{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence du syndrome grippal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import isoweek"
   ]
  },
  {
   "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": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici l'explication des colonnes données [sur le site d'origine](https://ns.sentiweb.fr/incidence/csv-schema-v1.json):\n",
    "\n",
    "| Nom de colonne | Libellé de colonne                                                                                                                |\n",
    "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n",
    "| week           | Semaine calendaire (ISO 8601)                                                                                                     |\n",
    "| indicator      | Code de l'indicateur de surveillance                                                                                              |\n",
    "| inc            | Estimation de l'incidence de consultations en nombre de cas                                                                       |\n",
    "| inc_low        | Estimation de la borne inférieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc_up         | Estimation de la borne supérieure de l'IC95% du nombre de cas de consultation                                                     |\n",
    "| inc100         | Estimation du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants)                                   |\n",
    "| inc100_low     | Estimation de la borne inférieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| inc100_up      | Estimation de la borne supérieure de l'IC95% du taux d'incidence du nombre de cas de consultation (en cas pour 100,000 habitants) |\n",
    "| geo_insee      | Code de la zone géographique concernée (Code INSEE) http://www.insee.fr/fr/methodes/nomenclatures/cog/                            |\n",
    "| geo_name       | Libellé de la zone géographique (ce libellé peut être modifié sans préavis)                                                       |\n",
    "\n",
    "La première ligne du fichier CSV est un commentaire, que nous ignorons en précisant `skiprows=1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>202222</td>\n",
       "      <td>3</td>\n",
       "      <td>22047</td>\n",
       "      <td>17173.0</td>\n",
       "      <td>26921.0</td>\n",
       "      <td>33</td>\n",
       "      <td>26.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202221</td>\n",
       "      <td>3</td>\n",
       "      <td>13505</td>\n",
       "      <td>10130.0</td>\n",
       "      <td>16880.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202220</td>\n",
       "      <td>3</td>\n",
       "      <td>19787</td>\n",
       "      <td>15756.0</td>\n",
       "      <td>23818.0</td>\n",
       "      <td>30</td>\n",
       "      <td>24.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202219</td>\n",
       "      <td>3</td>\n",
       "      <td>17884</td>\n",
       "      <td>14079.0</td>\n",
       "      <td>21689.0</td>\n",
       "      <td>27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202218</td>\n",
       "      <td>3</td>\n",
       "      <td>30353</td>\n",
       "      <td>25089.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>54.0</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1957</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>1958</th>\n",
       "      <td>198447</td>\n",
       "      <td>3</td>\n",
       "      <td>72029</td>\n",
       "      <td>54274.0</td>\n",
       "      <td>89784.0</td>\n",
       "      <td>131</td>\n",
       "      <td>99.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>198446</td>\n",
       "      <td>3</td>\n",
       "      <td>87330</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>106974.0</td>\n",
       "      <td>159</td>\n",
       "      <td>123.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1962 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202222          3   22047   17173.0   26921.0      33        26.0   \n",
       "1     202221          3   13505   10130.0   16880.0      20        15.0   \n",
       "2     202220          3   19787   15756.0   23818.0      30        24.0   \n",
       "3     202219          3   17884   14079.0   21689.0      27        21.0   \n",
       "4     202218          3   30353   25089.0   35617.0      46        38.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1957  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1958  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1959  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1960  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1961  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          40.0        FR   France  \n",
       "1          25.0        FR   France  \n",
       "2          36.0        FR   France  \n",
       "3          33.0        FR   France  \n",
       "4          54.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1957      176.0        FR   France  \n",
       "1958      163.0        FR   France  \n",
       "1959      195.0        FR   France  \n",
       "1960      308.0        FR   France  \n",
       "1961      213.0        FR   France  \n",
       "\n",
       "[1962 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, skiprows=1, encoding='latin1') # Problème d'encodage, j'ajoute \"encoding='latin1'\" pour corriger le problème.\n",
