXAwwceLE9K4r3oH/+7snWPycHx5IktQUowd04WsfHNPq/27mExNnDkktb1ivTlz/qal0qK7k1Evn8/jaV4pdkiRlwQJgeEQMi4gaYBZwy3bn3EzDrCEiohcNbWbPtmKNkiQpAzI/c2jbmkNVrjkktaS9e3bi+k8dwqyL53PqJfO5+pNTGDe4W7HLkqSSlVLKRcQ5wO1AJXB5SumJiPgGsDCldEvhumMjYjFQD5ybUtoIEBF/BUYCe0XEGuCTKaXbW3scxfj0U5Ik7Z7MT6ep27pbmTOHpBY3uEdHrv/UVLp1rOEfLr2fRas2FbskSSppKaVbU0ojUkr7ppS+VTj21UIwRGrwuZTS6JTSgSml6xrd9vCUUu+UUoeU0qBiBEOSJKk0ZD4x2bqVvW1lUusY1L0j1509lZ571XD6ZQ+wcMVLxS5JkiRJkvQOMp+YvB0O2VYmtZYB3Tpw3dmH0KdzO06//AHuf3ZjsUuSJEmSJO1Ek8KhiJgZEU9FxLKIOG8n55wUEYsj4omIuKbR8SER8ceIeLJw/dBmqr1JbCuTiqNf1/Zcd/ZUBnTrwBlXLOC+Z14sdkmSJEmSpB3YZWISEZXARcB7gdHAKRExertzhgPnA9NSSmOAf2109S+A76WURgGTgfXNU3rTvL0gteGQ1Nr6dGnPtWdNZUiPjsy5cgH3Pm1AJEmSJEltTVMSk8nAspTSsymlOuA64ITtzjkLuCiltAkgpbQeoBAiVaWU7igcfz2ltLnZqm+CbW1l1YZDUjH07tyOa86awtCenZhz1QL+/FSr5sOSJEmSpF1oSmIyEFjd6PKawrHGRgAjImJuRMyPiJmNjr8cETdFxEMR8b3CTKS/ERFnR8TCiFi4YcOGPRnHTm1rK6s0HJKKpede7bj2rKkM77MXZ//iQe5asq7YJUmSJEmSCporMakChgNHAqcAl0REt8Lxw4EvAJOAfYAztr9xSunilNLElNLE3r17N1NJDba1lVW7ILVUTN071XDNmVMZ2b8zn7r6Qe5YbEAkSZIkSW1BU8KhtcDgRpcHFY41tga4JaW0JaW0HFhKQ1i0Bni40JKWA24GDn7XVe+G2i1uZS+1FV07VnP1J6cwZkBXPv3LB7nt8eeLXZIkSZIklb2mJCYLgOERMSwiaoBZwC3bnXMzDbOGiIheNLSTPVu4bbeI2DodaDqw+N2X3XR19XkioKoiWvOflbQTXTtUc/UnJzNucDc+e81D/P5RAyJJkiRJKqZdhkOFGT/nALcDTwI3pJSeiIhvRMTxhdNuBzZGxGLgbuDclNLGlFI9DS1ld0bEY0AAl7TEQHamNpenXVUFEYZDUlvRuX01V82ZzIQh3fnn6x7itw9vPxlRkiRJktRaqppyUkrpVuDW7Y59tdH3Cfhc4Wv7294BjH13Ze65ulyedlWuNyS1NXu1q+LKOZOYc+UC/u36h6nPJz5y8KBilyVJkiRJZSfzC/HU5uqpcb0hqU3qWFPFFWdM5pB9e/L5Xz/CDQtX7/pGkiRJkqRmlfnUpHZL3sWopTasQ00ll31iEoft14sv3vgo1z6wqtglSZIkSVJZyXxqUltvOCS1de2rK7nk9IkctX9vzr/pMa6ev7LYJUmSJElS2ch8alK7JU+Naw5JbV776kp+dtoEjh7Vh6/c/DhXzl1e7JIkSZIkqSxkPxzK1TtzSCoR7aoq+cnHJ3DcmL58/XeLufSvzxa7JEmSJEnKvMynJnU528qkUlJTVcF/n3ow7zuwH//x+yf5+T3PFLskSZIkScq0Jm1lX8pqc3k6t8/8MKVMqa6s4MezDqKy4hEu+MMScvnEZ4/ar9hlSZIkSVImZT41qc3l6eWaQ1LJqaqs4AcnjaOqIvje7U+Rq0/8y9HDi12WJEmSJGVO5sOhulw97aptK5NKUVVlBf/5sXFUVgQ/+NNS6vN5/u2YEUREsUuTJEmSpMzIfDhUm8vTrtJwSCpVlRXBdz86lqqK4Md3LSOXT5x73P4GRJIkSZLUTMojHHLmkFTSKiqCb3/4QCorgp/8+Rk219XzheP2Z692mf8RJkmSJEktLvPvrBp2K3PNIanUVVQE//GhA6iurODK+1bwPw+u4WMTB3PGoUMZ0rNjscuTJEmSpJKV+XCoNldPjVvZS5kQEXz9+DF86KCBXDF3Ob+Yt4Ir7lvO0aP6MnvaUA7Zp6ftZpIkSZK0mzIdDqWUGtrKDIekTBk/uBs/mnUQX37fKK6et5JrHljFHYvXMbJfZ+ZMG8bx4wfQvtoZg5IkSZLUFJlOTXL5REpQ44LUUib17dKeLxy3P/edN53vfnQsAF/8n0c59MK7+M/bn2Ldq28VuUJJkiRJavsyPXOoNpcHcEFqKePaV1dy0qTBfGziIOY9u5Er5q7goj8v42f3PMP7x/Zn9rRhjB/crdhlSpIkSVKblO1waEs9gAtSS2UiIjh0314cum8vVm58g6vuW8kNC1fz24ef4+Ah3Zg9bRgzD+hHtbMJJUmSJGmbTIdDdfUNM4dckFoqP3v37MRXPziazx07ghsXruaK+1bwT9c+RP+u7TntkL05ZdIQuneqKXaZkiRJklR0mU5NarcU2soMh6SytVe7Ks6YNoy7P38kl31iIvv07sR3b3uKQy68k/NvepSl614rdomSJEmSVFSZnjm0bc0h28qksldREcwY1ZcZo/ry1AuvceV9y7lp0VqufWA1h+3Xi9nThnLU/n2oqIhilypJkiRJrSrTU2rqcraVSfp7+/frzAUfGcu882dw7nH7s2z963zyqoXM+P49XDl3Oa/X5opdoiRJkiS1mkynJrW5rQtSZ3qYkvZQj041fPao/fjrl47ix6ccRLeO1Xz9d4s55Nt38s3/XcyqjZuLXaIkSZIktbgyaSszHJK0c9WVFRw/bgDHjxvAw6tf5oq5y7nqvhVcPnc5R4/qy5xpw5i6Tw8ibDmTJEmSlD2ZDodsK5O0u8YP7saPZh3E+e8dxS/nr+RX96/kjsXrGNmvM3OmDeP48QNoX+06ZpIkSZKyI9OpydttZb6Rk7R7+nVtzxeO259558/gOx89EIAv/s+jTLvwLv7fH59i3atvFblCSZIkSWoemZ45tK2trDrTGZikFtS+upKTJw3hpImDmffsRi6/dwX/ffcyfvrnZ/jA2P7MnjaMcYO7FbtMSZIkSdpjZREO1VQaDkl6dyKCQ/ftxaH79mLlxje46r6V3LBwNTc//BwHD+nGnMOGMXNMP6r8eSNJkiSpxJRFOOTMIUnNae+enfjqB0fzb8cM58YH13DlfSs455qH6N+1PacdsjenTBpC9041xS5TkiRJkpok06lJ7RbXHJLUcjq3r2b2tGHc9fkjufT0iezTuxPfve0pDrnwTs6/6TGWrnut2CVKkiRJ0i5leuZQXb1b2UtqeZUVwdGj+3L06L4seeFVrpy7