diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index d4a4fd9e964c7338274a1ebcd4a5a760e9dbf55b..3e7a959da4dd3f74840627f5bad18dcff132b733 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -32,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -73,7 +73,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -82,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -142,53 +142,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "ImportError", - "evalue": "cannot import name 'STL'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mstatsmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseasonal\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# Décomposition de la série temporelle\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mstl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSTL\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'CO2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseasonal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m13\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mImportError\u001b[0m: cannot import name 'STL'" - ] - } - ], - "source": [ - "from statsmodels.tsa.seasonal import STL\n", - "\n", - "# Décomposition de la série temporelle\n", - "stl = STL(data['CO2'], seasonal=13)\n", - "res = stl.fit()\n", - "\n", - "# Extraction des composantes\n", - "trend = res.trend\n", - "seasonal = res.seasonal\n", - "residual = res.resid\n", - "\n", - "# Affichage des composantes\n", - "plt.figure(figsize=(12, 8))\n", - "plt.subplot(4, 1, 1)\n", - "plt.plot(data['Date'], data['CO2'], label='Original')\n", - "plt.legend()\n", - "\n", - "plt.subplot(4, 1, 2)\n", - "plt.plot(data['Date'], trend, label='Tendance')\n", - "plt.legend()\n", - "\n", - "plt.subplot(4, 1, 3)\n", - "plt.plot(data['Date'], seasonal, label='Saisonnière')\n", - "plt.legend()\n", - "\n", - "plt.subplot(4, 1, 4)\n", - "plt.plot(data['Date'], residual, label='Résiduelle')\n", - "plt.legend()\n", - "\n", - "plt.show()" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", @@ -213,7 +170,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Importation des bibliothèques nécessaires\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import isoweek" + ] + }, + { + "cell_type": "code", + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -228,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -258,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -288,1010 +258,123 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", - "# Definir una función para extraer la información relevante de cada línea\n", + "# Définir une fonction pour extraire les informations pertinentes de chaque ligne\n", "def process_ping_data(df):\n", - " df.dropna(inplace=True) # Eliminar filas incompletas\n", - " df.columns = [\"date\", \"size\", \"bytes\", \"from\", \"url\", \"ip\", \"icmp\",\"ttl\",\"time\", \"ms\"] # Nombrar las columnas\n", - " df[\"time\"] = df[\"time\"].str[5:].astype(float) # Convertir el tiempo a float\n", + " df.dropna(inplace=True) # Supprimer les lignes incomplètes\n", + " df.columns = [\"date\", \"size\", \"bytes\", \"from\", \"url\", \"ip\", \"icmp\",\"ttl\",\"time\", \"ms\"] # Nommez les colonnes\n", + " df[\"time\"] = df[\"time\"].str[5:].astype(float) # Nommez les colonnes\n", " df[\"date\"] = df[\"date\"].str[1:18]\n", " df[\"date\"] = pd.to_datetime(df[\"date\"], unit='s')\n", " return df\n", "\n", - "# Procesar los datos de ping para liglab2 y stackoverflow\n", + "# Traiter les données ping pour liglab2 et stackoverflow\n", "df_liglab2 = process_ping_data(df_liglab2)\n", "df_stackoverflow = process_ping_data(df_stackoverflow)\n" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date size bytes from url \\\n", + "0 2015-01-20 13:48:02.052172 665 bytes from lig-publig.imag.fr \n", + "1 2015-01-20 13:48:02.277315 1373 bytes from lig-publig.imag.fr \n", + "2 2015-01-20 13:48:02.502054 262 bytes from lig-publig.imag.fr \n", + "3 2015-01-20 13:48:02.729257 1107 bytes from lig-publig.imag.fr \n", + "4 2015-01-20 13:48:02.934648 1128 bytes from lig-publig.imag.fr \n", + "\n", + " ip icmp ttl time ms \n", + "0 (129.88.11.7): icmp_seq=1 ttl=60 22.50 ms \n", + "1 (129.88.11.7): icmp_seq=1 ttl=60 21.20 ms \n", + "2 (129.88.11.7): icmp_seq=1 ttl=60 21.20 ms \n", + "3 (129.88.11.7): icmp_seq=1 ttl=60 23.30 ms \n", + "4 (129.88.11.7): icmp_seq=1 ttl=60 1.41 ms \n" + ] + } + ], + "source": [ + "# Affichage des premières lignes des données pour vérification\n", + "print(df_liglab2.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " date size bytes from url \\\n", + "0 2015-01-20 16:26:43.082701 1257 bytes from stackoverflow.com \n", + "1 2015-01-20 16:26:43.408254 454 bytes from stackoverflow.com \n", + "2 2015-01-20 16:26:43.739730 775 bytes from stackoverflow.com \n", + "3 2015-01-20 16:26:44.056630 1334 bytes from stackoverflow.com \n", + "4 2015-01-20 16:26:44.372224 83 bytes from stackoverflow.com \n", + "\n", + " ip icmp ttl time ms \n", + "0 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", + "1 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", + "2 (198.252.206.140): icmp_seq=1 ttl=50 126.0 ms \n", + "3 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", + "4 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n" + ] + } + ], + "source": [ + "# Affichage des premières lignes des données pour vérification\n", + "print(df_stackoverflow.head())" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - 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datesizebytesfromurlipicmpttltimems
02015-01-20 16:26:43.0827011257bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50120.0ms
12015-01-20 16:26:43.408254454bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50120.0ms
22015-01-20 16:26:43.739730775bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50126.0ms
32015-01-20 16:26:44.0566301334bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
42015-01-20 16:26:44.37222483bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
52015-01-20 16:26:44.688367694bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
62015-01-20 16:26:45.0055141577bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
72015-01-20 16:26:45.321112632bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
82015-01-20 16:26:45.637464405bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
92015-01-20 16:26:45.9534721419bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
102015-01-20 16:26:46.269163329bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
112015-01-20 16:26:46.585098868bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
122015-01-20 16:26:46.9019721714bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
132015-01-20 16:26:47.2178631053bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
142015-01-20 16:26:47.533900349bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
152015-01-20 16:26:47.8511481598bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
162015-01-20 16:26:48.1667941412bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
172015-01-20 16:26:48.482159167bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
182015-01-20 16:26:48.79815560bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
192015-01-20 16:26:49.1144801038bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
202015-01-20 16:26:49.430586949bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
212015-01-20 16:26:49.746729279bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
222015-01-20 16:26:50.062322757bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
232015-01-20 16:26:50.3781131355bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
242015-01-20 16:26:50.6940151151bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
252015-01-20 16:26:51.009670237bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
262015-01-20 16:26:51.3248561221bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
272015-01-20 16:26:51.6405441063bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
282015-01-20 16:26:51.956109445bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
292015-01-20 16:26:52.2725041619bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
.................................
68572015-01-20 17:04:11.530711234bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68582015-01-20 17:04:11.847515231bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68592015-01-20 17:04:12.1638371495bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68602015-01-20 17:04:12.4798341313bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68612015-01-20 17:04:12.795239182bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68622015-01-20 17:04:13.1115702000bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68632015-01-20 17:04:13.4271101396bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68642015-01-20 17:04:13.742351515bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68652015-01-20 17:04:14.058100590bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68662015-01-20 17:04:14.373566229bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68672015-01-20 17:04:14.689196806bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68682015-01-20 17:04:15.007766422bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50113.0ms
68692015-01-20 17:04:15.3245711939bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68702015-01-20 17:04:15.639814365bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68712015-01-20 17:04:15.954957502bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50110.0ms
68722015-01-20 17:04:16.2729511738bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50113.0ms
68732015-01-20 17:04:16.5919151148bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50114.0ms
68742015-01-20 17:04:16.915868294bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50119.0ms
68752015-01-20 17:04:17.2316171534bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68762015-01-20 17:04:17.5464041103bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68772015-01-20 17:04:17.8614991121bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68782015-01-20 17:04:18.1770301219bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68792015-01-20 17:04:18.4934441880bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
68802015-01-20 17:04:18.808864986bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68812015-01-20 17:04:19.124524357bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68822015-01-20 17:04:19.4405171696bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68832015-01-20 17:04:19.756250561bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68842015-01-20 17:04:20.071820773bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68852015-01-20 17:04:20.3873851009bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50111.0ms
