{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Incidence du syndrome grippal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>201911</td>\n",
       "      <td>3</td>\n",
       "      <td>35094</td>\n",
       "      <td>29031.0</td>\n",
       "      <td>41157.0</td>\n",
       "      <td>53</td>\n",
       "      <td>44.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201910</td>\n",
       "      <td>3</td>\n",
       "      <td>50623</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>57246.0</td>\n",
       "      <td>77</td>\n",
       "      <td>67.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201909</td>\n",
       "      <td>3</td>\n",
       "      <td>88354</td>\n",
       "      <td>79564.0</td>\n",
       "      <td>97144.0</td>\n",
       "      <td>134</td>\n",
       "      <td>121.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201908</td>\n",
       "      <td>3</td>\n",
       "      <td>172604</td>\n",
       "      <td>160024.0</td>\n",
       "      <td>185184.0</td>\n",
       "      <td>262</td>\n",
       "      <td>243.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201907</td>\n",
       "      <td>3</td>\n",
       "      <td>307338</td>\n",
       "      <td>291220.0</td>\n",
       "      <td>323456.0</td>\n",
       "      <td>467</td>\n",
       "      <td>443.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201906</td>\n",
       "      <td>3</td>\n",
       "      <td>394286</td>\n",
       "      <td>376782.0</td>\n",
       "      <td>411790.0</td>\n",
       "      <td>599</td>\n",
       "      <td>572.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201905</td>\n",
       "      <td>3</td>\n",
       "      <td>355785</td>\n",
       "      <td>339295.0</td>\n",
       "      <td>372275.0</td>\n",
       "      <td>540</td>\n",
       "      <td>515.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201904</td>\n",
       "      <td>3</td>\n",
       "      <td>241090</td>\n",
       "      <td>227261.0</td>\n",
       "      <td>254919.0</td>\n",
       "      <td>366</td>\n",
       "      <td>345.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201903</td>\n",
       "      <td>3</td>\n",
       "      <td>147063</td>\n",
       "      <td>135890.0</td>\n",
       "      <td>158236.0</td>\n",
       "      <td>223</td>\n",
       "      <td>206.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201902</td>\n",
       "      <td>3</td>\n",
       "      <td>75548</td>\n",
       "      <td>67632.0</td>\n",
       "      <td>83464.0</td>\n",
       "      <td>115</td>\n",
       "      <td>103.0</td>\n",
       "      <td>127.0</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  \\\n",
       "0  201911          3   35094   29031.0   41157.0      53        44.0   \n",
       "1  201910          3   50623   44000.0   57246.0      77        67.0   \n",
       "2  201909          3   88354   79564.0   97144.0     134       121.0   \n",
       "3  201908          3  172604  160024.0  185184.0     262       243.0   \n",
       "4  201907          3  307338  291220.0  323456.0     467       443.0   \n",
       "5  201906          3  394286  376782.0  411790.0     599       572.0   \n",
       "6  201905          3  355785  339295.0  372275.0     540       515.0   \n",
       "7  201904          3  241090  227261.0  254919.0     366       345.0   \n",
       "8  201903          3  147063  135890.0  158236.0     223       206.0   \n",
       "9  201902          3   75548   67632.0   83464.0     115       103.0   \n",
       "\n",
       "   inc100_up geo_insee geo_name  \n",
       "0       62.0        FR   France  \n",
       "1       87.0        FR   France  \n",
       "2      147.0        FR   France  \n",
       "3      281.0        FR   France  \n",
       "4      491.0        FR   France  \n",
       "5      626.0        FR   France  \n",
       "6      565.0        FR   France  \n",
       "7      387.0        FR   France  \n",
       "8      240.0        FR   France  \n",
       "9      127.0        FR   France  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
    "raw_data[:10]"
   ]
  },
  {
   "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>1557</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",
       "1557  198919          3    0      NaN     NaN       0         NaN        NaN   \n",
       "\n",
       "     geo_insee geo_name  \n",
       "1557        FR   France  "
      ]
     },
     "execution_count": 4,
     "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>201911</td>\n",
       "      <td>3</td>\n",
       "      <td>35094</td>\n",
       "      <td>29031.0</td>\n",
       "      <td>41157.0</td>\n",
       "      <td>53</td>\n",
       "      <td>44.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201910</td>\n",
       "      <td>3</td>\n",
       "      <td>50623</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>57246.0</td>\n",
       "      <td>77</td>\n",
       "      <td>67.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201909</td>\n",
       "      <td>3</td>\n",
       "      <td>88354</td>\n",
       "      <td>79564.0</td>\n",
       "      <td>97144.0</td>\n",
       "      <td>134</td>\n",
       "      <td>121.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>201908</td>\n",
       "      <td>3</td>\n",
       "      <td>172604</td>\n",