gpsWreHaB1Zx+PBezJ42lCNH9KGiIopdqiRJkiT9nUynJrVbXHNIUusa2a8LF350LPPOn8G5x+3P0nWvMefKhcz4/j1cdd8KXq/NFbtESZIkSfobmU5NanN5aior/LReUqvr0amGzx61H/d+aTo/PuUgunWs5mu3PMEh376Tb/7vYla/tLnYJUqSJEkSkPW2slyeGlvKJBVRdWUFx48bwPHjBvDQqk1cMXcFV923givmLufoUX2Zc9gwpgzrQYQhtiRJkqTiyHQ4VJurd70hSW3GQUO6c9CQ7nz5faO4ev4Krrl/FX9cvI5R/bswe9pQjh83gPbVLqAvSZIkqXVlOjmpzeUNhyS1Of26tufc40Yy7/wZfOejB5LPJ75446NMu/Auvv/Hp1j/6lvFLlGSJElSGcn0zCHbyiS1Ze2rKzl50hBOmjiYec9s5PK5K/ivu5fx03ue4f0H9mfOYcMYO6hbscuUJEmSlHGZDoca2sps0ZDUtkUEh+7Xi0P368WKF9/gqnkr+PXCNdz88HNM2Ls7s6cNZeaYflS586IkSZKkFpDpcKgul6ddtW+mJJWOob068bUPjuFzx4zg1wvXcNW8FZxzzUMM6Nqe0w4ZyimTB9OtY02xy5QkSZKUIZlOTrZuZS9JpaZz+2rmHDaMuz5/JJeePpGhvTrxnduWMPWCO/nybx7j6XWvFbtESZIkSRmR6ZlDtbk87Z05JKmEVVYER4/uy9Gj+7LkhVe5cu4K/ufBNVxz/yoOH96LOdOGccSI3lRURLFLlSRJklSiMp2c1OXyrjkkKTNG9uvChR8dy7zzZ3DucfuzdN1rzL5yAUd//x6uum8Fb9Tmil2iJEmSpBKU6XCoNldvW5mkzOnRqYbPHrUf935pOj+aNZ4uHar52i1PMPWCO/mP/13M6pc2F7tESZIkSSUk821lLkgtKauqKys4YfxAThg/kEWrNnHF3BVced8KLp+7nGNG92X2tGFMGdaDCFvOJEmSJO1cpsOhhrYywyFJ2XfwkO4cPKQ7L7xvFFfPX8E196/i9ifWMap/F+ZMG8oHxw2gfbVttpIkSZL+XqaTk9pcnhrDIUllpF/X9px73EjmnT+DCz9yIPl84twbH2XahXfx/T8+xfpX3yp2iZIkSZLamEzPHKrdUu+C1JLKUvvqSmZNHsLJkwZz3zMbuWLucv7r7mX89J5n+MDYAcyeNpSxg7oVu0xJkiRJbUCmw6G6etvKJJW3iGDafr2Ytl8vVrz4Blfet4IbH1zDbx5ay8S9uzN72jCOG9OXKhfvlyRJkspWZt8N1OcTW+qTbWWSVDC0Vye+fvwY5p0/na9+YDTrX6vls9cs4j3fvZuf3fMML2+uK3aJkiRJkoogs8lJXS4PYFuZJG2nc/tq5hw2jLu/cCSXnD6Rob06ceEfljD1gjv58m8e4+l1rxW7REmSJEmtKLNtZVvDIWcOSdKOVVYEx4zuyzGj+/Lk869y5dyGlrNr7l/F4cN7MWfaMI4Y0ZuKiih2qZIkSZJaUGaTk9pcPYBrDklSE4zq34XvnDiWeedN5wvHjmDputeYfeUCjv7+Pfxi3greqM0Vu0RJkiRJLSSzyUnttrayzA5Rkppdz73acc704fz1i9P50azxdO5QzVd/+wRTL7iTb/1+Matf2lzsEiVJkiQ1s8y2ldXaViZJe6ymqoITxg/khPEDWbRqE1fMXcEVc1dw2b3LOWZ0X2ZPG8aUYT2IsOVMkiRJKnUZDoe2tpW5ILUkvRsHD+nOwUO68/z7RnL1vJVc88Aqbn9iHaP7d2H2tKF8cNwA2lf7s1aSJEkqVZmdVrOtraw6s0OUpFbVv2sHvjhzJPPOm8EFHzmQXD7PuTc+ymHfuYvv37GU9a+9VewSpcyJiJkR8VRELIuI83ZyzkkRsTginoiIaxod/0REPF34+kTrVS1JkkpNZmcObdvKvtJwSJKaU4eaSk6ZPIRZkwZz3zMbufze5fzXXU/z0z8v4wNjBzBn2jAOHNS12GVKJS8iKoGLgGOANcCCiLglpbS40TnDgfOBaSmlTRHRp3C8B/A1YCKQgAcLt93U2uOQJEltX2bDIWcOSVLLigim7deLafv1YsWLb3DlfSv49cLV/OahtUzcuztzDhvGsaP7UmVIL+2pycCylNKzABFxHXACsLjROWcBF20NfVJK6wvHjwPuSCm9VLjtHcBM4NpWql2SJJWQzL5ir93imkOS1FqG9urE148fw7wvz+ArHxjN+tdq+cyvFnHE9/7Mz+55hpc31xW7RKkUDQRWN7q8pnCssRHAiIiYGxHzI2LmbtyWiDg7IhZGxMINGzY0Y+mSJKmUZDYcqqt3tzJJam1d2lfzycOGcfcXjuTi0yYwpEdHLvzDEg654C7+z28eY9n614pdopQ1VcBw4EjgFOCSiOjW1BunlC5OKU1MKU3s3bt3y1QoSZLavOy2lW0ptJUZDklSq6usCI4d049jx/Tjyedf5Yq5y/n1g2v41f2reM+I3syeNpQjhvemoiKKXarUlq0FBje6PKhwrLE1wP0ppS3A8ohYSkNYtJaGwKjxbf/cYpVKkqSSltnkZNuaQ7aVSVJRjerfhe+eOI55503nC8eOYMnzrzL7igUc/YN7uHreCt6ozRW7RKmtWgAMj4hhEVEDzAJu2e6cmymEQBHRi4Y2s2eB24FjI6J7RHQHji0ckyRJ+juZDYfqcg1rDtlWJkltQ8+92nHO9OHc+6Xp/GjWeDq3r+Yrv32CqRfcybd+v5jVL20udolSm5JSygHn0BDqPAnckFJ6IiK+ERHHF067HdgYEYuBu4FzU0obCwtRf5OGgGkB8I2ti1NLkiRtL7ttZTnbyiSpLaqpquCE8QM5YfxAFq3axOX3LufyuSu47N7lHDu6H7OnDWXysB5E2HImpZRuBW7d7thXG32fgM8Vvra/7eXA5S1doyRJKn2GQ5Kkojl4SHcOPrU7z738JlfPX8m1D6zitideYMyALsyeNowPjutve7AkSZLUwjKbnNTl8lQEVFVmdoiSlBkDunXgSzNHMu+8GVzwkQPZUp/nC79+hGkX3sUP7ljK+tfeKnaJkiRJUmZleOZQvZ82S1KJ6VBTySmThzBr0mDmLtvIFXOX86M7n+Ynf17GB8cOYPa0YRw4qGuxy5QkSZIyJcPhUJ521c4akqRSFBEcNrwXhw3vxfIX3+Cq+1bw64WruemhtUwa2p3Z04Zx7Oi+zg6VJEmSmkFmw6G6XJ4a3zRIUskb1qsTXz9+DJ87dgS/XriGK+9bzmd+tYiB3Tpw+iF7M2vSELp2rC52mZIkSVLJymx64swhScqWLu2r+eRhw/jzF47i4tMmMKRHRy74wxKmXnAn/37zYyxb/3qxS5QkSZJKUmZnDrnmkCRlU2VFcOyYfhw7ph+Ln3uVK+9bzg0L1/DL+at4