68862015-01-20 17:04:20.7043821948bytesfromstackoverflow.com(198.252.206.140):icmp_seq=1ttl=50112.0ms
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6824 rows × 10 columns

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\n", "text/plain": [ - " date size bytes from url \\\n", - "0 2015-01-20 16:26:43.082701 1257 bytes from stackoverflow.com \n", - "1 2015-01-20 16:26:43.408254 454 bytes from stackoverflow.com \n", - "2 2015-01-20 16:26:43.739730 775 bytes from stackoverflow.com \n", - "3 2015-01-20 16:26:44.056630 1334 bytes from stackoverflow.com \n", - "4 2015-01-20 16:26:44.372224 83 bytes from stackoverflow.com \n", - "5 2015-01-20 16:26:44.688367 694 bytes from stackoverflow.com \n", - "6 2015-01-20 16:26:45.005514 1577 bytes from stackoverflow.com \n", - "7 2015-01-20 16:26:45.321112 632 bytes from stackoverflow.com \n", - "8 2015-01-20 16:26:45.637464 405 bytes from stackoverflow.com \n", - "9 2015-01-20 16:26:45.953472 1419 bytes from stackoverflow.com \n", - "10 2015-01-20 16:26:46.269163 329 bytes from stackoverflow.com \n", - "11 2015-01-20 16:26:46.585098 868 bytes from stackoverflow.com \n", - "12 2015-01-20 16:26:46.901972 1714 bytes from stackoverflow.com \n", - "13 2015-01-20 16:26:47.217863 1053 bytes from stackoverflow.com \n", - "14 2015-01-20 16:26:47.533900 349 bytes from stackoverflow.com \n", - "15 2015-01-20 16:26:47.851148 1598 bytes from stackoverflow.com \n", - "16 2015-01-20 16:26:48.166794 1412 bytes from stackoverflow.com \n", - "17 2015-01-20 16:26:48.482159 167 bytes from stackoverflow.com \n", - "18 2015-01-20 16:26:48.798155 60 bytes from stackoverflow.com \n", - "19 2015-01-20 16:26:49.114480 1038 bytes from stackoverflow.com \n", - "20 2015-01-20 16:26:49.430586 949 bytes from stackoverflow.com \n", - "21 2015-01-20 16:26:49.746729 279 bytes from stackoverflow.com \n", - "22 2015-01-20 16:26:50.062322 757 bytes from stackoverflow.com \n", - "23 2015-01-20 16:26:50.378113 1355 bytes from stackoverflow.com \n", - "24 2015-01-20 16:26:50.694015 1151 bytes from stackoverflow.com \n", - "25 2015-01-20 16:26:51.009670 237 bytes from stackoverflow.com \n", - "26 2015-01-20 16:26:51.324856 1221 bytes from stackoverflow.com \n", - "27 2015-01-20 16:26:51.640544 1063 bytes from stackoverflow.com \n", - "28 2015-01-20 16:26:51.956109 445 bytes from stackoverflow.com \n", - "29 2015-01-20 16:26:52.272504 1619 bytes from stackoverflow.com \n", - "... ... ... ... ... ... \n", - "6857 2015-01-20 17:04:11.530711 234 bytes from stackoverflow.com \n", - "6858 2015-01-20 17:04:11.847515 231 bytes from stackoverflow.com \n", - "6859 2015-01-20 17:04:12.163837 1495 bytes from stackoverflow.com \n", - "6860 2015-01-20 17:04:12.479834 1313 bytes from stackoverflow.com \n", - "6861 2015-01-20 17:04:12.795239 182 bytes from stackoverflow.com \n", - "6862 2015-01-20 17:04:13.111570 2000 bytes from stackoverflow.com \n", - "6863 2015-01-20 17:04:13.427110 1396 bytes from stackoverflow.com \n", - "6864 2015-01-20 17:04:13.742351 515 bytes from stackoverflow.com \n", - "6865 2015-01-20 17:04:14.058100 590 bytes from stackoverflow.com \n", - "6866 2015-01-20 17:04:14.373566 229 bytes from stackoverflow.com \n", - "6867 2015-01-20 17:04:14.689196 806 bytes from stackoverflow.com \n", - "6868 2015-01-20 17:04:15.007766 422 bytes from stackoverflow.com \n", - "6869 2015-01-20 17:04:15.324571 1939 bytes from stackoverflow.com \n", - "6870 2015-01-20 17:04:15.639814 365 bytes from stackoverflow.com \n", - "6871 2015-01-20 17:04:15.954957 502 bytes from stackoverflow.com \n", - "6872 2015-01-20 17:04:16.272951 1738 bytes from stackoverflow.com \n", - "6873 2015-01-20 17:04:16.591915 1148 bytes from stackoverflow.com \n", - "6874 2015-01-20 17:04:16.915868 294 bytes from stackoverflow.com \n", - "6875 2015-01-20 17:04:17.231617 1534 bytes from stackoverflow.com \n", - "6876 2015-01-20 17:04:17.546404 1103 bytes from stackoverflow.com \n", - "6877 2015-01-20 17:04:17.861499 1121 bytes from stackoverflow.com \n", - "6878 2015-01-20 17:04:18.177030 1219 bytes from stackoverflow.com \n", - "6879 2015-01-20 17:04:18.493444 1880 bytes from stackoverflow.com \n", - "6880 2015-01-20 17:04:18.808864 986 bytes from stackoverflow.com \n", - "6881 2015-01-20 17:04:19.124524 357 bytes from stackoverflow.com \n", - "6882 2015-01-20 17:04:19.440517 1696 bytes from stackoverflow.com \n", - "6883 2015-01-20 17:04:19.756250 561 bytes from stackoverflow.com \n", - "6884 2015-01-20 17:04:20.071820 773 bytes from stackoverflow.com \n", - "6885 2015-01-20 17:04:20.387385 1009 bytes from stackoverflow.com \n", - "6886 2015-01-20 17:04:20.704382 1948 bytes from stackoverflow.com \n", - "\n", - " ip icmp ttl time ms \n", - "0 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", - "1 (198.252.206.140): icmp_seq=1 ttl=50 120.0 ms \n", - "2 (198.252.206.140): icmp_seq=1 ttl=50 126.0 ms \n", - "3 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "4 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "5 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "7 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "8 