TP : Telechargement des donnees de latence

module2/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.array([14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(data)\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "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": [
+    "plt.hist(data, color='b', edgecolor='k')\n",
+    "plt.grid(color='k', linestyle=':')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +90,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo5/exo5_fr.ipynb

@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -287,7 +287,7 @@
        "22   1/12/86      6           58       200            1"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,118 +321,20 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "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>Date</th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Temperature</th>\n",
-       "      <th>Pressure</th>\n",
-       "      <th>Malfunction</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>11/12/81</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>2/03/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>57</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>4/06/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>63</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>8/30/84</td>\n",
-       "      <td>6</td>\n",
-       "      <td>70</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1/24/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>53</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>10/30/85</td>\n",
-       "      <td>6</td>\n",
-       "      <td>75</td>\n",
-       "      <td>200</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1/12/86</td>\n",
-       "      <td>6</td>\n",
-       "      <td>58</td>\n",
-       "      <td>200</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Date  Count  Temperature  Pressure  Malfunction\n",
-       "1   11/12/81      6           70        50            1\n",
-       "8    2/03/84      6           57       200            1\n",
-       "9    4/06/84      6           63       200            1\n",
-       "10   8/30/84      6           70       200            1\n",
-       "13   1/24/85      6           53       200            2\n",
-       "20  10/30/85      6           75       200            2\n",
-       "22   1/12/86      6           58       200            1"
+   "source": [
+    "**Non, au contraire, ça apporte des informations très importantes de savoir qu'il n'y a pas eu de dysfonctionnement ! Il faut garder toutes les données !**"
    ]
   },
-     "execution_count": 2,
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "data = data[data.Malfunction>0]\n",
-    "data"
+    "#data = data[data.Malfunction>0]\n",
+    "#data"
    ]
   },
   {
@@ -448,12 +350,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -470,7 +372,7 @@
     "import matplotlib.pyplot as plt\n",
     "\n",
     "data[\"Frequency\"]=data.Malfunction/data.Count\n",
-    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
+    "data.plot(x=\"Temperature\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1], color=\"#1133ff77\")\n",
     "plt.grid(True)"
    ]
   },
@@ -483,6 +385,13 @@
     "dysfonctionnements d'un joint. \n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**En affichant toutes les données on voit effectivement que les dysfonctionnements semblent se produire plus fréquemment pour des températures plus basses**"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -500,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -509,10 +418,10 @@
        "<table class=\"simpletable\">\n",
        "<caption>Generalized Linear Model Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>     7</td>  \n",
+       "  <th>Dep. Variable:</th>      <td>Frequency</td>    <th>  No. Observations:  </th>  <td>    23</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>     5</td>  \n",
+       "  <th>Model:</th>                 <td>GLM</td>       <th>  Df Residuals:      </th>  <td>    21</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model Family:</th>       <td>Binomial</td>     <th>  Df Model:          </th>  <td>     1</td>  \n",
@@ -521,16 +430,16 @@
        "  <th>Link Function:</th>        <td>logit</td>      <th>  Scale:             </th> <td>  1.0000</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -2.5250</td> \n",
+       "  <th>Method:</th>               <td>IRLS</td>       <th>  Log-Likelihood:    </th> <td> -3.9210</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>           <td>Sat, 13 Apr 2019</td> <th>  Deviance:          </th> <td> 0.22231</td> \n",
+       "  <th>Date:</th>           <td>Tue, 26 Oct 2021</td> <th>  Deviance:          </th> <td>  3.0144</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>19:11:24</td>     <th>  Pearson chi2:      </th>  <td> 0.236</td>  \n",
+       "  <th>Time:</th>               <td>08:58:58</td>     <th>  Pearson chi2:      </th>  <td>  5.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Iterations:</th>         <td>4</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