z4jezJ42lCOG96aiIopdqiRJklQSMju1xrYyScq+0QO68N0TxzHvvOl8/pgRLHn+VWZfsYCjf3APV89bwRu1uWKXKEmSJLV5mU1PbCuTpPLRc692/NOM4dz7pen8aNZ4Orer4iu/fYJDLriTb9/6JGs2bS52iZIkSVKbleG2sjztqgyHJKmc1FRVcML4gRw/bgCLVr3M5XOXc9m9y7n0r89y7Oh+zDlsGJOGdifCljNJkiRpqyaFQxExE/gRUAlcmlK6cAfnnAR8HUjAIymlUxtd1wVYDNycUjqnGerepdpcnq4d3L1GkspRRDBh7+5M2Ls7z738JlfPX8m1D6zitideYMyALsyZNowPjOvv2nSSJEkSTWgri4hK4CLgvcBo4JSIGL3dOcOB84FpKaUxwL9u99d8E/hLcxTcVLVb6p05JEliQLcOfGnmSOadN4Nvf/hA6nJ5Pv/rR5h24d384I6lrH/trWKXKEmSJBVVU9KTycCylNKzKaU64DrghO3OOQu4KKW0CSCltH7rFRExAegL/LF5Sm6aunrbyiRJb+tQU8mpU4bwx397D1d/cjJjB3XlR3c+zWEX3s3nbniYx9e+UuwSJUmSpKJoSlvZQGB1o8trgCnbnTMCICLm0tB69vWU0m0RUQH8P+AfgKN39g9ExNnA2QBDhgxpcvHvpHZLnhrDIUnSdiKCw4f35vDhvVn+4htcdd8Kfr1wNTctWsukod2ZM20Yx4zuS5U7XkqSJKlMNNcr3ypgOHAkcApwSUR0Az4D3JpSWvNON04pXZxSmphSmti7d+9mKahhQWrXkpAk7dywXp34+vFjmPflGfz7+0fxwqtv8elfLeKI7/2Zi//yDK9s3lLsEiVJkqQW15SZQ2uBwY0uDyoca2wNcH9KaQuwPCKW0hAWHQIcHhGfAfYCaiLi9ZTSee++9HdWl3PNIUlS03RpX82Zh+/D7GnD+NOT67hi7nK+fesSfnDH03x0wkDOOHQY+/XZq9hlSpIkSS2iKeHQAmB4RAyjIRSaBZy63Tk30zBj6IqI6EVDm9mzKaWPbz0hIs4AJrZGMARuZS9J2n2VFcFxY/px3Jh+LH7uVa6Yu5wbFq7hl/NXccSI3syeNpT3DO9NRUUUu1RJkiSp2ewyPUkp5YBzgNuBJ4EbUkpPRMQ3IuL4wmm3AxsjYjFwN3BuSmljSxW9KyklwyFJ0rsyekAXvvexccw7bzqfP2YEi59/lTOuWMAxP7iHq+evZHNdrtglSpIkSc0iUkrFruFvTJw4MS1cuPBd/R11uTwj/v0PfOHYEZwzfXgzVSZJKmd1uTy3PvY8l89dzqNrXqFL+ypmTR7C6YfszaDuHYtdXsmJiAdTShOLXYfe1hyvwSRJUtv1Tq+/mtJWVnJqc/UALkgtSWo2NVUVfOiggZwwfgCLVm3i8rkruOze5Vz612c5bkw/5hw2jIl7dyfCljNJkiSVloyGQ3kA2lXbViZJal4RwYS9ezBh7x489/Kb/GLeSq59YBV/ePwFDhjYhdmHDuMD4/r7AYUkSZJKRibTk7pCOFRTmcnhSZLaiAHdOnDee0cy//wZfPvDB1K7Jc/nf/0I0y68mx/+aSkbXqstdomSJEnSLjlzSJKkd6lDTSWnThnCKZMHc++yF7li7gp++Ken+cndz/CBcf2ZM20YBwzsWuwyJUmSpB3KaDjkmkOSpNYXERw+vDeHD+/Nsxte56r7VvDrB9dw06K1TB7ag9nThnLM6L5UObNVkiRJbUgmX53aViZJKrZ9eu/F/z3hAOadP4N/f/8onnvlTT79q0Uc8b0/c/FfnuGVzVuKXaIkSZIEZDQcsq1MktRWdO1QzZmH78M95x7Fz0+bwKDuHfj2rUuYesGdfOXmx3lmw+vFLlGSJEllLpttZVsK4ZBtZZKkNqKyIjhuTD+OG9OPJ557hSvnruD6Bau5ev5KjhjRmzmHDeM9w3sREcUuVZIkSWUmk1Nr6uob1hyqqcrk8CRJJW7MgK5872PjuO/86XzumBEsfv5VPnH5Axz9/Xu4ev5KNtflil2iJEmSykgm05O3Zw5lcniSpIzotVc7/nnGcOZ+aTo/OHkcndpV8ZWbH2fqt+/kglufZO3Lbxa7REmSJJWBbLaV5QyHJEmlo6aqgg8fNIgPjR/IolWbuPzeFVx673Iu+euzzDygH7OnDWPi3t1tOZMkSVKLyGQ4tG23MsMhSVIJiQgm7N2DCXv3YO3Lb3L1vJVc+8Aqbn3sBQ4Y2IU504bx/rH9XVNPkiRJzSqT6UltrmHNIV88S5JK1cBuHTjvvSOZd/50vvXhA3hrS57P3fAI0y68mx/+aSkbXqstdomSJEnKiEzOHHIre0lSVnSsqeLjU/bm1MlDuHfZi1x+73J++Ken+cndz/DBcQOYPW0oBwzsWuwyJUmSVMIyHQ7VVBoOSZKyISI4fHhvDh/em2c2vM5V963gxgfX8D+L1jB5WA/mTBvKMaP7UVnhukSSJEnaPZlMT1yQWpKUZfv23otvnHAA886fwb+/fxTPvfwm//jLRbznu3dzyV+e5ZU3txS7REmSJJWQjM4cqqemqsJdXSRJmda1QzVnHr4Ps6cN447F67hi7nK+deuT/OBPS5k8rAcHDe7OQUO6MW5wN7p2qC52uZIkSWqjMhkO1eXytLOlTJJUJiorgpkH9GPmAf144rlXuPaBVSxYvol7li4lpYZz9uuzFwcN7sZBQxoCoxF9O9uCJkmSJCCj4VBtLu9i1JKksjRmQFf+40MHAvDaW1t4dM0rPLRqEw+tepk7l6zn1w+uAaBjTSXjBnXjoCHdGF8IjXp3blfM0iVJklQk2QyHtuTdxl6SVPY6t69m2n69mLZfLwBSSqx6aTMPrXq5ITBa/TIX/+VZcvmG6UWDundomFk0uCE0Gj2gi79PJUmSykAmw6G6+jw1LkYtSdLfiAj27tmJvXt24kMHDQTgrS31PL72FR5a9TIPr36ZB1e8xO8eeQ5o2PVzzMAu29YuOmhINwZ26+CafpIkSRmTyXCodku9O5VJktQE7asrmTi0BxOH9th27IVX3uLh1ZsKM4xe5poHVnL53OUA9O7c7m/WLho7qCsdazL5ckKSJKlsZPLVXG0ubzgkSdIe6te1PTO79mfmAf0B2FKf56kXXtu2dtFDq1/mj4vXAVARsH+/Lg0ziwqh0T69OlHhYteSJEklI5PhUF3OtjJJkppLdWUFBwzsygEDu3LaIQ3HNr1Rx8Or31676HePPMc1968CoEv7KsY3Wrto/OBudOtYU8QRSJIk6Z1kMhyqzdU7xV2SpBbUvVMNR43sw1Ej+wCQzyeeffF1FhVa0R5atYn/uutpCmtds0/vTn+zdpEkSZLajkwmKHX1ebo7c0iSpFZTURHs16cz+/XpzEkTBwPwem2OR9e8vG3tonuWrud