clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import cluster\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import geopandas as gpd\n",
    "import re as reg\n",
    "import random\n",
    "from yellowbrick.cluster import KElbowVisualizer\n",
    "kmeans = cluster.KMeans(n_clusters=2, max_iter=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "user='S'\n",
    "\n",
    "if user=='S':\n",
    "    lien=\"C:/Users/sylva/Desktop/git/Projet-Python-deputes/\"\n",
    "elif user=='G':\n",
    "    lien=\"C:/Users/Administrateur.LAPTOP-FH8TB47B/Desktop/Projet_assemblee/\"\n",
    "    \n",
    "mandat_dep=pd.read_csv(lien+\"table_mandat.csv\")#on récupère le csv de la liste des mandats\n",
    "df_depu=pd.read_csv(lien+\"table_depute.csv\")\n",
    "table_depmt=pd.read_csv(lien+\"table_dpmt.csv\")\n",
    "lstRegime=pd.read_csv(lien+\"regime_wiki.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Essayons de faire un clustering sur les département selon leur orientation politique sous la troisième république"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mandat_dep_3=mandat_dep[mandat_dep['Régime politique']=='Troisième République - Chambre des députés']#on prend la database de la Troisième\n",
    "def datagroupe(mandat_reg) :\n",
    "    liste_groupe=mandat_reg['Groupe'].unique()\n",
    "    gros_groupe=[]\n",
    "    for groupe in liste_groupe :\n",
    "        if mandat_reg[mandat_reg['Groupe']==groupe]['Groupe'].count()>10 :\n",
    "            gros_groupe+=[groupe]#on va se concentrer sur les gros groupes pour diminuer la complexité des visualisations et faciliter l'aanalyse\n",
    "    datadep=[]\n",
    "    for departement in mandat_reg['Département'].unique():\n",
    "        newdep=[]\n",
    "        dep=mandat_reg[mandat_reg['Département']==departement]\n",
    "        nbremand=dep['Groupe'].count()\n",
    "        for groupe in gros_groupe :\n",
    "            pct_groupe=dep[dep['Groupe']==groupe]['Groupe'].count()/nbremand#pourcentage de députés du département issu de ce groupe\n",
    "            newdep+=[pct_groupe]#on rajoute les données du groupe dans le dataframe du département\n",
    "        datadep+=[newdep]#on rajoute les données du département au futur dataframe général \n",
    "    datadep=np.array(datadep)#on crée un dataframe avec les informations\n",
    "    depmt_groupe = pd.DataFrame(datadep, index=mandat_reg['Département'].unique(),columns=gros_groupe)\n",
    "    return depmt_groupe, gros_groupe\n",
    "(depmt_groupe_3, gros_groupe_3)=datagroupe(mandat_dep_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.21806700303927534\n"
     ]
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=2, max_iter=1000)#comme il y a beaucoup de variables et pas trop de données, on peut augmenter le nombre d'itérations.\n",
    "def clustering(depmt_groupe):\n",
    "    kmeans.fit(depmt_groupe)\n",
    "    idk = np.argsort(kmeans.labels_)#on trie les index\n",
    "    #affichage des observations et leurs groupes\n",
    "    clusters=pd.DataFrame(kmeans.labels_[idk], depmt_groupe.index[idk], columns=['Appartenance'])\n",
    "    depmt_app=depmt_groupe.merge(clusters, right_index=True, left_index=True)\n",
    "    #distances aux centres de classes des observations\n",
    "    dist_centre=kmeans.transform(depmt_groupe)\n",
    "    print(dist_centre.mean())\n",
    "    return depmt_app\n",
    "depmt_app_3=clustering(depmt_groupe_3)#les départements sont en moyenne plus proches d'un centre que de l'autre : bonne nouvelle ! Deux groupes relativement distincts a priori !\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Socialiste</th>\n",
       "      <th>Gauche indépendante</th>\n",
       "      <th>Fédération républicaine de France</th>\n",
       "      <th>Républicain radical et radical-socialiste</th>\n",
       "      <th>Non inscrit</th>\n",
       "      <th>Républicains indépendants et d'action sociale</th>\n",
       "      <th>Indépendants d'action populaire</th>\n",
       "      <th>Communiste</th>\n",
       "      <th>Indépendants de gauche</th>\n",
       "      <th>Gauche démocratique et radicale indépendante</th>\n",
       "      <th>Gauche radicale</th>\n",
       "      <th>Alliance des républicains de gauche et des radicaux indépendants</th>\n",
       "      <th>Parti socialiste</th>\n",
       "      <th>Députés indépendants</th>\n",
       "      <th>Indépendants</th>\n",
       "      <th>Indépendants républicains</th>\n",
       "      <th>Union républicaine démocratique</th>\n",
       "      <th>Républicains de gauche</th>\n",
       "      <th>Union socialiste et républicaine</th>\n",
       "      <th>Fédération républicaine</th>\n",
       "      <th>Démocrate populaire</th>\n",
       "      <th>Députés du centre républicain</th>\n",
       "      <th>Parti socialiste de France</th>\n",
       "      <th>Radical et radical-socialiste</th>\n",
       "      <th>Parti républicain socialiste et socialiste français</th>\n",
       "      <th>Parti républicain socialiste</th>\n",
       "      <th>Parti socialiste français</th>\n",
       "      <th>Démocrates populaires</th>\n",
       "      <th>Républicain et social</th>\n",
       "      <th>ZZZ</th>\n",
       "      <th>Démocrates</th>\n",
       "      <th>Gauche républicaine démocratique</th>\n",
       "      <th>Action démocratique et sociale</th>\n",
       "      <th>Entente républicaine démocratique</th>\n",
       "      <th>Gauche sociale et radicale</th>\n",
       "      <th>Action républicaine et sociale</th>\n",
       "      <th>Républicain socialiste</th>\n",
       "      <th>Républicain socialiste et socialiste français</th>\n",
       "      <th>Union républicaine radicale et socialiste</th>\n",
       "      <th>Parti radical et radical socialiste</th>\n",
       "      <th>Gauche démocratique</th>\n",
       "      <th>Parti républicain radical et radical socialiste</th>\n",
       "      <th>Républicains radicaux-socialistes</th>\n",
       "      <th>Action libérale</th>\n",
       "      <th>Républicain progressiste</th>\n",
       "      <th>Gauche radicale-socialiste</th>\n",
       "      <th>Socialistes parlementaires</th>\n",
       "      <th>Droites</th>\n",
       "      <th>Socialistes unifiés</th>\n",
       "      <th>Union démocratique</th>\n",
       "      <th>Gauche démocratique</th>\n",
       "      <th>Républicains progressistes</th>\n",
       "      <th>Radical-socialiste</th>\n",
       "      <th>Progressiste</th>\n",
       "      <th>Union républicaine</th>\n",
       "      <th>Défense nationale</th>\n",
       "      <th>Républicains nationalistes</th>\n",
       "      <th>Républicain radical</th>\n",
       "      <th>Union des Droites</th>\n",
       "      <th>Républicain</th>\n",
       "      <th>Républicain</th>\n",
       "      <th>Extrême-gauche</th>\n",
       "      <th>Gauche républicaine</th>\n",
       "      <th>Radical</th>\n",
       "      <th>Extreme-gauche</th>\n",
       "      <th>Appel au peuple</th>\n",
       "      <th>Centre gauche</th>\n",
       "      <th>Gauche</th>\n",
       "      <th>Union conservatrice</th>\n",
       "      <th>Opportuniste</th>\n",
       "      <th>Centre droit</th>\n",
       "      <th>Extrême-droite</th>\n",
       "      <th>Union des gauches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026683</td>\n",
       "      <td>0.003863</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.066091</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>0.001924</td>\n",
       "      <td>5.204170e-18</td>\n",
       "      <td>0.008238</td>\n",
       "      <td>0.005269</td>\n",
       "      <td>0.004223</td>\n",
       "      <td>0.122458</td>\n",
       "      <td>0.004507</td>\n",
       "      <td>0.068131</td>\n",
       "      <td>0.003817</td>\n",
       "      <td>0.003540</td>\n",
       "      <td>0.000462</td>\n",
       "      <td>0.011460</td>\n",
       "      <td>0.021218</td>\n",
       "      <td>0.004443</td>\n",
       "      <td>0.003950</td>\n",
       "      <td>0.000735</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.003809</td>\n",
       "      <td>0.021616</td>\n",
       "      <td>0.002261</td>\n",
       "      <td>0.001936</td>\n",
       "      <td>0.002189</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>0.001328</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0.014726</td>\n",
       "      <td>0.003120</td>\n",
       "      <td>0.015538</td>\n",
       "      <td>0.002336</td>\n",
       "      <td>0.003523</td>\n",
       "      <td>0.021308</td>\n",
       "      <td>0.007300</td>\n",
       "      <td>0.003296</td>\n",
       "      <td>0.01464</td>\n",
       "      <td>0.014663</td>\n",
       "      <td>0.024325</td>\n",
       "      <td>0.022181</td>\n",
       "      <td>0.006988</td>\n",
       "      <td>0.007901</td>\n",
       "      <td>0.028124</td>\n",
       "      <td>0.014826</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.005496</td>\n",
       "      <td>0.016469</td>\n",
       "      <td>0.014404</td>\n",
       "      <td>0.022961</td>\n",
       "      <td>0.033503</td>\n",
       "      <td>0.005202</td>\n",
       "      <td>0.093799</td>\n",
       "      <td>0.000498</td>\n",
       "      <td>0.001982</td>\n",
       "      <td>0.014864</td>\n",
       "      <td>0.009953</td>\n",
       "      <td>0.005646</td>\n",
       "      <td>0.004197</td>\n",
       "      <td>1.858089e-02</td>\n",
       "      <td>0.060959</td>\n",
       "      <td>2.904945e-03</td>\n",
       "      <td>2.171605e-03</td>\n",
       "      <td>0.006648</td>\n",
       "      <td>0.021150</td>\n",
       "      <td>0.001367</td>\n",
       "      <td>1.734723e-18</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>0.002328</td>\n",
       "      <td>0.000339</td>\n",
       "      <td>0.001884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008515</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>0.011024</td>\n",
       "      <td>0.045902</td>\n",
       "      <td>0.036412</td>\n",
       "      <td>0.003989</td>\n",
       "      <td>1.026852e-02</td>\n",
       "      <td>0.005462</td>\n",
       "      <td>0.005539</td>\n",
       "      <td>0.004389</td>\n",
       "      <td>0.068906</td>\n",
       "      <td>0.006521</td>\n",
       "      <td>0.028437</td>\n",
       "      <td>0.011524</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>0.001992</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>0.033840</td>\n",
       "      <td>0.003400</td>\n",
       "      <td>0.013436</td>\n",
       "      <td>0.007658</td>\n",
       "      <td>0.004945</td>\n",
       "      <td>0.001749</td>\n",
       "      <td>0.015466</td>\n",
       "      <td>0.001664</td>\n",
       "      <td>0.001535</td>\n",
       "      <td>0.001493</td>\n",
       "      <td>0.005609</td>\n",
       "      <td>0.002934</td>\n",
       "      <td>0.000851</td>\n",
       "      <td>0.004899</td>\n",
       "      <td>0.018399</td>\n",
       "      <td>0.002682</td>\n",
       "      <td>0.034300</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.004288</td>\n",
       "      <td>0.006936</td>\n",
       "      <td>0.002999</td>\n",
       "      <td>0.002450</td>\n",
       "      <td>0.00734</td>\n",
       "      <td>0.017482</td>\n",
       "      <td>0.017252</td>\n",
       "      <td>0.012247</td>\n",
       "      <td>0.041935</td>\n",
       "      <td>0.013308</td>\n",
       "      <td>0.010175</td>\n",
       "      <td>0.000958</td>\n",
       "      <td>0.008089</td>\n",
       "      <td>0.001958</td>\n",
       "      <td>0.016286</td>\n",
       "      <td>0.010398</td>\n",
       "      <td>0.029964</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>0.009469</td>\n",
       "      <td>0.035146</td>\n",
       "      <td>0.004528</td>\n",
       "      <td>0.004247</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>0.116768</td>\n",
       "      <td>0.004651</td>\n",
       "      <td>0.003717</td>\n",
       "      <td>-8.673617e-18</td>\n",
       "      <td>0.029283</td>\n",
       "      <td>4.336809e-19</td>\n",
       "      <td>-4.336809e-19</td>\n",
       "      <td>0.049346</td>\n",
       "      <td>0.029169</td>\n",
       "      <td>0.000989</td>\n",
       "      <td>3.060224e-03</td>\n",
       "      <td>0.004021</td>\n",
       "      <td>0.004321</td>\n",
       "      <td>0.002360</td>\n",
       "      <td>0.002733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Socialiste   Gauche indépendante   Fédération républicaine de France  \\\n",
       "0     0.026683              0.003863                            0.001249   \n",
       "1     0.008515              0.005188                            0.011024   \n",
       "\n",
       "    Républicain radical et radical-socialiste   Non inscrit  \\\n",
       "0                                    0.066091      0.018364   \n",
       "1                                    0.045902      0.036412   \n",
       "\n",
       "    Républicains indépendants et d'action sociale  \\\n",
       "0                                        0.001924   \n",
       "1                                        0.003989   \n",
       "\n",
       "    Indépendants d'action populaire   Communiste   Indépendants de gauche  \\\n",
       "0                      5.204170e-18     0.008238                 0.005269   \n",
       "1                      1.026852e-02     0.005462                 0.005539   \n",
       "\n",
       "    Gauche démocratique et radicale indépendante   Gauche radicale  \\\n",
       "0                                       0.004223          0.122458   \n",
       "1                                       0.004389          0.068906   \n",
       "\n",
       "    Alliance des républicains de gauche et des radicaux indépendants  \\\n",
