diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 61a5f90ea301f5e5f044155d89968aa07241d902..232974227e0e9a84282cf6b0b4b9a69a718cbf7d 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -7,9 +7,16 @@
     "# Analyse de l'incidence de la varicelle"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Obtention et pré-traitement des données"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,17 +51,1430 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Nous affichons les données"
+    "Nous affichons un aperçu des données, la première ligne étant exclue car il s'agit d'un commentaire."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "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",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>803</td>\n",
+       "      <td>83</td>\n",
+       "      <td>1523</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1918</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3161</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202014</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3879</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>5531</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7326</td>\n",
+       "      <td>5236</td>\n",
+       "      <td>9416</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202012</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8123</td>\n",
+       "      <td>5790</td>\n",
+       "      <td>10456</td>\n",
+       "      <td>12</td>\n",
+       "      <td>8</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>202011</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10198</td>\n",
+       "      <td>7568</td>\n",
+       "      <td>12828</td>\n",
+       "      <td>15</td>\n",
+       "      <td>11</td>\n",
+       "      <td>19</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>202010</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9011</td>\n",
+       "      <td>6691</td>\n",
+       "      <td>11331</td>\n",
+       "      <td>14</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>202009</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13631</td>\n",
+       "      <td>10544</td>\n",
+       "      <td>16718</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>8</th>\n",
+       "      <td>202008</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10424</td>\n",
+       "      <td>7708</td>\n",
+       "      <td>13140</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>202007</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8959</td>\n",
+       "      <td>6574</td>\n",
+       "      <td>11344</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>10</th>\n",
+       "      <td>202006</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9264</td>\n",
+       "      <td>6925</td>\n",
+       "      <td>11603</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>11</th>\n",
+       "      <td>202005</td>\n",
+       "      <td>7</td>\n",
+       "      <td>8505</td>\n",
+       "      <td>6314</td>\n",
+       "      <td>10696</td>\n",
+       "      <td>13</td>\n",
+       "      <td>10</td>\n",
+       "      <td>16</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>202004</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7991</td>\n",
+       "      <td>5831</td>\n",
+       "      <td>10151</td>\n",
+       "      <td>12</td>\n",
+       "      <td>9</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>202003</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5968</td>\n",
+       "      <td>4100</td>\n",
+       "      <td>7836</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>202002</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6534</td>\n",
+       "      <td>4530</td>\n",
+       "      <td>8538</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>202001</td>\n",
+       "      <td>7</td>\n",
+       "      <td>9835</td>\n",
+       "      <td>7019</td>\n",
+       "      <td>12651</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>16</th>\n",
+       "      <td>201952</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7941</td>\n",
+       "      <td>5246</td>\n",
+       "      <td>10636</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>17</th>\n",
+       "      <td>201951</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5823</td>\n",
+       "      <td>3675</td>\n",
+       "      <td>7971</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>201950</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6424</td>\n",
+       "      <td>4276</td>\n",
+       "      <td>8572</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>201949</td>\n",
+       "      <td>7</td>\n",
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+       "      <td>8702</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>201948</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5542</td>\n",
+       "      <td>3383</td>\n",
+       "      <td>7701</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>11</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>201947</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7536</td>\n",
+       "      <td>5058</td>\n",
+       "      <td>10014</td>\n",
+       "      <td>11</td>\n",
+       "      <td>7</td>\n",
+       "      <td>15</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>201946</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2638</td>\n",
+       "      <td>1316</td>\n",
+       "      <td>3960</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>201945</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4492</td>\n",
+       "      <td>2615</td>\n",
+       "      <td>6369</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>24</th>\n",
+       "      <td>201944</td>\n",
+       "      <td>7</td>\n",
+       "      <td>5728</td>\n",
+       "      <td>3627</td>\n",
+       "      <td>7829</td>\n",
+       "      <td>9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>12</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>201943</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4834</td>\n",
+       "      <td>2751</td>\n",
+       "      <td>6917</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>26</th>\n",
+       "      <td>201942</td>\n",
+       "      <td>7</td>\n",
+       "      <td>6279</td>\n",
+       "      <td>3989</td>\n",
+       "      <td>8569</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7</td>\n",