/Fq0pcqWSJEnaXibDodottpVJklRse7Wr4tB9e3Hovr0ASCmxZtObLFq1iQ99p8jFSZIkaZtshkMuSC1JUpsTEQzu0ZHBPToWuxRJkiQ1kskEpS6Xp11VZbHLkCRJkiRJavMyGQ7V5uptK5MkSZIkSWqCTCYotpVJkiRJkiQ1TSYTlLqcC1JLkiRJkiQ1ReYSlFx9nlw+ueaQJEmSJElSE2QuHKqrzwPQrjpzQ5MkSZIkSWp2mUtQ6nIN4VBNZeaGJkmSJEmS1Owyl6DU5pw5JEmSJEmS1FSZS1BqtxTCIdcckiRJkiRJ2qXMhUN19fUA7lYmSZIkSZLUBJlLUN7aNnMoc0OTJEmSJElqdplLULatOWQ4JEmSJEmStEuZS1C27VZmOCRJkiRJkrRLmUtQanMNaw65ILUkSZIkSdKuZTAcsq1MkiRJkiSpqTKXoNQZDkmSJEmSJDVZ5hKUt2cO2VYmSZIkSZK0KxkMhwprDlVnbmiSJEmSJEnNLnMJyrbdyiozNzRJklRmImJmRDwVEcsi4rwdXH9GRGyIiIcLX2c2uu47EfF44evk1q1ckiSVkqpiF9DctrWVOXNIkiSVsIioBC4CjgHWAAsi4paU0uLtTr0+pXTOdrd9P3AwMB5oB/w5Iv6QUnq15SuXJEmlJnMJSu0WZw5JkqRMmAwsSyk9m1KqA64DTmjibUcDf0kp5VJKbwCPAjNbqE5JklTiMpeg1NXXU1kRVBkOSZKk0jYQWN3o8prCse19NCIejYgbI2Jw4dgjwMyI6BgRvYCjgMHb3zAizo6IhRGxcMOGDc1dvyRJKhGZS1Bqt+Tdxl6SJJWL3wFDU0pjgTuAqwBSSn8EbgXuA64F5gH12984pXRxSmliSmli7969W69qSZLUpmQuRanNGQ5JkqRMWMvfzvYZVDi2TUppY0qptnDxUmBCo+u+lVIan1I6BghgaQvXK0mSSlTmUpS6XJ4awyFJklT6FgDDI2JYRNQAs4BbGp8QEf0bXTweeLJwvDIieha+HwuMBf7YKlVLkqSSk8HdyuppV1VZ7DIkSZLelZRSLiLOAW4HKoHLU0pPRMQ3gIUppVuAf46I44Ec8BJwRuHm1cBfIwLgVeAfUkq51h6DJEkqDRkMh2wrkyRJ2ZBSupWGtYMaH/tqo+/PB87fwe3eomHHMkmSpF3KXIpiW5kkSZIkSVLTZS5FceaQJEmSJElS02UuRXHNIUmSJEmSpKbLXDhkW5kkSZIkSVLTZS5Fsa1MkiRJkiSp6TKXotTl8rSrtq1MkiRJkiSpKTIXDtXm8tRUZm5YkiRJkiRJLSJzKUptrp521ZkbliRJkiRJUovIXIrizCFJkiRJkqSmy1yKUpvLO3NIkiRJkiSpiTKVoqSUGhakrnJBakmSJEmSpKbIVDhUV58HcCt7SZIkSZKkJspUilKbMxySJEmSJEnaHZlKUWq3GA5JkiRJkiTtjkylKFvbymoMhyRJkiRJkpokUylK7ZZ6ABekliRJkiRJaqJshUOuOSRJkiRJkrRbMpWi1OVsK5MkSZIkSdodTUpRImJmRDwVEcsi4rydnHNSRCyOiCci4prCsfERMa9w7NGIOLk5i9/e2zOHbCuTJEmSJElqiqpdnRARlcBFwDHAGmBBRNySUlrc6JzhwPnAtJTSpojoU7hqM3B6SunpiBgAPBgRt6eUXm7ugQDU5gprDlU7c0iSJEmSJKkpmpKiTAaWpZSeTSnVAdcBJ2x3zlnARSmlTQAppfWFP5emlJ4ufP8csB7o3VzFb29bW1ml4ZAkSZIkSVJTNCVFGQisbnR5TeFYYyOAERExNyLmR8TM7f+SiJgM1ADP7OC6syNiYUQs3LBhQ9Or3862tjJnDkmSJEmSJDVJc6UoVcBw4EjgFOCSiOi29cqI6A9cDcxOKeW3v3FK6eKU0sSU0sTevfd8YtG2tjLXHJIkSZIkSWqSpoRDa4HBjS4PKhxrbA1wS0ppS0ppObCUhrCIiOgC/B74Pyml+e++5J1ztzJJkiRJkqTd05QUZQEwPCKGRUQNMAu4ZbtzbqZh1hAR0YuGNrNnC+f/BvhFSunG5ip6Z97ercxwSJIkSZIkqSl2maKklHLAOcDtwJPADSmlJyLiGxFxfOG024GNEbEYuBs4N6W0ETgJeA9wRkQ8XPga3xIDAdhc19BW1r7atjJJkiRJkqSm2OVW9gAppVuBW7c79tVG3yfgc4Wvxuf8Evjluy+zaVa/tJmuHarZq12ThiVJkiRJklT2MtV/tWLjGwzt1anYZUiSJEmSJJWMbIVDL25mWM+OxS5DkiRJkiSpZGQmHHprSz3PvfIme/d05pAkSZIkSVJTZSYcWv3SZlKCYbaVSZIkSZIkNVlmwqEVGzcDsLdtZZIkSZIkSU2WnXDoxTcAZw5JkiRJkiTtjsyEQ8s3vkG3jtV061hT7FIkSZIkSZJKRmbCoZUb33AxakmSJEmSpN2UmXDIbewlSZIkSZJ2XybCoa3b2A91vSFJkiRJkqTdkolwaOs29kNtK5MkSZIkSdotmQiHlhd2KnPmkCRJkiRJ0u7JRDi0cuNmAIa65pAkSZIkSdJuyUQ45Db2kiRJkiRJeyYT4dDKjW+43pAkSZIkSdIeyEQ4tOLFzbaUSZIkSZIk7YGSD4fcxl6SJEmSJGnPlXw45Db2kiRJkiRJe67kwyG3sZckSZIkSdpzJR8Obd3GfpgzhyRJkiRJknZbyYdDW7ex79qxutilSJIkSZIklZySD4dWvOg29pIkSZIkSXuq5MOhlRvdxl6SJEmSJGlPlXQ45Db2kiRJkiRJ705Jh0OrCtvYDzMckiRJkiRJ2iMlHQ6tKGxjv7drDkmSJEmSJO2R0g6HNjaEQ25jL0mSJEmStGdKPBzaTHe3sZckSZIkSdpjpR0OvfiGLWWSJEmSJEnvQsmHQy5GLUmSJEmStOdKNhxq2Mb+Lfbu2bHYpUiSJEmSJJWskg2HVr20GXAbe0mSJEmSpHejZMOhrdvYD3XNIUmSJEmSpD1WuuHQRsMhSZIkSZKkd6tkw6HlL7qNvSRJkiRJ0rtVVewCtrf6pc3887UP/c2x6soKzj1uf/p1bb/t2MqNbmMvSZIkSZL0brW5cGjzlnoeW/vK3xxbufENenWu4fz3jtp2bMWLbzBln56tXZ4kSZIkSVKmtLlwaP++nbn7C0f+zbEzr1rITYvWcu6x+1NVWbFtG3vXG5IkSVkWETOBHwGVwKUppQu3u/4M4HvA2sKh/04pXVq47rvA+2lYRuAO4F9SSqmVSpckSSWkJNYc+tjEQWx4rZZ7lm4A3t7GfmivjsUsS5IkqcVERCVwEfBeYDRwSkSM3sGp16eUxhe+tgZDhwLTgLHAAcAk4IjWqVySJJWakgiHpo/sQ6+9avj1wjUALHcbe0mSlH2TgWUppWdTSnXAdcAJTbxtAtoDNUA7oBpY1yJVSpKkklcS4VB1ZQUfGj+QO5esY+Prtax0G3tJkpR9A4HVjS6vKRzb3kcj4tGIuDEiBgOklOYBdwPPF75uTyk92dIFS5Kk0lQS4RDAxyYOZkt94uaHn3Mbe0mSpAa/A4amlMbSsK7QVQARsR8wChhEQ6A0PSIO3/7GEXF2RCyMiIUbNmxoxbIlSVJbUjLh0P79OjN2UFd+vXA1K158g6G9nDUkSZIybS0wuNHlQby98DQAKaWNKaXawsVLgQmF7z8MzE8pvZ5Seh34A3DI9v9ASunilNLElNLE3r17N/sAJElSaSiZcAgaZg8teeE1Hly1yZYySZKUdQuA4RExLCJqgFnALY1PiIj+jS4eD2xtHVsFHBERVRFRTcNi1LaVSZKkHSqpcOj4sQOoqaqgLpc3HJIkSZmWUsoB5wC30xDs3JBSeiIivhERxxdO++eIeCIiHgH+GTijcPxG4BngMeAR4JGU0u9adQCSJKlkVBW7gN3RtWM1x43px+8eec5t7CVJUuallG4Fbt3u2FcbfX8+cP4OblcPfKrFC5QkSZlQUjOHAE4/ZG/aVVUwdlC3YpciSZIkSZJU8kpq5hDApKE9WPyNmVRWRLFLkSRJkiRJKnklN3MIMBiSJEmSJElqJiUZDkmSJEmSJKl5GA5JkiRJkiSVMcMhSZIkSZKkMmY4JEmSJEmSVMYMhyRJkiRJksqY4ZAkSZIkSVIZMxySJEmSJEkqY4ZDkiRJkiRJZcxwSJIkSZIkqYwZDkmSJEmSJJUxwyFJkiRJkqQyZjgkSZIkSZJUxgyHJEmSJEmSypjhkCRJkiRJUhkzHJIkSZIkSSpjhkOSJEmSJEllzHBIkiRJkiSpjBkOSZIkSZIklTHDIUmSJEmSpDJmOCRJkiRJklTGIqVU7Br+RkS8BjxV7DqKqBfwYrGLKJJyHjuU9/gde/kq5/GX89gB9k8pdS52EXpbRGwAVha7jjam3J+nxeB93rq8v1uX93fr8v7+e3unlHrv6Iqq1q6kCZ5KKU0sdhHFEhELy3X85Tx2KO/xO/byHDuU9/jLeezQMP5i16C/tbMXi+Ws3J+nxeB93rq8v1uX93fr8v7ePbaVSZIkSZIklTHDIUmSJEmSpDLWFsOhi4tdQJGV8/jLeexQ3uN37OWrnMdfzmMHx6/S4P/T1ud93rq8v1uX93fr8v7eDW1uQWpJkiRJkiS1nrY4c0iSJEmSJEmtpGjhUETMjIinImJZRJy3g+vbRcT1hevvj4ihRSizRUTE4Ii4OyIWR8QTEfEvOzjnyIh4JSIeLnx9tRi1toSIWBERjxXG9Xe71USDHxce+0cj4uBi1NncImL/Ro/nwxHxakT863bnZOpxj4jLI2J9RDze6FiPiLgjIp4u/Nl9J7f9ROGcpyPiE61XdfPYydi/FxFLCv+vfxMR3XZy23d8jpSCnYz/6xGxttH/7/ft5Lbv+PuhrdvJ2K9vNO4VEfHwTm5b0o/9zn6/lcvzXqVpR8/ZHZxzZOF5+URE3NOa9WXRru7ziOgaEb+LiEcK9/ns1q4xS5r43iOTr7+LoYn398cL9/NjEXFfRIwrRq1Z0JT7u9G5kyIiFxEntmaNJSOl1OpfQCXwDLAPUAM8Aoze7pzPAD8rfD8LuL4YtbbQ+PsDBxe+7wws3cH4jwT+t9i1ttD4VwC93uH69wF/AAKYCtxf7Jpb4D6oBF4A9s7y4w68BzgYeLzRse8C5xW+Pw/4zg5u1wN4tvBn98L33Ys9nmYY+7FAVeH77+xo7IXr3vE5UgpfOxn/14Ev7OJ2u/z90Na/djT27a7/f8BXs/jY7+z3W7k87/0qza8mPGe7AYuBIYXLfYpdc6l/NeE+//LWnxNAb+AloKbYdZfqVxPfe2T+9Xcbu78P3fo7Dniv93fL3t+F6yqBu4BbgROLXXdb/CrWzKHJwLKU0rMppTrgOuCE7c45Abiq8P2NwIyIiFasscWklJ5PKS0qfP8a8CQwsLhVtSknAL9IDeYD3SKif7GLamYzgGdSSiuLXUhLSin9hYYXdI01fm5fBXxoBzc9DrgjpfRSSmkTcAcws6XqbAk7GntK6Y8ppVzh4nxgUKsX1kp28tg3RVN+P7Rp7zT2wu+xk4BrW7WoVvIOv9/K4nmv0tSEn1enAjellFYVzl/fKoVlWBPu8wR0LvzM3Ktwbu4dztc7aOJ7j3J4/d0qmnJ/p5TuK/yug4y/Jmxpu/He+p+A/wH8Gb4TxQqHBgKrG11ew98/gNvOKbyZegXo2SrVtaJoaJc7CLh/B1cfUphO+4eIGNO6lbWoBPwxIh6MiLN3cH1T/n+Uulns/M1hVh/3rfqmlJ4vfP8C0HcH55TD/4E5NHxCtyO7eo6UsnMK06gv30lrUdYf+8OBdSmlp3dyfWYe++1+v/m8VykbAXSPiD8XnpunF7ugMvDfwCjgOeAx4F9SSvnilpQN7/Dew5/BLWAX7/W2+iQ7f02o3bCz+zsiBgIfBn5ahLJKRlWxCyhnEbEXDenlv6aUXt3u6kU0tBy9XliX42ZgeCuX2FIOSymtjYg+wB0RsaTwCVJZiIga4Hjg/B1cneXH/e+klFJElN2WiRHxf2j4BPRXOzklq8+RnwLfpCEA+SYN7VVzilpR6zuFd541lInHfvvfb40n/pbr814lrQqYQMOs3w7AvIiYn1JaWtyyMu044GFgOrAvDT8P/7qD18vaDbt476Fm1pT7OyKOoiEcOqw1a8uiXdzfPwS+lFLKZ6QZqUUUa+bQWmBwo8uDCsd2eE5EVAFdgY2tUl0riIhqGv7z/iqldNP216eUXk0pvV74/lagOiJ6tXKZLSKltLbw53rgNzS0kTTWlP8fpey9wKKU0rrtr8jy497Iuq3TlAt/7mhqZ2b/D0TEGcAHgI+nlHb4BrkJz5GSlFJal1KqL3z6ewk7HleWH/sq4CPA9Ts7JwuP/U5+v5X1814lbw1we0rpjZTSi8BfABePbVmzaWjlSymlZcByYGSRayppu3rvgT+Dm1UT7m8iYixwKXBCSikz73OLoQn390TguohYAZwI/CQiPtR6FZaGYoVDC4DhETGsMItiFnDLdufcAmzdqeRE4K6dvZEqNYX+6cuAJ1NK39/JOf22rrEUEZNpeKxK/odGRHSKiM5bv6dhgd7td6q4BTi9sGvCVOCVRu0IWbDTmQNZfdy30/i5/Qngtzs453bg2IjoXmg9OrZwrKRFxEzgi8DxKaXNOzmnKc+RkrTd2gUfZsfjasrvh1J1NLAkpbRmR1dm4bF/h99vZfu8Vyb8FjgsIqoioiMwhYY1LdRyVtEwU4uI6AvsT8Mi9doDTXnvQfZff7eaJr7XGwLcBJzmLMR3pyn3d0ppWEppaEppKA3rGX8mpXRz61VZGorSVpZSykXEOTS86KsELk8pPRER