{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence de la varicelle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import requests\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence de la varicelle sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.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 1984 et se termine avec une semaine récente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file doesnt exist: dowloading\n",
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b'199314,7,12425,9045,15805,22,16,28,FR,France\\n', b'199313,7,12266,8927,15605,21,15,27,FR,France\\n', b'199312,7,10821,7884,13758,19,14,24,FR,France\\n', b'199311,7,11021,8006,14036,19,14,24,FR,France\\n', b'199310,7,15847,12158,19536,28,22,34,FR,France\\n', b'199309,7,15084,11059,19109,26,19,33,FR,France\\n', b'199308,7,16967,12936,20998,30,23,37,FR,France\\n', b'199307,7,16057,12350,19764,28,22,34,FR,France\\n', b'199306,7,15887,12275,19499,28,22,34,FR,France\\n', b'199305,7,15954,12443,19465,28,22,34,FR,France\\n', b'199304,7,12374,9426,15322,22,17,27,FR,France\\n', b'199303,7,15283,11831,18735,27,21,33,FR,France\\n', b'199302,7,13366,10262,16470,23,18,28,FR,France\\n', b'199301,7,18678,14977,22379,33,27,39,FR,France\\n', b'199253,7,17829,13827,21831,31,24,38,FR,France\\n', b'199252,7,14330,10582,18078,25,18,32,FR,France\\n', b'199251,7,14635,11262,18008,26,20,32,FR,France\\n', b'199250,7,15252,11566,18938,27,21,33,FR,France\\n', b'199249,7,14411,11187,17635,25,19,31,FR,France\\n', b'199248,7,10048,7396,12700,18,13,23,FR,France\\n', b'199247,7,13069,9752,16386,23,17,29,FR,France\\n', b'199246,7,6900,4727,9073,12,8,16,FR,France\\n', b'199245,7,10791,7794,13788,19,14,24,FR,France\\n', b'199244,7,10205,7482,12928,18,13,23,FR,France\\n', b'199243,7,7196,4908,9484,13,9,17,FR,France\\n', b'199242,7,5748,3738,7758,10,6,14,FR,France\\n', b'199241,7,3587,2045,5129,6,3,9,FR,France\\n', b'199240,7,5654,3442,7866,10,6,14,FR,France\\n', b'199239,7,4747,2751,6743,8,5,11,FR,France\\n', b'199238,7,2842,1306,4378,5,2,8,FR,France\\n', b'199237,7,4103,2203,6003,7,4,10,FR,France\\n', b'199236,7,2801,1200,4402,5,2,8,FR,France\\n', b'199235,7,6428,3717,9139,11,6,16,FR,France\\n', b'199234,7,7517,4190,10844,13,7,19,FR,France\\n', b'199233,7,9951,5955,13947,17,10,24,FR,France\\n', b'199232,7,16090,11337,20843,28,20,36,FR,France\\n', b'199231,7,17156,12441,21871,30,22,38,FR,France\\n', b'199230,7,21780,16301,27259,38,28,48,FR,France\\n', b'199229,7,18926,13397,24455,33,23,43,FR,France\\n', b'199228,7,28144,20384,35904,49,35,63,FR,France\\n', b'199227,7,29388,22331,36445,51,39,63,FR,France\\n', b'199226,7,27933,21686,34180,49,38,60,FR,France\\n', b'199225,7,26446,21153,31739,46,37,55,FR,France\\n', b'199224,7,19614,14769,24459,34,26,42,FR,France\\n', b'199223,7,23628,17852,29404,41,31,51,FR,France\\n', b'199222,7,20438,14868,26008,36,26,46,FR,France\\n', b'199221,7,22595,17395,27795,40,31,49,FR,France\\n', b'199220,7,21092,15963,26221,37,28,46,FR,France\\n', b'199219,7,19210,13934,24486,34,25,43,FR,France\\n', b'199218,7,30273,22605,37941,53,40,66,FR,France\\n', b'199217,7,19162,14251,24073,34,25,43,FR,France\\n', b'199216,7,15583,11158,20008,27,19,35,FR,France\\n', b'199215,7,15473,10723,20223,27,19,35,FR,France\\n', b'199214,7,14455,10040,18870,25,17,33,FR,France\\n', b'199213,7,17146,12105,22187,30,21,39,FR,France\\n', b'199212,7,15838,10923,20753,28,19,37,FR,France\\n', b'199211,7,20003,13824,26182,35,24,46,FR,France\\n', b'199210,7,22906,16060,29752,40,28,52,FR,France\\n', b'199209,7,21824,15687,27961,38,27,49,FR,France\\n', b'199208,7,15243,10265,20221,27,18,36,FR,France\\n', b'199207,7,22633,16160,29106,40,29,51,FR,France\\n', b'199206,7,17009,12219,21799,30,22,38,FR,France\\n', b'199205,7,15997,11590,20404,28,20,36,FR,France\\n', b'199204,7,20690,15075,26305,36,26,46,FR,France\\n', b'199203,7,18586,12684,24488,33,23,43,FR,France\\n', b'199202,7,16588,10590,22586,29,18,40,FR,France\\n', b'199201,7,23607,16552,30662,41,29,53,FR,France\\n', b'199152,7,15184,10392,19976,27,19,35,FR,France\\n', b'199151,7,15043,10624,19462,26,18,34,FR,France\\n', b'199150,7,14115,9737,18493,25,17,33,FR,France\\n', b'199149,7,13304,8548,18060,23,15,31,FR,France\\n', b'199148,7,15370,10164,20576,27,18,36,FR,France\\n', b'199147,7,12813,8139,17487,23,15,31,FR,France\\n', b'199146,7,6453,3123,9783,11,5,17,FR,France\\n', b'199145,7,8048,4083,12013,14,7,21,FR,France\\n', b'199144,7,9882,5623,14141,17,10,24,FR,France\\n', b'199143,7,14307,8645,19969,25,15,35,FR,France\\n', b'199142,7,6419,3334,9504,11,6,16,FR,France\\n', b'199141,7,6168,3113,9223,11,6,16,FR,France\\n', b'199140,7,3615,1159,6071,6,2,10,FR,France\\n', b'199139,7,3105,472,5738,5,0,10,FR,France\\n', b'199138,7,4446,1435,7457,8,3,13,FR,France\\n', b'199137,7,1457,0,3262,3,0,6,FR,France\\n', b'199136,7,3053,29,6077,5,0,10,FR,France\\n', b'199135,7,805,0,2556,1,0,4,FR,France\\n', b'199134,7,5512,856,10168,10,2,18,FR,France\\n', b'199133,7,7347,2903,11791,13,5,21,FR,France\\n', b'199132,7,16099,7334,24864,28,13,43,FR,France\\n', b'199131,7,16803,8145,25461,30,15,45,FR,France\\n', b'199130,7,15915,6368,25462,28,11,45,FR,France\\n', b'199129,7,23744,14703,32785,42,26,58,FR,France\\n', b'199128,7,24222,15617,32827,43,28,58,FR,France\\n', b'199127,7,20309,12868,27750,36,23,49,FR,France\\n', b'199126,7,17608,11304,23912,31,20,42,FR,France\\n', b'199125,7,16169,10700,21638,28,18,38,FR,France\\n', b'199124,7,16171,10071,22271,28,17,39,FR,France\\n', b'199123,7,11947,7671,16223,21,13,29,FR,France\\n', b'199122,7,15452,9953,20951,27,17,37,FR,France\\n', b'199121,7,14903,8975,20831,26,16,36,FR,France\\n', b'199120,7,19053,12742,25364,34,23,45,FR,France\\n', b'199119,7,16739,11246,22232,29,19,39,FR,France\\n', b'199118,7,21385,13882,28888,38,25,51,FR,France\\n', b'199117,7,13462,8877,18047,24,16,32,FR,France\\n', b'199116,7,14857,10068,19646,26,18,34,FR,France\\n', b'199115,7,13975,9781,18169,25,18,32,FR,France\\n', b'199114,7,12265,7684,16846,22,14,30,FR,France\\n', b'199113,7,9567,6041,13093,17,11,23,FR,France\\n', b'199112,7,10864,7331,14397,19,13,25,FR,France\\n', b'199111,7,15574,11184,19964,27,19,35,FR,France\\n', b'199110,7,16643,11372,21914,29,20,38,FR,France\\n', b'199109,7,13741,8780,18702,24,15,33,FR,France\\n', b'199108,7,13289,8813,17765,23,15,31,FR,France\\n', b'199107,7,12337,8077,16597,22,15,29,FR,France\\n', b'199106,7,10877,7013,14741,19,12,26,FR,France\\n', b'199105,7,10442,6544,14340,18,11,25,FR,France\\n', b'199104,7,7913,4563,11263,14,8,20,FR,France\\n', b'199103,7,15387,10484,20290,27,18,36,FR,France\\n', b'199102,7,16277,11046,21508,29,20,38,FR,France\\n', b'199101,7,15565,10271,20859,27,18,36,FR,France\\n', b'199052,7,19375,13295,25455,34,23,45,FR,France\\n', b'199051,7,19080,13807,24353,34,25,43,FR,France\\n', b'199050,7,11079,6660,15498,20,12,28,FR,France\\n', b'199049,7,1143,0,2610,2,0,5,FR,France\\n']\n"
     ]
    }
   ],
   "source": [
    "csv_name = \"incidence-PAY-7.csv\"\n",
    "data_url = \"http://www.sentiweb.fr/datasets/\" + csv_name\n",
    "file_exists = os.path.exists(csv_name)\n",
    "if not file_exists:\n",
    "    print('file doesnt exist: dowloading')\n",
    "    r = requests.get(data_url)\n",
    "    data = r.text\n",
    "    with open(csv_name, \"w\", encoding='UTF-8') as text_file:\n",
    "        text_file.write(data)\n",
    "\n",
    "with open(csv_name, \"rb\") as text_file:\n",
    "    print(text_file.readlines())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>202217</td>\n",
       "      <td>7</td>\n",
       "      <td>23490</td>\n",
       "      <td>17979</td>\n",
       "      <td>29001</td>\n",
       "      <td>35</td>\n",
       "      <td>27</td>\n",
       "      <td>43</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202216</td>\n",
       "      <td>7</td>\n",
       "      <td>19696</td>\n",
       "      <td>14751</td>\n",
