From ab6b3ee808aa6e7ceb753bd8cadfe1657c25234a Mon Sep 17 00:00:00 2001
From: 6b9fcb02f874d4fa73786fcf6e721560
 <6b9fcb02f874d4fa73786fcf6e721560@app-learninglab.inria.fr>
Date: Tue, 5 May 2020 14:31:28 +0000
Subject: [PATCH] Exercice fini

---
 module3/exo2/exercice.ipynb |  490 ++++++++++-
 module3/exo2/varicelle.csv  | 1536 +++++++++++++++++++++++++++++++++++
 2 files changed, 2023 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo2/varicelle.csv

diff --git a/module3/exo2/exercice.ipynb b/module3/exo2/exercice.ipynb
index 0bbbe37..daf80c3 100644
--- a/module3/exo2/exercice.ipynb
+++ b/module3/exo2/exercice.ipynb
@@ -1,5 +1,490 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Incidence de la varicelle"
+   ]
+  },
+  {
+   "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\n",
+    "import os\n",
+    "import urllib.request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les données de l'incidence de la varicelle sont disponibles du site Web du Réseau Sentinelles. 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 1991 et se termine avec une semaine récente."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_url = \"https://www.sentiweb.fr/datasets/incidence-PAY-7.csv\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il se peut que l'URl change et devienne caduque au fil du temps. Ce fichier va d'abord tenter d'importer une copie locale si elle est disponible, sinon charger l'URL et créer le fichier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>week</th>\n",
+       "      <th>indicator</th>\n",
+       "      <th>inc</th>\n",
+       "      <th>inc_low</th>\n",
+       "      <th>inc_up</th>\n",
+       "      <th>inc100</th>\n",
+       "      <th>inc100_low</th>\n",
+       "      <th>inc100_up</th>\n",
+       "      <th>geo_insee</th>\n",
+       "      <th>geo_name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>202017</td>\n",
+       "      <td>7</td>\n",
+       "      <td>147</td>\n",
+       "      <td>0</td>\n",
+       "      <td>410</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>202016</td>\n",
+       "      <td>7</td>\n",
+       "      <td>758</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1438</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>202015</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1918</td>\n",
+       "      <td>675</td>\n",
+       "      <td>3161</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>202014</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3879</td>\n",
+       "      <td>2227</td>\n",
+       "      <td>5531</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>9</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>202013</td>\n",
+       "      <td>7</td>\n",
+       "      <td>7326</td>\n",
+       "      <td>5236</td>\n",
+       "      <td>9416</td>\n",
+       "      <td>11</td>\n",
+       "      <td>8</td>\n",
+       "      <td>14</td>\n",
+       "      <td>FR</td>\n",
+       "      <td>France</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     week  indicator   inc  inc_low  inc_up  inc100  inc100_low  inc100_up  \\\n",
+       "0  202017          7   147        0     410       0           0          0   \n",
+       "1  202016          7   758       78    1438       1           0          2   \n",
+       "2  202015          7  1918      675    3161       3           1          5   \n",
+       "3  202014          7  3879     2227    5531       6           3          9   \n",
