diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..7443250eae8f9c32b8017a1d0485b2aafb129e25 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,1469 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyse de l'incidence de la varicelle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import isoweek"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](https://www.sentiweb.fr/france/fr/?). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1991 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Si le fichier local n'existe pas, alors nous le téléchargeons depuis le site web de Sentinelles. Dans le cas contraire nous utilisons une copie locale déjà existante. Si le site web change ou que les données ne sont plus accessibles à l'adresse indiquée dans l'entrée ci-dessus nous aurons toujours une copie de travail pour effectuer l'étude."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Le fichier n'existe pas en local, téléchargement de celui-ci\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_file = \"incidence-PAY-7.csv\"\n",
+    "import os\n",
+    "import urllib.request\n",
+    "if not os.path.exists(data_file):\n",
+    "    print(\"Le fichier n'existe pas en local, téléchargement de celui-ci\")\n",
+    "    urllib.request.urlretrieve(data_url, data_file)\n",
+    "else:\n",
+    "    print(\"Le fichier existe déjà en local, pas besoin de le télécharger à nouveau\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pré traitement des données"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
+    "\n",
+    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
+    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
+    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
+    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
+    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
+    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
+    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
+    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
+    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
+    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
+    "\n",
+    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202039</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1017</td>\n",
+       "      <td>216</td>\n",
+       "      <td>1818</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202038</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2253</td>\n",
+       "      <td>782</td>\n",
+       "      <td>3724</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202037</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1584</td>\n",
+       "      <td>405</td>\n",
+       "      <td>2763</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202036</td>\n",
+       "      <td>7</td>\n",
+       "      <td>919</td>\n",
+       "      <td>100</td>\n",
+       "      <td>1738</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202035</td>\n",
+       "      <td>7</td>\n",
+       "      <td>828</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1694</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202034</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2272</td>\n",
+       "      <td>371</td>\n",
+       "      <td>4173</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202033</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>177</td>\n",
+       "      <td>2391</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202032</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2650</td>\n",
+       "      <td>689</td>\n",
+       "      <td>4611</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>202031</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>100</td>\n",
+       "      <td>2506</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>202030</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1385</td>\n",
+       "      <td>75</td>\n",
+       "      <td>2695</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>202029</td>\n",
+       "      <td>7</td>\n",
+       "      <td>841</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1672</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>202028</td>\n",
+       "      <td>7</td>\n",
+       "      <td>728</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1515</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202027</td>\n",
+       "      <td>7</td>\n",
+       "      <td>986</td>\n",
+       "      <td>149</td>\n",
+       "      <td>1823</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202026</td>\n",
+       "      <td>7</td>\n",
+       "      <td>694</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1454</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202025</td>\n",
+       "      <td>7</td>\n",
+       "      <td>228</td>\n",
+       "      <td>0</td>\n",
+       "      <td>597</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202024</td>\n",
+       "      <td>7</td>\n",
+       "      <td>388</td>\n",
+       "      <td>0</td>\n",
+       "      <td>959</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>202023</td>\n",
+       "      <td>7</td>\n",
+       "      <td>558</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1115</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>202022</td>\n",
+       "      <td>7</td>\n",
+       "      <td>277</td>\n",
+       "      <td>0</td>\n",
+       "      <td>633</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>202021</td>\n",
+       "      <td>7</td>\n",
+       "      <td>602</td>\n",
+       "      <td>36</td>\n",
+       "      <td>1168</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>202020</td>\n",
+       "      <td>7</td>\n",
+       "      <td>824</td>\n",
+       "      <td>20</td>\n",
+       "      <td>1628</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>202019</td>\n",
+       "      <td>7</td>\n",
+       "      <td>310</td>\n",
+       "      <td>0</td>\n",
+       "      <td>753</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>202018</td>\n",
+       "      <td>7</td>\n",
+       "      <td>849</td>\n",
+       "      <td>98</td>\n",
+       "      <td>1600</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>202017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>272</td>\n",
+       "      <td>0</td>\n",
+       "      <td>658</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>202016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>758</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>202015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1918</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3161</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>202014</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3879</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>5531</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7326</td>\n",
+       "      <td>5236</td>\n",
+       "      <td>9416</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>202012</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8123</td>\n",
+       "      <td>5790</td>\n",
+       "      <td>10456</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>202011</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10198</td>\n",
+       "      <td>7568</td>\n",
+       "      <td>12828</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>202010</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9011</td>\n",
+       "      <td>6691</td>\n",
+       "      <td>11331</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1526</th>\n",
+       "      <td>199126</td>\n",
+       "      <td>7</td>\n",
+       "      <td>17608</td>\n",
+       "      <td>11304</td>\n",
+       "      <td>23912</td>\n",
+       "      <td>31</td>\n",
+       "      <td>20</td>\n",
+       "      <td>42</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1527</th>\n",
+       "      <td>199125</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16169</td>\n",
+       "      <td>10700</td>\n",
+       "      <td>21638</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1528</th>\n",
+       "      <td>199124</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16171</td>\n",
+       "      <td>10071</td>\n",
+       "      <td>22271</td>\n",
+       "      <td>28</td>\n",
+       "      <td>17</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1529</th>\n",
+       "      <td>199123</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11947</td>\n",
+       "      <td>7671</td>\n",
+       "      <td>16223</td>\n",
+       "      <td>21</td>\n",
+       "      <td>13</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1530</th>\n",