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "9 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "10 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "11 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "12 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "13 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "14 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "15 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "16 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "17 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "18 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "19 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "20 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "21 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "22 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "23 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "24 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "25 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "26 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "27 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "28 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "29 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "... ... ... ... ... .. \n", - "6857 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6858 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", - "6859 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "6860 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6861 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", - "6862 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "6863 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6864 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", - "6865 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6866 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", - "6867 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6868 (198.252.206.140): icmp_seq=1 ttl=50 113.0 ms \n", - "6869 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "6870 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6871 (198.252.206.140): icmp_seq=1 ttl=50 110.0 ms \n", - "6872 (198.252.206.140): icmp_seq=1 ttl=50 113.0 ms \n", - "6873 (198.252.206.140): icmp_seq=1 ttl=50 114.0 ms \n", - "6874 (198.252.206.140): icmp_seq=1 ttl=50 119.0 ms \n", - "6875 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6876 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6877 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6878 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6879 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "6880 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6881 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6882 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6883 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6884 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6885 (198.252.206.140): icmp_seq=1 ttl=50 111.0 ms \n", - "6886 (198.252.206.140): icmp_seq=1 ttl=50 112.0 ms \n", - "\n", - "[6824 rows x 10 columns]" + "
" ] }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "df_stackoverflow" + "# Graphique du temps de transmission dans le temps\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(df_liglab2[\"date\"], df_liglab2[\"time\"], label=\"df_liglab2\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Transmission Time (ms)\")\n", + "plt.title(\"Transmission time evolution (df_liglab2)\")\n", + "plt.legend()\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1303,14 +386,12 @@ } ], "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Gráfico del tiempo de transmisión a lo largo del tiempo\n", + "# Graphique du temps de transmission dans le temps\n", "plt.figure(figsize=(10, 6))\n", - "plt.plot(df_liglab2[\"date\"], df_liglab2[\"time\"], label=\"liglab2\")\n", - "plt.xlabel(\"Tiempo\")\n", - "plt.ylabel(\"Tiempo de transmisión (ms)\")\n", - "plt.title(\"Evolución del tiempo de transmisión (liglab2)\")\n", + "plt.plot(df_stackoverflow[\"date\"], df_stackoverflow[\"time\"], label=\"df_stackoverflow\")\n", + "plt.xlabel(\"Time\")\n", + "plt.ylabel(\"Transmission Time (ms)\")\n", + "plt.title(\"Transmission time evolution (df_stackoverflow)\")\n", "plt.legend()\n", "plt.show()" ] @@ -1320,30 +401,48 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Graph of transmission time based on message size\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(df_liglab2[\"size\"], df_liglab2[\"time\"], label=\"liglab2\")\n", + "plt.xlabel(\"Message Size\")\n", + "plt.ylabel(\"Transmission Time (ms)\")\n", + "plt.title(\"Transmission time evolution (liglab2)\")\n", + "plt.legend()\n", + "plt.show()" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Graph of transmission time based on message size\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(df_stackoverflow[\"size\"], df_stackoverflow[\"time\"], label=\"df_stackoverflow\")\n", + "plt.xlabel(\"Message Size\")\n", + "plt.ylabel(\"Transmission Time (ms)\")\n", + "plt.title(\"Transmission time evolution (df_stackoverflow)\")\n", + "plt.legend()\n", + "plt.show()" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "\n" + ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "\n" - ] + "source": [] } ], "metadata": {