+       "4  202013          7  7326     5236    9416      11           8         14   \n",
+       "\n",
+       "  geo_insee geo_name  \n",
+       "0        FR   France  \n",
+       "1        FR   France  \n",
+       "2        FR   France  \n",
+       "3        FR   France  \n",
+       "4        FR   France  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filename = \"./varicelle.csv\"\n",
+    "\n",
+    "if not os.path.exists(filename):\n",
+    "    urllib.request.urlretrieve(data_url, filename)\n",
+    "\n",
+    "raw_data = pd.read_csv(filename, skiprows=1)\n",
+    "raw_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Y a t-il des points manquants dans ce jeu de données ? Non, parfait ! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_data[raw_data.isnull().any(axis=1)]\n",
+    "data = raw_data.copy()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nos données utilisent une convention inhabituelle: le numéro de semaine est collé à l'année, donnant l'impression qu'il s'agit 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 semaine. Il faut lui fournir les dates de début et de fin de semaine. Nous utilisons pour cela la bibliothèque isoweek.\n",
+    "\n",
+    "Comme la conversion des semaines est devenu assez complexe, nous écrivons une petite fonction Python pour cela. Ensuite, nous l'appliquons à tous les points de nos donnés. Les résultats vont dans une nouvelle colonne 'period'."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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 comme nouvel index de notre jeux de données. Ceci en fait une suite chronologique, ce qui sera pratique par la suite.\n",
+    "\n",
+    "Deuxièmement, nous trions les points par période, dans le sens chronologique."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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 le début de la période qui suit, la différence temporelle doit être zéro, ou au moins très faible. Nous laissons une \"marge d'erreur\" d'une seconde.\n",
+    "\n",
+    "Ici, aucun souci de ce coté !"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "periods = sorted_data.index\n",
+    "for p1, p2 in zip(periods[:-1], periods[1:]):\n",
+    "    delta = p2.to_timestamp() - p1.end_time\n",
+    "    if delta > pd.Timedelta('1s'):\n",
+    "        print(p1, p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f805e0eba20>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "Prenons seulement un échantillon de ce graphe pour voir les pics d'incidence plus en détail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f805bef7eb8>"
+      ]
+     },
+     "execution_count": 12,
+     "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": [
+    "subset = sorted_data[sorted_data[\"week\"] < 199500]\n",
+    "subset['inc'].plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pour obtenir des résultats fiables, nous coupons les années entre les minimas des pics d'incidences, du 1er septembre de l'année  𝑁  au 1er septembre de l'année  𝑁+1 comme on peut le voir ici.\n",
+    "\n",
+    "Notre tâche est un peu compliquée par le fait que l'année ne comporte pas un nombre entier de semaines. Nous modifions donc un peu nos périodes de référence: à la place du 1er août de chaque année, nous utilisons le premier jour de la semaine qui contient le 1er août.\n",
+    "\n",