+       "      <td>199122</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15452</td>\n",
+       "      <td>9953</td>\n",
+       "      <td>20951</td>\n",
+       "      <td>27</td>\n",
+       "      <td>17</td>\n",
+       "      <td>37</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1531</th>\n",
+       "      <td>199121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14903</td>\n",
+       "      <td>8975</td>\n",
+       "      <td>20831</td>\n",
+       "      <td>26</td>\n",
+       "      <td>16</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1532</th>\n",
+       "      <td>199120</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19053</td>\n",
+       "      <td>12742</td>\n",
+       "      <td>25364</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1533</th>\n",
+       "      <td>199119</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16739</td>\n",
+       "      <td>11246</td>\n",
+       "      <td>22232</td>\n",
+       "      <td>29</td>\n",
+       "      <td>19</td>\n",
+       "      <td>39</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1534</th>\n",
+       "      <td>199118</td>\n",
+       "      <td>7</td>\n",
+       "      <td>21385</td>\n",
+       "      <td>13882</td>\n",
+       "      <td>28888</td>\n",
+       "      <td>38</td>\n",
+       "      <td>25</td>\n",
+       "      <td>51</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1535</th>\n",
+       "      <td>199117</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13462</td>\n",
+       "      <td>8877</td>\n",
+       "      <td>18047</td>\n",
+       "      <td>24</td>\n",
+       "      <td>16</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1536</th>\n",
+       "      <td>199116</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14857</td>\n",
+       "      <td>10068</td>\n",
+       "      <td>19646</td>\n",
+       "      <td>26</td>\n",
+       "      <td>18</td>\n",
+       "      <td>34</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1537</th>\n",
+       "      <td>199115</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13975</td>\n",
+       "      <td>9781</td>\n",
+       "      <td>18169</td>\n",
+       "      <td>25</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>199114</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12265</td>\n",
+       "      <td>7684</td>\n",
+       "      <td>16846</td>\n",
+       "      <td>22</td>\n",
+       "      <td>14</td>\n",
+       "      <td>30</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1539</th>\n",
+       "      <td>199113</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9567</td>\n",
+       "      <td>6041</td>\n",
+       "      <td>13093</td>\n",
+       "      <td>17</td>\n",
+       "      <td>11</td>\n",
+       "      <td>23</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1540</th>\n",
+       "      <td>199112</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10864</td>\n",
+       "      <td>7331</td>\n",
+       "      <td>14397</td>\n",
+       "      <td>19</td>\n",
+       "      <td>13</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1541</th>\n",
+       "      <td>199111</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15574</td>\n",
+       "      <td>11184</td>\n",
+       "      <td>19964</td>\n",
+       "      <td>27</td>\n",
+       "      <td>19</td>\n",
+       "      <td>35</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1542</th>\n",
+       "      <td>199110</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16643</td>\n",
+       "      <td>11372</td>\n",
+       "      <td>21914</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1543</th>\n",
+       "      <td>199109</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13741</td>\n",
+       "      <td>8780</td>\n",
+       "      <td>18702</td>\n",
+       "      <td>24</td>\n",
+       "      <td>15</td>\n",
+       "      <td>33</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1544</th>\n",
+       "      <td>199108</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13289</td>\n",
+       "      <td>8813</td>\n",
+       "      <td>17765</td>\n",
+       "      <td>23</td>\n",
+       "      <td>15</td>\n",
+       "      <td>31</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1545</th>\n",
+       "      <td>199107</td>\n",
+       "      <td>7</td>\n",
+       "      <td>12337</td>\n",
+       "      <td>8077</td>\n",
+       "      <td>16597</td>\n",
+       "      <td>22</td>\n",
+       "      <td>15</td>\n",
+       "      <td>29</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1546</th>\n",
+       "      <td>199106</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10877</td>\n",
+       "      <td>7013</td>\n",
+       "      <td>14741</td>\n",
+       "      <td>19</td>\n",
+       "      <td>12</td>\n",
+       "      <td>26</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1547</th>\n",
+       "      <td>199105</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10442</td>\n",
+       "      <td>6544</td>\n",
+       "      <td>14340</td>\n",
+       "      <td>18</td>\n",
+       "      <td>11</td>\n",
+       "      <td>25</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1548</th>\n",
+       "      <td>199104</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7913</td>\n",
+       "      <td>4563</td>\n",
+       "      <td>11263</td>\n",
+       "      <td>14</td>\n",
+       "      <td>8</td>\n",
+       "      <td>20</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1549</th>\n",
+       "      <td>199103</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15387</td>\n",
+       "      <td>10484</td>\n",
+       "      <td>20290</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1550</th>\n",
+       "      <td>199102</td>\n",
+       "      <td>7</td>\n",
+       "      <td>16277</td>\n",
+       "      <td>11046</td>\n",
+       "      <td>21508</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>38</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1551</th>\n",
+       "      <td>199101</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15565</td>\n",
+       "      <td>10271</td>\n",
+       "      <td>20859</td>\n",
+       "      <td>27</td>\n",
+       "      <td>18</td>\n",
+       "      <td>36</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1552</th>\n",
+       "      <td>199052</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19375</td>\n",
+       "      <td>13295</td>\n",
+       "      <td>25455</td>\n",
+       "      <td>34</td>\n",
+       "      <td>23</td>\n",
+       "      <td>45</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1553</th>\n",
+       "      <td>199051</td>\n",
+       "      <td>7</td>\n",
+       "      <td>19080</td>\n",
+       "      <td>13807</td>\n",
+       "      <td>24353</td>\n",
+       "      <td>34</td>\n",
+       "      <td>25</td>\n",
+       "      <td>43</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1554</th>\n",
+       "      <td>199050</td>\n",
+       "      <td>7</td>\n",
+       "      <td>11079</td>\n",
+       "      <td>6660</td>\n",
+       "      <td>15498</td>\n",
+       "      <td>20</td>\n",
+       "      <td>12</td>\n",
+       "      <td>28</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1555</th>\n",
+       "      <td>199049</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1143</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2610</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1556 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202039          7   1017      216    1818       2           1   \n",
+       "1     202038          7   2253      782    3724       3           1   \n",
+       "2     202037          7   1584      405    2763       2           0   \n",
+       "3     202036          7    919      100    1738       1           0   \n",
+       "4     202035          7    828        0    1694       1           0   \n",
+       "5     202034          7   2272      371    4173       3           0   \n",
+       "6     202033          7   1284      177    2391       2           0   \n",
+       "7     202032          7   2650      689    4611       4           1   \n",
+       "8     202031          7   1303      100    2506       2           0   \n",
+       "9     202030          7   1385       75    2695       2           0   \n",
+       "10    202029          7    841       10    1672       1           0   \n",
+       "11    202028          7    728        0    1515       1           0   \n",
+       "12    202027          7    986      149    1823       1           0   \n",
