{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence du syndrome grippal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek\n",
    "import requests\n",
    "import csv\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Les données de l'incidence du syndrome grippal sont disponibles du site Web du [Réseau Sentinelles](http://www.sentiweb.fr/). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1984 et se termine avec une semaine récente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get(\"https://www.sentiweb.fr/datasets/incidence-PAY-3.csv\") # on envoie ne requête http à l'aide de la bibliothèque \"requests\" et on récupère le contenu dans l'objet 'r'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\" \n",
    "if (Path(file).is_file() == False): # on teste si le fichier des données existent\n",
    "    file_data = open(r\"C:\\Users\\renau\\mooc-rr\\module3\\exo1\\incidence.csv\",\"w\",encoding = \"ANSI\") # création d'un fichier vide accesible en écriture\n",
    "    file_data.write(r.text) # écriture dans le fichier du contenu de la requête au format texte\n",
    "    print(\"Ce fichier a été téléchargé depuis l'url du site du réseau **Sentinelles**\")\n",
    "    file_data.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\renau\\\\mooc-rr\\\\module3\\\\exo1\\\\incidence.csv'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>202106</td>\n",
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       "    <tr>\n",
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       "      <td>33</td>\n",
       "      <td>27.0</td>\n",
       "      <td>39.0</td>\n",
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       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202102</td>\n",
       "      <td>3</td>\n",
       "      <td>17320</td>\n",
       "      <td>13906.0</td>\n",
       "      <td>20734.0</td>\n",
       "      <td>26</td>\n",
       "      <td>21.0</td>\n",
       "      <td>31.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>1889</th>\n",
       "      <td>198448</td>\n",
       "      <td>3</td>\n",
       "      <td>78620</td>\n",
       "      <td>60634.0</td>\n",
       "      <td>96606.0</td>\n",
       "      <td>143</td>\n",
       "      <td>110.0</td>\n",
       "      <td>176.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1890</th>\n",
       "      <td>198447</td>\n",
       "      <td>3</td>\n",
       "      <td>72029</td>\n",
       "      <td>54274.0</td>\n",
       "      <td>89784.0</td>\n",
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       "      <td>99.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>1891</th>\n",
       "      <td>198446</td>\n",
       "      <td>3</td>\n",
       "      <td>87330</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>106974.0</td>\n",
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       "      <td>123.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1892</th>\n",
       "      <td>198445</td>\n",
       "      <td>3</td>\n",
       "      <td>135223</td>\n",
       "      <td>101414.0</td>\n",
       "      <td>169032.0</td>\n",
       "      <td>246</td>\n",
       "      <td>184.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>1893</th>\n",
       "      <td>198444</td>\n",
       "      <td>3</td>\n",
       "      <td>68422</td>\n",
       "      <td>20056.0</td>\n",
       "      <td>116788.0</td>\n",
       "      <td>125</td>\n",
       "      <td>37.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1894 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          43.0        FR   France  \n",
       "1          40.0        FR   France  \n",
       "2          46.0        FR   France  \n",
       "3          39.0        FR   France  \n",
       "4          31.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1889      176.0        FR   France  \n",
       "1890      163.0        FR   France  \n",
       "1891      195.0        FR   France  \n",
       "1892      308.0        FR   France  \n",
       "1893      213.0        FR   France  \n",
       "\n",