+       "      <td>13</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>201941</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4130</td>\n",
+       "      <td>2030</td>\n",
+       "      <td>6230</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>28</th>\n",
+       "      <td>201940</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4211</td>\n",
+       "      <td>2218</td>\n",
+       "      <td>6204</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>201939</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3137</td>\n",
+       "      <td>1310</td>\n",
+       "      <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",
+       "      <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>1503</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>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",
+       "      <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>1509</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>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    803       83    1523       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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data = pd.read_csv(data_file,skiprows=1)\n",
+    "raw_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On recherche de potentielles incohérences sur le fichier de données. Il y a-t-il des données manquantes ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Parmis les semaines renseignées toutes contiennent des données. On cherche dorénavant à vérifier que toutes les semaines sont présentes. Pour cela on commence par convertir les numéros de semaine à l'aide du script qui suit, pour que Panda puisse les interpréter correctement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=raw_data\n",
+    "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": [
+    "On effectue quelques modifications pour des raisons pratiques. Premièrement, nous définissons les périodes d'observation\n",
+    "comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique.Deuxièmement, nous trions les points par période, dans le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_data = pd.read_csv(data_file, skiprows=1)"
+    "sorted_data = data.set_index('period').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il est maintenant possible de vérifier que toutes les semaines sont présentes. Pour cela on utilise un script permettant d'afficher les semaines qui seraient séparées d'un intervalle de temps non nul (fixé ici à 1 seconde)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "Aucune anomalie n'a été détecté dans le fichier du jeu de donnée, nous pouvons donc commencer l'analyse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ba573c7f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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NwFwAox2XnAfgHiFEmxBiMYAFAI4nolEABgohponga74TwPnKNXeEx38CcAbVakbWCEnOoTv6sOfMVtuj1IsjyyIi6KUopPWF1DNgXY2fwedT4AiBVayUcdWrFefw3tZATDZ75RZrnZcXbcCHf/Y86/Hd1ejpHE69kUnnEIp7jgEwPSy6gojeJKLfEtGQsGw0gOXKZSvCstHhsVmuXSOEKAHYAmBYlr7VG+Y02YPW6W6FuYDVS6xU9hQrPTp7NdZsbQMQcA66WMmvH1y9es8XnnPg62Zd82qV7McnFfe//uUtzF29Fcs27HTUqi/MkOWNCm/iQET9AfwZwDeEEFsRiIgOBDARwGoAP5FVmcuFo9x1jdmHS4loJhHNXLeua4OZJXftuSkrADz1zhq8unRjd3cjFb4f+pd//xoenfMegCTn0AkfuLrrqPjlmy/9wYNzsHZrK3uObaVGPLwaIdYGqRvqzthlaoKkRobXGyCiZgSE4X+EEPcDgBBijRCiLISoAPgNgOPD6isAjFUuHwNgVVg+hinXriGiJgCDACRWHCHELUKISUKISSNGjPB7wjqhO+ZNT5ysX7h9Ji64eVrm67paRFbN3W59YbFOHDwb4Z6tUzoHj8U5i7XSys278N2/zPa+f61MWaPEQ4460qps2qINmLV8c03umx1hxrxcrORGKPu/DcBcIcRPlfJRSrVPAJCz7UEAU0ILpP0RKJ5nCCFWA9hGRCeEbX4OwAPKNZeExxcCeEr0MAF7TxArdeeIvPPeVnzq19OwfGN92f16OZpV0+7mnR2YtSxeoFI9pD2jt2aFM0+FcW/9OjvKGXQwNbNWinbk9iod4cmr738L5//yxdrcNwUvLVyPD/74qei3D4fTCPDhHE4CcDGA0w2z1R+HZqlvAjgNwDcBQAgxB8B9AN4G8CiAy4UQUrv0FQC3IlBSLwTwSFh+G4BhRLQAwJUArqrJ02VAqVzBI2+ttk6InmDK2p0bmXtfWY4Zizfiiblr6nqf+o1rde1mcYJz1auXh3RUhyEFtRrJmomVwr9usVLX+xb8+1/nYvnGXdFv2c9u6EqPQqqHtBDiBfCbkIcd10wFMJUpnwngCKa8FcBFaX2pJ257YTH+85F38PPPHIOPHb1PskIifEbX9KunQC4+tbbgMBe1eo1rte2qC6O/zoETK9V3wnCbe9ctsxgD1spw0KedUgZ/FABYvnEnBrY0Y1Df5mq7FUWrjX5Tfeb67obcQzrEll0dAIAl63ew5xN+DnXvEd+L7kKW9WHmko1YuG57/TpTBaofufjBvZ3gas05+NTxqLTYMrfTUCvCFnMO9jpmOJU0fPDHT+OMnz5bfaeQ3KDkYqUAeWylEIP6BDsPSSRMNHrIbp8PW+LCXwUK6iXXnmut09XhM7py7NRbEQX3rrtC2s/gr1sRK6Tt/apmQV6/va3qPgF2k9+snuR7GnLOIUSvMJ6OaqHw6OzVGHfVQ1i6IbnjqodsPO3DOGbfwQC6J9ZNrGyN+1gTtjvh51CfD7La96UuzL7WYmy1ThGH6hTSPW1ti0xEe5os3+pJ3sX96GHIiUOIKPSxMk9kopfZK7cmv+06TJy0+D+jBvUBAJw0fnjtb+4JdcGpljgs27ATf351BXuup3EO6nXeCmlO51ATwZajTh0DCtSKyMROcPYGfSyzag1zsyWft9FjK+VipRCuZOoCIrGjrcW0WbB2O3YpyejbSuWIg+Egd65d7e/w5Nw1+M3ziwHoz13tgnf+TS86s5vVA9WOWBY/h7p5SHusl9xC1pWzZO3WVry2bDMmH7G3vVIky3c01B1csaW80RXSOXEIIRcBdaLIXUxrRwVrt+lyzVqIP840FGntJTe/LedqV4sLbnluUXTs2kmv2rwLw/v3Tm1PJQy1iMrqg2rfV7mKkN2aziH8Xe9XxsnHazWWPpuAKbe8jEXrd2D+1A9bvZujNKW16VbNYOO6Gl3nkBOHEHIecMnUv/3HN5L169CHNEsNuTvsiXN2V3sZJ177FD55rCsmowc64wRXh3FRDRR8CYxarxZd8hG1DO6TzZQzywbd57GXeThHFmK5khVZ+sXpAjl8/ncz8JEjR+GiSWPZ8+Y9d4T5HBpdrJTrHELE6Rb90NmFiEuPmc45hMShi/deNlGSOgatHYF47Mm5azO1PX2xHiWlmmdLE7d3lCv439dXZm7XRFrPXBZdneE0fdQJvA686xc3H98KUyza2lGOuElf1cmG7W045fpnvOo+PW8d/vlPb1rPm6LkbSFxaHTOIScOIVwZtfj6ncNfmMWqp4qVVGhiJWUUerJt+CduehFPz8sWqFFahmmo4tF8IpH6tmFi7bY4eF49I8HWTCFtCS9y0a+m4dgfPZ56/evLNmHcVQ/hrRVbMiVvSu8YX9zoOoecOISIxEqeO5fOLIK2a9tSiIOIOIcuhuWG6mPUSp5cD9oye+XWzNc0MRPBdyde64Xatl+5a9rSuH3WK9veZhajBlvNjnKlKh8Dc/6/peR3cInQZOiWZ99dm/Bq9sE5NzzHltvu2BMDXXYlcuIQQsoXfdnazkybne18IpO0uRhN1i6es7ZFkRM3dfZ76szltRSjsFZrqdZK9n50zpQ1fVJmHfdaRBz93l9mY9K/PxGJFAH3c67avCusY4f39+drVqxUnLdmW6Z79jh/jC5GThxCyG9FWxRcE7UT35b6Mel9cDcq+9idOxpN2aocxyKv7H2ztdmd4IiD73paayc4n/tk1Tm0dfivfLZ38vDs1UFbnsEJv37PrNQ6tdb5+bwzW1u5ziEHgOw6h4feWl31vWwTNm0qlrtLrKTA9r1UOtG3nvgNcmGqvcVKynHk+9CJvvhMydb2MraHitSoH46bZknDWevXU+0GQL3Md4PUGb2BzVrp+fnrIgfZPRk5cQiR1VqpM7AtMmkTPtI5dHWiHA9iFhGHKrpWTba1eoMlDlWIMrJey8FHIf2dP7+JI655zLvN1gycQxrueGlJpvqdfcdEVDVx4IiFrSUb53DxbTPwtbtf97q/L7a1dmBNhux8XYGGJw7vvLcV47/7MFZuCuShqjihXoTCutimiZXC77mrF1BhOxbJ42pk6xWmne6AOv485+DZDtNmp/QhDOuwva2E+2byIUh8kIVzSOv6Tx9/N9JhqO/vwTdWJbgZs44Jb2tBz+E0F/gSp0iwccNdaK109g3P4f3/8WSX3c8HDU8c7p6+DKWKwGNh3uCuCGpnJw7u6zqzO68VhIVS1I5z6Bm8A5saswfJv9oseisVru5mMQW1vRM+DmxQd/bKLfja3a/ju/e/5d1eFngTB+M5uXwRtv6kiaT+85G5NcuMuHpLz+IagJw4JBxz6hi/LIJdrOS+LlL61rg/LsxdvRWvLt2k9IFfyLm+PfF2fbPGSdQjWBsXAcKbc1AquuItcXhlSSJ1Ovt0P3n8Xc/edA+kl/HqLbsS52qikPZ8G0nOwf/rSaOfv352Ea6osXipJ6HhiYNEOTJlrT91sM1PX51D2krTXqrg+sfewbirHsKvnl3orLti005sbeVzWADAh3/2vPb7jRVKTmWVcwgfSnXkm7vaz79Ae+4ewhVVp3Owu7z5PtZFYS4MF1Zs2ok/TF+WWq9WeROyMEw+dZ27bcfnp+u4/Ppj7v5LTIgaq5GFx0249vYUNDxxiGLMd6HIwDdPtQm5C0qbs79/eSl++XRAFG57YbGz7snXPY2P/fwFd4MKnlE8jXep9u2dGL5Kz6ANGng/B09rJaaaz7XPzONDj5hXfuu+ZKwv337Y2nS2Yynf2sroE4y/HFe3tbWEBWt5nwNfVKuQ5qIBZ1VIu9rfk5BKHIhoLBE9TURziWgOEX09LB9KRI8T0fzw7xDlmquJaAERzSOic5Ty44jorfDcjRRu04moNxHdG5ZPJ6JxtX9Uy/NBipWC311hCVS1QtpT6avanfvwQUs3ZJObXv/YOwCA7yjxariP1ZcJyxIW24lOvjr1ci6yqKv5kuItrNbzzaC3YO12/MPvXuH7ZVzMcTVZUa9p7vv9rAgNQNYZ0Y5r7edgLvCX3fUq05ZNzJt+k6xpTXcn+HAOJQDfEkIcCuAEAJcT0WEArgLwpBBiAoAnw98Iz00BcDiAyQBuIqJi2NbNAC4FMCH8Nzks/yKATUKI8QBuAHBdDZ4tEyrMrrxeIia7r0DadX5KX3Vdq8cjSK5kzqo47AG3y/K2POmB31cvjjg4xl21HKpm4WWtaCzt9e1V5Cua12Xvhtf9a4ECEWav3IL3TX1CK/edMwvW+uUoNxXSizLk0fYTKzUw5yCEWC2EeC083gZgLoDRAM4DcEdY7Q4A54fH5wG4RwjRJoRYDGABgOOJaBSAgUKIaSJY5e40rpFt/QnAGVSvldlAHDBO/1tP2MNRpHEOvsQhfq31zKylGS51YuC0SK91ECztNSA9xwSgP0Pv5uSn4Z0mVHueAGnWKFniID3hGfm2VlxwlncSiZViuRILIn6B9/3qv/z7JAfAwWdnbxUreWxaOhqcc4gQinuOATAdwEghxGogICAA9gqrjQawXLlsRVg2Ojw2y7VrhBAlAFsADGPufykRzSSimevWZYuyaX0m43emD6HKj8+ukE65ztPPoahGAKkjiVV3Vp0RvXbWzyHtGasZg5Zmv925xICWODWK+gxSJv3Vu19zXu8mDpm64oV6mQybz2EbetZU2BMPzPIPv+7DYXQmfEZOHAAQUX8AfwbwDSGEywyFN3+2l7uu0QuEuEUIMUkIMWnEiBFpXfaCOUc1M8SUa6v9aKuVcUbhM1LqqTLperJfaeEMbN+/Wd5ZD+m0b9h3IVKb6c2ka3U7b7nrpfkVOC2LqlzIu0Ws5FmXwM8Pnzf17ho/kRLg5+xn/R49PvC0MPu7M7yIAxE1IyAM/yOEuD8sXhOKihD+lbzuCgBqyqUxAFaF5WOYcu0aImoCMAhA0uC7DjClV1m+A9di3lYqY9xVD+GmZxYkzlmv8tQ5pEETK9WRdVCfnxOZ+4q06m0pVs0uldU5OF6Q+qxcvbQeOC2Lqt6EdK7NH513eHU3RjpB4+ZlPYxBss4ttVt+nEMD6xxC2f9tAOYKIX6qnHoQwCXh8SUAHlDKp4QWSPsjUDzPCEVP24johLDNzxnXyLYuBPCUqKPZ0Kd/PQ03PjkfAPPRZrita+JtD838fqPkX45vUR3n4BuV1ZLCNxN8hl/dWHEK1WrCL1fz2mtF/9Rb96ox59AZqDvYbOPj4kY8UEXQQJMo+HKPQDCfNu20+9xUA5/hUqtMPf/IuD8enMNeA/30Wb54d8027LKE9O9q+CwjJwG4GMDpRDQr/PcRANcCOIuI5gM4K/wNIcQcAPcBeBvAowAuF0LIp/0KgFsRKKkXAngkLL8NwDAiWgDgSoSWT/WAEALTF2/ETy0epllERT5yYn6H5L7GBim/rsXis2T9jihkiIllG3Zi/6sfxv+lRJ5UF4JrH3kncd62Zif0PJ3UOUj8miHEAKpKDJM1tlIafSIC/v2vb2PcVQ+x5311DmkJoWqKKqw0fKty3Fya/L660CzpddR2mxWFnY8Pw8njh2fvlANn3/Acm0K4O9CUVkEI8QLsc/8MyzVTAUxlymcCOIIpbwVwUVpfaoFEIDBT55CyT7ritPH4xdOBqMi2g28rlTE7zG7FmaRbQ3anzH65O0+bsuo3Ztu5nfpfz1ivnxV6QduIh4T6HC8t3JDSK1c79WXNVZHPN888CDc8kR56IgtR96m3fns7bg0dEssVkSA+vjoHW6Io337UGyJxwIObl1mezRc+c0sdX9W/xYew1MOUdQYTQqU70HAe0qb0w5SLp82lQ0cNjNtSxSrlSsSGfvf+2fj87dKhyT8vQNpklBMx3VkuPl+NyEWKxPr3Ttk7VLEABOX6iVoSh3FXPYTfvbhYK1PX4UtO3A+H7D2AvVbLiZ1y3oQmVkoZGC7Zk69+YGd70ivZep3j3Lptbd6WNp15Oza9E7dp2uURTDAzMna+SeEcfOalq849M5bhwptfytYBBF7cWd5zvdBwxCFNJpo2HYoF4HvnHgpAnxgHf/9RTP5ZkKN25tKY8mcJ7pm26EdipZQ+quer8XPY3hbIfdOIQ9rKKDyZAAAgAElEQVTHw3kZA8nnrJVYSeKH//e29ruf8hwE8lLS+7y3ZRt24tDvP4rFhmNV2jOwxMFRXx3nWsqjr39snvO8fH9Z3ol/nvPkAO+qw4LoxTkoVZoUGaSPWMmltL7q/rcwUwlamQU72rpf79B4xMF4ly75N2tqRxTJS1WFVbkiIhM7tQ1erMRPqHRxkZQBu+t1Ng69TATDOYJp96mVk5VKHJSHm7ZwQyabdhs0j2JXYLeUxzGJ2v/OWoldHWXc/5qeVyFtVLgdsmtjoHMOtRMrLUzxAWiqwrLBvKWNDrOcQ3uKzqEKHsbnCqvOgRlA18bGBi6e0+6AxiMOqedTLIGIoontE12V27nbFdLue3d46hyueXBOfP9OWPKkcR2+CvREu0an3lMyYKlD8JnfvIyv3zMLb63YAheymIkS+dnSs2IlT9FPWqjy1o4KLrtrJv7yuhJyw1FfnRdZFNKdNfiTC2U9Irhy3Fs9RCl+OocYms6Bmb+bDWsqH+7i2B89nlrHRE8I6Nd4xMGYLC4nOA7FAqFQ0HNAuJBld23Ohy07O/C+qU/g9WUBayrjxLja3GTsUrokBokFvo9+1Z/fdJ7vrCxaF7P5EUxWIW3+VgrUdycNFmxo7SjjsTlr8M174+iqvtZKXRk9OBIrVXGt7OY7723Dydc9hf99XecAOc5hZx10DtxwuTjrphRrJZOb8PGFqAauWFtdhcYjDsbvpELa/bKJkgmCEvewLBpxme06vfyVJRuxblsbfvFUsNhwqRhNfOjHTxv97T7y4JthS03Awl3R2SCk6rgSkSdx4NqJj19csB5/mLE0qItsIg9O5+BegpXxMaqdddhIj6t4pI1DU8rAf+zofZh76nfduKMdKzbtwjc8zDM/b4lK2xlw37O5oKt1VM6BFyult18L9ISAfo1HHEydg4Nz4LJGFQuEopHdyzVBsiik/zaHF0fI6rFC2n6/bYapbjXraq3IiS9nrIaq4MaylgTObGnFpjhcuSZ+Yq6VRL1cEfjsrdOxZmscblq9lgu9oULqdFQ4OQeluvnuLzpuDGxQ2/zjlz/g7BOH5ia3Qpqd957vvKsYIG4OukQ2KkHkOAzzmesl/smSsa5eaDziYMxecxev/jLD/QKB806sc4gXC62NDKamKh56a7X221wTpelhpg+LJU7uBmo1LX1FIKriuy6cg3Ic6BziBl9ftjl5QYgbPn209lu+53tfWa5XJMIby+N2hvd3e812xlrJrMh5cnMoEPDV08ez516Yv54tby64xUou2pDGSdVy6XOFMOfmujkv1fehcw5Me8bvesXdy3UO3QFjzE0KrXMOyTdfUK2VwroJNlU59ooomAJhEKEsxIHC63/x1PwoPaOpVKsXfPs5rJ97Me1s/BqdI9DFSlb/BKKEyFEaBGzaaVifCBE5uAHA371/X2d/WplgcK6xUkNKmNVcxMFcoL919sFsvZcWJonDuUeOwuH7DGRqB3hm3lotn0fi3imvrFbimOYieetrJMxvfrtiNprm59BlYqVc59D1SFD+srmwx7+5RalYiBeXimWxTjOH9bfo0H9Xy2qu2LQL//W3d/GlO2bijeWbcfX9b1V1/6zw/XDOOHSv6Ji75GGDo8rcD+XY95k4PYKUA6ft6tIytXG+Cq6xemLuGjwSjoG5YPXtpfuiCCuXYe8T9zj/8YkjI8MLrm//8LtXsGTDTs30M6hrvY1eD37iwi0pGxkCpXiXJ2HGqtq6K75HmrWSea/OKqQrFYHx3304UZ5zDt0A8+NKKqeUc8wLKlA8gaSYR6139f1vam3ypqzZXvyO9jJ2tpcUzsH/+uZiIbpuV0cZn7/9FTyaEhajVvBeKLR6vCivlh3xMmVlKslxNOfFesNCLE2c1sqYo6YN1fTFgWOl2XT/3rpIpZq1inNnoIIfIU0QJ0++2LefP39qvvP8oL7NTt0Wq5DWiIOeEKg5xQnObK4zi3hrRxk72kvspi/XOXQDzJdr7g5uf2lJdMyKlQqEllBGLhWLKjG4e8ZyLS8uyzl49lUSlhmLN+LoH/4ttlbyvB4I2GRZnwgY1Kc5w9V2qIpcG3znd1oO6VpEmZUghy2rKX5Sfw/r1yvaDJiL/x+mL7O2w6HNI3zGP5w4TvstuRGzaTMpkS03hmuhLzInC+TnWz9+r/44cvSg+J4ieW8V+w7ty/TOjjQC9cljRzub4gh12TJGgIdYyfztOce5UCWHfP/RhHVh1MecOHQ91CE/9fqncce0pda6POdA6N0UfJAykYgrBzJr9eL74pWLVRFXFlv3ohGS1BYSY2trNj3Eydfxk1qF7y7y7VVx7ihuaNI4h7QFRNcB6YueawlUh7mpSN5ipTTOjhUrGWP1g4/ruRSkpMps28w7Ycuq5xqiAiMGU4tcj9NUIDx4xUlx3agf/EUnHjgstU0VpCuFNHzimNEoEjm/B97PQT1vjGdTGudgSB48v+VTr3+GNUSwhSjvCRnmGo84KC93yQb37vfY/YYkyopEkXUNxzmY8HGmklCD+gH2DzqL6KDoKZE56gd/q6p9F1gfD6bwj6/GnsJ8Rrn4IbKK5Lbs6sCbioe1i5C4dtpNhUKkkE6TM6uPaL5TgFdIp9HRWP6vl5vxq3TOIW2sgjatnEOUz8HeTnOxwOsOLJfYOCB3D3mUKwIFImdbrCkrwzlcftqBuPMLx2uWZqy1klE2bdEGlDwW8pWbd+E/H56bWk+iB+ijG5E4+NftzcgzCoWYlZcfeVYWMJPdOIO129q847UUCxS1m9VZqzMgssh708xomdO/enah87yryZWbdun9gqeHNPQFrFnlHNJSfiqnRw9uSZzn4iOlvZWisVDvMyhot9mwVrKNhUv5W2R2EL5hRpoSCumgA7bdfFEhcl77FkelckWAyM1Jc/NdfX/S2KFPcxEfOihIPXz/P52IQX2aLX4OyXss3ZguYgWC79YXXfWdupCaz6GRwb2eAhFapFgpZBOzO8H5ylvtX8ayjTsxtF8vrWzu6mRqby2fNLnN/uJ6Xt1zomC5VxohTROZcWez0GZyyNJ1T2r9d7FA3puANB0Kl3c47b1I0Zqs999TjsHAPk0JMWHavTnYOAc52q52TM4l0jmkcQ6+34CWflVHqVJJndOsWEnI5xL4+j2B5/bc97ZF54/ddwgOGtmfFysxM1CGuK8leoDKIeccstYtFiia4JKbzGrOVov33qc56fjz4Z89nygb3r93t+xBCBZloGXWR6GeUzrLtcmVLbBEHA04B5Vg8vcJTCQDnHjgMDQXC5EcOM1UNW28fZ9BRaxzCP62NBdwyN6ByOpLJ++faKe9VNHiGbl6zD1PQQkz4uqZacpq9sNEU0axkgvlioiey0ZsuHJZoloEmaFCyKLLcBGbNGRZJroyhpYNjUccPKZlHJM+mbGrQMqHGrbl2lFmy+eg//6hEl3VhO2jNKFOenLcm8PPn3IHkHPBJgu2EdKHLHb8Jny5EZnFzhx/V7gUsxVp2XXUmME655CmADc4EBNc3Jy011IwFlVVSX/kGMVaKPx70zMLcL9KHCx9LlcEFq5LEtKC4UluQ5MlB6vtkzA5oM6gFIqVXPdTy6UyXM4xVelrPodN0d1ZztUX9XKuy4JU4kBEvyWitUQ0Wyn7ARGtNHJKy3NXE9ECIppHROco5ccR0VvhuRsp3L4RUW8iujcsn05E42r7iDpcYz5mSB+tTkUkQzf0KhYTE9LVJveB2fM56OWLjCQyehv2e9rqLVq/A28zoqdEP1LaTosdBACwyIJt8vo7XwqsxtIeiyPuXv2R3fI00QQBZx82Ev910dH45lkTNGKXZj3lGr/h/XtrCvL4GveTS9FPKvEM17v12/3k2zc8/i7unrE8Ua4bAdivNz2047pp4kHP3bajXrkios2PuUFYsWkntreVovF645qzMeX4fcM+hsShZOccbGJElhNhyh7prONm99MGL87hdgCTmfIbhBATw38PAwARHQZgCoDDw2tuIiIp/7gZwKUAJoT/ZJtfBLBJCDEewA0ArqvyWbzgM+YCwHPvrsPNzyxMeEmPHdontuII3+Daba1mExGWMcoqTjcQtJfeN2lN4buzEJ7t+uKA4f1w5qH2SKASBVOjG8LGOchFoBrOoS9jnsut3y/8y2mJc5qlpNK2FD9deNwY9G4qoqAQu7RYT65nWL+9DfPWbEuUe3MOknmx9Fve29wJ20jiK458xbFYyd67fkZco/g92hqNKvrpthwDUyqLKCGR6ZN08nVP44KbXorGZmBLk0JggzLV+c1UrBcK5BVbCQCmMlZIX/mf1xJlWZxPdwudgxDiOQC+Ga/PA3CPEKJNCLEYwAIAxxPRKAADhRDTRLCq3QngfOWaO8LjPwE4g8hr2lQFH+WxEEIzr9TrkCLnDP5ecPM05z3f26ITjzdW8MHefObDpNC81lfPESwWtZtpxYI7XIEEgWfLbT4esSIzO3HwxZghfaO+pcGcgoEMOjhO5RyUY5kdMBXMc11/4VHRcXzPoKKuqI0vjomD2X/LbTs5NUzCHHPdfMOy37bbTmLMx20oV4QSrSDZ4rw122JLPUWHwomVTPFxkfyisgLu4I3VYnfXOVxBRG+GYif5RkcDUHnUFWHZ6PDYLNeuEUKUAGwBMKwT/XLCl3NQp8rcf9MZp0huWuWi21EWOGTvAYmonz7cgGTjfe2ghRCsGKNaEPndO9hpJ8ttYQFkadqOiR1zzjeik4HHzasLivVS2ocrzy9ct53lHDlwz3XRpLHRsTQKSuccwvoeOikiv02Gq4rJOfhc4zr/z+fowQFdzZQqlUj3ZnMaE4g5vSiacljV5WhmFyvx9WutI+gBtKFq4nAzgAMBTASwGsBPwnJuRpprrVruuiYBIrqUiGYS0cx169Zl67Fs2GPQhcHy9jE+ADImWfY+BIpuNewA4Ee4JPvru7OoVIAr73sjvaInAtl7+r2zmrLG5oXudmvCblttWe2XFBQFpTRltkE+w5xV6fod8xrX/QGw3IumVA9/NFsUxSZc88iHfzfDd8REvjqdg7mDdy26KudgS45TESLiAk2nPhdxUN+3CpsVnFpVbdeVjMmF3UIhzUEIsUYIURZCVAD8BsDx4akVAMYqVccAWBWWj2HKtWuIqAnAIFjEWEKIW4QQk4QQk0aMGFFN1+FaAWKWVzj3nTHn4AdTHhoouikhuvBpUH703uZzVXA3add4LdAWhbSt37LNdJ0Ds5vjbk/6X6Nr7DF3vUSBKNoMNKUEe5J95DO+2a5xn49TdkoxiXKtUi/iHIxFVvbliNG6x7Z3KJeUfkV9SSHyseiWP2+G8nCNS0lRSNsW+pcXbVQ4B11v016yN27jHDhdAqC/AzWk+xH7DEpW9sBuoXPgEOoQJD4BQFoyPQhgSmiBtD8CxfMMIcRqANuI6IRQn/A5AA8o11wSHl8I4ClRR7LptCxSJu767ekeyL4LtDnJKkKEpoJG3zzaKhb1HWQaaj3JfB3pbHJ5a7RJb5FN+r2rhUoUORPYiLvxJJ5ZosmmPVZEHKRYyXpvXuewPcwQePc/nhAFjhTCPZ7RZkl5J60dZdz4ZBwpNbHTj/7adA56PROcQ54NpbKIxKxX3c/nIX916aZoE5aWpEtFgSiT/5I6RjuU/BC+yZhM9ASdQ6qHNBHdDeBUAMOJaAWAawCcSkQTEbzjJQAuAwAhxBwiug/A2wBKAC4XQsiR+goCy6c+AB4J/wHAbQDuIqIFCDiGKbV4MBtcQy6n5R9nLscLC4IEKDKw2cNf+2BUzxXnnoO5IAZu/5RYPHzas5nu2VANnXXJ64Pbp7dpC2tgVUiHf1OVe34qByfn5xc+w7BeoXgn6StPz5LBTr6nn02ZiKPHDE6cl+LESBartK3G9hFRfX1RkgvWgJZmHDiifyjyEs5FyHS8A4DbXliMnz7+bvTb5hBoE7mmzRwbseFQqlQiq6wXF2yw1pMtmiboLm/yQsG9Cbr+wqMwfEDvKO+1zr3Fv3z9kUx0P2nwIA5CiM8wxbc56k8FMJUpnwngCKa8FcBFaf2oFXzWypcWxhNN7rIOU7JimR/NoaMGWs1TgaQ8VIjgI6gmT0HMGnvqHGrOOfi1adU52ExZw+LrH5vnbLcWMWfUhd9qymqKlQr+Gelik9fsnMOBI/pj3PB+ifPme1dFkrsU8ZUkvibnMHHfmOCo+guXXoSUehJqYhyA0xGE/ahCPAjYw3FwOHBE/4QJKpDcgMQiRl1f5+IMmovEhjmRGNDSjGFK+BoboTHfgy96gs6h4WIruRYXzoLWVEYD8eISO8G5X2RS5xCKlbKrHLqEc3AhWPQ9OAfwC8RrS3nOwFvBznIOyUJ39NXsY6IqKP1jY/m3z1khqTAV9mo1GR3Y1c7owX0S/fIO2aGMV7sh20+KgYTyfxJpYiVzLZ2/NvYJUcf92k8eiXOPGqX5aYhQ+Wwu+jLEvqlzcL3HgS3NCUKoPQelGwUAQLHKZCQ9QazUeOEznDLWZB0uhpH50aS9SJNzkBYUrlAOqzbr0UQlaqlz+OQxo9lyJwF1tDlxbLw7JUv4jO/+hU9R6vstmOPSViqz/Ynl5clzbZYdoVPkqPg5pFtUJXf36QivsQnE5L2RbFvjHDy4Fm6es/UYzsFU/No4B9vCq7prcM9qjtnz8+P81qp49kMHjcCAlmbtOW1GDfIbNnNiuL6NwX2bsa2tZA3HTbCPsdpsFh2KCk4st3ZrKx6d3TnP6yzIiYMK5j2apnpqvXgyuu9p6hxkWI6EzkGZVjZ2Pwrd7L3Tttc7ZNQArzY0MIv+mWEOaLVc9QuQsBE8wH+n9Mun43hP7aUKDv7eo/jPR5Ieqi6rGC7ZTvJ6U+egWOI4rhvQuylVacwhjXOQc4NrWw0L7uXFncHajgjaIHYYFj5WnYOl8fF79Q/vzVdw9ZtzWmti0nqaC6sUDZvmwFq6UKM/MtrtDstcISKo1sI65xAfZxErPXB5MmmSiim/eRlf/v1rTnFXLdF4xCFlV2yCIw4FY+VJ5xz0lylEMknJeRP30SaVbccSRYStAdt53H5DM9Xv16vILvpnH753oi7nLLc5JVm8D1ROTubTcOkCuHejcw6qaEAwpQEKnpxDr6aCYtXkD1nXJA7//emJ2j05IvKpSWPxd+8P4gZFllKORSnmHHzFg/HvVM4hajvZ1hWnjccBw/tbzwNubktdFGU1dYGWi32HMfHkNyxbrnh8t1E/bP2EIVZivNQBPnyODaplE9e3ZWFysq7K9dB4xCHjuMqPU0U8yYIPbNE6e4A8wMY5kLaDLRrWER2WrZf8GM3Ttg89i+wybbEYPaQPG9nVlg/AnMQuvyyzn+ceNYqt16LogKqljz47r3VGYhY1tlKa3ipeILOMfXi9QZZOOEBGEkX4Nyk2IiIl/Wa6SMu1mb3sQwdg5vfOVOrqzmBtnmIlbt71b2lK93Nwcg5CqRdUVOee3DD9wogmLPWGpkLa5cQaS7/s4jFbKlX1eBST7MmGpgLhnktPCNtI3tdFeOuBhiMOWZFmOfLUO2tT25A25hKBzgEYoaQkNBcFG+cg2VTTIsPqeZyBAzWVjSaCAHSE2av0cBxy0VefgNNNuOTg7xtncDGWD8CWA9uEa3fok5932iLdNNJX50Cq+CnDR8w5t6m/I7GS7b6GkYRrkS0Yi6SKpiJpqTJNDrejlOSCtd+R+CvZNin9THsODipRj4iD8qAy4u8cY362NOk6B3lzH+9wO4ejE2AuPev3P3oYPvO+fbXr0sL77zesb9ietVpOHOoF94ctJ6579FV76Q0e6TqfNghIJcx9O6hvM8YN6xtZLqkflM1ZbK8BLeG9DeJQBedgLkTS6sV2SbEQKNFN8RC36HPOci6PYXVBAuzv4IjRfh6nphhGRVp8JwB43zg9AJyvziGoJ9urhnPQkVikGLGSvK96Txchlue4hSoxNqTPIZOwbrNkQeOeXEsgJETiGQC3hZe6eYm8nlXiIE1UjWfoG3IOBYPrdn4b4V8rETP8lNR6stmxQ/okxHtpGxNXvgvf2F61QsOZsvpMiDTERMTvmnteWY5rL4gjbFZEvON54spTAABX3/+W7kijTPAnrvwQ9h3aD7NXbYmc8hJe11U6HaloK5UBNNu9VwvE7uy4pPEmsQOAL9/1qvXeQgivUA5am67dVfg3LRudbTE696h9tN+aeMUxh3STV3v/TMj8wgnOwfBS5qyV1N9yHrgV0tDqqjAt68zQ60/P02OamfMwEisx71LzKbF0zbWz5sSBqlhJmoybzyDFSqaHtI/OwSUaVO/NiZU4At1erqClucgmWBrQ0uw0M3bN6Xqg8TiHGrSh7n5cOzSZScyE9HMAAk/WpmIhXEz5dsbvNQC9mgo4dt8hCS9PCZNzGBUmoM8i926T9vKWaySHk8yOZ9M56Fi1hc97USwEIhuf/Mc6bfAgJkyZNdmSUuxUSMNOVKQ47aUF66Pk9T740V/fZu9s6kX//OrKsD9m/4K/HGH645c/wNblQsebi7Maen0LY1BgiiJjnUOiqma+bXu/nGhzV3sZb67YnPAXAvS5J0+b3E9sxGDXOZj9SdFHo1IxOB+lYmzKnLxOiuVeXbJJKz/n8JEYObDF6eTqGtt6oPGIQwYxiw0q66deI830JMYN74cTDxyWIBIVwez8PPkWW4J286O+6sOH4OTxwzNNJCn2+d9Zq6Kyzxwfx1FUxQIq5HiQVua/w5F11b7aLlXb9JHLcmarPhyKSfCI9IWXwM8XKU77u1un44m5bn3UMfsmw2QkOQdE92ztKGNG6PTFhfeQ9QB9UdtvaF+2LufvYS7AqpisrZwcy7YOgzg4dCN66lH/iXnlfbPw8V+8qMUs4sWFQV/Mb8HkHFxKcwnZS1udckVoIiN1oyKPWOIQcjVmzKWDRw4w7mvtWpd5TzcecahBG+rEURcRKSKKbyaw96AWDGgxE6IIlu3nJpiJSF5sTBBzwSuVhTW+EQAcsveABDmSOgfV/E790JqKpMUYkuBs3VUFbhpkXU6pZ0InIPYbyNShX737dWcb+ubPLm5Sw4EI8LGx5HW+H6+Pg5S6k1THJ9G/8EvmOAfz/bhua4pktPfIPNaJ4/nUK1aFtLJAc904aKTue3P02MGYtTzgcLYoHstyYVafRdK1kQN1C6GYOOg6xc6YslaMb1gw85KzGJM6BzNMiBms0TWHcs6hTuDG/Lgw+5RveF01ZLfrQ2srVUDQFbPbWjvwznvbEvJTU6xkk72ajjwSSw176s27OqJd7IEjYourK04bH/RdBKapKqTfgAr1PjbOgYsQkGWRjDmH9PppnMOU9wWcTu/Q8WkjYzDgcx9O4WtyDiZNDMwbec9wDlw9U2GpihFtRC2oZ4hMlGf0TRkKJEUyRG7x3eHGN+PalRcKiljJ2mKMiWMHa8mE2ksVHDxyAG767LGsyFZyDup8B1QPaV0v44xIm9LPUkVEYTmCtpLzkhtlWS+Re9swJHD1Ldc51A3JgT1v4j549Xtn4piQSKSNvao0ctmTt3aUE4vkTc8sBJBU7AUfYQw7cQjvbZy/9M6Z2u9eRYosbNTnGdgn5mKk5ZPaXxMfPiJ2cOMU0hceNyYqU4eC4KeQfebbp6JIhEpFaM+8z+A+bP3YTFQkrMAANRmS/Z62j2vqQ7GnNSe2UZ3biIAfflyPI9kc6o5s7T/6jQ9qv7l6iU2DYvqp1Wc4G7VNtWUzK5xtyu4zqAWXfuiARLu293jdBUfyJ6C/+8F9m6Muu8KamOhVLGjP3FYqY5/BLfjIkbwPjM35sG/k56DX0zlVHWn9LFcEhvbrFfnj6NXsnIOc42a0VnkfMjhADjlxqBO4cS0WCMP6985grRS35bpmV0c5WKCVMrsDlv4R2swtYyc4/bwpW//M8ftGSlS1LZcCvbUj2bczDh0ZpW60cQ7cIKhOcJWKwIsL1ifqHLvvYIwb3i/qp/rIpnhBQtb565ur8Z0/J2P4czkITNgI7wOKriUh+6c42U/w3kkjnADQHEbatd36YOOZuHqJ+RHNNQGhnDLfo/R/icRCSuOm+Mo2B57/l9MT467pWoz6Rcar0ak0VRXSEHj2XXc2x+De8e+2UkXbrZuQ89zcOEnOwTTmSPM5kP3kIJ/zhP2Har8B4P/eCAwRONGxjXMwuQ3X+t9FtKEBiQNT1sTIL11QFyDXYrv3oD6atUdwraVN0ntXDlci6TEpYbKd89dsw462ErYZjnZNxUIUGbXsIA6vfu9M