       "      <td>160024.0</td>\n",
       "      <td>185184.0</td>\n",
       "      <td>262</td>\n",
       "      <td>243.0</td>\n",
       "      <td>281.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>201907</td>\n",
       "      <td>3</td>\n",
       "      <td>307338</td>\n",
       "      <td>291220.0</td>\n",
       "      <td>323456.0</td>\n",
       "      <td>467</td>\n",
       "      <td>443.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>201906</td>\n",
       "      <td>3</td>\n",
       "      <td>394286</td>\n",
       "      <td>376782.0</td>\n",
       "      <td>411790.0</td>\n",
       "      <td>599</td>\n",
       "      <td>572.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>201905</td>\n",
       "      <td>3</td>\n",
       "      <td>355785</td>\n",
       "      <td>339295.0</td>\n",
       "      <td>372275.0</td>\n",
       "      <td>540</td>\n",
       "      <td>515.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>201904</td>\n",
       "      <td>3</td>\n",
       "      <td>241090</td>\n",
       "      <td>227261.0</td>\n",
       "      <td>254919.0</td>\n",
       "      <td>366</td>\n",
       "      <td>345.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>201903</td>\n",
       "      <td>3</td>\n",
       "      <td>147063</td>\n",
       "      <td>135890.0</td>\n",
       "      <td>158236.0</td>\n",
       "      <td>223</td>\n",
       "      <td>206.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>FR</td>\n",
       "      <td>France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>201902</td>\n",
       "      <td>3</td>\n",
       "      <td>75548</td>\n",
       "      <td>67632.0</td>\n",
       "      <td>83464.0</td>\n",
       "      <td>115</td>\n",
       "      <td>103.0</td>\n",
       "      <td>127.0</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  \\\n",
       "0  201911          3   35094   29031.0   41157.0      53        44.0   \n",
       "1  201910          3   50623   44000.0   57246.0      77        67.0   \n",
       "2  201909          3   88354   79564.0   97144.0     134       121.0   \n",
       "3  201908          3  172604  160024.0  185184.0     262       243.0   \n",
       "4  201907          3  307338  291220.0  323456.0     467       443.0   \n",
       "5  201906          3  394286  376782.0  411790.0     599       572.0   \n",
       "6  201905          3  355785  339295.0  372275.0     540       515.0   \n",
       "7  201904          3  241090  227261.0  254919.0     366       345.0   \n",
       "8  201903          3  147063  135890.0  158236.0     223       206.0   \n",
       "9  201902          3   75548   67632.0   83464.0     115       103.0   \n",
       "\n",
       "   inc100_up geo_insee geo_name  \n",
       "0       62.0        FR   France  \n",
       "1       87.0        FR   France  \n",
       "2      147.0        FR   France  \n",
       "3      281.0        FR   France  \n",
       "4      491.0        FR   France  \n",
       "5      626.0        FR   France  \n",
       "6      565.0        FR   France  \n",
       "7      387.0        FR   France  \n",
       "8      240.0        FR   France  \n",
       "9      127.0        FR   France  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = raw_data.dropna().copy()\n",
    "data[:10]"
   ]
  },
  {
   "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": 6,
   "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": 7,
   "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": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1185ebfd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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99q5+vCmX/dchAs2COUKp1lF/VFEMKY7G5Ee/WuikqTXVCjgdR/3Ryvt/8DQAYMsNH/NN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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ee35390>"
      ]
     },
     "metadata": {},
     "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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ee52c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BZ54ai4qgn2VN1bxiQiMvBuNjaBpB0zSMKWZI08jdPFXhmqcGStw81R9NjLsa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\nDMMwcub/B764808d1n7FAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c8d06a0>"
      ]
     },
     "metadata": {},
     "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": 11,
   "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": 12,
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ee62cf8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eWuZQ7gcWHCN9RUTMztdTAJJmUWzfe3GW+ZakUzL/KuCzFHvMX1Sq82bgQERc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\nBxQzM6sLBxQzM6uL/w8T9rXtfaQJQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c90b128>"
      ]
     },
     "metadata": {},
     "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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    1600941\n",
       "1991    1659249\n",
       "1995    1840410\n",
       "2012    2175217\n",
       "2003    2234584\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": 14,
     "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c9e4048>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f768be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "yearly_incidence.hist(xrot=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}