       "[1894 rows x 10 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(file, sep=',', encoding = 'ansi', skiprows=1) # on lit le fichier csv, en tant que panda data frame\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
    "\n",
    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
    "\n",
    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y a-t-il des points manquants dans ce jeux de données ? Oui, la semaine 19 de l'année 1989 n'a pas de valeurs associées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>week</th>\n",
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       "      <th>inc_up</th>\n",
       "      <th>inc100</th>\n",
       "      <th>inc100_low</th>\n",
       "      <th>inc100_up</th>\n",
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       "      <th>geo_name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1657</th>\n",
       "      <td>198919</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "1657  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
       "\n",
       "     geo_insee geo_name  \n",
       "1657        FR   France  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[raw_data.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous éliminons ce point, ce qui n'a pas d'impact fort sur notre analyse qui est assez simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1890</th>\n",
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       "      <td>20056.0</td>\n",
       "      <td>116788.0</td>\n",
       "      <td>125</td>\n",
       "      <td>37.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1893 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202106          3   23816   18867.0   28765.0      36        29.0   \n",
       "1     202105          3   22491   18436.0   26546.0      34        28.0   \n",
       "2     202104          3   25804   21491.0   30117.0      39        32.0   \n",
       "3     202103          3   21810   17894.0   25726.0      33        27.0   \n",
       "4     202102          3   17320   13906.0   20734.0      26        21.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1889  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1890  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1891  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1892  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1893  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          43.0        FR   France  \n",
       "1          40.0        FR   France  \n",
       "2          46.0        FR   France  \n",
       "3          39.0        FR   France  \n",
       "4          31.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1889      176.0        FR   France  \n",
       "1890      163.0        FR   France  \n",
       "1891      195.0        FR   France  \n",
       "1892      308.0        FR   France  \n",
       "1893      213.0        FR   France  \n",
       "\n",
       "[1893 rows x 10 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data"
   ]
  },
  {
   "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": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_week(year_and_week_int):\n",
    "    year_and_week_str = str(year_and_week_int)\n",
    "    year = int(year_and_week_str[:4])\n",
    "    week = int(year_and_week_str[4:])\n",
    "    w = isoweek.Week(year, week)\n",
    "    return pd.Period(w.day(0), 'W')\n",
    "\n",
    "data['period'] = [convert_week(yw) for yw in data['week']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Il restent deux petites modifications à faire.\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": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_data = data.set_index('period').sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vérifions la cohérence des données. Entre la fin d'une période et\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",
    "Ceci s'avère tout à fait juste sauf pour deux périodes consécutives\n",
    "entre lesquelles il manque une semaine.\n",
    "\n",
    "Nous reconnaissons ces dates: c'est la semaine sans observations\n",
    "que nous avions supprimées !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1989-05-01/1989-05-07 1989-05-15/1989-05-21\n"