    "raw_data"
   ]
  },
  {
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>1725</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator  inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
       "1725  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
       "\n",
       "     geo_insee geo_name  \n",
       "1725        FR   France  "
      ]
     },
     "execution_count": 7,
     "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>week</th>\n",
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       "      <td>40.0</td>\n",
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       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>202221</td>\n",
       "      <td>3</td>\n",
       "      <td>13505</td>\n",
       "      <td>10130.0</td>\n",
       "      <td>16880.0</td>\n",
       "      <td>20</td>\n",
       "      <td>15.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>202220</td>\n",
       "      <td>3</td>\n",
       "      <td>19787</td>\n",
       "      <td>15756.0</td>\n",
       "      <td>23818.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202219</td>\n",
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       "      <td>17884</td>\n",
       "      <td>14079.0</td>\n",
       "      <td>21689.0</td>\n",
       "      <td>27</td>\n",
       "      <td>21.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>202218</td>\n",
       "      <td>3</td>\n",
       "      <td>30353</td>\n",
       "      <td>25089.0</td>\n",
       "      <td>35617.0</td>\n",
       "      <td>46</td>\n",
       "      <td>38.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>1957</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>1958</th>\n",
       "      <td>198447</td>\n",
       "      <td>3</td>\n",
       "      <td>72029</td>\n",
       "      <td>54274.0</td>\n",
       "      <td>89784.0</td>\n",
       "      <td>131</td>\n",
       "      <td>99.0</td>\n",
       "      <td>163.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1959</th>\n",
       "      <td>198446</td>\n",
       "      <td>3</td>\n",
       "      <td>87330</td>\n",
       "      <td>67686.0</td>\n",
       "      <td>106974.0</td>\n",
       "      <td>159</td>\n",
       "      <td>123.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1960</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1961</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1961 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        week  indicator     inc   inc_low    inc_up  inc100  inc100_low  \\\n",
       "0     202222          3   22047   17173.0   26921.0      33        26.0   \n",
       "1     202221          3   13505   10130.0   16880.0      20        15.0   \n",
       "2     202220          3   19787   15756.0   23818.0      30        24.0   \n",
       "3     202219          3   17884   14079.0   21689.0      27        21.0   \n",
       "4     202218          3   30353   25089.0   35617.0      46        38.0   \n",
       "...      ...        ...     ...       ...       ...     ...         ...   \n",
       "1957  198448          3   78620   60634.0   96606.0     143       110.0   \n",
       "1958  198447          3   72029   54274.0   89784.0     131        99.0   \n",
       "1959  198446          3   87330   67686.0  106974.0     159       123.0   \n",
       "1960  198445          3  135223  101414.0  169032.0     246       184.0   \n",
       "1961  198444          3   68422   20056.0  116788.0     125        37.0   \n",
       "\n",
       "      inc100_up geo_insee geo_name  \n",
       "0          40.0        FR   France  \n",
       "1          25.0        FR   France  \n",
       "2          36.0        FR   France  \n",
       "3          33.0        FR   France  \n",
       "4          54.0        FR   France  \n",
       "...         ...       ...      ...  \n",
       "1957      176.0        FR   France  \n",
       "1958      163.0        FR   France  \n",
       "1959      195.0        FR   France  \n",
       "1960      308.0        FR   France  \n",
       "1961      213.0        FR   France  \n",
       "\n",
       "[1961 rows x 10 columns]"
      ]
     },
     "execution_count": 8,
     "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": 9,
   "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": 10,
   "metadata": {
    "collapsed": true
   },
   "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": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='period'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "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": 14,
   "metadata": {
    "collapsed": true
   },
   "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": 15,
   "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": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021     938731\n",
       "2014    1600941\n",
       "1991    1659249\n",
       "1995    1840410\n",
       "2020    2053781\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": 17,
     "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": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "68a78512f08647dba0d657fd7997aaa8ad8c9d9af1a9b906b8cea64366ea2030"
  },
  "kernelspec": {
   "display_name": "Python 3.9.13 64-bit (windows store)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}