       "0                                           0.004507                   \n",
       "1                                           0.006521                   \n",
       "\n",
       "    Parti socialiste   Députés indépendants   Indépendants  \\\n",
       "0           0.068131               0.003817       0.003540   \n",
       "1           0.028437               0.011524       0.009127   \n",
       "\n",
       "    Indépendants républicains   Union républicaine démocratique  \\\n",
       "0                    0.000462                          0.011460   \n",
       "1                    0.001992                          0.039216   \n",
       "\n",
       "    Républicains de gauche   Union socialiste et républicaine  \\\n",
       "0                 0.021218                           0.004443   \n",
       "1                 0.033840                           0.003400   \n",
       "\n",
       "    Fédération républicaine   Démocrate populaire  \\\n",
       "0                  0.003950              0.000735   \n",
       "1                  0.013436              0.007658   \n",
       "\n",
       "    Députés du centre républicain   Parti socialiste de France  \\\n",
       "0                        0.002649                     0.003809   \n",
       "1                        0.004945                     0.001749   \n",
       "\n",
       "    Radical et radical-socialiste  \\\n",
       "0                        0.021616   \n",
       "1                        0.015466   \n",
       "\n",
       "    Parti républicain socialiste et socialiste français  \\\n",
       "0                                           0.002261      \n",
       "1                                           0.001664      \n",
       "\n",
       "    Parti républicain socialiste   Parti socialiste français  \\\n",
       "0                       0.001936                    0.002189   \n",
       "1                       0.001535                    0.001493   \n",
       "\n",
       "    Démocrates populaires   Républicain et social      ZZZ    Démocrates  \\\n",
       "0                0.000269                0.000901  0.001328     0.000220   \n",
       "1                0.005609                0.002934  0.000851     0.004899   \n",
       "\n",
       "    Gauche républicaine démocratique   Action démocratique et sociale  \\\n",
       "0                           0.014726                         0.003120   \n",
       "1                           0.018399                         0.002682   \n",
       "\n",
       "    Entente républicaine démocratique   Gauche sociale et radicale  \\\n",
       "0                            0.015538                     0.002336   \n",
       "1                            0.034300                     0.000381   \n",
       "\n",
       "    Action républicaine et sociale   Républicain socialiste  \\\n",
       "0                         0.003523                 0.021308   \n",
       "1                         0.004288                 0.006936   \n",
       "\n",
       "    Républicain socialiste et socialiste français  \\\n",
       "0                                        0.007300   \n",
       "1                                        0.002999   \n",
       "\n",
       "    Union républicaine radicale et socialiste  \\\n",
       "0                                    0.003296   \n",
       "1                                    0.002450   \n",
       "\n",
       "    Parti radical et radical socialiste   Gauche démocratique  \\\n",
       "0                               0.01464              0.014663   \n",
       "1                               0.00734              0.017482   \n",
       "\n",
       "    Parti républicain radical et radical socialiste  \\\n",
       "0                                          0.024325   \n",
       "1                                          0.017252   \n",
       "\n",
       "    Républicains radicaux-socialistes   Action libérale  \\\n",
       "0                            0.022181          0.006988   \n",
       "1                            0.012247          0.041935   \n",
       "\n",
       "    Républicain progressiste   Gauche radicale-socialiste  \\\n",
       "0                   0.007901                     0.028124   \n",
       "1                   0.013308                     0.010175   \n",
       "\n",
       "    Socialistes parlementaires   Droites   Socialistes unifiés  \\\n",
       "0                     0.014826  0.000408              0.005496   \n",
       "1                     0.000958  0.008089              0.001958   \n",
       "\n",
       "    Union démocratique   Gauche démocratique    Républicains progressistes  \\\n",
       "0             0.016469               0.014404                     0.022961   \n",
       "1             0.016286               0.010398                     0.029964   \n",
       "\n",
       "    Radical-socialiste   Progressiste   Union républicaine  \\\n",
       "0             0.033503       0.005202             0.093799   \n",
       "1             0.006792       0.009469             0.035146   \n",
       "\n",
       "    Défense nationale   Républicains nationalistes   Républicain radical  \\\n",
       "0            0.000498                     0.001982              0.014864   \n",
       "1            0.004528                     0.004247              0.004368   \n",
       "\n",
       "    Union des Droites   Républicain    Républicain   Extrême-gauche  \\\n",
       "0            0.009953       0.005646      0.004197     1.858089e-02   \n",
       "1            0.116768       0.004651      0.003717    -8.673617e-18   \n",
       "\n",
       "    Gauche républicaine       Radical   Extreme-gauche   Appel au peuple  \\\n",
       "0              0.060959  2.904945e-03     2.171605e-03          0.006648   \n",
       "1              0.029283  4.336809e-19    -4.336809e-19          0.049346   \n",
       "\n",
       "    Centre gauche    Gauche   Union conservatrice   Opportuniste  \\\n",
       "0        0.021150  0.001367          1.734723e-18       0.003930   \n",
       "1        0.029169  0.000989          3.060224e-03       0.004021   \n",
       "\n",
       "    Centre droit   Extrême-droite   Union des gauches  \n",
       "0       0.002328         0.000339            0.001884  \n",
       "1       0.004321         0.002360            0.002733  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "centres=pd.DataFrame(kmeans.cluster_centers_, index=['0','1'],columns=gros_groupe_3)\n",
    "pd.set_option('display.max_columns', None)#on peut observer tous les partis ainsi\n",
    "display(centres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>ecart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Union des Droites</th>\n",
       "      <td>0.009953</td>\n",
       "      <td>0.116768</td>\n",
       "      <td>-0.106815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appel au peuple</th>\n",
       "      <td>0.006648</td>\n",
       "      <td>0.049346</td>\n",
       "      <td>-0.042697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Action libérale</th>\n",
       "      <td>0.006988</td>\n",
       "      <td>0.041935</td>\n",
       "      <td>-0.034947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Union républicaine démocratique</th>\n",
       "      <td>0.011460</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>-0.027756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Entente républicaine démocratique</th>\n",
       "      <td>0.015538</td>\n",
       "      <td>0.034300</td>\n",
       "      <td>-0.018762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radical-socialiste</th>\n",
       "      <td>0.033503</td>\n",
       "      <td>0.006792</td>\n",
       "      <td>0.026711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gauche républicaine</th>\n",
       "      <td>0.060959</td>\n",
       "      <td>0.029283</td>\n",
       "      <td>0.031676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parti socialiste</th>\n",
       "      <td>0.068131</td>\n",
       "      <td>0.028437</td>\n",
       "      <td>0.039694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gauche radicale</th>\n",
       "      <td>0.122458</td>\n",
       "      <td>0.068906</td>\n",
       "      <td>0.053552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Union républicaine</th>\n",
       "      <td>0.093799</td>\n",
       "      <td>0.035146</td>\n",
       "      <td>0.058653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           0         1     ecart\n",
       " Union des Droites                  0.009953  0.116768 -0.106815\n",
       " Appel au peuple                    0.006648  0.049346 -0.042697\n",
       " Action libérale                    0.006988  0.041935 -0.034947\n",
       " Union républicaine démocratique    0.011460  0.039216 -0.027756\n",
       " Entente républicaine démocratique  0.015538  0.034300 -0.018762\n",
       "...                                      ...       ...       ...\n",
       " Radical-socialiste                 0.033503  0.006792  0.026711\n",
       " Gauche républicaine                0.060959  0.029283  0.031676\n",
       " Parti socialiste                   0.068131  0.028437  0.039694\n",
       " Gauche radicale                    0.122458  0.068906  0.053552\n",
       " Union républicaine                 0.093799  0.035146  0.058653\n",
       "\n",
       "[73 rows x 3 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plusgrandecart=centres.T\n",
    "plusgrandecart['ecart']=plusgrandecart['0']-plusgrandecart['1']\n",
    "plusgrandecart.sort_values(by='ecart')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les écarts les plus importants en faveur de l'un des centres correspondent à des partis à droite, tandis que ceux en faveur de l'autre correspondent à des partis de gauche : le clustering semble donc avoir séparé un groupe de départements plus à droite et un autre plus à gauche. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#récupération d'une carte des départements français\n",
    "import requests\n",
    "import tempfile\n",
    "import zipfile\n",
    "temporary_location = tempfile.gettempdir()\n",
    "def download_unzip(url, dirname =temporary_location, destname = \"borders\"):#fonction qui dézippe\n",
    "    myfile = requests.get(url)\n",
    "    open(dirname + '/' + destname + '.zip', 'wb').write(myfile.content)\n",
    "    with zipfile.ZipFile(dirname + '/' + destname + '.zip', 'r') as zip_ref: \n",
    "        zip_ref.extractall(dirname + '/' + destname)\n",
    "download_unzip('https://www.data.gouv.fr/fr/datasets/r/3096e551-c68d-40ce-8972-a228c94c0ad1')#l'URL où on trouve les départements\n",
    "carte_departement = gpd.read_file(tempfile.gettempdir() + \"/borders/departements-20140306-100m.dbf\")#on va chercher le fichier là où on l'a stocké\n",
    "carte_metrop = carte_departement.loc[0:95]#on enlève les départements d'outre-mer pour avoir une carte lisible\n",
    "carte_metrop.set_index('nom', inplace=True)#on met le nom du département en index pour pouvoir joindre les bases\n",
    "carte_metrop_5=carte_metrop.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "depmt_app_3.index=[i[1:] for i in depmt_app_3.index]#on commence par enlever l'espace qui est au début du nom des départements\n",
    "ind_val=carte_metrop.index.values#on remplace les anciens noms de département par les actuels\n",
    "ind_val[ind_val==\"Côtes-d'Armor\"]='Côtes-du-Nord'#et on traite chaque cas de changement de nom\n",
    "ind_val[ind_val==\"Seine-Maritime\"]='Seine-Inférieure'\n",
    "ind_val[ind_val==\"Alpes-de-Haute-Provence\"]='Basses-Alpes'\n",
    "ind_val[ind_val==\"Loire-Atlantique\"]='Loire-Inférieure'\n",
    "ind_val[ind_val==\"Pyrénées-Atlantiques\"]='Basses-Pyrénées'\n",
    "ind_val[ind_val==\"Charente-Maritime\"]='Charente-Inférieure'\n",
    "ind_val[ind_val==\"Hauts-de-Seine\"]='Seine'\n",
    "ind_val[ind_val==\"Paris\"]='Seine'\n",
    "ind_val[ind_val==\"Seine-Saint-Denis\"]='Seine'\n",
    "ind_val[ind_val==\"Val-de-Marne\"]='Seine'\n",
    "ind_val[ind_val==\"Haute-Corse\"]='Corse'\n",
    "ind_val[ind_val==\"Corse-du-Sud\"]='Corse'\n",
    "ind_val[ind_val==\"Essonne\"]='Seine-et-Oise'\n",
    "ind_val[ind_val==\"Val-d'Oise\"]='Seine-et-Oise'\n",
    "ind_val[ind_val==\"Yvelines\"]='Seine-et-Oise'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = carte_metrop.merge(depmt_app_3, how = 'inner', left_index=True, right_index=True)\n",
    "df.plot(column = 'Appartenance', cmap = 'RdYlBu_r', legend=True)#on crée une carte"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut représenter ces résultats avec une ACP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "acp=PCA(n_components=2)#une ACP sur deux axes afin de pouvoir la visualiser\n",
    "proj=acp.fit_transform(depmt_groupe_3)\n",
    "#Fit et réduit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def nuagepoint(base, nbre_cluster=2) :\n",
    "    proj0=[i[0] for i in proj]#on prend la projection sur l'axe des abcisses\n",
    "    proj1=[i[1] for i in proj]#puis des ordonnées\n",
    "    appartenance=np.array(base['Appartenance'])\n",
    "    if nbre_cluster>2 :#avec des couleurs aléatoires\n",
    "        for num_cluster in range(nbre_cluster) :\n",
    "            appartenance=np.array((base['Appartenance']==num_cluster).astype(float))\n",
    "            plt.scatter(proj0*appartenance, proj1*appartenance)#on projette les données du cluster\n",
    "    else :#on prend les couleurs rouge et bleu\n",
    "        plt.scatter(proj0*appartenance, proj1*appartenance, color='red')#on projette en rouge les membres du cluster 1\n",
    "        plt.scatter(proj0*(1-appartenance), proj1*(1-appartenance))#en bleu ceux du 0\n",