     ]
    }
   ],
   "source": [
    "periods = sorted_data.index\n",
    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
    "    delta = p2.to_timestamp() - p1.end_time\n",
    "    if delta > pd.Timedelta('1s'):\n",
    "        print(p1, p2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Un premier regard sur les données !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 54,
     "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": [
    "Un zoom sur les dernières années montre mieux la situation des pics en hiver. Le creux des incidences se trouve en été."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DwHdilyaorkOUp1rnWIHqvaq6WFUX19XVDfU28gZVpScQpty1NMDWnxpLhsqeMkvDyGeSEg0RKcIRjJ+r6i8BVLVZVSOqGgV+ghNzAMcamBlXvR7Y75bXJyg/ro6I+IBqoH2ItgqeQDhKVKHcjWmAWRpjyaAzwm2ehpHnJJM9JcD9wBZV/W5c+bS4yz4KvOUePwEsdzOi5uAEvNeq6gGgS0TOd9u8Afh1XJ1YZtTVwHNu3OMZ4HIRmei6vy53ywqe2GS+cr+XyhITjbEmZmnE9tCIYcuIGPmOL4lrLgSuBzaKyAa37OvAtSKyCMddtAv4DICqbhKRR4HNOJlXn1fV2MJInwN+BpQCT7kPcETpIRFpxLEwlrtttYvIbcCr7nW3qmp7Km8034itNVVWfMzSsFnhY0cwEsHrEXzeRDPCLYvNyF+GFQ1VfZHEsYUnh6hzO3B7gvJ1wOkJyvuAawZpayWwcrh+FhqxXfsqLBCeFfpCUUp8JxrqRV6zNIz8xmaE5yi9rmiUxQfCTTTGjL5QhJIi7wnlNk/DyHdMNHKU2KZL8TGNI0dNNMaKvlA0sWjYPA0jzzHRyFF6+wPhPsqKvZQUeWjrDmS5V4VDXyiCv8jcU0bhYaKRo8T2By8v9iEi1JT7OWSiMWb0hSKU+BK7p8zSMPIZE40cJRbTKC12bly1lX4OdQez2aWCoi8coWQQSyMUUVsHzMhbTDRylD537aNS169eV1FslsYY0heK9gt2PLHJfhYMN/IVE40cpW/AMha1FWZpjCWDuqfceRs2V8PIV0w0cpRAOIIvbnJZbYWf9p4AkajdrMaCwVJui7zOlCbbU8PIV0w0cpSBKZ+1FcVEFTp6zdoYC/pC0cTZU+aeMvIcE40cJRCOHLfCam2lH8DiGmNEIDzI5D7X8rO0WyNfMdHIUU60NFzR6DJLYyxwlhEZPBBuabdGvmKikaP0hQZYGhVmaYwlR0ODp9yCuaeM/MVEI0cJhKP44yyNOhONMSMUiRKJ6pDuqVDYEhKM/MREI0cZaGlUlfoo9nos7XYMiM2RSWhp9AfCIyecM4x8wEQjRwmEo8fdtESEGpvgNybE5sgMHQg3S8PIT0w0cpRAgnkCzgQ/E41Mc8zSSBQId+dpWEwjbfz3mj18++mt2e6G4ZLMdq8zReR5EdkiIptE5Etu+SQRWSUi292/E+Pq3CwijSKyTUSWxpWfKyIb3XN3udu+4m4N+4hbvkZEGuLqrHBfY7uIrMAA3JjGgE2Aas3SGBMC4cFFo8hSbtPOI6/u4b/X7sl2NwyXZCyNMPBVVZ0PnA98XkQWAF8DnlXVecCz7nPcc8uBhcAVwN0iEvt23QPchLNv+Dz3PMCNQIeqzgXuBO5w25oE3AKcBywBbokXp0Im0Yzkmgq/pdyOAf3uqQQ791nKbXqJRJVtzV0c7g3R0WOf7fHAsKKhqgdU9TX3uAvYAswAlgEPuJc9AFzlHi8DHlbVgKruBBqBJSIyDahS1dXqLAH64IA6sbYeAy51rZClwCpVbVfVDmAVx4SmoOkLJbI0/LT1BGyF1QwzlHvKUm7Ty+62nn6R3tnWk+XeGDDCmIbrNjobWANMUdUD4AgLMNm9bAawN65ak1s2wz0eWH5cHVUNA51AzRBtDezXTSKyTkTWtba2juQt5SyJZiTXVhQTiiidR0NZ6lVhkFwg3EQjHWw72NV/vOuQicZ4IGnREJEK4HHgy6p6ZKhLE5TpEOWp1jlWoHqvqi5W1cV1dXVDdC1/SGRpTKsuBaCp42g2ulQwDJVye8w9ZdZeOthysAuPgEdMNMYLSYmGiBThCMbPVfWXbnGz63LC/dviljcBM+Oq1wP73fL6BOXH1RERH1ANtA/RVkGjqgktjVOmVACwvaUrUTUjTRxNwj1lMY30sPXAEebUljNjYik723qz3R2D5LKnBLgf2KKq34079QQQy2ZaAfw6rny5mxE1ByfgvdZ1YXWJyPlumzcMqBNr62rgOTfu8QxwuYhMdAPgl7tlBU0ookSVEyyNhtpyirzC283dWepZYdBvaSRYeyq2NLq5p9LD1oNdnDatioaacrM0xgm+JK65ELge2CgiG9yyrwPfAh4VkRuBPcA1AKq6SUQeBTbjZF59XlVj02M/B/wMKAWech/giNJDItKIY2Esd9tqF5HbgFfd625V1fbU3mr+0DdIymeR18Oc2nK2N5ulkUn6wrGYxuDuKQuEj57uQJg97b381eJ6WroC/PK1fagqbqa+kSWGFQ1VfZHEsQWASwepcztwe4LydcDpCcr7cEUnwbmVwMrh+llIBGK79iVwj5wypZI3mg6PcY8Ki4BraSQa/yKPBcLTRSwIftrUKsr9vXQHwhzqDlLnbgNgZAebEZ6DxNwjA91T4IjG3vaj9AbDY92tgmHg/uzxeDyCzyMW00gDe9odd9RJdeU01JYDsMvSbrOOiUYOEggPnvJ5ypRKALZbXCNj9IWieORY/GIgxT6PiUYa6O5zfvhUlRYxp8YRjZ0W18g6Jho5yNCWhpNB9bbFNTJGbDb+YL71Iq/H3FNpoDvgfM4r/D7qJ5bi9Qh7LIMq6yQTCDfGGUNZGrNryin2eUw0MkjfIFu9xijyegjaPI1R0xMI4/UIfp8HEWFiWTFtPba2WrYxSyMHCQxhaXg9wty6Ct5s6rTlRDKEs9Xr4F8dv7mn0kJ3IEx58TGLzlmQ09afyjYmGjnIYCm3MS6bP5k1O9v5h/9509wkGSDRYpHxFHnFxj0N9ATCVPiPOUMmlRfTbosWZh1zT+UggdDg8wQAvvKBUxARvv/sduZNqeCz7z15LLuX9/SFIgnTbWNYIDw99ATDlMeJRk2Fn42WTp51zNLIQWKWhj/BjGRwdvH7ygdO4aS6ctbv7hjLrhUEfaHooIINFghPF119A0SjvJg2szSyjolGDjKcpRFjwbQqNu8fam1JIxX6QpGES4jE8Ps8/ckKRuoMdE/VlBfT1Rfu3wTLyA4mGjnIsZTbwW9cAAunV7Pv8FEO99qvs3TiZE8N/tUpLfb2L2popE5PIEK5/9hnfFJFMQAdPbb0fzYx0chBhlr7KJ6F06sA2HzArI100heKUlo8uGCXFnk5GjTRGC3dgYHuKWf5ENvSOLuYaOQg/WtPDWNpLIiJhrmo0spw7qnSYl+/NWikTk9wgHvKtTQsgyq7mGjkIH3hCEVewesZerXP2go/U6r8bDLRSCt9oeiQ2VOlRR5zT6WBnhMsDUc0bIJfdjHRyEECoeiQv3TjWTi92iyNNBMIDRPTKPLSa+6pUREIRwhFdEAg3HFPtdkEv6xiopGD9IUj+IeJZ8RYMK2KxtZuc5ekkeGWESmxQPio6XHXnSqPix1VlfrwecTSbrOMiUYO0heKDBvPiLFwehWRqNpaVGkiHIkSiujQMY0iL8FwlEjUlnFJlZ6As8JtvHtKRJxZ4WZpZJVktntdKSItIvJWXNk3RGSfiGxwH1fGnbtZRBpFZJuILI0rP1dENrrn7nK3fMXdFvYRt3yNiDTE1VkhItvdR2w72IInEI4mbWmcOtVZKn3rQRONdJBM5lpsnw2z7lKn2xWNePcUOLPCLaaRXZK58/wMuCJB+Z2qush9PAkgIgtwtmpd6Na5W0RiP8nuAW7C2TN8XlybNwIdqjoXuBO4w21rEnALcB6wBLjF3Se84AkMk70Tz+yackqKPP27oBmjo39/8CHcU2WuS8VcVKmTyNIAmxU+HhhWNFT1Tzj7difDMuBhVQ2o6k6gEVgiItOAKlVdrc7Sqw8CV8XVecA9fgy41LVClgKrVLVdVTuAVSQWr4JjJJaG1yPMm1xpopEmjonG4OMfExSbq5E63YOJRkWxBcKzzGhiGl8QkTdd91XMApgB7I27psktm+EeDyw/ro6qhoFOoGaItk5ARG4SkXUisq61tXUUbyk3GG6ewEBOnVpp7qk00RcafC+TGKVmaYyaWCC8suR40bCVbrNPqqJxD3AysAg4AHzHLU80cUCHKE+1zvGFqveq6mJVXVxXVzdEt/ODQHjoBfMGctrUSg51B2izmbSjJpklXErN0hg13QFnqZBE7qnuQNjiRVkkJdFQ1WZVjahqFPgJTswBHGtgZtyl9cB+t7w+QflxdUTEB1TjuMMGa6vgGUn2FBwLhpuLavTErIf4NZEG0i8admNLmf6tXotPDISDzQrPJimJhhujiPFRIJZZ9QSw3M2ImoMT8F6rqgeALhE5341X3AD8Oq5OLDPqauA5N+7xDHC5iEx03V+Xu2UFz3BLcw/EMqjSRyxAW1Y8+FY05p4aPccC4ceLc2xWuK0/lT2G3YRJRH4BvA+oFZEmnIym94nIIhx30S7gMwCquklEHgU2A2Hg86oa++Z8DicTqxR4yn0A3A88JCKNOBbGcretdhG5DXjVve5WVU02IJ/XBMIjszTqKvxMKi/mmU0H+e2b+7nuvNl87Nz64SsaJxCb6T2kpVFs7qnR0hMI4/d58HmP/3E0uaoEgJYjJhrZYljRUNVrExTfP8T1twO3JyhfB5yeoLwPuGaQtlYCK4frY6ExUktDRDh1SiWr32kD4EhfmL88Z0b/3stG8vT/Ah7K0rCYxqjpHrCXRowpVY57qrmrb6y7ZLjYdq85SCA89Hajifj0hQ3Mn1bF5Co/33pqK5v2H+H0GdUZ6mH+ErM0yoZZGh3MPTUaBi5WGKO2wo8INJulkTVsGZEcQ1UdS8M3sn/d5Qun8v99eAHXvmsWxV4Pv3p9X4Z6mN/0BBPPH4inpNhmhI+W7kAk4RgXeT3UlPtpOWKWRrYw0cgxYtuIjtTSiFFdVsT7T6vjiTf2E47YlqQjpTcQwSPOlq6DYe6p0eNs9Zr4Mz6lyk+ziUbWMNHIMfpFY4SWRjzLFs2gtSvAG02H09SrwqEnGKa82DdkPKjI66HIK+aeGgU9wcTuKYApVSW0dJl7KluYaOQYgSTWPhqO2DawO1p70tKnQuJoMELZEJlTMUpsT41RMXCr13gcS8NEI1uYaOQYPUmkfA7HjAml+DzCrkMmGiOlJxgZMnMqRmmR12Iao6AnED5hYl+MyZUltPUECJl7NSuYaOQYyaR8DofP62HmpDJ2tZlojJTeQDgpS6PUNmIaFT2DBMLBcU+p2gS/bGGikWMMts/ASGmoKWPnod50dKmg6AmGh5wNHqO0yGuB8BRRVXqCQwfCwdJus4WJRo4x2D4DI6WhtpzdbT04K7YYydIbjBy3BelglBSZpZEq3YEwqlBZUpTw/BR3VrhlUGUHE40cY7B9BkbKnNpyeoMRWi0LZUT0BMKUJTH2ZcVmaaRKR4+zwu2EssSiMdm1NGyuRnYw0cgx0uWeml1TDsBOC4aPiGQtjVKzNFKmo9dZwXaSuzjhQGrK/Xg9Yu6pLGGikWMMtvrnSJnjioYFw0dGTyC5mEaJBcJTpt0VjQlliUXD6xFqK4rNPZUlTDRyjNg+A6PJngKYPqGEIq+wq82C4cmiqo6lkUz2VJGXPnNPpcThYSwNcOIazeZazQomGjmG80vXi8czuhVq+9NuzT2VNMFIlHBUk8+eMksjJdrdmMbEQWIa4MzVaO40SyMbmGjkGIOt/pkKDTXlFtMYAb39Vt7wlkZZsc0IT5XDvUE8AlWDZE8BnFVfzbbmLtbvPn6LnWhUaenqIxK1rMBMYaKRYwy2z0AqzJtcwTutPQTCdnNLhtgKt8lkT5UUeQmEo0Tt5jVi2nuCTCgrHtKa/uuL5jClys83ntjcP8YPvLyLBbc8zZLbn+XbT28dq+4WHMOKhoisFJEWEXkrrmySiKwSke3u34lx524WkUYR2SYiS+PKzxWRje65u9xtX3G3hn3ELV8jIg1xdVa4r7FdRGJbwhY0jqUxuiB4jEUzJxCMRNm0/0ha2st3+nftS8Y9FVse3QR5xBzuDQ3pmgIn5fzrV85n475OfvryLva09XL7k1s4s34CU6tK2NHaPUa9LTySsTR+BlwxoOxrwLOqOg941n2OiCzA2a51oVvnbhGJ3eHuAW7C2Td8XlybNwIdqjoXuBO4w21rEs7WsucBS4Bb4sWpUOkJJLf2UTKcM9sZztd2d6SlvXynf3/wJAPhYMujp0J7T5CJg2ROxfORs6Zz2fzJ3Pbbzaz46VqKPMJdy89m3pQKWruDY9DTwmRY0VDVP+Hs3R3PMuAB9/gB4Kq48odVNaCqO4FGYImITAOqVHW1OlOQHxxQJ9bWY8ClrhWyFFilqu2q2gGs4kTxKjjS6Z6aUlXCjAmlvL7ncFray3dGZGnY7n0p09EbZOIQmVMxRIQfXncOl82fzM5DPXzpsnlMrS6hrtLPIcusyhip3n2mqOoBAFU9ICKT3fIZwCtx1zW5ZSH3eGB5rM5et62wiHQCNfHlCeoch4jchGPFMGvWrBTfUm4w1D4DqXD2rAmsN0sjKfotjWSWEbHd+1KmozfImfXJbUXs93m5+7pzWbOzjXefXAtAXaWf1q4AqjrkvidGaqQ7EJ7oP6RDlKda5/hC1XtVdbGqLq6rq0uqo7lKOrOnAM6ZNZEDnX0c6DyatjbzlX5LI5llRFxLwzKoRoaq0tEbSsrSiFHs83DxvDq8buC8rsJPMBLlyNFwprpZ0KQqGs2uywn3b4tb3gTMjLuuHtjvltcnKD+ujoj4gGocd9hgbRU03UNsg5kKx+Iah9PWZr7Svz94MsuIFFtMIxV6gxGC4WhSMY3BqKt01qZqtaXTM0KqovEEEMtmWgH8Oq58uZsRNQcn4L3WdWV1icj5brzihgF1Ym1dDTznxj2eAS4XkYluAPxyt6xgCUei9IWiabU0Fkyrwu/zsGGvuaiGIzZPI9mUW7CYxkjpX3dqNKJR4YqGxTUywrCffhH5BfA+oFZEmnAymr4FPCoiNwJ7gGsAVHWTiDwKbAbCwOdVNfat+RxOJlYp8JT7ALgfeEhEGnEsjOVuW+0ichvwqnvdrao6MCBfUMR27UtXIBwc0352TRm7bTmRYYlZGqVJbLUbu8ZiGiNjuBVuk8Esjcwy7N1HVa8d5NSlg1x/O3B7gvJ1wOkJyvtwRSfBuZXAyuH6WCikay+NgUyfUMp+i2kMS28wQmmRt993PhT97ikTjREx3Aq3ydAvGmZpZASbEZ5DZFQ0Dts6PsMxkomVsbhHd58FY0dCxzAr3CZDdWkRRV4x0cgQJho5xLG9NNIXCAeYMaGU9p6gBW2HoTcYSWqxQqA/+6etxyaZjYSOntFbGiJCbYXf9hDPECYaOURPmpZFH8iMCaUA7DtsLqqhiK0wnAxFXg8Tyopos5nJI6K9N4SIYy2MhthcDSP9mGjkEOna6nUg013R2G+iMSTOXhrJj31NebH92h0hh3uDVJcWJRU3Goq6ChONTGGikUP0pGmr14FMn1ACmGgMR08weUsDoLbCb5bGCEl23anhqKv0W/ZUhjDRyCH6J5elWTSmVJXgERON4egd4WKRtRV+DvXYjWskHOoOUDOKeEaMuko/bd0B21cjA5ho5BAx91RlSXpFo8jrYWpVCU0mGkPSHQgntcJtjJqKYrM0Rsietl5mTiobdTt1lX6ieiwby0gfJho5RE8gjNcj+H3p/7c5abcmGoMRcXeEm1pVknSdmnI/nUdDBMPRDPYsfwiEIxw40sesNIhGrc0KzxgmGjmEs5eGNyMrd9pcjaE5eKSPUERH9Cu4psJxs9iv3eRo6jiKKsyuSY+lAdBiopF2TDRyiHTupTGQ6RNKOdB51LYnHYQ97jIrI/kVbL92R8ae9pGP8WDELMLmI/ZDKN2YaOQQ6V4WPZ4ZE0oIRdRSRAdhbwo3tNoKm+A3EvqFOQ2WxuQqR7APdppopBsTjRyiO5OiMdGZq2HB8MTsae/F6xGmVY8gpuFaGm0mxEmxp72X0iJv/yq1o8Hv81JbUcwBE420Y6KRQ2TSPTVvciUAG5s6M9J+rrOnvZcZE0rxeZP/ysRiGpZBlRy723qZNaksbTG7qdUlHLSFONOOiUYOsf/wUaaO4JfuSJg5qYz6iaW8vONQRtrPdfa0947Y117p91Hs9dhcjSTZ256edNsYU6tKzdLIACYaOUIgHKH5SIB6142UCS44qYZX3mm3YHgCUrmhiYjN1UgSVWVPe29aMqdiTKsu4aAFwtOOiUaOsK/DMbNnTkzfl2og755bQ+fREJsPHMnYa+Qi3YEwbT3BlLJ6bLXV5GjtCnA0FElL5lSMqdUlHO4N2erNaWZUoiEiu0Rko4hsEJF1btkkEVklItvdvxPjrr9ZRBpFZJuILI0rP9dtp1FE7nK3hMXdNvYRt3yNiDSMpr+5zN6YaKTxSzWQC06qBeCVd9oy9hq5SCqZUzHM0kiO/nTbNFsagFkbaSYdlsb7VXWRqi52n38NeFZV5wHPus8RkQU4W7kuBK4A7haR2JoM9wA34ewpPs89D3Aj0KGqc4E7gTvS0N+cpKnD+VJl0j01tbqEObXlvLzDRCOe2A1t5qSRj31Nud+yp5JgdwrzYIYjNlfjgAXD00om3FPLgAfc4weAq+LKH1bVgKruBBqBJSIyDahS1dWqqsCDA+rE2noMuFQyMR06B9jbfpQirzBlBMtYpML5J9Xw6k6La8QzGkujtqKYQz1BnI+2MRibDxzB7/MwO83uKbC5GulmtKKhwO9FZL2I3OSWTVHVAwDu38lu+Qxgb1zdJrdshns8sPy4OqoaBjqBmlH2OSfZ2+GkfI52n4HhOGNGNV2BsG3IFMf25m6qS4tS2hiotsJPMBzlyFHb9nUoNu7rZMH0qhGlNA9HTDQsgyq9jPY/dKGqngN8EPi8iLxniGsT3e10iPKh6hzfsMhNIrJORNa1trYO1+ecpKnjaEbjGTFOnVoBwNvNXRl/rVxAVXmx8RDnzZmU0vyBObXlADS22ngORjSqbNrXyRkzqtPablmxj+rSIrM00syoRENV97t/W4BfAUuAZtflhPu3xb28CZgZV70e2O+W1ycoP66OiPiAaqA9QT/uVdXFqrq4rq5uNG9p3NLU3pvReEaMeVOcSX7bTDQA2NXWy77DR7l4Xm1K9edPrwJg8wEbz8F451APPcEIp6dZNMAJhueypdHZG+LZLc282XQ4213pJ2XREJFyEamMHQOXA28BTwAr3MtWAL92j58AlrsZUXNwAt5rXRdWl4ic78YrbhhQJ9bW1cBzWoDO4R435bM+g+m2MapKipheXcLbB+0mB/DidsdyvWheaj9GpleXUFXiY4ulMQ/KW/ucVQjSbWmAOyv8SG66Wp/d0sw5/76KGx9Yx0d+8BJfffSN/j11sslo1qSYAvzKNdl9wH+r6tMi8irwqIjcCOwBrgFQ1U0i8iiwGQgDn1fVWAL154CfAaXAU+4D4H7gIRFpxLEwlo+ivzlLLL4wFu4pgFOnVrLVRAOAP28/RP3EUhpSTAUVEeZPq2LzfhONwdi4rxO/z8O8yRVpb3tadUm/KOUaP/rjDmZMKOVbHzuDF7cf4u4XdnDy5HL+7n1zs9qvlEVDVd8BzkpQ3gZcOkid24HbE5SvA05PUN6HKzqFTCx7ZyzcUwCnTK3kpcY2QpEoRWkMTOYS//HMVlRh9Y42/uKsaaNaD2n+tCoeeXUvkahmPJEhF9m4r5P509IbBI8xa1I5h7qDdPaGqC4beSJDtmhs6ebVXR3c/MHTePfJtbz75Fp+t/HAuFgbrjDvCDlG/zyBMXBPAZw6pZJgJMrutp4xeb3xxoHOo/zw+R3c/cIOugJhLk7RNRVjwbQqjoYiBTueQxELgp9Zn37XFMDpM5yY0lv7s3+zHQmPrtuLzyP85TnHwr0LplWNi9UaTDRygHW7O5hS5e/fnyHTnBILhh/sHpPXG288v9WJYzzw10v43scXsXTh1FG1N3+ac+PaYsFwOgbMWVm7q52eYIRzZ08colbqnD7dEaONOeSiCoajPL6+iUvnT+7fgRBg4fQqdrf10tUXymLvTDTGPdGo8nLjIS6aW5eRbV4TMXdyBR6BbQez/6smGzy/rYUZE0p5z7xarjp7xqhdSvOmVOD1SMEHw7c3d3HeN5/l289s6y97dN1eKvw+PrBgSkZec2J5MfUTS3NKNJ54Yz9tPUE+cd7s48oXTB8fPz5MNMY5mw8coaM3xEXzxm5OY0mRl3mTK3l1V8eYveZ4IRCO8FLjIS45bXLaRLqkyMvJdeU55yJJN79Yu5dgJMo9L+zgua3NdPWFeHLjAT581nTKijOzTww41kauBMNVlZ/86R1Om1rJewakeS90rabNWf4cmWiMc15qdPa3uPDk1OYJpMrlC6ewZmdbwa3QuuaddnqDES45bfLwF4+Ad59cy8s72jiSZddCtgiEI/zy9SYumz+F+dOq+NIvNvCVRzbQF4ryV4vrh29gFJxRX83utl46j47/sf/j261sa+7iby8+6YQfLZMr/dSUF7Mpy5l4JhrjnBcbD3HKlAomZ3jNqYF86MxpRBWefuvgmL5utvnDlmb8Pg8XnJxey27ZoukEw1Ge3lhY4xnj95uaOdwbYsW7Z/OTG87lXXMm8YctLZw6pZJFMydk9LVjkwY35YC1ce+f3mFqVQkfPmv6CedEhAXTsx8MN9EYx/SFIry6q50L546tlQFOBtXJdeX87s0DY/7a2aI3GOZXr+9j6cKplBR5h68wAhbNnEBDTRn/u2FfWtvNBXqDYe790zvMmFDKhSfXUj+xjJWfehdPf/li7luxOOOxutikwfHmHvzX/32Le17Y0f/8rX2dvLyjjU9f2ECxL/GtecH0KrY3dxMMR0f12t2BMFff8zJfffQNQpGRtWWiMY55dVc7faEoF2VBNESED50xjTU722jtKgwX1a837KerL8wNF8we/uIRIiIsWzSD1e+0FdRaSD2BMJ/66ats2t/JP39oPp64pILTplaNyYTVSW4w/E9vj5+tjDc2dfLQK7v5zu+30djiBLbv/dM7VPh9XHverEHrvWv2JIKRKI+/1jToNcMRiSpf+sXrvLang8dfa+JvHljHtfe+knR9E41xzPNbWyn2eXj3GMczYnz4rOlEFX6+ZndWXn8sUVUeWr2b06ZWZiz986qzZ6AKD72yKyPtjzcC4Qg3PbSO9bs7+N7ys7nyjGlZ68sNF8zmxcZD/THCbHP3C41UlvgoLfbyb7/ZzBt7D/O7jQe4dslMqkoGn4R46fzJLGmYxB1Pb6W958TNvfpCEf72wXU8vt4RlT++3crye1ez+N//wB1PbwXgO7/fxrNbW/jGRxbyLx+azx/fbqXpcG/SfTfRGMe8sK2FC06qobQ4va6SZJk3pZIrFk7lvj/vTPgBzQe6+kJ89qH1XPzt59l84Ag3XNCQMXfJnNpyli2azk/+vJM9bcl/SXORcCTKVx99g5ca27jjY2fykQQ++rHkhgsamDGhlG8+uSXre8U0tnTz9KaDrLigga9cdgp/3n6IZT98iSKv8OkL5wxZV0S47arT6e4Lc8dTW084/62ntrJqczP/8r9vsXpHG1/4+WvsO3yUObVl3PPCDm79zWbufmEHy981kxsuaOBvLj6J1Tdfwh//4f1J999EY5yy61AP7xzq4f2nZnfV3n9Yegq9wTB3P9+Y1X5kgqPBCDc+sI4/bGnmnFkT+ex7T+Yvz5kxfMVRcPMH5+PzCLf9bnNGXyebtHUHuP7+tfz2zQPc/MHTuPrczGZHJUNJkZf/d+mpbNp/hE/ev4bX9mQvnfz+F3dS7PXw6QsbuP6C2fzzlfP5j6vPZNVX3sv0CcMvFXTq1EpuvGgOj6zby/rdxxb9/uPbrfzs5V0sWzQdr0e47r5XUOC//+Z8/utvzuOMGdWsfGknp02t5BsfWdhfb1p16XFuw+Ew0RgjNjZ18tj6JiLD/MpRVUKRKC9sc1aUf9+p6U39HClzJ1fy0bPrefCV3eNihc10oap89X828Oqudu78+CLuuvZsvvbB09IeAB/I1OoSvnjpPFZtbuaBl3dl9LWyQSgSZfm9r/Dang7+85qz+Mx7T852l/pZtmg6//oXC9h2sItrfrSa17MgHEf6Qvzv6/tYtmg6NRV+irwe/vY9J3HN4pkjiu988dJ5TKsu4Z9/9RbhSJQdrd188Revc8qUCu742Jl87YOnEVW45cMLmDmpDL/Pyw8/cQ4fOnMaP7zunFF9zjM3o8bo5/U9HXzyvjX0BCM8vHYPnzx/NtMnlLJ49sQTFP7OP2znB89tx+/zclJtOQ3uJj7Z5Opz63n8tSZeajw06iU1xgsPv7qXJzce5J+uOC1hemMm+duLT2Ldrg7+7TebmD6hNGOzobPBQ6t3s72lm/tuWMxl4+x9iQg3XjSHq8+p58q7/sxXHtnA7754MeX+sbsN/nJ9E0dDEa4/v2FU7ZT7fdzy4QV89r9e46q7X6KjJ4TPI9y/4l2UFHn55PmzuWz+lP7dCwFm1ZTxw0+cM8p3YJZGRolElcfXN7Fi5VpqKvzcumwh21u6+fIjG/irH6/mpofW09l7bMLR9uYu7n6+kcWzJ/GeU2r5wiXZXQI5xuKGiVT6ff3WT64SjSr3/fkdvvroG9z6m81cOLeGz7znpDHvh9cj3HXtIhZOr+afHn+T3mB+WHAdPUG+94e3uXheLZfOz66FPBTVZUV896/OYnd7L3//i9c53Jv5eJ2q0tLVx0Ov7OasmRM4Iw0LNC5dOJV//YsF+H1eAuEI996w+DhrJV4w0onk255Gixcv1nXr1mW7G4QiUa750Wo27D3MGTOqueeT51A/sYyjwQj7O4/y/NYW7nh6K1OrS7jnunOZO7mCT/10LVsOdPHcV99LTYV/+BcZQ/7u5+t5bfdhVt98yZitgZUq4UiUqHJCrvu9f9rBN5/cyrTqEmbXlPH95WczZYwnTcazfnc7H7tnNV+/8jRues/4ceMkQlXZ1txFbYWfWvezuXn/Ee778zvUVflZunAq//7bzWzYe5invvQeTp1ameUeD8+Dq3dx6282U1vh58fXn8tZI5hk2BeK8NqeDlq7AkwoK+bUKZWD3qQ7e0N84r5X+mdyf+/ji7jq7MzGzlJBRNar6uLhrjP3VIZ4YsN+Nuw9zK3LFnL9+bP7b7SlxV5Orqvg5LoKzpk9kS/8/DX+8p6XKfF5ONIX5psfPWPcCQY4sZUnNx5k84Ej/WvgZJtAOMIPn2tkW3MXZcU+egJhmo/0sfVgF4FwlMoSHxeeXMsVp0/laCjCt5/expVnTOWHnzhnXAjfubMncfG8Wu790zt88vzZGV1/aTQ8s+kgdz27nU37j1DkFS6aW8uh7iAb93VS4ffRGwzz4z++Q2WJj+8vPzsnBAOcjKpzZk3kcz9fz3X3rWHlp97FkjmThq3XF4rw8R+v5o24vS08And+fBHLFjliEI0qwUiUvlCEmx5az/bmbr5+5WmcWT+B808au3XkMkFOWBoicgXwfcAL3Keq3xrs2nRZGtGo8vKONn775n5EYHp1KR86cxon1Q2/u1gkqnzgzj/i93l58osXDXmDau8JcvvvtqCqfOzc+qzM/k6Glq4+ltz+LF++bB5fvuyUEdff3dbDoe4gp8+owu8bfbD5rX2d/ONjb7L5wBFOrisnEI5S4fdRW+HntKmVVJcWsb+zj1WbD3Ko23E/zK4p44kvXER16fjZjGfdrnau/tFqLl8whX//6OlMrsye5TOQfYePcvvvNvPkxoPMnVzB9efPZuehHl7Y1kL9xDKWzJnEinc30NYd4NktLXzozGlJZf+MNw529nHdfa/Q1HGU25adzl+9ayadR0NUlfiO++6GIlEC4Si3/WYzj6zbyzc/egbvaphIR2+I//z9Nl7f08FfXziH37yxn/0DJnB+f/kxQRmvJGtpjHvREBEv8DbwAaAJeBW4VlUT5iwuXrxY1659le5gmEAoyjObDvL81hZOnlzBe+bVceHcmiFv4mveaWPlSztZv/swh7oDVPp9+Iu8tPUEUIWTasuZXVPGrEllzKopZ9akMqpKfPSGIvQFI/QGI2xr7uLeP73D3dedk9UJTenm2ntfYc3ONq4/fzahqHK4N8g/XXEaMyaU8sK2VgLhKOFolM0HjjC1qoTrzpvNxn2H+e6qt3mpsQ3A2dZzSgWnTK7kE+fNYnHD8L/s4tl68Ah3rnqbZzY1M7GsiP+4+qwhA66hSJTtzc6+IA21ZePy1/w9L+zgzlVv4/UIi2ZO4KJ5tax4dwPbm7t4bH0T5X4ftRXF1Fb4qav097uIJpUXn7Bse8jNpJlUVszkqhJUlZ5ghBKfh95QhJYjfcyYUDbo3J/uQJiXGw/x3NYWfvnaPhD40qXzuOk9J+X1Lo5t3QG++PDrvNTYRnVpEZ1HQ8yYUMolp01mcqWfbc1d/GFLM30hZ8mNL7x/Lv+w9NT++p29IT72o5dpbOnmgpNqOO+kSRT7PBR7PZw6tXLUG3mNBfkkGhcA31DVpe7zmwFU9f9PdH1Nw3yd/qnv0dV3LLg4c1IpzZ0BgpEoi2ZO4Kz6apqPBDh4pI+uvhBFXg/1E8uYUFbE4681UVfh58K5tbzv1Lr+dYiaj/Txv6/v442mw+xu62VPWy9dQ6SgnjNrAo999t0jyn8e73T1hfjmk1v5xdo9VPh9xN7ZxPLi/t0FAXweIRxVJlf6aekKMKXKzw0XNHByXTnrdnWwvaWbN5oOc7g3RF2ln86joUHX0vEINNSWM2NCKd2BMK/vOUyl38eNF8/hry+aM+Ts2VzindZufvrSLt5oOsybTZ1U+n10BcKUFXuJRJVAgvGpLPFx+YKp+Is8bD1whEPdQQ4e6SMYjuIReFfDJPa2957wq9fnERpqy/GIYxWrOvvPV5cWsWpzM0dDEUqKPHz07Bn8/SXzctJ6SIVIVFn54k4aW7qZVVPG+t0dvPJOG73BCBPLirjyjGk01JRTU1HMskUn7rPS0ROkuauP06ZWZekdjI58Eo2rgStU9W/c59cD56nqFxJdP2HWafp33/8f5tSU4/MKZ8+ayFn11QTCUX71+j5++HwjnUdDTK0qYUpVCdWlRQTCEd5p7WFXWw8ff9cs/uVD84dNw1NVDveG2N3eS08gTGmxl7JiL6VFXkqLvUwqK87InsfjgeYjfUwsK6alq4//55E3CIQj/N3759JQU46inFRbwcs7DvHjP77DolkT+PtL5p7wC783GObhtXvZfOAINeXF+Afkjce+jsFIlB0t3TR3BSjxeVgyZxI3XjSHCWVjs4thNtiw9zD3v7iTk2rL+dv3nER5sZfuQJjWrgCHuoMc6g5wqDvAG3s7+f3mgwjOQnZTq0qYXFXCgmlVNLZ084ctzcypLefM+gmEIlFKijzUVvjZ0dpNY0s3HhHnR43CjtZuWroCXL5gCh9ZNJ1zZ09MixsxHwiGo3g9kvf7u+eTaFwDLB0gGktU9e/jrrkJuAlg1qxZ5+7endpaSeFING9v9EZ+Eo0qIoyLwL6R2yQrGrlwh2wCZsY9rwf2x1+gqveq6mJVXVxXl7rv0ATDyDU8HjHBMMaUXLhLvgrME5E5IlIMLAeeyHKfDMMwCpLxl0oyAFUNi8gXgGdwUm5XquqmLHfLMAyjIBn3ogGgqk8CT2a7H4ZhGIVOLrinDMMwjHGCiYZhGIaRNCYahmEYRtKYaBiGYRhJM+4n940UEekCtsUVVQOdg1w+2Lmh6oymbqbajVELHEpjn4Z7zfHWbib7NNjYjrbd8TSGo/l8jrbddH9209Gn8fQdH83/e6jPbnz9qao6/BLFqppXD2DdgOf3DnFtwnND1RlN3Uy1O9h7H22fhnvN8dZuhvuUcGzHcX/H9HOfhnbT+tnN5HvNRp9G+f8e9LMbXz+Z61S1INxTv0nh3FB1RlM3U+2m2uZozo3HdkdTNxvjO5q6mWh3NJ/P0babztdL5ppc+46P5v+dDEnXz0f31DpNYv2UfKSQ33umsbHNLDa+mSPZsU32uny0NO7NdgeySCG/90xjY5tZbHwzR7Jjm9R1eWdpGIZhGJkjHy2NESMi3cOcf0FEzHROARvbzGNjnFlsfI/HRMMwDMNIGhMNFxF5n4j8Nu75D0TkU1ns0qAM98tnvGFjm3lyYYxzdWzBxjceEw3DMAwjaUw0chQRqRCRZ0XkNRHZKCLL3PIGEdkiIj8RkU0i8nsRKc12f3MJG9vMYWObWcZifE00jhHm+PEoyVZHkqQP+KiqngO8H/iOHNv3cx7wQ1VdCBwGPpadLvZjY5t5cmWMc3Fswca3HxONY+wGFoiIX0SqgUuz3aFhEOCbIvIm8AdgBjDFPbdTVTe4x+uBhjHv3fHY2GaeXBnjXBxbsPHtJyd27sskIuIDAqq6V0QeBd4EtgOvZ7dnw3IdUAecq6ohEdnFsV8/gbjrIkBWzHwb28yTg2OcM2MLNr6JKHjRABYCOwBU9R+Bfxx4gaq+b4z7lAzVQIv7wXg/MDvbHUqAjW3mybUxzqWxBRvfEyho0RCRzwJfBL6c5a4kTeyXD/Bz4Dcisg7YAGzNZr8GYmObeXJpjHNtbMHGd9DXsmVEcgsROQv4iaouyXZf8g0b28xhY5tZxnJ8LRCeQ7i/fH4B/Eu2+5Jv2NhmDhvbzDLW42uWhmEYhpE0ZmmMY0Rkpog8707K2SQiX3LLJ4nIKhHZ7v6d6JbXuNd3i8gPBrR1rTvZ500ReVpEarPxnsYLaR7bj7vjuklEvp2N9zPeSGF8PyAi693P6HoRuSSurXPd8kYRuStu3kFBkuaxvV1E9spIliBJZns/e2TnAUwDznGPK4G3gQXAt4GvueVfA+5wj8uBi4DPAj+Ia8cHtAC17vNvA9/I9vvLk7GtAfYAde7zB4BLs/3+sv1IYXzPBqa7x6cD++LaWgtcgDMH4Sngg9l+f3k0tue77XUn+/pmaYxjVPWAqr7mHncBW3Am6yzDuTnh/r3KvaZHVV/EmRUaj7iPcvdXWhWwP+NvYByTxrE9CXhbVVvd539gfM1kzgopjO/rqhr7TG4CStyJdNOAKlVdrc5d7sFYnUIlXWPrnntFVQ+M5PVNNHIEEWnA+cWwBpgS+0e7fycPVVdVQ8DngI04YrEAuD+T/c0lRjO2QCNwmjhr+/hwvqgzM9fb3COF8f0Y8LqqBnBuhk1x55rcMoNRj21KmGjkACJSATwOfFlVj6RQvwhHNM4GpuPMar05rZ3MUUY7tqragTO2jwB/BnbhrFNkMPLxFZGFwB3AZ2JFCS6z7B3SMrYpYaIxznFv+I8DP1fVX7rFza7Zjvu3ZZhmFgGo6g7XxH8UeHdmepw7pGlsUdXfqOp5qnoBsA1nmYmCZ6TjKyL1wK+AG1R1h1vcBNTHNVtPgbtWIW1jmxImGuMYN/5wP7BFVb8bd+oJYIV7vAL49TBN7cNZbK3Off4BHD9owZLGsUVEJrt/JwJ/B9yX3t7mHiMdXxGZAPwOuFlVX4pd7LpZukTkfLfNG0jif5LPpGtsUybbmQD2GDJL4iIcU/xNnCUBNgBX4mTsPIvzi/ZZYFJcnV1AO9CN8yttgVv+WRyheBP4DVCT7feXR2P7C2Cz+1ie7fc2Hh4jHV+ciWk9cdduACa75xYDb+GsAfUD3PllhfpI89h+2/0sR92/3xju9W1yn2EYhpE05p4yDMMwksZEwzAMw0gaEw3DMAwjaUw0DMMwjKQx0TAMwzCSxkTDMMYYEfmsiNwwgusbROStTPbJMJKloLd7NYyxRkR8qvqjbPfDMFLFRMMwRoi7SNzTOIvEnY2zNPUNwHzgu0AFcAj4lKoeEJEXgJeBC4EnRKQSZynq/xSRRcCPgDKcyWt/raodInIusBLoBV4cu3dnGENj7inDSI1TgXtV9UzgCPB54P8AV6tq7IZ/e9z1E1T1var6nQHtPAj8k9vORuAWt/ynwBfVWc/KMMYNZmkYRmrs1WPr+PwX8HWcDW5WuRvLeYH4fQoeGdiAiFTjiMkf3aIHgP9JUP4Q8MH0vwXDGDkmGoaRGgPX3+kCNg1hGfSMoG1J0L5hjAvMPWUYqTFLRGICcS3wClAXKxORInf/gkFR1U6gQ0QudouuB/6oqoeBThG5yC2/Lu29N4wUMUvDMFJjC7BCRH6Ms6ro/wGeAe5y3Us+4Hs422sOxQrgRyJSBrwDfNot/zSwUkR63XYNY1xgq9waxghxs6d+q6qnZ7svhjHWmHvKMAzDSBqzNAzDMIykMUvDMAzDSBoTDcMwDCNpTDQMwzCMpDHRMAzDMJLGRMMwDMNIGhMNwzAMI2n+L6kof3cPdnGjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sorted_data['inc'][-200:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etude de l'incidence annuelle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Etant donné que le pic de l'épidémie se situe en hiver, à cheval\n",
    "entre deux années civiles, nous définissons la période de référence\n",
    "entre deux minima de l'incidence, du 1er août de l'année $N$ au\n",
    "1er août 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 1er août de chaque année, nous utilisons le\n",
    "premier jour de la semaine qui contient le 1er août.\n",
    "\n",
    "Comme l'incidence de syndrome grippal est très faible en été, cette\n",
    "modification ne risque pas de fausser nos conclusions.\n",
    "\n",
    "Encore un petit détail: les données commencent an octobre 1984, ce qui\n",
    "rend la première année incomplète. Nous commençons donc l'analyse en 1985."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
    "                     for y in range(1985,\n",
    "                                    sorted_data.index[-1].year)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En partant de cette liste des semaines qui contiennent un 1er août, nous obtenons nos intervalles d'environ un an comme les périodes entre deux semaines adjacentes dans cette liste. Nous calculons les sommes des incidences hebdomadaires pour toutes ces périodes.\n",
    "\n",
    "Nous vérifions également que ces périodes contiennent entre 51 et 52 semaines, pour nous protéger contre des éventuelles erreurs dans notre code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "year = []\n",
    "yearly_incidence = []\n",
    "for week1, week2 in zip(first_august_week[:-1],\n",
    "                        first_august_week[1:]):\n",
    "    one_year = sorted_data['inc'][week1:week2-1]\n",
    "    assert abs(len(one_year)-52) < 2\n",
    "    yearly_incidence.append(one_year.sum())\n",