+       "13    202026          7    694        0    1454       1           0   \n",
+       "14    202025          7    228        0     597       0           0   \n",
+       "15    202024          7    388        0     959       1           0   \n",
+       "16    202023          7    558        1    1115       1           0   \n",
+       "17    202022          7    277        0     633       0           0   \n",
+       "18    202021          7    602       36    1168       1           0   \n",
+       "19    202020          7    824       20    1628       1           0   \n",
+       "20    202019          7    310        0     753       0           0   \n",
+       "21    202018          7    849       98    1600       1           0   \n",
+       "22    202017          7    272        0     658       0           0   \n",
+       "23    202016          7    758       78    1438       1           0   \n",
+       "24    202015          7   1918      675    3161       3           1   \n",
+       "25    202014          7   3879     2227    5531       6           3   \n",
+       "26    202013          7   7326     5236    9416      11           8   \n",
+       "27    202012          7   8123     5790   10456      12           8   \n",
+       "28    202011          7  10198     7568   12828      15          11   \n",
+       "29    202010          7   9011     6691   11331      14          10   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1526  199126          7  17608    11304   23912      31          20   \n",
+       "1527  199125          7  16169    10700   21638      28          18   \n",
+       "1528  199124          7  16171    10071   22271      28          17   \n",
+       "1529  199123          7  11947     7671   16223      21          13   \n",
+       "1530  199122          7  15452     9953   20951      27          17   \n",
+       "1531  199121          7  14903     8975   20831      26          16   \n",
+       "1532  199120          7  19053    12742   25364      34          23   \n",
+       "1533  199119          7  16739    11246   22232      29          19   \n",
+       "1534  199118          7  21385    13882   28888      38          25   \n",
+       "1535  199117          7  13462     8877   18047      24          16   \n",
+       "1536  199116          7  14857    10068   19646      26          18   \n",
+       "1537  199115          7  13975     9781   18169      25          18   \n",
+       "1538  199114          7  12265     7684   16846      22          14   \n",
+       "1539  199113          7   9567     6041   13093      17          11   \n",
+       "1540  199112          7  10864     7331   14397      19          13   \n",
+       "1541  199111          7  15574    11184   19964      27          19   \n",
+       "1542  199110          7  16643    11372   21914      29          20   \n",
+       "1543  199109          7  13741     8780   18702      24          15   \n",
+       "1544  199108          7  13289     8813   17765      23          15   \n",
+       "1545  199107          7  12337     8077   16597      22          15   \n",
+       "1546  199106          7  10877     7013   14741      19          12   \n",
+       "1547  199105          7  10442     6544   14340      18          11   \n",
+       "1548  199104          7   7913     4563   11263      14           8   \n",
+       "1549  199103          7  15387    10484   20290      27          18   \n",
+       "1550  199102          7  16277    11046   21508      29          20   \n",
+       "1551  199101          7  15565    10271   20859      27          18   \n",
+       "1552  199052          7  19375    13295   25455      34          23   \n",
+       "1553  199051          7  19080    13807   24353      34          25   \n",
+       "1554  199050          7  11079     6660   15498      20          12   \n",
+       "1555  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             3        FR   France  \n",
+       "1             5        FR   France  \n",
+       "2             4        FR   France  \n",
+       "3             2        FR   France  \n",
+       "4             2        FR   France  \n",
+       "5             6        FR   France  \n",
+       "6             4        FR   France  \n",
+       "7             7        FR   France  \n",
+       "8             4        FR   France  \n",
+       "9             4        FR   France  \n",
+       "10            2        FR   France  \n",
+       "11            2        FR   France  \n",
+       "12            2        FR   France  \n",
+       "13            2        FR   France  \n",
+       "14            1        FR   France  \n",
+       "15            2        FR   France  \n",
+       "16            2        FR   France  \n",
+       "17            1        FR   France  \n",
+       "18            2        FR   France  \n",
+       "19            2        FR   France  \n",
+       "20            1        FR   France  \n",
+       "21            2        FR   France  \n",
+       "22            1        FR   France  \n",
+       "23            2        FR   France  \n",
+       "24            5        FR   France  \n",
+       "25            9        FR   France  \n",
+       "26           14        FR   France  \n",
+       "27           16        FR   France  \n",
+       "28           19        FR   France  \n",
+       "29           18        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1526         42        FR   France  \n",
+       "1527         38        FR   France  \n",
+       "1528         39        FR   France  \n",
+       "1529         29        FR   France  \n",
+       "1530         37        FR   France  \n",
+       "1531         36        FR   France  \n",
+       "1532         45        FR   France  \n",
+       "1533         39        FR   France  \n",
+       "1534         51        FR   France  \n",
+       "1535         32        FR   France  \n",
+       "1536         34        FR   France  \n",
+       "1537         32        FR   France  \n",
+       "1538         30        FR   France  \n",
+       "1539         23        FR   France  \n",
+       "1540         25        FR   France  \n",
+       "1541         35        FR   France  \n",
+       "1542         38        FR   France  \n",
+       "1543         33        FR   France  \n",
+       "1544         31        FR   France  \n",
+       "1545         29        FR   France  \n",
+       "1546         26        FR   France  \n",
+       "1547         25        FR   France  \n",
+       "1548         20        FR   France  \n",
+       "1549         36        FR   France  \n",
+       "1550         38        FR   France  \n",
+       "1551         36        FR   France  \n",
+       "1552         45        FR   France  \n",
+       "1553         43        FR   France  \n",
+       "1554         28        FR   France  \n",
+       "1555          5        FR   France  \n",
+       "\n",
+       "[1556 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a-t-il des points manquants dans ce jeux de données ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [week, indicator, inc, inc_low, inc_up, inc100, inc100_low, inc100_up, geo_insee, geo_name]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pas de données manquantes, donc rien à supprimer à priori... Mais si cela devait arriver, la ligne de code suivante effectue la suppression du jeu de données d'une ligne sans informations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = raw_data.dropna().copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit de nombre entier. C'est comme ça que Pandas les interprète.\n",
+    "  \n",
+    "Un deuxième problème est que Pandas ne comprend pas les numéros de semaine.  Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque `isoweek`.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def convert_week(year_and_week_int):\n",
+    "    year = year_and_week_int//100\n",
+    "    week = year_and_week_int%100\n",
+    "    w = isoweek.Week(year, week)\n",
+    "    return pd.Period(w.day(0), 'W')\n",
+    "\n",
+    "data['period'] = [convert_week(yw) for yw in data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.\n",
+    "\n",
+    "Premièrement, nous définissons les périodes d'observation comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions la cohérence des données. Entre la fin d'une période et le début de la période qui suit, la différence temporelle doit être\n",
+    "zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n",
+    "\n",
+    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives entre lesquelles il manque une semaine.\n",
+    "\n",