    "nuagepoint(depmt_app_3)#il y a juste n points en plus en (0,0), mais ils ne gênent pas la vision "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le clustering semble bien avoir séparé deux groupes différents, dont un qui semble expliquer une très grande part des différences et qui semble bien être l'axe droite-gauche."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On refait pour la Cinquième République"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sylva\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "mandat_dep_5=mandat_dep[mandat_dep['Régime politique']=='Cinquième République - Assemblée nationale']\n",
    "(depmt_groupe_5, gros_groupe_5)=datagroupe(mandat_dep_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4631566592959512\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Union pour la majorité présidentielle</th>\n",
       "      <th>Union pour un mouvement populaire</th>\n",
       "      <th>La République en Marche</th>\n",
       "      <th>Socialiste</th>\n",
       "      <th>Union pour un Mouvement Populaire</th>\n",
       "      <th>Les Républicains</th>\n",
       "      <th>Nouvelle Gauche</th>\n",
       "      <th>Rassemblement pour la République</th>\n",
       "      <th>Mouvement Démocrate et apparentés</th>\n",
       "      <th>Non inscrit</th>\n",
       "      <th>Union des démocrates et indépendants</th>\n",
       "      <th>Communiste</th>\n",
       "      <th>Union pour la démocratie française</th>\n",
       "      <th>Nouveau Centre</th>\n",
       "      <th>Union pour la démocratie française et du centre</th>\n",
       "      <th>Gauche démocrate et républicaine</th>\n",
       "      <th>Socialiste</th>\n",
       "      <th>Front national</th>\n",
       "      <th>Communistes et républicains</th>\n",
       "      <th>Apparenté socialiste</th>\n",
       "      <th>République et liberté</th>\n",
       "      <th>Union du centre</th>\n",
       "      <th>Parti socialiste et radicaux de gauche</th>\n",
       "      <th>Union des démocrates pour la République</th>\n",
       "      <th>Union centriste</th>\n",
       "      <th>Républicains indépendants</th>\n",
       "      <th>Union des démocrates pour la République</th>\n",
       "      <th>Union démocratique pour la V° République</th>\n",
       "      <th>Réformateurs démocrates sociaux</th>\n",
       "      <th>Fédération de la gauche démocrate et socialiste</th>\n",
       "      <th>Progrès et démocratie moderne</th>\n",
       "      <th>Union pour la nouvelle République-UDT</th>\n",
       "      <th>Centre démocratique</th>\n",
       "      <th>Union pour la nouvelle République</th>\n",
       "      <th>Républicains populaires et centre démocratique</th>\n",
       "      <th>Unité de la République</th>\n",
       "      <th>Indépendants et paysans d'action sociale</th>\n",
       "      <th>Entente démocratique</th>\n",
       "      <th>Rassemblement démocratique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.040764</td>\n",
       "      <td>5.694960e-02</td>\n",
       "      <td>0.011309</td>\n",
       "      <td>0.045948</td>\n",
       "      <td>5.208749e-03</td>\n",
       "      <td>0.017162</td>\n",
       "      <td>0.001913</td>\n",
       "      <td>0.116901</td>\n",
       "      <td>0.003028</td>\n",
       "      <td>0.033867</td>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.036117</td>\n",
       "      <td>7.219167e-02</td>\n",
       "      <td>0.00287</td>\n",
       "      <td>2.759765e-02</td>\n",
       "      <td>0.004182</td>\n",
       "      <td>0.079885</td>\n",
       "      <td>0.002384</td>\n",
       "      <td>1.663933e-03</td>\n",
       "      <td>1.140434e-02</td>\n",
       "      <td>0.003418</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>0.008555</td>\n",
       "      <td>0.066399</td>\n",
       "      <td>0.006368</td>\n",
       "      <td>2.868889e-02</td>\n",
       "      <td>2.388692e-03</td>\n",
       "      <td>3.189787e-02</td>\n",
       "      <td>0.004893</td>\n",
       "      <td>0.015714</td>\n",
       "      <td>0.011297</td>\n",
       "      <td>0.028657</td>\n",
       "      <td>0.010414</td>\n",
       "      <td>0.023678</td>\n",
       "      <td>0.007615</td>\n",
       "      <td>0.006277</td>\n",
       "      <td>0.013603</td>\n",
       "      <td>0.003822</td>\n",
       "      <td>0.005450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.024429</td>\n",
       "      <td>2.274527e-02</td>\n",
       "      <td>0.013360</td>\n",
       "      <td>0.205806</td>\n",
       "      <td>2.024291e-03</td>\n",
       "      <td>0.003317</td>\n",
       "      <td>0.004336</td>\n",
       "      <td>0.071223</td>\n",
       "      <td>0.005480</td>\n",
       "      <td>0.016206</td>\n",
       "      <td>0.000835</td>\n",
       "      <td>0.028402</td>\n",
       "      <td>2.638526e-02</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>2.409303e-02</td>\n",
       "      <td>0.001949</td>\n",
       "      <td>0.267869</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>6.497726e-04</td>\n",
       "      <td>8.577236e-03</td>\n",
       "      <td>0.003193</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>0.047837</td>\n",
       "      <td>0.031345</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>6.856032e-03</td>\n",
       "      <td>3.550543e-03</td>\n",
       "      <td>7.087766e-03</td>\n",
       "      <td>0.003630</td>\n",
       "      <td>0.072466</td>\n",
       "      <td>0.008628</td>\n",
       "      <td>0.013381</td>\n",
       "      <td>0.001170</td>\n",
       "      <td>0.015268</td>\n",
       "      <td>0.009519</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.007754</td>\n",
       "      <td>0.014932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.938894e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028042</td>\n",
       "      <td>8.673617e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.042328</td>\n",
       "      <td>1.387779e-17</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>3.469447e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.168404e-19</td>\n",
       "      <td>1.734723e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.469447e-18</td>\n",
       "      <td>4.336809e-19</td>\n",
       "      <td>3.469447e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.741799</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103704</td>\n",
       "      <td>0.055556</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Union pour la majorité présidentielle   Union pour un mouvement populaire  \\\n",
       "0                                0.040764                        5.694960e-02   \n",
       "1                                0.024429                        2.274527e-02   \n",
       "2                                0.000000                        6.938894e-18   \n",
       "\n",
       "    La République en Marche   Socialiste   Union pour un Mouvement Populaire  \\\n",
       "0                  0.011309     0.045948                        5.208749e-03   \n",
       "1                  0.013360     0.205806                        2.024291e-03   \n",
       "2                  0.000000     0.028042                        8.673617e-19   \n",
       "\n",
       "    Les Républicains   Nouvelle Gauche   Rassemblement pour la République  \\\n",
       "0           0.017162          0.001913                           0.116901   \n",
       "1           0.003317          0.004336                           0.071223   \n",
       "2           0.000000          0.000000                           0.000000   \n",
       "\n",
       "    Mouvement Démocrate et apparentés   Non inscrit  \\\n",
       "0                            0.003028      0.033867   \n",
       "1                            0.005480      0.016206   \n",
       "2                            0.000000      0.000000   \n",
       "\n",
       "    Union des démocrates et indépendants   Communiste  \\\n",
       "0                               0.003511     0.036117   \n",
       "1                               0.000835     0.028402   \n",
       "2                               0.000000     0.042328   \n",
       "\n",
       "    Union pour la démocratie française   Nouveau Centre  \\\n",
       "0                         7.219167e-02          0.00287   \n",
       "1                         2.638526e-02          0.00000   \n",
       "2                         1.387779e-17          0.00000   \n",
       "\n",
       "    Union pour la démocratie française et du centre  \\\n",
       "0                                      2.759765e-02   \n",
       "1                                      2.409303e-02   \n",
       "2                                      3.469447e-18   \n",
       "\n",
       "    Gauche démocrate et républicaine   Socialiste    Front national  \\\n",
       "0                           0.004182      0.079885         0.002384   \n",
       "1                           0.001949      0.267869         0.000261   \n",
       "2                           0.000000      0.000000         0.000000   \n",
       "\n",
       "    Communistes et républicains   Apparenté socialiste  \\\n",
       "0                  1.663933e-03           1.140434e-02   \n",
       "1                  6.497726e-04           8.577236e-03   \n",
       "2                  2.168404e-19           1.734723e-18   \n",
       "\n",
       "    République et liberté   Union du centre  \\\n",
       "0                0.003418          0.002785   \n",
       "1                0.003193          0.001406   \n",
       "2                0.000000          0.000000   \n",
       "\n",
       "    Parti socialiste et radicaux de gauche  \\\n",
       "0                                 0.008555   \n",
       "1                                 0.047837   \n",
       "2                                 0.000000   \n",
       "\n",
       "    Union des démocrates pour la République   Union centriste  \\\n",
       "0                                  0.066399          0.006368   \n",
       "1                                  0.031345          0.001170   \n",
       "2                                  0.000000          0.000000   \n",
       "\n",
       "    Républicains indépendants   Union des démocrates pour la République   \\\n",
       "0                2.868889e-02                               2.388692e-03   \n",
       "1                6.856032e-03                               3.550543e-03   \n",
       "2                3.469447e-18                               4.336809e-19   \n",
       "\n",
       "    Union démocratique pour la V° République  \\\n",
       "0                               3.189787e-02   \n",
       "1                               7.087766e-03   \n",
       "2                               3.469447e-18   \n",
       "\n",
       "    Réformateurs démocrates sociaux  \\\n",
       "0                          0.004893   \n",
       "1                          0.003630   \n",
       "2                          0.000000   \n",
       "\n",
       "    Fédération de la gauche démocrate et socialiste  \\\n",
       "0                                          0.015714   \n",
       "1                                          0.072466   \n",
       "2                                          0.000000   \n",
       "\n",
       "    Progrès et démocratie moderne   Union pour la nouvelle République-UDT  \\\n",
       "0                        0.011297                                0.028657   \n",
       "1                        0.008628                                0.013381   \n",
       "2                        0.000000                                0.028571   \n",
       "\n",
       "    Centre démocratique   Union pour la nouvelle République  \\\n",
       "0              0.010414                            0.023678   \n",
       "1              0.001170                            0.015268   \n",
       "2              0.000000                            0.741799   \n",
       "\n",
       "    Républicains populaires et centre démocratique   Unité de la République  \\\n",
       "0                                         0.007615                 0.006277   \n",
       "1                                         0.009519                 0.000000   \n",
       "2                                         0.000000                 0.000000   \n",
       "\n",
       "    Indépendants et paysans d'action sociale   Entente démocratique  \\\n",
       "0                                   0.013603               0.003822   \n",
       "1                                   0.010416               0.007754   \n",
       "2                                   0.103704               0.055556   \n",
       "\n",
       "    Rassemblement démocratique  \n",
       "0                     0.005450  \n",
       "1                     0.014932  \n",
       "2                     0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=3, max_iter=1000)\n",
    "depmt_app_5=clustering(depmt_groupe_5.fillna(0))\n",
    "centres=pd.DataFrame(kmeans.cluster_centers_, index=['0','1', '2'],columns=gros_groupe_5)\n",
    "pd.set_option('display.max_columns', None)\n",
    "display(centres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour la Cinquième République, les résultats sont beaucoup moins encourageants. Il y a beaucoup plus de valeurs manquantes, ce qui enlève de l'intérêt au clustering : les valeurs sont artificiellement proches de 0. On va essayer d'en refaire un en se concentrant sur les premières législatures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sylva\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "listeLegis=['Ire','IIe', 'IIIe', 'IVe', 'Ve', 'VIe', 'VIIe', 'VIIIe', 'IXe', 'Xe', 'XIe', 'XIIe']\n",
    "listeLegislature=[' '+i+' législature ' for i in listeLegis]#on crée une liste des législatures dans le bon ordre\n",