/M+X3g9AmrImIRXtgZ+DsQtlnklaNcnF9NO3TMNnb53Otg3EC9B3/hSnM7WF2pBj+Z7F8kl+kP/vgTlWxbMmnrG8P3OcdD+HYFdgXhpwVvadHRFFpsgAT8D6G/opdYEpO+ZRRBwqyYXcV+fAFavE7rcvLLb2zYR1DMK/QgQm3i4UDHa6rVRxJs+RhNF87S3NZlTWWDxoQ9oiHX1Thh5jZ3sJt7+0JGwjOUA2/Y3sk09++pxzqBO4cZUvxDf+vmr1YLvklouPw62fm+Q0UVVhimHkR65GOgWU3U9FQAiBs254Dl+6QxcpxXWDD1slDuZCMax/b4wdElizcJyDvBcQ7EC5BcFcYJuLBY1zeMUw2zMRJFYReGzOmqhszVZ+8U9Txql9sTlo+SBJ8HTFrCoikYhMkh3tnn7IXtGx+iwfPzrwqzhkbz2Np6pL0BXSyXsDsc5CJYy+wd9Y66vw3pt2tOPXzy1KbSPdgZTnfDkUCkE9WbW9VIkMDbg+28KbSIJipvf18pC2nJeEOrp9WFEm/wnux1wnuRuTOIR/pVjUFeKlqxTSDecEx+3WOFNMF9RQxrbvTgajM612rLs2U+cQ7oLMD1sVK8lHMUM9SEglqi5WStaLwoBbZp38EOTO2Oz35jBnrgxtEcje/a2VCkxd2w4xzQxVu873hTLgrIFMnQZ+rtYAACAASURBVENS7i8JcvqiE7SjE+2xQ5N6FnUH6+JApQy7VA6cCdWEVaaXrp17TZ6RsaI4MSc3lyOxkhrxVLmUyy4n8cEJw7XfZlwnIDkv1BkZWZMZbUvLoCxpQtPEk/L5TE5++uI4vwQ30DYHPHmf3k1F9G4qJCQB3L3rjcbjHBznfC0p1FAFP35Uz1zWq8jtbOIWJYv7pZP31+tBX1Tkx2hzOCt7WPdIrkWtx0XrVD8a7mOQH1GBESsBsengqQcHu+LmIoWckLt/8iznE/GNMyfgV39/LH584VFaedozf2jCiOh4w/Y2R003OA/kOLaSCBYPoysFkkYAfvdQXQoqQrCmreqc1PTRjEgLCObNb19cjL+9HXBhv/y7Y5k2/ammNKjg3iXnYR3vyvn2oqjCTAXpuKndw+DOTZ3DIXsPwLgoHhG/8MZi43ghF0Jgpyt0u4OIAQrn4NBNsGKlyFJKr68Ox4CWZmzdZScOPUbnQES/JaK1RDRbKRtKRI8T0fzw7xDl3NVEtICI5hHROUr5cUT0VnjuRgrfFBH1JqJ7w/LpRDSuto+ogxtYWeTriKZOskXr9YisL119uubvYC4W8vhfzz3UaDNprcTJ+NWdCjdHjt9/KB7/5oeiuhUhtCB+tgiqsk31o/3rV08GAJx12EiceehI/Mvkg5PmmyCccMAwPPfPp+HzJ47DuUeOwm8+NwmFgv8k5riMvr2aMPmIURhjmNumbZpOVnaf0jIMsFvW2HfR+m9N5xAuWKavyZGjByV0TCZs8XgCLtT+bkwib5pyyt1xqVLBYmVOnjxe341nhZy/3BNxHsucaCciLIqOTn2Ww0bporQBIQcqzYLVwJUm51AoEH7w8cPDNqPbaDA5ByEEbn9pCa55cI7SR/2atJVAficu3YS830mKL4gttIr6e2BLUyJYp4qepHO4HcBko+wqAE8KISYAeDL8DSI6DMAUAIeH19xERJLU3wzgUgATwn+yzS8C2CSEGA/gBgDXVfswPuAofMSmZRBDkKkkCDG8f2/NU9pcLFo7yujXq8gqdk2dA+dcppqycpPkmH0HY0JocSI/Lk0hzTqshW0KgZuVBVUGuevbqwm3XjIJY4b0hW2Q9h3WF4UC4ZefPRaTxg1NXSTNZ7Kxyv16JR0I1T67oFZRxSJHj0n3Z0kqpA1TViQ/0hs/c0y6jkkVKykVZaReG1QP8h9fcFRS56BYK6nzhpgvfKiS+zgN8j3y0W3t35KNiMfB7+IKHz06MAfdO+RAn/7nU/HEladEBHmHslCaOgdAfVcWzqFocg4Cf33THdqEFKLMISIOhrRBfS2sKauFu1F/92oqoN1iHMJdWy+kEgchxHMANhrF5wG4Izy+A8D5Svk9Qog2IcRiAAsAHE9EowAMFEJME8EXcadxjWzrTwDOIG5UawVmXEvGLsAHweLgUc/gCNZua+OzyxkoVyqsIlHdebFckFI2972tWLB2u7YwsqILRb76ytIU5bHnIJkmvO66dkJiihqyBLVTH1UNj35XaJ3lvJbtI6J7ExEG92nGocqut1/vJlZ/AgRpLQHgm2cehKPGDMJJ44clvGptHtcSFWNBUiF3x9+4d5bWDve65E7bBxHnwH03TBykErN9HxhyOQLx/FPH6OzD9sbPP3MMvnrGBADxBkvOC/XWnC7KlPub4y+dADVDEu5hFcTkhp9okajK0E2o3ywvinMTTyB4l64EVmn5Y2qFanUOI4UQqwEg/CtNMEYDUO3TVoRlo8Njs1y7RghRArAFAO+TXwNwQx7LD/3JA5fMxlovrPb0O2vx4Bur2DDfpiLTyjkoYYd54hAXykmkxtDhEu7EOpTYhNYGc4isQ9ZJhbTEiAF6KG9Zb/mmZIatl68+Q++CsgScddjI6HhgSyyScRkImL9VkQkhsBB65OsfTNQzF4XXvn8WfvcP7wMQmAU/eMXJGNjSrHvVVtxipY072qP3zdVrUpyqVHEPN6f7925K6MZs4EKbxPfhF79trR24MUy489+fnhgRRlKs3XSdg8DHjt4nEVKCKBgX9Qm4fqsWfLI9FVJZ7+N9bLYpH7tcCSy2JD4VeuKb9dRvlrtNTMD0s98886DouFdTwRna+5/+57X0B6gBaq2Q5j414Sh3XZNsnOhSIppJRDPXrXM70NjALaiVKjiHIMZQ/PsXf3eMta6cCC9brIqivinH5YqwcA5hmxaxkjrxpfOa2W9bWaUiErF1TPjqZQrMIqni6DGDcM3Hgh1soJ+I6375lAPj+xn9lc9857SliTb3NriMNBbfBU6sJESwSP/l9ZXWycKFz2guEmtYoNaziZVkP257YXH07ByBV9+LFpKc76b3ZI8V0slzagTVaz52WHBvIbBQ2dmef8zo2PsX8eZGfd+2dVBaSqlzekd7UhavinY6yhUmPpT+l/tuPnKk7tBoiot+8OAcHPOjxwEA35l8cKQYN8VPatOucPFaSloCBvWNNyzNRdJMWSsVgWseiFS+XpvSWqBaU9Y1RDRKCLE6FBnJOAYrAIxV6o0BsCosH8OUq9esIKImAIOQFGMBAIQQtwC4BQAmTZpU1QixOgdDju0jOgnCTMdt7Tc0mTEualMAv3x6AR6ebZdzynoSAeeQXAU0U1amHVW514cRX8ndmbpYqTsqm2d2VNfokm3NdXkKA8ADV5ys1VXH8jIjhIOKLOLWWgonpUXVN++d5cyF3V6qYJERq5/1nyF98bAppOV7OmTvATFxYOqpHFYbk2uZub0XOL2VxLlKGIujxgT+OPymJu43l2jIFSrG3GBw9v+qqHXCvz6SOL+rvaLVMxMNvXHN2QkFvyku+vNrseBD5V5M8RNnfKKb2/qJlVQ9y6L1O3CHshny3aB1FtVyDg8CuCQ8vgTAA0r5lNACaX8EiucZoehpGxGdEOoTPmdcI9u6EMBToo4xabmWpdxYThhOnGOiqUDaDsW5SAK4/rF5WL5xl7U9gr6TLJdtnEP4cVmUhJ88dnR0zD3HwXsPwGWnHIBfX3xcfG8lnksacfC26IK/4swUK5mxgFRUKgI7md1jrWFTSG/a6c78N2/NNizZoIu8rByBqXOwfI1Hjx2MkQNblE0MT0Su/vAhAPhcyyZ8CacUp5mWWSMH9sZ+w+INUZOyaWky3p+UchHxOge7N3VSF2WhswDsGwf5GcScgz6PecfOsM3wt/pd9FY2XaZYSYvXFB7uNTAm3JxC2rx9L0PnYC6HddTIakjlHIjobgCnAhhORCsAXAPgWgD3EdEXASwDcBEACCHmENF9AN4GUAJwuRBCqt2/gsDyqQ+AR8J/AHAbgLuIaAECjmFKTZ7MAnWYB/RuwpPfPiUKQCcnjI+ndLFImnzetUPzjQIqJ8FvX1iMV5ZstFgrxWws16y6cHCTvqlAuPrDuhmt2qZUMp6hePKa/TRK2HppnIPZpjpGrlDWFQHcy4Rd4HIjuOLwSNj6mNQ5BATMx5gg0RYzRqZvhxn+XUWRgt21XJ9sexd5/T6Mv4BPn2xtCpHc3d/wqYl6H83YTgqi7HWINyJrtsUe8NZFnTGHZseyEM9fDh84cFj0LFw912ZQCODW5xdpHEv/3gxxCH+b7xQAfnTeEWhpLuIP05fhvx6bhw9OGJHKOWhiJXMMegpxEEJ8xnLqDK5QCDEVwFSmfCaAI5jyVoTEpSugUuFCgbTIpHLQfcINFIm03YTtYwtCQ6T3S5Uq/VuYFUwmG9faU3QO5o4i4WHqIC5smyLWOZi7v6if3lvO0PzSQ/tnEhLXx2qz0rr0g0lRFGf2aILLPAZw1krB3OFEdWmw7Xb13bN9bJsKhZA4BBfYiKcsbmF2tiZ8uOO4nwI/+Zvu7Hni+KQ3M6DPIQlVkS77/utn41AcrlhU5jmu26bns4oPThgejaumc1Da4a3EgrK1W1vx70q0XkB3/jPFT+o7lYf9ejfhgmPH4A/Tl+GNFVu0+uq9JJoNhbQpQO7pYqXdFuowW3dgPsShoCe9qQ3nYNzDoTwuM4vkF07an62rlTnNY3nHJr2u87RWTyDpKAbolkOyrubF7Vj8bBJH7rlsC78Km+UHnwmOJzi//+L78eMLjkqUq9dy7asffcA58NcXCiHnEIln7NwaoOeEsC0kLicrFVL6pca94hB7Pif1VtFP4t8tN0cAnvvknz0mTC7YEum4RFVctjxVKW5yDiq0cONGOHxXX5uLpM1d85M8aGR/dAUaLraS+haTC4C/WKmpkMyIxoL4iZOoRknT2DRTVnOCmfU54uIW2cQ6B6u4JdFvWz2CEBVtjAa2NOFv3zwlYVVkZpez7WpdJq/cc7nMAdNgNid1DlzfTp7g9kK2KqSNHMa2eddUKGBXuawopG33Cf6qz+1LzG1IixUlIXW0ZSEicestoV5LjTjK6VVs3CW3sUrjfF1QN0FqK66wJZwOTg2eSBaCY5apibQ27WjHTx9/N27DuC7QOahiJb3t9x9QN0t/DQ3IOehiJRXyl48YKNA5JJVPJkzFow3EtGFfJEOxktknj9DMvJ9DXFGKBGxd9hUrSXmxpksoUIIwyDbVb9C1+FlNPpnnskWZ9QEXW0mI6uS93CXmIleuuDiHQISpimds9QDdoqez/qQFSu5cOUjLOtUcWgZilK+2YOEcrN7UzKdjVe4DaC+5PzR5aUUIPeUoRxwgOSH94Yf0bQ4jBehtct+/eqmaa/s7f35TM045fLTusW86wZltd2bTkwUNxzkIxyLExX2xoalQ0HYVNttjX7ESt4LY5P7SjPZZI5ucOcldCm29LPhbqYhIrOStqOWrRZYmPhxBseCWwarl9oUkeU0trZoii5cq5L1stFMklZd2nQNpfi02qyZ5/WZl4eusdLpg6NZskLvvUkUkgkaqpuLcLp0LuCfv7SuSBfhFU50XqkL6nfe2Ja7n2jSf/Zh9h7D10sRKx4ZZJg8a2R9blffzsykTcepBuvFHc7GADoXAm2I3L4lFDdBwnINmMsnIlYM6Pmy0H+fgyTgEbRi/Z6/cammTUBYC3/rjG1q5LYKrVsehx9AD79mInd9yI2X02ng7xEVPMik/k/UcOgfmuba3OaJuZoRvrg8T3z77ICuRNm3ibSK/Ysg5SD8ZK/EM/67bFkej7axlS+/mopfuRhKsikIc5OZGtfc3+3P9hUdh7NCk4UV4QYJr4V4/p2tpLhJGDuyN6xRdkC0Fp414A0nrq4QoGvozqlDn/siBLRjevzcmjRuqmcIeOXqQ5gAHAM1Nus7BDFPiCq1RSzQccXDtUGO2M72dpgKlKm8BuQh47H58lROQi2Sy3IwswItf7PLVihDRxEuzGXfdI6iXdJ6yW9l4iqrCnSRXm7Mw2+lQuk79RMJwLuXemaoDAE47eASuOH0Ce46I4xz4dopE2Nleiix8rGKlsHxra0fU586KlVqaCmjtKEchMIA4Wq/WR6mQVnQOMqaRyjmY/RluhEdRIZ/HZlYtIZtUxWn7D++H6d89UxNjxkSkem7EnGcx58C1aeoEA/8l1YmuT6+k9Zupc1D7O6ClqcvESo1HHJTj0w4ZoZ3LyjmkhZoAkiaL6n3MMgHdPPX4cUP5exOxSjwfsRJ/b4osgeQuxa5zsJxg6gkhsHpLLFu1WYGlLbzD+/cK69nFSr0ZE1OXRc6xhnggDdUssq5rZGgICVssLSDQb5U0xza+zUghXRI4euxgLPrPc7N2OYHezUW0lSo4YHjs8GZ6EwMx4S9