+       "  <th>No. Iterations:</th>         <td>6</td>        <th>  Covariance Type:   </th> <td>nonrobust</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -538,10 +447,10 @@
        "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th>   <td>   -1.3895</td> <td>    7.828</td> <td>   -0.178</td> <td> 0.859</td> <td>  -16.732</td> <td>   13.953</td>\n",
+       "  <th>Intercept</th>   <td>    5.0850</td> <td>    7.477</td> <td>    0.680</td> <td> 0.496</td> <td>   -9.570</td> <td>   19.740</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Temperature</th> <td>    0.0014</td> <td>    0.122</td> <td>    0.012</td> <td> 0.991</td> <td>   -0.238</td> <td>    0.240</td>\n",
+       "  <th>Temperature</th> <td>   -0.1156</td> <td>    0.115</td> <td>   -1.004</td> <td> 0.316</td> <td>   -0.341</td> <td>    0.110</td>\n",
        "</tr>\n",
        "</table>"
       ],
@@ -550,24 +459,24 @@
        "\"\"\"\n",
        "                 Generalized Linear Model Regression Results                  \n",
        "==============================================================================\n",
-       "Dep. Variable:              Frequency   No. Observations:                    7\n",
-       "Model:                            GLM   Df Residuals:                        5\n",
+       "Dep. Variable:              Frequency   No. Observations:                   23\n",
+       "Model:                            GLM   Df Residuals:                       21\n",
        "Model Family:                Binomial   Df Model:                            1\n",
        "Link Function:                  logit   Scale:                          1.0000\n",
-       "Method:                          IRLS   Log-Likelihood:                -2.5250\n",
-       "Date:                Sat, 13 Apr 2019   Deviance:                      0.22231\n",
-       "Time:                        19:11:24   Pearson chi2:                    0.236\n",
-       "No. Iterations:                     4   Covariance Type:             nonrobust\n",
+       "Method:                          IRLS   Log-Likelihood:                -3.9210\n",
+       "Date:                Tue, 26 Oct 2021   Deviance:                       3.0144\n",
+       "Time:                        08:58:58   Pearson chi2:                     5.00\n",
+       "No. Iterations:                     6   Covariance Type:             nonrobust\n",
        "===============================================================================\n",
        "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
        "-------------------------------------------------------------------------------\n",
-       "Intercept      -1.3895      7.828     -0.178      0.859     -16.732      13.953\n",
-       "Temperature     0.0014      0.122      0.012      0.991      -0.238       0.240\n",
+       "Intercept       5.0850      7.477      0.680      0.496      -9.570      19.740\n",
+       "Temperature    -0.1156      0.115     -1.004      0.316      -0.341       0.110\n",
        "===============================================================================\n",
        "\"\"\""
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -593,6 +502,26 @@
     "estimations avec des pincettes.\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<statsmodels.genmod.generalized_linear_model.GLMResultsWrapper at 0x7fa39e9db7f0>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logmodel"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -605,12 +534,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -625,11 +554,65 @@
     "%matplotlib inline\n",
     "data_pred = pd.DataFrame({'Temperature': np.linspace(start=30, stop=90, num=121), 'Intercept': 1})\n",
     "data_pred['Frequency'] = logmodel.predict(data_pred[['Intercept','Temperature']])\n",
+    "\n",
+    "\n",
+    "\n",
     "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
     "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])\n",
     "plt.grid(True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**On n'a pas de barres d'erreur sur ce calcul ?! Affichons les intervalles de confiance.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = logmodel.get_prediction(data_pred[['Intercept','Temperature']])\n",
+    "conf_interval = res.conf_int()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7fa397d57b00>"
+      ]
+     },
+     "execution_count": 36,
+     "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": [
+    "data_pred.plot(x=\"Temperature\",y=\"Frequency\",kind=\"line\",ylim=[0,1])\n",
+    "plt.fill_between(data_pred[\"Temperature\"], conf_interval[:,0], conf_interval[:, 1], color=\"#3333ff33\")\n",
+    "plt.scatter(x=data[\"Temperature\"],y=data[\"Frequency\"])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -646,6 +629,13 @@
