diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index fd0c793fa9f4c20ff81db6f159212af6e839daf4..ec6b28a7e0d2acd9f0ad70362236412da2e26a52 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -28,15 +28,6 @@
     "Les données de l'incidence de la varicelle sont disponibles sur le site web du [Réseau Sentinelles](https://www.sentiweb.fr/datasets/incidence-PAY-7.csv). Ces données ont été téléchargées sous la forme d'un fichier en format CSV. Elles seront extraites ici à partir du fichier télécharger et non pas à partir de l'adresse URL du fichier, afin de prévenir un potentiel changement d'URL ou une modification de la version du jeu de données utilisé."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "raw_data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -61,10 +52,1396 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
+   "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",
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+       "</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>202016</td>\n",
+       "      <td>7</td>\n",
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+       "    <tr>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
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+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
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+       "      <td>7</td>\n",
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+       "      <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>4</th>\n",
+       "      <td>202012</td>\n",
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+       "      <td>France</td>\n",
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+       "      <td>7</td>\n",
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+       "      <td>7568</td>\n",
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+       "      <td>15</td>\n",
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+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
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+       "      <td>11331</td>\n",
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+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
+       "      <th>7</th>\n",
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+       "      <td>13631</td>\n",
+       "      <td>10544</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
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+       "      <th>8</th>\n",
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+       "      <td>7708</td>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>202007</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8959</td>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>7</td>\n",
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+       "      <td>12</td>\n",
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+       "    <tr>\n",
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+       "      <td>8538</td>\n",
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+       "      <td>France</td>\n",
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+       "      <th>15</th>\n",
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+       "      <td>7</td>\n",
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+       "    </tr>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
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+       "      <td>10</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
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+       "    <tr>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
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+       "      <td>France</td>\n",
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+       "    <tr>\n",
+       "      <th>27</th>\n",
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+       "      <td>3</td>\n",
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+       "      <td>FR</td>\n",
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+       "      <td>2218</td>\n",
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+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
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+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>201939</td>\n",
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+       "      <td>3137</td>\n",
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+       "      <td>4964</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>1503</th>\n",
+       "      <td>199126</td>\n",
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+       "      <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>1504</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>1505</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>1506</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>1507</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>1508</th>\n",
+       "      <td>199121</td>\n",
+       "      <td>7</td>\n",
+       "      <td>14903</td>\n",
+       "      <td>8975</td>\n",
+       "      <td>20831</td>\n",
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+       "      <td>16</td>\n",
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+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1509</th>\n",
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+       "      <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>1510</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>1511</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>1512</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>1513</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>1514</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>1515</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>1516</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>1517</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>1518</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>1519</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>1520</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>1521</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>1522</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>1523</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>1524</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>1525</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>1526</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>1527</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>1528</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>1529</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>1530</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>1531</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>1532</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>1533 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        week  indicator    inc  inc_low  inc_up  inc100  inc100_low  \\\n",
+       "0     202016          7    778       80    1476       1           0   \n",
+       "1     202015          7   1918      675    3161       3           1   \n",
+       "2     202014          7   3879     2227    5531       6           3   \n",
+       "3     202013          7   7326     5236    9416      11           8   \n",
+       "4     202012          7   8123     5790   10456      12           8   \n",
+       "5     202011          7  10198     7568   12828      15          11   \n",
+       "6     202010          7   9011     6691   11331      14          10   \n",
+       "7     202009          7  13631    10544   16718      21          16   \n",
+       "8     202008          7  10424     7708   13140      16          12   \n",
+       "9     202007          7   8959     6574   11344      14          10   \n",
+       "10    202006          7   9264     6925   11603      14          10   \n",
+       "11    202005          7   8505     6314   10696      13          10   \n",
+       "12    202004          7   7991     5831   10151      12           9   \n",
+       "13    202003          7   5968     4100    7836       9           6   \n",
+       "14    202002          7   6534     4530    8538      10           7   \n",
+       "15    202001          7   9835     7019   12651      15          11   \n",
+       "16    201952          7   7941     5246   10636      12           8   \n",
+       "17    201951          7   5823     3675    7971       9           6   \n",
+       "18    201950          7   6424     4276    8572      10           7   \n",
+       "19    201949          7   6621     4540    8702      10           7   \n",
+       "20    201948          7   5542     3383    7701       8           5   \n",
+       "21    201947          7   7536     5058   10014      11           7   \n",
+       "22    201946          7   2638     1316    3960       4           2   \n",
+       "23    201945          7   4492     2615    6369       7           4   \n",
+       "24    201944          7   5728     3627    7829       9           6   \n",
+       "25    201943          7   4834     2751    6917       7           4   \n",
+       "26    201942          7   6279     3989    8569      10           7   \n",
+       "27    201941          7   4130     2030    6230       6           3   \n",
+       "28    201940          7   4211     2218    6204       6           3   \n",
+       "29    201939          7   3137     1310    4964       5           2   \n",
+       "...      ...        ...    ...      ...     ...     ...         ...   \n",
+       "1503  199126          7  17608    11304   23912      31          20   \n",
+       "1504  199125          7  16169    10700   21638      28          18   \n",
+       "1505  199124          7  16171    10071   22271      28          17   \n",
+       "1506  199123          7  11947     7671   16223      21          13   \n",
+       "1507  199122          7  15452     9953   20951      27          17   \n",
+       "1508  199121          7  14903     8975   20831      26          16   \n",
+       "1509  199120          7  19053    12742   25364      34          23   \n",
+       "1510  199119          7  16739    11246   22232      29          19   \n",
+       "1511  199118          7  21385    13882   28888      38          25   \n",
+       "1512  199117          7  13462     8877   18047      24          16   \n",
+       "1513  199116          7  14857    10068   19646      26          18   \n",
+       "1514  199115          7  13975     9781   18169      25          18   \n",
+       "1515  199114          7  12265     7684   16846      22          14   \n",
+       "1516  199113          7   9567     6041   13093      17          11   \n",
+       "1517  199112          7  10864     7331   14397      19          13   \n",
+       "1518  199111          7  15574    11184   19964      27          19   \n",
+       "1519  199110          7  16643    11372   21914      29          20   \n",
+       "1520  199109          7  13741     8780   18702      24          15   \n",
+       "1521  199108          7  13289     8813   17765      23          15   \n",
+       "1522  199107          7  12337     8077   16597      22          15   \n",
+       "1523  199106          7  10877     7013   14741      19          12   \n",
+       "1524  199105          7  10442     6544   14340      18          11   \n",
+       "1525  199104          7   7913     4563   11263      14           8   \n",
+       "1526  199103          7  15387    10484   20290      27          18   \n",
+       "1527  199102          7  16277    11046   21508      29          20   \n",
+       "1528  199101          7  15565    10271   20859      27          18   \n",
+       "1529  199052          7  19375    13295   25455      34          23   \n",
+       "1530  199051          7  19080    13807   24353      34          25   \n",
+       "1531  199050          7  11079     6660   15498      20          12   \n",
+       "1532  199049          7   1143        0    2610       2           0   \n",
+       "\n",
+       "      inc100_up geo_insee geo_name  \n",
+       "0             2        FR   France  \n",
+       "1             5        FR   France  \n",
+       "2             9        FR   France  \n",
+       "3            14        FR   France  \n",
+       "4            16        FR   France  \n",
+       "5            19        FR   France  \n",
+       "6            18        FR   France  \n",
+       "7            26        FR   France  \n",
+       "8            20        FR   France  \n",
+       "9            18        FR   France  \n",
+       "10           18        FR   France  \n",
+       "11           16        FR   France  \n",
+       "12           15        FR   France  \n",
+       "13           12        FR   France  \n",
+       "14           13        FR   France  \n",
+       "15           19        FR   France  \n",
+       "16           16        FR   France  \n",
+       "17           12        FR   France  \n",
+       "18           13        FR   France  \n",
+       "19           13        FR   France  \n",
+       "20           11        FR   France  \n",
+       "21           15        FR   France  \n",
+       "22            6        FR   France  \n",
+       "23           10        FR   France  \n",
+       "24           12        FR   France  \n",
+       "25           10        FR   France  \n",
+       "26           13        FR   France  \n",
+       "27            9        FR   France  \n",
+       "28            9        FR   France  \n",
+       "29            8        FR   France  \n",
+       "...         ...       ...      ...  \n",
+       "1503         42        FR   France  \n",
+       "1504         38        FR   France  \n",
+       "1505         39        FR   France  \n",
+       "1506         29        FR   France  \n",
+       "1507         37        FR   France  \n",
+       "1508         36        FR   France  \n",
+       "1509         45        FR   France  \n",
+       "1510         39        FR   France  \n",
+       "1511         51        FR   France  \n",
+       "1512         32        FR   France  \n",
+       "1513         34        FR   France  \n",
+       "1514         32        FR   France  \n",
+       "1515         30        FR   France  \n",
+       "1516         23        FR   France  \n",
+       "1517         25        FR   France  \n",
+       "1518         35        FR   France  \n",
+       "1519         38        FR   France  \n",
+       "1520         33        FR   France  \n",
+       "1521         31        FR   France  \n",
+       "1522         29        FR   France  \n",
+       "1523         26        FR   France  \n",
+       "1524         25        FR   France  \n",
+       "1525         20        FR   France  \n",
+       "1526         36        FR   France  \n",
+       "1527         38        FR   France  \n",
+       "1528         36        FR   France  \n",
+       "1529         45        FR   France  \n",
+       "1530         43        FR   France  \n",
+       "1531         28        FR   France  \n",
+       "1532          5        FR   France  \n",
+       "\n",
+       "[1533 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(\"incidence-PAY-7.csv\", skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Aucune ligne n'est vide dans ce document, il ne manque pas de données."