       "      <td>24641</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202215</td>\n",
       "      <td>7</td>\n",
       "      <td>17722</td>\n",
       "      <td>13643</td>\n",
       "      <td>21801</td>\n",
       "      <td>27</td>\n",
       "      <td>21</td>\n",
       "      <td>33</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202214</td>\n",
       "      <td>7</td>\n",
       "      <td>17005</td>\n",
       "      <td>13162</td>\n",
       "      <td>20848</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202213</td>\n",
       "      <td>7</td>\n",
       "      <td>15448</td>\n",
       "      <td>11659</td>\n",
       "      <td>19237</td>\n",
       "      <td>23</td>\n",
       "      <td>17</td>\n",
       "      <td>29</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>202212</td>\n",
       "      <td>7</td>\n",
       "      <td>14702</td>\n",
       "      <td>10794</td>\n",
       "      <td>18610</td>\n",
       "      <td>22</td>\n",
       "      <td>16</td>\n",
       "      <td>28</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>202211</td>\n",
       "      <td>7</td>\n",
       "      <td>11729</td>\n",
       "      <td>8347</td>\n",
       "      <td>15111</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>202210</td>\n",
       "      <td>7</td>\n",
       "      <td>13314</td>\n",
       "      <td>10036</td>\n",
       "      <td>16592</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>202209</td>\n",
       "      <td>7</td>\n",
       "      <td>10485</td>\n",
       "      <td>7600</td>\n",
       "      <td>13370</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>202208</td>\n",
       "      <td>7</td>\n",
       "      <td>12088</td>\n",
       "      <td>8741</td>\n",
       "      <td>15435</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>202207</td>\n",
       "      <td>7</td>\n",
       "      <td>14003</td>\n",
       "      <td>10789</td>\n",
       "      <td>17217</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>202206</td>\n",
       "      <td>7</td>\n",
       "      <td>9798</td>\n",
       "      <td>7048</td>\n",
       "      <td>12548</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>12</th>\n",
       "      <td>202205</td>\n",
       "      <td>7</td>\n",
       "      <td>10851</td>\n",
       "      <td>7797</td>\n",
       "      <td>13905</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>202204</td>\n",
       "      <td>7</td>\n",
       "      <td>9547</td>\n",
       "      <td>6721</td>\n",
       "      <td>12373</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>14</th>\n",
       "      <td>202203</td>\n",
       "      <td>7</td>\n",
       "      <td>13972</td>\n",
       "      <td>10680</td>\n",
       "      <td>17264</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>202202</td>\n",
       "      <td>7</td>\n",
       "      <td>8495</td>\n",
       "      <td>6026</td>\n",
       "      <td>10964</td>\n",
       "      <td>13</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>202201</td>\n",
       "      <td>7</td>\n",
       "      <td>13793</td>\n",
       "      <td>10597</td>\n",
       "      <td>16989</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>202152</td>\n",
       "      <td>7</td>\n",
       "      <td>13239</td>\n",
       "      <td>9611</td>\n",
       "      <td>16867</td>\n",
       "      <td>20</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>202151</td>\n",
       "      <td>7</td>\n",
       "      <td>13326</td>\n",
       "      <td>9629</td>\n",
       "      <td>17023</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>202150</td>\n",
       "      <td>7</td>\n",
       "      <td>14128</td>\n",
       "      <td>10312</td>\n",
       "      <td>17944</td>\n",
       "      <td>21</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>202149</td>\n",
       "      <td>7</td>\n",
       "      <td>13674</td>\n",
       "      <td>10369</td>\n",
       "      <td>16979</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>202148</td>\n",
       "      <td>7</td>\n",
       "      <td>11549</td>\n",
       "      <td>8503</td>\n",
       "      <td>14595</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>202147</td>\n",
       "      <td>7</td>\n",
       "      <td>11419</td>\n",
       "      <td>8376</td>\n",
       "      <td>14462</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>202146</td>\n",
       "      <td>7</td>\n",
       "      <td>8216</td>\n",
       "      <td>5724</td>\n",
       "      <td>10708</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>24</th>\n",
       "      <td>202145</td>\n",
       "      <td>7</td>\n",
       "      <td>8965</td>\n",
       "      <td>6468</td>\n",
       "      <td>11462</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>25</th>\n",
       "      <td>202144</td>\n",
       "      <td>7</td>\n",
       "      <td>8736</td>\n",
       "      <td>5636</td>\n",
       "      <td>11836</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>202143</td>\n",
       "      <td>7</td>\n",
       "      <td>8145</td>\n",
       "      <td>5164</td>\n",
       "      <td>11126</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>202142</td>\n",
       "      <td>7</td>\n",
       "      <td>9443</td>\n",
       "      <td>6037</td>\n",
       "      <td>12849</td>\n",
       "      <td>14</td>\n",
       "      <td>9</td>\n",
       "      <td>19</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>202141</td>\n",
       "      <td>7</td>\n",
       "      <td>4021</td>\n",
       "      <td>2239</td>\n",
       "      <td>5803</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>29</th>\n",
       "      <td>202140</td>\n",
       "      <td>7</td>\n",
       "      <td>4441</td>\n",
       "      <td>2454</td>\n",
       "      <td>6428</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</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>1609</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>1610</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>1611</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>1612</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>1613</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>1614</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>1615</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>1616</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>1617</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>1618</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>1619</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>1620</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>1621</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>1622</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>1623</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>1624</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>1625</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>1626</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>1627</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>1628</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>1629</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>1630</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>1631</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>1632</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>1633</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>1634</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>1635</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>1636</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>1637</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>1638</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>1639 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
