{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Subject 1: CO2 concentration in the atmosphere since 1958"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Téléchargement et chargement des données\n",
    "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Charger les données à partir d'un fichier CSV local\n",
    "data_file = \"weekly_in_situ_co2.csv\"\n",
    "\n",
    "import os\n",
    "import urllib.request\n",
    "if not os.path.exists(data_file):\n",
    "    urllib.request.urlretrieve(data_url, data_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date  Concentration\n",
      "0  1958-03-29         316.19\n",
      "1  1958-04-05         317.31\n",
      "2  1958-04-12         317.69\n",
      "3  1958-04-19         317.58\n",
      "4  1958-04-26         316.48\n"
     ]
    }
   ],
   "source": [
    "# Charger les données dans une table de pandas\n",
    "# data = pd.read_csv(data_file, skiprows=44, sep=r'\\s+', engine='python', parse_dates=[0], index_col=[0], names = ['Date', 'Concentration'])\n",
    "data = pd.read_csv(data_file, skiprows=44, names = ['Date', 'Concentration'])\n",
    "# Affichage des premières lignes des données pour vérification\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Toutes les lignes contenant au moins une valeur nulle (NaN) dans une colonne sont supprimées\n",
    "# data_subset = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Réalisation du graphique complet\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(data['Date'], data['Concentration'], label='CO2 Concentration')\n",
    "plt.title('CO2 Concentration Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('CO2 Concentration (ppm)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Réalisation du graphique pour montrer l'oscillation (300 dernières lignes du tableau)\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.plot(data['Date'][-300:], data['Concentration'][-300:], label='CO2 Concentration')\n",
    "plt.title('CO2 Concentration Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('CO2 Concentration (ppm)')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convertir la columna 'Date' a tipo de datos de fecha\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "# Extrayez l'année de la colonne 'Date' et créez une nouvelle colonne 'Année'\n",
    "data['Year'] = data['Date'].dt.year\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculez la valeur moyenne par an et créez une nouvelle colonne 'Concentration moyenne de CO2'\n",
    "data['Mean CO2 Concentration'] = data.groupby('Year')['Concentration'].transform('mean')\n",
    "print(data.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création du graphique pour montrer la concentration moyenne de CO2 par an au fil du temps\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data['Date'], data['Mean CO2 Concentration'], label='Mean CO2 Concentration')\n",
    "plt.title('Mean CO2 Concentration per Year Over Time')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('CO2 Concentration (ppm)')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subject 4: Latency and capacity estimation for a network connection from asymmetric measurements\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importation des bibliothèques nécessaires\n",
    "url_liglab2 = \"http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/liglab2.log.gz\"\n",
    "url_stackoverflow = \"http://mescal.imag.fr/membres/arnaud.legrand/teaching/2014/RICM4_EP_ping/stackoverflow.log.gz\"\n",
    "\n",
    "# Charger les données dans un DataFrame pandas\n",
    "df_liglab2 = pd.read_csv(url_liglab2, sep=\" \", header=None)\n",
    "df_stackoverflow = pd.read_csv(url_stackoverflow, sep=\" \", header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affichage des premières lignes des données pour vérification\n",
    "print(df_liglab2.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affichage des premières lignes des données pour vérification\n",
    "print(df_stackoverflow.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Définir une fonction pour extraire les informations pertinentes de chaque ligne\n",
    "def process_ping_data(df):\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)  # Extraire le temps en ms et convertir en float (supprimer la chaîne \"time-\")\n",
    "    df[\"date\"] = df[\"date\"].str[1:18] # Extraire la date de la première colonne et la convertir au format \"datetime\"\n",
    "    df[\"date\"] = pd.to_datetime(df[\"date\"], unit='s')\n",
    "    return df\n",
    "\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affichage des premières lignes des données pour vérification\n",
    "print(df_liglab2.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Affichage des premières lignes des données pour vérification\n",
    "print(df_stackoverflow.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Graphique du temps de transmission dans le temps\n",
    "plt.figure(figsize=(10, 6))\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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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": [
    "# 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": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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