    "mandat_dep_5b=mandat_dep[mandat_dep['Régime politique']=='Cinquième République - Assemblée nationale']\n",
    "mandat_dep_prems=mandat_dep_5b[mandat_dep_5b['Législature']==listeLegislature[0]]\n",
    "for legislature in listeLegislature :#on concatène les bases des douze premières législatures\n",
    "    mandat_dep_prems=pd.concat((mandat_dep_prems, mandat_dep_5b[mandat_dep_5b['Législature']==legislature]))\n",
    "    \n",
    "(depmt_groupe_5b, gros_groupe_5b)=datagroupe(mandat_dep_prems)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "depmt_groupe_5b=depmt_groupe_5b[depmt_groupe_5b.index.notna()]\n",
    "depmt_groupe_5b.index=[i[1:] for i in depmt_groupe_5b.index]#on commence par enlever l'espace qui est au début du nom des départements\n",
    "depmt_groupe_5metr=carte_metrop_5.merge(depmt_groupe_5b, how = 'inner', left_index=True, right_index=True)#on enlève les députés hors france métropolitaine\n",
    "depmt_groupe_5metr.drop(['code_insee', 'nuts3', 'wikipedia', 'geometry'], axis='columns', inplace=True)#on enlève les colonnes inutiles\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sylva\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:213: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KElbowVisualizer(ax=<AxesSubplot:>, k=None, model=None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = KElbowVisualizer(kmeans, k=(2,12))\n",
    "visualizer.fit(depmt_groupe_5metr)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3883105428006415\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Union pour la nouvelle République</th>\n",
       "      <th>Républicains populaires et centre démocratique</th>\n",
       "      <th>Unité de la République</th>\n",
       "      <th>Non inscrit</th>\n",
       "      <th>Indépendants et paysans d'action sociale</th>\n",
       "      <th>Entente démocratique</th>\n",
       "      <th>Socialiste</th>\n",
       "      <th>Communiste</th>\n",
       "      <th>Union pour la nouvelle République-UDT</th>\n",
       "      <th>Centre démocratique</th>\n",
       "      <th>Rassemblement démocratique</th>\n",
       "      <th>Républicains indépendants</th>\n",
       "      <th>Union démocratique pour la V° République</th>\n",
       "      <th>Progrès et démocratie moderne</th>\n",
       "      <th>Fédération de la gauche démocrate et socialiste</th>\n",
       "      <th>Union des démocrates pour la République</th>\n",
       "      <th>Parti socialiste et radicaux de gauche</th>\n",
       "      <th>Union centriste</th>\n",
       "      <th>Union des démocrates pour la République</th>\n",
       "      <th>Réformateurs démocrates sociaux</th>\n",
       "      <th>Rassemblement pour la République</th>\n",
       "      <th>Union pour la démocratie française</th>\n",
       "      <th>Socialiste</th>\n",
       "      <th>Apparenté socialiste</th>\n",
       "      <th>Front national</th>\n",
       "      <th>Union du centre</th>\n",
       "      <th>Union pour la démocratie française et du centre</th>\n",
       "      <th>République et liberté</th>\n",
       "      <th>Union pour un mouvement populaire</th>\n",
       "      <th>Union pour la majorité présidentielle</th>\n",
       "      <th>Communistes et républicains</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.069724</td>\n",
       "      <td>0.025001</td>\n",
       "      <td>7.256894e-04</td>\n",
       "      <td>0.022758</td>\n",
       "      <td>0.040555</td>\n",
       "      <td>0.011095</td>\n",
       "      <td>0.045083</td>\n",
       "      <td>0.012325</td>\n",
       "      <td>0.039963</td>\n",
       "      <td>0.016621</td>\n",
       "      <td>8.472120e-03</td>\n",
       "      <td>0.048541</td>\n",
       "      <td>0.045898</td>\n",
       "      <td>1.864636e-02</td>\n",
       "      <td>0.017414</td>\n",
       "      <td>0.081291</td>\n",
       "      <td>0.008833</td>\n",
       "      <td>1.125719e-02</td>\n",
       "      <td>2.215534e-03</td>\n",
       "      <td>0.006668</td>\n",
       "      <td>1.454481e-01</td>\n",
       "      <td>0.112547</td>\n",
       "      <td>9.132538e-02</td>\n",
       "      <td>0.005010</td>\n",
       "      <td>0.001953</td>\n",
       "      <td>5.126811e-03</td>\n",
       "      <td>0.037034</td>\n",
       "      <td>2.215620e-03</td>\n",
       "      <td>0.003549</td>\n",
       "      <td>0.054206</td>\n",
       "      <td>0.000544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.045500</td>\n",
       "      <td>0.014176</td>\n",
       "      <td>-2.710505e-19</td>\n",
       "      <td>0.019335</td>\n",
       "      <td>0.025871</td>\n",
       "      <td>0.014955</td>\n",
       "      <td>0.162008</td>\n",
       "      <td>0.073682</td>\n",
       "      <td>0.019345</td>\n",
       "      <td>0.003131</td>\n",
       "      <td>1.224010e-02</td>\n",
       "      <td>0.011879</td>\n",
       "      <td>0.010110</td>\n",
       "      <td>8.247797e-03</td>\n",
       "      <td>0.063988</td>\n",
       "      <td>0.047990</td>\n",
       "      <td>0.040118</td>\n",
       "      <td>1.464878e-03</td>\n",
       "      <td>3.729311e-03</td>\n",
       "      <td>0.004900</td>\n",
       "      <td>6.993767e-02</td>\n",
       "      <td>0.040015</td>\n",
       "      <td>2.066957e-01</td>\n",
       "      <td>0.011194</td>\n",
       "      <td>0.003757</td>\n",
       "      <td>1.139836e-03</td>\n",
       "      <td>0.030343</td>\n",
       "      <td>2.957684e-03</td>\n",
       "      <td>0.004615</td>\n",
       "      <td>0.032319</td>\n",
       "      <td>0.002930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.628643</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.167022</td>\n",
       "      <td>0.071429</td>\n",
       "      <td>0.040867</td>\n",
       "      <td>0.066453</td>\n",
       "      <td>0.025586</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.734723e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.734723e-18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.673617e-19</td>\n",
       "      <td>4.336809e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.387779e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.775558e-17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.673617e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.336809e-19</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Union pour la nouvelle République  \\\n",
       "0                            0.069724   \n",
       "1                            0.045500   \n",
       "2                            0.628643   \n",
       "\n",
       "    Républicains populaires et centre démocratique   Unité de la République  \\\n",
       "0                                         0.025001             7.256894e-04   \n",
       "1                                         0.014176            -2.710505e-19   \n",
       "2                                         0.000000             0.000000e+00   \n",
       "\n",
       "    Non inscrit   Indépendants et paysans d'action sociale  \\\n",
       "0      0.022758                                   0.040555   \n",
       "1      0.019335                                   0.025871   \n",
       "2      0.000000                                   0.167022   \n",
       "\n",
       "    Entente démocratique   Socialiste   Communiste  \\\n",
       "0               0.011095     0.045083     0.012325   \n",
       "1               0.014955     0.162008     0.073682   \n",
       "2               0.071429     0.040867     0.066453   \n",
       "\n",
       "    Union pour la nouvelle République-UDT   Centre démocratique  \\\n",
       "0                                0.039963              0.016621   \n",
       "1                                0.019345              0.003131   \n",
       "2                                0.025586              0.000000   \n",
       "\n",
       "    Rassemblement démocratique   Républicains indépendants  \\\n",
       "0                 8.472120e-03                    0.048541   \n",
       "1                 1.224010e-02                    0.011879   \n",
       "2                -1.734723e-18                    0.000000   \n",
       "\n",
       "    Union démocratique pour la V° République   Progrès et démocratie moderne  \\\n",
       "0                                   0.045898                    1.864636e-02   \n",
       "1                                   0.010110                    8.247797e-03   \n",
       "2                                   0.000000                    1.734723e-18   \n",
       "\n",
       "    Fédération de la gauche démocrate et socialiste  \\\n",
       "0                                          0.017414   \n",
       "1                                          0.063988   \n",
       "2                                          0.000000   \n",
       "\n",
       "    Union des démocrates pour la République  \\\n",
       "0                                  0.081291   \n",
       "1                                  0.047990   \n",
       "2                                  0.000000   \n",
       "\n",
       "    Parti socialiste et radicaux de gauche   Union centriste  \\\n",
       "0                                 0.008833      1.125719e-02   \n",
       "1                                 0.040118      1.464878e-03   \n",
       "2                                 0.000000      8.673617e-19   \n",
       "\n",
       "    Union des démocrates pour la République   \\\n",
       "0                               2.215534e-03   \n",
       "1                               3.729311e-03   \n",
       "2                               4.336809e-19   \n",
       "\n",
       "    Réformateurs démocrates sociaux   Rassemblement pour la République  \\\n",
       "0                          0.006668                       1.454481e-01   \n",
       "1                          0.004900                       6.993767e-02   \n",
       "2                          0.000000                       1.387779e-17   \n",
       "\n",
       "    Union pour la démocratie française   Socialiste    Apparenté socialiste  \\\n",
       "0                             0.112547  9.132538e-02               0.005010   \n",
       "1                             0.040015  2.066957e-01               0.011194   \n",
       "2                             0.000000  2.775558e-17               0.000000   \n",
       "\n",
       "    Front national   Union du centre  \\\n",
       "0         0.001953      5.126811e-03   \n",
       "1         0.003757      1.139836e-03   \n",
       "2         0.000000      8.673617e-19   \n",
       "\n",
       "    Union pour la démocratie française et du centre   République et liberté  \\\n",
       "0                                          0.037034            2.215620e-03   \n",
       "1                                          0.030343            2.957684e-03   \n",
       "2                                          0.000000            4.336809e-19   \n",
       "\n",
       "    Union pour un mouvement populaire   Union pour la majorité présidentielle  \\\n",
       "0                            0.003549                                0.054206   \n",
       "1                            0.004615                                0.032319   \n",
       "2                            0.000000                                0.000000   \n",
       "\n",
       "    Communistes et républicains  \n",
       "0                      0.000544  \n",
       "1                      0.002930  \n",
       "2                      0.000000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=3, max_iter=1000)#on choisit trois clusters car le gain du troisième est considérable\n",
    "depmt_app_5b=clustering(depmt_groupe_5metr.fillna(0))\n",
    "depmt_app_5b=depmt_app_5b.drop_duplicates()#on supprime les doublons créés par les changements de nom\n",
    "depmt_groupe_5metr=depmt_groupe_5metr.drop_duplicates()#de même ici\n",
    "\n",
    "centres=pd.DataFrame(kmeans.cluster_centers_, index=['0','1','2'],columns=gros_groupe_5b)\n",
    "pd.set_option('display.max_columns', None)\n",
    "display(centres)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les résultats sont ici plus intéressants, avec un groupe très lié à l'Union pour la nouvelle République (parti gaulliste)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depmt_app_5b=depmt_app_5b[depmt_app_5b.index.notna()]\n",
    "df = carte_metrop_5.merge(depmt_app_5b, how = 'inner', left_index=True, right_index=True)\n",
    "df.plot(column = 'Appartenance', cmap = 'viridis', legend=True)#on crée la carte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proj=acp.fit_transform(depmt_groupe_5metr)\n",
    "nuagepoint(depmt_app_5b, nbre_cluster=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On a toujours le groupe parisien votant Union pour la Nouvelle République qui se détache, le reste est plus fluctuant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour l'Empire !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2522591131733693\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Centre droit</th>\n",
       "      <th>Opposition</th>\n",
       "      <th>Centre gauche</th>\n",
       "      <th>Gauche</th>\n",
       "      <th>Majorité dynastique</th>\n",
       "      <th>Tiers-parti</th>\n",
       "      <th>Majorité dynastique</th>\n",
       "      <th>Indépendant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.109686</td>\n",
       "      <td>0.002311</td>\n",
       "      <td>0.017651</td>\n",
       "      <td>0.013935</td>\n",
       "      <td>0.810457</td>\n",
       "      <td>0.011329</td>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.017547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.107732</td>\n",
       "      <td>0.038921</td>\n",
       "      <td>0.058156</td>\n",
       "      <td>0.052971</td>\n",
       "      <td>0.523471</td>\n",