     "défaillance d'un joint:\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pas du tout, une analyse avec barres d'erreur montre qu'on ne peut rien déduire sur la probabilité d'échec à basse température. En fait, l'incertitude est déjà très grande lorsqu'on prend en compte toutes les données aux températures mesurées.**"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -686,6 +676,90 @@
     "analyse et de regarder ce jeu de données sous tous les angles afin\n",
     "d'expliquer ce qui ne va pas."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Si on s'intéresse à la prédiction prenant en compte toutes les données, sans même regarder l'intervalle de confiance, on voit que c'est plutôt mauvais signe:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = float(data_pred[\"Frequency\"][2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.963654715320592"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 - (1-p**2)**3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Affichons l'intervalle de confiance à 95% de la probabilité de défaillance d'un des lanceurs.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PolyCollection at 0x7fa397151cf8>"
+      ]
+     },
+     "execution_count": 49,
+     "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": [
+    "ci_flight_failure = 1 - (1 - conf_interval**2)**3\n",
+    "\n",
+    "plt.fill_between(data_pred[\"Temperature\"], ci_flight_failure[:,0], ci_flight_failure[:, 1], color=\"#ff333333\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**On ne peut absolument rien dire sur ce qui se passera à 30°F. C'est plutôt mauvais signe.**"
+   ]
   }
  ],
  "metadata": {
@@ -705,7 +779,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

module3/exo3/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Estimation de la latence et de la capacité d’une connexion à partir de mesures asymétriques"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ce document correspond au Sujet 4 du module 3 du MOOC \"Recherche reproductible : principes méthodologiques pour une science transparente\".\n",
+    "\n",
+    "On s'intéresse à comparer un modèle simple de la performance d'une connexion de réseau avec des données réelles."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import exists\n",
+    "import requests\n",
+    "import gzip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Téléchargement des données"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filenames = [\n",
+    "    \"liglab2.log\", \n",
+    "    \"stackoverflow.log\",\n",
+    "]\n",
+    "urls = [\n",
+    "    \"http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/liglab2.log.gz\",\n",
+    "    \"http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/stackoverflow.log.gz\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {
+    "hideOutput": true
+   },
+   "source": [
+    "# Facultatif : suppression des fichiers pour forcer le re-téléchargement\n",
+    "for filename in filenames:\n",
+    "    try:\n",
+    "        os.remove(filename)\n",
+    "    except FileNotFoundError:\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Le fichier liglab2.log existe déjà, pas besoin de le télécharger.\n",
+      "Le fichier stackoverflow.log existe déjà, pas besoin de le télécharger.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Si les fichiers n'existent pas encore, on les télécharge.\n",
+    "\n",
+    "def download_archive(filename, url):\n",
+    "    if not exists(filename):\n",
+    "        # Le fichier est une archive .gz\n",
+    "        archive = requests.get(url)\n",
+    "        content = gzip.decompress(archive.content)\n",
+    "        open(filename,'wb').write(content)\n",
+    "        print(f\"Téléchargement de {url} et extraction vers {filename}.\")\n",
+    "    else:\n",
+    "        print(f\"Le fichier {filename} existe déjà, pas besoin de le télécharger.\")\n",
+    "\n",
+    "\n",
+    "for filename, url in zip(filenames, urls):\n",
+    "    download_archive(filename, url)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on Response in module requests.models object:\n",
+      "\n",
+      "class Response(builtins.object)\n",
+      " |  The :class:`Response <Response>` object, which contains a\n",
+      " |  server's response to an HTTP request.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __bool__(self)\n",
+      " |      Returns True if :attr:`status_code` is less than 400.\n",
+      " |      \n",
+      " |      This attribute checks if the status code of the response is between\n",
+      " |      400 and 600 to see if there was a client error or a server error. If\n",
+      " |      the status code, is between 200 and 400, this will return True. This\n",
+      " |      is **not** a check to see if the response code is ``200 OK``.\n",
+      " |  \n",
+      " |  __enter__(self)\n",
+      " |  \n",
+      " |  __exit__(self, *args)\n",
+      " |  \n",
+      " |  __getstate__(self)\n",
+      " |  \n",
+      " |  __init__(self)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |  \n",
+      " |  __iter__(self)\n",
+      " |      Allows you to use a response as an iterator.\n",
+      " |  \n",