+    "Comme l'incidence de la varicelle est très faible en septembre, cette modification ne risque pas de fausser nos conclusions.\n",
+    "\n",
+    "Encore un petit détail: les données commencent an octobre 1991, ce qui rend la première année incomplète. Nous commençons donc l'analyse en 1992."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_august_week = [pd.Period(pd.Timestamp(y, 9, 1), 'W')\n",
+    "                     for y in range(1992,\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": 14,
+   "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, d'une part sur une graphe et d'autre part sur une liste. On voit que l'année où le pic d'incidence est le moins élevé est en 2002, et le pic d'incidence le plus élevé en 2009."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f805be780b8>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "yearly_incidence.plot(style='*')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2002    516689\n",
+       "2018    542312\n",
+       "2017    551041\n",
+       "1996    564901\n",
+       "2019    584066\n",
+       "2015    604382\n",
+       "2000    617597\n",
+       "2001    619041\n",
+       "2012    624573\n",
+       "2005    628464\n",
+       "2006    632833\n",
+       "2011    642368\n",
+       "1993    643387\n",
+       "1995    652478\n",
+       "1994    661409\n",
+       "1998    677775\n",
+       "1997    683434\n",
+       "2014    685769\n",
+       "2013    698332\n",
+       "2007    717352\n",
+       "2008    749478\n",
+       "1999    756456\n",
+       "2003    758363\n",
+       "2004    777388\n",
+       "2016    782114\n",
+       "2010    829911\n",
+       "2009    842373\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "yearly_incidence.sort_values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +501,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo2/varicelle.csv b/module3/exo2/varicelle.csv
new file mode 100644
index 0000000..9789d7d
--- /dev/null
+++ b/module3/exo2/varicelle.csv
@@ -0,0 +1,1536 @@
+# @source="r�seau Sentinelles, INSERM, Sorbonne Universit�, http://www.sentiweb.fr", @meta={"period":[199049,202017],"geo":["PAY","1"],"geo_ref":"insee","indicator":"7","type":"all","conf_int":true,"compact":false}, @date=2020-05-05T15:51:27+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
+202017,7,147,0,410,0,0,0,FR,France
+202016,7,758,78,1438,1,0,2,FR,France
+202015,7,1918,675,3161,3,1,5,FR,France
+202014,7,3879,2227,5531,6,3,9,FR,France
+202013,7,7326,5236,9416,11,8,14,FR,France
+202012,7,8123,5790,10456,12,8,16,FR,France
+202011,7,10198,7568,12828,15,11,19,FR,France
+202010,7,9011,6691,11331,14,10,18,FR,France
+202009,7,13631,10544,16718,21,16,26,FR,France
+202008,7,10424,7708,13140,16,12,20,FR,France
+202007,7,8959,6574,11344,14,10,18,FR,France
+202006,7,9264,6925,11603,14,10,18,FR,France
+202005,7,8505,6314,10696,13,10,16,FR,France
+202004,7,7991,5831,10151,12,9,15,FR,France
+202003,7,5968,4100,7836,9,6,12,FR,France
+202002,7,6534,4530,8538,10,7,13,FR,France
+202001,7,9835,7019,12651,15,11,19,FR,France
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+201950,7,6424,4276,8572,10,7,13,FR,France
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+199644,7,12095,8643,15547,21,15,27,FR,France
+199643,7,14958,10739,19177,26,19,33,FR,France
+199642,7,14106,10196,18016,24,17,31,FR,France
+199641,7,10302,6968,13636,18,12,24,FR,France
+199640,7,9514,5990,13038,16,10,22,FR,France
+199639,7,6376,3651,9101,11,6,16,FR,France