       "      <td>0.027082</td>\n",
       "      <td>0.035065</td>\n",
       "      <td>0.133162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Centre droit   Opposition   Centre gauche    Gauche   Majorité dynastique  \\\n",
       "0       0.109686     0.002311        0.017651  0.013935              0.810457   \n",
       "1       0.107732     0.038921        0.058156  0.052971              0.523471   \n",
       "\n",
       "    Tiers-parti   Majorité dynastique    Indépendant  \n",
       "0      0.011329               0.001956      0.017547  \n",
       "1      0.027082               0.035065      0.133162  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=2, max_iter=1000)#vu le faible nombre de grands partis, deux clusters sont suffisants\n",
    "mandat_dep_emp2=mandat_dep[mandat_dep['Régime politique']=='Second Empire - Corps législatif']\n",
    "(depmt_groupe_emp2, gros_groupe_emp2)=datagroupe(mandat_dep_emp2)\n",
    "depmt_app_emp2=clustering(depmt_groupe_emp2.fillna(0))\n",
    "centres=pd.DataFrame(kmeans.cluster_centers_, index=['0','1'],columns=gros_groupe_emp2)\n",
    "pd.set_option('display.max_columns', None)\n",
    "display(centres)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>ecart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Indépendant</th>\n",
       "      <td>0.017547</td>\n",
       "      <td>0.133162</td>\n",
       "      <td>-0.115615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centre gauche</th>\n",
       "      <td>0.017651</td>\n",
       "      <td>0.058156</td>\n",
       "      <td>-0.040504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gauche</th>\n",
       "      <td>0.013935</td>\n",
       "      <td>0.052971</td>\n",
       "      <td>-0.039036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Opposition</th>\n",
       "      <td>0.002311</td>\n",
       "      <td>0.038921</td>\n",
       "      <td>-0.036610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Majorité dynastique</th>\n",
       "      <td>0.001956</td>\n",
       "      <td>0.035065</td>\n",
       "      <td>-0.033109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tiers-parti</th>\n",
       "      <td>0.011329</td>\n",
       "      <td>0.027082</td>\n",
       "      <td>-0.015753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Centre droit</th>\n",
       "      <td>0.109686</td>\n",
       "      <td>0.107732</td>\n",
       "      <td>0.001954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Majorité dynastique</th>\n",
       "      <td>0.810457</td>\n",
       "      <td>0.523471</td>\n",
       "      <td>0.286985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              0         1     ecart\n",
       " Indépendant           0.017547  0.133162 -0.115615\n",
       " Centre gauche         0.017651  0.058156 -0.040504\n",
       " Gauche                0.013935  0.052971 -0.039036\n",
       " Opposition            0.002311  0.038921 -0.036610\n",
       " Majorité dynastique   0.001956  0.035065 -0.033109\n",
       " Tiers-parti           0.011329  0.027082 -0.015753\n",
       " Centre droit          0.109686  0.107732  0.001954\n",
       " Majorité dynastique   0.810457  0.523471  0.286985"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plusgrandecart=centres.T\n",
    "plusgrandecart['ecart']=plusgrandecart['0']-plusgrandecart['1']\n",
    "plusgrandecart.sort_values(by='ecart')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un groupe clairement pro-pouvoir, l'autre qui regroupe les mécontents (le centre droit faisant office d'entre-deux)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "depmt_app_emp2.index=[i[1:] for i in depmt_app_emp2.index]#on commence par enlever l'espace qui est au début du nom des départements\n",
    "df = carte_metrop.merge(depmt_app_emp2, how = 'inner', left_index=True, right_index=True)\n",
    "df.plot(column = 'Appartenance', cmap = 'RdYlBu_r', legend=True)#on crée la carte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "proj=acp.fit_transform(depmt_groupe_emp2)\n",
    "nuagepoint(depmt_app_emp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.01245552,  0.09971621,  0.12341285,  0.12435676, -0.89625841,\n",
       "         0.08970181,  0.070648  ,  0.37800728],\n",
       "       [ 0.83922823, -0.05123824, -0.18827126, -0.12595902, -0.20966967,\n",
       "        -0.12329484,  0.22178893, -0.36524726]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acp.components_#comme il y a peu de grands partis, on peut regarder facilement les vecteurs propres de l'ACP."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le vote pour la majorité dynastique est largement l'élément le plus contributif au plus grand vecteur propre : il est donc correct d'analyser la séparation opérée par le clustering comme une séparation entre départements pro-régime et ceux davantage liés à l'opposition."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Analyse des départements selon leur caractéristiques électorales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dpartement=table_depmt.set_index('Unnamed: 0')[0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "dpartement=dpartement.dropna(axis=1)\n",
    "dpartement=dpartement.T[dpartement.T[\"nombre_mandat\"]>40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "centre_red=sc.fit_transform(dpartement)#on centre et réduit les données pour éviter l'effet de taille \n",
    "#du nombre de députés par rapport aux ratios par exemple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "centre_red=pd.DataFrame(centre_red, index=dpartement.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sylva\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:213: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "KElbowVisualizer(ax=<AxesSubplot:>, k=None, model=None)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualizer = KElbowVisualizer(kmeans, k=(2,12))\n",
    "visualizer.fit(centre_red)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il semble qu'utiliser 5 clusters est optimal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6379149692712556\n"
     ]
    }
   ],
   "source": [
    "kmeans = cluster.KMeans(n_clusters=5, max_iter=10000)\n",
    "centre_red_clust=clustering(centre_red.fillna(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "centre_red_clust.index=[i[1:] for i in centre_red.index]#on commence par enlever l'espace qui est au début du nom des départements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = carte_metrop.merge(centre_red_clust, how = 'inner', left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UZLg7M+pJMPI7HF16W2+0DZPTxQIMKICrVq3i4MGDPPLII1RUVOBwOJg/fz4bN25k9uzZ5OXlMWfOnIFYq+RsxusVQlddB59thG17RYzti83hDbfdsV/8O2087DoIPl9k1xsMCbEiLhgtAfb5YPebkPwDqPLCnmbxCMSeZniyBG4dAjaZKC45cwn47b7yyiu57777uPbaa1EUhV/84hfEx8fz0EMP8Zvf/Ibs7GwuuOCCgVir5GzC64W//hv+uwa27hG1e6oaeaHavg+mjIWdByJ73EBYTJCaBPv67/rsk7gMOBjGZ7YiRYqfJGzOGAvQYDDw61//+qTnX3vttagsSHKWU1kDf/kXPPUGFJUE3j4SaLWBt4k0Pv/AnDf+K+AJI9a5zQF7nLDXCQYNXBZsNo1EAl6NeIS6z0Ajb/Ekg4+zWVhgP38WPlo38O7I+ka4YD4cPg4FAzQhwuuDePsAnGcvMCP0/T6pg5zNHb/nWGFpHPxsBMTIy4YkADog1DyqQfhayW/yac5x7kSLHT1pJHE7Cj0nLfhw4OIgLgrQYMNMDjqS0TBI6e6qCv/4L7zxgXBztroGZx1DkoUl9uFa0QR78lhR24dKU5MDe6tH1PfZLJCWJDqlFB4XpQyhkJEqjlFVK47j8YrYZUqCmEQRcRSYdjkUzyEizqV8p3gcbYUfZ8E0m7AspRhKekIPhNouN4Bg+nw+HnzwQY4cOYJWq+Xxxx8nKyvrxOufffYZTz31FDqdjuXLl3P11VcHPKX89p7m2FlCHf+ggpWUcB9mpqAnhTiuQYMRHYn4aKSSJ2jisy77pvEgQ/jZwC9aVeHfX8KKuwf+3N1pdMLwoWIEUmFxl5fsINqltbQKwStvG8ZnNMDYERAfI0Rs/5GuHWY6o9PBjIng8cCeApH4crAI9HqxjyZKbtCF/wer7UQ8svKvGvEAMCpw/zB4eHhkzyE5/dETugUYYPvPP/8cgDfffJONGzfy+OOP88wzzwDg8Xh4/PHHWbVqFWazmWuvvZYlS5aQnJzc5zGlAJ7mxHEZcVzGMexU8yxORH1mPW8H3LeOVaTyI7SENpKn3/j98Lu/DOw5e0OrhQNFvb/eU7s0l7tre7PRw2DMMDFXsLgCauph3Aghnn6/6ETTTmml+Nfrg+R4KKmMxLs4QeP0HxGjZsNmgCjXObpUONAMdx+GSxJhSWQbektOY6JgAZ533nmcc845AJSWlpKU1BGXLigoICsri9hY0VkpNzeXLVu2cOGFF/Z5TCmAZwhaQrv4JHAzqdw78OIH8NOn4dMNA3/envD7ISEGaoJvZn0Sh452/X3eNDhUBFUBjnmsTIxlWr8j/HN3w3LoL+B9FFrDKA0JhAaYaAWjBnQKbGoUbtHNTfCbYnhrIlzR8x23m1L0pPfqopdIgkGn03HPPffw8ccf84c//OHE8w6H40S/agCr1YrD0YtXphOyg/UZQCMfUsGvQtpHixUzY6O0oj44WASPPT/w5+2NJqfo26mLoCty3fbA4geQGAcNPbRS6wd+gxnio/RnPScGdjthSxNsaIQ0A+xywqEW8frTJaioNLONev5FCfeRzzAO81UOMIN9TKaFPoYZqqq4IfEOYmMCSWRoT4IJ5RGkObZy5Uo+/PBDHnroIZqbRSzeZrPhdDpPbON0OrsIYm9IATwDiOECUvlRSPtU8RRl/BQVFT9RsBZ6Y/gQuGRx39tMGAkLpg9ceUJZlbDaBhpVhZKKiB7SlXUVlEZJQLqXU5R2bXfsczayr34y+8mlkGVU8H+4OUYjH+KhjFbyOcgiKjbehPfWu6CuQXwGLje88A/IOhcs0yFtkSiFqW8USUNFJWI7yemDIcxHH7zzzjs899xzAJjNZhRFQdt2jRg5ciRHjx6lvr4et9vNli1bmDYt8N+0dIGeISRzKxU8HtI+ZfwEF4Vk8RwwQG3qDAZ4baVIHDEbYft+qK0XnVmKSoRbcG9bcfjksaDXQk1DR02gVhtamcT08YAiLD2bBexWQIUjJR3TG9KT+575Fy2SEyA1AdZsD6+bTU/4q4ARkTlWd/Y6waCAu2cx0mxRseVPonV+PrrGVBI3XEbd9I9xJ3fES33UUDLrLyQ8ejnMvJpxJj1U1HWdp+hyw033gUbT8bnMmQJ33QhXfVXOKDwdiEIZxFe+8hXuu+8+rr/+erxeL/fffz8fffQRzc3NrFixgnvvvZdbbrkFVVVZvnw5qamp/T2l5PRBg4IeNcT+C7W8gpFs0tWH4P0vhCiUVopSgOsugSEpkV/qJ+vhd6+KKQ0ut8iMrGs8+S5/V1t3ltREMU2hph5qG2HqWHB5xFSHvgbRDhsCR8t7ju/ZrWJUkVE/eNZFamL/hTdzMoy4Gpy74MCn+I0JMLctrqtToN4r4nSReIsjzcLl2RupGtKeuJmhN9+GpkDcpAyJ+zr79n6b1vSOTjuaFjPaww1QUEafQ5063xRs2An3/Q6Gpg2OtS4JjSgIoMVi4fe//32vry9dupSlS5dG8pSS0wUDQ5lMHTX8iXIex0uwrjWFJj7DmNdAwtf+0/Wle38LsyfDxFGiXi4uRpQAZKRAi0tYVUOSRY1bckJAsdSXV8M1d8Pfup2nrlHU4PU2pqiiRjzaWb1NrGt8Nqze2nXbtCQYngEGHTha4Ghpz8dscopG2O2MHnZyMku0GJEhyiP2hNEGTaOHUXMg7avgM0OtFfJ8wDmQdC6WQ17xeXYm1waHmqGxH1bm/BgR++sDpcSHocQAdFjoSr2PMV9/ml2fnnviOb+lhbofWUj8TohrKDwOf1olEoekFXhqE4RLs8d9BhgpgGcQWqyk8AOMjKKQK1CDGkmp4uBLMl7u4avg84mEjnXbg1vArEnwrSshd6KI9bW6xZSG9Tvg/S+ZtHqLKCQ/aQkqxNiEcAXT/qw9djZsCIwdLsoYJo4SdXmFxzvm/4VCetLACGBirHADHykGd4jdEkfNhYqb4KAfDrY/2ckdXO1D31OW5VYHKIgpEHoF9rYV8Q81gFkDh3oo9eiOVhFlD2GgPaQn85VHOX7TT048V3zdZ+iqv0rs/ftCO9g/PoSZk+Bby0UtpeTURHaCkQwWsVxMKvdRzqNB71PxqJusT03ojgdxMeyNTbvFoxf6vGdvr4sLtv/n8XLxGJICC3NFJ5nO9XbBkhgHo7ICrS5y1DSIR7Bi35mjOyDxG9AUhiWn0pGtmWYQrtFiN6ToIVEHQ43CZVruFqlxLX6YYIUyF6QboTj8Tj2+bC9llz/Z9TlrM8e/lYfxmbGYjofQCcfRDLf/VDQxfz7477dE0hNSAM9QUrkb8FPOzwkmAFSftRrHkWRSPppJwsO1VP8mjoRfODH9tzrqawXgaJmI7YVKaWVHcXk4TBwl2pL1FotLTxaT3Y+VhX+OzkwaLdzFHq9o/N0cwg2H1wMjHVDez/Z15Z08A5UeMGlgZ7fYXqIO8hrAphFGZmH4N0aq0Y+21Y43pqrL8wkbctA6+ogp9sVL/xSf5f98ve33t0U2r6MZLl4s4oTSTTp4RKEQPhpIATxD0WInie9QHkKrM6+2itILP6D0QrDX5pL2ZT+swVBpbBKtyHS6gasDMxrEqCWrWZRexNnF+QG8XporqrEcKRUCmJUu3Lpuj7if8PtFG7XyKmgIXHB7gn1HhAXo8UJ8LAzLCH4kkj0BjvwJFlwA60d38X72i56K5mva/g8mWWF9/2oV9R9pid/yVcoverrL8+7UBhQ1zFIXrxdef1+4w4emiZmO7d2FHn8BHr4NfnizqO+0WrruW1kDt/9MuKNnTYZbloe3BknvRKEVWjSQAnhGo2l7hO4yc8TvpfLtJaRdeCTwxpGiuBzmTYV1OwbmfLMnC8vP2dJjQ+oTl02XW1iAvVmBE0eJC7GKELPO6fvd8XqFxWo2weIZYqhvMNgSQfszKPVBGVHvcnaC9U2i8fX2EES+Byz7JhAz/BwaJ3wBgP3QSJLeGYmufk/4B924Szx64q//FolT2/bB3CmiBMbtETceL6yC5jZ38Aerxf/DqGHhr0NyMtIClAw2rewhHPEDUJUWKr6ynlRSB659VfZQKK0KvF24GA0wYqgYQ2Qyipl808aLeFJinOjfuW2vyHANhT2HO45/3lyR9JM7UTS7tlmEW85mEcLo8wnxcjjFFAydTjTWPhDgRsMaD0P8UMfAiV87pv73y4j734lY1o8i/801oPOS9ZNpGN/oh/gF4vAx8X9cWgmbe49LU1wO59wMV5wnag2vu0TcvCiKsBRT2/pN+v3i/08SHDIJRjLYmJmKkbG4OYpK6O5MS302arwHpS7U2c5hotVCRpIoXYhGbd7syaJsovOxNRrh2iwqhbXbYdEMcbFztuAtOIau0SlcoCoBXbPq2BEo2/YKizLY+j6vV7jiApExFcz5MHoKHBrgVmGqCqPMcLilX4fRf2nBXjibpjFrOfDcf0m7aAExDzsxHYlCnNmgF1nI40YE9iiUVMAfXxdu1J8/K27CstJh90G4bKlwcZdUwK0rMA+JhenTZXwxENIFKhls9CQzlF9TwCUh72tqGcOIXNAMgPipqCgo+MuLUYwGlGgVppdXiwtb59pAv1+IXzudhEuNtwuL8XiZSKrYc0jE79rwpaWgLRcJOBWzFnOZ80HWaVaE3l9w2z6Rydq9prHLNm+JEUdHB3hYMIgEmCH97xTkneWiaYQYsuu1Oyj++n+xJk5hzHeTobQWJZKDkGdMFMJXXC4ybrPSRKbxjv09Jx5lDxXlPp62m4vdbXHPf3UaIXb3L5kA8OK/4cKFIm6cmQYTRkVu3WcK0gUqORXQkYiCIaiaQGvjZDLuSUHVKdiePI5CiENfg8AzwUbtL5LRlYEvUcExpwFXYg3WI0NoGn2YUdepGIMMi4WMaRIMV6D430G1U9PXNUFd24UwbwvkTqQ1PpmXRn6XDdaZ/GVdCkumHEVB5bNjwwHYOOUK5n76p9DW1eoS4rcoFyrrRMPw7jHEhSthtY0B938OMYgSiaP9S4hSk7S0zCnAUjqB5mE7TjzvvHAn24sg65WLsG6yoq3woT3Wgmb7MZRw2sPF2WHSmI52eq0uUW7SXnIycZRIempuFc9lZ0KMVcQLPUFa1h+vE4/MNPjyFBnrJQkLKYDh4PeLAacGw8muEFXteM7nG5CGzioqNVQDCgkkoOlkgzjZEGRBPLTYC3BNHkri7UeIdF2cqlc4/kgSrludNCV8cPK5J+5Do9ppuMaHJW4Ihm0O1BgdSosfw6b6yCwibhJ8NBQW6SAv8LzELuvX6fhTwvV8v/xiMur1jIiFBbOgoGQYx9qurYmGZiZW9mHFBSJvqyiRmDpOxCIBsmeB/1uwZhAmJKTqodZ7UtPrcHBd1MDh+78L2h5uPDRw7BsfwDc6nsp85UKSby4K/URen0h26U082+O17ew60PN2gYiLgU9fFB4CycnIGOAZjLNZdDTRaEQSQ5MDduyBz7eKouqC4/DhWnj3SSgtg9ZWmDk9astRUIjBggYjaicLwUMltbwRcH+NamHIW+eQ9J1iNHW9J2N4Jtpp/Uoc1pfL8Yy10vDDBJqnOWkckU/swcmk3eai8ncW9BU6zGv92F6soPXcRNxTDJR+bx8uQ16f6/ArTRRf+QFc2fGcrW46o9M1KK5+tPEaNQ/ir4cdOsAHh2bDyB1QUBj0IWqmzOI7u5cxcyrs3gcFbU1j5s+ECWPEV+LYcRPaln6KRVWt6G86bIh4OJfD1kEaD6QoonNMBKphdCWWnsWvF2wbbKg6HUqoJTGOZpEZarPAjByRZTt7smjQEEnX+r3fgtHDI3e8Mw3ZCu0MpLkZvtgE331U3Gn+z/VwrBze/EDMddPrRGPnihpxJ3/59+FrS+B/vxn1pRl6aCusIxkbC2lmU4C9FRoX1ZDkFSKjooJRe0J0VFTcc+Kp+0k8pRf8B+XXelSl64W+Zvxn1H5uQlXarpYXgO6RJHzK/o7nwsARv42CY7NIfSwW6wulaFr6EkIF9CbwdEvWSFsOaxROFM6V2WF4aBfW+KMHWDXtVW7KX4FHNXDBOVBSDgcKwG6DI8dAr9ewbcq5LNzXz+xGVQVVA/7pcHgQ232NMcPGxsDbBYE/NrRYcsFjnzC68VyMf92DqtWGHh/0eMFkEElNeVsg1g5TxooYYGP4JR0+qwntrCnwnavCPsZZgbQATzN27g9cgK3VwjufQ3Fbo+kHunUmNxnFHaheJzqIrN4qUurLq+GGr4npAwOIghLUpHi/4qQxZSN7azK77B23cxz6SgstoxqoHfP5iVd6c6l2FzqvJjLZfY0pm2j8PZhWjmRsth1tWQ9lCjlfBXMiHFsMw9xgbAHFDX4D7LByUjlI1nlQ8peg4z5aq5Hlro9ZmL2ZX6nXUOkeyZZCOzVuC9VtJYQeDxxrjevXez3BsGmwbQI4B+G2uJ28BlEDWObu2j0mRPzjtex/6YaQ9nEn1LHvxX9heCIRndNC6ltTsOSBbksFSmUQw4Zd7q6t8RqahBAGSjbqi69fysGvzGD8DVL8AiKTYE4DVFXE6arr4ON1JDTWwbRposnu5t1CuHx+OHcOWExgNIohnb0VOje1tXUy6EUiAwgXzJZ8EZd45A5IiIvse2h1QUureA89uGRSuRsNFkr4YcBDufVdpzFUzjiGjlRMjA06kSaqqBpUY1tMdca1YEoG30hoNMFeX5vGeaFCA1jbHtBzLaQz+KSHWZOEC+1oKSnAL415YNDTmj6UsqzRmLwtWJvrmNb8R5aVvNXPN9nG6vdg7AE48ANCjsfG6cDth3EWkTNj1ECL7+R2Z8Gw3QHjLf0SQKUZrEem0zDlw5D28xs9tKaLmY2OewrhHrAcH8rwh+Zg/LIapSjEYcJjR/ReOB+IqePgyQdpPnww8LYSWQZxSlFaKe7+1u+AH9wgRuY88Dt46R3hDklJgIoaUiaMgNQ0uGAe3HBvh4hZzXDBAoi1CRdKoOy0nrr8//F1UWf20/+BixadnDzTXmjr9wtBc3vg5XdEs+cVF3bdtqUFquoYcc8fYPM+sf2bv+5RADWYSOb7WJhCDa/i4ThNfBrUxwbgpQIHFdhYhIO+Y3jRptV8iPJ/fIXk5+/A+NeR4Gz/fwgjfb4xO2DbNU+MFX3WEFEf1gnF5QaXG3PTAbIPdiRRHFxwJ9o9vYx0CofUXDgQgvhNsODQtmLb4wWrBrZ1cvXlWHrfrzNDDKLmD6DBCz4VkvUQ4tCGzihHfaCJzMDf5sxi9r68Cvv+MaS/NBnTJje