    "    year.append(week2.year)\n",
    "yearly_incidence = pd.Series(data=yearly_incidence, index=year)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici les incidences annuelles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 58,
     "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 valeurs les plus élevées (à la fin)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    1600941\n",
       "1991    1659249\n",
       "1995    1840410\n",
       "2020    2042389\n",
       "2012    2175217\n",
       "2003    2234584\n",
       "2019    2254386\n",
       "2006    2307352\n",
       "2017    2321583\n",
       "2001    2529279\n",
       "1992    2574578\n",
       "1993    2703886\n",
       "2018    2705325\n",
       "1988    2765617\n",
       "2007    2780164\n",
       "1987    2855570\n",
       "2016    2856393\n",
       "2011    2857040\n",
       "2008    2973918\n",
       "1998    3034904\n",
       "2002    3125418\n",
       "2009    3444020\n",
       "1994    3514763\n",
       "1996    3539413\n",
       "2004    3567744\n",
       "1997    3620066\n",
       "2015    3654892\n",
       "2000    3826372\n",
       "2005    3835025\n",
       "1999    3908112\n",
       "2010    4111392\n",
       "2013    4182691\n",
       "1986    5115251\n",
       "1990    5235827\n",
       "1989    5466192\n",
       "dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_incidence.sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enfin, un histogramme montre bien que les épidémies fortes, qui touchent environ 10% de la population\n",
    "  française, sont assez rares: il y en eu trois au cours des 35 dernières années."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DS3nsUh436dExdiMrattrbJ9r+3ZJ50jaLenHo1p/EbYnJc0o+/jhyZI+IEmRwF9cba+z/WFJF0q6TNK5EZHM59kZu+GlOnapj5v06Bm7It9MLMsaSeskXRsRfzTC9fZk220P6omSro+ID9o+VNK1tp8XEV+vMd8hEfGgpAOSviKpFRGXrphWS6ZUx64tX4pjd2RE3Ldi/UmMXVu2NRHxgBIat3zdR0XEPW35pHTGbmW2Useu9C1q2+ttPzf/+dfLzw/o9EVJB2y/wPbrbE/ZPjqfd03ZWTpkW2f7r2xfYvucfFDbf/OOS/qu7aMjYknS5ZJebHtt1dl65HtQyj7PruzLRyfbvtD2ZyRttf2c/LaVvjvKH9dzbH9S0httjyU2dp3yJTF2+TqOsr1T2ZEoteIFO656x25ltgfy/1MYt3X5Lo0vSfpQe77cuGoau17Zyh67Ugfa9tsk3SLpf2w/bnmrq22WHyjbsv4PSU+TdJakS6WHDX7pbB+v7EGclPQRZbtfXmv7yLbZfixpQtLyjv//lnSaOh/vpI58t0lazH++QNlYflB62Au/7GzrJX1J0unK3lqekWdr/+Va59gVyVfL2LVZK+kISU+0fXKe+7B8Wm1j1yPb8tjVNm75+OyS9HJJ74iIP26bttxdtYxdr2xtyhu7iCjtn7KSebqy/TLn5detaZt+iKSnSDosv3y4pO9J+u38ssvMsyLbWmVH/Vu+fLakj+Q/H5r/f6KkncoO7bomv+5/JT2tqlwF8x2yPD6Sjlpxu92SnjmCfMe0/fw3ki5IZez65Kt97PJ1vVLSOyS9VdLbExu7TtmWc9Q9bpdJOrvt8hEr8tX5mu2WbfmLhKWNXdlvXa6KiO9K+oKkP5QO3lKOiAcj4qaIuD+/fEDZFvhEfrnKPwDcJ+mbbVv4uyQ9OV/vUr7/6IeS5pUdz2T5QFTXqMPRAkecb/ktfETEPcs3sH2EpAWN4G8NEXGX7aNtf0jSm7PVe30+dq557Hrlq3Xs2h7PQyR9X9JNyjZmVPfY9cm2/Ba+tudc7iJJ59vebvvLkt5u+4S2fHU+77plizxbaWNXalG3lfIXJW1wfiaY9regzj79cbjtTbbfK+leZb8RK5UPWvuD90ZJH8szOR56K/IeST+SdJHt2yTdHRE31J1v+Urba20/Nx+7a5S9vVqoOl+e8W5J35T0PEkbJL3F9kltuWsZu375lqfXMXZtY/MSZbtlvizpWNvvs/3sOseuX7bl+Wp+zl0u6VZJP1O25f8kSW9Y3kWTq+s12zdbaWNX4duC90jatuK6dcp+ObxZ0tfz/4+vKkOPbCcoO8jUk9que4yk32m7/HxJTxh1th75jpG0Kf/5NZLeIunxdeTLM5wq6RJJTUlHpzJ2PfLVNnaSxiS9V9kW2DeUHUz+qvy18DhJzbrGrkC2Z9f9nJO0bsXjerGkZ6TwvOuTrbTnXJVvX3ZIepezb+Q8Rdn5Fo+T9D5J74uId1S47n6epWwn/222z1H2R87jJf3C9mERcX9EXJVgvn22vx0RH6gx27K7JD1W0v9J2izpPtuHRsRSzWO37C4dnO9AjWO3pGwj5X5JfyfpAUlvi+yP7S/Is9U1dv2y3W/7ujqfcxHxy7aL9yj7BXKrEnje9clW2nOusqPn2T5T2Vv3+5T9Nvl0ZPuTamf7a8repuxW9pbp7RFxY62h2qSaz/YGZZ+o+HNlv3gvjIh31pvqIannW2b7N5R9WuDDEfGzuvO0SzFbvm93s6RXKPu02Psj4l9rDZUbVbZKitr20yX9s7KP3l0cEfeVvpIh5R+rOV/Zb72PRsSvao50kJTzOftCwWsl/UpZtmQeV+kRkW+NpAejqq2jVUg5myTZfq2kByX9e4KPa+XZOB41ACSOo+cBQOIoagBIHEUNAImjqAEgcRQ1AKyS7Yts32n7+oLz/6ntG2zvcnZigd7z86kPAFid/MtBLWUHUju1z7wnSfq4pNMj4hf5kUbv7HUbtqgBYJUi4kpJP2+/zvaTbX/W9oLtq2w/JZ/0Gknvjohf5LftWdISRQ0AVdmh7BRcmyS9Sdnxj6TslGEn2/6a7attb+63oFGeigsAHhVsjyk7iuMn2g5+eUT+/6GSTlJ2/P4TJF1l+9SIuKvb8ihqACjfIZLuiohndph2u6SrIzsu/622b1ZW3Nf2WhgAoESRHRv9Vtt/ImXHlLf9jHzyf0qayq8/VtmukFt6LY+iBoBVsn2JsuN5n2L7dtuvVnY6vVfb/o6yMza9LJ/9c5J+ZvsGSVdIenO/IxXy8TwASBxb1ACQOIoaABJHUQNA4ihqAEgcRQ0AiaOoASBxFDUAJO7/AV8CY7c0xslbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}