+199638,7,3835,1587,6083,7,3,11,FR,France
+199637,7,2746,1100,4392,5,2,8,FR,France
+199636,7,4577,2320,6834,8,4,12,FR,France
+199635,7,2624,902,4346,5,2,8,FR,France
+199634,7,6280,3139,9421,11,6,16,FR,France
+199633,7,10194,5347,15041,18,10,26,FR,France
+199632,7,10116,5942,14290,17,10,24,FR,France
+199631,7,14292,9640,18944,25,17,33,FR,France
+199630,7,20862,14786,26938,36,26,46,FR,France
+199629,7,17243,12463,22023,30,22,38,FR,France
+199628,7,16021,11796,20246,28,21,35,FR,France
+199627,7,19880,14406,25354,34,25,43,FR,France
+199626,7,12565,8783,16347,22,15,29,FR,France
+199625,7,19360,14570,24150,33,25,41,FR,France
+199624,7,20778,15610,25946,36,27,45,FR,France
+199623,7,17991,13162,22820,31,23,39,FR,France
+199622,7,18294,13971,22617,32,25,39,FR,France
+199621,7,14000,10136,17864,24,17,31,FR,France
+199620,7,16598,12361,20835,29,22,36,FR,France
+199619,7,11710,8148,15272,20,14,26,FR,France
+199618,7,12140,8325,15955,21,14,28,FR,France
+199617,7,17984,13059,22909,31,22,40,FR,France
+199616,7,10796,7250,14342,19,13,25,FR,France
+199615,7,12766,9101,16431,22,16,28,FR,France
+199614,7,14952,10656,19248,26,19,33,FR,France
+199613,7,12861,9266,16456,22,16,28,FR,France
+199612,7,11592,8202,14982,20,14,26,FR,France
+199611,7,15991,12026,19956,28,21,35,FR,France
+199610,7,13551,9873,17229,23,17,29,FR,France
+199609,7,9737,6799,12675,17,12,22,FR,France
+199608,7,11835,8569,15101,20,14,26,FR,France
+199607,7,9296,6455,12137,16,11,21,FR,France
+199606,7,11330,8078,14582,20,14,26,FR,France
+199605,7,10249,7092,13406,18,13,23,FR,France
+199604,7,9004,6336,11672,16,11,21,FR,France
+199603,7,5904,3709,8099,10,6,14,FR,France
+199602,7,6166,4027,8305,11,7,15,FR,France
+199601,7,7238,4902,9574,12,8,16,FR,France
+199552,7,7808,5225,10391,14,10,18,FR,France
+199551,7,10505,7602,13408,18,13,23,FR,France
+199550,7,9513,7014,12012,16,12,20,FR,France
+199549,7,10263,7482,13044,18,13,23,FR,France
+199548,7,9491,6551,12431,16,11,21,FR,France
+199547,7,11221,8016,14426,19,13,25,FR,France
+199546,7,5104,2852,7356,9,5,13,FR,France
+199545,7,5017,2797,7237,9,5,13,FR,France
+199544,7,8374,5440,11308,14,9,19,FR,France
+199543,7,6365,3429,9301,11,6,16,FR,France
+199542,7,5827,3427,8227,10,6,14,FR,France
+199541,7,5884,3274,8494,10,5,15,FR,France
+199540,7,4653,2571,6735,8,4,12,FR,France
+199539,7,4334,2014,6654,8,4,12,FR,France
+199538,7,3821,1909,5733,7,4,10,FR,France
+199537,7,2297,884,3710,4,2,6,FR,France
+199536,7,2317,870,3764,4,1,7,FR,France
+199535,7,2531,1035,4027,4,1,7,FR,France
+199534,7,5067,2070,8064,9,4,14,FR,France
+199533,7,4709,1793,7625,8,3,13,FR,France
+199532,7,8030,3945,12115,14,7,21,FR,France
+199531,7,12034,7228,16840,21,13,29,FR,France
+199530,7,16283,11419,21147,28,20,36,FR,France
+199529,7,15520,11209,19831,27,20,34,FR,France
+199528,7,21385,16186,26584,37,28,46,FR,France
+199527,7,31707,25490,37924,55,44,66,FR,France
+199526,7,23680,18921,28439,41,33,49,FR,France
+199525,7,27941,22665,33217,48,39,57,FR,France
+199524,7,24813,19263,30363,43,33,53,FR,France
+199523,7,19371,14610,24132,34,26,42,FR,France
+199522,7,16158,12114,20202,28,21,35,FR,France
+199521,7,14986,10952,19020,26,19,33,FR,France
+199520,7,12831,9059,16603,22,15,29,FR,France
+199519,7,15806,11585,20027,27,20,34,FR,France
+199518,7,15333,10987,19679,27,19,35,FR,France
+199517,7,15551,11471,19631,27,20,34,FR,France
+199516,7,15939,12349,19529,28,22,34,FR,France
+199515,7,16126,12689,19563,28,22,34,FR,France