6Lw4Ft3Nygpgp2dcMye7odcJ788cHwGIOa81nJdIFOoi0tMJ7n0HeNmG5rfpIjJ8B+CBPdInoTNusOVNhCfzwlzBmuIjxteNsgepaePvj/q1r2174+j1Qtaaj5yQIS9TjEc99/xfw/D/ETb9BD7eugH9/CU/8r5h+8PCTUN8EFy8m4VNRU4VGIyzBimoxVqi2QXSxOHyMVv0Rar9WglEzEhUPLgrQYMNPaHGQQRoZexKVMz5CW5tD+gv9nHq+IxMmPAD6d2H/HuEy64a21SWSKVqD6Axj0KOrqjp5Fl9/KPwShn4J478JHw/pfTudAhMtcKgZW3Pb/1T3iRH6IKsTS93Q5IMRpo7htzPtMC9GxAPDLNVLfe1mvLfV4BwexsSOHmgad5CmlQfBq0HvsJP55yXoi/VomsGYV4dSUIHS3cIvLheu0GDRamH9G6Krj+SM5MwRQL8f8g8JC+3LTbB+p5gw4HJ3rfnqLn6d0La2XQTze7ij3Lgb5k8XxbL9ySZrdMDN98PPfyAKdEEc7xcvwB9eEy7Wdjxe+PXL4uf2TveqKmqY/vYBpd+4lCEvvSfe+/U/7vF0JsBVkEJ59mPhrxnw0xB4ozY0qgXQ4lf610S5N8rO/x36F+OJ/e0k9LuD6KLSG3tTwXAzzHgf9m7s+tmbjPjMBjQ9fRd6wmISNyaRpK4Oxn5fiN8suyhJ6N6NZYpV3CztCODe3O4QxzjaChV9JKRMt0FBS9fJ75vb3tdwI2SaYGsTNAdv0TnvLufgYzeBLgrZrDo/nrgGCu9+58RT+vpYRt91PqaXOyUjGQ1iXmQoI6i+e7UUv3CRMcABxOWC5XfCR2uFaGg0Qij0uqAKnoM7h1u4UOdPhzX9qPXy+0UX+9ffF3VENfXwzN/g84197/fJ+o6fFQVSk9A2OUV88tMNfe4a/6aZuvtPfj79I+FadUyvxhVbgVt3TFxMVdA2x+Gz1mMgGw0WWuhjyKyqgCJuCgyeLGIOLiJzys2Uf/QeZUt/3/t+4aL4OfaNh9DfMIRRy5/A/G56+Mdy62Hd5bAoBfJe73h+2jh0m/KD//5MHht8+7NgyB4BGZNhY5vlt6lJuDEXtQm+TxVCZtHC+iCtzk1Nojf6KDMk6ESPz2IXGBQYZhKipiDihT1R5BKPeC1Mj4GjLrF/gPtBy5PpZKf/jsK7vxfcOvuJJ66BvS+uYkz217F+qaC4VdhbKHq+hkK0OhKdDeiBUO93pAD2QVmVSDIZM/zk146VwZebO5Ia2mN0oU7cDsS4EX031g2V5XcKqy7UWidVhboGEj/aCInxQhD7+GONe+AomSMuovqyQlos+7FXzyT9oVhszxZ1HBIL6gWv4PcNxZvlwPhaLJ6LW2m6aCfHbvkJKKDzJ6F3pdFizkfnSyPlvzcS+8w0jF8k0rKsDFduJfE/moZq86H4tNj+NRGW9v42NK1GbIUjsBSmUb1wG97Y0NyHHm05fnuE/o/rOrlUhw+BLXtCS733RSbGdYKMHFh7Hvg7NVLI76EzT6j9Of097GNSoNwjhuQGQ50P1rT9X8VoRZLM/mZo6PZ5aQGrBqXRj2XPRMbf/hbGDUmU/+zvlF/8VGjr7oSuKQmvvZcMYxWS824g/qPzsfx3GOjXw8ZXQj/JqCy4++aw13jWoyd0d7kUwF5ocsK7n3XMZDN0s60PFAkhCQaDPnxhLCwW2WDhZpJ1p74f8SKXG53LLSyP9vhmHyRfd4SEZCPuWYsw/bcKxdc1BqigoLTo0eRp0BEDqBj+aSTxn7OwP/sGFX96l9RvfQ0UFU9OI9aXu46Psb6ehfX1LHGsBnHRtj03gmHjHkdp1XHszkfwK8KtZipNJ+Mvc4h57BiKoxmoIN02mvJXYylb9hlBo/hxT6k+cd5+sXsIxP0aJq8DfQIUvxTa/poIN0d2je4qftEkWS+mvm8Kw4Xb6IONTSIGGeOD4y5ROmHWgFaBtQ2QrMfwkgdIQNWpaNxGNM2x+C09u9U1Lgu6xlTcyV2bMliOTSHjhbuwvZhN/p4VeGLLTiTImkrH49e3MvoHT2J8oy3hR+OH5HWhvyeAK86HkRH4Xp2tyCSYCGK3wndXiJ9VFapqOsoJtNq+Lz7DM4TlVlgsYmfF5eGvY8zwnuODg0kIXem1VW7M/+5j3JDrGHCyO9GwNY7MaTd2/L4lcG0hgOLSkXj7HFS9l/pzRuGp9BNfm07yA26UI/u7butoJmbtCMqWBXVosQ7PCOzPhjFFvjfqzZB3LtjcMG9FV5donwvRi/+HGFu/iqzRGsHnhgnnEkT5ZuQ47hbW34IY2N8C1WHcIDZ6IVEvBKn77MCqjuOpWRoSX/4qqqJStuy3AEIMzQ2Mv/0tvCmNWN7PRnEoeKY34ZpYivWdMSgtCpq9PlSTgtLqIyfpr5CixTO3GU92LZbfDwGdgtK5UUKiDhorQ38vGg38+JbQ95N0oCf0zDlpAQaBokByooj7eb2iZi8jVQS5l84WnVj2FYqC15uXieLzFT8U2XkLpovRM7sOhuff33WgoybsFMBv0KEJJkMxWIq3ALMjdzybGyasQtm3g+zp7VmWvZcJ6AsCuxH13iHYSmZi2TWOpFvOQVtlitBiO+EwwOHJqMpfu06miLGDzyuygtvJSAW3O/T437xbQZsNxXEiVmIBylTxvSxQoVQLmVoRqyt1dxGRqNDkF27N6TbRAWZ9Y2gXsAYvxOthuAmcvo6J8t3QFPrQFOpJPXw59g25uHcbia1Nxp/gQf+BDkigvW7TeNCCkVF0ruNUWsWiFB9Q5sPwthED6YAKnm4LtgDaVIhJgYoQblz9fjGvMTEu+H0kXZECGGWMRvEA0f29boMQwfYEmO/9XNTvVdaI2rtYu0iB/sNrYnzJJ+tF38VQOVYm4m41IdQSRZqMVCivpnVYOpYtERwqmjkXKhTwRij4b/LA/p2iV2oQ6P9TyISbrsQ5v5Xq8/bgzD65L2n6a7eS+I0F0R/SG+cUn0VKCgwdCYoG6r4CrXrI3QeFEyD7IGiq4Yt3Qzu20Qb7p0Gtj14zBVxeYZUdd0GWEbJNYixRtNnmEAIYrxNZp8HS6O/IHJ1qFd+hZv/JotSGZq8P2962LGhUtNG4FB31AnfCMCOcUw7eStj+F3AG+Nv9+fcj37BCckpy+gpgZzQaMLdZAj4fPPWGaEidc5lIjLFZYPQwUYc3JAWS44VYmk0iHhhKskNFDeROGHgBVBRhfRoNYlCnxYTlUASLrnUm2DUDvBFM5sioh53Bx5WUVhemv+zB9BeIPX8yR5+w0JJRRuoHs6g5J5/mzGJql/2HxG8siNwae6NlOD7LH9CVKVDWTWyPzxH/Fs+AcZUw5Tjs3B78sV0OmOiDYO+/jrnESKJEXa+WVUQ52CJKJsKJCYIoyYjRQpoeit2DW0QaqxU1kF/EA/Ew51E4+hSU9TIFYngG3P9dOfC2v4RjzUkLMAJotfD9r4sSiPpGeOv3wiX6zqdCKLfki84wIL7kJmPvad89kZkmrMBAZA8FiwWKioNP0OmLGTmRS77pCW8rTPFDPyo8TqIhyO4jPaD7+CAjpwCkoupKSBw1jr2rW3Akrcdx1yHsvx0TsWX2SLIe3ZEgisf3pwDfhulHYdvKwNvrzTD7O7AmxPUoQNMAToXQ9VMAGn3ikW2CVEPwpRqRZopN9DRtZ4MO+AGM1kF6Eez4U0ecUKMR1p8Uv/4TjrLIJJgIMjMHLj1HuAszUuG8ueL5H66E37alRauq6BoTCEWBoali9lerS4hsVrr4t6ZeuFf3FYgkm+yh4vfdh0SMcmQWTIwTglxaKZJxeiMjFZLiRcu13Qc7uoqkJUNh7wX8EcNaRk9JMCFjd8G0DbDrP/0/FqB4vSj7S5g4fSJqnAVNfDHkGmFnBnij8BWeZgtte40PrDUnP28ywqQZcGAX6LQwJBOsd8K6MOK2Ni2k2GCXA6baYUcTRDMsGGxJRCC0iML6wWBeTFfx68whLxwaCjPOhS1tI8N+cw9cf+nAre9MRk/oI0VPVQGsqanhiiuu4MUXX8TtdvOTn/wErVbL8OHDeeyxx9BogmyzNJDMyOn42e8XVpjNAlec1yGAwWA2QXqSEK7j3TJI9bqO2sNYO9i1UNJN5AqOiQeIO8xhQ4R4Hi0VYmfQw4a2IvORmR3JFKOHwbjsDuE83Ef2ZqQ49HfgB/0/jskHxZv6V+bRA8rxKpTjgK4E5n4XRrqgySIKuw80993hJFRCcf/llIPSBJMehNga8OtAWwSOHNicAfOmQv5IyDcBbTG9Rl9oIrOvzYuQZhB1fHNjYU1Dz32+wyVFDzE6KHNBRYQan6ca4VDwXYQiSjAX4C0LYeEUYQ3+z/XRXtHZgx4IVRYGqOqnMwEF0OPx8PDDD2MyiRjbk08+yR133MHixYu5++67+eKLL1i6tI9q54GkuhbKqkW7s4NFwoo6Wgr7j4gGuGNH9NjzsU98PmGZ9WS5de41GEyPQb9frKe8WiToHCsT88oApo3vmkl46Kh4gNhmSIqIa5aE2AE/FMoOwBQN7OzPVVUFrxK5DjzdMZtgxgWwrq1Flc4DmUYYa4ERftgQgnBNsrZ5Afwihd+kEX0wu6fxB2JXBtCe0NHes3N8x+vta23nmAsWxooauVA/6vapDHkNMMce2vvti9GmjlZrCp0ajfeDbJM4ll0rPteBZJIVdgc5/WJ1DPxqmbhBlUQGHaEL4CB8/AEFcOXKlVxzzTU8//zzAIwfP576+npUVcXpdKLTDaIX1e0WHTjMJpGU8qMnhNjtOgjNLRAX09UKOdD7tPPez+ER88MmjRZuzUjQNk0AEC7VuVNFm7WeMBvF+6ysja74tRNbCqSFt+/wOjC9Cvv3Q7RyhGxWaBnT0WXCp4pCbBAX2kCikGYQWY4Aqxs6EjQKBiDDsjOrG2C2HbY0hd1gmhI3jDLB4fDWrqLS+PAeNE0GjJ+loo9JR6nxRi5ppbBVPIYYYKpNvOfeSNKHV3/YEwk6KHEJKztYIiH4kg4MhH5zF0AAPR4P999/PyUlJbjdbm677TbOPffcE6+/9NJLrFq1ioSEBAAeffRRsrOz+zxmn+r19ttvk5CQwMKFC08I4PDhw/npT3/KM888g91uZ/bs4OrG8vPzg9quX3xvOQCa5lZMRaUoPj+J760m8d0v0Xh7/mPwGw1ogrAKWxqb8E4ZDRoNWkcLpiMlKH4/ir+fV4sANYVeg56WlhbUOBvascMwFxT3+l76g3PcRaj6ONQGHfYwj+E/bsOZkxj2/kFRVUNT5m7s9DANoskHG5ponKbBWO7H2Jar5LWBzwJ+i4I71Yu2pgnbnsG/4KmbmmgdpsFcFOZajrtomq6ByRrsu3o+hs8I2l5CjqX3rafi0QdO/K49NhLD8WxGLbsPfXUEE0FK3fir3KhW0LSKGj53koKhWqVpmgbrbj+aag+tWQqmY/37e3JM0mA64kUXohGvPlpEoaWc+qWBUxG3bo1kplj0Od3W2xvvvvsucXFx/OpXv6Kuro7LL7+8iwDu2bOHlStXkpOT08dRuqKoau8V4ddffz2KoqAoCvv27WP48OHs37+ff/7zn4wePZrXX3+dw4cP85Of/KTXE7hcLvLz88nJycHYXrc30GzeDefd0nOXjlmTRJPcYIejtjN1nJgMn5oIew5HZp3Bnnf3oci6GOf/D6wdH3i7vohrgdQmSD0Eq/8a3UbCsxfBxmsCbxevE02eK92imBzEnelIS0dMbbDQAfPa3KD9/a9cFCsK5XUKrXXNmNJt4m5aqwgLeZeDot1/xJVUgst2BENrOi5zET6l4UQT8xP4tMQcWEjm/92N8dUQk4ECMccurEudIt6z09fVTTnFCgaNaKPmU8HhE82+azyiNAMg1wZ13t5rItMM4Q3vTTPAF1OFK70Ptm7dSm5ubujHHyR6W2+0rsvtx73UmUOZGtpx0xUX71l7X5PT6URVVWw2G3V1dVx55ZV8+mnHbNMLL7yQ0aNHU1VVxTnnnMN3v/vdgOfs0wJ8/fWOVlA33HADjzzyCHfccQc2m/jDSElJYdu2yMz3iiozJ8FLj8Hybgke2ZnCZdomfvULpxGXXxDcTLcd+0XMINRG1v2luVUM9iyPUFKMLRHqc+j3VXjyRsj7O/RSXhURFAUW3AJHRgW3fZ0X6rrd9LiBWk9kXW6hMsokRKC3DMVQ6XQcE0BxVxew9zw/Hnc1zlgxNcRr7cOVrvXhGLmJxvO30HpLIUOXfhMlUsbyhiYR+yxo6bgh6UxvE+vjtaIu0aQR7zVWC3NiYHsTuDoJ+HATVIbZGenmtIDiJwmBKNT0Wa1WABwOB9///ve58847u7x+8cUXc91112Gz2fje977H559/zpIlS/o8ZsgBvJ///Ofcdddd6HQ69Ho9P/vZz0I9xOBw+Xkis7I9seScmbB0jrAA9xbA1j2UXDqPuOlTYcbVHdaixSziiZ1JS4bGJpg46qRp4VFHUWBUZugC2NvEiKlfhzX9FL+hjYA/4FSKfqOqUDoMimP6d5yhxg6LYjBwqwNT0N6G89xDGKszCDZdxm9s5vgNjwJgeX4cid+a1/9FtI9xCkf063xds3IbfLChEVL1QrS2O4T726SB5jC/fzekhrefpGeiVNReVlbGHXfcwXXXXcell3aUrKiqyk033YTdLgIwixcvZu/evZETwFdfffXEz2+++Wao6x58Plkv3JWqKmr3nC1w2VIxTeEC0VmkdetWGD0c3n8avveYGJ45f5oQzaOlHW7SjGQhipsHIK7ZnQNHRJ2g2RRcDSOIHpZTx4omANmZ4n0cPgYxMeAbffL2GkRCSffxNr2R3ACNR6IrfooCC2+CvIT+H8s6CFmJnWnyDmjSRex9Y7Hljqd6819Drs0qWfFrYv86B91nYabojbMIl+bGxq7WWiSo8EBFAyyMEf+nH4WZeZVjldZfpNEReh2gSp+OqOrqar75zW/y8MMPM3fu3C6vORwOLrnkEj744AMsFgsbN25k+fLlQS3z7OD8eeLRG50v3gtnwM5/ijibVgt1DaKbzP5CkY25vxC27u3YPiFW1OtV9FAMHWnGZ4uaxmDEb+5UUf5RWtlRYtE+zcKgh5xsWP0/kD0TWhshdQKYEkGfAjv/DPMuB81I0HjgcCKUdrJapipgb4C6L8G+AHRTiWwbmTYUBabPBJZA3rCAmwdFjQfidGIM0KEBsgRn28GoAbdfuPrcA9sfTLX5Qr4gaZpjGXPni+GLH0CzT3RairT4tTPJKrKA/Yjs3hQDHGkREy6CZYpVxEslkSOcOkA/fQrgs88+S2NjI08//TRPP/00AFdddRUtLS2sWLGCu+66ixtvvBGDwcDcuXNZvHhxwFOePQIYiOpqlKoaIYTtrZC0bZWZ8bEwb5p4gBCf+ib4cgvMmQwTRsKrITZEDpfkBNi5/+TnJ44SYqfVgNUittu6p/e6R7dHFPYvyoU1W0SNYmm34657puNnrVGk76UMF7/v6Jz409bxpb2m0dksXMe7D4rPqT8suhH2L4hskfueZkg3CPFLbqv/Ox7BqRo9YdR0LbsYIFSjQum7r1N5zquBN+6G39JA2W1/Zvird4Ndg1ITgtUcp4V6n2iGHami+p6waztuJva3iAeIWGNfZRedka3PIk+4AtjHff2DDz7Igw8+2Ovry5YtY9myZSGdUgpgO8nJqMmJwf0xmIzw9h/Ez4oCFdViXuH/PAYfrY3uOlUVGhwwZwoOpxObwQh7D5+cidoe6+yLohLxyJ3Q1aLtCV+bQFT0kfHauZB/9mQhfjodxFihNsTYj0EP4yYCyUI0sk3Q6hfxO4MGjrb2T7TK2i7KVR4Rnyp3g0UD461gUKDOS5OuFbu9WybkoZaOfUMhr0G0WMt39johISqkaPDEV6EawqsVrJvyH7z51WidcQy75V40+8A3z4vu025Xt0lWkXXrbxtLVOuFyQZhAYbzeQVLfi+JM+4QXMzXpERmLZLTDimA4VBZA39aJcYwfW2pSIpJS4b/Pi/GLd332w4Xpc0iur40NYu44cJcyEgR1teWPiy03thfCMPSYcNOIpakXlUHMyaKZKDmCBWEb9zV0Rd1+z7ITKMpOQ77/qKOcyTGgU4PldVC2BdeCqoPPONgbybsak+F9kBlmwVY6hYtk5INYoAriEbL/Qnp5TVApkEUlm/oyAAW4fROwq0BzouHkWY42NyxJoXgLLvtjoEba9RGw507qZ35TvgH0HlpGr0egPot/0HTKj6VrBcfIf7BmeKmRKugWr14Misxvm7t2Hcg3MtOH4w0dW1koFdC6z96zV54bTxclhT59Z2t6Am9tdkghOWlAIZDahI8cOvJzysK/OAGEad7498iQ3TscGEJLT9fJK+kJIFeK0TsjsfElIpQ0OugOIwp132h1YrYZkqi6FMa6nDX3ujcPu54Ofbj5W2T0+2QMxIKSyD1O2AzQXoJuPJh0zUE9J34EHf4a9rEaoJFlDX0p6wgUMxoQQxsa+qaaDHTDsdahVXq9AnLdFcvFgmIi0KWUZRnmDWih+nmJtGKLQr4ZisU3XZ/5A6ogN8sXNpFt/4YXe1zFN31Y4wVI/GZHbQO2cckx4fo/2WI3Dl7o130fECGsasAelQxgWKkWfyfBfKeO3zw31opgJFER+jqMgieaCmA0cBqEkN4M9smKzibhZX1wj+Ey7HJKcYqvfOpcKeOzxZikZEi6hL9vVwQY2wii7M0wo2xM9M6RK+oJLLH7o6qihKSdTtg4kTYnwouHRQkApODP07nYa17m0Xn/2hS5YFJNlF71q6Vm9vim53jkyNN4neHTwhcvVfUGy6OFRbg2kbhJqxDWLOLYvtu5L0wVlilIbpN/eO0HPjnt08IVsTR+jj80LcA8NraBhv6tHhTneiJoABaNWKwbvvbn2kXNZSVblEfOMEq6jpzbeL5HQ6RcLOnrdHBgpiOG6WesGnhhTFwjSyDiCh6Qi+FkAJ4pqDAn9+Cn9whLB6rRTweuxNeexfWbof50+H8+TBtnBhyGx8r4l6/eA7++HpHRum8aaLYvsEBibGwJgqNB3bsF9MzVFWsRasR/U+jTWtL5EondjlEXVgkk2U6c6BFXIxnB0iuKGgVUxUmWIQItvpgrh3WNcAws2g23Zl2q3WOXViQ7ZPU3X5hJR5vFQk7WgWOBO86LXrjcVrTo9mVoCtZL/6cmH/PwPB2hLqKTLWJcpEqt7iQTreDRoEGb4e4gbihaCdRB5NtIhZ5c5qID46zwIwYeKrk5JuI8+PF4N4rkiOzZkkH4QjgICAFMBrMny4ePV3cv/418egJv1/sYzGL+KJOK2r33FHuWNLoEOdpR6MRrtBgBv/2h6RlUBChvxKHH4YYhehEq8bOoBElFIGo7BSzBFjfZoV1bzvWmUBTHbKMwspt9cO2Tt1tUvRdzwX4x2tBHbjbacVtwp1UhX6ThX4HcowKjLFAYTM0dvp/DGbqRY0XZuhgeTJc28miG9MIOx2iFnGmXWQbuvzw0DBYGNe/9Up6RkfoAjjAGdIgBTCyNLcI8WpHUYT1dvgYDM8Q7s6e8Ld1UVm9VWRjutxwpI/BudHG7xdzC6MpgLF2OJwReLtQMCjREb9RZpFuf6xV9KYMl3QjHAozAeaYSzxAuE1VhHtVixBFnXLCLVz6h1eon/ZB+OsMEdXQSvnXfk/Cu0swfJmA5nAYIji2rYZvf7Ow3MK5GCbp4ZHhMCe26/OzY+CzqUL0jHLk0YCgh5A94VIAT3Ou+5FwXX7jcvjO1eI5nQ5GZQnLrrgc0pM76gvb0WhEXPCfn4SeFBMuRoOIOSYnCJen2yOyM+1WqK6Pvgt08hJYG+FkiWgVlzf7xJBYpx9qwoypDTPC1gjF4zon+6TqhQjatVDrxWeGtMdvpGncRlqGDmynor1/Wsboh1/G/rMgGhYMMUClB1cyGEfFwF6ncDP3h0sSRTLLc2Ui7vr2RNB3EjwpfpJuSAGMJN+6Er77CKz8M7z1MXxrOVz1VSGCAGlJvdcZ2iyiNduHa0WpQ08kJ8DMHNEG7fBR2BlmjGfMcDErcdOungf9RptFyyFvKRGPeqcYRH2gSxWp8JGqt2tPVulPlmmrX8S11vaRkBEO7THPOh/Mj8F7uBHjZ35idy8ccAEEcGWVYksaiVLdQ6/TRB2Mt4gWe/udYNFiKPdBWYSagr9cLhKIh5ngzQnCKpYMDlq/eIS6zwAjBTCSXHIOlHwhWqhV1wlR60xfw4MVRXRzuecWMSPw6q/Cb16B9z6HkZnw/a8LS/GfnwjRKjwe/joPFol/J4yEsqrgpl9ECpsFNi8kKilfaxqEpdXoFdl9itLhNmxHQUwWaLcGVERWZmdhWhQLpS6aYjzYFbMo5N4T5HTx3qjwiMc0mxgB5I2Ctbq2kXYnu8/iwFA3BHd8aeTP0we2jRNQ9BqY2xavtGtPNBZgq6NrRqbHF/lvwZXJ8LeJkT6qJGQ8hP43PvDTWaQARgOtVtQKhkpKItx8uXiAaC12sKgjfrjse/DZxsitc1+hENXMNFGYPxBkj4FdUawTO9omeHU+MRk8Xidmy021iR6RyQbhiuxuoIy3iGzLJp9IpDnc2lYIH+Jk1UBsdwiRPhrd1mtDL/wWQ/XfouyNv1F+0dNRPVc7htqhGPISxA1D5+4vs+2wr583EMEw2w4/GBr980iCwEvoAjjAo+WQAnhqo9HAuGzxc0MTJMVH9viqKqzV9JQBEUDnuEuwpkSooXUw1HphqlUkVbS7L3sreN/XLFx0m6NUN9eZNEPUBVBp8UOLiqbFFNXzdCb1/W+hOdhDAszGJpGgMkLftYQhknwtEVZ1i/lJBpFwrDlpAUp6orYe7lopShVMRmiN4MVzfLaIBUabhXdjXT0SqnQwxyySHpz+6Lc/2uGEyda+O7SAyEJs7LSYyVZaa5oxlUTBVZnvFK7BKIxk8lqb0Y6uxXFlPtqGKlIeOErD9OE4M4+BLnoxltj8c0m6bym9/odWe4SVHWlyrHBdirD8pPidQngIPa1TWoCSnkiIg1ceFz97vaK35i0PiWkL4RKbBuMWgnOnmAixdoewBkNh7BiIS4PSAhg2GdRscMZATQzEOsHmgPw/w+hfQpUG8IlarZomUYA80y4G09Z5o5MCbdeKETlbg3BjtmcgxutEfG6XEyVd6X/yS0+4/BAbnT+91ux92Hb8GfuOjufGZptRk2ZR+WQM7tRGqs7ZELHzmSpGkZh3BUm/uQiltI/vz0gTbIjw5/iDDHh0RNQ+S0l/8BL6H/XANwOV35xTGVUVEw9cfhjddves08HMSbD9LeqW3kV83ieBjzN8OiSNgy1/Bb0Z5v4v+GNgjRVmngOuTTDXBGvaJ1kE0d05KQGKb4diwGmA7jk5x9tqseIegSbTyR1QGn2iWbLTJ4RwS9tMt/6QaRTp9VpFtEcbZYItIcbw6jruQo1lKoxG/JVE8ubUi8hY9arBDx0OElPRWNFRqFvzBKW6gdRrGvBPycL+9WVUXbqHpjGH+pWLZCodz/gZz6GU+ejz+6JFjJ2K0YkYaCRYGgc3pEnxk/QL+e05lVEUeK8GHjwC23JhRKci++V7ic+7AhbNh7xHez+GwQpl34YYBfireG73UJEkArBZAWaDfg5k3wQxPijTwshmcPwHWgshbTagAX8taOIAHxyeCtVBJLPUmyGnbbvuIljTpir7m3u+fsZqRVcQvyrchn0NVZ3RJqLtI5IUQhe/nshriI4VWOoSyTaRZEw1raYPsO3qPZai2XmMuJ0Q+6AFdcxXyF+9AW9sgCxgFaxHRuAccUR8rn5I/XQ+hqr5qLGgBOqX4ENY4QqiNVmyXvRWPdgc2k2PWSP+LxbEwn3D5BDbUxo3p8M4CCmApzq3DxGNfvc44T+1cHsGvFYuhBEgLxVynwZrNWz8JbhbQe1krridMM8FxyyQPQuGng8berjqeFQobN/PCxUGGL4CkrWQ188MvjVtItJdANtp8QtrLcUg5vINMQrryN82V+5Qiyhd2NRHgoq5W/wnUi5VQ4ijdYLF5RelGpG0AA8mok4dDgR2cSouN8ru40w8dz5V97upXrwdd2ItqBC7dzxxa0YS97KK0ugGnQbNrmN4LliG4vSh3V+FUl0Li+KhuhGwBjzfiZrM/c2wH1GnOcEC+UEkxYwxw98niu1lnO80wUvoLh1ZByjpjqJ0tHb6WyX84BD8odvEhq0AScAvYbgOmv8PKovo+ELth5LJoN4MhRC0OhS1wjF67DcZMkf6EBGPKlqEtbcJ6zzapp1il2hG3VOrM50Cm6NUy+hWxfEjbQWOtcDOSJcGKCg+m2hyUB/c56HdWkTacoi/agnaei+ailY0u44BBSdtq/+wW+OFvL/ArAVgvAS8GqgIYUKlR4X4IJtFjrPAlIhNv5QMCOGUQQx8LzQpgKcTibqTxa87RV7gfyFOCwkKpDuh2NrVKgwFP6IXZrWn3zdojhwNtvwwD1LqhtFmkTzTPallvEUUl0eLYabIu0CPRWcoruXARJi9BFb/K6T9jP/YG94JN60B1sCY0TDqQlg7Lvh91zXQNE2DXWuFHT3UZrbT29R3ySnMwJc0hIMUwNOJyTaYE9Nlanmv1PugHig00u8MjnWNwgocZRY/h0OaEUNRP/8oDrUI19kQgxDEdqxRdIuNNkNJhMpOdApkGMRYnywjfBlhUQVcGeWYQxS/fqHVwuzboTwNQh0l6QP7dj/QBMNNYNP07BKVxe2nIeH+rQ/sDCXpUD+dSDHAumnwXo7IeBxIKj2io0q41Hoi0/2sfdr3aLOIDSXqItfzsycKW8Tn3l8mWTs6wBxpDd2azjSCvS07N66Fru4iFSaVwpx/gPpm/9caCj4flP4Lqi1Q1I9GDUWtUOIW3Xs6ExudeklJtPEiRDCUh6wDlLTzXKkYhvrNdMjuNmLpkiRYEEvjVzYQs3kALw75Tphu6zqPLlgKWnFN02CoUvvv6t/uEBfK4UaoIbg6v3DRKOI20aJAcwgLz7WJqeUjzGLAbXcX3+oG4bpN0HX0IZ1qg0PN4v94pKkjRjjcJCzw0eth//tQWg6TpkDZ12HCZqjdB7t3A2BmEHC5YEQ17OzHeKs5McLN3p4oZVDEXL9fZsPQgetmIzm7kAJ4qnJLGuzo48Iep6dwpZmpe9Phe4cGZk1Ov3BBhsMUK7ZdYc5564lar8gKrInyXaOnrVH2wgCT4NsZZRZp/uvbRK231msg2q+BqINs8on/77FtErbTKRpKe/ziM9vUBPrJMP2QEMDdO4GdkNefNxchyipgyCfADYTlVFoQKzKF2/lOOjw2ApKi2DNWEmU8hO7m0DDQt3BSAE9VdBqYEdPnJr4YBe7IECn1/1swMElUB1vEBTuUnpkxWojV4UlSMFREcJFVAxhoX90A82MCjzPqLH7B0vmz7DwTb3OjuIa0X0c8Oth4FYwrh/1hjsKKFkUHYYgDSvv+znZHVUBp18yhRnhuDFyUGPn1SQaYcAVwYJExwDOBH2bCt9MH5lx1XjCF+LVp9EFeA640TWS/cQYN5FhCr7cNl2CszXAt5J7osZRKA9ZlkTtHJEhOhAnLQxY/ALX9/+6OIXB4thS/MwYPohg+lIdshi0Jl9sz4PlALTkihDOMuKNVg6HaH9la1/bShwyDiJO1+qMXD9QA7h4Wb9WAXSesXK86MLW8VXGi/u7ofqipF/1hB5Oxs2D19LB21XgRJSYtftH6T3KG4CX0pBZZBygJlyk2cRe9qqpjSni0CMfImW7HGEwMLRxK3OIBsCCm69DVSOFHjPQpbKvfm2ARXWtqveB0Q/s0qcLo1Pd14VgsHLtO/LxgF3gPgKMa8ndH9jwpSZD2HWg1QeKXIjlHmw016dD8EhQdE9tt+gTmumH98vDOY9OKUUamgTLlJdEnnKxOKYCS/vDkGPjZCFEs/1JZ9GbOWcK4UO2P0hy47rT4RXZoUQjvXQvMb2vV5lNF02bAd7wFbbueZRo7+oxOsopszdZTwGJZMxmYLH5e9BnkrYrcscd9BfLaa/C6iVvmbbBoJ5h0UJ0Bh8IYAN3OA1mQJTM9zyzaSxtCQQqgpL/E6+Enw+GHQ0Xv0APNounyH0uEOEQCBZgXI1L7gy0N8Ku4kxQM1VH+ku92wuyYDgEcaxbv+5gLLBpRxqFRhDuzzitqCl1+4YZrH4LRZkFrAUaYILWtFVyrT/ycrIPdp4D4dac6zG4uPaHVQsHk3l8/HgvHF4lyj2Y/VId3g9M8WoNlhwPyHZDTe7uzZrbSykFUfHipxE8TftwYySaWi9AzQDFwyRmFFMAzFbsOrk7p+H24CR46IuJUrf6eJysYFNGj0qoVltDmpo6klc7aubdZuACVIMYmtVPjxTNGIwRQ19bN5ViELFQNMC9WCJjLD9uaYIYNGryih6hOgXNihXuyu3u0c9ZlT2/lSKt4dKaiAebaYf0ATI8PBWtc5I41/2rICyKpZatD3BTE6aC+m8tLp4jvW28sisW8pgEOVcHb1ZA3FebE4qWeUu7FyGiszKaOv1PLq/ioP+kQeoZgY6EUwFOO9kL4UBj46R5SAM8WbssQDxCtvS7YKbpv+ICrk0U8q9ItJqiDqHtbGNNRapCo7xg/s8MRVm9MQ7lfNJWu8YiMykAXyGCZbOtaRwYnj0La6QRXBJsGlHuEFQzht4eLFEYvzNwDx7sPZewBgx6MemjqZLElxoLFDMfbApkaDZSND/78FR6RiDTeLj53jyqSg4YaRRedElfX2KhREdM98ho6Lnk/zjzR9P0436OO14M6tYdSyniULJ5HG8xUCskAEY4LtG8B9Hg83H///ZSUlOB2u7nttts499xzT7z+2Wef8dRTT6HT6Vi+fDlXX311wDNKATwbqPEIK6m9+36GEdZME3fsB1rgggS4bHeH+MHJRd8TFdjTvzievpEO4ZwfI0Q3XGbaod4jbjRLg7AkJ1kj29C63TK0akSXlnqPyOTuET8ktoLDAK4I/smNrIG0UtBuh7wgp7xPHgvb9sKcKeJ3vQ7Kq8HjFUJY0wAz5sOmEGN6JW5xMzM7RnzX1jSI79aBFlHQ3z7iSoP43q3udtNwiSh/UPHj5ih6huChNKhT17XNuRwRpGhKBoL2MohQ6FsA3333XeLi4vjVr35FXV0dl19++QkB9Hg8PP7446xatQqz2cy1117LkiVLSE5O7vOYUgDPdOo8cO4OWJYECXr4RpqoVYvTi4dZC985AO/W9H2colZhFa5v7L/VFqeFsjDFz6KBXHtwXVnaGWUW8xSjgdMvHhMswjXcmUmlUB4H43dA3muQmQHl94iC9v4ypQQKfgsFzTB/GhgN4ArwmZqMYNCB3w8bdp78+vxpsHY7OJeEt6YKj3APd6e3xgBXJnN4voNRV00VLlQA/GTyJMe5PWgBNDOdLJ4Na8mSaBGOC7Tv5LqvfvWrXHDBBR1bazu2LygoICsri9hY4UXIzc1ly5YtXHjhhX0eUwrgmU68HlZPE3PtClrgx4XwbjV8LQl+lQ0/LYIXgqgfdPphfYOIte1r7l8XFovoDBOWC7TZH3qooLBFtNuq9QQ3gDUc9jYLS6f9Yj/9KLS+Bw2HYGvbn1lcEviawyoYP4lDyWCzgKNZiJZWC3On0NTait3rh/i2GZJeH5RXwZBUaG6BVjfYrdDUww2BowXOXQhfDFAx+vUpNGS2Co8E4KaUQ5yLi/0hHSae5WixR2OFkrBpL24Phb67ZFitwsXtcDj4/ve/z5133nniNYfDgd1u77KtwxG4JjgoAaypqeGKK67gxRdfJC4ujgcffJDGxkZ8Ph+//OUvycrKCuYwksHC3vbfnKiHWTHw6HCR4KIA30oXLtDvHepI8++N9qLl0SZRamDWhudWLHWLxxSraLDdPTSXbRJdXjqXTky0iPUDGENsJ+NvW3eWUaz9UJRq9VoKYOFxqE+Dw09DY1uSjLvtZmH3ThH70l0savlQCTvw36IFbacEHp8P1u/sWwbSksDn71n8AHbuh/Q6iHdDdfTG0jRfUUrz8iM4v/YXqD/OPqowMopG/oOf0G9Q3BylhlexMhcTo6KwYknohGMBBpajsrIy7rjjDq677jouvfTSE8/bbDaczo7vtdPp7CKIYZ/R4/Hw8MMPYzKJOp1f/epXXHrppVx00UVs2LCBwsJCKYCnGykGuLjtLn+aXUwhiNUJV2kweSKdBWSoAWw6EQdrbWvcvMPR+4iiGTbR51QDmDXCalrX2DXLNMMIWxtFwoxbFd/Sgy0dMchFsaG+Y8Exl0hcqfKIeYmRxl4Cq//U9zab1oBlC4zNgpgkOLxcaKBbK2KE7Ri9fccLVQ1MPR9WvxPc2lISYd2OwNul3wzbopNM0njPPhqv3EZjzhpaTcLKUxKgBWihB5dskFTzPNU8z3DelAJ4BlNdXc03v/lNHn74YebOndvltZEjR3L06FHq6+uxWCxs2bKFW265JeAxAwrgypUrueaaa3j++ecB2LZtG2PHjuXmm28mIyODBx54IMy3IxkUni+F61KEaLXjUWFxHPx+dOiTJYrbXB2dvVaTrBCvE5l/xW1W5dwYmiuasHTPzgSRRu/2CwHNsYl9mtXerUuvCmmG8JJo1jWK2sD6lsDbhsqmHJg+E7Zt7nu75lY4cBA4CKwTzxkNYDdAZhbojbB3N8xdBOuvRNxVqHR1ESngCzKuuTA3OPHTauFoSuDtwsS59DCVMwLcIPSDJj6illcZwd9kRuigE44F2LfX4dlnn6WxsZGnn36ap59+GoCrrrqKlpYWVqxYwb333sstt9yCqqosX76c1NTUgGfsUwDffvttEhISWLhw4QkBLCkpISYmhpdffpknn3ySF154gR/84AcBT5Sfnx9wm1OBrVu3DvYSQiLU9WasaSXh4UMUf9+IN1ahabYWbaOKqofEo14iYsu39eh0JYOSqqB1qHiLGrH0Fmrs3LotmCkT6xpRNeCcrMG2K/Ti/iarKzoRI5eOJv9Y7AQQwB73dYvH3o5idm/+RnwZ+9A2NuI3m8FzN4Y6Yf2qGh+OVntQ78NRV49meDqWguK+N/T58Hi9UZvJ3VwW3Z6lNbwIwI7Gc6DwV+CLTlzwTL9GRIZwYoB922MPPvggDz74YK+vL126lKVLl0bujG+99RaKorB+/Xr27dvHPffcg0ajOXGSpUuX8tvf/jaoE+Xk5GA0DvAU8xDZunUrubm5g72MoAlrvdfVwBv5ZD/QlsJfNk/ECOtd8EIhUBGx9RmrEOO9kvRoj0e2P6niB5vWAjpnaIk0mUbsegNomiLfuHp4PXb2RexwuiYnuvZ4XZMTRr4g3J4F/0aprsa+LbhYpm1PIcyYCIEEMD0VvdELUWrZOuKOi9l540uoSnQbCCgxW8iaepAkvhPxY58p1wiXyxVloyQcC3Dgm7r3mU3w+uuv89prr/Hqq68yfvx4Vq5cyZIlS/jyyy8B2Lx5M6NGSZ/7acVXE2HDdLgqWWR2vtRW/BxrEJ1gIolOEa3GIix+zLLDaLOYDB9qFulxl+iCE6sTiTWRwuyGxH/BjijebRcUwhfPQ1o8tITQRUdV4fAx4Wbti7IKOPYQLPoEFm4j0ncI7rkNmJQRYe2rJwMz04LaVsGAkdFhnUcSKdoFMJTHKSaAPXHPPffwr3/9i2uuuYbVq1dz6623RmNdkmiSa4e/T4TbhsDfKkWN3INH4M/lgfcNBa8KmVFocuz0waF+xPB2OERGaLUHDECmQWSkhoUKi1bDsGdg68bw1xT06VQhZKGODqprhOnjISWh7+38fsh7WyTzLFoT+LjTj8Lo38DcwKULtfevwUVBkAvuwMhYPJTjpQY9Q3vdTs8Q7JzLcN7ATpi1jJIIEeoswHBcpv0n6DrAV1999cTPL730UlQWIxlgVqQISypWC88FV3QckDgdpOlhf4uoay2IULKJAgwzivZakRh3tNMp1qcocNwtRpMvihUJOEGPNFJh3juQ93H/1xMKvjAts/zDoc0O3P8J5GaBVwcGF+hVcJjgUAq0GCCmFY4+DzV1UPQ0JD0O1T3fSKhaH1WLX8NPMzYW4cNBC9uCWoYGK+DDRzUmJuKlBhUf7RfMWC4liz+hIxllEPpJSnri9HCBykL4s5mhRlixB35UILIqrRrhFg2VXBvoNcIyq3QL8ZtpExMUIjWSKccqkmuCmcoeLD440QG72CUeQwzCKnSDzwTaVGMf70GBnRfDyMNQcCRy6+oLuxW0ITtuBFPHwcZdwW9fWQ2VvxQ/azQwexLs2gnDsyBrERz5HI7Xidc9Xkj8E/i+A3Xmkw7lHduMT1MLgIM8AKzMw9mWBasnEx8N+Om4uVGwYSD9hFD6aaa5LcHIRA6t5GNhFjYWoyd62auSM5cw/5IkZwQKIgPTh+jl2OoX5QvmEL8WrX7Y5xQC1Z7RudkROfE7x94x/b0pCvV7nbFqwaSB2Xbc6Yp4D3Pswjqc362Dy1AjaMxgfgiWXB/ddbXT5BQitmhG6Ptu2yuK4cPB74d9hbBgOpRXiNZux0u6bnPgAOgfFy3gOuGZ2EjjXScLr5N1qO5kDIzEw3F0JNK5MYCFabjouSynlXysLKCZzTTyYXjvSRJF2nuBhvKI8iDvHpAW4NnMEAO8Ok7MDXyjUghhXRgW1r5mUfu3M9L9NlVYtBGKD8OccaDoYF9udIrYx5lF9xkQjQI2NnHCjtnQlrVoUkSXmhSDuHXc2CgMyHwfDJkMA9WM2e2BkkrR27M1hJuMkVmw53B459RoIGc0bN/X9zkrq8H8Ckz4BuxNw5fRwoHt38etP9rj5oqhCjdVgOjoYmA4eoai0oKTvmOQ7a838TGHuZgkvk0cy8J6e5JIE840CCmAkoHEpIWvp4nH3UPhlQp4s1K4LkMhVhf5koLco2Atgry/id8PrwOdCdJnQD1g04AjAifNtYnWapsaA4cgWlURHzwpRqhC9mqC7N0cGQqOwezJcLwMSquC22fXAZg3DQ4dhara4M+VEAs5oyAvyAzXo8fB8ku8c+6l5JW3exW/k/Hj5ghuQncnN/IBjfwXC7nEcTlp3BfyMSSRRMYAJacDqircatNjYLIFHhsOF+eH1uOzziuGzy6MhYPNXQvbw6Wz+LXjdUPWLkiZACW/gSQ7uFvA2wree6A2BMtwqFFMew9lqkRvjKyFNe/3/zihsnEX2C2wKBc25QdnDa7bDiOGwogMaHCIBtmVtR2TJCwmESvUaqG6Hqprwa8GL37tNLfiuPBlakb/N+S3FT5+mtlMHFcM4DklPRPOOKSBtwBlDPBsR1EgxgaFx2HqFeBphn/mwJOjxRT5YPEjxGRsBGrrxlZBxfaeT7L2Sdh6O5QfhKKtULoXKgsh412Y64HpvWQBxmpFHG+aTdQROr2RET+AgkSYd0dkjhUqTc1CnGbmBL/PkWLYtBsOHBFDcMdnC2sSRJu24+XgbIF9BVBVBzX1YS0t7idFDPmw73E00aCF3QN+Tkl3Qq0BDMdl2n+kAEqgrgHO/aa46G3cJeYG3pEBB2fBK+NEQXuw5DXAgjDH/cS2wsxCKHqsrVdmCOz+N2y4G5RO9XGJOjF9fLIVfG29Rbc7YFMT1EU4juixi5uJwaKkAuLCbP11oAj2H4Fx2eJ3g6FjgkU/Sb2okLji+Sc9r/emo/OFmZATgEb+TTUvokbcLy8JnjO0EF5yBrL7EBSViMeF34Vr7hajcfQauDEN1k6DmL6HVZ5AofdJEL1h9MKiTaA8ApufCDzYtTdULxQ9DvPzYb4bUlxg04oM1UjEC/vClAM5D8Ci5TBrgShXGGjqw2wx1tIKjQ5hEV6wQMQX80Nsit4LiqqQscKP3jsERRWtELX+GHJMcYyfPAJN2VjMzePRqJH7vHw0cIzvsIfRuMKIJ0rOHmQMUALF3TrA/O0/8M9P4Hf3wbeWixmCb06Ai4JwLamIbEqFEyV2faL4YOjvIK8w9HX3RM1xWCs6xTP/dtg3gajd5y2IERmp+U5h/e0eAgxpO/dk2PmysKQyhoiSgb6K0A16yMyEjAWADpr3gjEOfGOh+Qs4fEC4Jzuj04ljLprRc32f2STELRjaO8vsDtHyDgLjunom6WNxLRxOw/ficY/0gKYY/V4HU4doUFHxTJlI4Zpmmm2R6k/pw00h+5lOCneRzsMROq4kOE6PGKAUQIkoYu6O2wO3/xSe/zus+h1ckAmZxk5Dc3sZ5jrJCo3e4MQPYESD6HEZDY79CyYmwZ606BxfRYhfugGOdhOatRNB93/i5yM6SHXA2O2Q90bHNkPSIDYeEodD8UIoiKOjU1jnOr+xsGgdFH4GadeDYbuI1RYshwmfgbsKcr8N3l24Sg5jTEqDmFxYNwXMPpjZJo5leXCo5zII1WrGe+5wVLMZw98q+/3R9IRxdR0pq+tOel5BwbCziaybU9m/KrLnVPFgZW7gDSURRpZBSE4XulsWndmxH752B2x7C26rhA/WgMYOKJC38OTtDzbDKLMYPFvjgQMBWqEVJkBmxslF1f1l4kRouRb2BOh9GS6LYkUPVYCynu50FdFCrJ0KG9TMhVnHYet6GDEcrD+GnUHGIvPmgmEmFOuBTg2l11wsGnG3GMA8GoNLA5WAp+3cLVrImwH4YdZB4GQBbLkth/2/+yeqYRvDXriEhM8TUbQ6SM8C3SLYkwm5a2DN22IHf+TdyaoWmq6PYHPyNtJ4gBjOj/hxJYGQFqDkdKEsQB3Z3gJ4+g2YNwHu/3fH85l54PNB1jjYcCWgEZMW2ie3g8i43NRLbGpKCdS+HHnxA0gcA5uiMA8uVQ/J+tDKRNrx6mDTdZByGRy2QKoGxhggRS9a0G3vYVjwCRRw9zKpr8Vw4l8FeqnJ1IBhEswzQVXRCUvQn5PJgV+/h2oQXoCj334fy/a3MD8TD2WdLPy8pTBhAiTtFUIYYRFsvD+Lkss/iOgxARx8CbImcBCQdYCS04WDRYG3eeZNUUTdmXbhKq+EIRdCaQ+Cs9MhXIQZRlzFDRizCkXjaQXY9Ov+rrx36gogbQYUxUfumJlG0CuQ3xx4276obEv4qPCIx8EWmG0Huza6rd7WTAImgd5L0y/+Sc2SjTizt+M3d/UAaKss9Oje3psGsXEwJxXWPRXRpammyGXQ6hmKmUmo+BjKkxE7riQUpAtUcrpwKIhOHYeO9r6d3w9ZZYAKpd1KIFyqcBHWOfFl/AE2hT4OJyxqKyG7KLICCOCKUjbpxiaYGwPrIzDpIgBqhpGi2/6JJ66sx9c9GY0Y6KUGtMEkhhBHmNgHi7B8fyLNtj1hH0NLPHbOZQR/Q5EJ7oOMdIFKTgdUNTgBDMS+P0NDE8ycB/VfhUPtNV5+MPphShH6Q9X9P0+wlJRD2m5Y1Aj+ODg8Espt/Ttmsh529eWm7CeqGlUrUE3U4vxuEe6hFb2KH4B7RBnWvqYrVGdFfnEahZHzrLjmnkfN3TpqxvxXBAaVrp+F1p+AX2nBqswklkuxkAuo1PAqydyKldmRX5vkjEUK4NlOTb1ohdZfGtrifJvXQcxuyJ0I1jFQvwvqqqDOhL62ARbmwuooTk3vzNYNwAbRNDr2Z/071iSraAkWzTDF/mbRqWaHAxojL4Kur9Vx7HuP4U443ud2DfO+IJ4pPb9odkPCFhiZfXL27pA0UQ5S0ru49obiA/3uJvQVRqwv/Zj0CbfiHd/E/jduACAp/wZi3s3F/ptxKBPsKJ/NRtF11KbaWRryOSXRRMYAJacDR4ojf8zGpg7x6c5gdEtpdcGszVBxDky1iakXflXM/2sIQmhybbA1ipZfO/U+KHFFRfzUGA2qwUdr+oGA29bOeIeMi+5A/0EPl4cWA2y6ANwXwaRyMLjBWgKqA47PgprH+7fQcReh5Gkx7IxFn29jfMMbuGdVYdiUjPk/beUsq93wQjncltG/c0miiIwBSk4HCqMggH2xbe/Anq8dbxKMMsGOTtZurDZw3E0DHGqB6TbYNgAi2OgTIr0jsuc6vOVemkavD25jBTR1enot5mzPRt3dVvTP8I7X5s2FdR+HuUrAUwwLTeDXoqydhPk/aR3C15kfFsCjRfD5VBg/CF13JAE4PWKAMlJ8tlPYtzsskjRNGweOfmZQhovfA4e7TUto8IkxSAtjxe9WjRh6m2aABbF4bUCMDlDDEz99iNauWRHnjtfBlMhc1Fu/0UjVy3l4LaGVbZT+4kXUILvfdaGpIoydOrH+M1j9POx/EzL7WHOrX2TQvjOAcWVJCJwevUClBXi2c/jYgJ1KO1jiB9BSKW73uidx+hBTIRJ1wj27ts0aLHe3/XF4RdF7qHV/U21Q5RYTNdb2YWFmm8R8wVyb6LKzu81CndX/GkbvQjjwx5vwWetD3lfrtHfPPwkOnSGMnXqgpg4mbIDjF/S9XYtfJA8NZiNySQ+0T3kPdZ+BRQrg2U64E8JDJc4uiua7MzMHjAZYu72jH2WkMVghdlLf29T0cffpC2FdWmBebMeopUyjsCrrvLC32w1Au1t1YawQv86DiDc1iXmFR1o7tlUQJSWlvV8oSn68HVO2B3dqOXWzPwhL/AAS/3pRGHupUBRmE+3JU0CrF/Md44eDJg78QWTt/uwofFAD9w2DxbGwuQmWxoshxxJJAKQAns2oqujyMhDUN2GpbxJZoF4voIj6wY27xHPREr+0MZD8Q8jrR/3e2kbR2m1jo7AY+2KKreucwQ1t2bFzu9VHDjOKIcLQ+1xCoyIabvsRwnekVViWMTpo9UFRh0u3+fYaWqYcpvLaX6Da+1dLmP2rpzD+NYy2ZCZv8JZYYjxMWIyjLgWb2Q6bs+mx+D4Ytjrgyk71gwYFjsyBIcbwjieJADILVHKqc6xUlEDMmQKb83u20CJN9xKIlGzQ2ejZP9lPTDGg+1/YHYE/rCOtYrRSX1mjQ42iEXhPrG8U3V62NAkRHWYK7Fbd3wJ066Xanhxj1sCiWNR1DfjO8bP/d9eBvv8upKGvP0TsfRPC29mrwMhbIKcG1BpY837XGxutFjQK6HUwdDjsXoit3tzvNfdIrUcK4CCiA3pp3NfnPgONFMCzmd2HYNp4Me2hyQl3/Aw+2xj982qNMPc2ULNgkxm+UGHuFbD+B5E9z7hz4XiELMsso+jW0hfZAUTtYLMQPy1wzNX7dsHQ4oc1DbhubWLvHy4HbWRuXuxrpoYX+wPR63TzSGCk+D1nKli/hMYq2LcPps6EQ1dDqxZ2hnp5DAG3Ci+Ww89HgCWcTB5JfzG0PULdZ6CRAng2U1QCX74CTc1iGvjbf4Cr7oKP10X3vDkXQOkEOOrtiK/lG8EcCy0NYI2HUYvA1QjWq0TvUO0a0FlAOw40lZC3sudjJw2D8VdCazZsVgjsswySei8MMYj4m0EBnQLNnSzWYBJl6nww2ix6o4bTTLs7fjCsjQVN5Cz3uos/x/zs8sgcLD8duEb8PLYKLMOgMcqJUEYFfpgJiXqR4XtOhFvhSYJCWoCSU5+EWPhyC1z+fbhkMWg0Yip4tNn5LkzygelSMQUBoMkPw34FKR44oOs2JkgF5nf8Oj9V/DvxAkicBYX/hOK2QaoTroG8YZFf84G2htVDDGIMkkGB6TEi5uVTYXOQcTenDzyRi3dqdvqJ33YpdbnvReR49o0zAm8ULMOMkKQHqxbKzLAxwGis/pCog4lWkfzyyHAxlFkyaOgJ3aILVjB37tzJE088wauvvtrl+ZdeeolVq1aRkCBGoD366KNkZ2f3eSwpgGczly2F7z8uklLe+XTgzjv321A6E5zd4mVHvXA0CKutyAaTn4VdbeJp+R7MawV1L6yOsPgl6YXYNfrEv6vb3KAtPlgTRrJJqVuURyyICW//Hkh5/Xrqpr0Pmv4Ja/KXN2D7RQQ/v2DinP0lViuE71CLONcTI6X4nQLoCd0CDGb7F154gXfffRez+eTY8Z49e1i5ciU5OTlBn1N+U85m3vsCXnyr43dtdJMGmkeeAwtXwoZpQuzCpcTbIX4gSgTq42DD1OAn0QfCrqVpqgZ8fiFaLT5YHaFJDR6E+E2IzABY629TmXTZh/06hqF2KEO/9i2USOUh2TRizFO0sGjETYRRA+saRQLpG+PhrqHRO6dk0MnKyuKPf/xjj6/t2bOH559/nmuvvZbnnnsuqONJC/Bs5lhb0+LMSTDsPNg9Dia0gm8LbHq1731DJS4dffVVUBCFgmWNcnKNXbhkGkXx+uEW7Ds6ZVVGI0E2lPrCAOjf1xO/NXxXqLFqOEpjP9UvQSdcxEdbhUt7pF64fMOdbrEwFmo8J//faoAZ9q7W5XATXJksvguSQSdaMcALLriA4uKe2zdefPHFXHfdddhsNr73ve/x+eefs2TJkn6fU3Kmop0HqWPhuBeOA/hgvR5yXWCJg+YGTphU6WPBmgiJo+HgJ1AX4hT35kb8CSo0ROECVRahDhIGBeo8oih9IChsiWij7dS/30Jj6m58Q4tC3teVFIGRWDpFWMsuVUzPKHYFFD9PLOinxIrykQoPlLthjl24nLc3gVaBmXZh4blV8KoQpz3ZtbqpCS7Nh7+Oh/goZphKgiKaMcCeUFWVm266CbtddFBavHgxe/fulQIo6YMtKlR0c0XGaMFxHriXQKoWslvApYdtba8fBubbYW2Ik7bdTlRtlIrdD7V0ZGEqhO8GdaswJyb6cat2PIgEkf6SbYIMI5ZfwrCtv6XwoytCjgeOu/wvJz+ZqocxFlHLFyheqUHE/CrdUOvtaOnWGzYtTLOh7m3o+LyTdDDGLBKL9nSy+jYHKD9pJ1EnsnWlAA460YoB9obD4eCSSy7hgw8+wGKxsHHjRpYvD5zNLAXwbGZfDxepRh+0X+sqvFDRw9dy7QQY+zzErg7OVZo+FkYswVfXCiUR6txv1cAEK3j8YoisXhEus0MtwooIl+0Okb14dICswNoIFOkPNZ4QEVWj9i1+Kic1XDGVj0a7ptt2emCkuaNLzcJYUVbg6uXYfoQFXeERdY4BO+ZYYXVDVyuh2gtuPxSEHjtUdQqtl5VjGjE23H4ykggyUBbge++9R3NzMytWrOCuu+7ixhtvxGAwMHfuXBYvXhxwfymAZysevxjAGi4HvGCZD/OngX8frH+h921TJ8CeaViDmb0XLOMsJ1sGQ41ianudp/cLdXeGGMSFXgFcfojTwYd1kVtnIGwRyENb1wjjzLC/BX21iYnX/hvvkEbKb3yZhikfonFZyF75R2yvjKbyl+9iPJZFzGtTaLhlM0W3/S+qxofSPhEDYH2DsE5b/SLLcrQZNjVA+z1Bql4IXXfWNoqJFsNMYqKFR4VdPdxkTbWJNfdEGHHIlttqOfDrm/Gbm0jlXjLo50xCSb+JZh3g0KFD+fvf/w7ApZdeeuL5ZcuWsWzZsqicU3Kmsa85eJHojWY/rDUB02DRo5D3k47XRs2DqkJoKIcd/4Lc8yGSg+C3OmCsWXRUaWm7aBa7xGOmHUwacQHe0OlCO8suYkiHm8Gug2yzENHSTi7PDIOwdrrvGy0ikbLvVSHJALRg226G7WDEQvYf76P1lu/QMrGImJ9kAz6S/+9SlAYFzSEfCdtyiX88D+d1JV3dvnNiRF1jqVtkqqqATiOstmY/NPvotc9jiyqmWwCMNnWdbRivE1mbnsi1vHMvc3Pkofvxm8XNUAW/QoOFdB6K2DkkoTPQLtBwkQJ4trI1yLhK0HT7+ppvhKEK5B6HykzYGoU0ygS9KFDvTrtlON4imlhXeiBFLxIl7FqYaheuzp6aUJe4IdkgekkOBHkNMLnNLdyTtRQsPeio4lExP5uAmYQTz2m3dBUfpdiH7ZdpQvTXNQjXpcsPo8zis+3sTt7U9rnOiRHZncNNoj1cb21cD7XCgk6WZZNPTLjY74xIuYr7Chf5q77azaXro5LfoSUeDUZi+Rp6Uvt/MskZiRTAs5UtERbAdfGQ+jzEAVYv7FXbXGjjID8KltSCmI5RQb2xr83FqwUOtwllnTdwkotNA3q9sC4nWYXl4vJDhVtknPbXcu7OLqfoMjPBAg5fR+zN2+08WUbR4mt7t6xRo9L/PuKrG4TlvLnp5ON3p90yrvCIptwaRDJP53FOC2JEPNmkiEzXQy3C8lOAsdbA5wiC8h++1OMACR+1FPM/AGi4CyMjyeAJbMxHQ++1l15qaWE3dgLHjiR9M9BZoOEiBfBsJdjMumDxtmWUnhgI3nbxLncLC8esocnTjL3VKKyHYld4tXVDDTDCLM5XEmSyS7DnyTaJOGKzD0wanOMUrJ2zGZP1MLttrNGmRmiNoBC2N9qeYoU1DaIe0aCIrMbqNmEZ3tZZJUEHOVZhUWkQH/WaCGSu6sJIH2l3P48wC4He6hAW4jEX+Ns+n86WbYRKPgAM5ZkBt/HjpIVdHOYrWFmAldnEcD42FuKmFCfraOUAreTj5jhGRkoBjABnVC/QmpoarrjiCl588UVGjhSd3t977z1ee+01/va3v0V1gZIo4PBGwQXaC4UdVpqo0GkWgljjFdZOIIxKh8U1yiQuuL3NzwuVdIMQEAXIMIqLdifr8KR81SoPVLW9btLAohjY7RBNriPBdJuwND1qV+vWqAjBb19bbRBWbDhoFeEqrgzD/ZvfJnLdW7wVR2/Kd+q3voZz1G4apgTXBcfJGpysoZLfAz4U9KjdppC3so9a/kYCK6Kw4rOH02UaRMAIvMfj4eGHH8ZkMp14bt++faxatQo1WkNMJdFldUPER++FRLWnb/FbFCtid7PtwgqaYRcDZRN1EKsTr5sjkDxS5hbuxmK3sMBCKahv9QsRGmoSyTWRYJsDSl3i/c3sdEyXGp1ONN1Z0yAEeKQp8La9kd8s1j8AKPU+7HvDad7tBdSTxK/tqP1clQQ6LMBQHoNhAQa8iqxcuZJrrrmGlJQUAOrq6njiiSe4//77o744SZR4t2bwzj3LLjIJhxlhfox4zLCLi+bcmI4uH+sahSg1+WFbkxgou9Eh2mJ51Q7XW3+YYoUj/exXudspkkOm2/q/HhDWZF6DyFTNiVDNZCg0tE2rSA0zIlPfVss3QCTfeD6jHv0zmpbI3ISk85C0/iLA6SKAfZ7z7bffJiEhgYULF/L888/j9/t54IEHuP/++zEaQ2ucnJ+f36+FDhRbt0YyVz/6hLxeVSXnXScDNStb1ULrMA1+HWidKt4WB7Z6FZfOi3FtkMXmna6nTdO12DY09us+3TlBg98Eth3OiDV/bqlvJlKzzZ3jNWib/JjynSe95kqtwp3+CaDBcuBraFsiO1Hdr4cWqwfrsfA+GJ8V/Aea0CmgDICDSPGqxDySTcaBpzn+p++CJbzaVrV5NNQvoaRiHiX+vv+mzvhrRAQ4XVygfQrgW2+9haIorF+/nn379nHppZcydOhQHnnkEVwuF4cPH+axxx7jgQceCHiinJyckEVzoNm6dSu5ubmDvYygCWu9B5uhdFN0FtSZBTFwtBWlzou5yA96haYJGuwGC9CEsTq8w9q9Jpjd1vllbUNorsFELZgUrFkq/DeyPkVzoT+4obh9YfCAW4t12B5wuqFiEvgU8GvB6IXcQxidazDu2C62X5QIeedF5g0AzPWg0R/GSirExsA2vWgPFwJap3gwL6b3YvcI477chc1mY+qc/3LktZ/TMPmjkI8xxvIsdstSGNL3dmfKNcLlcp02Rkk06VMAX3/99RM/33DDDTzyyCMnkmCKi4v54Q9/GJT4SU4hPhgg92e9TxRSt+uMS8W+3Qf0M/mmPaNwiEF0banpo5WYAgzVQVoe6G1QuQuKtkHG1cCc/q2jJw61iE4oLWGYPsProPFXUN8I//WDVgvzL4PtH0Li/ZC1FfLe7rqPtxEWfACqF/adD7VhWoPJOsj4ANa/0/X52DTIvRSOz4TiEFq2GaMfR1