+    "Nous reconnaissons ces dates: c'est la semaine sans observations que nous avions supprimées !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pas de problèmes à priori sur le jeu de données."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Premières analyses graphiques"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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X3BR+vxPALClV7Ojw5ezqgavhFqmVYHhs2llcWaVBur1TUyv1wKPPwmz87fV13nVjO062m8grzmHeWxsAAK3tZpXNV+94Ef+ruA73FuohLX4WZLI5hOqeQwA8ExZ9mYheJqJfE9HIsGwiAJW1WR6WTQy/6+WJa4QQnQBaAIzOMraeRk/HOdQBbYjABrxVef++C/7+BauwZnMrgIA4qGPoa48+y3jI01VaR15qpchmZKnzx+dX4Af3v5pLf9WgyG/Gw5s4ENEQAH8AcKEQYhMCFdEeAKYBWAXgv2VV5nJhKbddo49hDhHNJ6L569b5c0/dgby8leKUyvZ6fXHi3vL021jVsj239jjPLdsW5fNIfJ/b+bc+H+n7O7oqCbVST9ocvJBhPHKPv/axpXhlRUvm62pFf0hJnzWnV73AizgQUSMCwnCrEOKPACCEWCOE6BJCVAD8CsCMsPpyAJOVyycBWBmWT2LKE9cQUQOA4QDe18chhLhWCDFdCDF97NjedTnLax5FRlAHdehrE3dVy3ZcctcrmHPzc7m1GXO5+d1rNU3d9PdliaC7vubKmmUuqLaD0376RFXX1YJYcnCP+brHl2ayEeWNXuy6T8LHW4kAXA9gsRDif5Ty8Uq1jwF4Jfx+D4CzQg+k3RAYnucJIVYB2ExEM8M2zwZwt3LNOeH3MwA8IurCdzMmDqpfPYfeJA5/fW0tPnP9M1G0MhC7/21p6zRdlhlsnEONrqzVXLf0va14Z/3WuI3quu429MRcyEut5JsBAAAum7sYd7240l2xRry9fiumX/ZQ9H+kVipsDgn4BMEdBeAzABYQ0Yth2X8C+CQRTUOwdpYB+AIACCEWEtEdABYh8HS6QAghrVHnA7gRwEAA94d/QEB8biGiJQgkhrNqu63s2NrWiaeXrsesfcd51c9rfTY1BBPTTRzy6a8aXHL3K3j3/e1YsWE7po4bCiAerzTi5gFOvLcxsD7voFr1n+oC29ektizDqXboeXmDZD2joyee9W/nvYP3tsRJFbOOsV7gJA5CiCfAz5X7LNdcDuBypnw+gAOY8lYAZ7rG0p249N5FuH3+u5j7r0dj/wnpHPk68ppGTb6SQ8J7RvToSV1S46Xq4TtCGbyxIc9xmO0v1W7yuaz3PmZzyMIoVPvc8ppervvSFQRDmu1b0vb2Lqxs2Y49xg6pekwplZk8y6IgDgkUEdIh1oaeKqtbWr3q56X1itRKDoWnuiH0tBTBHUgkiVlzQzkqE0Lg7hdXoLXKSFNOPx2fUJau350J8dTtoyced7b55F/39TXVZbPNe580tdemMUWDmsp8xRDn3/ocZv3332qyTejkKpIcCrVSAgVxCDFsYCMAeAfr5CY5NPhJDgnXyh7mcDh1TxujVvr7m+vxldtexBVVuiVyni21crDVPqpK4nnXNoa80dfGY4PLZqS7GrsM4X8PPcqyxK+YxuQaS72jIA4h4oyc8QT5wf2LMeWiufxmnLO3UhabQ09PYcm9qxtme6RWiqfQpjCTqa/0ZUKe6TOqtjko86AnUnZnUyv1gPdUTu24vJWy9qMfZ1oN6impYC0oiEMIOdfUafO/f1sKoHtVGs0Nvmql3udkE+sx/J7VqeXuF1cYf2MN0snuMiOPZ9XXOPWeYHDzkk7lRmzazPV+XL3KuKDODKnYdaTUSp75zeoNBXEIIScpx1Vwc6bWtSOEwP+9HLvt6bpXHYmgrB6UHa597E288/62YAw5rJ6v3Pai8TdOQoEl9sFnNP3EHp1pM+4re9jrazbj6aX2RIGulPR6ses5yPWZ5RCn9KCSa1z2WXgrJVGc5xBCTouSQi6JAiKwcVs7WrZ3sPWrxf2vrMaXf/tC9H82m0ONnWfA9++L7QemhSuEwLvv1x4p7ZNqISvy4IDrJOQmM0666jEAwLIrTjXWcR3glDmtRzhHarI5VH1lfaGQHELIyUvM1DnssofwrbteSZTVul+s35I8vN7tylpbf3kgecZB/P23897BsT98FM+/syH1WxbEBmk/tZLPpt1fJIcsNofDdgnSmB00ye1yXS1yyx0Wfhq5cmH9N4GW7R3Y1BoEXdqIw88fXYKL/mA+ZlXXDmxt67KPsU5REIcQkc3Bc43Wotp56d2NuEQ7JrO9y+7+2edsDiEIwHNvB0RBnsHsi7fX8/U5b6Vq73nuy6uquzAxoNqbcHaR4QblM5k4YmA3jSZHMG7QAPDelja0dXal15HlMRz/o79G322urD/882u47VlzWnN9ja/eFDhQFDaHJAriEEIuTl8OrpYN+tt3v5Iqy+Kt1FscjmkDi4/yTP7vwmlXJ3P9ZDUMuqr96rGluP6Jt/waC3HAxGGpsr7GUf7Pg68DyC9QjUNedi1TxtPplz2E83/zfGod6fWOuuIRnPnLvwMA1m9tj8q7Q63Ux15zr6MgDiHijS2GNSNoDX1tY3LbZ7I51NB3LeAiSAVU7j7byDZreZns2TGz3/Xl9y3OfA13jkE9bhqme27t6MLGbe38jwxsaqVHXl2beqv6nr9i43Y8u2xD6lofb6V9Lrkfazel3apNeaP6GhPQ2yiIQwi5sTHemqYLqu6LIw5ZjgntLQNpMpU2Kd8D5DWqhFqJKePq5QVu4+irW0ZvTINPXPs0pl36oHf9N9YGEdpGbyXtJnw3aB9vpdaOCv6a4WCkAkkUxCGEnGu+G28t65Kb2O6T4PLpuxaY0gvozHat+XwSBmlr5r2qurGClxy6/4lXk9enO4dlavuldzdW1Z4xzsGzXx2+cQ5ZUmIUXmlJFMQhhNycOa6Vw7c1g3IWsBuQ45recmVVkTCKM/mPajXo2drpzltWn21vSQ7VPDsBkfJ6ywt537PZDdqvXuo6z36zEF3TO3j01bW4f0EOjg39DAVxCCGiz+7fCsrl7NxpQrLoJeLAcX+EODakVs6LT7wn207X74531Vs2B99np56f8eeFa3DYZQ9h2XvZvMTygC3SnYM5CE5XK/m2V73kYHrWpjY/d+OzOP/W5/0G5onlG7Zhu+Vc7b6AuicO85e9j72+dT/eDz0hemIj4DYg5zGh6slkvUQdEqqtxBByOjWM6adaY3cWqE2XWWNl9z9v3ziWI37wcKps+Yb8jmqVcD1vLtJ9e3sX7nphBXttIkbGwuj4zm3f6cAxNMaQix5cVkdf+Sg+e8O8nuuwCtQ9cbjmr2+ivbMS+eonfey7x1eQV134Sw69pVYyqbZ0L6N1m2tTdSSD4MzvoMcM0j3wvH054c2t+Z28Z0M1t/y9uYtw4e0v4pm3Uif8GueOy1vJOD7P58WZJkxXutq89N5FUWr/PMA9p76EuicOaWNq94OVHBwdq/O2p1zuntHy5iTzO8XQVT/Pv1Od0dJ2pGRP0cOGXrA5CCHw++eWO+vp70OiryQZldl4tzAEzDR3utvmkMkg7fj910++hf+qwdbY31D3xEFXifSExwKnunD1a1pcXDvXPb4UUy6ai3+73ZzkDgj0nrbzKz5x7dOJ/99ev40dr9ycXl7eYu3P+Wy5OAdLhHReb0pth0282M1TYpVninP9fXQrcr7n95UANnUe6IyOL+PjrVbiYnMM1/r0XUvwXX9D3ROHlOTQEzaHKlQXvmqlpe9txWVzg+CvP71gNxoefeWjqShlG/6ycHX0fbUSXCRVP1va7CoP32fLxzn0zKLkjsTuThvPyo3b8fqazc56G7aaA8+6Q3Co5p7jLAPp31Qbhc0t2/c1+xKRlRvT9hjTvfkcLtdHhLQegZM4ENFkInqUiBYT0UIi+kpYPoqIHiSiN8LPkco1FxPREiJ6jYhOVsoPI6IF4W9XU6jUJ6JmIro9LH+GiKbkf6uG+9P+5zxlcu+TaThbnIO5btZNVKbj9sFLy1vw0KI1AGJXXiLKkI/KDls0LXdtXgRDbaehlF4SNmaxVh30v9z4LD57w7POev2JX7XNh/bOCjZu56UIIIvNwa/ezU+97X1tT6lr+0s8hY/k0Anga0KIfQHMBHABEe0H4CIADwshpgJ4OPwf4W9nAdgfwGwAvyAieTDsNQDmAJga/s0Oy88FsEEIsSeAqwBcmcO9eaE3JAcOrm5tHh4quvuUqz++kNaN+/boWhRcbqVaEu9Nmzwi8zXNDeklYRv3F255LnMfKl5d7ZYaAEdEcDe88u5aB1+4ZT5mXB57XLlyK8X1hPV/G3yv9TmvJA8C0l80U07iIIRYJYR4Pvy+GcBiABMBnA7gprDaTQA+Gn4/HcBtQog2IcRbAJYAmEFE4wEME0I8JYK3c7N2jWzrTgCzpFTR0+iJ98Zt4K4JoxrWbFV5V8z8wHkPeScrdLadrskeAOTZ3r7j00n0XONqbsymaW3ZZj9zPC8uUbX36PBNdJgF3bUOHn3Nkc7C0PFqLUdSlvHppyyaAuN8iEN7DSfQSfSXHE6ZVkKo7jkEwDMAxgkhVgEBAQGwU1htIgA1X+7ysGxi+F0vT1wjhOgE0AJgNNP/HCKaT0Tz163LJ2eKvrB64r2xnvRZ1EqWqn2FK2FPbnPZoyODNNNeFWOohr1oYowOtnEPHWA/L+vNdVuyD4LBp3/1jPG3vuKtVM078pUcfvHom171OLtXh7ahm9aIl0HaxzDhQD+hDf7EgYiGAPgDgAuFEJtsVZkyYSm3XZMsEOJaIcR0IcT0sWPHuobsBX1hJVNE9ByyGKRtkziPozytYN4UtzmxwUc1BDhVRWy8eku209xYTv9uG7dzZ65+51bv2XXGeN6oZgOT12SRZHwjpG95Omk7MI2vrcOd8djk3uolOTiyJ/tgh5IciKgRAWG4VQjxx7B4TagqQvi5NixfDmCycvkkACvD8klMeeIaImoAMBxAj0SI2OIcatlob35qGaZcNJf34GHVSi7JwY9odTtx0EDgN4MsLoQSXO7/WmwO1XDUvM3B0kc3jMGn3yxjyIJvnLx3jq254Ss56DDV46a/vqGb1ojP0ukoJIcYoe7/egCLhRD/o/x0D4Bzwu/nALhbKT8r9EDaDYHheV6oetpMRDPDNs/WrpFtnQHgEdGNJv1/+uVT+NkjbwBgNracur3u8bcAAO8x0cLcYs7krdTNkkPWR9/ayaQgr2ENscSBIYkuScTbdVZpJ6taybX51+Ig0JscZh7uuz7zaL3mouvbq6keN+4UcVDG9eXj9oy++wTMDR/U5DdAC9T3umilTQnTu/CRHI4C8BkAxxPRi+HfKQCuAHAiEb0B4MTwfwghFgK4A8AiAA8AuEAIIXeP8wFch8BI/SaA+8Py6wGMJqIlAL6K0POpOyCEwLxl7+NHf3md/z2vfsKWfDcH1zpK5qYx1/PZUN5Zvw0PvMJnmXz+nQ3Y7eL7zNG42v8CwG+feSdVr5q05Dawlzqaq6a3gU2cWskMH6XSlIvm4twb3e6qWfpN9NENRoeq1ErySzgcH0Zlzs3ztX69KTpfzEkO2hG8ap0GJQmmTwbX/TydHGxQeznl6sexfIO/O3lPwm5NAyCEeALmNTDLcM3lAC5nyucDOIApbwVwpmsseUA/fSw9Fvv1vz3vA/iUxTgIBIE3azcFEgPHyXAORa5+ffPX+0RwHvvDR42/PfpqoB18emltWj1OcnAbpBm1Ug3pwNX+5hy7O659bKnzmqaMrqwuzAvz5zz86lpHzTT8o4X7pp7CZ7PVJQffdBdml9d0mb4mVKLVqEiKPn3nYZDWx75peycw0lC5F1F3EdJd+iZrMUhzmDxyEFve3lmJFumRVzyCtlCU1T0lAJ7Tc/Xbpey2tqq1qpWkjWRwc5qDBtJjf8Sw6ZmOFLWBS89ty8qqlky5aC5OuupvxhqnHTQex+7FOzG49jCr5ODg2v/9Dy/bG7f1WwUT7SIULdvtrrdcm9WiJumjyra5daSvCXVuqrm0fIiZbXl9996F+M8/LXC2ITT6cvav7cxmb6HuiIP+blNqEsf8aCgTTth3XEK8XLlxO/b61v247dl3U/V9DViuadmZiHMw165VT701JA5Dmnmh0leBwadKto+tVlfW19dsMabwIE8fGh8340dfXYvplz2E1o6ubk2nUE26Edc1n3OkiY5sLjXMI/lMNjpiQDjUep4DV6pLsYmIeEVyqDUI7oYnl7Eq1vQYk21MakvnAAAgAElEQVS8t8X/TO6eRP0RB+3l6pyfa3qUiFCi5CSRuXHuY06L4tRB1RikfW0OtUoOrR3BShrAuHRmAe/Kaod8Fbc8tQyPvro2kRfH15W1o5OXsIg8PYc8Eu9dNncR3tvS1u264u5QK73kSI7YwBxE5Qt9HCs2Zn8+tWZl5VRDukSQVCvF98s9b709n3eyrd2uuu4rsUgu1B9x0P7XX75rcgbEgRIbhlQhNTekN1Rf/3SnzUEZp22C3vjkMq/+XDBtpL62z2pcWSVeWt6Cz934LE65+vHM19oSFFYtOdjqKxes3WTPs7Rk7WbM/vFj3qodf7fO+LvrCtf8lpx0LfuXZLjaqogJqJUgcsUptZIyLDWXFmcnW6t5G/owX5+/ab719x0qzmFHgv5esnLaJQqOxVRfcGsYeMN5unAGLG6D5Sb71+54Cd/7v0Wpdkwjbu3owgNK5tTuADevdxmVtsOwxj3Ho9Y9uzZu62DzLcXN2aWTRBJFC2XgbBym3/U+VGXVUwYPL4mrHnoDr67ejMde94vu952ZSYJov8rVZlPZfKaGL37z9Nv43A3z8CsPBwAd1ajSEuXMHeprXH1GjQ5vJb3MZ7v4+5v2edBfiIPTW2lHgz550i/f/uLKJQIRscRhAOPpwhqkGf6Um3R/eD7INnLJafslbQ6GIbrOb8gD3ALyjRp98s33rL9zB+1ERuoqoqtTkkOVLp9q33e9sAJL18kzmykhagysURWX6teT8c6y2ThtakxWWhUHThyOBSvsqqkHw8y91cDf5uBfbjNIu7yVUlljPQY4mGESk406m+gTqDvJQX8xaa7CfjkxaiWZjKuRIQ6sBwSzRy1Y0YI2JpiMHyc/yDyPHTRyZswPelI0gN+wbmHSJ6vgNiZbhDRfxg9cN0gvXBlvcMk07WbCvam1AxdqBFitPagpX15LfYa7jBoUudketmvS7zFBEJXyb56yb+Y+GyLJgX+OjRabRB4MsbdaybAGuHHrbapryRXnkNI0eIzPlfyysDn0UejvJUUcHNeXS2mDtJxB3JzgJrtp6vzpefPhPD6SQ0/AN/KZWwA+UpkO2xkPHEwkVBcaHl7Mu+ASAV89ca9EWVeo0rv4D2k3RZUgD2zKvpxs+4iuIpPHy+rHzJrsLETAVZ84mG17yVo+IWCjw+bQVza2WiSH7e0xE6YyJLyHnd6++wG4qvQXtVL9EQfHy3a9t3IoOajzSH5nuU5mwpm0G7Z00QlvJUMd0z5z45Nv4bXw3ACXJ0XUlqExf/WO3R7AgfOUsaV45tqzbZTqPak9JeohvWFLwryqJXmqmP6MnGorToi0XKO+KyFi4qkTUduG/bFDJmHC8AGp8gUr0ud8f3CvsZix2yhjW/ctWIVl67caf88DvkFwZtuK226gHo3r8lZKJQb0GJ+rRh7J+3oC9UcctFenu5q6Nj+5yXBcBn/Cm//YRgw0521R4yWyMh7fuXcRZv/kMby2ejO+dsdL2S7WUJNO2Knvrl1lkVSx6Oqi6lKZSOKg31PKUO0YI5dSxDaio698NNG2JAo6EVU3Sm7+cgSIG+uVHz8oegfc71+69fmqYheyQM++mhWsxKoVbtoeE11XnEOamXSPweYU8PTS9fjQj/7qbqQPoP4M0g4dolNyKBEaS6XEQpeT4Y757+KAicOT7XNExLAlDDIYst7f2q5JDtnFUiGAC29/EYtX1Zboy9+n3s2F6bBx0SxX55JOLJKDeQzpMukppo9hhXY+sevZyBgSV38chBBekkNSWgrqcTZmToVXonhuVqP4yCNZn+46akKW9Bn6GlQTRTaWVMmBaU9vy0etZChv2d6BXz/xlvP6voI6lByS0LmKn4bZWk0oEaG5sZTw4ZYttHZU8O93JtMlcBuGWWXD49DvPYh1yqKpNuPp8IG18wIVgUhF5arnU6bCtrn6GqRNYr9tD07YJkCp9yA9znQp85xfJ6ONXdtGK3PWgEoQPzF9cup3tW35fHQJyyc2x6eMiLyEq1MO3NldqZtw3dnTAZiZODaQzcYAKvebl7fStvb0e35vSxsO/u5f8JcaPLl6GvVHHJSXffjlD2H+2xsSv3PcnYoSBdHD6kK3rc0saiVbO2qCMhOH5oqfGNLcyF63udVfVdDRVcHJP37MWc/Xk0hVl9meVXWcbAx93zNy7JQce7lEEefpSoXi2jhaGV2zOoxZ++6E4/fZKVUHsLvomrqVbXOEwCQ5xH2Y72VAQxlP/Mdxxt91HLf3WIwZUnuqawDYa9xQANlOc9Nfm7p+1Oo++cB8jclXPvBq4v/NrX62vr6EOiQO8fd1DhH20F3SB9QTEQY0BJKDXEC26eIjhkZjs7SU0CtXSXBMG+KB3/mLMgY7fHNF+Z75fOm9i6zX+PxmqvenF5LeX5TY/NTvqm0iiXKJ0BGKaq5od9cIuVPK1I27ROb8TwICwwYGxF3faGxR4UG7XBkvTfiouRrKxKZXMb2icqmUm4edLRGjaQy6rUcI4KPTJuD6c6bjyD3i04h5ySH5/58X+nH+1/z1zcRZDVy2376O/jfiHsTOjJcHEBwlKYRfaoxMx1taFpAq0eibng2ujSMrfFVavmqgl5envWb4BqvvF+CDEc1dxXUbSxSpk9ocUqWLgHFqJdVDLbCL8LuzEHEk+r7jh3r1K5viVUjp+lbipPTRUC4ZEhTy1zaW06q6auE6GZBVNSqFC5a3YPmG7WjvqmDWvuNARJj7r0dj52EDDIxcumz9Fj+7yPaOmIj7emH1JdQdcaiW61Yhj5KUG3bWk9myZJSUUAPNbngybdRasXE7m90x2X3tE5TzuFEhuVRfyUGFbXNlU2V4GBAlOroqRkeApPopGeDYUC5F79B5VobjZ45AqVHVQpjjHioCmDRyEO7/yjH4z1P3TahpzB5a4adHShAAoJL5d/XWG0uU6YS7hnIpU3JAG2S/xiA4plydsh/52RMAgPsWxGlm9p8wHAdMHJ5SPwG8+sqU+XdHQ/0RhwwbpFlMDvP9hDMnq82h1mXCJfg76opHUmW7jBqUe8CNKxeVNf2CYyw2uuPLeN3OpE0HQuKgxjkY9ja1+JMzdkFDiSJVmmuDc42Rexe6esa86QoQAfuOH4bmhjLuuuAott2/MGoPvs30WAK1Er/56pJDFuLQWMpfcsgSBOfTd7nkp1ay9W27tr8EvqmoP+KQ4R2ZXmjMvXi0keFcA7X4rffMwUYDLMFyKnQjYJZ7/8ptfJ4mF/ccEU7WayRdX00hbZUc2EWfLvzl395kr+/oEmYvMc0uM2pw8Nz22XlowiDtsh+5GA/u/poVXbSAxZNNJAnXmCHNqfGv39KWSO8hN/pUWnohsHhV2uOsZHFWUt9dQ9nPq0miXKJMc8+G+GRA0xqKyyeNHGitq6JcIoNB2k/yX7vZnpG3H2qV3MSBiH5NRGuJ6BWl7DtEtEI7U1r+djERLSGi14joZKX8MCJaEP52NYUzloiaiej2sPwZIpqS7y0mYXtHOw1tTi46Qz3SVCdZD98x6kuVdl54ZwNfCcCQAX4uqRWR7N/nqEoXdzxl9GDr7zFx4Mbj2lxtv9W2utT36sI/z9wVPzzjIPzzzF0TebRcUpPpZ7lJrdmU1lWrhyoJIYwcuU441HryuZqIV1lb5X98fgV+8nDaZVtVu6XVSnFBc7nEH3VreEd5qpV0qV2iZXsHVm7cHr2DX/7zofjt52cG4/Lou6Ql05Tw9br78UN2F/gdVXK4EcBspvwqIcS08O8+ACCi/QCcBWD/8JpfEJGUm68BMAfA1PBPtnkugA1CiD0BXAXgyirvxQu2iRL4jwe//+qxpcbskpHoHTbFcWESzy5LbvJdFYGXDVktbe6KKmz+8In2kB/HMj1M9jZuGG+klyhHEbb+RDH+3UZkufr29iQe+doHsedOQxKbX4Kb1iSHcolw5vTJqTxa1Y5/+EDehRhIZgUVMB+2oxOOpNtp8Kmr9EwGaVMgpBooqN+JemuDmhuyqZVyNEjLdBe6/ebE//kbjrziEciRD2pqQGODmVHRUSLyViv95OElqTLuBDhVKu6HtMFNHIQQjwHwTfd5OoDbhBBtQoi3ACwBMIOIxgMYJoR4SgQr6GYAH1WuuSn8fieAWaTLwTnCNlFKivj744deB+BIIx1WtnkPybTbEsvWbzXmVhGG7zoadFbQACFEZo7F9OjlZtDlMEjbJAfXSKyLuIbVtfvYIcEXnwhprRIpebSckk/4s36Ghy1Lp8ptd3RVcMlp+7FSTkVTKxEjOei9EFMXMEsYgbcSP1b13gc1lTMRh3KJ8vCFABDPfd0xQkZWR3nOKJ6zeauV7n1pZeJ/UzZleRYL4Ce99DXUYnP4MhG9HKqdZA7hiQBUi+DysGxi+F0vT1wjhOgE0AJgNLoN5pdUVgxnckEdtecYLPjOSYl6WWwOOqRb5CWn7ZdKiOaaQHKT8T2gqCIE3n4vn6Ms5V7g0rvHxDRdzxUIZfdW8iurFfqeVyrF78WtFgt+//Y9C7U2LcRBabK1o4IxQ5rxg388kKknEu2o9EZOB6MaVPvf5FbJSSNxHypxaPD2gAr6t0sOXz9pL8uvSZgkBwl5b2rMhs++HEgO6XKfa33yTe2QNgcDrgGwB4BpAFYB+O+w3OQWYXOX8HOlAEBEc4hoPhHNX7fO7zStVMOWl1QmijYCOagRgxoxdEBSLaDaHLL6L8tFNnHEAIzSDcaOa8uROsuvTyGAr/++tkR7ErHkYK/XYJEc9h43NF2owOr1lcPqsgWYmaDqog+amA6KVCE3F52z1AUHg0YrioPgpFV9hJzkoM+Lh8K05HpzpkdpC4JTrxnYWOaJA38piOxzNouioDFUnZmCMWPmTmHivCQHnul6Y607Vcz7SvaCD+411jCu/kcdqiIOQog1QoguIUQFwK8AzAh/Wg5AVYhPArAyLJ/ElCeuIaIGAMNhUGMJIa4VQkwXQkwfO5Z/Cc6xW34rqS534XxtZFQ4JYUjabUc0MMhUgEQ48GRsDmkRypVylkyo25nAq9sMC0k2bdTrWQ5ZtLp7ZNVcshJZWaDmp59tzF2Y7wcjR4sp5+/wF4EhThwdgdh82QSelMAgLfD9Nr64UCuoLmgrWSdpCtrtjiH2JJn+D3DaymFdiA9z5XE9WFiOwIpaqV0Pf3QIpNa6d9udzNXGxTicMDEYWydavOh9SaqIg6hDUHiYwCkJ9M9AM4KPZB2Q2B4nieEWAVgMxHNDO0JZwO4W7nmnPD7GQAeEd2ooHNLDoHOWKYoUInDiEGBBKG603FJtnz610+TA9zcRTni3n0lB8FG5VYLIg9XVoue18X9237OQyw3Sg5K2zoBIdUg7euqmlJN+dkcRoeSJLfxCkN50K/WfwgZpPkfs/fB6dMmpOrrUO9dbWpTaweuVoywDSVzJDXfrn3dZSE0QDD+nz26hGUOpBOJ6pbLzUXdeG8ySPtgq7IHcMykaQx9HU6fSCL6HYAPARhDRMsB/BeADxHRNARzdhmALwCAEGIhEd0BYBGATgAXCCHkkzsfgefTQAD3h38AcD2AW4hoCQKJ4aw8bswEqwqhFKiVvqvk+5EHrt/6+Q9g97EB56jqMn1zDUnIScLZ6Fzzp1Qip4iu95UXcZCcmGsBxd5K6d9shKVSEdazibMeHlQtf6FvUyoRd3srmdqwX3PE7qNx3rG74bi9g6R7psyiZuLGGx2kVNtQLmGfnYfh7lBYt71DbqO+9N5FuPO52GRYyhghTUQO1Z13Uwm0dVbYHE+yz1itlP5dl85MrqwSPzlrGgA+/ke9zpRDqR/SBjdxEEJ8kim+3lL/cgCXM+XzARzAlLcCONM1jrxg52CC9XX/K3FovXzZR+05RqkXT7qszIasX1LsG+w4mTIKr/NN5heovfKRZ2VWU5fkIDkyn7N8VbzuodvVYXsM3G9GtYylTsKV1TkeqfvX27BJDoHK7vh9xintcG2bx28ySJ93zO7Rd/XaecvMzoeRPU15zxu3JdOyNJQMtgmTRIJ8JQeJza2dFuIQpwPh5p3O4TeWS9b8W8MGNBq9ztTmmwySQ93YHPoznPuqtgg5MTGLQXr/CUkdpFQJEaWlDtfYiAhlogw2h+wGcxtc3BVgd2X93Tw+tYUPsorlJuKaFeo9u4Yg71kn3lZXViFS7qMml0qTzcQ0vn84OFYlqVfaou85Tzx90ywraTZUGI+5tXiyEqXXmIlpuvfLRyf+tyVtbG4oWSUH/Z0MG9iALW2dKTfkeKBmIqaOt9kgOdSTt1K/hY2CE6Vdq3iDdLyAsoqLciKVS4Q31yUXqdqUfmiQHJ+qA3f3Za47y3RugKEteRCMyRA4c/fg7GEpruvP2XV29fxl5ohwALhNIyydXRVrEsAsxCSZslu3OZDCmdvblL/rfVtdWZGWCEwBhC6biT4+VXXiy5xzenqdiTHZUExR6GShDiWilIpHSu46Y3PgpOQpi8vWm920BzSWrTYH3UlABipuMpy7EEjtfF9q6ya1UjU2h4cXr0mdNtiTqD/i4NQbJytwLzshOTga1I3HqlrJ1XeqX2QznAmYORZ9oblA4Z/rfk0eIl+45Tnrdd+66xXr70s1bvcD338Ys3/8uLF+tTpeTq1k0unriIhISq1kvoZXf/EG6aySgzUJogFcbIBOHExnfZvKA6aLf3iE2D1VYku4QbtS4ttyjA1sLLNSkDomFTKNyVZDxlUySEtA8lmZDNLc2r709P3ZuhLn3jQfp15tnuPdjbojDjbIYJ2kWik9IdT0Ga7NUl9YsStrXHbM1MCe4drPiAjlUja1kgnHTM3mCjxkQJAyQbc5SCO97Co+oD5Z7/E33svUnwvqyXgcuM3ItNkkbA7ab4Gklq6nInp/hudtc2XlNv1jQvvWsAHJvEt6M3/80pEAzN5K6mbt618Uz+24LT2i3yQ5cPPyD+cfabU5dFZESnKQ/zvPz7AshObGUioHmlpfH4+eEkeHTXJQn7tZ9ZQumxyez2GDT4Bdd6HuiIOPx4m6kD45Y5dUHYrqikQADAd9M429leI+Bjc1eI2NkE2tlKf73MQRAwNXVk2tlPLMsXgr9SS4/ts73YPSiU7S5mDgfl0bi7ZhJOoxXkgN5RJOO2h8KvOqXk/as0ySQ9lTrfThA3bGs988Ifpf96Rr7zITnaBf8/MZEG7Stievp4OR9oC2LrunnVxbnJ0gITkYbEEqojVtGGmJyEgU1SvURIoqODp23N474dgwaK4vpteoP+LgcKmrKBzagMZSKjo6qBeLq5+67hlrf1vbkhNczgGVOMQcrUOtRNnUSlkCbyJDueH35obg9K8nliQlANJE90hy0O7FrlrJf2FwTfq4HT+refKowYqmUUZBkcaNRRub9p3buEtEznqSialGYlHRUC5h7NCYEJHmeJCyORiIHTcKma/J9o4btQck1WEuyUHOWS7Qc4ASxS3Xiy0+iFOn6b+7DNI3fO5w7DOezwJgYtQO22Uk229fIBb1RxxszzxckHIKmCaDKq6akuhJvLelLRFBqcY5/OH8QC1wxO6j3WMDcOguIzOplWzQb03eh3kD5H3boxKpVgq5QJ0w1RrgJtVXvuAWo8mArVY9dJdkNLFPVtbI8GmYCrpnjH4eODfLdAmR82oqaRtaSnLwDCDQOe/AzhL/rxOH1P2En9wzl04U1UgOLpuD3OxbGSLSqBxIJPu2ra+IOJh+h8UgHV6066hBRjuPjegA6WfnG+janag/4uBRQXLDJuKgL0oXvnpHHDgTZ40kHLbrSLz6vdk4Zi+zzeHxfz8OL1xyIu798tH48VnTUCJ3Goq4L/8J5gqWK9tOglEQSw5uHBQaxX3GOWmkWz+rgmvRZ8GdvP/Oif9LHpKDLj3p0NUR6u0uWNESReMn2tTqCaQ3J934n/JW8iUO2nMhJBmQtzWvIGO8CPMA5LnUtles2xwaGZsDR+jkOjDNXf3IWnXd6M/KJYVZXVkRq4pNPgA/efh16xj1Xn3XeHei/oiD46H7BavIRWmv+0/Tg3RS6uJTJQdAutyZddY7Dx+AkYObcOCk4RjU1OAMnlORiTg4ckSVDVGxepHtJDgdUj/rUzermO17Eh2QfOfWIDghsOvoNJFS7/k3T7/tHJvsT0pr899Ou/GqUcWvrt4UzA1tcCmDq3J/n/7ALt6p3XWiqXoXvbo6ffZDh2a7sWWt9SFPeuBYuUR4dfUmrNvSlihLjbvLThxII55eaiXT7zBHhUuJkcisynt6aVJd+fi/H6eNMdlzX8jFVH/EwfKbi8OR4CSHWfvslPAuAYCzj5gCADhaia4WEXEwGwsTSc60RWFKLazixP3G4ZMzJmcyCkvRfO7Lq6KyUw+MU2iZMnZGhC18slzmWKNOPDrVyz2+zEFwbPpl95hSen3VIA2eG29Q2D/OJVfvS96v7Z5IUe38xx8WAAC2aW6WRMl0Kmo/e+88NFXXhJTkoPTNSTX6+QU2tQ1JvZIF+jMtlwizf/w4zvn1vHQnCmLJIfmy1e5UV2QfW53plXDeYvrQCOStypOHZplsHYXk0Atw6R0ZBo2pl+b0Tz1oPG4+9wOJenFAWAy5MfDJ1dIcoL6ofdRKnV2VKHiLi9gcNbgpxdG1dXalFr3KaZVLZt24WreBycrK6YQBNb23eyH4clIymOn6J99K/WbkHNXnzej1o8sEv8m6pKWUsdFQriIw4gbfX3o3iATmDK9q1li1uZSrprkrxuZACsFJ19/VcFQsJ3WToo3kCPKE4QNSEg63NiaNGpgqM53trc55lbhbvZWiPvk6XcJyhGvYLpE9yWKyv+DTdCBRYXPoFaQf+l7jgpPCJo8clOAQjOmrI0ZR5UTTOmFu87tvQcCZq/MsXjzBpy3yt1RKp7DQxeqW7R2hFCRw+JRRUfkFx+0RfZ84MrnYWjsqqY1A99/WF8fnj94t+i69HWXQoDq3txqiowdnUCv51ClRIDUBwAImtYLPekurlZSNEnzyO5edZVBTUqKM1UAuycG9YZgM5vq1NoZHlxxUgsNB9WxS++WmrevQnUN3HZmKJdKfy5c+tAd+d97M1LXyeej1mxvifEuqcd0mOehrkOurqcFEHMI2LGolHXItmeyXhbdSL4B75nOO3QPPX3Iidh09KPRWsr/gWNertIs0x8PldvljeKQop1aS1WxcA5fq+/Znk6klRg1uilwhVUIzTHHL3Wlo8hQ6Tm97xB7xgXxlJtnaaQdPiHWm4Zgl16YSTi7lxo/OPBiHhJ5BPpt2woi7nM/eOmHEQDaORCLp/ROXf/++xdF37h2qkc9EwCSNsJZLkiDyN/K5o6ZoN5Mejw5CmthwSQ+J1ESM8e++jOeQ5gb8x+y9U32bxnbDZw9PlZnShsi2YtVjGuUSpTx89LH/w7QJ7Nnlcp3o3aYlh7C+gxibxij72HOnoZgRMlsJFSWk5OCvVpK1CsmhD4F75I1lilQtcgMw1QX4KFJOJ2nLYJpcD8n2rNdReuLo9X905sERR6lyc7aJ29rRlUw9XC7hs0dOwfTwoJjgWs0gqnyXY5LGRTmkVS3bWaPmGYdNUrimuN9/1zYqCXVsH/nZE2wdlXBy4r1pw7vrxfjkNv0q1aVUiGCj+/JxeybqxFHhbPOplAqcAVkHxwRwwV4NJYoMs7b2TG/+ka99EIftOipZV+G2dQ6We65RXaZ9VXLgnn+JKCU5pFLYGAzrMXHQ6ivEQbU5WA3SFqcQ9doj90y7nd/w5LKoL9Ma0+9R3yv0oRU2h14A98yjFxq+MZeIKX93Sw5hPWZSlhnJQUIu9j13GpK6TlUrrWrZjjWbWvHTR95I1BkxqCngmCoiITnohr/nvnUCbvhcwAm2dVYS91MqBURwani0Z6BW0sZCpDyL4GK5EQoh0NbZhSN+8Ag+e8OzqfuQ1wPAm+u2RGXmCFP3Ynl/azv+9MIKrN/Sxvqkq4TSJP1zaiXVVZQovZDj0+/4MUq1pUTE5zs4Wf2eOaahoRSnNBHafEy2Z7phru9AlSaEwLWPLU31ZwJ3P6rN4d3304nygsR7elbWZJ1mQ1pued/6Y0lIDojXi/r+UzYZRlXM9aVLQRu3tePV1Zuj30xqpXNC55S4P6lW4jnRvuCt5DzPYUcDN4HlhNdfqy2UXtZQKkflTQ0l/M8/HYwpoeGOa0VdrGmbQ/DlnCN2ZfuWk/2IHzzCji9oP2hP1eiUtUU4ekgzJofxA7rkEJ3oFo6FUyupjiiR5CDVSiLmqEyQ+8wdzy631gP81CRbQm+e3817J5NaSQXnAKAbZ3WuzmZz+OSMyak2dcnhix/cQ7+MDRzj1HON5VLEACRtYNVznjJ9xhtrt+DhV9dqv9mIQ7pMNdJy5yVMHTckRXB0Dt8kOZjSmqiSmmpz8EqfYaii50QL+iRMu/TBRF8+KTaSYw3qB6lCYrVvITn0Ani9Z/AY5It358oJPpOSQ5xAbHBTGacdNMGaT16dQ3EQVVL8LTMRNSXy4yqkzUE985njalSjuer+KccuJ2mZMUgTxYnBBjUF3F0kOcB9hKp+34DdldAXxIwV8JM+eMkh3IQQc9UqYpfcdPuqcVQiMpCGXyaOSOvToXgryTFxjgrlEkVE44V3YiP8qMFN1vuKe0n/ID18uOh/Tm2i34/eluybS18yRzmQSCLlfWTIvhr3myxX370q+bkcAGx19PQybC3t0QxUJB5Tu8NC77otmstwnuewVIv6Iw7MM48lBz9jErfpC4GIC99/QhD5a5tw6uJNSw6VxLj0vn09dypCoKuSLDPdS6WSHOeg5nJYHpSVSumnUyLCkaHRWnqwRN5KFZh3+hCmI0V/d95MPPK1DybKXGtFVcF1dgnMXbAqVcfHyJe2OcRxJYHNIb1wYwLrbD5sR0oOkhvl3jMgtyBdolQhTzDb2taJi/8YxEOcdtB4fOyQiV5j4YiG5LatKlgFnAu22tsa70cAACAASURBVJZkSjjiwHHauoSkSw4//9ShYX+SaCfrqzyVqp7b1mZmVlwMYVpyYNrQZo+PpDo0jI3Sz5HoC2dOO4kDEf2aiNYS0StK2SgiepCI3gg/Ryq/XUxES4joNSI6WSk/jIgWhL9dTeHbIKJmIro9LH+GiKbke4tJmHyx1c+4Lg9dzy7R1FDCAxceg59/+tCwvaS6QQiBcolw/of2wIhBCnHQJpzcjLiF6CIOd37xiKjvgDjEC5JbiCoBUxfvbXOCdr5+8t44Yd9x+PABO7Ob2GUfOwBPXXw8LjltPxy711jM2nen6J5d09uUOuCIPUZj97FJPb1rsXz/YwdG3xevig3gu42JffJ91htnN4r7DmwOunZEpl72z5abHI8p8lzW2zu0+3z+6DSX3VAmdFYqiQ31tIMmsO+KA18r4La5tcLNSVtKczWy2HQMp3Rp3j18V7qEpBOHU8OMtabzM5L2vEDSe2VFS8KRQR+J62nFJzimpV0J/dGo88E0N2RiT11y6C/eSjcCmK2VXQTgYSHEVAAPh/+DiPYDcBaA/cNrfkFEUra6BsAcAFPDP9nmuQA2CCH2BHAVgCurvRkvMM884go8m9DdN9Vm99l5WBSIFdRFNHs7ugS6KgKDm5KqBt3IFUkOzFkSrsR700NXO2lzULlNTq2kEqbvzY1dOuWmOmHEQFx3znQMbm5guczmhjLGDx+IXUcPxs3/MiNTSgyOWzOpj1xrRd0/1A1MHcdBHgcccWoloWxCROkxnj5tYqovU3tBO0nJgZPo1Eym40cMwMGThuPoqWNS9cqhQVrdrLgDa0yGfpvUwj1z7h5NXkOyrdjmwDtHjBrchHu+fBR+9E8Hp/poKpdYpkaVrGwn70n7yUuWI0XVa1zeSlbJQXuW6gZvWg+S8LVr6cn7heQghHgMgH4i+ekAbgq/3wTgo0r5bUKINiHEWwCWAJhBROMBDBNCPCWCGXSzdo1s604As8iX7akC3COXczZpbDJUVuptURahzbNJzpFNrYF+3nQoejye4AJecvCbOKtbWtHWWUGrovc3SSJAwAk98cY6a5ucQTpdRy4y4eTUSxYuTIfL5jB2SKy3VzeTPRUJ5IqPH+Tsh7OrJGwOIHzk4AmYOGKgck3wyW2mMl//RR/eJ8q1FevA+T5lv7K5ijB7Gy1dtxVzX16VOCmPM2ZKApbqhyujtJpRgjOKSyaJnZcUPx+VOPz1Gx/C7XPiwLaDJo2IzjVR+zAdu6mmkUlJDiVK1vNhVMJPk1FftmlzeU1LDvx3FdJGp581YpKyehLV2hzGCSFWAUD4KQ8knghAjchaHpZNDL/r5YlrhBCdAFoAjEY3gXupusgY1XV4K6npDExHFqoJ1KZf9lBYV5McNKIkuX3O5kBEXiLnopWBamVlS2uqH+5eKsJ8PrRe114n+BQGtQRXV602+4A4n9MJ+8bnXMsFzumtH/7aB7GLkhBPfWzfPHXf6LuLKPNjjM9VkLEs44YNwJMXHZ+oA6RVAa9dNhvH7R3cwxc/uAe+eep+QTvynjRuVIUQsbFfCMFKFyqeV5L3cYS0XCJMZVyjTXNCgCfu3PPvrAg89/YGPLlkPQDgN0oamTLFgWHqhjdp5CB8YPfkMpf3qEq7ppT4KtHWN3/d2aMi3PZEXSLoqgi8tyVOtf+Rgyck63HqaaaPCiNV7a6oOmXUtf5cfc4e6W7kbZDm3oCwlNuuSTdONIeI5hPR/HXr7FyuCdxL9c2JIyEnX5sycU2cmepKJ2HapGQ1m7dSuZQOjuJw4Ql7sddy4wOCZ2ALvgMYYy3n6YKY2JjGKVNccGf87jw8lgCO32dc9F0O7eePLkm1t4dmn1D7zUoQOOmoIgTeXr8Vj762zppfSjdU655KOhNgszk8Fkpxaze3oiLMeX3ituPfTfsKK+mx71C+v/QLVDetUw4M0ptXhMAtTy2LylX1V+ACzW+ApvEliIPhGkm0KxWR4rJVgiLVgPq9n6ukflH7li1deu9CHH55wMxd/OF9Ig4/4me4uc08Xy7G4ktKEKVsV302z739Pn5w36tMBz2LaonDmlBVhPBTOkMvBzBZqTcJwMqwfBJTnriGiBoADEdajQUAEEJcK4SYLoSYPnZstjOQ4zbSZbongimkXSI6jESZhKbISAJh7eY2fOnW56KyMUMMboZhdzbJwaZWkoZLgBfH5X0lzhZWJQeHj6yPtk/NO8WN8sg9RuNXZ08P2wvrelC7KPBvY6ujZlKtYhqyqUcufYYQwKlXP2FsUF6zepN9bLqXW2RzYFah9Dba2taFSsUstX1yRrDc1M3FND9Y7tmgGgwkvzQOnjQi+n5seA55V0VEqrwJw5NuuaUSWb2V9H4BfzfOihA4/9bncN7N8xPlqgs15/p9y7kzUrEl+nkOf3x+RfRbUyIdR/DJjZDbAuRcNL0Tjjh8/Jqn8NTS9Wz9nkS1xOEeAOeE388BcLdSflbogbQbAsPzvFD1tJmIZob2hLO1a2RbZwB4RHRj1imuYWl8lRNETnTTHI2OMXScAhc2ijufW477FqyOivQ8Mek4h6BdjuDY1Er/HRr0gmvTv48c1IT/d/ye+O15sdivprBwSg4eNge52ZgkBz2ZH+D2agrGF3zePv9de0XEEeZqH77Qa0tiLO1Ltql56zPveLWtq0O4Mco52VURqDCcr8S/HBVwwB2dHsTB81FIbltv5ug9x2Ck4oIt10lXJa5b1pwoGkpxZL3r1ET5HFzzMOgbgAD+vHBN6rfEGEMVmToqNopZ2/Q7FIqiSoC2Q4E45ilmBPj7iGwOfcDGoMPHlfV3AJ4CsDcRLSeicwFcAeBEInoDwInh/xBCLARwB4BFAB4AcIEQQpLx8wFch8BI/SaA+8Py6wGMJqIlAL6K0POpu6C+1LFDmzHvm7NScQmuE7Qkp6enuGbrMk3pXL0qqr6yogU/f/RN4zjKZFYr6QnHdBABXztpb+y509BUvYqywLmIXXWcNsTpAHjZQeXibEGCqesy8AsJycFQx1jOSA5q31xgny8B0omhPc4h3oSEMPfBqWyyuEGabQ5pSfLiU/ZJ/B9F0SuMhZ5ELzgdLajHeVElxhJ+dnlEedoMzTIOIq6nXcsxXeGnEMCNT76VSDM/uFkhDh6Sw1+//iHsFMb9zHvr/bBdfqzSW6nDQjh7K0OrM32GEOKThp9mGepfDuBypnw+gAOY8lYAZ7rGkRfUx1wmSmQnlRPEtdh9D0AP2uQ3+EQdZcKd9tPYF5u1EZRiLmOvcUPw+po4L5EaCGY971ltj5nsJkOyj1op5o7BrqCk5JDu2wTTRiDVKioSCeoMQzZJffojl0FwMoeRfuBOUIfvw1RPT5/BMRDy3XeJQHJoNDAsEVeucJ4m4sBKooZxVoTApfcuSpRLJio1xoqInrneR7lEeC880e07Wns6skgO0i7CQU0prhqu9X4S7YVlW9o6reO0ubzKtT5FMTjft2AVjt1rbGIMau+NBoO0is6KSCXu6wnUXYS0uhOlN4Kw3PFU5ALwUSvZPISiOgb3OC7OQeWY1MXxr7OmJjZv37zy+lGK3Djivt3tJThe5vcrPh4Hq0kOzsvmYDSypgelbi4mQv8zxrDNtSdzK0lpb6siOVz58QNx6+c/4C056B4x8j1y70r1gLIZpOMAs/gBmR4nl5bdJLUIgSihnAnqIUfjhweuvbMUDzMguDc9wMsEOZQuDxWL6kXmalPGp8TjZuqFnxwHr7qsxxIGp1aKv8v3IdewS61kIw69FRBXf4n3lCnFqRAA98baEBEHH7USz/2rMLnH8bmV1PMF4vrp40SdQ0vU8zpC05KyWx0fkPZ2efDfjsW44QMSZ0q4DP8qjEeNMs9XXUy18lvyecuxtmzviH77xOG7APA3oOr66vjsYUa6jPI12eMcZLGPQfrNdVtTZTbJwQU5xs6KwIQwP9SXPphMZ14qpbOumsB5K5kr+41RHtakzl3bWehc3x/ae6dUPSHS711tVrqFN5ZLeOndjbjnpZVsPblubTaHjq5KVW7YtaLuJAd1Ppk8jFxH/WWSHCzX63VSkoPJW4lJ0ay3yafKMHOo1SSl4yshak9tcuiAxgRhCPoOPl3xFUF7fDn3qtSNstZ4ylIpuBdbK75dqDEggD1CWu6nwXM0xzlwkkOWjJ4m26xPE6p0ox8Tm6znN5Ysc1E9oc9VryKSpxHy6jVJ6JJresSgxkTAY7RWkWQU1DaA2AW3qVzC6T9/MlFvzBBV7UVoKpf6pORQEAcFciNxSg4yza6HzYHbVVKci2PhqwjSZwis3Lg9ERVrOoUu0Y1lLD5qJX2j5dtT2lHLmZlmCh7jYNowOCKoBif6bkwmqCeJ2er4tgX45lYKn40QCcklXS/4lAFzgL8kAxhsYiVyujXLekF/ivs1Qxx8h+PKwZSs60fApBT0XcWOYFsb+iZ9yOQRWr1Y+tOJsNrs/zs+kKD087Zv/NzhUdS8RGOZrAbp3oqWrj/ioHw37dGuo/4k8aharWRY6PoU4Baa3Ky+ddcriXJdcveU5FNGUm4cUV2v9njuz7Yg9TMD9N+D9vj+uHa3K3YB30y7JkibQ9ZcNzf9ywy2LYBxZWXelZxjb67dggUrWpySgzT6AuYgOA7cVGxuKKfcTvVzo4F4jnUJRXJgbqaSsAEBiy49OVVHHYuPt5J6iI9EY5nwv585TKtnT6+h1gOADi2Nha7aVZ1HdCKszsVTDgwi/QdpedRUFVU07oZCcugTUEVRLo8O4EEcajRIp9VKmr4hhPFA+Up6s0rfi3kBcONTmzMapFOzxaxCECJJILi+s8QgmFQInOotQRy0MXObtg3SASCLqubbp+2HD+6VDtLUs/TGCR957h0AvnHny4lrufEBSRXHh8PI5WoxoLGEts4K9hs/LCr705eONPateivZAsGAIAProCZTEsCwvo9AzkgOD1x4LE7eP3nvMs5BBbu+wyI9IltnslQVcEpyYPrw2dgbyyWnzaE3UH/EQfkuIzwlfCWHLEFwrGrIQJT06bHPzkOhQ6qV9DmXsjlkVCtxB6anrvfgwtX0GWs3x9yszd4hsauSHwkAxivRtibOfTvjgbOtQ5UcklCjyH0gdda1xg6ov+lnGtsSIsb/m8YXfErVw4LvnIS9MtyjSXJo66zggIkxcRiq2YuApLeSdLfk3nNScjA/nJjY+MY58Nen6lXc9eS81SUmk+pZKNISENgR1Gb1w7IA4MzD1CQRMZrKJTZAUB4W5Do0q7tQf95K4bu69jOH4fh9kiJe6lxXA8qRzcH90lhu3cKNcOPRy7pCI6UKfcy+rqxZbA4LVrRoY0nXUT2v3lOIw0DG20Lf8H7x6UMT/39o751w25yZuG/BKtz94kpwUM9ukFAPdfGxk9ggddaZiIPlNzUFuNzQfYy4nLomGJ/U05uj6rOiuaGEDdvaE44CnJ99OdrMg+dj6lv1ALISzvCzwyfOgdLSJCsQUJqx4DMPhH1rXLpqjFbrBWql4PuPzjwYZ2gbP3c6IEdggdDmEParqqoHNzdge0dXwpjek6g7yUHy55NGDkq52MkX79pAGmpVKxlUQFyCMB1yc0mplVIcTvraprJ5g86qUz/vmN0S2SX1foVIbgoDm9J96xv3yEFNqTozdx8dSUsc1LMzJFTVQCqWhW3FjPh5Z7jGskET4mctjb6NjIHIx/sMUFwwu8xSSFYMaAwkB3WTto2xK0x+ZyJgsxQmzEtyqDLOwXzkqX5tur2IMGnE4asn7s32oaqVOPueGsQoscaQe6tBOQf8qCvic+GHhJHZ23tJcqg74iDflcmFD/A5WCYLcTDrk/V+BdybV5nSbqJBuSY5KH1ceMJUfPcf9k+oCfTxqfc8TfPQ0DF2aDO+eep+VlXRwpUtTl2pvkhtgV4m2uXaDG2qsCeXvGe9FgDWbW5LBEH5wCk5hN+jQCk2Ej5ZZvJAiqOKQ8khq2jEoKmhhLbOroR6h03lohCHrkolIQF9/aS9cOguwTw6cs8xUfS+7XVxqhgTiNIxCWbvOR/niLRa6bxjdksxNVKCau+sRNKkzelEfW8btrWn6skxyketpgmXJ+T1llqp/ohD+MkbioPH0enY1CJvpWrVSlqhVFN1VdypmUtEUdRsoo1Sup7EmCHNOOfIKRajZiC1HDhxOPYaNwQfdZw/bMs9Jfu9b8HqSG3iG09iunWZ/I6D+qr2Hc8RP70g/vrp655ho4ZVqMFL3rC8w/auCq75a5A7q0MJlNKhb/Im11Ld5pBVcjBFZ1cqSRdKlhFQuOPOikjMiy8fPxV//NJR0f/ROe2WZyPVrV5nfROl1il/YlxacrCplVTDMBd4Nik8J/6d97fFEe4Owik9ljYbIsVNeaJkrrTCIN1DiCQHZtuWnI/z0JsSgSg/tZL8/7K5i93EocSrOWwGTCd3HU7Ojq4KpoxOq4okvnpicEaEjbFTe5KL15QXxuQtxtWrCIGHFqUzcE6bHOf7+eknD0mPJ6VWShbsc8kDfKcGHDjR46hRz7Y6LcRBfzamDVO1OZTIP+ZCgoteLlHSA8mEKPFeRaCzy2xzAOI5aJuK8ievrKyUXqdc0xxjYXNlVSUHjjgMGxhw81vaOqO03hyBjewxIo51+MwRuxruhY+lke+mSJ/RQ7BFpcpNrM2DUjeUKCIOn5ieTv4mYQpkM/3v0v3Lya4b42w6ahdxKFHAJbZs72A3KgmZX8gm9qu3q0aJchg2IDn9TCogqTe+7dlkuu4fnnEQPn5obAhUs2ea2qxV6zL7ALebqG8ftrPCdfW9KRBKjTLPKjWYqksbj2uTlv1t2NaB2+e/y8ZCSESSg4V0ZvFWIqTdTlnGigjrtyTVOZxnEGeQnjRyYKqe6qH1y78FEiArscgI94rAkOYyjtxjNP7JsE/IKHwdjeW4r95A3REHm1pJGtRs0YoS5RJFngVfPSl96poEr1ZKljZkIg5SrZQst0kSLj00EeHax5YCMHP5wW9uTka9t62h15DpHOCd9YNhLGolIUR0BrfEvuOHJRYmr/s1DjUzBjeVcb4hnbkK38C7SK1kOPFPhemZR1HFlUoiZbsPbOo+KUnaIPu+6sHXAQT2GVdfPjYHn3QqRIS1Ldu1snS9pWu3YLNmM+KJWCyBSchkgipUDy29LFFPUbnZItzl9dy6b4jU3EUQXI8g5rjTL0tujK3hpj95VHpySDSUSpHkYOPYfMT8pORgrxss3DQR0VUA6oTlONNEm8rPtiRpkeTgKeZKyeA3n/8A+3v6LG2z+ok7tjKd3TaNlCurbcAO7D9xuDPvVtCnX3vynXHvx9/mENQTws0EfOPkpOeNacMqhwyI6z3Leetzv3Kjs9ocwp/UflXJUMVzb29InI+ujkeFThgAXl0U2RwcpzuqBxxZ6ylExHZYU9B3ML83bE1KOHKNZwnAzBN1RxwkbJLDu+9vw97jhuKBrxxrvL5comgi2Qy0PguHWzDHKOfwqpBctJ7XSefy1CbdaqX4d6taqZxeGByGhkShvauCfzxkIvbZOW0oBtIbtU1y4GIN5OHsUXucF4r+v+WFHD5lZKrs15+d7hyfDt960lXUlCZFhYl7VKu5CNdumuuxaV6USxS5p9ogr+diWHTIe7Slw4+Ig0eGYHY8jsW26+hBuNkQIR/ZHJR1xK3rBoY4cM9djXOoOBxNZNaDQ773YKJcMp9F+oweQmyQTkNO4I4ugb12Hhq5knFQF5Zdckj+/5/aaVpZUaIgCG6tJsJv2JZUuSTG5+EBJWFTK/lKDmcdPhmDmoL8PCaVkt4vYN645ZnG+l6lp2EwqJy9IT1RVIweHKsgbO/5Y4qHl02tpLoTS2839RhKU18m/b96f84TDLWfjWqlkJN1Jd+T169qcZ/rHauV3DYHlRBm0be7iOPHD52USnonwZ2oZzpPBUgSMLtBOlAr2eaOyVupIA49DJkagtuIVK7ZtdDUl20K/gHSi+Hco3f3GqetvUpFJBKtAenUEwmbgzMWIIbtXnxsDtEYhUCbgzikPImMkoPk1pKb1WCNONj81yVsnCj3m/rsbBtb4ghNy+M+as8x0TORi39Ao9uV1aTPz/Ke9YGZJYfAG+eVFenoc26MPnEgsUHaPTqVKGXZFl33b7PJRJJDpyo5mG1BiZQgXMZhVXKwpFyXdTni8JVZQWbXwiDdQ7BKDhk8fNS6Np2+TmRqjWDV3d4mjxqIW8+dmbKPZHNlVeua60mvI9dklTrU9s6K0VNJ1lNhDoILPnXVih6g5PNkbSPn3Qn9iIO6mdvGUSKKBtHW0QUi3ptL33C+/48HpuroY/LxSjNdm+zbb47qYxyvORiokDFEPpKDygNkYZpdDJ3twBw5LDVXlzV+wSE5yLpScrCpMwO1abLstjkzo9xq/dIgTUTLiGgBEb1IRPPDslFE9CARvRF+jlTqX0xES4joNSI6WSk/LGxnCRFdTbWe0GKBLUJaNca6VDG+ahtVGvmvj+znOUpbv0nOXQhgl9GD0httFldWz82g0cOVFYh1qO1ddslBh2kUcny6akVv2yfLq42wcb+o3KNLNSBhNboqY2jrDDyMXEkJD91lBJvlNagXf8+iPuT+920nqqc9jz8pQW864iA4c3vyt4TkkEWt5Bi3XXIIrlWjkfnDttKSA5caBpCGfYQ2B/u49Qj4D+w2KuE22xvIQ610nBBimhBCWu4uAvCwEGIqgIfD/0FE+wE4C8D+AGYD+AURyad6DYA5AKaGf7NzGBeL2FfJzBUAcdSyCerEsW2ukvPcb/wwfO6o3TKMlEeJKGE0M6bXzsRRxr9z+u/ot5DQueZqcFhMYEDORBwMw+S8WN76wSmpeuoiPeOwSbjzi0ek6tjGzm1Eifds4/6Uei7JQcap/O9jS9FqODDKV52lEhbXcZw6p2+q7ivd2lK2pOpKg7THvTz/zsaozPS6uNTrtshnAGhm1Hd6va2Kioz1ImMM0qYU5DJ+oSLcAYI6ASCixDGsvYHusDmcDuCm8PtNAD6qlN8mhGgTQrwFYAmAGUQ0HsAwIcRTIlidNyvX5A65AXBzNOHSmcHmYIPkPPNIiAYAm1o7NcmBnzjlTMQh/s7pvyUaPTd6dcMyZaLkYFJB6Udh7jt+GMttNzWUIvXamCHNmD5lVKqOlTgwZeqz26QdC5kco/Ld8phKoVbJteDVTdRXsnO5LOsMkUlC8D1nQx+Xbc34SA5B38n/hxg2Xj7tR7qeOqcGWBgfiaTkYLYFqZoeu+QgXVntRJGbDllOSuwO1EocBIC/ENFzRDQnLBsnhFgFAOGnTMk4EYAa4ro8LJsYftfLUyCiOUQ0n4jmr1u3ruoBm5BYkBnUSjY0Ri582YiDyb/7d/PeSfx//nF7svXUtOA2IzOQTPZl08va7Acq1FvlMreaYOJ8ZXtygxk3zByJKwPKTBuV6awKgN881Ocxb9n7xmvLCcnBrjsRwq1HTkoO1qqR2zMXTKd1nYBpTvozPmZVpqlNn9xhEqcdNB7fOm1fvh5zqyzDoMypnSzzRl6qEgc+ziH4fEVJX99o8voKXYKFIwhOumrrkM83y7GveaJWg/RRQoiVRLQTgAeJ6FVLXe7pCEt5ulCIawFcCwDTp0+v7olZbA5Z3AL14wNNiCSHDLThb9/4UOrsWQ7f++gB+MxMc74WCd8jQwFg8qi0O6eELQbC1LdNlPeFbG/CiIFYtn4bfvyJaca6smvTRrXzMN5oesDEYfj2aWmbkIkrNI1RHQNfL/jUUz+Y6ultc5BxBi4GJHXmh6G+zqlyaSS46/0kB58xBv2fd8zuRsnTV7ppaigBoaPX2CFmg7kk6J3OOIdgPv/phRVRmSltiFQXdTm8lUwR0v1arSSEWBl+rgXwJwAzAKwJVUUIP+UBwcsBqMlFJgFYGZZPYsq7BTZX1mrdP21o8NC1AsHhQxJ6WgkV/zpravR9qC0OI3Ev/q/5pP3GGX/ztR+oj86XoNgg31VbZwX7TxiGEcy5DxJyGZk2KiLCt05Nc6NX/ONBGD4ovRH5BHgB/puV3IScqSk8bQ4A0ByOkTv4KNGm1s7nj+HdqtWxnXrgeNz/lWPYevq7tevVpbeSdYiJhWUjoL7SjTpnbWo32Z7aJ6dG1ZfS2UfsaiR4aqS5zchPlD6tTpabpIqeQNUrl4gGE9FQ+R3ASQBeAXAPgHPCaucAuDv8fg+As4iomYh2Q2B4nheqnjYT0czQS+ls5ZrcYXNlVRePnsdHh+8LkwvIxdWpE9emHpi5W6xHHz3EvEkm3FMzOH/ZODtftVLCSJqDrUU+mvbOitPoKqlDVhvPMAOH6m2cVdVKDhUCAHztjpfs7XlKIkFd9/j0dl76r5OMUqfqzjlp1EDLCWb+xKHRk0lSbWh6FgAVvsRYXVe28TVF6bEVLySGMdDXkjXGKZQcOruElTBJAmByne0tyaEWtdI4AH8KF0IDgN8KIR4gomcB3EFE5wJ4B8CZACCEWEhEdwBYBKATwAVCCDkLzwdwI4CBAO4P/7oFkbcS867Ud7OlzZ7n35SbXYfcHLO4GdoIifqbGr2rwzeCOwv8JQeF0OUgOch7bu+sRKk5TLDl2JfgNu9a1V8Jacnj/f3tdbvNLK93pkK9bdt0VNVKNkYllV3YFgfiaZBW+27vMq9B38ejMjQ2gsLNbVucg4Qto4CUHDordqamRITWji7W8My5ufYUqiYOQoilAA5mytcDmGW45nIAlzPl8wEcUO1YssB2noO6abj8q1ds3G79XUJuju6gOl8bRtzOGIvkoC4ElxeLL2wLIdl3/N23b5MBHojfS3tXxdmeS60E8FKjjyeLDercyWNjV/exx9+wn1jnu3X4OlyoaWN8319DiTyTEtrrqPugKScXkEWtFL9Xq+RQhVQcFJjrBjmq3MGg5VI6rbhEQy9KDnWcPiP9Wzcwa9GG6lQNeHauYH3iNAAAEVNJREFULsCRg23EQf2eE3GoQnLwVStd/jEzbxAZcTsrTknET3JIl+VhOJewqxCSv5kCI/M47tPWt22zOvfo3aLvvu/P5uUGADc8uQwA8NK7G631JL55yr6YMMKcFdnbIK2qlTJKDrVixcbt+MPzy7GptROLLPYgoqQqT4X0eOoN1B9x8LQ55IUGX8khA4cmYdsoVQ4na55/E/y5q/i7r0Rk21yiY1k7K87NSnjYHLhffO/NBzYHAH2KjTN4T2WRPuQ9Txlt9jTT+7ZJgWOGNEcn3jltPB7tVQNXviZ/m4OiVrLcir5GvpNDNgMV894yu0GXyCwdNDABcj2F+iMO8gsrOfhxVlkgF02WdBx51FPR08Qhb5WWbK+9s8u5WXkRB85TLUex0Wpz8DYeZyAO4ef/O36qtZ5vig8g1v37Sg6mSG8J6Vnn257Lhbia9eKrVjpklxH4bA7ZDHxhs6P1pkG67oiD3D14m0P83ZauW8XXLafAZUG1+Wx80OzpjulCqUQ45cCdccNnD7fXS3CoebiyBp/tXRUnhyptRVnO2DDlLZIwna1hgl1qSf6WB+GO7tnxbLJw99vaA87ddx2Y1CIS1/xz4Krtyyx88vBdrL/7Tqss6U9kdoA8POyywCQ9AmGsREEcegY2byW1zBaFm2jP8d5+Ny8ICn/41bXWetVGpfogL8kBAH7x6cNw3D47WetkSfrnA9lGoFZy2RzCMWTgvP999t7W3285lz/JzgTbBqgPy5bLyhdyCrru2VfFB8RR877rwIXhA/3TqABgY05U+L7fLF57E8JjQbNw6r5HwtpglRwsKqfuRv0RB0+bw5xj3WcFA9nyzeeBauwieRIHH5RytneoR2G6vZXcXLT+y/4ThjvHsM/OQ3EJE0HNwWZz0N+fzRDusiFE8JyEWVR8Uufv2tQ/9QE7hy+RxeB78CT3+xjiKdGohzC5pPOl720FALzwjt1ofsFxfnvD/3fGQdH3Q3YZYaxnYxBKvSg51OF5Dn4R0r6T+aT9zRHF3YEs3J+Er/761IPGZ26bg/ocXV4sPkgYUh33v2ZTkCvBtkFXw4k9cKH5yFgJebymj0orCHwCDplc3aahQtoHspxB4gvXfPN1AY689hyc9kvfPsnLc2zMED+J5h8PnYSvhgGHedmV9hvvJl5AkhhdfdYhxno2BqqhRFGK/I3b2rFgRQuOmWpXg+aF+iMO4ScvOfi3s/jS2WjvrDjF37zhSiVeC37+qUNzaScR7Z2DzSFx6p7n/bdadOBtnXbjaa3wOfdhwoiBmLrTEKuBXd7rN062q722hvYBFzddzbvIKxhPMiguG4bvetI3elO0d3fA13ajPjub27nOiJ6wb8xwlhSD9Hk3z8ezyzbgle+e7C051YL6Iw7WxHv+C2FgU9k7KVsW7DveHPgDACMzEKOXv3NSzcFd1SBvg141cRM2tHcTcSgR0AX75hEZ1z1Sgci6LoO4jNZ3bbzVeI75EpS9xw21/j5xxEB8aO+xuPCEfBw4VLxwyYkYkQOTJiU/F9TnOOdYPj8VkCRgts1clRze/P4pqaMDpFrptdWbAQSxIkftmc1JohrUn80h/OTE2+5wUvjpJwNx0pcDc9Ua1NSAWfvshM8eOcXZ1rABjd0S3NPTSEZce96PZY23ddo9a6qFZDxsqhg12tvFgco56nJ6+MoJUzGkuQF772zfoKuRAnyvOXO6OcIdCLjjGz83A9MsarRq0dzIn6aXFVLlfMcX0odEqVDTeo+ySASSkXGtQdWbsFyixL2oMRCbQibg09c9g7+/aY+azwN1KDmYLdLdEQR36K7BKakuw6xchD7SyPUOV9LeRt72s2riJmxHme4+ZkjNY+IgF7HN+yRLtLe8bdfjPG7vnfDKd0921HLba9hrPB0AehN5qC5VuKSQrY4APQk5b4c58oHZ9gaTK+uy97bhSD+7eNXo/2xlleDTZ+RPHCQxMmX9lNhn56H4txP2ws8+ZTZc9RfIiM7/v71zjbGqugLwt2aGmXYQQccBYQQFQgUcfM1UpQUxUhGslsYHsTGCj0ZJbNWaJqJt0x+0KdpqqtKEkgrR2lZt2qZofZQ+0FhtBSo+EFC0JqK0tKkioDJSV3+cfZjD3HvPOfdy7nnMrC+5mTN79t2z1173nnX22nuvdWFX+NNkXIJPUnFvcGHugfNPLptLKjHCst/5s4GeGNtyZf8urWRuwLW4leoRADBpknJj+qMcddjT391294Lu0Hr+2EW5+8JmFk2NDXxU5rOchkdgwBmHsK2sUofRaB/SwrgjBvO9C6aE1hMRrvvcBEYOrRxPpijs3xGWUHvBG33cG1zYDVVE6nLQyfe7h31x/X+772ON4VbySGom5vermkN9UU/lvWt42RmRqP89LaZ/3pclKoZYZ8dQNi+ezcxJ4TsV/aGL+qyFzRxamhroKeMGTTpcSTkGnlspZrKfpGhpauRPXz8j8XbzTC0H0cIIrhHEdSFELSzWwxnywNWn8fa7H4bWOTBsdvj43Dh7Ijc8uIGJEWsJcWlpauThr05jXHsVqVsLMHOIYsVln461zrRozkRu+/0WhlfI7BYkzhbtxv0payPWHEI2jbQ0NZSNM1XLlvZqsZlDgH7wPcgFvlspqc9vcHdRXDdH1NP2eQmd6QgyrLWZyaPCd5sFDULffOB9mTq+jWdumhk7hEUcOjuG0tocv724N6E8f3WamxpCXX0+C2eM55XvzElsDcM/5xD1mY2aOZRLehSVSTAJBp5xcD/D1hySjNBZBG6fd0JoXuZq8b+I7TEPKkURPJfwzp7yce99Jgz3FpujDid+/6KSVCSpkOebaJBWf2NERIf9cT5tXFude5QOSbrHfLsa5QoNNw6N9PzvYz7oOXDmU68dd0EGnFtpTueRfGrEIaFTuQzdp5lwfkiinVo4d8pI3t+7L7F2gzOHqCCCq2+YEavNpHe4xCV48zk75dP11fDg1VN5cN2bkTttPjP+CN5Y8vmUenUgV50+rm5nVpLAnzlEGZywNarGBmHrjt2ct/SpA8rTiLc04IzD0W2DObqtvM/V96/GjRdjlKehQbj4lOTGMJj0fcHU5E7CPnvzzLI7QepJ0B2wzEUqzSOdHUPp7IgXJiIrbj5nUtZdCMV3B+6NiFg7rLWZWy88nm637T3IYxv/CcDWHbv3l51/UgeXnFr/E+G5MQ4iMhu4A2gEfqKqS9LuQ1NjA5sXzx5wbqUsue/KUyP3lc+aPIIlj24GoC0hVxXA8JBQyfUi+KSb5Q4fo/74objbQtL5+szrHl22fFCDEHSkbl48O5F4ZXHIhXEQkUbgR8BZwDZgrYisUtWX0+5LWgNveEyLsa1yXHt9Dq1lSb3PWhjZ0z6kheWXdtFVZkYQl8MGN7OnpzdffZr3p7w8Ip8CbFXV11W1B7gfmJtxn4wc8fSiM3ns+ulZd+Og8aOJ1nJa2Sges4478qBmu9fO7M3uF3byvh7kYuYAdABvBn7fBlSXYcXo14wa9klGUfwDgrOOG8HCGeP58vSxWXfFKADzukczr3s023d+kHoQzbwYh3LO15KVQhG5CrgKYMwYWzQ2isegxgYWzZmYdTeMgpFF5IS8zG23AcEVmaOAt/tWUtXlqtqtqt3t7ekkvDAMwxiI5MU4rAUmiMhYEWkGLgZWZdwnwzCMAUsu3Eqquk9EvgI8jreVdYWqbsy4W4ZhGAOWXBgHAFV9BHgk634YhmEY+XErGYZhGDnCjINhGIZRghkHwzAMowQzDoZhGEYJklR+2rQRkV3AljJ/GgOEZ1HxGArszKBeNXWzkqUebZosydTNuyz9SS/9SZZg3WNVNTq9oKoW8gWsq1D+75jvX55FvSrbzESWOo2PyZJuH00vJkvZupXunX1f/dGt9G7Meg9lVK+aulnJUo82TZZk6uZdlv6kl/4kS7V1C+1WWqeq3XHLi4jJkk9MlnxisiTbdpFnDsurLC8iJks+MVnyicmSYNuFnTkYhmEY9aPIMwfDMAyjTuTeOIjIChHZISIvBcpOEJFnRORFEXlIRA515c0istKVPy8iZwTe0+XKt4rInZJBAt8EZVkjIltEZIN7Dc9AltEi8mcR2SQiG0XkOld+uIisFpFX3c/DAu+5yY3/FhE5O1CeqW4SliVT3VQri4i0ufq7RWRpn7YKpZcIWYqml7NEZL0b//UicmagrXT0EncbVFYv4HTgZOClQNlaYIa7vgJY7K6vAVa66+HAeqDB/f4sMBUvsdCjwJwCy7IG6M5YLyOBk931EOAVYDJwK7DIlS8CbnHXk4HngRZgLPAa0JgH3SQsS6a6qUGWwcA0YCGwtE9bRdNLmCxF08tJwCh33Qm8lbZecj9zUNUngf/2KT4WeNJdrwYucNeTgT+69+3A2w7WLSIjgUNV9Rn1Rvde4Iv17ntfkpAlhW7GQlW3q+rf3fUuYBNeute5wD2u2j30jvNc4H5V3auq/wC2AqfkQTdJyZJmnytRrSyqukdVnwI+DLZTRL1UkiUP1CDLc6rqJzzbCHxCRFrS1EvujUMFXgK+4K4vojeL3PPAXBFpEpGxQJf7Wwdetjmfba4sD1Qri89KNz3+VtrT/b6IyDF4Tzp/A0ao6nbwvhB4sx4onye8g5zp5iBl8cmFbmLKUoki6iWKourlAuA5Vd1LinopqnG4ArhGRNbjTdF6XPkKvMFaB/wQeBrYR8wc1RlRrSwAl6jqFGC6e12aao8DiMghwK+A61X1vbCqZco0pDx1EpAFcqKbKmSp2ESZsrzrJYxC6kVEjgNuAa72i8pUq4teCmkcVHWzqs5S1S7gF3g+X1R1n6p+TVVPVNW5wDDgVbyb7FGBJsrmqM6CGmRBVd9yP3cBPycjl4aIDML7oP9MVX/tiv/lpr6+a2KHK6+UJzwXuklIllzopkpZKlFEvVSkiHoRkaOA3wDzVfU1V5yaXgppHPydBiLSAHwTWOZ+bxWRwe76LGCfqr7spmu7ROQ0N52cD/w2m94fSLWyODfTEa58EHAunmsq7X4LcDewSVVvD/xpFbDAXS+gd5xXARc7v+lYYALwbB50k5QsedBNDbKUpaB6qdRO4fQiIsOA3wE3qepf/Mqp6qUeq9xJvvCeprcDH+FZzSuB6/BW+18BltB7mO8YvEitm4A/AEcH2unG+0C8Biz131M0WfB2ZKwHXsBbqLoDt1MmZVmm4U1nXwA2uNc5QBveQvqr7ufhgfd8w43/FgI7LLLWTVKy5EE3NcryBt5Gid3uczm5wHopkaWIesF7UNwTqLsBGJ6mXuyEtGEYhlFCId1KhmEYRn0x42AYhmGUYMbBMAzDKMGMg2EYhlGCGQfDMAyjBDMOhlEHRGShiMyvov4xEojWaxhZ05R1BwyjvyEiTaq6LOt+GMbBYMbBMMrggqM9hhcc7SS8Q4rzgUnA7cAhwH+Ay1R1u4iswYt/9VlglYgMAXar6g9E5ES8k++teAeXrlDVd0SkCy+G1vvAU+lJZxjRmFvJMCpzLLBcVY8H3sPLsXEXcKF6sbBWAN8N1B+mqjNU9bY+7dwL3OjaeRH4titfCVyrqlPrKYRh1ILNHAyjMm9qb1yb+4Cb8RKvrHYRnxvxwqH4PNC3AREZimc0nnBF9wC/LFP+U2BO8iIYRm2YcTCMyvSNLbML2BjypL+niralTPuGkRvMrWQYlRkjIr4h+BLwV6DdLxORQS7efkVUdSfwjohMd0WXAk+o6rvAThGZ5sovSb77hlE7NnMwjMpsAhaIyI/xombeBTwO3OncQk14iZg2RrSzAFgmIq3A68DlrvxyYIWIvO/aNYzcYFFZDaMMbrfSw6ramXFXDCMTzK1kGIZhlGAzB8MwDKMEmzkYhmEYJZhxMAzDMEow42AYhmGUYMbBMAzDKMGMg2EYhlGCGQfDMAyjhP8DlaW2i7BjREsAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Un zoom sur les dernières années montre mieux avec un haut taux annuel excepté en Septembre / Octobre où les cas sont bien moins nombreux"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_data['inc'][-200:].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval entre deux années civiles, nous définissons la période de référence entre deux minima de l'incidence, du 1er août de l'année $N$ au 1er Septembre de l'année $N+1$.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de syndrome grippal est très faible en été, cette modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1984, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1985."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