+199514,7,15247,11965,18529,26,20,32,FR,France
+199513,7,16864,13368,20360,29,23,35,FR,France
+199512,7,15875,12429,19321,27,21,33,FR,France
+199511,7,12844,9644,16044,22,16,28,FR,France
+199510,7,15346,11782,18910,27,21,33,FR,France
+199509,7,14754,11260,18248,26,20,32,FR,France
+199508,7,14521,11077,17965,25,19,31,FR,France
+199507,7,14315,10809,17821,25,19,31,FR,France
+199506,7,18089,14383,21795,31,25,37,FR,France
+199505,7,13414,10176,16652,23,17,29,FR,France
+199504,7,15743,12316,19170,27,21,33,FR,France
+199503,7,13763,10614,16912,24,19,29,FR,France
+199502,7,11495,8490,14500,20,15,25,FR,France
+199501,7,13733,10266,17200,24,18,30,FR,France
+199452,7,12696,9453,15939,22,16,28,FR,France
+199451,7,10390,7458,13322,18,13,23,FR,France
+199450,7,8048,5524,10572,14,10,18,FR,France
+199449,7,8212,5651,10773,14,10,18,FR,France
+199448,7,6481,4250,8712,11,7,15,FR,France
+199447,7,7140,4666,9614,12,8,16,FR,France
+199446,7,5021,2972,7070,9,5,13,FR,France
+199445,7,6759,4267,9251,12,8,16,FR,France
+199444,7,9276,5996,12556,16,10,22,FR,France
+199443,7,5622,3091,8153,10,6,14,FR,France
+199442,7,5848,3383,8313,10,6,14,FR,France
+199441,7,4921,2796,7046,9,5,13,FR,France
+199440,7,5936,3694,8178,10,6,14,FR,France
+199439,7,4090,2080,6100,7,4,10,FR,France
+199438,7,2734,1164,4304,5,2,8,FR,France
+199437,7,4364,1957,6771,8,4,12,FR,France
+199436,7,1991,509,3473,3,0,6,FR,France
+199435,7,3676,1494,5858,6,2,10,FR,France
+199434,7,6830,3728,9932,12,7,17,FR,France
+199433,7,6933,3534,10332,12,6,18,FR,France
+199432,7,4851,2299,7403,8,4,12,FR,France
+199431,7,7346,3935,10757,13,7,19,FR,France
+199430,7,17674,12807,22541,31,23,39,FR,France
+199429,7,17175,12461,21889,30,22,38,FR,France
+199428,7,16743,12021,21465,29,21,37,FR,France
+199427,7,22335,17343,27327,39,30,48,FR,France
+199426,7,23679,18832,28526,41,33,49,FR,France
+199425,7,24951,20263,29639,43,35,51,FR,France
+199424,7,29139,22549,35729,51,40,62,FR,France
+199423,7,13583,3641,23525,24,7,41,FR,France
+199422,7,16336,4640,28032,28,8,48,FR,France
+199421,7,16477,11025,21929,29,20,38,FR,France
+199420,7,12872,9458,16286,22,16,28,FR,France
+199419,7,13769,10440,17098,24,18,30,FR,France
+199418,7,13066,9917,16215,23,18,28,FR,France
+199417,7,18648,14838,22458,32,25,39,FR,France
+199416,7,17629,13660,21598,31,24,38,FR,France
+199415,7,23128,18550,27706,40,32,48,FR,France
+199414,7,20128,15971,24285,35,28,42,FR,France
+199413,7,16259,12721,19797,28,22,34,FR,France
+199412,7,15215,11581,18849,26,20,32,FR,France
+199411,7,16439,12812,20066,29,23,35,FR,France
+199410,7,20681,16433,24929,36,29,43,FR,France
+199409,7,22652,17519,27785,39,30,48,FR,France
+199408,7,22067,17044,27090,38,29,47,FR,France
+199407,7,15332,11563,19101,27,20,34,FR,France
+199406,7,15321,11710,18932,27,21,33,FR,France
+199405,7,15264,11523,19005,27,21,33,FR,France
+199404,7,10871,7697,14045,19,13,25,FR,France
+199403,7,10882,7808,13956,19,14,24,FR,France
+199402,7,11834,8348,15320,21,15,27,FR,France
+199401,7,11104,8142,14066,19,14,24,FR,France
+199352,7,9874,6746,13002,17,12,22,FR,France
+199351,7,9565,6818,12312,17,12,22,FR,France
+199350,7,6195,4181,8209,11,7,15,FR,France
+199349,7,10095,7443,12747,18,13,23,FR,France
+199348,7,6413,4261,8565,11,7,15,FR,France
+199347,7,7543,4934,10152,13,8,18,FR,France
+199346,7,7862,5175,10549,14,9,19,FR,France
+199345,7,5648,3511,7785,10,6,14,FR,France
+199344,7,6964,4509,9419,12,8,16,FR,France
+199343,7,5976,3632,8320,10,6,14,FR,France
+199342,7,7669,4853,10485,13,8,18,FR,France
+199341,7,9657,6648,12666,17,12,22,FR,France
+199340,7,5725,3082,8368,10,5,15,FR,France
+199339,7,5520,3207,7833,10,6,14,FR,France
+199338,7,2179,888,3470,4,2,6,FR,France
+199337,7,1762,557,2967,3,1,5,FR,France
+199336,7,3214,1526,4902,6,3,9,FR,France
+199335,7,2335,910,3760,4,2,6,FR,France
+199334,7,3770,1712,5828,7,3,11,FR,France
+199333,7,5925,3250,8600,10,5,15,FR,France
+199332,7,12711,8079,17343,22,14,30,FR,France
+199331,7,12481,7874,17088,22,14,30,FR,France
+199330,7,12584,8619,16549,22,15,29,FR,France
+199329,7,13179,8991,17367,23,16,30,FR,France
+199328,7,15293,11335,19251,27,20,34,FR,France
+199327,7,14134,9791,18477,25,17,33,FR,France
+199326,7,17937,13577,22297,31,23,39,FR,France
+199325,7,19581,14916,24246,34,26,42,FR,France
+199324,7,18285,13450,23120,32,24,40,FR,France
+199323,7,16003,11927,20079,28,21,35,FR,France
+199322,7,12652,8597,16707,22,15,29,FR,France
+199321,7,15218,10970,19466,27,20,34,FR,France
+199320,7,11343,7655,15031,20,14,26,FR,France
+199319,7,12086,8856,15316,21,15,27,FR,France
+199318,7,18898,14305,23491,33,25,41,FR,France
+199317,7,14969,10565,19373,26,18,34,FR,France
+199316,7,15102,11226,18978,26,19,33,FR,France
+199315,7,15058,11183,18933,26,19,33,FR,France
+199314,7,12425,9045,15805,22,16,28,FR,France
+199313,7,12266,8927,15605,21,15,27,FR,France
+199312,7,10821,7884,13758,19,14,24,FR,France
+199311,7,11021,8006,14036,19,14,24,FR,France
+199310,7,15847,12158,19536,28,22,34,FR,France
+199309,7,15084,11059,19109,26,19,33,FR,France
+199308,7,16967,12936,20998,30,23,37,FR,France
+199307,7,16057,12350,19764,28,22,34,FR,France
+199306,7,15887,12275,19499,28,22,34,FR,France
+199305,7,15954,12443,19465,28,22,34,FR,France
+199304,7,12374,9426,15322,22,17,27,FR,France
+199303,7,15283,11831,18735,27,21,33,FR,France
+199302,7,13366,10262,16470,23,18,28,FR,France
+199301,7,18678,14977,22379,33,27,39,FR,France
+199253,7,17829,13827,21831,31,24,38,FR,France
+199252,7,14330,10582,18078,25,18,32,FR,France
+199251,7,14635,11262,18008,26,20,32,FR,France
+199250,7,15252,11566,18938,27,21,33,FR,France
+199249,7,14411,11187,17635,25,19,31,FR,France
+199248,7,10048,7396,12700,18,13,23,FR,France
+199247,7,13069,9752,16386,23,17,29,FR,France
+199246,7,6900,4727,9073,12,8,16,FR,France
+199245,7,10791,7794,13788,19,14,24,FR,France
+199244,7,10205,7482,12928,18,13,23,FR,France
+199243,7,7196,4908,9484,13,9,17,FR,France
+199242,7,5748,3738,7758,10,6,14,FR,France
+199241,7,3587,2045,5129,6,3,9,FR,France
+199240,7,5654,3442,7866,10,6,14,FR,France
+199239,7,4747,2751,6743,8,5,11,FR,France
+199238,7,2842,1306,4378,5,2,8,FR,France
+199237,7,4103,2203,6003,7,4,10,FR,France
+199236,7,2801,1200,4402,5,2,8,FR,France
+199235,7,6428,3717,9139,11,6,16,FR,France
+199234,7,7517,4190,10844,13,7,19,FR,France
+199233,7,9951,5955,13947,17,10,24,FR,France
+199232,7,16090,11337,20843,28,20,36,FR,France
+199231,7,17156,12441,21871,30,22,38,FR,France
+199230,7,21780,16301,27259,38,28,48,FR,France
+199229,7,18926,13397,24455,33,23,43,FR,France
+199228,7,28144,20384,35904,49,35,63,FR,France
+199227,7,29388,22331,36445,51,39,63,FR,France
+199226,7,27933,21686,34180,49,38,60,FR,France
+199225,7,26446,21153,31739,46,37,55,FR,France
+199224,7,19614,14769,24459,34,26,42,FR,France
+199223,7,23628,17852,29404,41,31,51,FR,France
+199222,7,20438,14868,26008,36,26,46,FR,France
+199221,7,22595,17395,27795,40,31,49,FR,France
+199220,7,21092,15963,26221,37,28,46,FR,France