       "0     202217          7  23490    17979   29001      35          27   \n",
       "1     202216          7  19696    14751   24641      30          23   \n",
       "2     202215          7  17722    13643   21801      27          21   \n",
       "3     202214          7  17005    13162   20848      26          20   \n",
       "4     202213          7  15448    11659   19237      23          17   \n",
       "5     202212          7  14702    10794   18610      22          16   \n",
       "6     202211          7  11729     8347   15111      18          13   \n",
       "7     202210          7  13314    10036   16592      20          15   \n",
       "8     202209          7  10485     7600   13370      16          12   \n",
       "9     202208          7  12088     8741   15435      18          13   \n",
       "10    202207          7  14003    10789   17217      21          16   \n",
       "11    202206          7   9798     7048   12548      15          11   \n",
       "12    202205          7  10851     7797   13905      16          11   \n",
       "13    202204          7   9547     6721   12373      14          10   \n",
       "14    202203          7  13972    10680   17264      21          16   \n",
       "15    202202          7   8495     6026   10964      13           9   \n",
       "16    202201          7  13793    10597   16989      21          16   \n",
       "17    202152          7  13239     9611   16867      20          15   \n",
       "18    202151          7  13326     9629   17023      20          14   \n",
       "19    202150          7  14128    10312   17944      21          15   \n",
       "20    202149          7  13674    10369   16979      21          16   \n",
       "21    202148          7  11549     8503   14595      17          12   \n",
       "22    202147          7  11419     8376   14462      17          12   \n",
       "23    202146          7   8216     5724   10708      12           8   \n",
       "24    202145          7   8965     6468   11462      14          10   \n",
       "25    202144          7   8736     5636   11836      13           8   \n",
       "26    202143          7   8145     5164   11126      12           7   \n",
       "27    202142          7   9443     6037   12849      14           9   \n",
       "28    202141          7   4021     2239    5803       6           3   \n",
       "29    202140          7   4441     2454    6428       7           4   \n",
       "...      ...        ...    ...      ...     ...     ...         ...   \n",
       "1609  199126          7  17608    11304   23912      31          20   \n",
       "1610  199125          7  16169    10700   21638      28          18   \n",
       "1611  199124          7  16171    10071   22271      28          17   \n",
       "1612  199123          7  11947     7671   16223      21          13   \n",
       "1613  199122          7  15452     9953   20951      27          17   \n",
       "1614  199121          7  14903     8975   20831      26          16   \n",
       "1615  199120          7  19053    12742   25364      34          23   \n",
       "1616  199119          7  16739    11246   22232      29          19   \n",
       "1617  199118          7  21385    13882   28888      38          25   \n",
       "1618  199117          7  13462     8877   18047      24          16   \n",
       "1619  199116          7  14857    10068   19646      26          18   \n",
       "1620  199115          7  13975     9781   18169      25          18   \n",
       "1621  199114          7  12265     7684   16846      22          14   \n",
       "1622  199113          7   9567     6041   13093      17          11   \n",
       "1623  199112          7  10864     7331   14397      19          13   \n",
       "1624  199111          7  15574    11184   19964      27          19   \n",
       "1625  199110          7  16643    11372   21914      29          20   \n",
       "1626  199109          7  13741     8780   18702      24          15   \n",
       "1627  199108          7  13289     8813   17765      23          15   \n",
       "1628  199107          7  12337     8077   16597      22          15   \n",
       "1629  199106          7  10877     7013   14741      19          12   \n",
       "1630  199105          7  10442     6544   14340      18          11   \n",
       "1631  199104          7   7913     4563   11263      14           8   \n",
       "1632  199103          7  15387    10484   20290      27          18   \n",
       "1633  199102          7  16277    11046   21508      29          20   \n",
       "1634  199101          7  15565    10271   20859      27          18   \n",
       "1635  199052          7  19375    13295   25455      34          23   \n",
       "1636  199051          7  19080    13807   24353      34          25   \n",
       "1637  199050          7  11079     6660   15498      20          12   \n",
       "1638  199049          7   1143        0    2610       2           0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0            43        FR   France  \n",
       "1            37        FR   France  \n",
       "2            33        FR   France  \n",
       "3            32        FR   France  \n",
       "4            29        FR   France  \n",
       "5            28        FR   France  \n",
       "6            23        FR   France  \n",
       "7            25        FR   France  \n",
       "8            20        FR   France  \n",
       "9            23        FR   France  \n",
       "10           26        FR   France  \n",
       "11           19        FR   France  \n",
       "12           21        FR   France  \n",
       "13           18        FR   France  \n",
       "14           26        FR   France  \n",
       "15           17        FR   France  \n",
       "16           26        FR   France  \n",
       "17           25        FR   France  \n",
       "18           26        FR   France  \n",
       "19           27        FR   France  \n",
       "20           26        FR   France  \n",
       "21           22        FR   France  \n",
       "22           22        FR   France  \n",
       "23           16        FR   France  \n",
       "24           18        FR   France  \n",
       "25           18        FR   France  \n",
       "26           17        FR   France  \n",
       "27           19        FR   France  \n",
       "28            9        FR   France  \n",
       "29           10        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1609         42        FR   France  \n",
       "1610         38        FR   France  \n",
       "1611         39        FR   France  \n",
       "1612         29        FR   France  \n",
       "1613         37        FR   France  \n",
       "1614         36        FR   France  \n",
       "1615         45        FR   France  \n",
       "1616         39        FR   France  \n",
       "1617         51        FR   France  \n",
       "1618         32        FR   France  \n",
       "1619         34        FR   France  \n",
       "1620         32        FR   France  \n",
       "1621         30        FR   France  \n",
       "1622         23        FR   France  \n",
       "1623         25        FR   France  \n",
       "1624         35        FR   France  \n",
       "1625         38        FR   France  \n",
       "1626         33        FR   France  \n",
       "1627         31        FR   France  \n",
       "1628         29        FR   France  \n",
       "1629         26        FR   France  \n",
       "1630         25        FR   France  \n",
       "1631         20        FR   France  \n",
       "1632         36        FR   France  \n",
       "1633         38        FR   France  \n",
       "1634         36        FR   France  \n",