X4g2GfJ7IWMnBtXKQOiZuAVUhd+/vrok9013iU5/FVfbV5JqS3HnwV3626vlJxvdbDDl+1eJt1KBkgqfmYkHj4tUN1rB+vRL54uuFhtU5/OZzk3DqwSOMs6F4xYN1aCrorJ9drOQn2qGwDXUivP8AnjgUwknGlbt+uyAX3thaiX+gh4xQx65drBBBFFoJ04SVa2MYE076sW98COu3t+PCX71klbdyJqZcHB6JrGt9NWIl1xWmiKxUqSQCz0kUibQFzSWeA4COSgX9KLs/BoeWpgLWdpQ1Lpm7v6pfKCfESnYrK9fGQHKKqVZI4bA9Nue9qIwXFSXFTzbIqzsMU9bRQ8zFXNerlCsCXzt9PK48m4lrFuqO0oidnAftpQr69Cpio+KVP6Cl2Tmva4mG4xzkPmb04D5sFEjAj3MoFEizQLAqpNkPgt/RC+hchm2xsDnWVatzCOoapqxsLWCHsms545C9cOVZB7H1Ao5JaKz+dz9yKFtX7fYpB5kEO8hzffDeAyIRHffhtzQzxKGmOofs17g5B/1dd5REFD3URFOBDHk6Py9UUUiTTWudEZJzSOOSVQ/pSOcQWTAFdVxBHzlEeivV5NeRce3osYOUMr49wM//xcY5qNFT1TaFiDeVNg65WAg2dfsMTnILKuQ86KhU8PQ7a/G1u1+PzvXrXcTOGs5rFxqOOETKMU68Ev71Eek1FQhtiu2y3S/Avssy6wljl2SzVioQb7GQ5Bzi42s+dhiuu+BI9OvNM4uBdVFsOXKChy31f3zySAzuyyeOIQLmrNqqORHZFzVij00EVlr8OS5UhjOblvKOX13KxnnUIFNKrs+QetTFbJi6qI5KBU2OzYAqd07jHEple5ymrOjdVEBbR1m7P5tTQeEcpGhSyucvO+UA9Gku4v37B5zwV08fH13nornSfDjdfyH4q5qyuky2O1JMXtV+qc/95VMOTOz65ULeVqrEudZthgUUGLH0SxOTSauqCjBjiT43+/ZqyjmHeiGWfybPqakW01AskLbw2aYb9x3b2HLdWghYu5VfiMyIsLZ21d9jh/TFp9+3r6WXsY5gv2F9sc+gFmtk1EP2HuB8Dokn5gbWRw+nZNwC9DFyL6i62EIFdx0XwVNC3YFecPM0tHa4d2N/ejWIyqklkE+Ba+F7Yf56bNrZgfe2BDGGSmWBZtuOk3TjB1t4F7kgdpTt+ouskHJy9f7D+yeVyKrOwbRWOmbfIZj7o8kYFl6nZapL0TmYGyYOsg0tj4UlnhXgq3MI/qpziONOx4Y+D8s37tRyrXMohN+tzSNcQu4JubzizUU/XWct0HjEQTGrMyEns8/kaSrowbFsMnperJQskxPrhQXro7LXl/FZ2Wz2/ubGU+UkXJFOZV2BYFe137B+1o9WzRHh8raW2Jmy6AJm+HB7PfncMvaUir5KOtEfemQ6M1/XId9/1FnfZ8NgwjU8Mvf4Gys2B+2XXZyDblJpExmpJp1cuHcXVIWzfu8gb4mvWKms6hwsfVA5SD+dg/PWrGURr9/IoHNgRFBc0EWZ5W5HewlPh+bYVsOCkHOQc+/EA3nuvKCMpUlopGiqK9B4xEFGiGTen5zMrh2nhMk5HL4Pn5eYtdBgJo+0Tb/srlejMpvJnfSQTpYbYiXlPmkLeYGC5968q90qzgKMD9tjd9riEfxOy8zm2G/b/Bye/NYpWl7kkQPt5pESWb8vc3E881Beua7CxydE9qOjIuw6JtId0dKCIpbKFaTsBRKwiRuLBWni7P4mVJ3DtY+8o5XZ6gLuORTorfzDYqjfC6uQDovWbG1NnEveW4qg4uceO4QLhBmK0yoCXwnjdFn1eiGhld/unV84nr+3bJOJt1UsFLwMZmqBhrVW4l6flB+2e7BtTQWKRBHXfvJIVg4L+CukuU2WzTKnSH5iJfVnWqTZAhFuf2kJAP4jkFCf00d0wQUuM6HZojtoic1j9sAReiAyH7PTrEnaTU/Zn3/Gnvkv7od/+x3lilUhbc4Xq1gp2kHziaJcsCpRw91umpe0XBDnrt4ahaZwKdjNPnOQcZ3UW09hRKOyDdXz2GayDQBPz/PIIslwDqMGJz255bOo42NXSOu5Mewh2kOdg0gSmiKlJ+SqFRqQcwjAcg7hzs2MoMhB5Rxci6RL9qn1i3nfn1AS92htepqyqhMrbSFXL7XtYM1zPmKlXeFYfv+jh1nr6ITVxTn45aX2WZOzfl4HK7qWYf16pZojAr7EIehJqSwc4iL9t61etNutVBIiRhPnHjVK+23jNKT5ZXqGwKABVTTmwzm4dQ6kWQEByVSwQPzcakgMn7D7wT34clmsckxs8i0pAtKcPfk2JaGN8nKkEPlyRWD+WiOrYMEz4GcN0HjEIVpUGZ1D+FaWbdyJgS1NeOP/nW1tp6kYWyu5FlPu/Q/rlxR7cMThwuPGJAsRfFw+pqyUgThoIihH3d6enIMkBm0dZRABXzhpnP3myrO4d5KeZsad5ByO2y/pIPcfnzgSQOC96usQ51NPbjBcnIPZjsuKDQh3nCnv+ygj2JtTTq5YILlgGkrYiJi6yLp6Kd93ukI6WeYzB943bgge+fqHLG0G16ucb5p/R1TmsDoL8pHYgyyqfX9g1io8+MYq7Zx3NOgaoOGIg4TLWmlnexkHjOifiHmiolgoJDxBfe9z6yWTEmWc/XazYyfpY8pa9FzwE9c6qjZ77AwB4NgwM1trRyXTgur2mPVMnORxK1dIkzEJR6cgbAEQiBlcQ3nDp4+O++G4/93/eAIARItFW6liTU5kjrNtV6xvBtyftjlGrl1+uZJM4MOhXBF4cm6c98EeOFJVSLvet/T98eNa9HukT4KvnHqgxhGqiDmH+N7cJjC20kqWcXXLFRFG4HU8d9j3ee8lY6vlCuk6wqVzUCdzGluqzntXXXMSjB7cxx5ozLyHQwa9fFOcp3hAqEw09R5qt9KJQ3zsqqnew9WkvN+ujrJXRrbo3s4dVe04B1cz3DldTm5v/xPHjMGBIwLLHxdB3CeUX1cqsVybCwES3E//bU/2Ex+nKaR9Qq0AobNnqZKwt7dBDZNuWyTV7yxtM+BlrcSU+RCHFkca2diUNRYx8/lVgr96aHq+TSkSqoh0oggkRZ+TD9874M5yzqE+iK2Vki9H3amnTa6iZ12TcPS3WIX4XCtRKBBWb44tLr730UPx/HdO0yx2ZD2fPgbw29mroo80eTEAtHaU0csjl7N5ne2cl87Bh3NwnOMIUFPBjygC8Vi7qkXhoxEvqDYiao7JEAtHW8jEOehtpilHq4FNpq6JlZxiRAo9pN0v3YeL5tCb8VuI7g25uXHrHFT/jjRIQxIh3E6Ktr7/6uLjEumJ64lOEQciWkJEbxHRLCKaGZYNJaLHiWh++HeIUv9qIlpARPOI6Byl/LiwnQVEdCN1NpSkA96cQ8rWS50oLp2DqqA7fv+hrEhJ7ZftHioKpGeWIhDLjaiTLC2cgsY5OIbfZpVlQjbhwzn4TnVO5/C+cUn9gJe1kuMD486oXJyPz0jQD3sdea4iRKS74uzoAX2R/dmUiU5/iKiPKa8paSJp34hUg1s/x89z815p4TN8nOA48a8P58B51av3BoAdipe9S+eg5ZO3EdqIc/ATK6mQYWW60pS1FpzDaUKIiUIIORuuAvCkEGICgCfD3yCiwwBMAXA4gMkAbiKKooPdDOBSABPCf5Nr0C8WrvAZvpMWMBYLx0RUd9rfPvtgq0iJXZCsxIG0yIy2uE66WMnaxahNiV6Oyq5zXHttHRVvguLTpqor+JfJh+C+yz6QqKdGDb3yrIPw6Dc+mKjj/L5SCHW6z0g656Cml/zFUwsAuDiH5HUcdrUru9yU92S24pKTVwMXAVU3KmlixI6ywGvLNjvvNbClGedP3Ee/v49Yyck5BFCJA7cJJKKE35ErU19ZBPoJH7GSiu9/NIhLVizs3qas5wG4Izy+A8D5Svk9Qog2IcRiAAsAHE9EowAMFEJME8GXf6dyTc0R2yol34D6vtJ0Dup5V90mTfzk6Bfzvm0LwTvvbdOsKHyyo6WJGdRHcH00vgt9lLqxXImUuT54UfEQ59pUHZXPOmwkO0b7K96++w/vh0P2HpioYyOotnPqO16yYWfivAq5MHnJlQVw57SlABycg9KOa1qqXJQtFEfUjukT47DNrwYuouK7CcsUVdjovw9Rc3EOcnnY2e7WOchyVUlsG/piSEQ45za9ntw4xGWSMBV2I4W0APA3InqViC4Ny0YKIVYDQPhXZuoYDWC5cu2KsGx0eGyWJ0BElxLRTCKauW6dhyML12El8YiJLIupryVQs6bAtddzLVZpsAXJ054n5WNZtSXWYdgWKcAtQtPvHR/vbwnNwCFNFKN+GHansfi4mhhDnKIybSfO3t/1LFGo57jMxjkQ+S2mw5SYR2nvySSqLq/eauDaMDVrCml7G+apWy4+zlo34Szm0e8RjkRD8v2oxMEu5qUoRpbr3sVCIQyfkQyLod07fBY9HAhFfdhdxEonCSGOBfBhAJcTEW80HIAbDeEoTxYKcYsQYpIQYtKIEcnQzj5wDatOHNztaJyDK9yEJxG56Lix2u8l1/olannqW6dYF1+NgGWIp2B6HKvwdS5SFx+XVYgJ126uQHHo6gl79cd+wyzxgDz8O2yJlI4eMwjXfCw9NpML8sNPCwUC6DoUVz6H+Dr3jlwSmDRi5q1zMO7Xx7Fx8GnPPJeWEEnF2Yfvba3rCh1jg2sTJJtLy2Mhy//y+sro9zmWfqoe0j5cpdo/WX23UUgLIVaFf9cC+AuA4wGsCUVFCP/K5MArAKgr4BgAq8LyMUx5feDQOajvPk2Bq3MWDrFS0Y9z+MCBw3DS+IAD+NQk3vlNQpX793eIbPTn8ScOU9431nrO319BuXcKYVL1CC6xVaGAKIfGJ4+1j5H6Edueu6lYwOcZx7zrLjzK6d/iA7mYp1niAMZmxVJffYS01yjFjW+tdMvpTcJlc7hUnd/OPHQvvHz1Ge4OhHBb8PkH3vOFSQx8UvO6IPvf7hFyXn3WQX2arURHKqTTdA6yPc58Wq47XcE9VE0ciKgfEQ2QxwDOBjAbwIMALgmrXQLggfD4QQBTiKg3Ee2PQPE8IxQ9bSOiE0Irpc8p19QcLlNWtWzWcvfHpS869hfla9MNACs3BSk10+IRff7kcdHxUEs+BUD/YDrzoVUD9YPxFUUBwKGjax2XOAAAEzNJREFUeKckIFh0pU+ATaQE6Ity1mcZ0NI5wgD4KqSDv/Kdu+pX8x5nr0w6UGltKs0sufZcnGbJ07xLCSUzenAfb8LpyzlkVcza2/Sv6wO5AVOf38dJ0TUvZQbHksMbXtYDeLGSfM6u4B46E3hvJIC/hAtqE4A/CCEeJaJXANxHRF8EsAzARQAghJhDRPcBeBtACcDlQgg58l8BcDuAPgAeCf/VBS5TVnUyrty8i6mh1PWcuVl8J6Si89l33foUuTMd0rfZKT7QTVk7v+BnQbX3dsVgIqJoJ+freOiqx4l9fKLIpkG+cp938+aKeBNid25TFtMaLYK+RKakJRnyv7mTc9B8ZextZDGUMp/nb3PWWGr6QXKwKgNiNVH1NBmXDmwlRwTe4D7B3/+dFQtQ5HCqSZXqjaqJgxBiEYCjmfINAFjeUwgxFcBUpnwmgCOq7UsWyCHNmrLQxPRFG+I2He+pWTN59Wvb11JqGJN0RYUmq+5i4qBZfmXY1rl1DsAuSRw8iWLW0CY2L+UsUJWH9jrBX/Uj33tgMqicWhfwF+uliVV8p7oaytu12zXhEg/6epurOOdwd4h0sx2fVKAuZDG/Vt+JS4Qq41S1lyvuety8CYu4KLD1QsN5SFc8rZXSsGFHe3ol+OscsvRDLvQukRKgP2OtMoP5Qn0G30Xls++3Z6oDEEbClcEO/cQRrp0c10ItOAc51lmj9dri/GSxOpNIWzp85+Lfn7BfdJymO1LhMkLQ54Z9vFXu/dcX253qgNrP7ywhX9TUsa7nWbZxJ5Zv3IWH3lyNRet2WOvJ8VGV/7JM/u2K4HsNRxxcYqUsbKyvqKTZ01kuS9vyfEtK2Gj1flkmey2QxTtbvpP9hrljTuliJRdb7setce87C5djg3x2p14kvE3WQIK+73FqGEXWp00XhvbrhZPHDweQ/h5VuEJTqI5lrh36GkuaXA7mJ3PCAbw1mi/Mfv3ofD/BhuvbnbmUz+xoQr4bVd9hcqM9WiG9uyIa0k5yDurCa9vxAaZlhl/7aWanUvabJbha1+sc4uOdNUqIXqBYXOC7i3XJyesVpUV6rzvvHf71+cQrntZcKj5g8X2RyLLTlrvULGIlF+cwUPFgdz1Plnljik0/fjSfC8UXvYtx/08aPwwXKxyUC1mML2wYyBhFyMfjwnXUCw2XCU5u1dKS8AxICZCnLrYuCxfN4cebc0gzow3bSxU/xce1XAivOG08BqdYrWTJJeHbtWKBIlNW34/QrZDWcZ4RgsHEZaccgF8/uyj1njKCqcuxMXp3Ht+4annk9OpVkLaQyznmM/ZykVbzdKfB5UNwhJJLwtVPGfTum2celHo/U9zW2ene0iv7pg7IRkBt2ItJcyu/p65USDcs55Cmc9g3RcThK37ImjkN8AgXXohd6V3wyWFcDb59zsH40gcPcNZRu5a2yfGd5wUitJXTrZVUOAmTceprZ0xwtnX1hw/1uqeE67mi3MMeD3/ph+Kx9l180uan5Lx8Fj6Z33ykRWHOwVf85YrV1RpyYOcckZ6v25Uitxr0bipiWD+3To+Da9y/ePL+Xm24OIdcIV1H+OocfjbFnSPYe6HXOAevS9LDhYen0z6AzjoCdQa+8YCAmHikLVSqzsGXc8giPnF5hkuceehe+MJJ7o/805MCJ0LX+MtH9bGqUXfavpvYNOIpiYzPPN4e6gjSuMW7vnh8dOzLJbs42lMPCaIg7DUgnSiZofBt7X7USI/qgjQ6eX6+Pd4XAFx3QazfcY3nP5z4/9s70yArqisAf2dmQB0YkR2UVYEhaFQEMS5IMKWIJmIiVolGcYtLaWnMopIYTcVYamKsuFWpZXCLiamUWXBJjKZcYtSoRFERcQsVsaiQREXUiIOe/OhupnnvdfftR/d73TPnq3r1eu7cd7vPO/3u6XvvueeMczpvLcNauSDdiDWHXjet1B1bqVqJ4bLK3AiVzOocylOr3mbBjHgPm3pGDoP7x587SEOY1PHVc//MnDgk/YdqkCaXxKcxOgmzWYIl5zWH6HqfOGQ3q+SmhXsm1tlpmBfWI842bzIOvrG75MuuntxucidFz21NMa0U3EdJBiduKqkezp0zma/N3DHxtwiwQ0X2vqgHjWsWTOWe59dkcn0BLg8VsPlv4ryDJ0fWq/U7CG6l4L5vhLdS7zMO/nvSbyKpIz991k7MnzYqcahdj0/3FUdWbR+p2WZSZxqE1th5++qopFHcftJeznXj6JNiZ3hgsJP6+3X/69p07Oo5E9ehJe1Er5dgOi/u5xvUCYzDzAluscKSPLoCkp7cg5hfLgvcgfFOMshZu5O2tghDEvbyBPSrWA/ZbdSAmvXycEJwHsWGzr3jULdglAPb+/DOh12bfk/Bb8kWpHMgLp9DmKS+p6VFnOZgwzp0HWon/SCC/2/oiu/c+m/VxrILD4qNv5QXrrmmITStlFAvHNffde49zjB9nJdxCNaaY9ccvPcNG9N5X2XhDeOdz2vHZW0geEpNMshpPOIWzZ2caQcXfC/9+ray7KKDMnFJDkgauYR1cukR0S7E4a/PNVLx+XMnM3/a6FC8JT+2UpF3SJeVuHwOYdL4dMcxJDRF5PpklTRqmT52IKMHbcPhU+O9a6B2lqxG0JZiWukTx2mlzdp39VaK6XQ3hLLpZYnL7zac7wKy6/SP32ccq/4bvcEqIPheXM7rOnIIZJoyMnmkeuqsnRLrpCG4NhHJ1DAA3HFy/Gg6/KASH9G4+7pGDnBb3G9raalID+C9b6xjSjQtvc84xK1Ih8gqhk24w0sKd9zW4oXjTXqCHtivL38594BMri8vwnIHm6ii6J5WSmEcMvBWmjxyW3gu+wDAweal2ExjFWsOWbhAAnz/MLdw40HMLzfj4L1n9cCUB1l9f7UI78uoRZdjRx2+v11zyVca5DRebltKcbWdM0n90JYkVo9sM6FDu/esmVwYE3iurOw2ervY/wfTFmmmrN33OUTXOyXBHbdeAuMQ9zAQGM+03ldZ0eXnKdh2m+ROynXk0EzyNFxJi/tDOrzZgaTfbpp9R20V00gBwX3StYWxo1zohSMH7z1xQTrDxbVvz+lkWUIIcPB2Wsfttu6puK45hHH2VooLCZ3TrvFRvufMhGHxXiwi3VNbje54g7WGmROTF8KD30wfxzAoOW08jyXoNF1P3Z4QeiZMknEY1rE1L198cOL6TZo+pdWfRai8L4Jz5OVMEab3GYeYfA5hsvRqOGP2hMza6ol8umnk4P6dJ3VUAWky4GXFgj3HsNPQ/uw1Pj6+T4tId2iKBHkO3XUkYwe5eSq5sPP2A7jr9H3YPWFUB93Tna7fZXOMg/tJX/rBHKd77czZE7jtiVVOoysXN960xgGqp0+DmFVmHHLAdeRgNI5g2iLNzIrrk3ZSrdmdQ3loZX35yKNoaZHIvN5hwr7qSaOY647eY4uvq5JpYwc61Wvv28p7H23cLOVt0djkfeXQSbuGAfnWnE6+edCkzB4U00xVB7mrKx26gvApG7rycaYI0+vWHOLCZ/RmrlkwlR/P37Up5/50k8F2V4preIakJ7qkUNBGd06HrgTX02A67egZbkHqsiRYuxmYsXdeljMI9UxVr13/0WZ/B04OH9nIIXvm7jKCScP7Owcw6y18abdkt9i03HHyXgzrSN7EFJdjI4qBCbksVl12qFM7aZK69FZuOWEGtzy+ipEJ+3oG9uvr/L1nzdjB7Uwa3p+L5zUkZ1hd1GNoxlRMJTZy5NDrjMPYwf0YOzh+A8r+k9x2qxrx7JvgwhoQdPQdKTbrZbmY/OSiL8RGUM2bCw5NF9Cv0XSO6ODSr8Tnh2g2W/dp5U/nzGr2ZWTGA+fsz/Or11U5DPTKNQcRORi4CmgFblLVy5pxHa/8cG7Ds6b1dhYdMplJIzqY3Vk7yX2Aa8jstIxw3JCUF/tlFM/K6DlMHN7BxOHVnouD2vvy4Df2Z1iKCLn1UgjjICKtwHXAgcBq4GkRWaKqLzX6WmyaofG0921zSqZy3pzJ3PDIG+waETenrEwe4R77yig3vztj30TX2DjaWluYMKwx7u6FMA7ADOA1VX0DQETuBOYBDTcORnFpaRHuOn0fJjhGwTSMouHiOlwUimIcdgDeDP29GsgmPKjRo3B1vywD1391WrMvwTAiKYpxqDXJX7VCKCKnAKcAjBkTn0fBMIrOwbuMaPYlGEYkRZlgXw2MDv09CqiKiKaqN6rqdFWdPnSoeRQZhmHkRVGMw9PARBEZLyJ9gaOAJU2+JsMwjF5LIaaVVHWjiJwJ3I/nyrpYVZc3+bIMwzB6LYUwDgCqeh9wX7OvwzAMwyjOtJJhGIZRIMw4GIZhGFWYcTAMwzCqMONgGIZhVCHagETVeSAi64GVNf41BvinQxMDgHUZ1sujTVdZ8jh3Hm02U5485M76XktTt7fKk+bcPakvgOzk6VTV5ABNqlrKF/BMRPm/HT9/Y5b18mjTVRaTp2lyZ3qvmTyZn7vH9AVZyhPVd1a+euK00ruO9e7OuF4ebbrKkse582izmfLkIXfW91qaur1VnjTn7kl9AeSjn0jKPK30jKpW5XiMKi8jPUkWMHmKjslTbLKSx7WdMo8cbkxZXkZ6kixg8hQdk6fYZCWPUzulHTkYhmEY+VHmkYNhGIaRE4U3DiKyWETWisiLobLdROQJEXlBRO4WkW398r4icrNfvkxEPh/6zDS//DURuVpEmpIoOkN5HhaRlSLynP+KT8CcjyyjReQhEVkhIstF5Gy/fJCIPCAir/rvA0OfWeTrYKWIzAmVN10/GctTOv2IyGC//vsicm1FW6XTT4I8ZdTPgSKy1NfDUhE5INRW9vpxdaNq1gvYH9gDeDFU9jQwyz8+EbjYPz4DuNk/HgYsBVr8v58C9sZLLPQHYG7J5XkYmN5k3YwE9vCPO4BXgCnAj4Dz/fLzgcv94ynAMmArYDzwOtBaFP1kLE8Z9dMP2A84Dbi2oq0y6idOnjLqZyqwvX+8C/BWnvop/MhBVR8F3q4o7gQe9Y8fAI7wj6cAf/Y/txbP9Wu6iIwEtlXVJ9T7Jm8DDs/72muRhTwNuEwnVHWNqv7dP14PrMBL+ToPuNWvdivd3/U84E5V3aCq/wBeA2YURT9ZydPYq44mrTyq+oGqPgZ8FG6nrPqJkqco1CHPs6oaJEFbDmwtIlvlpZ/CG4cIXgQO84+PpDuL3DJgnoi0ich4YJr/vx3wss0FrPbLikJaeQJu9ofE32vGMD+MiIzDe7L5GzBcVdeA9wPAG/VA7VzhO1BA/WyhPAFl008UZdVPEmXWzxHAs6q6gZz0U1bjcCJwhogsxRuOfeyXL8b7Yp4Bfgo8DmzEMUd1E0krD8AxqvpZYKb/OrahVxxCRPoDdwFfV9X34qrWKNOY8qaQgTxQTv1ENlGjrAz6iaO0+hGRnYHLgVODohrVtlg/pTQOqvqyqh6kqtOAX+LN9aKqG1X1HFXdXVXnAdsBr+J1sKNCTdTMUd0s6pAHVX3Lf18P/IImTWeISB+8G/sOVf2NX/wvf6gbTEms9cujcoUXRj8ZyVNW/URRVv1EUlb9iMgo4LfAcar6ul+ci35KaRwCzwIRaQEuAK73/24XkX7+8YHARlV9yR+arReRz/nDx+OA3zfn6qtJK48/zTTEL+8DfBFvaqrR1y3Az4AVqnpl6F9LgIX+8UK6v+slwFH+POl4YCLwVFH0k5U8JdZPTUqsn6h2SqkfEdkOuBdYpKp/DSrnpp8tXdHO+4X3JL0G6MKzkCcBZ+Ot7L8CXEb3Zr5xeJFaVwAPAmND7UzHuwFeB64NPlNGefC8MJYCz+MtTF2F7yXTYFn2wxu+Pg88578OAQbjLaS/6r8PCn3mu74OVhLyqCiCfrKSp+T6WYXnMPG+f39OKbl+quQpq37wHhw/CNV9DhiWl35sh7RhGIZRRSmnlQzDMIx8MeNgGIZhVGHGwTAMw6jCjINhGIZRhRkHwzAMowozDoaRAyJymogcl6L+OAlF6jWMZtPW7AswjJ6GiLSp6vXNvg7D2BLMOBhGDfxAaH/EC4Q2FW+D4nHAZ4Argf7Af4DjVXWNiDyMF/tqX2CJiHQA76vqFSKyO96u93a8TUonquo7IjINL37Wh8BjjZPOMJKxaSXDiKYTuFFVdwXew8uvcQ0wX704WIuBS0L1t1PVWar6k4p2bgPO89t5AbjIL78ZOEtV985TCMOoBxs5GEY0b2p3DJufA9/BS7LygB/huRUvFErAryobEJEBeEbjEb/oVuDXNcpvB+ZmL4Jh1IcZB8OIpjK2zHpgecyT/gcp2pYa7RtGYbBpJcOIZoyIBIZgAfAkMDQoE5E+fmz9SFR1HfCOiMz0i44FHlHVd4F1IrKfX35M9pdvGPVjIwfDiGYFsFBEbsCLkHkNcD9wtT8t1IaXhGl5QjsLgetFpB14AzjBLz8BWCwiH/rtGkZhsKishlED31vpHlXdpcmXYhhNwaaVDMMwjCps5GAYhmFUYSMHwzAMowozDoZhGEYVZhwMwzCMKsw4GIZhGFWYcTAMwzCqMONgGIZhVPF/r2l1iAXzOlQAAAAASUVORK5CYII=\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": [
+    "On remarque le présence de pics à intervalles réguliers. Mais le graphe obtenu est difficilement lisible, nous effectuons donc un zoom sur les dernières années"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ba36469b0>"
+      ]
+     },
+     "execution_count": 9,
+     "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": [
+    "Avec cette vue plus précise, on remarque que le nombre de cas est très faible au début de l'automne, puis ne fait qu'augmenter au cours de l'année jusqu'à la fin de l'été où il chute pour retourner à une valeur similaire à celle de l'année précédente."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Etude de l'incidence annuelle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On s'intéresse dorénavant au nombre de cas par années. Afin que le choix de la période n'impacte pas les résultats, nous définissons la période de référence entre deux minima de l'incidence, du 1er septembre de l'année $N$ au\n",
+    "1er septembre de l'année $N+1$.\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er septembre de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er septembre.Comme l'incidence de la varicelle est à son niveau le plus faible à cette période, cette modification ne risque pas de fausser nos conclusions.\n",
+    "Encore un petit détail: les données comencent en décembre 1990 et finissent en avril 2020, cela rend les années incomplètes. Nous démarrons donc l'analyse en 1991 et la finissons en 2019."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_september_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1991,\n",
+    "                                    sorted_data.index[-1].year)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Period('1991-08-26/1991-09-01', 'W-SUN'),\n",
+       " Period('1992-08-31/1992-09-06', 'W-SUN'),\n",
+       " Period('1993-08-30/1993-09-05', 'W-SUN'),\n",
+       " Period('1994-08-29/1994-09-04', 'W-SUN'),\n",
+       " Period('1995-08-28/1995-09-03', 'W-SUN'),\n",
+       " Period('1996-08-26/1996-09-01', 'W-SUN'),\n",
+       " Period('1997-09-01/1997-09-07', 'W-SUN'),\n",
+       " Period('1998-08-31/1998-09-06', 'W-SUN'),\n",
+       " Period('1999-08-30/1999-09-05', 'W-SUN'),\n",
+       " Period('2000-08-28/2000-09-03', 'W-SUN'),\n",
+       " Period('2001-08-27/2001-09-02', 'W-SUN'),\n",
+       " Period('2002-08-26/2002-09-01', 'W-SUN'),\n",
+       " Period('2003-09-01/2003-09-07', 'W-SUN'),\n",
+       " Period('2004-08-30/2004-09-05', 'W-SUN'),\n",
+       " Period('2005-08-29/2005-09-04', 'W-SUN'),\n",
+       " Period('2006-08-28/2006-09-03', 'W-SUN'),\n",
+       " Period('2007-08-27/2007-09-02', 'W-SUN'),\n",
+       " Period('2008-09-01/2008-09-07', 'W-SUN'),\n",
+       " Period('2009-08-31/2009-09-06', 'W-SUN'),\n",
+       " Period('2010-08-30/2010-09-05', 'W-SUN'),\n",
+       " Period('2011-08-29/2011-09-04', 'W-SUN'),\n",
+       " Period('2012-08-27/2012-09-02', 'W-SUN'),\n",
+       " Period('2013-08-26/2013-09-01', 'W-SUN'),\n",
+       " Period('2014-09-01/2014-09-07', 'W-SUN'),\n",
+       " Period('2015-08-31/2015-09-06', 'W-SUN'),\n",
+       " Period('2016-08-29/2016-09-04', 'W-SUN'),\n",
+       " Period('2017-08-28/2017-09-03', 'W-SUN'),\n",
+       " Period('2018-08-27/2018-09-02', 'W-SUN'),\n",
+       " Period('2019-08-26/2019-09-01', 'W-SUN')]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first_september_week"
+   ]
+  },
+  {
+   "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": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "year = []\n",
+    "yearly_incidence = []\n",
+    "\n",
+    "for week1, week2 in zip(first_september_week[:-1],\n",
+    "                        first_september_week[1:]):\n",
+    "    one_year = sorted_data['inc'][week1:week2-1]\n",
+    "    assert abs(len(one_year)-52) < 2\n",
+    "    yearly_incidence.append(one_year.sum())\n",
+    "    year.append(week2.year)\n",
+    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On présente désormais les incidences annuelles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ba3508d68>"
+      ]
+     },
+     "execution_count": 13,
+     "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": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3ba3635f28>"
+      ]
+     },
+     "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.hist(xrot=20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On peut également classer les années par nombre d'incidences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "1992    832939\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/module3/exo2/incidence-PAY-7.csv b/module3/exo2/incidence-PAY-7.csv
new file mode 100644
index 0000000000000000000000000000000000000000..40452db8f6bc405e1965918f6c44f1c8545de9e9
--- /dev/null
+++ b/module3/exo2/incidence-PAY-7.csv
@@ -0,0 +1,1535 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[199049,202016],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-04-27T17:04:56+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202016,7,803,83,1523,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
+201952,7,7941,5246,10636,12,8,16,FR,France
+201951,7,5823,3675,7971,9,6,12,FR,France
+201950,7,6424,4276,8572,10,7,13,FR,France
+201949,7,6621,4540,8702,10,7,13,FR,France
+201948,7,5542,3383,7701,8,5,11,FR,France
+201947,7,7536,5058,10014,11,7,15,FR,France
+201946,7,2638,1316,3960,4,2,6,FR,France
+201945,7,4492,2615,6369,7,4,10,FR,France
+201944,7,5728,3627,7829,9,6,12,FR,France
+201943,7,4834,2751,6917,7,4,10,FR,France
+201942,7,6279,3989,8569,10,7,13,FR,France
+201941,7,4130,2030,6230,6,3,9,FR,France
+201940,7,4211,2218,6204,6,3,9,FR,France
+201939,7,3137,1310,4964,5,2,8,FR,France
+201938,7,3078,1416,4740,5,2,8,FR,France
+201937,7,970,162,1778,1,0,2,FR,France
+201936,7,1277,263,2291,2,0,4,FR,France
+201935,7,922,0,1857,1,0,2,FR,France
+201934,7,1997,605,3389,3,1,5,FR,France
+201933,7,3279,1103,5455,5,2,8,FR,France
+201932,7,3627,1576,5678,6,3,9,FR,France
+201931,7,7611,4605,10617,12,7,17,FR,France
+201930,7,8875,5629,12121,13,8,18,FR,France
+201929,7,10658,7151,14165,16,11,21,FR,France
+201928,7,11251,7604,14898,17,11,23,FR,France
+201927,7,16602,12349,20855,25,19,31,FR,France
+201926,7,18851,14491,23211,29,22,36,FR,France
+201925,7,18309,13972,22646,28,21,35,FR,France
+201924,7,13382,9659,17105,20,14,26,FR,France
+201923,7,19264,15027,23501,29,23,35,FR,France
+201922,7,16861,12783,20939,26,20,32,FR,France
+201921,7,17572,13140,22004,27,20,34,FR,France
+201920,7,16180,12310,20050,25,19,31,FR,France
+201919,7,16646,12721,20571,25,19,31,FR,France
+201918,7,13803,10159,17447,21,15,27,FR,France
+201917,7,15868,11815,19921,24,18,30,FR,France
+201916,7,17127,12815,21439,26,19,33,FR,France
+201915,7,21449,16525,26373,33,26,40,FR,France
+201914,7,16131,12254,20008,25,19,31,FR,France
+201913,7,17264,13409,21119,26,20,32,FR,France
+201912,7,14133,10499,17767,21,15,27,FR,France
+201911,7,12655,9528,15782,19,14,24,FR,France
+201910,7,14062,10547,17577,21,16,26,FR,France
+201909,7,13725,10221,17229,21,16,26,FR,France
+201908,7,17186,13156,21216,26,20,32,FR,France
+201907,7,10206,7388,13024,16,12,20,FR,France
+201906,7,13504,10402,16606,21,16,26,FR,France
+201905,7,12799,9843,15755,19,15,23,FR,France
+201904,7,11842,9041,14643,18,14,22,FR,France
+201903,7,8616,6114,11118,13,9,17,FR,France
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