+199219,7,19210,13934,24486,34,25,43,FR,France
+199218,7,30273,22605,37941,53,40,66,FR,France
+199217,7,19162,14251,24073,34,25,43,FR,France
+199216,7,15583,11158,20008,27,19,35,FR,France
+199215,7,15473,10723,20223,27,19,35,FR,France
+199214,7,14455,10040,18870,25,17,33,FR,France
+199213,7,17146,12105,22187,30,21,39,FR,France
+199212,7,15838,10923,20753,28,19,37,FR,France
+199211,7,20003,13824,26182,35,24,46,FR,France
+199210,7,22906,16060,29752,40,28,52,FR,France
+199209,7,21824,15687,27961,38,27,49,FR,France
+199208,7,15243,10265,20221,27,18,36,FR,France
+199207,7,22633,16160,29106,40,29,51,FR,France
+199206,7,17009,12219,21799,30,22,38,FR,France
+199205,7,15997,11590,20404,28,20,36,FR,France
+199204,7,20690,15075,26305,36,26,46,FR,France
+199203,7,18586,12684,24488,33,23,43,FR,France
+199202,7,16588,10590,22586,29,18,40,FR,France
+199201,7,23607,16552,30662,41,29,53,FR,France
+199152,7,15184,10392,19976,27,19,35,FR,France
+199151,7,15043,10624,19462,26,18,34,FR,France
+199150,7,14115,9737,18493,25,17,33,FR,France
+199149,7,13304,8548,18060,23,15,31,FR,France
+199148,7,15370,10164,20576,27,18,36,FR,France
+199147,7,12813,8139,17487,23,15,31,FR,France
+199146,7,6453,3123,9783,11,5,17,FR,France
+199145,7,8048,4083,12013,14,7,21,FR,France
+199144,7,9882,5623,14141,17,10,24,FR,France
+199143,7,14307,8645,19969,25,15,35,FR,France
+199142,7,6419,3334,9504,11,6,16,FR,France
+199141,7,6168,3113,9223,11,6,16,FR,France
+199140,7,3615,1159,6071,6,2,10,FR,France
+199139,7,3105,472,5738,5,0,10,FR,France
+199138,7,4446,1435,7457,8,3,13,FR,France
+199137,7,1457,0,3262,3,0,6,FR,France
+199136,7,3053,29,6077,5,0,10,FR,France
+199135,7,805,0,2556,1,0,4,FR,France
+199134,7,5512,856,10168,10,2,18,FR,France
+199133,7,7347,2903,11791,13,5,21,FR,France
+199132,7,16099,7334,24864,28,13,43,FR,France
+199131,7,16803,8145,25461,30,15,45,FR,France
+199130,7,15915,6368,25462,28,11,45,FR,France
+199129,7,23744,14703,32785,42,26,58,FR,France
+199128,7,24222,15617,32827,43,28,58,FR,France
+199127,7,20309,12868,27750,36,23,49,FR,France
+199126,7,17608,11304,23912,31,20,42,FR,France
+199125,7,16169,10700,21638,28,18,38,FR,France
+199124,7,16171,10071,22271,28,17,39,FR,France
+199123,7,11947,7671,16223,21,13,29,FR,France
+199122,7,15452,9953,20951,27,17,37,FR,France
+199121,7,14903,8975,20831,26,16,36,FR,France
+199120,7,19053,12742,25364,34,23,45,FR,France
+199119,7,16739,11246,22232,29,19,39,FR,France
+199118,7,21385,13882,28888,38,25,51,FR,France
+199117,7,13462,8877,18047,24,16,32,FR,France
+199116,7,14857,10068,19646,26,18,34,FR,France
+199115,7,13975,9781,18169,25,18,32,FR,France
+199114,7,12265,7684,16846,22,14,30,FR,France
+199113,7,9567,6041,13093,17,11,23,FR,France
+199112,7,10864,7331,14397,19,13,25,FR,France
+199111,7,15574,11184,19964,27,19,35,FR,France
+199110,7,16643,11372,21914,29,20,38,FR,France
+199109,7,13741,8780,18702,24,15,33,FR,France
+199108,7,13289,8813,17765,23,15,31,FR,France
+199107,7,12337,8077,16597,22,15,29,FR,France
+199106,7,10877,7013,14741,19,12,26,FR,France
+199105,7,10442,6544,14340,18,11,25,FR,France
+199104,7,7913,4563,11263,14,8,20,FR,France
+199103,7,15387,10484,20290,27,18,36,FR,France
+199102,7,16277,11046,21508,29,20,38,FR,France
+199101,7,15565,10271,20859,27,18,36,FR,France
+199052,7,19375,13295,25455,34,23,45,FR,France
+199051,7,19080,13807,24353,34,25,43,FR,France
+199050,7,11079,6660,15498,20,12,28,FR,France
+199049,7,1143,0,2610,2,0,5,FR,France
-- 
2.18.1