       "1635         45        FR   France  \n",
       "1636         43        FR   France  \n",
       "1637         28        FR   France  \n",
       "1638          5        FR   France  \n",
       "\n",
       "[1639 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, encoding = 'iso-8859-1', skiprows=1)\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Non"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On regarde rapidement si il y a des risques de valeurs suspectes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_data.describe()\n",
    "data = raw_data.copy() # does not seem required"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nos données utilisent une convention inhabituelle: le numéro de\n",
    "semaine est collé à l'année, donnant l'impression qu'il s'agit\n",
    "de nombre entier. C'est comme ça que Pandas les interprète.Un deuxième problème est que Pandas ne comprend pas les numéros de\n",
    "semaine.  Il faut lui fournir les dates de début et de fin de\n",
    "semaine. Nous utilisons pour cela la bibliothèque `isoweek`.Comme la conversion des semaines est devenu assez complexe, nous\n",
    "écrivons une petite fonction Python pour cela. Ensuite, nous\n",
    "l'appliquons à tous les points de nos donnés. Les résultats vont\n",
    "dans une nouvelle colonne 'period'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       202217\n",
       "1       202216\n",
       "2       202215\n",
       "3       202214\n",
       "4       202213\n",
       "5       202212\n",
       "6       202211\n",
       "7       202210\n",
       "8       202209\n",
       "9       202208\n",
       "10      202207\n",
       "11      202206\n",
       "12      202205\n",
       "13      202204\n",
       "14      202203\n",
       "15      202202\n",
       "16      202201\n",
       "17      202152\n",
       "18      202151\n",
       "19      202150\n",
       "20      202149\n",
       "21      202148\n",
       "22      202147\n",
       "23      202146\n",
       "24      202145\n",
       "25      202144\n",
       "26      202143\n",
       "27      202142\n",
       "28      202141\n",
       "29      202140\n",
       "         ...  \n",
       "1609    199126\n",
       "1610    199125\n",
       "1611    199124\n",
       "1612    199123\n",
       "1613    199122\n",
       "1614    199121\n",
       "1615    199120\n",
       "1616    199119\n",
       "1617    199118\n",
       "1618    199117\n",
       "1619    199116\n",
       "1620    199115\n",
       "1621    199114\n",
       "1622    199113\n",
       "1623    199112\n",
       "1624    199111\n",
       "1625    199110\n",
       "1626    199109\n",
       "1627    199108\n",
       "1628    199107\n",
       "1629    199106\n",
       "1630    199105\n",
       "1631    199104\n",
       "1632    199103\n",
       "1633    199102\n",
       "1634    199101\n",
       "1635    199052\n",
       "1636    199051\n",
       "1637    199050\n",
       "1638    199049\n",
       "Name: week, Length: 1639, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    " def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\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.Premièrement, nous définissons les périodes d'observation\n",
    "comme nouvel index de notre jeux de données. Ceci en fait\n",
    "une suite chronologique, ce qui sera pratique par la suite.Deuxièmement, nous trions les points par période, dans\n",
    "le sens chronologique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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": [
    "Un premier regard sur les données !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55aea79588>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XGm0kUrUbRkX49z+/iyUbduKjHa3JlauEnq7LqwWsiQMRDQTwKICvMcZ2whMRHQRgCoANAH7Kqyou1/m0B5Z9ht/EMVxFRPOJaH5jY6PiktogUz+HPcTRoaOzhNtmLU9vfVSj21NxYPJJ++9vm427XvViE7UXS5Fram3iqeMCTJuajpm+278nG9Riz+woep1kEaCyXOwhy6pbw4o4EFEdPMLwB8bYnwGAMbaRMdbJGCsBuBPANL96A4B9hcvHAVjvl49TlEeuIaICgCEAYiEvGWN3MMamMsamjhw50u4Oq4AsNhKeE6J9D0n4fv+c1fjxM8uCzTUtuttB7o1V2yJ6ilqPT+ezYBIz6gjK959YYt1vLYhg0TcOeHbJRqxo3FX1/kxjcCgfNtZKBOAuAEsZYz8TyscI1S4EsMj//jiAGb4F0gHwFM/zGGMbADQR0fF+m5cBeEy45nL/+0UAXmDdmS/MYGTc3t5WkVpLLFi7HZ/9zRxs3hXKlne0dABA2XLkam9K5UwXVTrNWkHPORiuyUCwVItVxefIdx9fjOn//XJV+/pgYxOOnflcrNyl1a0cNk5wJwK4FMBCIlrgl/0HgM8R0RR4W+UqAFcDAGNsMRE9BGAJPEunaxljXBZxDYB7APQD8LT/D/CIz/1EtBwexzCjsttKjxWNu9Da0YlJ+wxJrJvFtOOpRm09e2uJ22ctx9yVWzF/1TZMn7w3AE+sBIREzRa1EtdUnibUok6G96Lb5k09ZGHKWou3IW7M1T78/G72KqWC3NYp0kGPROLAGHsV6rn8lOGamQBmKsrnA5isKG8FcHHSWKqJM376EoB4mGQVsmBq+Mmxe1t0hPfJx1luitRqn1grbd608VcjHlFZnMMeopHuSLExb2tuR3N7EeOG9i+rL90j2VN0ed0ZzkO6DGSx0fE2uqPOIcw2FpbxcfYRUqSu3bobs1dsrunYdKgF51ALlKOQzqr9rNAhzOkkTvOEm57HSTe/WHZfukfiOIfK4WIrWaCaC6qjOxIHRaqJUKyUD8o+ccuLYMyS28pygFWA1fgyvImyFNIZ9FuL9yDeQlLWxNaOyua/zjHQNpyKgx6Oc9Cgpb0T469/Ev/z4vLYqTKTAGx+K0ky2aP22wsAMGHUwAx6TYcI56AwT0xDM6t9Yq1UH2A+sVfUtKZRzThMlxgGYvt8a80hVVvnpBUrdRdWMGPsbi/izpc/xJL11TemcMRBA26dc9+cVbHfspx3SZzDRJ8oTN5ncHadJkCVwpR/TxPC4ZUPGmtmjVWxWCmbYVQM032YFNL2UpTa3mm192gdweyp1kq7WouY+dRSvFVmxsQ0cMTBAvI0q/Q09PqHWwLv0SSFNJ/jtZrqLy7bhKcWfhT/oYwBXHrXvEouTwVV+2kUybUK2c1RjrWSiS7bboZJ99nS3omH56+14kS6s7V5T+Uc+GvO1yAKo9M5SGCMRU4jnSWgYdtuqU5lfcy44/Xge5JCmk/yWh2ERG9b3X02txXRUqM8DdaoeDOorqw/1qZmpzeKtwwjsSYOCb//v6eX4r45qzF6cF984mCzo2mJASrjNaLwdVR72uo42R7KOITpXmtgueY4BwlyMpbNu9qqGoEySawULLIuOAnpejz71pcx9YdxxyNjW93dlNXQQDWGrlvbHxvn+dmodExGziGjB7xpp+cz0NyWnONadzq33be2Nrcn1lm9pRmn/WQWNjWp4zTpnklPFSvx+6pFXFJHHCTYTKlyN+qOzhI+c/vsSFmSVQVfgLWa6uKtRe5TmIs8YU0aLN/UpOmv8jvb1VbE04sUorAU6C5bCX8c+w2L2/2bNoROS91OlkRaJA6dJYaNOxUbuKG/o3/wbGIf985ejZWbm/H4AnUo896mkOa3lWQFlgUccZBgJWsts+1121rw5uqoIimJcwgOQDWa69WyLvnM7XPU/WXQ3eTv/jXIkWyDvQf3TTWO6oiV1OX/7+n3yurTlnPIkgMVwxfd+uz7OO5Hz2PDjhYjEfvkL17BmT97ybqPvkJyJhVyGvlKTyUOgVipBju3Iw4S7DiH8tpWyemTFdKcc6j9ZI8ssCp13xWLWKXMq7XYLungp9pgTZfYBprT3eWmna2pvYrFdzfrfS/DXVKuh8XrdyoJue75cye6W/66DH+atyb2u+6Z9FSxUqhzcJxDzWGzWZW7Ue9ujxOHpO74oumKg5Bqv8l6SnbFElYSB4vraklA0iocbcOwqG5h7dbdmPaj53H7SyvCehZtiWtFVJaLQ7ddKzqP5nrB6fKGPy+MV9A8p54alJU/c6dz6AIECmmLOmmhEiElNcUnea32JbGfWpzquw/noK9fjXWYdNtpT4Y6sUusX8WM4zqkl5aly5Gi2s8ZK+956XRvhSQqqXmOWSnouxsCU1ZHHLon/jA3zt7aQDVfk06jnTUWK9WaOHTFGlatq1qL7ZKkHiXGsE2y5jHtB9ZJmBT9qpq12XpsxFC27zfr56+au9ua23HzM+91y0jItnCmrF2Iam5WqgWQ1F2XipVq0Gc170u3mSpPXRbjKIe469uKX9ciiB3/tmQjjvrBs9jZ2mHsn8Oec9Bj3qqtaG4vGuuJ49Zl3xNFTLZPR3dvSURD96uKcH3/iSW4fdYKPLd0k+WozNjZ2qG20KoinClrDfHbVz7EWYL1BJ+MVdmzNGy4CbX2kI72HfZarf6reWLXPdu0OgeT81m5xE113T/8Jm7R1dSa7G8ApOAcEvDKB9Eou50lhofnrw02JXHP1YmVyoGOS03ksDQVVArptmKn9rdycPpPZuG4Hz2fSVu2CExZnYd09fHDJ5dG/g6dzrLvS7mYEjbHUo05B3E8qoWnOrC0F0upkwAFfXQB1VPJ88ve5Mscg2ozXLhuh/Ea02GxEoW0si//8w9zV+O/HluM5rYi/vnEA6IHBlEhTYqLYc9Z6TkVu/HKUM0rTuQfeGMNBvUtJHqAJ2HzrmQnvqzhxEpdCBZ8Zr9rKcVKlpxDLXiHNVt24/UPw9Tdthv3dY+8U3afXeH5reYcshMPyVDlUZavWrW5uaz+w3FY1rO8T15ri78Bbt3tibfEU7dK6SuXWIuVylQDaMVKhgfyygebcdnd87S/d2fwx+9MWbsA1ZTxK2XWGY3nkTcbMP76J/E5IW6Tqq33PtKH+p1xR1Ss8f7GuFezahzPLdloHpwBXcI5KIhDNcdx5T1vKPqLdnjqT2Ypr7XdAqw3fdWJOsVGI457lyDy0jAO1tCNv1yirRQd7SGZ9EwIdQ7V78sRBwl8TlXDUkfZpoXVikU1/NvD3ul9zodbtHUent+A6f/9Cl5cplbIbdvdEfn7iXc3BN9XGk62FT2pLlFIK4ZRrmI54fe312xTih+sxTvCWE26D1u7/nIfN+9Z3HPPuvXlePuMRcZszdHoFNJM/psZf+dQcTU9gDYE918LnUMicSCifYnoRSJaSkSLieirfvkwInqWiD7wP4cK19xARMuJaBkRnSOUH0NEC/3ffkH+kYWI6onoQb98LhGNz/5WLVFFnYOqyUSdg7/osyBWSzZ4XMPKRjsRxo6WDvzfO15MmwVrt2vrVTK0rvBzKChiD5hG8cJ7m7R1koZ/4W2zsUsRxM6WGNmGHbe3Ckr3vOXaqhP5jpYOa2spHXTzQB7v29I81HIcPdzPobuIlYoAvskYOwzA8QCuJaLDAVwP4HnG2EQAz/t/w/9tBoBJAKYDuI2IuJvj7QCuAjDR/zfdL78SwDbG2AQAtwK4OYN7KwtVtXcvy1qpdgpp1Xx78I21iXUqeWZdQRyUynPDMEzcWLn3bivGsm3f9jmqatnsM7yOykjhyO/9De99FIogbQia3I6tQnrdthbj7xx7sCuDEd1KrMQY28AYe8v/3gRgKYCxAM4HcK9f7V4AF/jfzwfwAGOsjTG2EsByANOIaAyAwYyxOcwj6/dJ1/C2HgFwBtXCkFeBalorlefnYFevWrB5C5U8q664r3oFcSh3k29uK8+EVDzZtijCqnBYE5FqEVmpXRsiZDNnZIKra1YOq2FLBFUcThdtKZkiECt1E84hgC/uOQrAXACjGWMbAI+