3wAWppRuoeEBvjoiltGwCN2sYtTaQqYBpwGPxdvbGX8ZGAKQUvoZDYHYpyMiB7wJzMpIONYX+E0h/6gCrkkp3RYR/wjbxn4rDTsmLAM20/DpUSYU3vAdA3yq0bHGY8/U4x4R19KwA1uviFgDfA24ELghIj4JrKRhcV4iYiLwjymlM1NKL0XEN2kICgC+kVLak8WNi2YnYz8faEfD9HiA+Smlf4yIAcClKaX3sZPnSBGG8K7sZPxHRsR4GtrKVlB4HjQe/85+P7T+CPbcjsaeUrqMHaw1lsHHfme/38riea/StJOfV9XQ8Ls5pfRkRNwGPArkaXjOllRw29bs6j6nofX4yoh4jIbds75UmLWlPdOU9x6Zff1dBE25v79Kw3q6Pyn83s+llCa2fqmZ0JT7W00QJfy+U5IkSZIkSe9SsdrKJEmSJEmS1AYYDkmSJEmSJJUxwyFJkiRJkqQyZjgkSZIkSZJUxgyHJEmSJEmSypjhkCRJkiRJUhkzHJIkSZIkSSpjhkOSJEmSJEll7P8DvIpvjiWkuU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 2, figsize=(20, 20))\n",
    "g1 = series_abc_jan.plot(ax=axes[0][0])\n",
    "g1.set_title(\"Average area burnt for each false negative in function of C\")\n",
    "#g1.set_ylim([0, 20])\n",
    "g1.set_xlim([0, 20])\n",
    "g2 = series_abcr_jan.plot(ax=axes[0][1])\n",
    "g2.set_title(\"Average area burnt for each false negative  burnt in function of C (smaller range)\")\n",
    "#g2.set_ylim([0,20])\n",
    "g3 = series_ac_jan.plot(ax=axes[1][0])\n",
    "g3.set_xlim([0, 20])\n",
    "g3.set_title(\"Accuracy in function of C\")\n",
    "g4 = series_acr_jan.plot(ax=axes[1][1])\n",
    "g4.set_title(\"Accuracy in function of C (smaller range)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "id": "cf911400-7a5a-48b4-8a8c-6ebc6a22d7d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.549999999999999"
      ]
     },
     "execution_count": 285,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = SVC(kernel = 'rbf', gamma=0.3727, C= 3)\n",
    "rfc.fit(X_jan, y_jan)\n",
    "y_pred = rfc.predict(X_test_jan)\n",
    "area_burnt(X_test_with_area_jan, y_pred, y_test_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "id": "24a784e3-cc17-4de6-851e-90274f24fbd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9310344827586207"
      ]
     },
     "execution_count": 286,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, y_test_jan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62aca40-613f-4c9d-bc1a-2e698abc4c79",
   "metadata": {},
   "source": [
    "Even with a lower accuracy score, there seems to be a local maxima of accuracy and a global minima of false negatives at this point of the model space. Exactly this model seems a perfect option for our preclassification task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "4d37db78-3111-4525-8dcd-b592c1846a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"classification_jan_may.pickle\", 'wb') as f:\n",
    "    pickle.dump(rfc, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f014b5f-6d36-4c30-aaeb-634d2d155d58",
   "metadata": {},
   "source": [
    "alternative model, maximizing accuracy using scikit-optimize\n",
    "\n",
    "*(note: The accuracy was maximized using stratified cross validation, here the score is calculated only in a subset of test data, which can yield wrong estimations)*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "3100b969-7aa0-45d9-ae92-a20ea43ea40e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.32999999999999"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc = SVC(C = 36.016330496165466, kernel = 'rbf', gamma= 0.008066428945580253)\n",
    "rfc.fit(X_jan, y_jan)\n",
    "y_pred = rfc.predict(X_test_jan)\n",