+   ]
+  },
+  {
+   "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.\n",
+    "  \n",
+    "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`.\n",
+    "\n",
+    "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": 4,
+   "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",
+    "raw_data['period'] = [convert_week(yw) for yw in raw_data['week']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il restent deux petites modifications à faire.\n",
+    "\n",
+    "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.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans\n",
+    "le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_data = raw_data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous vérifions la cohérence des données. Entre la fin d'une période et\n",
+    "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\"\n",
+    "d'une seconde.\n",
+    "\n",
+    "À la différence de l'analyse de l'incidence du syndrome grippal, il ne semble pas y avoir d'erreurs ou d'incohérences dans le jeu de données :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "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": [
+    "De la même manière que pour l'incidence du syndrome grippal, la représentation graphique de ces données est la suivante :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6856327b8>"
+      ]
+     },
+     "execution_count": 7,
+     "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'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Par un zoom sur la situation des dernières années, on remarque que les incidences semblent plus réparties sur l'année entière avec un creux cependant en début de période hivernale."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6834529b0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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NsBxgtRy6TxysFc885lDKJM+oxW4lh4MgIEuIddHrshqiJSyH5aSC9WEvhgKitchq4DEHj8sBn1xbF2RrKqui6dB0Cq/sxGBAFq99CxDiAGvMoXvcStxPa3Urhb0uOEi+5fD4kTmomtGioDCVFTDiDsKtZBGHlIqwz2WIQ0z4vWuFu4Y8zK1U6bP2nefO4NhszEwEyGR1M8PO63JiKODGImvrImgeQhxgtO+WHKSr+iupJVqTOxwE/ZZah+Nzcdz+9efw89dnzPYZhQQ8Us8HpJOKZt7coikVYa9LWA6rJGURB79bKplODRijRO+6fz/u/tkRJBQNftnYuPG0YcNycCOrU5FK3GSEOAAghBi7ma4KSOuQHKSoYV6/XzYth5mo4bedj2eQVjV4XM6inxP0uHo6lZWn9y4mFFBq3JDCXhciQTcWkwqyml7lpwhmo2mzVb7XtBxKv6dOLyYAAI8cMgZL8my7Jdb2xbAcmHs0IcS5mQhxYPjdUpdZDjTPpcQZ8MlmzIH3WYqm1PKWg1vqyYA0pRSxtIpNA34AhjgkFQ2qRtHndSESkEFpfpprr/LQ/mm8fHa55PeOz8Vx5V8/jMeOzAPIuZXKZQaemjfEgY/zHTXFQTHPH2L9meaEW6+pCHFgVPODdhpKVs8LRnP6/S7zg8bdSyumOJSyHHozIJ1QNOgU2Mgm6C0kFNONwd1KAETcAcD/fOggvvbEiZLfm1xKgVLgqeOGOHiZW6nchuNUQR+rsT4PgNwEQ5+cEwdhOTQXIQ4Mv1vqqmwlRdPz0lg5A343FhPGB3PZFAkVmk5LWg7BHo058JvXBBOHpYRiWlp9PheGgkwcuizuUE8r7LSql32P8GFTp9lN3+NyYOOAD9PRdF63Vc6p+QQG/TJ2j4UAAKNhZjkkrDGH8ll3gsYhxIHRbZaDmtVLu5WY5aDr1BSJ2ZgReyidreTqyWwlfrPbNGi4layWQ8hqOXSROHz250dw8xeeWrVAKFm9rCUQLVj3uJzYGgmAUuAkcyFZObWQwMSQH2/dEQEhwHi/IQ58SJXX5US/T4aDdNdrb0eEODD8crfFHEpbDv0+GZpOEUtnTcuBt4Eo5VYKuCWkVM3MfuoV+M1urN8Lp4NgMZEpcCsZu1f+2n3zyZP4wD3PtudiG8SxuTheObuMo6zzaa0oWb1s0gKfYQ4AhABuyYGtkQAAIx7BeeLoHKJpFacXktg06MNHrtuGf/7QlYgwC23ZEnNwOggG/LLozNpkhDgwfG6p69pnlLIcuEm+mFTMYGpOHEq7lQAgkcn2VF45L4ALeVzo97mwmFCxkjJerz6fjIBbglsyGsBRSvHNp07hmRMLZXfdX3viBJ4/tdiy668HnlH0swPnV3deVivvVrJYDh7JCUIItkT8IAQ4PmtYDolMFnd8/Tn8xQOvYXoljYlBPwJuCdduGzI3LDxbycdSW43+SsJyaCZCHBj+Cul1nUimjFup35frr7TEAtK8TUEptxJvqfHtZ89gz1/9HC+eWWrWJduKmCkOEgb8cpHlQAhhtQ4KDk7HcGohCVWjZvFhIf/w8FHc/+JUy66/Hngtws9er10csqwVd7WYAwAzLdjjcmK832taDktJBTqFOchnYshfdA63HLws3drvdnZVF2U7IsSB4ZOlrqtzkEtkK3EzfTaaNndjHE+ZbCXAaDi3lFTxwa8/hyMzsSZcsb3gbiUeX5iNZbCcVOF0ELMwKxI0CuF+sn/aPK/Qx87JZHVzZ25XeIuLA1NRTC7VNv2Op5zGy1iWKykVY32Ga85rqaPZGgmY4lA4upYnAQCwWA5MHNhr73E5Swa0BY1DiAPD7zYsh24ZXF4u5rBxwPjgnV5MmpYDp1xAGjCCh1dtGYCqUXz7mdNNuGJ7wX3lQY+ETYM+nF5IYiWloo9ZDQAwFHDjyEwMP3xpCrzW0Opj51BKoWR1288oTykatg8b8YCv/+pUTecoFkspXsLyjqazGAq6MTHoyyuy5OKgWyqdg8xK5UkAQM5yWEqoZswCMCwIu7+enY4QB4ZPlkBpbvfU6ahaabdS0OPCgF/G8dk4Ypkswl6X+b1yAWnOuy4exWBA7om6h1haNXe7m4f8WEwoOLOYzHu93rJjCCspFVPLKdy0e515HmclqULVdLN9ut13umlVw851Qdx+9SZ8/cmTeOrYfNVzrG60Uq6laEpFyCPhzdsj2D4SMNe3RgJIqzrOraRMy+HPf30Xfv/67XmvMReU5aQCr8tpCrOwHJqPEAdGt7XtVsoEpAHDeuAVrfkmfPmANAC8YWN/xerWbiKWziLokUAIweYh46b2ytllhH25G9ftV0/g9U/fhOf+5Hp88JrN5nmcX/vsY7j3qVOm68XuO10+L+Gud16ILRE//uyBA1XPybMcSolDWkXI68JfvGcXvvyBveb61ohhHRyfS5iWw1t2RPCJG3bknc/fkwlFy3NLGeLQHRs5uyLEgeHrsoE/SpmANABsGvThGPP3Wk34kpYDEwe/7MQF64LwyVJN8387lXgmi+88dwaLScUUxs0sQBpN51tagNHMcDjkMde5OKiajrlYBjPRtHkDtfvNLMX6a3llJ96xax0mF1NVz8m3HIrjLdFU1gzgW9nMxOHUfALLZhaYq+h8T4Eg5B47hOXQZMSwH0Yf+3AvJDJmy4ROxog5FAekAWDTgA88tJJnOZSIOYRYzOGyjX2QnA4jS6SL3UoPH5zBXffvh9NBsHMkCMCwtBwE0CmKxIHDhYQHpLmVkMnqFnGw980srepmwDfgdkLRjGsvFbviKNXcSmnVfA9ZGfS74XQQzMUyZnysVONHa1dhfm2AIRR2t8Q6HWE5MMZYJSYfAdnpGNlK5SwHf8nHpdxKbsmBdSEP3rZzGADgddU2xcvuLMQzeMiSZcThhVWaTs0bviw5sIEF8vuqiAPfPafZa5RRjVkZgL3FgU9a87D3gN+dq2+pBH9uAIpiUWlVg5LVEfIW70GdDoJBv4zZWBrLSbXs6+pwEFOcfBZx8LKYQ7ckkNiRquJACPk6IWSWEHLAsvYpQsgUIeRl9vUuy/fuIoQcI4QcJoTcaFnfQwjZz773OcLsTEKImxDyPbb+LCFkorFPsTZMcVjqEnGo4lbiTAwVpw1aIYTgsT+6Dr9zreFT97tLz/9Nd1gV9f/48ev4yLdfxEpBGuVSQoGDANuHA/mvExPRcpaDX5bgILndc85y0MzdNV9T2VQzO8HdQx52A+bTEau1a1cquJV4jUMpywEAhkNuzMWM+pFSLiUOF6xCt5JOc7PSBY2nFsvhmwBuKrH+WUrpZezrJwBACNkF4DYAF7FzvkAI4X/RLwK4E8B29sV/5ocALFFKtwH4LIC/qfO5rImQx4WgR+oay0HRKFxl3AFWt1mptMFC3JLTnAtRrgfV+7/6DP78R9UDmHbg1HwC//bKOQDATCx/FvFiUkG/T8aPf/9N+Ov/dIm5zuMOoQo73IBbMm+IXAgUTTdvvHzAzW1feQZ//ZODDXxGa8ec1CbxIjNmOVRJ0FC08gHpqKVWpBQRXj+SUtDnlcv+DjcThcKANGD/IH8nU1UcKKWPA6i17v99AL5LKc1QSk8COAbgCkLIKIAQpfRpatiB3wJws+Wce9nj7wO4nhRGr1rEeL8Pk11iOShZraxbKRJwwycbffUH/bKZo1/KcijEJxd3rz10PoqXzix3zND3Lz9+HHzjPhvNb8GwlFDQ75fhlowePpwtLIDa5yt/Ewt6XDnLweJW4jfQNBOJ0wsJ21Wa82A59+ub2XtVEjQqxRxWUrkq81IMBz2YY8WF4UqWgytX25BbMx7bvbCwk1lLzOFjhJBXmdupn62NAThrOWaSrY2xx4XreedQSrMAVgAMruG66masz9s9biWNlpznABiuoo0DPvT7ZBBCzJ1dpcAjxy87kSzw9f7opXPsd9rfrbSUUPCDF6Zw7TbjLTZbYDksJRUMlBAAbjmU840Dxg45WuRWygWklazhTopnsiU7kraT3BhP4z0QqDnmUMGtVM1yYBXmS0mlrLsOyG1aCmMO1usWNJ56xeGLALYCuAzANIC/Y+ul7ka0wnqlc4oghNxJCNlHCNk3Nze3uiuugfF+L6aWU10R5CpXIc25fGO/WZQU9rrgcpK8nXI5vAXFgrpO8eDLU+bvtDs/enkKiqbjEzfsBADMxgotBxX9/uIb1dVbBvGp9+zCm3cMlf3ZQY9k3hDTlpiD9QaaVLJIqzqWkqrZL8gO8OvN9S6qTRzyLIeCY6OWXlSlGA65oVNgJpqpKLpmTya52HKwe3pwJ1OXOFBKZyilGqVUB/BVAFewb00C2GA5dBzAObY+XmI97xxCiAQgjDJuLErpVyileymleyORSD2XXpGxPi/imWzJFgh259HDszi9YOxGdZ0iq5cvggOAv7p5N+65440AjHhLLS4lIOdu4O3NXzyzhHMraUgOknejsCOUUnzv+bO4ZDyMPZuMgr5Ct9JiUsGAv9hykJwOfPDazRVfp5BlMFJKMV4Lq+UA5A+osZP1wHfg3L+/2oB0qaFQ1s62pYiwmRhA6RoHDo+DWN1KXtmRd92CxlOXOLAYAuc3APBI5IMAbmMZSJthBJ6fo5ROA4gRQq5i8YTbATxgOecO9vgWAI/QNm3decbS5HJtTcfsxB985yV8lY1qVHXjA1tJHJyOnKUQ9rpKprGWgn9AeUfME3PGDW77SBCKzTNHXjsXxaHzMdy619i/DAfdmLO0faaUGjGHCnGFSoQ8LtO1YgakC8XBMtrSTuLAU2+tXU+B2lNZhwLustlKwXIxh1BOHMIVXnN3qZiDxC0HIQ7NomoRHCHkOwCuAzBECJkE8BcAriOEXAbD/XMKwIcBgFL6GiHkPgCvA8gC+CillP/1PgIj88kL4CH2BQD3APgnQsgxGBbDbY14YvXAp05NLqVw0fpwuy5j1eg6RSyTNecZ85tRuYB0IasRh8IsFt4tczjotn2m1y8OzoAQ4L2XrAdgBERno7mYQyyTRVanJS2HWrDunvNiDlruBmYdUHPKTuKQ5TGHwmylyjdf7jIb9MtFVkY0pcJdprgNACIBj/m4UszBtBysbiVZxByaTVVxoJS+v8TyPRWO/wyAz5RY3wdgd4n1NIBbq11HKxjr68xaByNADHN4D8/9riXADAC/9cYNeONEf/UDkQsKcsthOaVCchD0+1y22gmXIpHJwiM5zcyYSMiNg+ei5vd5l9p6LYcgsxwopZYiOK2sW+mEjV4v7gbju3O35IDkINVjDizONBiQceh8fit33lepHLx9PFA50F/JchDZSs1DVEhbGPDL8Lgctt8BF8I/wIsJLg7V3UpW3rojgg+yIrdqFPagWk4aBUyy5LB9QFrJ6nktQoaD7ryANH/96rUcQl4JOjV226WylQCY08u8LidOLdhHHAoD0oQQ+N1SzQHpAb+7OOaQypZNYwUMS4C36a4p5mDNVpJFQLrZCHGwQAjBeL8vb7ZtJ8A/lHznyz+w5VJZ1wK3HLhbaTmpoM8nw+W0vzhksvktRSJBN+KZrBlc5y6y/rrdSrz5nponDpm8mIPxO3atD+HkXMI2mXGFqayAkc4ar1LnYEwcJAh5JcTTWbx2bsV0l62kKlsOgGG9AaijCE4EpJuNEIcCrtsRwZPH5osG4dgZ7utdSirQdWqa+rW6lVYD90XzIi/eF8fldJQdkWkXMkWWg+Hz5hlLiwkjgFqqzqEWzOZ7qWyuCK4glXWeWQ6714eQUDRzfne7MSukLbtzn+ysyXKQnQ6EPC4omo7bvvwM/uLB1wAA51ZSGA17Kp7PM5YqFcGZA35kEZBuJUIcCvjNPeNQNYoHXzlX/WCbwD/AOjV2a6t1K62GIsuB9cVxd4pbyZKKOsxHprIbtBlzKFHnUAshi+WQNvso0Ty/OI85XDAaAgCctUl8q7B9BmBsBKq2z2BdW3nRXCyTxYn5ODSdYnIxZTYsLMdwyANCclPgSuEpYTl4RUC66QhxKODC0RB2jYbw/Rcmqx9sE6y+3oWEAjXLAtJNFAe+M17JcyvZw0VSjkxBSxGeSsl374tJBS4nyZt+txpynVmzeTcta3EYT2XdGjEKEGei9mg5klZ1OB0kzxU6KZ+QAAAgAElEQVQZqDHm4Jac5nMPeiRMLaUwuZSEounYNOCveP5F60PYMRw0e3eVopTlwNdEzKF5CHEowW/uGcf+qRXbZ99wrB/gpaRiupXKNd5bCzwgzXvuLDG3kiw5oOnUdt1GrZR1K7EWGrzGod7WXjzmEE2rpngCBeLNLAfeq+m8TfpR8Xbd1ufudzvL9lbS2d85k9UgSw5cu20IH7hqE/7whh3QKfDU8QUAuZnl5fjwW7bgpx9/c8VjSlkOhJCuGvjzX7/9Aj7F3HF2QYhDCfiQl/m4PfzB1bDmly/EFYtbqfEBaaeDwC05WBsIIyunz+cyXVh2di1lsnpePUef1wXJQfDIoVkcnI5iMVG6OrpW+NyCaIHlwPP9AcM68bqMhoey5LCN5ZBStbydOWC4lUpVSFNKceP/eRxf+OUxKKxNy0jIg7+8eTd2jxn1QU8cNdrbVBMHQkhVMS5lOQDdNUf64HQMR2dj1Q9sIUIcSiCxm6pq8wArJ15oOayyCG61+N0SkoqW653jk00hsnNQOpPVIVt86g4Hwc2Xj+Gp4wt45z88gaePL9Rd4wDkYg7RlJp304