+    "\n",
+    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Voici les incidences annuelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pcjOrd04mw8ALLJpZvfPU4CHkp8nNbKRwy2QIeelrMxspnEyGkJ8mN7ORwslkiA106WsP1ptZLSoqmUj6qKSNkn4laY+kz0i6R9Lbkl5L2xdyx98pqVXSXkmLcvG5kl5P362RpBQfI+mZFG+RND1XZpmkfWlbVr6qD49Hbp7H6qWXM2vyOFYvvbzbUti98WC9mdWiopZTkbQOeDkivi/pI8A5wNeB9yLi2z2OnQWsBxqBycCLwJ9ExElJ24GvAf8EPAesiYgtkr4C/OuI+FtJTcB1EfGXkiYAO4F5QACvAHMj4uiZrrVWl1PpOVhf4MF6MxsOQ76ciqRxwGeBRwEi4g8R8bs+iiwBno6Izoh4E2gFGiVdBIyLiG2RZbDHgaW5MuvS/kZgQWq1LAKaI6IjJZBmYPGAa1kDPFhvZrWsmG6uPwbagR9KelXS9yWdm767XdIvJf1A0vgUmwK8lSt/MMWmpP2e8W5lIqILeBe4oI9z1R0P1ptZLSsmmTQAnwYejogrgN8DdwAPA58A5gCHge+k49XLOaKP+GDLfEDSCkk7Je1sb2/voyrVze+pNrNaVcxDiweBgxHRkn7eCNwREUcKB0j6HvA/c8dfnCs/FTiU4lN7iefLHJTUAJwPdKT453qU+VnPC4yItcBayMZMiqhTydqOneD29a/y4I1XlK314PdUm1mt6rdlEhHvAG9JuiyFFgBvpDGQguuAXWl/M9CUZmjNAGYC2yPiMHBc0pVpPOQW4NlcmcJMreuBl9K4yvPAQknjUzfawhSrOM+6MiuOp7uPDMUup/JV4EdpJte/AF8G1kiaQ9bttB+4FSAidkvaALwBdAErI+JkOs9twGPA2cCWtEE2uP+EpFayFklTOleHpPuAHem4eyOiY3BVLQ8vkWI2MPk/vFZf96lKX44NEb9pcYDajp1g9XN7eGH3O5x4/xRjR49i0eyPc9cXP+nBcrMcT3evLX7T4jDzrCuz4ni6+8jiVYMHoTDr6sbGaTy1/QDt7gs2+xD/4TWyOJkMgmddWbkMxazAauI/vEYOj5mYVdDdm17nR9sPcFPjNA9OW0WVOmbilolZBXhWoNUbD8CblVkxz1V4cNrqjZOJWZkV80CrB6et3riby6xMBtp15cHpD6v3CQn1zAPwZmXiB1pL5wkJleMBeLMq4a6rwfOEhNrnMROzMvJrBAbHExJqn1smVtVqrQ/dD7QOjlt1tc8tExt2A1mS3Ev9jxxu1dU2D8DbsCtmkNUrzpoNr1IH4J1MbNgMJEF4ZpTZ8PIS9FYzBjLI6j50s9riAXgbNgNNEH6oz6x2OJnYsBpIgvDMKLPa4TETMzPzmImZmVWek4mZWQ8DeRbKMk4mZmY9+GHZgSsqmUj6qKSNkn4laY+kz0iaIKlZ0r70OT53/J2SWiXtlbQoF58r6fX03RpJSvExkp5J8RZJ03NllqV/Y5+kZeWruplZd5fdvYXpd/yEJ1sOEJEtODn9jp9w2d1bKn1pVa/Ylsl3gZ9GxL8C/hTYA9wBbI2ImcDW9DOSZgFNwGxgMfCQpLPSeR4GVgAz07Y4xZcDRyPiUuAB4P50rgnAKmA+0AisyictM7Ny8oKTg9dvMpE0Dvgs8ChARPwhIn4HLAHWpcPWAUvT/hLg6YjojIg3gVagUdJFwLiI2BbZFLLHe5QpnGsjsCC1WhYBzRHRERFHgWZOJyAzs7Lyw7KDV0zL5I+BduCHkl6V9H1J5wIXRsRhgPQ5KR0/BXgrV/5gik1J+z3j3cpERBfwLnBBH+fqRtIKSTsl7Wxvby+iSmZmvfOCk4NTzEOLDcCnga9GRIuk75K6tM5AvcSij/hgy5wORKwF1kL2nEkf12Zm1ic/LDs4xbRMDgIHI6Il/byRLLkcSV1XpM+23PEX58pPBQ6l+NRe4t3KSGoAzgc6+jiXmZlVkX6TSUS8A7wl6bIUWgC8AWwGCrOrlgHPpv3NQFOaoTWDbKB9e+oKOy7pyjQeckuPMoVzXQ+8lMZVngcWShqfBt4XppiZmVWRYtfm+irwI0kfAf4F+DJZItogaTlwALgBICJ2S9pAlnC6gJURcTKd5zbgMeBsYEvaIBvcf0JSK1mLpCmdq0PSfcCOdNy9EdExyLqamdkQ8dpcZmbmtbnMzKzynEzMzOpApdcTczIxM6sDlV5PzC/HMjOrYZfdvYXOrlMf/PxkywGebDnAmIZR7F19zbBdh1smZmY1rFrWE3MyMTOrYdWynpi7uczMalxhPbEbG6fx1PYDtFdgEN7PmZiZmZ8zMTOzynMyMTOzkjmZmJlZyZxMzMysZE4mZmZWMieTGlfp9XjMzMDJpOZVej0eMzPwQ4s1q1rW4zEzA7dMala1rMdjZgZOJjWrWtbjMTMDd3PVtGpYj8fMDLw2l5mZ4bW5zMysChSVTCTtl/S6pNck7UyxeyS9nWKvSfpC7vg7JbVK2itpUS4+N52nVdIaSUrxMZKeSfEWSdNzZZZJ2pe2ZeWquJnZcKvn58IG0jK5OiLm9GgGPZBicyLiOQBJs4AmYDawGHhI0lnp+IeBFcDMtC1O8eXA0Yi4FHgAuD+dawKwCpgPNAKrJI0fRD3NzCqunp8LG4oB+CXA0xHRCbwpqRVolLQfGBcR2wAkPQ4sBbakMvek8huBB1OrZRHQHBEdqUwzWQJaPwTXbWY2JEbCc2HFtkwCeEHSK5JW5OK3S/qlpB/kWgxTgLdyxxxMsSlpv2e8W5mI6ALeBS7o41xmZjVjJDwXVmwyuSoiPg1cA6yU9FmyLqtPAHOAw8B30rHqpXz0ER9smQ9IWiFpp6Sd7e3tfVbEzGy4jYTnwopKJhFxKH22AZuAxog4EhEnI+IU8D2yMQ3IWg8X54pPBQ6l+NRe4t3KSGoAzgc6+jhXz+tbGxHzImLexIkTi6mSmdmwKjwXtukrV3HT/Etof6+z3zK1NGDfbzKRdK6k8wr7wEJgl6SLcoddB+xK+5uBpjRDawbZQPv2iDgMHJd0ZRoPuQV4NlemMFPreuClyB6AeR5YKGl86kZbmGJmZjXlkZvnsXrp5cyaPI7VSy/nkZv7f6SjlgbsixmAvxDYlGbxNgBPRcRPJT0haQ5Zt9N+4FaAiNgtaQPwBtAFrIyIk+lctwGPAWeTDbxvSfFHgSfSYH0H2WwwIqJD0n3AjnTcvYXBeDOzelWLA/Z+At7MrMq0HTvB6uf28MLudzjx/inGjh7Fotkf564vfnLIxln8BLyZWZ2pxQF7L/RoZlaFam0hV3dzmZmZu7nMzKzynEzMzKxkTiZmZlYyJxMzMyuZk4mZmZXMycTMzErmZGJmZiVzMjEzs5I5mZiZWcmcTKxu1NK7H8zqjZOJ1Y1aeveDWb3xQo9W82rx3Q9m9cYtE6t5L3/zaq6dM5mxo7P/nceOHsWSOZN5+VtXV/jKzEYOJxOrebX47gezeuNuLqsLtfbuB7N64/eZmJmZ32diZmaV52RiZmYlKyqZSNov6XVJr0namWITJDVL2pc+x+eOv1NSq6S9khbl4nPTeVolrZGkFB8j6ZkUb5E0PVdmWfo39klaVq6Km5lZ+QykZXJ1RMzJ9andAWyNiJnA1vQzkmYBTcBsYDHwkKSzUpmHgRXAzLQtTvHlwNGIuBR4ALg/nWsCsAqYDzQCq/JJy8zMqkMp3VxLgHVpfx2wNBd/OiI6I+JNoBVolHQRMC4itkU26v94jzKFc20EFqRWyyKgOSI6IuIo0MzpBGRmZlWi2GQSwAuSXpG0IsUujIjDAOlzUopPAd7KlT2YYlPSfs94tzIR0QW8C1zQx7nMzKyKFPucyVURcUjSJKBZ0q/6OFa9xKKP+GDLnP4HswRXSHLvSdrbx/XVgo8Bv630RQyxeq9jvdcP6r+OI61+l5RysqKSSUQcSp9tkjaRjV8ckXRRRBxOXVht6fCDwMW54lOBQyk+tZd4vsxBSQ3A+UBHin+uR5mf9XJ9a4G1xdSlFkjaWcp871pQ73Ws9/pB/dfR9RuYfru5JJ0r6bzCPrAQ2AVsBgqzq5YBz6b9zUBTmqE1g2ygfXvqCjsu6co0HnJLjzKFc10PvJTGVZ4HFkoanwbeF6aYmZlVkWJaJhcCm9Is3gbgqYj4qaQdwAZJy4EDwA0AEbFb0gbgDaALWBkRJ9O5bgMeA84GtqQN4FHgCUmtZC2SpnSuDkn3ATvScfdGREcJ9TUzsyFQd8up1ANJK1LXXd2q9zrWe/2g/uvo+g3wfE4mZmZWKi+nYmZmJXMyGSaSfiCpTdKuXOxPJW1LS8z8D0njUvwjkn6Y4r+Q9LlcmZ+lZWpeS9ukXv65YSfpYkn/S9IeSbslfS3Fy7bsTiWVuX51cQ8lXZCOf0/Sgz3OVfP3sJ/6Vd09HET9Pq/s2cHX0+e/z51r4PcvIrwNwwZ8Fvg0sCsX2wH8u7T/N8B9aX8l8MO0Pwl4BRiVfv4ZMK/S9emlfhcBn0775wH/DMwC/gG4I8XvAO5P+7OAXwBjgBnAr4Gz0nfbgc+QPWe0BbimzupXL/fwXODfAH8LPNjjXPVwD/uqX9Xdw0HU7wpgctq/HHi7lPvnlskwiYh/JJuplncZ8I9pvxn4i7Q/i2y9MyKiDfgdUNXz3SPicET8PO0fB/aQrVZQzmV3KqZc9Rveqx6YgdYxIn4fEf8b6PYmsnq5h2eqX7UaRP1ejfQMIbAbGKvskY5B3T8nk8raBVyb9m/g9MOevwCWSGpQ9qzOXLo/CPrD1LT+T9XQfdCTslWfrwBaKO+yO1WhxPoV1MM9PJN6uYf9qdp7OIj6/QXwakR0Msj752RSWX8DrJT0Clmz9A8p/gOyG7gT+C/A/yV7Zgfgpoj4FPBv03bzsF5xPyT9EfDfga9HxLG+Du0lVvQSOpVShvpB/dzDM56il1gt3sO+VO09HGj9JM0mW6n91kKol8P6vX9OJhUUEb+KiIURMRdYT9avTkR0RcQ3IlvyfwnwUWBf+u7t9HkceIoq6jqRNJrsf+IfRcSPU/hIajYXuj9KWXanospUv3q6h2dSL/fwjKr1Hg60fpKmApuAWyLi1yk8qPvnZFJBhRkgkkYBdwP/Nf18jrKla5D0eaArIt5I3V4fS/HRwH8g6yqruNTMfxTYExH/OfdVOZfdqZhy1a/O7mGv6ugenuk8VXkPB1o/SR8FfgLcGRH/p3DwoO9fpWYejLSNrOVxGHifLPMvB75GNuPin4G/5/RDpNOBvWQDaC8Cl6T4uWQzu35JNmD2XdIMoUpvZLNeIl3ba2n7AtmrBLaStay2AhNyZe4ia43tJTdbhGyywa703YOF/y71UL86vIf7ySaWvJf+v55VZ/fwQ/Wr1ns40PqR/QH7+9yxrwGTBnv//AS8mZmVzN1cZmZWMicTMzMrmZOJmZmVzMnEzMxK5mRiZmYlczIxM7OSOZmYmVnJnEzMzKxk/x+qftJYVEr3+gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Une liste triée permet de plus facilement répérer les valeurs les plus élevées (à la fin)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ainsi l'année avec le plus d'incidences a été en __2009__ et l'année avec le moins d'incidences à été en __2002__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.hist(xrot=20)\n",
+    "plt.show()"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +1480,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..d732695db650c648cbe7aae4dc93cd789a2b0c15
--- /dev/null
+++ b/module3/exo2/incidence-PAY-7.csv
@@ -0,0 +1,1558 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202039],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-10-07T00:09:24+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202039,7,1017,216,1818,2,1,3,FR,France
+202038,7,2253,782,3724,3,1,5,FR,France
+202037,7,1584,405,2763,2,0,4,FR,France
+202036,7,919,100,1738,1,0,2,FR,France
+202035,7,828,0,1694,1,0,2,FR,France
+202034,7,2272,371,4173,3,0,6,FR,France
+202033,7,1284,177,2391,2,0,4,FR,France
+202032,7,2650,689,4611,4,1,7,FR,France
+202031,7,1303,100,2506,2,0,4,FR,France
+202030,7,1385,75,2695,2,0,4,FR,France
+202029,7,841,10,1672,1,0,2,FR,France
+202028,7,728,0,1515,1,0,2,FR,France
+202027,7,986,149,1823,1,0,2,FR,France
+202026,7,694,0,1454,1,0,2,FR,France
+202025,7,228,0,597,0,0,1,FR,France
+202024,7,388,0,959,1,0,2,FR,France
+202023,7,558,1,1115,1,0,2,FR,France
+202022,7,277,0,633,0,0,1,FR,France
+202021,7,602,36,1168,1,0,2,FR,France
+202020,7,824,20,1628,1,0,2,FR,France
+202019,7,310,0,753,0,0,1,FR,France
+202018,7,849,98,1600,1,0,2,FR,France
+202017,7,272,0,658,0,0,1,FR,France
+202016,7,758,78,1438,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
+201952,7,7941,5246,10636,12,8,16,FR,France
+201951,7,5823,3675,7971,9,6,12,FR,France
+201950,7,6424,4276,8572,10,7,13,FR,France
+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
+201947,7,7536,5058,10014,11,7,15,FR,France
+201946,7,2638,1316,3960,4,2,6,FR,France
+201945,7,4492,2615,6369,7,4,10,FR,France
+201944,7,5728,3627,7829,9,6,12,FR,France
+201943,7,4834,2751,6917,7,4,10,FR,France
+201942,7,6279,3989,8569,10,7,13,FR,France
+201941,7,4130,2030,6230,6,3,9,FR,France
+201940,7,4211,2218,6204,6,3,9,FR,France
+201939,7,3137,1310,4964,5,2,8,FR,France
+201938,7,3078,1416,4740,5,2,8,FR,France
+201937,7,970,162,1778,1,0,2,FR,France
+201936,7,1277,263,2291,2,0,4,FR,France
+201935,7,922,0,1857,1,0,2,FR,France
+201934,7,1997,605,3389,3,1,5,FR,France
+201933,7,3279,1103,5455,5,2,8,FR,France
+201932,7,3627,1576,5678,6,3,9,FR,France
+201931,7,7611,4605,10617,12,7,17,FR,France
+201930,7,8875,5629,12121,13,8,18,FR,France
+201929,7,10658,7151,14165,16,11,21,FR,France
+201928,7,11251,7604,14898,17,11,23,FR,France
+201927,7,16602,12349,20855,25,19,31,FR,France
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