AABjlVxsLQDxuNvhlY/3vcnnkGsZYEcAOAMMV/V9FRPOJaH5jYzpXf1tU01pJJRdOmuwh56Cvl9XGUO50q6T3ruAc6usUxMFiGKo6v3pheVljEPeu/3lR30bmuaErfNw2YSls5pF8z7r7XL0lKgK15pB6qM4hECt1B50DBxENBPAogK8xxkzZrVWjZoZy0zXRAsbuYIxNZYxNHTmyMhtlHapqrVTGNTaLYY8OQNkFY++TL484qNBZZoQ3sbum1g59PaGi6cCStZhK20/C7TLYndBnr5A4B009OVOi3L+OqPTc2ErefXUbPwciqoNHGP7AGPuzX7zRFxXB/+QmMA0A9hUuHwdgvV8+TlEeuYaICgCGANiKLgCTPsvB9P9+WRmzXjWRrT2kDfW6PDxxRQrp7IZhi4KKOGjqVktcI7bbbkjUE3U607eXdIj42pkTtW0oT2aSOJPrEaq16eqalYmB7Vzv8jVRJfD76hYKaV/2fxeApYyxnwk/PQ7gcv/75QAeE8pn+BZIB8BTPM/zRU9NRHS83+Zl0jW8rYsAvMCqaG5w/q9exW+E8MQiQp1D+d2/91GTMmZ9OfM14GSMp8bKHlWae816TnZFngr1Zqgeh/jOVGMtV44tdmfybrbnCMzgm3s5vjaAWSEdaYtBaZ2VBK21kjQ6mZMQf73unEOM7VVrP120bkfNiBG3Cis3IkEa2PRwIoBLAZxORAv8f+cBuAnAWUT0AYCz/L/BGFsM4CEASwA8A+BaxhjXuF0D4LfwlNQrADztl98FYDgRLQfwDfiWT9VAZ4nhnYYdQcYtGeFmXA2kbzWIyloh5/Dm6q24Z/Yq5W8H3PAUrr5/vtV4VOOohDilXVNZxBBKE3fPhnAe/YNncf7/vJZqDKUI56AnDmL/ppFUEqZC7Rin/i35XZc3F3RXyfNDJqTieOoEB5Za5CLh+NQvXy1b95QWPOy/SjSaNRJjKzHGXoX+uZ6huWYmgJmK8vkAJivKWwFcnDSWLLB9tzkeSiBWUszWGcfuiwck004ZKq/ioO0y1g2Pc28kDhYN69J0cvx18UYMrE8OtaUiRJUQ0rQc2q62IvrW5bW/Hzt+KN5YtU37O6A2tdQ6YSWM58Vlm7C1uR1bm9PF2REfo4ngRTgXo1GCub9ws49XVG34ehNRc0fl+jvoOBJ5HHL/Il0VN8xa6xwWrzfHxcoKAXHoJpxDj0LSpAl/jtebPHaI8hr+wppaO3C2wmuUoxzOky8Gk/jFNrm8Ckmbs7xx3PxMnOOqRASXVow39YfPGRPT33LRkeWORFmadFJevWV3mb2F7ZqIg+1zWWY4lET7jSMN55c0h9s6snYwkIhBzENa4ByEDTNtylMRX7j/TaMFmQp/qyCETBrwvaauBpxDryMOSUfBIGS3ol6OCFd94sAg6TngyRsnfvtpvPDeRqO9uth2GgTEoQLOwXTaU6UuFSGz56pQECoFry3K8SvZ2twe4QAjISZSOHSpxsHxjQcX4D/+srBqzodiu7acgwm3z1Lr0DgKqpghirFwyM+I/7mjRW9ZBZQv9tMqpBM5B1GsJBAH5T3ZCZaeWfwRbvnrMqu6tQY3XnDEoQpIlJhKVhoi8jlv0YgT+XXfmUeOg29qOw2KAeegR9IpSZWelCNpMdsQtPoKJupTizbgkTcbUpNNUU4/pF9dqmttdA5/fnsd/lhmxj8biCfeVsNpOys/kDo/ZIitzihcB9HfNuxoidUVUbZYyTJ8hjzXxYOR6NyoOjAlHd5kdMeMcR3F2ukceh9xSOQc9PWICDmiyG98MfStyycTHutRhghORoaLX19ptvo1KjyF76pzlY0ZfyXyz5uefg//9vA7+Ovij9JdKAx8UN9QV2IXiyiFzkEo/91rq4ytLli7HZ/+1atWp2dxjzMR74ifQwV0IuQc7PULIjhBNY0VqIBz0JTHOIeYWCn8Lm6YqgNTw/Z0IsBf1kjJnAaBWKngkv1kjsS0gwbT0TwRchQ95fDF0LeQTyY8lqv7wttew8XH7ItLjtsPRX93Np0gb9ZYXnFiYZ7RAAAgAElEQVTIFh7iydnWQ9uEwSlP7irsTBBXyBDXvshil2uuqJsXaU7u3318Md5t2IGlG3biqP2GGuvaNhvNxlc+deCiv7TWZvynP81bi3mrtqFhq3mDbSlbrKThHKS/Y5yDRqyUhWnpooTkS10BvpZrIVbqfcQhcQPX18vlPL2DuJg451Bfl7MgPHZjfHvNdry9ZjsuOW4/QSGtxpPvbsC67WZW33jaS2AdbIZ82JhBsbJNKXPrpt3UxWedlh6YdA6MsYi8uVo2L+Ic2ntI30jgOtW4KkUfn3NQNWfTx7rtLYnzDAC+939LUo7Mg24vl4mGzDlETFlFhbTiptLGI8ri0JM1+FoudKfwGT0FtmtNp5AmoshEFil5sshKXWGbwQwy0DloGldlGZNhcrJKeh42JzCV6MkU4lsF29DUHBFxi6aOTg+kFJ/5DT67ZCNuE5S7tfCQHmAwIdZ5SP/vtSem6q+QUudQe+h0DtG/5XOOOPaIWEnpBJdujtUiZ0JatHcy9CnkahJEsPcRB0sximojz/liJbEd/pmj5I1WJ7+/ySAW4maqlSxfkw1+FqKwcqLNxpByrpc01MHWWunmzxyhbO+q+9+MlCdZ54hIQ0iY5g95PxLric2PH94/omvh0BHzQsA5xH830f9akQ29tZKeUwCi1naiE5yKWU6716elmZWYz9qio7NUE2U00CuJg93vqnr5HAUxTeR5QEjeHHS/miIsFi1MWVW4fdaKIKKlKRF6oijMoq8s1kRzypALNpyDCfLJq0PjK/Jfjy0Ovo/dq5+xTROHJkMk2OI7kBMR6U71dfkcHv/SSbHyjxTivFMPGYlD9x7s9SU1t3lXGx58o3pWWbbQi5Wif8vEr0lIVVpI4BzScgJpdTxyePFqoKOzFCGC1USvIw5JML3eHIWnD1kXQJIVk7JtTYW9+utlm0k6B9U02dbcjpufeQ+X3DkXzy3ZiJ89+75hTNqfANiJHLKQStz58oeVNwI70QEhzubpTtzbBH+KkYPqje2mIQ5/mLsmSL0qPj85SZ0pfIZKhq4yv5x54RHQHTa/+Ie38NxSddpYeWzlwsYk1NYgIE4cQs5OlMOrTvFpRZemzeD4Hz0fK6tFfCWPONRm23YK6djv+pN6jig4ncjOab96cXki26rrey+N4quptSOwVkqzSrnSrqWjE/9ynzlmkk68wWFjypqFXD6tDDUqi0+nnCaKb0a6DUzMPJZ0l7IZZ9JzWbN1Nw4YMSDyaj3OIRyLbr8hihMSr09FXfF36bfGpjbjGLPAgrXbMXX8MGMd3TyTxysTC9FHpC4hfEZ6o4c4OjpLaC+WlByat1b1oV2yQFuxdsSh13EOSaziovU7tfVyRIGjTVuxM1KvsakN/ymIINL0LTpxiRvKETf+DRZuDlrYrAWxPxWXUGIs0aQvi/NS6oVbgViJEN9EdSKBTSk2z1aJc5D7+JeTDoj8zU/+DCw4WMR0DuJ9RoggKcUkqrvwRKE8Kqv+aX359Ana31TYe3Bfq3qmd/tfnzrcG5fuLSZweOJ1+STOIeUce7chblRx6V1zMem7f1XWrw3nwGoSVwnojcRBVOpd/2Tsd+4zoDNlrS94JwNuwmo6HMoLUVdXfNk2zlhJSKorxo0R66pOW+u3t+BTv3zV2J6ttYtJ7JJeHix8T6mQVsFqYSfcZ5vMOUi/f8ffCDlC4wa93XrUzyEKW9PMHNk9lxnT9rNqDwAmjx2Mg/eOmzCroe983FBPj1OuQjrKdQnEQdFeWgZ3RWMz5khJiV7/UO9wqtNbZYmOotM5VA1Jr8/kdBbhHHx21tSePEF1dcWubOPac6hDQTDtb4CnoH7lg8Z434q9e2drsqLYdtF9+y8Ltb+ltUHnz2nTzlasERyzbOTKKhGWzcJOFCvFOAfzFSQYNwwf0AcA0CQp5nXzwRMrqTy9VSfm8KmYhlSn5ETUF+RzuUzFidpDkfB99OD6OOfAgL87ch/cc8WxGD9iQFCuOujIJTaRVD935+uJdcKx1Ebn4DiHKiHpBZoUwDmiIFw0FyuZkHTqCcYUqaNua9G6nZmayvG4QeLiLzfMse1Vbxt8Hz7c3Kz9Tdmn3+lSjfNY4vXS3zbpPsXHM2JgXDkdF3mYEXJLDEP6e8ThWEk2H3klUoMqgqqet8ImbBhVmgCKaRg9E90Pn4B6XOKaKeRyERPVpRt2Yt32FhQ7Szj1EC+F/e+vPM67Tsk6RP984t0N8SoVbPDVJw1eKBync6gSkl5ghyF/Qj4Xcg5cEWaaSyYWOFpulvvrfmsrduLl95MD/qn7jI+pXGco60QzmZ6s7DkpVR15KHacQ7rxJ90uX+OMeZvtc984BXddPjUiNojqVqINqjmHeD8mzkF8J8rIrZp7UOltdDC9kpzwDFSQLbnE8Z7781cAAE8vCuNynTRxBPr3yauDCUo3oyIgSfckRmSWUQtnwlpaK/U+4pDw/jh7r1oVROFpLzxlGzZzORm6bkzmIQWQT/Y/enIp5q1SyECDask7pU52bwvVRqtDlvq6BzVJl9JKY087ZCTyOUqtTLQhQknEJDzNe+1NGDUQg/rW4cmvnBzU4RtOa0cn/u+d6ElXradRiUPtxlunMn8yjN2WWJos0TjZsonKKoeu0SFHpHSCky+tV4hnkrhnUwTgGtAGtBedE1wVYX6Dpx3qsadKzoHImFFLRpxzSBasPrdUnzREvlwlihk/vH/w3WoDq3BG1+Vy1iemlSlFRybc+crKsq8lULDIJ48dgjyRlQNT2keVyDkE8nYW0ZWMFERW/Nn+6KmleHV5yCUSqcVKKxrjz1jcnE1jSmMUkBXnwH/UOsEJ33NS6Bod5OCYqrYAtRGAykJJhOykKKI2nAPrPgppIrqbiDYR0SKh7EYiWifllOa/3UBEy4loGRGdI5QfQ0QL/d9+Qf6MJaJ6InrQL59LROOzvcUojAq5fOjIpqpWJ8Q0CUxMsxArCb395a11GbTnwWTDHiQ10tbwcPiYwcbf8zm1818tTlFKWIqVzjtib/zk4iPx5dMnKv0eVMj6nvhmzDkHjpxiM98o2dWrTFm3Nbfjain8h9deeEI33UKaTScNx2gCtzCS5/aKxl1oL5ZQYgynHDwSS75/Dojs9GI6TlA+CPXrE/dJ+MqfFqQZvtR+2Zdao7uJle4BMF1RfitjbIr/7ykAIKLDAcwAMMm/5jYi4m/gdgBXAZjo/+NtXglgG2NsAoBbAdxc5r1UjJzAKl/863jO5YNHDxL2Hq/eIoPFQ5Nk6WNjHWHWOUT/1sU0SnOCSap64MgBxt8LOTWrXwvLDSBKC+Z9+ww7ayV4p+mLjhmHPoVcLEeHDmIVOx8Si0p+vUh7wh/82apOrDJx2KUJQZKz5HhV4h9dbUIasZL+N35fYuTgxqY2nPHTl/Ddxxf7Zr6E/n0KyBNZzSud+Eku+eGTSyP9FjtLysizuucaa78GU76t2I2slRhjLwMwZ5MJcT6ABxhjbYyxlQCWA5hGRGMADGaMzWHe270PwAXCNff63x8BcAaZhJQVwvT+dKdgjv51+VhspUXrdmrr/1jKt7zMIiyzim0+ws9drTLjk1FizIr1DhXS+so69lxEPk/KZ1rrBO8AMGqQnVOWjBylD5pm4xyn2jxnXjhZUS+6MYt7Pn+MMiEgilsMmcxexb5sEKbL1WukdT/JYUZMBJtzK0XBIGBHixeyZO7KLf5c9urkiKw89nPaA0u87purtwn9qoMs/u7VUIRp2plqIVba3V7EgD61CWxRCQn6EhG964udeGaTsQBETWGDXzbW/y6XR65hjBUB7AAwvIJxGWGUuSre/Iv/dmrwPZcTT2DJp+NmKS1hRyfDiROGY8ax+0bHJHw3BQyzipDKgPdTmHeaWvQUe+Y+Cxp791p4i6pQTg5pW1k2v09xQzHXj5f943H7x35njGnFSiHnEL8x+Qyle+Yi5yC/8HLfEhmu/eZZB1u3w81ni8Kuz5+Lp9cInw1ZHFYAbx0rxUqKugOFcOnbdquJg21AvVrM+N1tnehfX90QHRzlEofbARwEYAqADQB+6pfrzCdMZhV2JhcAiOgqIppPRPMbGxvTjTho2HBSznlsq7jZySECRPY8KV+u6mQ3oE8BE0YNjI6JRevIyAdy2Wi5TtZ/9e/jcmcdTGuNKHmTL2i4ra7KEWDDcsYYU8tNh8M2V0VSi+HpPDruaKY+71M2M1XdpykOU5KfgzxXk8RzZKAOsomt0bfCryuaEgcbgy/uC0OL2ForKdKLlhjeUbw3Ue+wXQiyeMjo0Ptb7LMrOQfGGJq7O+fAGNvIGOtkjJUA3Algmv9TAwDxWDwOwHq/fJyiPHINERUADIFGjMUYu4MxNpUxNnXkyJHlDD1xM/Rk9mGZbO4Wmt5FY8nb9s0TBkXKhcWjYps5RxNTSKt0DoxZRQdlwadBBg0LziGvXrDdMDd7AHl9iyf1A0fodSz8NpMcIINnm7BZhMYPTBIriQppr1Je0jko9QOa/nJGPwfvU9ZpJJrhGnQOMgdumkJcuSqKlfiYlm/ahfU7WoI1l89ZWivlKCYmvHf2KmVd8XlsFziHg0aF80DkHExEs9p6to5OT2Rs8rXIEmX14usQOC4EwC2ZHgcww7dAOgCe4nkeY2wDgCYiOt7XJ1wG4DHhmsv97xcBeIFV8SkbiYP/uximWT4FBZwDWOp8uSXGkMvFNydrzsFK52A3llCkoa9DlMxSF3JqnUMtEp+oUI66StStmEbNlZc5Us8JDmbRlvh7TCGtqKNLC/nCN08JvuseeUSsJGD28s1h6BHNALQqB4POQdaPmE7UnCO69o9vhX0KT66ptRg4ytnowHj/ss5r8y6djiis19wu5IYQiKXtXK42s8wPavJBoVpI5E+I6E8ATgUwgogaAHwXwKlENAXek10F4GoAYIwtJqKHACwBUARwLWOM76DXwLN86gfgaf8fANwF4H4iWg6PY5iRxY3pYDwp+9ZKU3/4XKR81r+dii3N3uTiGwNjXhAsE+RNpMS8E6Ip21casZIK9qxtKNJQwRNFJIuVdEp8E1FJk10tLaxIg1LnwJ+HftxcjKjiPMTNSCa8/3LSAbjw6LGQEfapN2XldWIKaf/zwJEDY3VliFeKNS757VyhT/kaC7GSBvJYTc9U5XgnV+djkVP0csiEM6+oJz7Tl647FafcMivWl/hdbDPCORjFSvrfKsHu9iJaO0IrpRpZsiYTB8bY5xTFdxnqzwQwU1E+H0DMVIMx1grg4qRxZAUbzkHG+BEDgqBeokI6aSPeR8ocphMriVBNsFxAHGSxUhxp56c2oB/sxEpe7myFWMnwbH75/Aepxpg15I1P3HRMd8v3sbhCGxB5SCZ9GbNXP0zaZ0isvYhYSRiTSuegslaSoXMyJBL8HAzcRWRsFn4wut/kzdq0TOoK8RuJratA56A+xcv6mEKeYgc3cUhiRj+xtUhGvkjKUVGspEfa8Cq2+LtfvooVjc245tSDAMTfVbXQCz2k9eA6BxP4JLMxGeXhiDlKjEWyyQUQFoOScyDNb4r+bSVycrIiGZzDSTRl1YiVHntb78xXzXSK5VkrCaIgw9BG+6ayMnGJcYjS/Rmk1GGfGs5BzFEeaVNxo1/8w1uxMl5XFIcq62hGqJNvE/Q+B7KT1sadapHO0189OUL05GRJHPV5fmJW67dk7mNQ3zo0tUncqVanA+V3MQihreWdjZltOeBe77fPWgEgfXj7ctHriIN5r7NxhuJWH8kbsTypSj7n8MaqqCmkWOvtNXGLilDnEC1XK6SNQwpgCjAIhI5iiTqHfDx8BmMM8