pnsmbWDqXGjpwQgnUhj2062aZVrag1iF8uHXMwbmRxHJuNmwFpDm8B/6uj83A6CEb7Kgeka6Hf7wIhxW04vF0kDtGUatYO2QUhDiUwd8E2dpFYiWey4JuvxYTS1IA0YHwoE0oWS2xQTr8vV2Fta8tB1Yoqwe++9VI8/6e/hndfMopYJlt3MBowdrI+2YmlhJIfc0hn4XE5zOwxnvG1LuTBeZtYDukylkOpmeqPHTGsgmgqywQ395oOBWT4ZSei6SzG+rwNeQ++++L1uP8j1+QVzQF8VKh932+1QiktckXaATFDugR8J9QplkMiYwxxV7J6njg0I5UVAJsjrZldRfu8sjnoxc7ioGh6yTYhA34Zn7/tclw23me6Reql3ydjMaEgpWhwOQlUjSKWVuGWnPBIDihZ3YzbjIQ9eHVyeU2/r1GkVb1o2FO5OdKPHZkFYGRlUeSPlyWEYNOgH69PR6u6lGpFlhy4fGNxBX+3uJXSqg5Vo8Jy6ARMt5KNb3RW4uksAm4JA34ZSwnFbIDXNMtBlpBUNSwzt5I15mDnzqwZtXwbc4eD4L+8ZQuu3rq2USIDfhmLSQVpVUeYFXbF0lnIToe5Mw+wpnbrQm6cX0nbohAupWh5AV+gdNvueCaLfaeMQUWxdLZIOIDc6NlqaaxrpVsC0rxhoRCHDqDT3EqxTE4cjFTWJsccZCeSmaw5f5m3zwDsLaiZgjqHZtDPBJoH6gEmDpYGdPymOxLyIJPVsZJSy/68VpHOakUN8vh1WmNaTx2bR1anWB/2IJpWWSpr/vuMxx0aZTmUo1tiDnwGSKnZ7O1EiEMJOtGtZFoOeamszZm26pONgDRvN8HrHABAydpXUJVsccyh0QwyyyGlaGYzOZ7R4y2YlTAaNho9tjMoPRtL4+xiEimlWBwCJeZIP39qEbLkwFt3RgzLocRQqQk2o3zTYLMtB2dXFMHxcaqFExbbjYg5lKDj3EqZLPp9MgZ8Mo7OxHMxhyZZDj7ZiaSSNTuy+mUnZCZEio1fs8JU1mbQ75OxEM+3HADkta7msxLWhY0A6/loGheyiulW85c/PoijMzFksnqROJjV8Jag9CuTK9g1GsKg35jfEPRIRdbY3okBjPd7cemGvqZeu2E52Pf9VivccqAUJf8O7UJYDiXoNLdSPJNFwCMZLg1LKmsziuAAVhylaKwjq1E0JjuNN7RdBZVS2hJxGPC7TN9x2NJMzhAH43ebAemQkeY500bLYTGRwZGZGJaSCrxFAen8mIOmUxyYWsEl42EEPUYH2pWkWrQJ2RoJ4Fd//HazBX6zcHdNzCFnmdkp7iDEoQSd5laKp7MIMrdSUtHMN1vzLAcJKUVjHVmN3bHL5rUhvLWHu8m7MmtHV+sAG6tbid90eYV2O9NZExkNOjUaKJaLOXBxOD4XR1LRcMl4n1m3Ectkm5YVV41uyVaKWmJOSRvFHYQ4lIC7lbK6PW90hSQyWfjdEgbZjYm3ZGhWtpJP5nUOiulX51ZKxqaWAx9n2SzB5Fg7ulrdSrIz51byMbeSLDkwFJDbWiVtza0vzFYKFASkXzlrpN1eyiwHTrvEodvcSgBsVesgxKEEuVYQ9ncr6TpFQtEQcEtmkdC5lRQcBOaM6EbjkyVQaoxT7WM3Q7tbW7xy2+1qcszBX0YcSlgOAB9V2r42LUk1t1MttKoKLYf9Uyvwy05siQTMPlIAmu6qK4fH5eiKgHQ0JdxKHQN3kdg5Z5/DM0kCbsl0U0wupZq6m+OBysmlFLayBnL899k1IM3/lq3IVuIUupX4rAQecwCMfkyxKp1Pm0kly4EHzvkc6VcmV7B7LAyng+RNeGun5aDp1LZxrlqJpa1uJfuIg8hWKgEhBJKDdIRbiZv8AY9ktqCeWk419SZ43c4IbtkzjnfuXoe37RwGYLEcbPpB5ZZDs29k5WIObskJnqXIi+CMxy6ca+NYWuvNqLBC2i05IUsORNlc7EPTUfz2VZsAIM9yaGfMATCaBjbLhdoKopaAdEq1T8xBiEMZOmE+AZAz+XnMgRBjl1xpeMpa2TTox923Xpq3xmMOqk3rHHjModlFcH0WQfC7JbOFhiw5wL18Vssh6Cnd+bQV6DpFStUwFHBjPp4pshwAIOSRzPkUmaxuui7zLIc23Zi5OKRVragpXycRTanwspoNO1kOnSu3TUZyko5wK/HMpKBbguR0YNBvfHhbvZMyu7LayHJ44fSiKQqtcitJTodpMXhdTlOMZKfDnLLmtwh3oExb7FaQzmqgFNi7yehb5CuxoQh6XIils+b7jN+E82IObcrL5+KQ6fCgdDStYh0bpyrEoQOQnY6OcysBudGXrTb13bzOwSaC+quj8/jNLz6NL/3yBIDWuZUAmDMhvLLTFCNZsvZWsoiDRzKbFrYafiO6eusg/vaWS3D9BcNFxwTcEmJp1Uy35FlKHpfD3BC422Q5eC1upU4mls5ihLmE7ZSaK8ShDC6nw7YuEiumW4m5KnjcoRmDfirBW3W0OuZAKcXzpxbz2g5oOsVf/fvrAIDvPX8Gmk7N3WWz3UqA0cIc4JaDg/1eSyqrbI05SFA03bRwWgkPRvtkJ35r74Y8i4YTZG4l7hfn9Q2EENN6aF/Mwfi9dkr/rIdoSjULIoXl0AFITmLb4KoV061UYDm03q3Unq6sB6aiuPVLT+PRw7Pm2g9emMSh8zG899L1OLeSxuNH5qBoPObQCsvB+Bt4XU7T5eKWHHjHRSP445suwHh/rnKY/93aYT0kTXEoH58yxEE1c/GtsQZ+7e0ShzH2Or4+HW3L728U0bSKSMB4zwhx6ABkp6Mj2mdYA9IAzIBhqz+wkoOAkNZbDtMrRqbPgancDeKxo3MY6/Pi7lsvxVDAjW8/e8a0HFrjVmKWQ4FbaTjowUeu25o3FrOw0KyV8Gpcn1zemuIxh5xbKRdr4PGHdgWkd44EMTHow7+/Ot2W398IMlkNaVVHn89lBKVFhbT9MdxK9rccZmOZvLxzXuvQasuBEAKX02HOkmgVvDPs4fO5+bvRlIqhoBuy5MBNu0fw9PH5XBFcC8RhMOA2Z23LFnEoBReHWBssB+6OKZwAZ4W7lcyAtLfYcmh2YWE5CCF418WjePrEAhYTSluuYa3ELO46o6GlsBxsT6e4lU4tJLCh3wuJiYEZkG7Dbk52OlruVlpMGDvaQ+dzlkM0pZoZQ0MBNxKKZhYLtiKz5o6rJ/Cl394DQkjOcnCW/r08kaA9lkMu5lCOoMeFeCZrTv2zpoyabqU21hi8+5JRaDrFz14737ZrWAvWLDCv7LRV/ESIQxlcHeJWOjmfxMSQ3/y/GZBugx9YlhwtF1RuOZxaSJqZHtF01rSkuEjMxYwWFa24ka0Le3DDrhEAuQB4Ocsh6M4NBGo1CdOtVD7mwF/H6ZV0Xn8o43vtDUgDwK7RECYGffiPDhUHaxaYsBw6BLkD3EqUUpxeSGBi0CIOzK0ktzhbCQAr+Gq15WCIg6ZTHJuNAwBWLJZDoTi02gVizVYqRc5yaP00uFRNloNxfeeWU3kuJeN77RcHQgguWh/GqYVk265hLZiBfq/LHL9rF6r+VQkhXyeEzBJCDljWBgghPyeEHGX/9lu+dxch5Bgh5DAh5EbL+h5CyH72vc8RFpUjhLgJId9j688SQiYa+xTroxPcSrOxDJKKhs0WyyHSpmwlwLhJtNqttJRQTL/9ofMxUEoRTalmymWROLS6/sNVW8yhvdlKld1KgNGSpbAK2Yw5tCA9uBKjYQ+mV1K2mqJWK0ts1G7Y64KvAwPS3wRwU8HaJwE8TCndDuBh9n8QQnYBuA3AReycLxBC+DvniwDuBLCdffGf+SEAS5TSbQA+C+Bv6n0yjaQT3Eon5xMAkCcOHpcTIY/UFnEwAtItthySCi4eC0OWHDh8PoqkoiGr0yLLYbaFbiUrVd1K7AbbjuZ7vHisUkCai9fUUiqvTTeQq3loV1dWzrqwB2nVHrO4V8vkkmHxjPV5TbfSgakV7J9cafOV1SAOlNLHASwWLL8PwL3s8b0Abrasf5dSmqGUngRwDMAVhJBRACFK6dPUkPdvFZzDf9b3AVxPrLl+baITspVOlRAHAPitvRvw1h2Rll9POwLSSwkFkaAb24cDOHQ+ZsnHL7YcZMmBVr+1uBiVEyW35IDkIG2yHLJwOkhFweSCkFA0Uww4l2/sw8VjYdNabRd8Fve55fbNxaiXMwtJDAVk+N2SGZD+0x8dwH9/8ED1k5tMvd3ZRiil0wBAKZ0mhPC6+zEAz1iOm2RrKntcuM7POct+VpYQsgJgEMB8ndfWENrhPweAhXgGqkbNXiuVOLmQgOx0YH3BOMY/+/Vdzbq8irQjIL2YUDDgl6FTPw5MrZi98YtiDvFMW3a43K1U2PGUQwgxWmi0KVvJ53JWFMxSdQ2cN2zsx7/93puadn21MtrHJ+qlsGt9e2Zx18vphSQ2DvgA5IZoTa+kzRYs7aTRn5ZS7zJaYb3SOcU/nJA7CSH7CCH75ubm6rzE2nA5Hci2wa30xz/Yj//67RdqOvbUfAIbBrxNG+qzWlrdyTar6Yims+j3ycbQnFjGdC3w4Cnf7SotmB9dimqprABrvtcOyyGjVXQpAfkV0YUBabswyjZS022cxW2FUorPPXwUx+fiVY89s2gVBwkz0QxSqoa5WKbtMZR6Py0zzFUE9i/vXTAJYIPluHEA59j6eIn1vHMIIRKAMIrdWAAASulXKKV7KaV7I5Hmuk1cbXCRUErx0pklnF2qrb//qflkkUupnbha3Ml2mQnBgN+F4ZAbSUUzK6a5xeBxOU1/fzsCp9ViDgBrbtcOy0HVKgajgXzLIWjTttiRgBsOAkzbxK20kFDw9z8/gh+/UrlyW8nqmF5JYSPLNrQKtaK1P4ZSrzg8COAO9vgOAA9Y1m9jGUibYQSen2MuqBgh5CoWT7i94Bz+s24B8Ahtt2SiPW6l89E0FhIKFhMK9CpWi6rpOFWQxtpuZMnZ0oD0Ektj7ffLZvEfT2e1ukDCbQycuqtUSANspkNbKqSzFWscAMMdJjHLNOSxp+UgOR0YCXlsYznwGe7WCW+lmFpOQacwLYfCeRo8iaJd1JLK+h0ATwPYSQiZJIR8CMD/AnADIeQogBvY/0EpfQ3AfQBeB/BTAB+llPLE3Y8A+BqMIPVxAA+x9XsADBJCjgH4BFjmU7tph1uJZyhoOjV3xeV4dXIZmayON2zqr3hcK5FbLKi8xmGAuZUA4OiMIQ7WKWz8cTvy8fv8MmSno+IOvV0zHZJKdcvB6L6a76KzI+vCHpyPtm+inpWZqCEO0SricHrBSCixxhyszLVZHKpuBSil7y/zrevLHP8ZAJ8psb4PwO4S62kAt1a7jlbTqGyl2VgaQberqm8XAA6cy7WAWIhnKgalnjg6D0KAa7YOrvkaG0WrXXG8OrrfL8PBgqrHmJ/XmnbZTsvh1j3j2LupP6+yuJCAx4VTC0l87Qlj9sTvvnlLS64tqWhF6amlCHpcWEqqNR3bLtaHvThok+6sM1Hjpl6u6l3TKc4uJnF20Uhj3TTILAd2j1gf9uDcSrrt4iAqpMvgcpKGuEh+4x+fwj8+eqymYw9M5XKb5+LFb4x4Jot3fPYx3P/iJJ48No+Lx8Lo87U/q4HT6mwl3ldpwOJWOjWfQIBNxePkxKH1MQePy4kLRytn0PCBOl967AQeePlcxWMbSaoGywHICa2dR3GuCxtuJRt4pHE+yt1KpcXhX/edxXV3/xLff3EKbslhtuvmf4trtg0BMDaW7USIQxka4VZKqxqmllPmm6UaB6ZWcPFYGACwEM91mXzg5SksJRR897kzODITx1/++HW8dGYZ17I3kV1odbYStxz6fC70+Vxseh8t8o2H2jx3oBpBj4T5uIL5eMZ8Tq0gUUPMAUBHuJVGwx6kVM1MZW4nMyuV3Uq/PGxkWr5ydhkbB3xwsJiO12W8zm/Y2A+PyyEsB7siOQk0nUJbg0DMMvOylk6Ls9E0ZmMZXLfTyMKaZ5bDyfkE/uC7L+N3v7UP33jyFLZE/FhJqcjqFG+ymTjIksNsjd0KFlnrDLdk5OrzYqzCm1g73Uq1YB0bupJsXYZKSqmeygoAATefG21ft5JZCLfS/rhDJctB0ymePrGAXcya5PEGAFjP6jUu3WAUFgpxsCm8/cRa3CQzzCxM1NAv5cA5w6V07bYhOB3EtByOzBhzCl44vYSp5RT+5J0X4v++chPCXhf22CgYDbBmhS1yK00uJTEXy6DfnxOCauJgV8vBKg6xTLZlryEvgqtGqAPcSrwQbqrGNPBmMhMtn610cDqKlZSKO9+yBX97yyX4f67ban7vkvE+PPcn1+Oi9WGzbqed2Hcr0GZ4S4G1uJb4m6SWNrwHpqIgBNg9FsaAXzYtB56a+ftv34ZD52N4+wXDeNsFw/j4r22vGORsB62qc3jpzBJ+4wtPAQAuHQ+b61wcwgXiYJceQOUIFOzIV1IqhgJrb0lx1/2vYsdIEP/52s156z977TxUTUeqhjoHoDPcSptZSjfvN9ZOzGylEi6uJ48ZjR+u3jpozo22MszWIgF3TUV0zUSIQxkk1vJazepAnZ9T7lZK1mA57J9aweYhPwJuCYN+GfPMcjg+G8e6kAefeMfOvOMHG3DzaDStCkjzIsEbLxrBDbvWmes8KF24w21nQLoWgsxyuHRDH145u4zlpNIQcXj44CxePxctEofPP3IUp+eNTBlvDTGH0T4vQh4J/hqEpF30+2UM+uW231DTqoalpGqM/FQ1pFUtbxP35PEFbBsOlBQGK5GgG8+cXGj25VbEnlspG9BIt1ItlsNrUyvYvd7YBUeC7pzlMBfHtuFA3dfQSngQv1oB31rhA1L+8ubduGVPrvCe1zoUWg52dyttHQ7ALztx82XrAQDLDYo7JBUNB6djRdbc1FLKrMj2u6vf8D94zQQe+vhbWt60cLVsjQTaLg48TsA/s9a4QzSt4tkTCzXFCoeDbiwnVWSy7ZvvYM9Piw3gbqW1tO02LYdM5T/wQjyDcytpM1Np0C9jIWH0Vjk+21niAKDpVdJm/6QCC4FPwSvsAWT3gPSOkSAOfPpGM4a01ABxoJQioWShaHreCNWkks37+YVVuaXwuJwYK2juaEe2DvtxfK69biUejN7OPrPWjKUHXz6HTFbHf3rDWMlzrXAX6Xy8fbOx7flpsQF5bqU6mTUth8puJV78dtGYkcEwGHBjPqZgeiWNhKJha4eIA7/5Ntu1FE2pcEuOopjLcLWYQ4unwK0GQgj6Wc3KcgPSWVOqBp7y/4plNgAP2PLXqJZU1k5haySARdZ+pl3w1hnbRooth3/ddxYXrAuam8BKcHFoZ8aSfT8tbaYhbiUz5lDZcuDFbxcxt9JQwI2UquFV9qHeFukMcci9Zk12K6XVksFR7lYqF3Oo1BnVDoR9xnU2wq2UsFirr55dNh9PLhvi8MFrJgDAFq2hG8VW9jk50UbX0oxpOQQB5DKWDp2P4pXJFfzW3g01ued4G/6T8+17LkIcytCIGx1/o2R1WjGL58DUCjYN+syb2GDA+MA+ywJSHedWanLGknVGtJVd60P4b+/YgV+7cCRvfcAvwyc72z6UphpBtwSng2A5tfadb8LSq+nVEpbD+6/YiAc/di2u2jKw5t9lF7g4tDPuMBNNwy05MN5v3Ny55cAL397L4krV2DESRNAj4bmTJRtUt4TusSkbjIu7leq0HFKKhlg6axazJJUsZKn0Lu3AuRVcMtZn/p+X0//swHmEvS4MBTpjdye3yK1UThycDoKPvX170brH5cTPP/FW83W1K4QQ9HldDYk58NqaC9YFcWQmhkQmC79bwtRyCi4nwXDQXdNAqU5irN8LWXK0Ne4wF8tgOOQ2LVuePDETTSPglmrOQnM6CK6YGMCzJ9onDsJyKMNa3Uo83sDzrxNlXEvLSQVnF1PYbfFDcsthLp7BH990ge2zRDhcUJsdkI6msqtuHz3W57VttpKVPp+rIVXS3K30jovWQafA5x4+CsCwHEbDXrNlQzfhdBBsGfLj+Gz7LIe5eAaRgDs3G5xZDrOxjBkTq5UrtwzgxHwCszW232k09v+0tIm1upV4vGFiyCiPT5UJSr/GgtG7x3LN2S5aH8YnbtiBH330WvxfV26s6/e3A7nNbqVuoM8nN6S/ErccrtsZwW9ftRFffvwEHjsyh8mlpNmmoRvZOODD2aVk037+clLBbV95GlPLpSux52IZRIJuBGQJhORiDnPRzKrdmldtMTouP9Mm15IQhzKs1a3E4w2bhww/aKJMOisPRvMaB8DYAf3+9dvNAHWnwHfmze6v1M3i0O9zNSggbYhDwC3hz969C9uHA/j0v72GyaUUxvp8Vc7uXAIeqexnrREcmYnjmROL2Heq9A17LpbBUMANh4Mg4JYQNS2HtFn9XCu7RkMIuCU8e6I9xXBCHMqwVrdSThyMD2K5jKX9UysY6/OivwuyRiaG/HA6CD7/8NE1NSyshK5TxMpkK3UDYa/ckFRWXlvjk53wuJz42Nu34cRcArOxDMb67V+zUC8Bt1RTL7N64WnpMyVcPUpWx1JSzfX48rjMOod63EqS04HLN/bhZUu2WSsR4lCGtbqVzi4mEXRLWMe6RZardXjtXDTPpdTJbI0E8Kn37MLDh2bx+UeONuV3xJUsdFpcy9At9PtcVacA1kLcYjkAwLsuHsUoC0CPd0BBW7343VJeplajSauG6PICVysLCWONi0PQIyGWziKeySKpaKsWB/6zGlUxv1qEOJRhrW6l04tJbBz0mf1oSlkO0bSKk/OJPJdSp/OBqyfwxol+PMpS9xoND9Z2q+XQ53MhqWhrbpvANyO8yM3ldOA/XzsBABgf6GJxkJ1QNdq0thMpLg4litN4wRrPigt5XIimVDOgzCv4V0PI46o6i7pZCHEow1rdSmcWk9g44IOP7dwKLYd/fPQY3vUPTwAAdo93jzgAwEjIY6bwrYX9kyv47a89a+7WgPKtM7oFPtlvrRlL8YwG2enIy9C645oJ3H3rpbhys31GyzYaP/+8NSnuwDd5pdxKvB9aoeXAhYQXaa4GPl+8HRPuhDiUwcU+VNk63EqaTjG5mMLGQZ/ZL7/Qcrhv31lIDoI/vGEHrt1qr6E9a6XP5zJv4mvh6RPz+NWx+bzZwNyH271uJUMc1lrrkFSy8BU01XNLTtyyZxzOLkxj5XBxiDfJtcQHd5Vqa2FaDpa5IrGMahGH1VsOQY8EndbWvLPRCHEog8tRf87++WgaiqZj04Df/IAW/nHnYxm8/YIR/N712zsi/341hL2GOKx1t7PAeuQcs+Stc4ukW8Whz2yhsbagdCKjwd9FfZNqhcdYmhWUTlvcSkkliw//0z48zzKXuDjwQregR0I0lc25leqwHILMQi43j7qZdNddqYGsxa10esGo0Nw06IPsdMDpIHlupaSSRULRMBTs/AylUoS9Lmg6XfPubZHPtLBUvJpuJW933vi46K3V8jIqou3dS6oZ8OFFzUpn5Zu8eCaLJ47O42evzeCOrz+HZ08sYC6WQcgjmQ0hw14jXnB6IQlZctT1ng2YxXStjzsIcSgD78paj1vpzIJRhLNxwAdCCHyyM+/NOh8zbnqNGOhiRxp1g1ssaTlk835Ht1FYWVsvCSXbVR1Xa8W0HNawMVGyOrJlNoUpS/zr8SNG0sVQwI3f+85LOB9N5xW6Xb11EDoFHnh5CsNBd12dDsz3QxMzsMohxKEMa5lNcHoxCclBzM6KPtlp+ioBo8QegO17/dRL2MuCqmsUB+5WsnbZXEmpcJD8ucvdRM6NsHbLoVtfo0r4GyAO7//qM/jrhw6V/J41OeKXh+cwEnLjj27aidlYBr86Op8nDlduHsRw0I1oOltXvAHIze8WbiUbsRa30pnFJMb7vWbgzy/nF+bwrIautxzWGFTllsPpxaTZkmMlZRTAdUq/qdXSKMshqdQ2H7rb4HGWcr3MauHw+RheKVN4Zo0dTi2ncMG6EN62cxhuyWG4ii2faaeD4D2XGl1Y64k3AEDA3ZjNQj0IcSiD00HgIPW7lTayhnsA4HPnWw6mOHRxzAFojFtp0C9D06kZx4mmu7d1BmBsSjwux5rdCPGetRx4zKG+1y+laIhnsji9WLo/U0rRzGJCALhwNAS/W8Lbdg4DQFH/pPdycaijxgHIbRbiwnKwFy6nY9WWA6XGjWzTQK5/jc9VYDmwmMOgv0stB9/axSGTNT6kb5ww5g3wuEM391XiBBtQ+JRUtKJU1l5gramsfOPG2+wXklI1jIQ8ZobhhaPGUJ93XrwOQLE4XDIexu1Xb8I7d4/WdT2NsiTrQYhDBVfq7rkAABiDSURBVGSnY9XtM04vJBFNZ3EBe9MApS2HsNfVdSmsnL4GWA7cpbR3wpirzAe4rKTUri2A4wQ9uYZt9RJn8xt6DbdUnB24GqyVz2cWk/jbnx7C1544Ya6lmLuOxxB2jRqtb66/cARXbB4oKjAkhOB/vG83rt5aX+Ghv6C7ayvpzrtTg5CcZNWWA5/cdMVEbsKWT3bm+UDn45mOGeBTDz7ZCclB1iQOCyyNdbzfh7E+Lw5Ox6DpFMdm49jQxe0fAG451C8OqqZDyeo9WedACIG/IDtwNXDLAQBOziVw71OncP+LU+ZaSs2Jgyw5sHnIcB8H3BLu+/DV2LOpf21PoACHgyAgS23JVuq9d88qqMet9NypRQz45bzRnj5ZQjKTH5Du1mA0YHxAw961NZDjlsNgQMaVmwfw2JE5HJyOIpbO4orN3TPashQhj7SmnSJvHdGLlgOQazlRD9bK518cnEVC0XBiPg5dp3A4CFKqBo/LiUvG+xDyuiA5m7+/5m04Ws2anhkh5BQhZD8h5GVCyD62NkAI+Tkh5Cj7t99y/F2EkGOEkMOEkBst63vYzzlGCPkcsUkqiqsOt9Lzpxaxd1N/XjaNT3YiqVotBwVDNp9nvFZ4lXS9cHEY8Mu4dtsQFhIK7n3qFAB0dW8gYO03Ax7f8vdgthIA+NxS3W6l+XgGhBjzvH96YBoAkFZ1c7hPWtHgdTnxqfdehG988I0Nu+ZKNCIGVQ+NkL23UUovo5TuZf//JICHKaXbATzM/g9CyC4AtwG4CMBNAL5ACOHv3i8CuBPAdvZ1UwOua824VulWmommcXohWbSzNSwHaxFcpmtrHDhhn2tNzfd4jcMgEwcAuP+lKWwY8Jr1I91K0L22mwHP1OlVy8HvlhCv0600F8ug3ydjc8Sf5wrmCRFJNZci3Ko9bMBTvyW0FpphE70PwL3s8b0Abrasf5dSmqGUngRwDMAVhJBRACFK6dPUaMbzLcs5bWW1biUeb3jjRKE4OKFoOlRNR1rVEMtkuzrmAKzdcliIZ+B0EIQ8LqwLe7BtOABNp7hiorutBsC4GazNcuBupd60HAJuZ92prPNsBvRGlm34ho19AHIJESlFg6fFFllHupUAUAD/QQh5gRByJ1sboZROAwD7d5itjwE4azl3kq2NsceF621ntW6l16ejcDkJLlqfP7zHZ5npUNicq1sJe1c/7vLxI3Nmk7LFhIJ+nwwHKyR8E7MertzS3fEGwLgZJBWtbAuHavD4Vi+2zwCM512vOMzFMhgKytg0aIjD9ReOYMAv49isEXfIZHV4Xa0Wh7UlKNTLWsXhWkrpGwC8E8BHCSFvqXBsKRuMVlgv/gGE3EkI2UcI2Tc315xhMlZW61ZaSigY8MtFQSqeermcVLq+OpqzWstB1XT8zjefxzdYXGGBFcBx3n3JKAb9Mt6yPdLoS7UdvIVGva6EwilwvcZaRoXOMcthEytivXxDH7ZFAjg2Gzf7KrW68jzgXluCQr2s6d1DKT3H/p0lhPwQwBUAZggho5TSaeYymmWHTwLYYDl9HMA5tj5eYr3U7/sKgK8AwN69e5s+/WK1biW+2y1k24iRuXT4fMz0U3Z7QLrPa8zP5Vke1ZiLZZDVqWlZLTKh5bxxYgAv/PkNTbteO2EtfOor8X6qBm/x0IvtMwDDnVZPKiulFPMxBUMBN9518Sji6Syu3DKIrcPTeOjAtPm6ttpyCHWaW4kQ4ieEBPljAO8AcADAgwDuYIfdAeAB9vhBALcRQtyEkM0wAs/PMddTjBByFctSut1yTlvp87nMfPtaWEqWFocL1gVBCHBwOobD543BNRODvqLjuomQ1wVKa+8mOWsRBf7vQJfHZcrBm61F69wt8lkQ3TpKtRr1zpFOKBpSqoahoBsBt4TfedNmOB0E24YDWE6qZsaSp+VuJQmZrG72F2sVa7EcRgD8kO2EJQD/Qin9KSHkeQD3EUI+BOAMgFsBgFL6GiHkPgCvA8gC+CillMv7RwB8E4AXwEPsq+1sHvLj8aPz0HRa0/SspaSKHSOBonWfLGFi0I+D01Eomo5tw4G6doSdBG9xEa2x3QWPNfAspfl4Js+t1EusdcDL9EoasuTAQJe/x8rhl42baVbTV1WHMB8r3S15a8RwMb12bgVA62M53D0YS6sYbKE7uu5nSSk9AeDSEusLAK4vc85nAHymxPo+ALvrvZZmsSUSgJLVcW45hQ0D1Xf6S2XcSoDRg+XAVBTRtIobd61r9KXaDi4Iy0kVG2qIIXPLYSGeMTK60lmMhOrrZNnprLWfztRyCuvDnprced1Irm23hrCvNnFQsjrOsw1KocuXp06fZEOnvHJrG0tYY1AdIQ69wBZWGn98Ll5VHHSdYjmllheHdSH8ZP95AMAbNvU19kJtSJ85C7k2t5w1S2k2Wv/M3W5grTMdzi2nur4WpBIBlsIbV7JmE8hq3PQPj+MccxsVWg58k3KKdQZuh1sJaH3zPdFbqQJbIoaL6OR