+      " |  __nonzero__(self)\n",
+      " |      Returns True if :attr:`status_code` is less than 400.\n",
+      " |      \n",
+      " |      This attribute checks if the status code of the response is between\n",
+      " |      400 and 600 to see if there was a client error or a server error. If\n",
+      " |      the status code, is between 200 and 400, this will return True. This\n",
+      " |      is **not** a check to see if the response code is ``200 OK``.\n",
+      " |  \n",
+      " |  __repr__(self)\n",
+      " |      Return repr(self).\n",
+      " |  \n",
+      " |  __setstate__(self, state)\n",
+      " |  \n",
+      " |  close(self)\n",
+      " |      Releases the connection back to the pool. Once this method has been\n",
+      " |      called the underlying ``raw`` object must not be accessed again.\n",
+      " |      \n",
+      " |      *Note: Should not normally need to be called explicitly.*\n",
+      " |  \n",
+      " |  iter_content(self, chunk_size=1, decode_unicode=False)\n",
+      " |      Iterates over the response data.  When stream=True is set on the\n",
+      " |      request, this avoids reading the content at once into memory for\n",
+      " |      large responses.  The chunk size is the number of bytes it should\n",
+      " |      read into memory.  This is not necessarily the length of each item\n",
+      " |      returned as decoding can take place.\n",
+      " |      \n",
+      " |      chunk_size must be of type int or None. A value of None will\n",
+      " |      function differently depending on the value of `stream`.\n",
+      " |      stream=True will read data as it arrives in whatever size the\n",
+      " |      chunks are received. If stream=False, data is returned as\n",
+      " |      a single chunk.\n",
+      " |      \n",
+      " |      If decode_unicode is True, content will be decoded using the best\n",
+      " |      available encoding based on the response.\n",
+      " |  \n",
+      " |  iter_lines(self, chunk_size=512, decode_unicode=False, delimiter=None)\n",
+      " |      Iterates over the response data, one line at a time.  When\n",
+      " |      stream=True is set on the request, this avoids reading the\n",
+      " |      content at once into memory for large responses.\n",
+      " |      \n",
+      " |      .. note:: This method is not reentrant safe.\n",
+      " |  \n",
+      " |  json(self, **kwargs)\n",
+      " |      Returns the json-encoded content of a response, if any.\n",
+      " |      \n",
+      " |      :param \\*\\*kwargs: Optional arguments that ``json.loads`` takes.\n",
+      " |      :raises ValueError: If the response body does not contain valid json.\n",
+      " |  \n",
+      " |  raise_for_status(self)\n",
+      " |      Raises stored :class:`HTTPError`, if one occurred.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  apparent_encoding\n",
+      " |      The apparent encoding, provided by the chardet library.\n",
+      " |  \n",
+      " |  content\n",
+      " |      Content of the response, in bytes.\n",
+      " |  \n",
+      " |  is_permanent_redirect\n",
+      " |      True if this Response one of the permanent versions of redirect.\n",
+      " |  \n",
+      " |  is_redirect\n",
+      " |      True if this Response is a well-formed HTTP redirect that could have\n",
+      " |      been processed automatically (by :meth:`Session.resolve_redirects`).\n",
+      " |  \n",
+      " |  links\n",
+      " |      Returns the parsed header links of the response, if any.\n",
+      " |  \n",
+      " |  next\n",
+      " |      Returns a PreparedRequest for the next request in a redirect chain, if there is one.\n",
+      " |  \n",
+      " |  ok\n",
+      " |      Returns True if :attr:`status_code` is less than 400, False if not.\n",
+      " |      \n",
+      " |      This attribute checks if the status code of the response is between\n",
+      " |      400 and 600 to see if there was a client error or a server error. If\n",
+      " |      the status code is between 200 and 400, this will return True. This\n",
+      " |      is **not** a check to see if the response code is ``200 OK``.\n",
+      " |  \n",
+      " |  text\n",
+      " |      Content of the response, in unicode.\n",
+      " |      \n",
+      " |      If Response.encoding is None, encoding will be guessed using\n",
+      " |      ``chardet``.\n",
+      " |      \n",
+      " |      The encoding of the response content is determined based solely on HTTP\n",
+      " |      headers, following RFC 2616 to the letter. If you can take advantage of\n",
+      " |      non-HTTP knowledge to make a better guess at the encoding, you should\n",
+      " |      set ``r.encoding`` appropriately before accessing this property.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  __attrs__ = ['_content', 'status_code', 'headers', 'url', 'history', '...\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +277,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-