    "area_burnt(X_test_with_area_jan, y_pred, y_test_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "id": "d0776509-3206-46a8-9ea4-369b1feef8b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7931034482758621"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_pred, y_test_jan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "3329c137-823b-4383-96c8-f200aba55515",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"classification_jan_may_skopt.pickle\", 'wb') as f:\n",
    "    pickle.dump(rfc, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30a4b9a0-dec1-4259-bf28-17d7ff5f1497",
   "metadata": {},
   "source": [
    "After trying more complex models, the conclusion is that trying to preclassify the instances won't work"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9856b092-9f1a-4df6-af5d-4b31a8a65b3e",
   "metadata": {},
   "source": [
    "# Back to a simplified model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "c11de240-be46-4397-9fc5-4b2547a16d7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fires = pd.read_csv(\"fire_data_processed_after_pca.csv\")\n",
    "y = fires.loc[:,\"area\"]\n",
    "drop_cols = ['Unnamed: 0', 'area_bool']\n",
    "X = fires.drop(drop_cols, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "545adb31-7db7-4b3b-ba0f-b993f2fb589e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict_gbr = hyper_opt_gbr_2(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "4f704deb-da88-404b-98ce-3112741218a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': 0.013059042524457578, 'deg': 1, 'lr': 0.05, 'n_estimators': 100}"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_gbr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "c663f6ce-7332-400a-9803-8e22deed1b29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': 0.01526792823709005, 'deg': 1, 'lr': 'mse', 'n_estimators': 100}"
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict_rfr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d942dfd-9402-4e3a-b29b-59e713cd998c",
   "metadata": {},
   "source": [
    "The Gradient Boosting regressor seems to perform slightly better, so it's going to be the choice for fine-tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "effede28-ecc7-4330-bd33-c9044fe2e257",
   "metadata": {},
   "source": [
    "## Feature engineering: fitting a model with polynomial features to extract important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "daf1cab0-2214-4e5b-8bf8-091d19074136",
   "metadata": {},
   "outputs": [],
   "source": [
    "poly = PolynomialFeatures(degree=2)\n",
    "poly.fit(fires.drop(['Unnamed: 0', 'area', 'area_bool'], axis = 1))\n",
    "transformed_features = poly.transform(fires.drop(['Unnamed: 0', 'area', 'area_bool'], axis = 1))\n",
    "model = GradientBoostingRegressor(learning_rate=0.05, n_estimators=100)\n",
    "model.fit(transformed_features, fires[\"area\"])\n",
    "importances = model.feature_importances_\n",
    "imp_series = pd.Series(data=importances, index=poly.get_feature_names(fires.drop(['Unnamed: 0', 'area', 'area_bool'], axis = 1).columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1d88c4d2-6a37-4392-97cb-9f7097f08618",
   "metadata": {},
   "outputs": [],
   "source": [
    "imp_series.sort_values(ascending=False, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e70507c4-1770-4253-9a2b-d2c6d5211aba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22 sat          0.059662\n",
       "wed pc5         0.054814\n",
       "tue pc2         0.038749\n",
       "94 pc6          0.034369\n",
       "sep_bool pc4    0.027146\n",