8osqRxh9O0yn6y7hCLhzNpbc2uv+KHeExleOWQT2V4JZDUtFwetF4vYd73HKoN1vp3HK6p8WBu32SNce70jgxl8BQwI2xPi82FsQDQx4JXpcTJ9h9oNUB6cAaY1D1IsShAkMBGUGPhBNz1cWB75D7y+xULmS1D2GvC1uGiuMS3UYk6MZQQMbr56JQNR2ff/goFixNzQqxdsM8NB0DAIzU2QO/06lnpxjPZPHC6UWomo6ZWG+LQ2CVbbsPsvfb/77lUjz5ybcXpQATQjAa9pjjf1sdc+Cp8K2e6SDEoQKEEGyJBHBivvrud5GLQxnLYX3Ygz6fC2/Y2NcTvmBCCC4cDeHg+SiePDaPv/v5EXzxl8fLHj8bS4O/LAenjYyueqdndTpuyQlZcuD4bBzX/e9H8bv3Po8njlau6/mnp0/jli89jf1TK6AUGOvrzdcOyI851AJ/v+0aDZU9ZiTkQVY3sudbbTnw+F2tLtpGIcShCluH/LVZDgluOZQWB0IIPv/+y3HXuy5s6PXZmV2jIRw5H8fjR+YBAPftO1u2IdpMNGO68XileTkrrBcIeST8eP80Ti0k8cLpJXzyB/srHn9kJgZKgX9/1WgW18uWA6/vqN1yiGJ92FMxPrHOMv3N0+KANB8gxGNxrUKIQxW2RPyYXklX7fK4xFpFVEoffPP2CHaMBMt+v9vYtT4ERdPx/RfOYihgDFr/0UvF9Y2aTrEQz5hxmeNzcQwHPV07J7oWgh4XlKyO3WMh3LJn3Kz/KAeP7fxkvxCH8X4vPC4H/p29FtU4OB3NiwmWwioOrXYreVxO9Plcea7XViDEoQp8N3t8trL1wC2HPn/v7nYL4R+4aDqL26/ehF2jIXz3+TNFxy3EM9BpbuSiqtG6Z+52Czzu8OuXrEfY60JK1ZDJlnaTUEpN63Z6xcj6Wh/uXXHo88m4881b8G+vnMMLpxcrHptWNRyfS1QXB0tyhKcNExxHgh7MsIy+ViHEoQqXb+yDW3Lgs784AqNpbGmWkgokB0GwR/vZlGLLkN8chXrttiG8afsQDp+PQdfzX0e+I9oyFIDLaVgLIz0ab+BwcXj3xaMIM2u0XK+q2VgG8UzWDKQO+GV4e7R1BufDb92KkZAbf/PTwxWPOzYbh6bTquLA01llp6MlA34KGQ65MSMsB3sxGvbik++8AI8cmsV3njtb9rilpII+n9zTrpBCJKcDF6wLIuCWcOl4GBv6vchkdcwVZC3xHdFIyG32U+rVTCXOJeN9uGHXCDYM+PKqzUtxnM0aePfFxhD79T0cjOb43RJuvmwML59ZhqaX3tQ9d3IR/+cXRwHkrNZycLdSu0R3OOgxa4FahRCHGrjj6gm8YWMfvvHkSXMtnsni7Xf/Er88bPQVNBrFCZdSIf/lzVvw/964E5LTYRYSnl1M5h3DLYfhkAeDfrf5uJf545suwFdvN+Zn9VmqzUtxnOXf37rX6F/Zyy4lK1sjASiajsmlZNH3dJ3iA/c8i6ePz+OWPeOYYF1YyzHKxaHFmUqckZAbc7FMkdXdTIQ41IDDQbBnUz/OLiVN19LRmRhOzCfwBZaeuZQsXx3dy7zn0vW445oJADDF4UyBOHA/eSTgxiBrtter1dGl6PNVEYfZOPyyE3s29WPbcAAXj4VbeXm2ZetwrsNBIUtJBZmsjj+66QLcfeulVdPLhwJuOB2kbZYDT6VdbGE6qxCHGtkw4ENazblETrOCmOdOLuLw+VjFvkoCgzGWQXN2MZW3/tKZJewcCRrN4phbqdctByvcrVQu5nB8Lo4tkQAIIfjpH7wZH3v7tlZenm3hxaalUtH55zhS4ybE6SCIBNwtb53B4ZulVgalhTjUyHh//o3t1EIChBgBqn9+5rRhOfRoF9Fa8bicWBfy5FkOmayG508t4uqtxvhPEXMops9rvCbLJcSBUorjs3Gzc6jkdIi4F6PfL6Pf5yppOfC5IbWKA2DEHfztijmwzVIrax1Eak2NbOg3XCKTS0ns2dSP0wtJrA97cdWWQfzzs6dBafnWGYIcGwa8OGvxAb98ZhlpVcc1TBz4h7XXs5WsBD0SCAFWClwKWU3Hn/xwP86tpHHF5u6frV0PWyMBHC9lOZRpz12Ju955QekRlS2Ab5ZmY62zHIQ41Mh4f34w9dRCApsGffj0+y7CgN+Fbz51qqcK3OplQ78Pz5xYwCOHZvDwwVmEvC44CHDlFuPm9lt7N2DjgE9YYRYcDoKQp3js6hd/eRz37ZvE71+/He+/YkOZs3ubLRE/HjlU3HqkHsuBv0fbQcR0KwnLwXZ4ZSeGAm7TrXR6IYkbL1qHgFvCn757F/7opgsg9UDPpLWyYcCHH748hf/+wGuYXEqBEOCSsbDpVx8KuPHrl6xv81Xajz6fK8+tpOkU//LcGbxlRwSfuGFHG6/M3myJBHDfvkmsFAydmotl4JOdZh8mu+OWnOj3uUTMwa5wl0g0rWIxoWCTpbWvS/h6a2LDgA+UApNLKbxlRwSUAtdsG2r3ZdmesNeVl630xNE5TK+kcdsbhcVQia0RHpTOjzvMxTOrshrswEjIIywHu7Kh34eXzi6ZrXu7fQ50M9jAAvujYQ/uuWMvnjw2jzdO1DAqrscJe/PdSt97/iwG/DJ+7cKRNl6V/dnCAvXHZuO4fGNujspcLLOqeIMdGA55MNfCmIOwHFbBhgEvzi2nzeyHTVUKZwTFbIkE4HQQ3HHNBFxOB67bOdwxpn076fPJpjikFA2/ODiD91223mxPIijNpgEfZMmBw+djeetzsc6zHEZDHkwtp6of2CDEO2sVbOj3QdMpnjmxAAB5biVBbUSCbvziE2/FnW/e0u5L6SjCXgnLLFvp5HwCqkZ7YqLgWpGcDuwcCeLg+Wjeeie6lbaPBDAfVyoOzWokQhxWAa/w/eFLUxgJuVveurdb2Dzk74mBR42kz2tYDrpOzbG1m4eE5VoLF44GcXA6ZnY3yGQ1LCfVjnMr8WzIwzOxKkc2BiEOq2DXaAibh/x407Yh3H3rpe2+HEEPEfa6oFMgrmRxkk0mrNYPSGBw4WgIiwnF7OG1EDcssE6zHC5YZ4jDkfOtEQex9V0F/X4Zj/6369p9GYIehE8pW0mqODmfxLqQR8RqaoS34359OorJpSQyqg6g88QhEnSjz+fC4ZnqY4sbgXh3CQQdQJ+lv9LJ+bhwKa2CC9cZ4vDtZ07jFwdnMcSaO3aaOBBCsHMkiMMF8ZNmIdxKAkEHELa07T45n8Dm/7+9e4+x4izjOP79ld1aoNulwILlooihFyRtsaS2WjHRNAFjUpJqZDWC9Y9aL1H/kxoTjdE/SrRpWkyQWEyrpqKpF9DahjbaWO8gBAobKDTELqWlKF1usS3J4x/zHjxydoHdnXNmZs/vk0zO2ffMvnneJ7PnmZmdeafHxeFCdU/oZOak8TzZd5iOi8SRip5WArjqzV3sffnEOR88lhcXB7MKmJRm/D3wr5McPfUGc33kMCy1h/l8c9kCrpvVzUXizLNDquTK6V2ceO00Lw40/34Hn1Yyq4DaXu4vtx0EfKXScC1bOJNLOsfxkUWzWXxlDzv6Byp5j0jtn9J7Xjp2Zgr8ZnFxMKuAyRMvpvfG2WceVeviMDwfunbGmTm7Zkwaz4wmf7E2y7zpXXRcJF4aaP69Di4OZhWxask1bN59mKOnXj9zz421l+7xnez+xpKWHPW4OJhVRPeETtZ8bCE7+l+lc1z1TolYPlp1Oqw0W5ikJZL2SNonaVXR8ZiV0U1zp3Dn4rcXHYa1gVIUB0njgO8CS4H5QK+k+cVGZWbWvkpRHIAbgX0R8XxEvA78BLit4JjMzNpWWYrDTOCFup/7U9v/kXSnpC2StrzySuOj/8zMLB9lKQ6DTdHZcAtgRKyLiEURsainp6cFYZmZtaeyFId+oP55h7OAFwuKxcys7ZWlOPwdmCfpbZIuBpYDGwuOycysbZXiPoeIOC3p88ATwDhgfUTsKjgsM7O2VYriABARjwGPFR2HmZmBWjH1azNIOg7sGWU33cBADuE0q7+pwJGc+ir7WPPuryavHFZhvGXe/qD8ORzr+avF89aIOP8VPRFRyQXYkkMf63KOKe/+Rj3GCo011/7yzmEVxlvm7a8KORzr+RtuPGX5h3RRNpW8vzyVfaxlzh1UY7zOYbn6y1tL46vyaaUtEbGo6DiaqR3G2GzO4cg5d6NTtvwNN54qHzmsKzqAFmiHMTabczhyzt3olC1/w4qnskcOZmbWPFU+cjAzsyZxcWghSbMl/U5Sn6Rdkr6Y2idL2izpufR6eWqfktY/IWlNXT9dkrbXLUck3VfUuFoprxymz3ol7ZS0Q9LjkqYWMaZWyTl3H0152yVpdRHjabUR5O9WSVvTNrZV0vvr+rohte+TdL+kweaXK1ael1p5Oe+lZFcA70zvu4C9ZM+vWA2sSu2rgHvS+4nALcBdwJpz9LsVWFz0+KqUQ7IbQA8DU9PPq4GvFz2+iuRuCvBPoCf9/BDwgaLHV8L8LQRmpPcLgIN1ff0NuJls0tHfAkuLHt/Zi48cWigiDkXEP9L740Af2dTkt5H9gZFel6V1TkbEM8B/hupT0jxgGvCHJoZeGjnmUGmZmPbaLmOMT/aYY+7mAnsjojZv/pPA7U0Ov3AjyN+2iKhtU7uASyS9SdIVwGUR8efIKsXDtd8pExeHgkiaQ7Zn8VdgekQcgmwDJPuyv1C9wIa0kbWV0eQwIt4APgPsJCsK84EHmxhuqYxy+9sHXC1pjqQOsi+22ef5nTFlBPm7HdgWEa+RFZT+us8GfX5N0VwcCiDpUuBR4EsRcWyU3S0HHhl9VNUy2hxK6iQrDguBGcAO4O5cgyyp0eYuIo6S5W4D2RHrAeB0njGW2XDzJ+kdwD3Ap2tNg6xWup07F4cWS19KjwI/joifp+aX06Em6fXwBfZ1HdAREVubEmxJ5ZTD6wEiYn866vop8O4mhVwaeW1/EbEpIt4VETeTzXH2XLNiLpPh5k/SLOAXwIqI2J+a+8meWVNTyufXuDi0UDq3/SDQFxH31n20EViZ3q8EfnWBXfbSZkcNOebwIDBfUm0CslvJziGPWXluf5KmpdfLgc8C38832vIZbv4kTQJ+A9wdEX+srZxOPR2XdFPqcwUX/jffOkX/R7ydFrIrP4LsFMb2tHyQ7OqPp8j2vp4CJtf9zgHg38AJsj2O+XWfPQ9cXfS4qppDsqtw+lJfm4ApRY+vQrl7BNidluVFj62M+QO+CpysW3c7MC19tgh4FtgPrCHdkFymxXdIm5lZA59WMjOzBi4OZmbWwMXBzMwauDiYmVkDFwczM2vg4mDWBJLukrRiGOvPkfRsM2MyG46OogMwG2skdUTE2qLjMBsNFwezQaSJ1R4nm1htIdn0zCuAa4B7gUuBI8AnI+KQpN8DfwLeA2yU1AWciIhvS7oeWAtMILvp6VMRcVTSDcB64BTwTOtGZ3Z+Pq1kNrSrgHURcS1wDPgc8ADw4YiofbF/q279SRHxvoj4zln9PAx8OfWzE/haav8B8IXI5icyKxUfOZgN7YX435w4PwK+QvbQls3pwV3jgEN16284uwNJ3WRF4+nU9BDws0HafwgszX8IZiPj4mA2tLPnljkO7DrHnv7JYfStQfo3Kw2fVjIb2lsk1QpBL/AXoKfWJqkzzdU/pIgYAI5Kem9q+gTwdES8CgxIuiW1fzz/8M1GzkcOZkPrA1ZK+h7ZjJsPAE8A96fTQh3AfWSPgDyXlcBaSRPIZtK9I7XfAayXdCr1a1YanpXVbBDpaqVfR8SCgkMxK4RPK5mZWQMfOZiZWQMfOZiZWQMXBzMza+DiYGZmDVwczMysgYuDmZk1cHEwM7MG/wW9K1CX1HjHAQAAAABJRU5ErkJggg==\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": [
+    "# Étude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Malgré que les incidences de l'épidémie ne forme pas un pic précis, nous allons essayer de définir la période de référence entre deux minima de l'incidence, soit le 15 octobre de l'année $N$ au 15 octobre de l'année $N+1$\n",
+    "\n",
+    "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 15 octobre de chaque année, nous utilisons le\n",
+    "premier jour de la semaine qui contient le 15 octobre.\n",
+    "\n",
+    "Encore un petit détail: les données commencent à la fin de l'année 1990, ce qui rend la première année considérée incomplète. Nous commençons donc l'analyse au 15 octobre 1991."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_october_week = [pd.Period(pd.Timestamp(y, 10, 15), '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 le 15 octobre, 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": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "for week1, week2 in zip(first_october_week[:-1],\n",
+    "                        first_october_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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6833dc748>"
+      ]
+     },
+     "execution_count": 15,
+     "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": "markdown",
+   "metadata": {},
+   "source": [
+    "Une liste triée permet de plus facilement répérer les baleurs les plus élevées (à la fin) :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    532097\n",
+       "2018    539810\n",
+       "2017    552448\n",
+       "1996    584922\n",
+       "2019    586670\n",
+       "2001    606610\n",
+       "2015    612835\n",
+       "2006    612861\n",
+       "2000    627919\n",
+       "2005    629150\n",
+       "2012    639641\n",
+       "1993    640388\n",
+       "2011    641667\n",
+       "1995    649640\n",
+       "1994    663465\n",
+       "1997    666802\n",
+       "2014    672296\n",
+       "1998    693579\n",
+       "2013    699031\n",
+       "1999    734329\n",
+       "2007    741092\n",
+       "2003    747377\n",
+       "2008    751881\n",
+       "2016    768084\n",
+       "2004    784636\n",
+       "2010    819324\n",
+       "1992    834024\n",
+       "2009    839723\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, un histogramme montre que l'épidémie touche généralement autour de 10 % de la population française chaque année."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6832e0198>"
+      ]
+     },
+     "execution_count": 18,
+     "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)"
+   ]
   }
  ],
  "metadata": {