PTtVSv/BT8Go5//aeg9aFxWfAbQ7MADYxgDHkYkGOUIskZlQUqOYN4K0zTK1ScvugmbpS6RaJITZugaoEUHaT4Ybe/w206xAWFa6Gq06T7TX+BhRNhdYSSV9qp9UCuPTjLZ3gdpH4IzIBWM8RUwOr6jtd9vg7Ba3oAjvUQrl/3ScfP2fvBdCuU2gh5zKdfhX2fnPx8Q7locRf3Pkx/qKMheiCGGkVJxcJYON4KRRHqq6pH5K8o4B+iUjf3QxyjNpx4OVTxA3BzDEVeBiPOGVcH2H38fOd+bJLThCq3cBsOBJnGwIXq/cWvwoUVsP55aHWK7NKyFhg1F9Ivh8oPYGMePaa8rr4H5t8BaydGbj2utvZpC2K71uV1LuVoR+eHLWvBlxfcsf0BYnKFRcC9OCdMwVp/HZSGIO5JCtT0UZ9XXwYVT8H8W2CbJXACUkGreICw1CdYgpvXmKoXrdLSDeK7U+kRdZ82rTinXrRR8yeo7PvoKtwJpcG/xx5RMJCFh1L0pPXzWJLOyFZoklOPz+ojNzE9EAORCLjVAZokmJMD6z7ueP7wevEIxJ63Yf7Itn6mEcKPEL8pVlGy4VNF15NpVtGGrT1GGOMQVl6Ese7dCQlHYdHFkDePoNL6lTcCb1OyB0p+CPYkmHc1eBrA2wr2CbB7aO+1kKVu8RlcAuw1QapBWIcKsKEB3MBwo6jDPNQiajGPuUSZy7wY0blnX1fx1ByFiRPexDvNTfWt71N22e8Cr78TOpIxMIJh/BkDWWiJCWl/SWDOOAtQcgYwUNZfjjUynUmCwa+FdctgUSbkvRjavvVl4NkOGQuhJAJjiTqzs1sG53YHjNfDzL9CfTkYohhzqq2HvNdh+lDYNuzk17XAjE1QXwgps+BobfDHbqqGdU93euIt0Jlg9jXgmwm7NR3Wrl0D00rAsQ8++BdM+iOs7+QVmGHH4XZiO+o92U3a4u/TlaxU+ND/V0ui7YKgBFDBQDo/Q4sVG+dgJoKWv+QkZAxQcmqhqqJ910AQN9BfKwXypsN8M9R/AXv2BL+rq0HElSLJPDewH9yTYY8GprhBXwRHP4V9O8U2hw5H+KQ9oHwO3Hzy8xP/AxvfEz8fCNIF2xfeVtj4MvCyEMOsseDzQlURrPXC/J/AiK/AwW5ugS1N9Ld6Uv+plaTV11G94K89GrsaYhjBX1EwEcO5/TybJFikC1RyarGqqqPfZDj01vE/zSA6tLRP8NYg3FfTbcLdWuMRRe9uFd+uJrT9rDvvc4FrJ0JGJizaBXl/DW63ne/DFGDEebDXKBo495fqt+DgatDowWSFDfX9P2YoJCdC1kgov+Lk16waiJsLvE9U/OHeVji2s+P3GddBXhyRv8sQKHU+shZ9m4y538FxyQEK7v9ul9fjuIxYLo7KuSWnP1IAzwaKW+Hewp5fyzSCRdN1rFCGQXRbGdWWVu9VRYuu4WaR3alXRBxH21aLN8IkEh0SdKLLy8fdBsoOM8HGRpqnabHv9kXnWhjfAsOrwLoL8teFsKMfdr4LvAuKDqY8CTv7GcCMzwZWg98DzfX9O1awJMXD15ZwPMZI5pAR8NIYKOkUO8vUQUYrNP0b8oLrnRkRHGUwA9gS3dNo16v4V7SgwUIsl+GnhSS+g48BcsVLuiBjgJJTg1YPfPuAELCFsSJV36YFjQKoYirEOIso4vapQtQqPWJafPep38d76ZW5PoBrdXUDTLVh3+4QfSbTDWKfSOaATDwEa57t3zFULxQ9AgvvgsJ4XD43RqsBCkJU7P3zwPgWuCI3+aBXxmfDw7fDsnPBZKRy61Yyp0+HT3eJ5JEYLUwthSP/hA27or+e7uz/HOYuYyAub7raWOK4iuG8jJsyFGzoiXCpiyQoZAxQcmrwnQPw3zaLrLcpCvnhtY8KmiQdaEVbNKU9Rb6/XVM6E9MK+UG6PAPRUC5KJAD/8LlQc3PX160aSDeK+rbe5gJOaIUtA9C9+qZl8PwjYOh2r60o8PeJounB0RIo3AuZWeBIguPlMDxD3AAdPgY79vd8bKsZnBHyV/u2gmZO1DODbb8difXWZZAOBtKjezJJn+gJ/b9bWoCSyKCqYt7eo0Xw6gAVvgdCq9AyUoOlcyJEmxEaNDEumHAUND6xY2k6FMXDpEOwNgIJPjPnQ9VFMORz0A7D21QEmQbY2Cqmy9d7hct4TaPo+DLbLtZf4hJWdZIWtB9D3quBztTPdU6CP9wPsycLseuJGB1ckwqkAtN7P9aPfgVPvNT1OZMRVlwIC6bDsCFw431QUtHz/oEwxYDHCQuqIS8pvGP0emwN3JsJx10wzoKyLAklvfeBt5KBQwqgZOA51gr3Foj08VqvGJg62OgQNV6bmvBP6tShJK9BuGQVROKJ2w9HW8HZy5/NsHpI/QA2rOl4zmiABedCVUFk1lpyKZTGQNHlgIKdXKDNCtrpFJPi69rcoS3qyUlFSfm9i9/0mbBja+CC9t6wW+G398JVF4DNApoIjIOqa4CdB0629lpdYNDDBQtgSAq8/BhcdCt4urmCdTpIS4Li8t7PYTDBvvMiOzy4neVJ8JMRkT+upN/oCF0ApQtU0j8yjHBhIvy7VsTZWlvFaJvBJNskrJGpVvB1c6l1dslaNKLx8qJYMWvPqBHiWOsA5RjUvwybSrru73LDmv/0ff4haeByQU1d79ssuh6Kx0NVe7P2XqwqR4A/6bIMmLUQNq2GYZlw9Lh4Pnc26ObDgqFQfQA87tDKIIZnwH+eg3HZwe8TDPGx8NGfREF+Q5P4vaUVNu4SQj1EjEDjvHnwwk+htkG8brUAKpw/DzLTYfkP4ONeEo9i08CshYMRzHzSAvdmwQ8DT4SXDA7hWHPSApT0D60CX0+Fy5KE6Fy9R0x/+O0oeL9GCM7R1vAmfoeLXQfNPojRYcvrQ0Ca217rHhec44W9L0BjZejnjrWDehckuCDuZSgohIXLoO4wNNRBfQPMOh8+nUvI/TN7ojAeqq+AKQuhOBEm/Fk8v+Pr4NMCo4DzYWI5LNwF2z6EFldXq9BkFFbe0tmw/wgUHodf/1gITbTQaiEhTvxsMcOS2Sdvc9Oyk5/z+aCoGC5c0LsAHt8F2v+Byb+HXREIAo4wwePZEKsVbmeJpB9IATzTUBQhfgB/GisswEQ9LIkXz31UCxcMYDagwwf1HhgRRmxmohY23B7+uRuaIOYPkHYhtF4B5U+BYxzsOxd8GhhdAzvSIJG+p0qEQqMRdrZ1eam5HfT+NvHrxJ40IA0MSyDFBUNqoPU9OG863PttSE+OzFqijVYL2Vl9x3GzZ4HvW7ArQp/vkVa4Zi/8KBMuSIjMMSURR1qAksEnpof/3p0DkJrfmTIXTLH3noHaG4uqYdMT4Z93ai7EjIPWVFg/HLw6MPwfbO/0Z3YoCfBGcXadBjx9WJZuPZTrQY2Ht/4E8+OisIYooariZsvvh8vOgQvng94Ax8pEjPLi22DxTIhbCn+OkPiNNMHiOOHN+GpC7wlAkkFHT1BdaLsgY4CS6OL2w2+KB/acmabQxW/hYcj7TWj7jB8PSWNELV/pHjDHgisVNo3q2Mbdyz3mukYRq8wwigv7oZaTayCjxSgzfDFVnPt0ol18tFoYObzj+VFtvUePfQpaDTQpoBTCbkfonYhitPC9DOH2TNDDkjiI14tmDFbp/jyV0RG6AA7G/6gUwLOFEpdwHZW7YXEsXJwousNEqzZLhxC/PWHUGB4dBwt/BBueBE8QtWiLroLjU2F1m5tXfwHEvAyuEIqgC1vFA8Rf4mx7/1rHBUOsFj6YdPqJXzCY2yZsJAIvjIU6N/yoEP5cDndmQI4FXOB56DD6eKOYGnFjKnwrHVIM8GEt7HHCYz0k/kjxO+XRE3pUXQqgJHok6sSg0gQdzI6Bc+NhWZKI37xfE9ls0UQdxOvCnwd4zAvHRsCi+2D1T4VV1xtaLeyYJ2Jv7Xh0sONb4Z0bRIcaYwSSYgLxWDaMPkvq1qw6+FqiaJT+P0PF1W6oiV2zysgdMxl+Vwq3DhHiB/DtIR37+trmLM6PHZSlS0JHT+iCNgB/cSchBfBswaSFNyYIF59bFRf4t3LEa01e0Sz73RrY4YCifgyyjdfCcJOY1dcfdAqoRhj7FBy4o3cR9Plg3PGurs5IsLkRn5noNe8eZYZvnkVDWA0a+FqyeHRGUcCigzuHiH60H9TAQjuMtIHTC+9WwxAj5PR3bsRpzB4nfFwrbmCXJ58WsU8dobdaH4x3JQXwbENRxITyzth18I108QB4pVwIYqxWtC0bahQdUH4bRPzQoOm/+IFowL3aDnYVxiyAA190fT1rKAybAb6hsC7C9XEA0+z4DjWhbYlSHeWNqaI+TgI6DcQYYKYBJtnEzRoIq/Ga1NPigh9V9jrhrrZmDxVzICWCA5xPQ3bu3MkTTzzBq692bTrx2Wef8dRTT6HT6Vi+fDlXX311wGNJAZSczE1p4tGdd6oDuzXHWKCyIXKTdqYVQd4XXZ+bOBG0l8HqKA6VrXSjbY7Qm8g0iDjfhk4xxYKozYU6vTF1uyk428UPunZ0+qwBrjn1BdBAdCzAF154gXfffRez2dzleY/Hw+OPP86qVaswm81ce+21LFmyhOTkvkuKBsPtKjldeX08/CCj72+q299/8ZughYUOmKHCut+J59KTRTswAEcT7BrS6+79xqYBnYISiUTQ+TFiisaGJpHNuCgWhhvBIq0/SZC83KnV3E7n4K0jBNqnQYTyCMYay8rK4o9//ONJzxcUFJCVlUVsbCwGg4Hc3Fy2bAk8g0tagJLgmRsrHsuSYMlOSNEL1+hWh4h4PzwclsbBS+XwYh/9IQORuB9W/7bT73FQ8KHoUbluBxw6Cp+Z4L1exjP1h4Wxomxjf0vfd4dTrKLfql0rBgCXuMW/8TrRkcekAZe/a5PuI63ioQDTzuKYliR4mn1iNFk7uwa4jjdMwimDCOa++YILLqC4+ORQjMPhwG7vyPq2Wq04HIE/KymAktA5Jx625op6rAWxYvJEjA5i275OC+LgfzPh83q483DoGaZ+V9ffJ48VLcLMJrh4sXjuTmBjoxhPtLpBlHdsboL/1Pbvva1uCG5U0/5mUUJyvNt7OxLEOVR6blIgkXTHooXvpMNjx8TvaaGOmR0cDIQngOHe0tpsNpzODuvY6XR2EcTekH+FkvCY3unLldlDTGK8VTyS9GImYUO3yRQTLDDVJgSrfcKCWQMtflC6/ZEfK4WqWkhJ7Pr87Bjxb+cMwUt2wSd1Yk3HW8X5y9yh1TvucsCiWJqamrD7zULY1zWKxJx2fKqYdF8QZsasOshNyiWnJjuaYJsDUg3i5jJW13Xu5GnSAFxH6PE1P+EL4MiRIzl69Cj19fVYLBa2bNnCLbfcEnA/KYCS6HJ1inArPnaUEl81GReMFjWI1rap9KoKy/eIGrHLk+F3xZCcCZdqIaWt3dWho2IKQTD8M0eUeZjb/vw0Cjx+FO4PxjRro94HeQ1ts8Tb7iqHG8WFyKCIWrUyd/jiB7KRs+RkHizssPRAdCd6ajT8sc3ll6SHcadH3Wg4dYDhDG977733aG5uZsWKFdx7773ccsstqKrK8uXLSU1NDbi/FEBJ9Ek3wpNjKN/aREZuD3Vgb+d0/P6T4W0/jAzvXHrNyV1178kSsbePauGWdNEc/NfHhdu0uXfTsGmKRliApS6Ridde33jU1es+QWOS+WdnPQ4vbG2EJr9w3z9b2vX1wla4cHfH701eMc0lu2sG5NnG0KFD+fvf/w7ApZdeeuL5pUuXsnTp0pCOJQVQcuajUeDR4SK29/W28o7bM0SSitMHjx2F3xeDXhEup1FmqPRg39nCCQsQoCaCCQh7muE8Oc3grMKnigSp3U3wVCm8VQ3VIaQau1S4cJfoHZt+arfPk51gJJJTiXRjh/i1Y9SIx9eSRIxvW5Nogj0QjbD3nR7p7JII8lktvFIh+p5+Xh/eMQ62wI8L4dXxEV1apNERurjITjASyWDwnxrRa7IbqhaUcAITwfBcmcg0/WNbTFRyZvNZnWgGHok6vsoolP9EmPbavlCQAiiRDAb/NxIeGi5mF/78KHxaD0vjaDpQTczGCChgrBYuSRR53hcnQr0X1jbAXyvhjoOwJRds8k/xjOaAM3JF7MURiEFHmXAEcDCQf3USCYis1FEWeLnNtXRlflfxuyFVFP63l3bsdMDTJb27S0eZxT7FLrhrqNivM7cOERmyWcbw0t8kpw9vV4l4X6So62M6yimCHlELeKojBVAi6Ym7MvF+Uo0uxgjXpcAvskUyTTuXJYnON8WtsGKviCHqFeHHUYFPpog6wd7QKOIYkjOXFi/8sQS+bOjfhJXujDj1e4G2t0ILhWiNJu0LKYASSU/Mj2XnhzZyZ8/oe7uhJvh8qhjjk6gXxf0HWzrqECVnL2YdLIwTSS8f1AprP8so6lQBNoU5cDnfCR6/KPmR9AspgBJJb+iCDMsbNLCsU31jbgiT6CVnNnNjYYZdxH6v2APH2uJ3BiDXFt7osEafaNm39NRNnlJ8oIToqY1awlkfSAGUSCSSaOD2wxuVYr7mDgc4Ol3h3YhSmHkxUOWBQyGOx4o5xTsJhZOoOgjJrVIAJRKJJBoYNKL4vbeav2ZVxI4BJlnhQHOHezQQ9ad4IoyX0OsaBuEtSSeyRCKRRItxluD6vu52wpyY4I9rPsUtQA/CogvlMQD9J7oTlADW1NSwePFiCgoK2LdvH9dddx033HADt9xyC9XVEUzvlUgkkjOJebFQPg9eGQdXBMj63dQo2vUFIr6tXd+pjCfMxwATUAA9Hg8PP/wwJpNIvX3sscd46KGHePXVVzn//PN54YUXor5IiUQiOW2xauHGNPjVyL6Fq1WFPU4xZLk3LBr4YJIYl3Qq4yV08TsVXaArV67kmmuuISUlBYDf/OY3jB8vioV9Ph9G46ndlFUikUgGnT+XwXk74XCAZJcar3CZzu4hk1inwJsTYE4QVqIkKPpMgnn77bdJSEhg4cKFPP/88wAnhHDbtm289tprvP7660GdKD8/v59LHRi2bt062EsICbne6HK6rRdOvzWf0ev1qwx51o1thw/7kSDz/MvcuD3ukzqpFP7USN2Qo7D1aPDnZ5A+Xw+hdzgahEr4PgXwrbfeQlEU1q9fz759+7jnnnt45pln2Lx5M8888wzPP/88CQnBjXTJyck55a3FrVu3kpubO9jLCBq53uhyuq0XTr81n/Hr9fpBfwgqamCiUbS/+//27j+26TqP4/hza9cBK2w3DhI8hWPEabidhyP8is4xE4KgJIuAG5UJ8seBGfJrQRIiYyLsRy4acp5sQk7UAbLAzEX+8U5+xEXxxg+Zl21mxOWyiKAMBVkHt3br9/6YKyBjXUvbb2tfj6TJ2m+/3++bkvTdz893bTv8Z+B9QW2WBLgvHr75ed1gHKQtnwyp/u2vcqd4u7q6QtsocRNYSfgwGzAB3ty6KygooKSkhOPHj1NTU0N1dTUpKSmhjk9EJHp1G1CeBlUP3HitudNnAuR7d+/SiG+6epcTzE31O/mZyo3/yyAGuQIkmPxaB+jxeNi2bRtjxozhxRdfBGDKlCmsWrUqJMGJiES1IZbeR5/zXfCvy4M71+WBKXa47oHma6GJL1QCndAS5oV5g06A1dXVAJw4cSJkwYiI/Gr99zpM/WLw1RxabpowMyXKttdz43+LLg4I8yiZFsKLiIRa2//gL9/ArN8MbmH8L0VBDcBbRMk6QG2FJiISauOGwI703kkx/77auzfoyQ7Yf3Fw259N82OXGBk0JUARkXCxxsMfkqCjB3ZdGFzyG26BR6IsAXbj/6xOE/ojlQBFRMLFMGBRM/xzkBNh8kbB+KGw+t7QxhVsgawDNGF7U40BioiES1wc5P4WxgxyK7OjV2BcIhgmFMu7GxoDFBGR26z4HTySDHu/h6rz8NMdklsccPRPkGGHnihMgP4uhTAhGykBioiE2x/tUG6Hzb+HL5zwj0tQdwVOdNx4zwPDIPnnr2hLhJc/+qW+zbAjnBKgiIhZhlp6W4OPJIOzG3Ib4cgVGGmFv90P9w0xO8LAmNSl6S8lQBGRSJBkgb/eD0cvw8F2GA14PBCvqRqhogQoIhIJ4uJgYlLvY+W90P5T9Ca/vorwEU4JUEQkEo2K4rp/gYwBah2giIhEPRf+twB9VI/weDyUlJTQ0tKCzWZj69atjBs3znt89+7dHDx40Fui75VXXiEtLW3AayoBiohIcAXSAvQx0fXw4cO4XC5qampoaGigvLycyspK7/GmpiYqKirIyMgY9C2VAEVEJLgCSYA+stHp06fJysoCYNKkSbcV9G1qamLnzp20t7czc+ZMli9ffre3FBER8VMgXaA+WoBOpxO73X7j7RYL3d3dWK29aezJJ5/E4XBgt9tZuXIlx44dIycnZ8BrRukUIxERiSV2u53Ozk7vc4/H401+hmGwZMkSUlNTsdlsZGdn09zc7POaSoAiIhJcfV2g/jx8bJ2WmZlJXV0dAA0NDaSnp3uPOZ1OnnrqKTo7OzEMg/r6+kGNBaoLVEREgiuQdYAJAx+eNWsWn332Gfn5+RiGQWlpKYcOHeLatWvk5eWxdu1annvuOWw2GzNmzCA7O9vnLZUARUQkuALZCs3H++Pj49myZcstr02YMMH7d25uLrm5uX7dMuQJ0DB6Cz66XFGwLQDQ1dVldgh+UbyhFW3xQvTFrHhDq794+76P+76fgy6QWaD+Vo8IgjgjZJ9Ar46ODs6ePRvKW4iISADS09MZPnx40K7X1dVFY2MjGXvmkdhxwb9zh4+hcfEhMjIySExMDFpMAwl5CzApKYn09HQSEhKIi/Ox1F9ERELOMAzcbjdJSUmhuUEIukBDIeQJMD4+Pqi/MERE5O4NGRKlpZaCSJNgREQkuAKZBfprbAGKiEiMiZJJMEqAIiISXBoDFBGRmKQuUBERiUlR0gWqvUD70drayuTJkyN+wWtHRwcrVqxg8eLF5OXlcebMGbND6pfH46G4uJi8vDwKCgpoa2szO6QBud1u1q9fj8PhYMGCBRw5csTskAblhx9+IDs7m9bWVrND8emtt94iLy+Pp59+mgMHDpgdzoDcbjdFRUXk5+fjcDgi/vP98ssvKSgoAKCtrY1FixbhcDjYvHkzHo8nPEG4AnyEmRLgLzidTioqKrDZbGaH4tPu3buZPn06e/bsoays7LZtgiLFzYUsi4qKKC8vNzukAX344YekpKSwb98+du3axauvvmp2SD653W6Ki4ujYmp7fX09Z86c4f3336e6uprvvvvO7JAG9Mknn9Dd3c3+/fspLCxk+/btZod0R7t27eLll1/2/ngvKytjzZo17Nu3D8MwoubHXLgoAd7EMAw2bdrEunXrGDp0qNnh+LR06VLy8/MB6OnpCdvuCf7yVcgy0jzxxBOsXr3a+9xi8VGoLAJUVFSQn5/P6NGjzQ7Fp08//ZT09HQKCwtZsWIFM2fONDukAY0fP56enh48Hg9Op9NbgicSjR07ljfeeMP7vKmpialTpwLw2GOPcfz48fAEEoJqEKEQuf+TIXbgwAHefffdW1675557mDt3Lg8++KBJUd1Zf/GWlpby0EMP0d7ezvr169m4caNJ0Q3MVyHLSNO3O4bT6WTVqlWsWbPG3IB8+OCDD0hNTSUrK4udO3eaHY5Ply9f5vz581RVVXHu3DleeOEFPvroo4jdKWrYsGF8++23zJkzh8uXL1NVVWV2SHc0e/Zszp07531uGIb3c01KSqKjoyM8gUTJGGBkfgOFwcKFC1m4cOEtr82aNYva2lpqa2tpb29n2bJl7N2716QIb9VfvAAtLS2sW7eOl156yftLL9IMVMgyUl24cIHCwkIcDgfz5s0zO5wB1dbWEhcXx+eff85XX33Fhg0bqKysZNSoUWaH1q+UlBTS0tKw2WykpaWRmJjIjz/+yMiRI80OrV/vvPMOjz76KEVFRVy4cIElS5Zw6NChiO1xuVl8/I1Ovs7OTkaMGBGeGwcypmfCGGBkfwuF2ccff+z9+/HHH+ftt982MRrfvv76a1avXs327dsjstXaJzMzk2PHjjF37tzbCllGokuXLrFs2TKKi4uZMWOG2eH4dPOPtIKCAkpKSiI2+QFMnjyZ9957j+eff56LFy9y/fp1UlJSzA7rjkaMGEFCQm+xuuTkZLq7u+np6TE5qsGZOHEi9fX1TJs2jbq6OqZPnx6eG6sFKKH22muv4XK52LZtG9Db0qqsrDQ5qtv1V8gyklVVVXH16lV27NjBjh07gN7JBdEwwSQa5OTkcPLkSRYsWIBhGBQXF0f0OOvSpUvZuHEjDocDt9vN2rVrGTZsmNlhDcqGDRvYtGkTr7/+OmlpacyePTs8N46SdYAhL4ckIiKxwVsO6c/zSGz3sxzSqDE07gxvOSTNAhURkZikLlAREQkujQGKiEhMipIxQCVAEREJLlWDEBGRmKQWoIiIxCSNAYqISEzyWHof/p4TZloGISIiMUktQBERCbIEwN+ScgmhCGRASoAiIhJkVvxPaOFPR0qAIiISZAn4nwDVAhQRkainLlAREYlJwe8C9Xg8lJSU0NLSgs1mY+vWrYwbN857/OjRo7z55ptYrVbmz5/PM888c5d3FBER8Vvwu0APHz6My+WipqaGhoYGysvLveXf3G43ZWVlHDx4kKFDh7Jo0SJycnJ81sVUAhQRkaByj04N+jmnT58mKysLgEmTJtHY2Og91traytixY0lOTgZ6iy6fOnWKOXPmDHhNJUAREQkKi8WCxWKh5e9b7ur8/jidTux2+y3v7e7uxmq14nQ6GT58uPdYUlISTqfT5/2UAEVEJCisVisZGRn09PQEdL7FYsFq7T8t2e12Ojs7vc89Ho/3vb881tnZeUtCvBPtBCMiIkFjtVpJTEwM6HGn5AeQmZlJXV0dAA0NDaSnp3uPTZgwgba2Nq5cuYLL5eLUqVM8/PDDPmONMwzDuPt/soiISOj0zQI9e/YshmFQWlpKc3Mz165dIy8vzzsL1DAM5s+fz7PPPuvzmkqAIiISk9QFKiIiMUkJUEREYpISoIiIxCQlQBERiUlKgCIiEpOUAEVEJCYpAYqISExSAhQRkZj0f9NYxPONBN1pAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(column = 'Appartenance', legend=True, cmap='gist_rainbow')#on crée la carte"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "dpartement.index=[i[1:] for i in dpartement.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyse=dpartement.join(df[\"Appartenance\"],how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age moyen des deputes</th>\n",
       "      <th>nombre_mandat</th>\n",
       "      <th>nombre député</th>\n",
       "      <th>nombre moyen de mandats par député</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Appartenance</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>48.768821</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>804.0</td>\n",
       "      <td>2.033582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48.358941</td>\n",
       "      <td>276.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>2.262069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.509394</td>\n",
       "      <td>299.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.568654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>49.147059</td>\n",
       "      <td>479.5</td>\n",
       "      <td>207.5</td>\n",
       "      <td>2.253323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age moyen des deputes  nombre_mandat  nombre député  \\\n",
       "Appartenance                                                        \n",
       "0                         48.768821         1635.0          804.0   \n",
       "1                         48.358941          276.0          125.0   \n",
       "2                         52.509394          299.0          118.0   \n",
       "3                         49.147059          479.5          207.5   \n",
       "\n",
       "              nombre moyen de mandats par député  \n",
       "Appartenance                                      \n",
       "0                                       2.033582  \n",
       "1                                       2.262069  \n",
       "2                                       2.568654  \n",
       "3                                       2.253323  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "analyse.groupby(\"Appartenance\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On peut remarquer un groupe concentrant les départements ayant le plus de députés, et un autre ayant des députés plus âgés et plus souvent réélus, que l'on peut interpréter comme des départements \"conservateurs\" au sens où ils ont moins de renouvellement de leurs élus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On voit assez bien que deux groupes ont des caractéristiques assez extrêmes, alors que les autres sont plus centrés"
   ]
  }
 ],
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