w3mnn8xEA4gmk+6VGJWEXA5UqeChG8iyRPuGY4HTKgvIn7qTm4LEDmH6Oav8nOo9LSoU0iHfarb13njmg5SspOWqE8QcdiYwRHldcO23cox1vuRkEljrZSXOIfBfQvY2RJ1XBOfadTvQzyYheVi+HKRCzZz/fGxPbZgHXZoTGQ5in52OVs44lAlmHUOXg0TRFPWpH1YjvRZ8m22P9i0KzqmhIbyGrGSCrY6B25XbsoTQUhWxqlMWf93gXnzT9I5vPJBGEPovx5fhEO+84y2bjl7pkpnIJqV6lAKTvGSjFujgA3s9XXKXv7JZLFSXOdQsV9owDloftY0b3r9umelU56rID47nYVbP584qExUVf0NqC9EDhgAtM83yjmEf+Q1Cmn5zh66+gT1oAF82LgLX31gAb7+kDkkxz/+di4O/s7TxjoinFipSihH5yBCVEgnbcRyjgCuc4hniEsW3QB2sZVsJTbFBM6hf59CLJbNSRNGxOoVFOaF72/cFauXBqLt+e9fX5Pu4rLESoLOQfM8Tp44QrvZx7qU9Bfy75cev3+kL1khDQC3/+PRAML3WelhMdgcdUprLQWL13/265/wjTe0nWmail+RixAH73fZRJyLtjwP6Xgb8thVY7O5PfGaOo3OQYb4XuT1ya0ZN+yI55sWMXelbQAKD7UKTdP7iIPhN9k872Pj4krEgD0Hw5Zd7bHfRag4B9UiT9ZzcOKQfJ3txGnvNGeyGzOkL4gIHUWzPqSQjxO7Sjey/oqAaLawOVS9tjya+tHG81Yk6kmmrCGhUZ/6z528t1/PJ9CKNib7IVN01ko2GLtXP7zx7TMjY9Tdpa551b5YX8h760DzzGSRDodKRJlXcElffzB60uYJtnQ6BxneIc9uToqcs3gN7xOQiIOinRvOPdS/Xmq7Snv4kg3lJbhKi95HHAxvTHbs+czR42J1go265LGDJuxuj6d8zBHhkuPsc/UC+vAZqnuxnZBc+ad7HvV1eRAByzaGE1FJHHK5WJ+Vsr2qOPtZQvZwFgPv6Z6HKGOXN5qY6aZk6RN7HNIhXhYrAcKJOuBW0j/TXC6Mc5R0tS50hOqdczNnBt0cVD9DlVxdfHaceMjB7zjnoDNljRk3KfQhuuenUkg//IUTInkbIjoHRRs8l7aNKXE5GDGwT+TvGkmVeiFxMPwmcw6q00bSCUzEo1L47RLzJuklfiJ3TnySNvS9h3hWMrJ5qOoyW50DZ911tfNkdtbj8Jzgoj+YdArVZolt1o0cEsXGCS4SuTVR5+B9BmKo2Bi5UYOecxCt4lRt2KBTkd0u7eNXVef6KMbU7XVoxDAq4iDet058I+ocbOaPSjycRqy079D+kbWfZJTBq8rVAh1VhbtsF/mTJvs59DQk6hzEv1NaJsjY2tyOna0dGNy3zu/bEysV8jks++F0tLR34tG3GpQL8KXrTsWIgfV476OmIC+DbK1kG1xMhZBzUP+eI4pb4aiIQz5+mrtvzupYvT6FHNqLJavNqZLFYErGwnHjpydF/rbRORD0G3UsHIpfL3g/OjGUoU9ZlFgON6aysuFj29psFonyS1XznPvq6IwZdM6h7QqNs0qsJKNvQBzins8qqMame35RayUuBoy+06IwbjmZ0rABfQQdiY5zqOyoLz+XGjEOvY9zMG2fRCRxDvHXICqkrXoT6nFTVsCX20oLVlS27T98AAbUF3DM/kNjp0gO1UnMlnMIlb7q+mIEWlPbuqisMgb3LViPr5IAZjantJj8PsI5MAzqGz8z8bkxf9VWPL5gfeQ3ncf7r1/y7NKfWxLN7ifShm3N7Viwdrt2AwgtpMLfuB4hCSrnLV5y6V1mkWgAxauIcA6KS1REYO3W3UonvbxCIS2Dz1UxZLdZPBxfn1qdg0KsRIjS86Imv/i+w/rhwJEDg7o2gTHLQVdFOO51xCHphYknCZNYSVwW08YPi9X7hh+2WJzEskJa3nx17KuOIMlRYa8+5UDrCXnIaC/D2ztrwwREY4aE4hbvRCeLS9TyZ5s+dUp1FdKInlokyxYbzkF+rTlBSM0Y8MkjxsSv8QnIRb+eE8vbHeewvMbW+7JzOYtbLhe+zwfnexHu40pyLnqKXgPE8yXooAr7wB/tkg36PCRifT3nwMVK8d9VOY5P/vGLmHHH64p+LIhDxJSVGet6jSrESprzNlN8J4pGMdCty0P3HuyPS70+NVLI1LAVkWWN3kccDL+RJFcycQ7ifLnixPG46e+PiNTjp88I51CKRt8MTnN+HW08fv8tySx1m+RRaspo9vUzozH2uWPZ959YEpSJ/edz8RhQquEZTRoFpPPVSG6PcyJXSWkxbYx6VPJ9k+VQUrs6E2PuQNYhnaRFruAjjZljoI8WRB1pIeoc5DShiYckg8iL/P/rmrjgqGgcqStPOiBWZ6wUWgawIQ7h/I6uBZnrij8svc6Bxb5zzoiDv7+4QUi0bV3OeN2rK5UYviH4QDDGsLBhB657+J2IFMFxDjWCUecgWTmoJpR8AgO8a+SNW2bjNzW1YqeUNlSOsd+h8b8PCVK0j/U75LzCXh2Rkzl49MBg3KceEoY5V+WiEOegSqz0hVMOUozN7qTPrT+sdA5JCkACpvvmoPHfkndRmehHA+8BquVM0JtRDu0ftSaR4yHJi1s0atDJwkOrOBb5Ow0iJ16J2Mh49Bq1M5fyVZDZMEMUFQ3ok489zc9N2w+PXvPx2HV8DX36yH0i5dxDOpcL6ySuY0vuM0JiNGIl/t7jYiO++dsffERsbGrFnwWjlc4SwxX3zMPDbzZgc3MYcqSrcqP0QuIQf9CPXnMCFt54dixejGpByrJgr834BhCKDrzy7/zFi2o+e0Xo/Su3rpNthmyreZLk/FO8SKi4MhwArjvnkOC7ijhcd07IXXjWStERHr3fXrFrPIJkHBb++ePjcf4U7zQpiu2G9q9T1uft6U7V+wzpl1ppHZH7yyqHiIkk0x4KdI//JxcfGfmbvycevC0ulgjfpy7CJn/nnUy8Ih1u/eyUsMeEBo7ZPyoaDTd/tVgJ8MaftG+psux96mNjAgs8EZwQyk2KnIONWEkSAETGzPGt6Yf49xCWiX4s4tzXKecDkZ98EpT/1Dx8eb0XRcmCKHGQ+h0/3JzXPSv0PuKgKOtXV8CgvnUxzkElvpZlwd53FnvRIRHxPvlmrFpMvKyYyDkofwbg2WZzubi4GYmyavGEK4ukAOCzx+4XLBqVWEl1KhdDT3SWGGYv3xyr89lj9w1CHIj38J1PHq68F75IP37T88rfbfUcIr76wNvh9dJvcuA91VLmhFeFoQPUnAO/59jcENa/LjY/jxcke9nb4leXHKXlrmyQJFbiz2i95JOgqixvbnLsJQ4+b+X6okJaLVaSulTpHKSXGkbJDSve9drKoK7K81krNtKsT15d72AYveDvb5sd2zdaOzpjzrSfPzEupqsGeh9xUMwpfsIj6Xcl56Bgz1WcAwIiot4YIm35f/NNRA5TwSdXZ4mhVPLkku99FFUo7jesf2BRI4plRFPBffbqh+e+cQrOO2JvbYrTA0cM9PuMhxZXHoAoNLH99K9ejeQIUF37yvthelc51DIHH76OGG7c2YpH32pIlRdCJJIqD2cxZLf2PjUb0j6xU7BXb8RAT3E8cXQ0LSwJ1XScQ0BYNKfpJMQVsmH5piZzOAexP2XIC0Eh/d/PvW9sR7WGVAprQJi3Updh+AxhozZyDnFTVtPzAIAduzuwdmtLcH1UrKRuQ+Yc4vODi53UkDnKJRt2CvPQ+7JqS9zCS05AVi30Pj8HxTLjMlJZuao7KQNxnQN/0acfOgpfPPWg0LOYqa8HBCUhPw35bcgKPTGfw+0vrcAtf12Gw8YMjtTpLIWWUKICVD6YThg1EP37FJScA+8DUIdrMCnoAWDxer0FDK/3478uC8q2aWztk2Ss/CT1xLvrjfVEiPcTV0iTwDnEvZV5HdUOnSOVn4P3yYmCzCHlhIODnFKTQ+Y6KhU7i/qtrz1gDgQnQm2EEIp4NjXpMqyFdW05h07NCZ0r9nMU5hcRx6XiEuSymKmwdDA7dmaY4ItyUZHqSJ/Iy20EHKLG8CBJ9KkicDxjXSBN0Iiaa4FexzmoFjhfiB7nIIhkVLLnoBlZ/uj9fdDIAZg6fpigpPKvI/4Z36QChbQ/ueQokyJBWuqbIK6RThQlYVMTuRjVhl5QpFHk4NfmiGKERXOgtlKY8VsSY/bnNSdI2+WQxrkoryDKwd9EiZyD7j4H1MfPVwHn4+8Vck6EwLqlZBArCZxDqcQiuqpyIJ6Ud7Ymc1xMs1ED3vPixMFG5yDX0REHnSiL52sQ/ZCMOgcFHY/7kUQPZqJvBkHN4esU0gP9ObBLigTLx6gzkjB5XvNru8pSCeiVnEMcwalS1jkYxEqiKJgxhiPHecraUTwhjLTx7+Vb63xu2r7xMfFTgj8RZHGLyLZywiHLIQu5XFCvmEAcdDFqeB+Ab60kx/shngAoWmZzqlVls/vUEWOwV786tLR34luPvhsbQxK2t4SchxwSQ9c/YDZlBXQ6B/XcUdWVYyvFxFiCWalOrEREKOQIxc4Sfj93dRDG/I//cpz6ggSIJ+U0agzlPZP3PBmLH5IGScRSlTBKFit9duq+eHD+Wq1sn6+HfC78TfZvkUYYm5PyepFNhSNXE+HMw0YHfyelkOVm1bI1IucMdEcY08YfEIcuslQCLDgHIrqbiDYR0SKhbBgRPUtEH/ifQ4XfbiCi5US0jIjOEcqPIaKF/m+/IJ+cElE9ET3ol88lovHZ3mIUSp0DP5kAkdWg4hxCkUAUJ04YgUev+TiuOHF82JbQ36C+dRjct4DLThgfXCNvUpyFlDkHfvLoLLHgpCmedH79T0dj7yF9gw1Q9JwOg/aJ7cUnOk9BeNbho3HmYaPx79MPiZ+gCXj7P8/GO/91Nn5wwWR8/cyDlWIDFcJ7iI7j747cJ5Yxz2SVJVpc3Tt7VfD9xk+rldscEc5BKVbifauv1yWaUZ0KQ50JPznK1/C+wvd57WlxM+FC3hOjrNq8Oyg7ZvzQWD0biCdlm+3GtCGGh4RkzgGIH0TkwIpf8Ode8A6kFjhx4NzK1uZ2fOKWF/U9ygsZcfKRJ/wAACAASURBVJEPX6Cq/TlHUSMDfqkudtJAnzjskomDJDWQYSQOCQSpFrARK90DYLpUdj2A5xljEwE87/8NIjocwAwAk/xrbiMiHqfhdgBXAZjo/+NtXglgG2NsAoBbAdxc7s3YIFnnIJwgDfZ/sikrAByz/9BA/igTkdaOzpgIQj6Zc2sl2ctX3OBViVSmT4569CaJlXIUV6Jf4Jua9u9TwG8vn4pxQ/srFbdD+tdhSP86XHr8/vjqmROtnOAYE5XqpUg5APSXnovpZCvqDsTxJQVHi+gcYhxRdDOU3/uFR41VhmTQgUkLWx/XJ8SXT58Y+72Qy6GjM+pVX27EW5GTtdlwOoMwFYq2EMY54j9PHuvpwA4Y6ZlZzv2PM/DSdaf6Y5c4B4k4yKJaeXwFQaxUYskKddW7kkPNyMEPVb9xyO8zLPc++2h0DkmnftPvoVjJ2ERVkUgcGGMvA5CzUZwP4F7/+70ALhDKH2CMtTHGVgJYDmAaEY0BMJgxNod5T/g+6Rre1iMAziDTrlwh1JxDqHMQNyblxurXFdlaZQCyQPzk/da4q00bippPOhuxkpwSUQQPpiZa8QQSM2mDKTEWmcx1inblEp2YzWazCez2FZv46MHRcBAmTmTiqNDyRxzP8AFhGy9885TYdWL49NhJXnBw4z3/8ILJwe8HjRwQkXeLmOGLCW8491Ccedgor42Ac4iPU+yfMT13AXgy7LtfW4lmcezxakr/E9MbsSFyfN7qQqYEcY78n/sW8vjdFcfiniumAQBGD+6L/YcPiMRD4pCJgxgGXzU+TthzpFbiyiUqnUPMSz2kSMr7ExEqytX18r7DaFHqg/8tpwUO2jUcaLgUQa5zuGSIUk2Uq5AezRjbAAD+5yi/fCyAtUK9Br9srP9dLo9cwxgrAtgBYHiZ40qE6nXwDVcWkZgU0mK2qr6FeHIa8dJnFn2EWcsasWrL7mgdYZMARLGSZvEw9Zg4VjR6GdhaRKWvxuqoxKILRhmTKLaJxsG5kCSI5rgcPMSIrC/QrZkfXXgEzhBkweKQTzgonDIHjoyajgLRfAWqzTq4B+b9/U/H7x+I2igw3YwObNH3zsH1071EL1efclBglcQPC6qgeWL/LR3FRO4CABq2hb4EqnPTKQePipXpCLZIkEzoZAybdrbiofnesv3dPx8bGT/3DQl8XBjDaYeMwrAB8dwDOusjsQ4Qrk3d+HiynyRDBNmZdWdrB5r99frnL37crxPtUzUeDllMyHHLRUf69Ql1uRzaJb1GTJQlwUQcdPqXgYqgkNVC1tZKav2cvtx0TbxxoquIaD4RzW9sbFRVSYRq0YScQ1Q+qlqvfBGLL/6cSXFnozDiKrSWJvIE5ScNmXMgYWMVM7PJUDnH6DadEos67qlogyrMhAxTWAkAmLLvXjho1IBQ5yDU5SI4XchrGQdL/gI6M9Ak6HQOb6/Zhqa2YiwcQrAZSu3UF3JK67OSJKOW74/Pt9+/vkbLXYgQRSKqWjaPQaxjIg188+wsMbwupK887dCQAHkOkr5YSeKSZKicB+X3FloDedysznqTH2rk+/2Xk6PzXuYcPnbj3/DHuWswclA9jt5vqF8nXJ8ydGIleZ7vI8SHqstTZE+YvXwzbn32A/WN+DARh50+999VoTOA8onDRl9UBP9zk1/eAEA0xxkHYL1fPk5RHrmGiAoAhiAuxgIAMMbuYIxNZYxNHTlypKpKIlSPmi9M+XSo9HPwn5i4YFVOKbzo3jmr8OAba2O/i+3HrJWk9sRMcDxgngryiQwIWfiY3JpFYzmpOAy5RGniqdg0RfzvtSeivpCPiZVOO0T//krMUlSVwhnoyH1D0YvcdM63grnwttkA4hwd70bOBRx7PoIVkne9mnM4yOdsDh8zWMtd8N+BaKgT5TuIFynqhHNI3nD+6fgwM+F+w/oDiFrGycgTIZfzNmr5XlWQ+5PfW0AYGTDx20/j5ffVBz/OhYhX//JzR+GLp06I1oN60xetpGRrQrkfEXzOmqZkXSEXEStd8tu5kSyKKpiIA3cmlet86bQJqupVQbnE4XEAl/vfLwfwmFA+w7dAOgCe4nmeL3pqIqLjfX3CZdI1vK2LALzAqqmiN+gcAFmsFF8cBYW1kAr80rteXan1Rg45B65z4JyDXqwkD1+Mbqlay1efciAuPX5/fF6IjMkVsCLnwL15Vfeg+5uPLY1YiRPAwf3UcZUAb2w6Oa3cty0OGqGPRyPG7AHC9yIGvePiFRE6D3I5P0eMA8sR+hRy6F+f13IXAPDNs71YVxHOQaP3SUKE4Env64cXhBGF8wIR1+WtzuUEsZLmVB3WjfenG5tNHu9SiUW4FOWhhij2DoColVSw9hRdyu9LJ+IRUVCIlZKQ5MPAGMNvXvowUnbyxBGa2tnDxpT1TwDmADiEiBqI6EoANwE4i4g+AHCW/zcYY4sBPARgCYBnAFzLGONH3WsA/BaeknoFgKf98rsADCei5QC+Ad/yqVoQTwpH7bcX3vrPsyLijagNf/x6PhlViXZE2DhoiXNwwdrt+PUsbyLEneC8zxJjOO6AqDpGDGCmWigD6wv4wQWT0b9PKKvkpz6ROKi4Dp19vgw7J7gol2Ta2EuM4R9+MydWHsbD8SA7l5UL+b2Hm1X0bxnx2FPeZ5JCGvA24VLJ21x1DBC/TBWkTh5/EkQRpu6wAoSn+s6SnnPgY9u8qz0g9rqzkkx4dXX42GR8dmooiMj78zbJGo+3tWFHC375wvKgTMWBKXUO/udDV3uRal//0BNkmPbyPpJYyQZJxGHx+p2Y86E6z0ctkKjdYIx9TvPTGZr6MwHMVJTPBzBZUd4K4OKkcVQDffK5iAKNkMw58A3YJN4BdKdsdV3GgAv+57Xg77jOIZSBy+3+fMaUWD3VeCPt+W0lcT8ytH4fFgemuIWQHqq9ZEi/OiF7nQddjJ4kyIypHHacP0fevG6TlJ+3GBYDECyRFMPkG11JYTort6+zcpP7NUEUYa4TguX956ei/iFi3g2ZgxXxp3meqPTdhh1+u+pJ4M21hLH5nyoictqhofiRi5XkvCOx9ggAA666700sXBcms1oXCRIYitmU1wOYdkA0Uq0pnpMsVrJBkvl1F6obAPTC8BniA5cnlmxZoVpzhYA4JImVVHoI9aYvz4GYn4NAHMTx7T+8P8YN7S+0Hx+HSmnLfRN0UWDl8en+9sq4M1R8Jv/sH8JQ1rp7V0FnPinDhotfu3V3rEy+TN7AeFdfOWMirjhxPGZM2w82kDkOE5dE5AdSZPpDA78uiUtV7eHyI5RFmNq2ArFSeM0YBecin3q1YiWFQlqGLqopEL0PLsLsjBzgFO3B61P0oI/36bdvGI8Ij8vTNoe6fC7mhZ2EJI5KF5iyVujVxEEZnTPBzyFvSxwUZVoFqjRJdLGVSqVoVbk1XaiM2Dj8k3JSUK+4Caa6DkN0YR84YgDe+PaZ+Pujxwn11KIyFdQWJHHYhLP+kOctFhqIKaQp6vz4mJ8jelDfOnz37yYFCe6TIIvOTMrmfI7A8yHoCCW/LonDk02kKwE/l5QYCzbhn884KvE6rYiEkjdBfvtJJ+8ceYRBfO+qNcUNS0zTI7iK2Rk/dDKGu/2Q3ioUcqR9TwMV8beAKOfwxJdPiv2u8j2qJXofcRC+yxNLNsvUyYoBoK3DTiGtulauF+McNKas8gldvk7FYtv6OagQV0iriY/M0fQp5GJ5jnW2/irYhqmwiVipiz4rty1uJB/tTA5prWzH/5Qzh+mi2XayBJ0D7DiHZingm7ItSR8ij5lDVEjzNKP9LIijbn+1ETuqzMODdqWxlRhDc1tnpEyGL1WKEaw3v3NmWEcQAdqc+Jd91IR7Zq/S/t7HIFbSbfKimEp1ANEFZawVeh9xMLCk3okD2t8BBOkzk05zurAVMgjxhSUrhyMhu2et0PZpq+cIrJUSZJ62SnUmKQl1FiRJY+Ww5RxaLDb+VsXGKotWVMHhyoEsJjR5P3vhp706+lSh3mdaRadubLLYVAUxtSmfHzqrJRFnTRqtLLd5trz1pE2ae6lfdve82Hij9RATPwHRCLqBkUfJ7t2LdfYb1h+zrz898rtJrKRbZ2K5rE9T4azD1c+4Wuh9xEH4HrfGkXUO6kVRyFHiidRWrCTHcwLiEz7UOcTzRkfaVxEkJUXyFllHwonUxo2A/AiY4nNTcyvyECrXOZgjc3poVb0nlVgpJW0QAwByhKdzFvlUiye9sT3wxlotCebPKNFs2nLM9YVcjAs5ZO9BsT5lxW+S7PvkiSPwrXMO1YzN3lpJHtvBowdGNkRTlGT5HhhYTIEc8XMQfFJMVkP/cZ53X+KcPnDkgIgDHGAWK+naj3AOCqMD8bk9es0JuPOyqdpxVgO9L2S3iTMgWaykbiNHZKGQjpfZOJoBYfx6eRy2p75ImYGDSbRWsrCC8XQOLDFMuC6+kArqBDPxC2w4B5t4+NwJzhbHjh+KaxXOSHyEsimr8qBAhL+8vU7xi1jH++Sb5gDN6dLWvLG+kI/N2xMnxO3m80Q+5+DVTeIchg3oo60jc+PqSt6HaCBx0oQR+L0Untza4AJqzkEVtp0JuhUVVHP5Y+Pisaz6FHKxfA4cOs5PXDP1CrGSKOXgnt21RK/jHMRjo2yq5nEO4d86dr+Qo0Q5sGpL0NpkS3NzSP+og1hSjtqw/Xjbpo062VcjGd4pE9i0M8wIpjL9lMWnoySdhM4RkaNRkXFMyRVIsNnzbU63cn0VQoW015ZJLJPG/LSjk2H4gD5Y/H05ODIfTxwqq6Q+hVyiCTbgbaKdIueQQBxM98L1W+brvc/3N+4KypSiOEudGs/L0mmhS2CInuDHShwBb18UGV0pOJRy1OVzSh0YkXetikBEQsko7oP//KtLjqqpfwNHr+UcfnPpMThbkuF51kos8rcK+RwlLjLbjVrW140Y2CdWJ5SPRidfLAyxJdttyzksWLvd+DvAxUossnmfcnA8NIY8NvnkPfuG0/FhYzNm3PG6dQx7G0WiapOUSyx0puhbl0MrN0LQzAvZlLXYWUIhF8/FDcSJpQoi52AU7Sh+Uj3C+kIu0ZACCB30bHUOpn2LWw6Zr/caeHbJxliZCFuxKfnUwcQRiAp6TgS/9+lJuPzj4yP1gox8wlpR+dcUcqETnLg3DKwvoKm1iJaOzpgusdOvL+cz4Qg5z9oTBqAXcg58uuw/vH9cSYro6Vx34vGIQzl+Dop6kOXd+tORfLoVw1ADemso3diSuZ8Q3zzrYGV5zuccREKjErnIC1teKKMG9cXBoz35t4UkqCIoTVkT+jSZEAflkkK6WGLaTd0maCBvr61YUkfNDcZjK1bKoc1CuZ3PecrykHOI9z3j2NBz2dS/nYe093nkuCGxMlU9uX0Z5Is6RZHi98+fFK0jeH5wIqKOkRbnHFTv1FNIe8/21FtmBeUD/MgEKv0Yb/LJr5ysvA9+sEkRQixT9D7iYKDGRBSZUDp5dT4XV+zF2lJep2OBzdyKTqzULE04cYJdfcqB+PFFH0O9Kpy4JMtOwvjh/fHlM+LJaMTGdoo5JCwU0irY6lbS4Oan30s8ua5o3IWVm5uNdcQWdPu6rJAudjLtpm4TNDDkHDqNNu+2Eoe6vB3nIFuz8Xl77WkH4SRfR3HDuYfFxqkbWxKDF5jsChV1ehoZJp2DOI+mT45GTubPs61YCsyYVW2FubzF8PZmCYAYoLF/vbf+dquIg6DTUb1D3mVXiJSA3kgcoDcvzOcoMgl0xMFkmcChM1+M9emz8Bwq2bpOrCSPT2x+v2H98Q9T4/mqxXHwe3jmaycr6wX1Dav//Y+8yJM/eGKJuY0UJ+UkzkH2oTBhZ2sRq7fsjuzusqhp8fqdyQ1FOAezzuGHTy4F4G0oOs4hrc7BFMpC1dKhe8eTwnDHO45p44fF6vB6np8Dz0zo9XDdOYcGSmIx6ZTpXkia3+o63qcourEWISkPU3ErJNmPgFsbNWxrCTgH1SPmBKMYETerOQzVfXIRlMphk99uIaeeUUs37AzupyvQ+4iDwYLEi8luwzlQ4glMPbnj9Vo6OnHnKyut2kr0rRAWj0mJKFvB7NUvrucAgG9wUZJhbb/vhyXepCBqImwmuHzyFvExQeSQ1qTv1J/Mwp8Fy6AkxuQ3lx4TKxMJik7CI99ih4FzsBEribqhJKWwjMP3URMHUQ4/fkT/WB2xXsA5KAicOB7TreTIJkyL9ykqbU26MhE63Z8Xmjz8W45PNaC+gHyOsLu9iL8u+shvX098khwueawsGTxkvsrXgRMML19IvO/tPjdebmrYStH7iIP/qXreBUlcZCIOfKO+YMo+6o4sOQcbWMfYEdo3nub8wW3x04rqTrd8YpsUezv9pOpJm9fgvvoQ3Rx8zLJJ4PlT9sEf//X44G9dOIKscNDIeHhvloJz4Ch2lrTiIJupIObAUEXNTdMWb0+c06b76OxkuPmZ9wCo320kJ7dxriWbE6vk+rYK6abWuPmo6r5USmTPZBf4vs/1msLlcK7m1/8UPzh441WLQ0WnQhmvLfcirhY0YiXume50DjVCeCpVn4YiYiXNpljIhYvs6xpFrVLnUC5x8N9SkqesOIlMFiZ8GNzbWvar4KgLWGL94m7xleJJFi2jBieLgngT24WUnoAnIhMJQjUPUseOH4oJowbFytPoHDiMCmmLFU+W71NOs6pDPmcXVjqfIyzZsDPYrJP8Z0y3IhMkFfjl23e3x8qibcXLjlT4HMjWZ3mDxViS82Zgyhrk5lAMDHqjBr62VMwTD8ed04yP7z9OrFRjKDmHfNQKaR9NHH1RfKN3/rGTj9rA2i+BkscltsdRV9BwDv7GZpIZ85AE/GT2b2eriaVKMa4bl2wJJo+uUjZ7tGEz1SVwLyf/VLFkECvZEAfhzk0K6WtOtcsOlidKzCoHeM9XJGqqe+AJf0zt8LZEkYpqTfH3KRpYKH1DFGVDB8RFogQ767K8RLiM1krFUuTveD2P0OxoiR5sOPExcd86cG7FKaRrBJPOoZDPBafW7316EiaOjp8ggSibrbVGUXQgJ19XQSWmUi0eFfIWREs1Nt09BJyDYWLfc8U0AEBf33v38woHIVWfKuisqOQAZJUuFTlkxI1/F+Y00Cnfo8mA7MVKtrkglO0Jt20yZbWN3pnLRQNG6sYgzx3du+NjMhJrisrrVTNJqV9IcBzsV5fHbf94tKZPefya95WLEgcbayVtdjzfZPesn70UKefiZ5sIwjL4c+sixqEXEofAWkktVuIQFaAybDZhldzzl5/TTOYEhOIWfXx6sR5gFmHJC0+3uYRiJX2f+w33lJrcW1mXgMdqMwxkz9EO5YxvWSvoxkRSrarbfvgLJwTfdb33KeQwqL6AQT431V4soV6Trc5mPxfHYnKCs30eOYpa2ekua+noxNtrtgv1zETE7CEdFd0ogypamqiK0/bsSaNx3hFjlH3KV+qG5ymRRbFSvA6/N8796EWKnv5CNszgxLic2IkdJTO3Um30PuJg4hyE05nppJaPcA56NlNE37pcYrpHHXgwtKd9qwqO8cP7x+qpxhhrT9G+CnW+Qtrkd8C7aesoIUfx/NdyPRN04jOZc8l6rRQsiP2x44cFnt+m/s84bFQg6mgrlrTiNJsFLw4lC4V0sZNh+aZdifVU5tQq2IrGRLFSUrIhDrUvQVhmyo4nPw/ds46JlQwEiZ/itW3l1KLHS0/YH4BdjC8ZHUVzn9VGrw2foXre4gnaFN4gwjlYKhyH9U8WKXnj0rOt4iY5/ztnxmy37RXSdpON6xxME1s0szUuWAvmWBfWIx4mJLGpVIha3iTXM3UvJg5qK3Zqn4nIvf3T8bpMc8mHEMD+fcp5Kip9jFacQy6qs7LdI5PESiYdljzXdI+O5yLhMCqkO5N0DnFP8Ie/cEJw7+U4dnJR1h6pkCaiVUS0kIgWENF8v2wYET1LRB/4n0OF+jcQ0XIiWkZE5wjlx/jtLCeiX1AVNTChrZJ+IgBmzqFgwTmIJ+gDRw7AHwRTzHIgdzNiYH3MpNNe52D3eG2slcSm+qQ4zanrqMVKJ0mRQ7OeHuK7NorjyKJ/Ci1TPM4h2c/hm2fFw3+L/QFmzsEWJqdJHe5Q+HxwFALioL9eVkjbKvaTxEqZcA65qBWRKuEOJ1JFC2sleZlM3X+o1pTV5jlwa7E9kjj4OI0xNoUxxj2TrgfwPGNsIoDn/b9BRIcDmAFgEoDpAG4jIv42bgdwFYCJ/j91+MkMwAzmYdHNVd+GONl0E0/kQr56xkQcMCJuO58GaWT2QDqFtA6Bn4MF5wCkW7CmsYkWNe9892xMlTx59+oX+kxcfsL++OvXPmHXuAZpiWrSZggAq7c0492GHVityGENRDlT/eZlp3OwRTkWV6Z+A04qYWhrhGegG8K/nnxA5O+k8CI6XQ6QQucgceNKfw6uc0iwHFL5ORCRljjYiJn4IaknBd47H8C9/vd7AVwglD/AGGtjjK0EsBzANCIaA2AwY2wO82bvfcI1mcP0SqKbq4FziJj5aTgH8TSagReLTRykiF28YcXabtQ21kriMzM5p6VSmgr3OqRf3HlOzOh1wIgBMeujtBDfp1m/wusZnq3fBtcPfdiojtkkPg/STLV9h4Y6JRMna4uY7sZK1KevE3IO9lzqXv3VzpByvSRGyShWkjkHnV4wFw15YQoJ3tGZzDmopo6OONhEFC4GoqzEqlVBpTOOAfgbEb1JRFf5ZaMZYxsAwP8c5ZePBbBWuLbBLxvrf5fLYyCiq4hoPhHNb2xsLH/E0Mdk4TBtrnmLjV/kHNI4v31SY4EhQrbeUfWThc6BEweTn4PYzf7D9dyR7RPIkZ2jFkcWhFfkeJ54d0NiX0l2/Ywlv/PIQcRgasnzC2SRbF5+rDbTwGiYkefPw55Lve/K49T1pIpJz88UX0sej4kzE4PkqaZSaK1UUo5TvFbFDQRe7hLlSEy0BcHxrouoQ6XE4UTG2NEAzgVwLRGZ+HvVHTJDebyQsTsYY1MZY1NHjoznDLCByZQ1smBN7LTwkzaVqJiS0HIz/tvXP4EzE/LEjh5cj4U3nqP8zV6sZDeePhacg60FibXFBYXs9Pc+PSmhsp7De+jqE2JldXnCE18+KVbeX8iwZoqhw2/BdCdcvJB0u+L7MT0bLj7JggjKYqVJivhLMkwMCyccRhWM8OO5k/eOJdMJ+rE87XOMGGgffFHHDeaJAi9lQO1tHbdWUvehC00ehNuXCAfnCuRQ4iI4B91FjENlxIExtt7/3ATgLwCmAdjoi4rgf27yqzcAEMOEjgOw3i8fpyivCkymrGKZLeegg415JAA8eFWoqB5jYerary6vVU5GOB9LncNPLz5SW497TieJqnl72cT/CRfF1PHJqRF1XcpZ/gDgS6dNxOSxcf8VURFpQ1STNkOGZGIYESsZqnKxlJgIJwmf+pia+xQVwzece6g2aq8IG5Nusw4m/G46PMjrLYlzMFlvye9Qx/mKBGjEwHqlGbYsVtJaE+Y8jlFew4FYianFSqo188q3TgMAbN7VZuyz2iibOBDRACIaxL8DOBvAIgCPA7jcr3Y5gMf8748DmEFE9UR0ADzF8zxf9NRERMf7VkqXCddkDlPgPZuwGN5vyf0UIiGN9fXEfkyb6+mHetI5VbgADms/B+EnU8wjWwsZvtFlIcrKCzoHGzm7DaHmGNhXrRPpJxAHm03HJKsn8k7o308MYS5+T342SVFvOX51yVH41SVqZ0tRl3PYmMFW78Q0BWx0Drbn3phYKcGU1cRZyPNWNy9FAqQT2/F+ePRXnZMnd/aL607UOocbH1+sHCsQhneZtcwTnacRs2aJSvwcRgP4iz/BCgD+yBh7hojeAPAQEV0JYA2AiwGAMbaYiB4CsARAEcC1jDEeD+IaAPcA6Afgaf9fVWBO9hN+N210Nrba4sZmmsi2xIFPuuEWITjE+km/merpFoK6PX2QORkHKqKeim1xeaxNe2ksPHUn13597DgH/qjM3sp2eaujYqXk+rYwEa5SgmWOCqb5Yef3EX43ydnlfuav3havIx7eTPNWes+m8BkcSRn7uAWdKT9HJ2NYt71Feb1MHJ5Z7BksqIhSn0IOA/rkg3A5crymWqFs4sAY+xBATCbBGNsC4AzNNTMBzFSUzwcwudyxpIEp2Y+NkhCwY/EjDnUWC0z+LoMvZpv4TIB5AyNL4mDLOfAmdNFdZXz6SE2Yc78tfsK1aS8N56Ajdn0LdsSBzwmz93lU9nykJgyLLYFOC9OeP6C+gLYiD9Nu99xMvis2Yo+tzWHIl78/apy2nvxM31QQh0gkWMPw66V70+oFLfya8jmJOGjqUQprJTGpka69vfr3QXO7R2iSkiVVCy58hoCIh3GF1iHi4jPrLyzFLQFxsFPE2Thzyd9l8I0haYiBWMnymX35dE3KUXinOX7CtGkvVRh0TVVbcaKN+CxHUWuKn312SmKfWYqUTfb/d10eJkmynXcmk9GNOz3iYCIgIgdgMraweQbinm9673KUYR3HuHDdjuC7PgyOV/5/73hqUK34KWFuiQcG0fdFTBFw5mGjgu+DBRPucyZFU5zWCr0vfAb/UgHnYANxstmG4jCBT66B9cmhrwHzgrWX3drIlL1AbYC9VUXSBhxyDjZiJfv3ZFPTODb/t6RQFuJJT+f7YWPxJuLrZ6pDocvoa9jMj9ovVPDbmsaalMhp2zJBXm9XnDg+Vsd23vbJh8+gb10O91xxbGL/2phgUrGOm9atkULAOYRlokWcmIvjN5dODfQL/ersxNLVRK8jDpx1UOscwjLT5mqDCHGwcCRKAicOtuIA04lPZH9txEq2Yo+5K7da1TPBU+x53202/jTEoX+f5Olu2qjDhPBmqyyRc9C93xUa5zgdzp5kNnHmMHEOIqw5B4v2bMSPR+8XNxMVIc8xHuRQtviU+wAAEKhJREFUhK35r0isvn3eYdrQ+7prRLRK6YB14lrdISDM5he2wwnAV06fgOMOHB6U53OEfM5bt7sTwvPXAr1PrOR/qnUO4XebxXPeEXp2z1aXYCsz5xumrR7AdOITzepM+359IYezDh+Nu/7ZLmdz2jzHKkTDVKczGU6CzQZr4lYemu/5aspKRxGEqOx5gGbTEEUaNrA/6dtxljaEMqm9c/znabNWfnyR3mQaiJ+OlRFSLdeU+Mxt15duHu2WUtbq9GA6qz+VzoF/n2IgmO991KQfbI3Q+4iDQeeQlERchsnUMqr01bdhK77iogrrTcJw4hPFHjwPgwpEhDsvm4qTJ9o5HGYRHE7cFGzu1Zbl3ntwX+P4xHwNSTD16Fkrhc9XR6Sv+sSB1v0B9uEzbMRAADDK4GEsGj2Y2jN5KcuYMGqg8Xf5VauWRd5S7Cv6GthOSd3znSwZFOg4h0GaHOmBtZKwtRQtONDugO49uiogDLwXf8l/fqshVmaCvWOXgXOw3Oy5WMl2AzaZoYrKMTlfcyXQhfVIg7Tcmy3n8PMZasUwx6F7D8Kg+gK+ZpDtf/u8wxL7IbLLGmey2FLB1kxYFVlUBR1HAwD3fX5a8N2W2OgwMYEocPSXxqNaMyKBMa09MR6XSUz428uSFfSD+9bh5IlhVGAdEdH6P/jFpYi1kr+Wu0iXYIvep3MwwDbP64RRA7F80y7MOFYXhz8KI3Gw5RxSipVMIhmRxZ26f7IXsi1sNyYTxMVsY8pqqw9JElEN6luHhd9ThyXh2HeYF/ohKXijTez+tOEwbHVglW7mchuVeuf+35dPiuUEV2GfIdGwGqpu99mrHw4bMxhLN+w0+pKI77rNwBnvOywMbGhjNDJqUH3EJ0aE7v1wYsK90xet24EP/IRLtvrDrkKvIw5hPoc4bDfqJ79yEprbOq19Dsw6h7ScQ+WnDU4c/mHqOIwaXF52OhWy2JhEeb6NyMj2RJ2FuShf6MbQ175COkfAF0+doK2W9j0O1ogtZNjqHEywnZM7Wzx5vCkab9+6vNWhob9khXfgCDXHwfdTU0Y58RRv4oxt3wHfF45VhGThkOc+5zYCzsGfM5/65athu45z6F4wZYKzFfHUF/KpFmFSjl0AGKoJZczB7Z6TTqULbzw78ZSZxhooDboinWGaUOCVwoYQ5chLPVli5vqc0HBuJAm2XJmttZIJtvPiS6dPwLbd7Tg7Azt8kUtcPvNc7amaWxmaloG46Y835FERufCZFxyhrce72n9Yf20dkTi88e0zg0gGOg9peZzdEd2br6kCTJngsoiZr0IWzX7+xAMwdq9+OEIROE7EoL51iYSr06B3qQRdcRKy7bJWI8tRuBHYhENJkkDdcO6hODRFvoosuDf+HgdrYlFxHDx6EO6/8jgj52CLSI4UiwCOpscmzkNdEEIg+n5MRGRXq8chmXLAiweyPoVcwPXqYisB1dtvskIv5Bz05kpZb26D6gtoaitigMFskG8SJgUhAByz/1C8dv3pmYyLK8eyMD0Vcf6UdErWLGBrX5YF58Bt3k2nePHQYXaW8z6TiMPVpxyEq085yHqMWRD8rti0bE/RFBBVuzDyxlwTlrfZ5JuzmkR74oFMfO9EpMwSB9hxoqpkV7VC9yZdVUSSn0MW4JNq+EC9bmLogD749+mH4veaJCjVQJgPN9sbnj45OVFR1jDFnfnjv2b7TLnZbz8DcRDnUHdXOOrQFQdaW4LEH28W4Yb4oe0rZ+jDuQBAMycO/fQHOJFzkDf9unxOGXTQ5M3Ocfc/J3t3Vwu9kHPwPlXbYta5Wn8+Ywp+89KHiQ5H15xqfzLMAvzU1RU6gqxh2iTE0AQdpcrDHvMEM4ebkuRY+mlwsUM5eZ2rjTCJT+3mh61hwdmTRmPB2u1WuU+SMKC+gPd+MD1RFMdFQiZxrdiGTOj61eXRqvB4ttEPHZOhNWFa9D7iYMoE9//bO/tgq6oqgP8W7/Ee8o18RTy+EngJyIc8CAVCLDLCxBltlBQILLKR1P5oUsupmaYJyxw/aIaoIKzMpq8JjXLQSRzTUggRCPlSR0BGrAQfGAjM6o+zz7zLu/fce+7lvLvP4a3fzJl77rr77rv2XvfedfY+e6+V+DTLQOaOL5jx1Cvhlz2jF7ZnUCxRe+4rcXJwl2LaiD48umQKk4dGr1o5Y+RQ5Gq4b9d6hvTuzN1zRp21XgDP3XF5rCW0cfCxiCbuEu0vzbiAeZMGF81rAkG+ipH9S++xiHOjP9y0VkzH3JFD6/47r2MN/zt5Om+UG2fk4JP25xyKjBzCYFelNkxlnfArmvYdmnEoduXdq3PLH0gSzgFgSk4snELkjj63HjgMFN4LU1fbgQ1fnZmIThDsAUgKH4OZuPe/RKSkYwBYd2t+OthKCWeEiq0CbH2fIZfOdTW89/5pNu87Mwx5sZHDI5//CNveLC/EStK0P+fgHguNmMPXGnpFL1k7F5g/ZQivvn206tNZSRJkXIPRRVZvnd+ljnENPdiy/0isjVhJ8J9jLRnbfCVpOVvCPQdfmD6sap8ZLgbpErHJrFySnBILg+YVizpQ7PPOq6vh+MnT7HGb30KK1Xfp8D5cOrxP5OvVoN05h9ljPsDI/l0Lzh+Go76U7005a7rU15YMhFYOa5dOPSM/cTV47btzYpX72IX92bL/CH2KLApIkubjLYHa5lxU/dVbpbj/uvElnVZ9bQ2vL4vXv0nRtb6WMQO7xw5NXk3C73ZdbWV/DM3HT7H9zUM8uePQGXJfobjj0u6cw5DeXRjSu/Ca5k+PHcCWfYcZ2Cu5IXp7YGxD8XDMlZDU72bpzOHMGNmXcYOS17EQuWrPKbLG3hdXT0jfPTAIVnY9/uXpvtUoSH1tDc2cKnlf5McLmgpGTXgjJ7lPyG/LCPToi9Q4BxH5JPAAUAP8RFWXVVuHm6YN48YpQxKJEWTks3rRpLwYOlFcFzNuVSk6dJCqOQaAE56SwRttx7iGHjz1yqGS5WYVyXQX0r1TLS9/q3gMr7SQijuSIlID/BCYDYwC5olIMss4ytPDHEMbMrOxH40ldvuuuHEiABf0jd6xmgW+f+1Y3yoYCXH/9eNZ/tkJkTMO5TC9QBKjtJIK5wBMBvao6quq+j7wKDDXs06GB64Y3Z/ViyaxeGr1boYmSbi3IoXbF4wK6dapI1eOrfz+0WWNLQ7hwzGy0qWFtEwrDQT25TzfD1Rvy7CRGkSEmY39ShdMKbd/fAQ1HYSrPIQSMdLJzxYF+TF2vdXMh4rEcEobaXEOhW4/5l17icgSYAnA4MHJzEkbRpJ069SRu2IkBTLaHyMzNGqA9Ewr7QcG5TxvAN5sXUhVV6pqk6o29e2bnbk7wzCMrJEW5/AiMEJEholIHXA9sNazToZhGO2WVEwrqeopEVkKPEGwlHWVqm73rJZhGEa7JRXOAUBV1wHrfOthGIZhpGdayTAMw0gR5hwMwzCMPMw5GIZhGHmYczAMwzDykDSmKYyDiDQDOwu8NBh4I0YVPYA42TTilEuyLki2DUnrFqdce7GBj76NW85s4L9cWn/HjapaekeeqmbyADZGyN+O+f6VSZVLsq6k29AGusX5zHZhAx99azbw37c+bJCwXgX/O1sf5+K00uGY5R5LsFySdUGybUhatzjl2osNfPRt3HJmA//l0vw7LkmWp5U2qmpTXHmWyHobsq4/ZL8NWdcfst+GtOofV68sjxxWlinPEllvQ9b1h+y3Iev6Q/bbkFb9Y+mV2ZGDYRiG0XZkeeRgGIZhtBGZcA4iskpEDonIthzZOBF5XkS2ishjItLdyetEZLWTbxGRy3LeM9HJ94jIgyKSUBr7qun/tIjsFJGX3FGVrDgiMkhE/ioiO0Rku4jc5uTni8h6EdntHnvlvOdO1887ReSKHLkvGyTZhqrboVz9RaS3K39URJa3qisTNijRhizYYJaIbHJ9vUlELs+py4sNyiLOkibfB/BR4GJgW47sRWCGO18MfNud3wKsduf9gE1AB/f8BeASguRCfwZmZ0z/p4EmD/0/ALjYnXcDdhHk+v4ecIeT3wHc485HAVuAemAYsBeo8WyDJNtQdTtUoH8XYBpwM7C8VV1ZsUGxNmTBBhOAD7rzMcAB3zYo58jEyEFVnwH+20rcCDzjztcD17jzUcBT7n2HCJaTNYnIAKC7qj6vgXUeBq5ua92dHmetfxXUjERVD6rqP915M7CDILXrXGCNK7aGlv6cCzyqqidU9TVgDzDZsw0SaUM1dC1Eufqr6jFVfRY4nltPlmwQ1QZfVKD/ZlUNk5ZtBzqJSL1PG5RDJpxDBNuAq9z5Z2jJJLcFmCsitSIyDJjoXhtIkHEuZL+T+aJc/UNWu2H03T6GoiIylOCK6B9Af1U9CMEPh2CkA4Vzgg8kJTY4yzaEeLNDTP2jyJINSpElG1wDbFbVE6TEBqXIsnNYDNwiIpsIhnjvO/kqgs7eCNwPPAecImae6ipSrv4AN6jqRcB0d8yvpsIi0hX4HXC7qr5brGgBmRaRV40E2gAe7VCG/pFVFJCl1QbFyIwNRGQ0cA/wxVBUoFjqlo1m1jmo6iuq+glVnQj8imBOGFU9papfUdXxqjoX6AnsJvjDbcipomCe6mpRgf6o6gH32Aw8QhWnOUSkI8EP4peq+nsnfssNkcPpikNOHpUT3KsNEmqDNzuUqX8UWbJBJFmxgYg0AH8AFqjqXidO1X9RFJl1DuHqBBHpAHwDWOGedxaRLu58FnBKVf/lhnvNIjLFDUEXAH/0o335+rtppj5O3hG4kmBqqhq6CvBTYIeq3pfz0lpgoTtfSEt/rgWud/Orw4ARwAs+bZBUG3zZoQL9C5IxG0TVkwkbiEhP4E/Anar6t7Bw2v6LIvF1J7ycg+DK+iBwksDr3gTcRrBaYBewjJYNfUMJorXuAJ4EhuTU00TwJdoLLA/fkwX9CVZubAJeJri59QBu9UwV9J9GMOx9GXjJHZ8CehPcPN/tHs/Pec/XXT/vJGclhkcbJNIGX3aoUP/XCRZCHHXfu1EZtEFeG7JiA4KLvmM5ZV8C+vm0QTmH7ZA2DMMw8sjstJJhGIbRdphzMAzDMPIw52AYhmHkYc7BMAzDyMOcg2EYhpGHOQfDaANE5GYRWVBG+aGSE7XXMHxT61sBwzjXEJFaVV3hWw/DOBvMORhGAVxgtb8QBFabQLBZcQFwIXAf0BX4N/A5VT0oIk8TxMGaCqwVkW7AUVW9V0TGE+yA70yw6Wmxqr4jIhMJYmm9BzxbvdYZRmlsWskwomkEVqrqWOBdglwbDwHXahATaxXwnZzyPVV1hqr+oFU9DwNfc/VsBb7p5KuBW1X1krZshGFUgo0cDCOafdoSE+cXwF0ESVvWuwjRNQRhUUJ+3boCEelB4DQ2ONEa4DcF5D8HZiffBMOoDHMOhhFN69gyzcD2Ilf6x8qoWwrUbxipwaaVDCOawSISOoJ5wN+BvqFMRDq6WP2RqOoR4B0Rme5E84ENqnoYOCIi05z8huTVN4zKsZGDYUSzA1goIj8iiLj5EPAE8KCbFqolSMi0vUQ9C4EVItIZeBVY5OSLgFUi8p6r1zBSg0VlNYwCuNVKj6vqGM+qGIYXbFrJMAzDyMNGDoZhGEYeNnIwDMMw8jDnYBiGYeRhzsEwDMPIw5yDYRiGkYc5B8MwDCMPcw6GYRhGHv8HT6JXuC4vQMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55ac28fdd8>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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() "
   ]
  },
  {
   "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\n",
    "entre deux années civiles, nous définissons la période de référence\n",
    "entre deux minima de l'incidence, du 1er août de l'année N au\n",
    "1er septembre de l'année N+1.Notre tâche est un peu compliquée par le fait que l'année ne comporte\n",
    "pas un nombre entier de semaines. Nous modifions donc un peu nos périodes\n",
    "de référence: à la place du 1er septembre de chaque année, nous utilisons le\n",
    "premier jour de la semaine qui contient le septembre.Comme l'incidence de syndrome grippal est très faible en été, cette\n",
    "modification ne risque pas de fausser nos conclusions.Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    " first_august_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 septembre, 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.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": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_august_week[:-1],\n",
    "                        first_august_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": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55ac232d68>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.plot(style='*') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020    221186\n",
       "2021    376290\n",
       "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": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
    "  française, sont assez rares: il y en eu trois au cours des N dernières années."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55ac1f7eb8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}