       "pc7^2           0.025914\n",
       "64 pc1          0.025817\n",
       "sat pc6         0.024862\n",
       "sun pc1         0.024560\n",
       "65 pc6          0.023853\n",
       "sep_bool pc5    0.023723\n",
       "86 pc1          0.022787\n",
       "74 pc7          0.018895\n",
       "44 pc4          0.017492\n",
       "pc1 pc3         0.016884\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "imp_series.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "57d89f81-c8c4-42ff-9ced-9196e94b21fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.1774842519463822,\n",
       " 1: 1.1672943180797808,\n",
       " 2: 1.1706739456215494,\n",
       " 3: 1.1613532872460774,\n",
       " 4: 1.156421714855043,\n",
       " 5: 1.1415335946102307,\n",
       " 6: 1.1453740833396528,\n",
       " 7: 1.1440121826017233,\n",
       " 8: 1.1453755496417544,\n",
       " 9: 1.1443048551395616,\n",
       " 10: 1.1488961532025403,\n",
       " 11: 1.152002937215305,\n",
       " 12: 1.1463670736843952}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = [[\"22\",\"sat\"], [\"wed\",\"pc5\"], [\"tue\",\"pc2\"], [\"94\", \"pc6\"], ['sep_bool','pc4'], [\"64\", \"pc1\"], [\"sat\",\"pc6\"], [\"sun\", \"pc1\"], [\"65\",\"pc6\"], [\"sep_bool\", \"pc5\"], [\"86\",\"pc1\"], [\"74\", \"pc7\"]]\n",
    "greedy_addition(fires.drop(['Unnamed: 0', 'area', 'area_bool'], axis = 1), fires[\"area\"], features)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "524a52d5-dc09-4438-aaad-2482ecfa4f06",
   "metadata": {},
   "source": [
    "There seems to be a slight improvement adding 5-6 features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "34bbe750-880d-4855-b975-60aea2fffb2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [[\"22\",\"sat\"], [\"wed\",\"pc5\"], [\"tue\",\"pc2\"], [\"94\", \"pc6\"], ['sep_bool','pc4']]\n",
    "new_df = fires.drop(['Unnamed: 0', 'area', 'area_bool'], axis=1)\n",
    "for feature in features:\n",
    "    new_df = add_feature(new_df, feature[0], feature[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "25ad4bd1-b4f5-485b-abf7-b0f776e0fe5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "features, losses_dict = greedy_feature_removal_kfold(new_df, fires[\"area\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "c3195a09-1e13-402d-a3fc-97e2d9630c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "series_feature_removal = pd.Series(losses_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "f42a4d1c-d261-42b5-8291-23c8b1238459",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=[10, 10])\n",
    "g = series_feature_removal.plot()\n",
    "g.set_title(\"Average absolute loss in functon of the number of features greedily removed\", fontsize=14)\n",
    "g.set_xlabel(\"Number of features removed\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3118dcac-a016-4d50-a6b8-29bdddc8b1b8",
   "metadata": {},
   "source": [
    "With this we observe a few facts:\n",
    "\n",
    " - Most of the features of the model can be removed, and there's a minima around 48. After that the steep increases fast. So around 36 there seems to be a local minima where it's \"safer\" to remove those variables\n",
    " - If we analyze the order of feature removal, the new engineered features are among the last features to be removed.\n",
    " - After all the effort the model still behaves close to random"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}