{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### TP 1 -- Informatique CM3 -- 2016-2017 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python @ Polytech'Lille"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le texte de cette session de travaux pratiques est également disponible ici: \n",
    "\n",
    "http://nbviewer.ipython.org/github/ecalzavarini/python-at-polytech-lille/blob/master/Python-TP1-2016.ipynb        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction aux commandes Linux"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avant de commencer avec notre introduction au langage de programmation Python, nous devons familiariser avec l'environnement du système d'explotation Linux. \n",
    "\n",
    "La première étape est d'apprendre les commandes les plus courantes. Ces commandes peuvent être tapées sur une console Linux.\n",
    "\n",
    "Pour savoir comment accéder à une console sur la distribution Linux Debian dont vous disposez, voir:  https://wiki.debian.org/fr/Console ou simplement recherchez l'application \"terminal\" sur le menu des applications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande pwd:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"pwd\" affiche le répertoire courant, c'est à dire le répertoire où l'on se trouve au moment où on tape la commande."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande ls:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"ls\" permet de lister ce que contient un répertoire.\n",
    "Il y a plusieurs manières de l'utiliser :\n",
    "\n",
    "\"ls\" donne la liste brute de ce qui se trouve dans le répertoire\n",
    "\n",
    "\"ls -l\" donne la liste de ce qui se trouve dans le répertoire avec des informations \tcomplémentaires (droits de lecture, écriture et exécution, propriétaire, taille, date de création ou de dernière modification).\n",
    "\n",
    "\"ls -a\" liste tous (pensez au mot \"all\") les fichiers du répertoire, y compris les fichiers cachés. Cette option est très utile lorsque l'on se trouve dans son répertoire personnel car il contient les fichiers de configuration de l'utilisateur dont les noms commencent généralement par un point et seule l'option -a permet de détecter leur existence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande mkdir:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"mkdir\" permet de créer un répertoire.\n",
    "\n",
    "\"mkdir nom_repertoire\" crée le répertoire ayant pour nom nom_repertoire."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande cd:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"cd\" permet de se déplacer dans l'arborescence de repertoires. Il y a plusieurs manières de l'utiliser : \n",
    "\n",
    "\"cd nom_repertoire\" permet d'aller dans le répertoire nom_repertoire\n",
    "\n",
    "\"cd ..\" permet de retourner dans le répertoire parent (celui qui est au-dessus dans l'arborescence de répertoires)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### L'éditeur de texte emacs:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour créer ou modifier un fichier \"emacs -nw nom_fichier.txt\"\n",
    "\n",
    "Pour sauvegarder tapez la combinaison de touches suivante: Ctrl-x Ctrl-s.\n",
    "\n",
    "Pour quitter emacs: Ctrl-x Ctrl-c."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande rm:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"rm\" permet de supprimer un fichier :\n",
    "\n",
    "\"rm nom_fichier\" supprime le fichier nom_fichier si il se trouve dans le répertoire courant.\n",
    "\n",
    "\"rm –r\" supprime un répertoire et ses sous répertoires."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### La commande top: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"top\" affiche en continu des informations décrivant l'activité du système. Elle permet surtout de suivre les ressources que les processus utilisent (quantité de mémoire, pourcentage de CPU...).\n",
    "Sous top il est possible d'expédier de manière interactive un signal à un processus, par exemple afin de le stopper, en tapant \"k\", top demande ensuite l'identifiant (PID) du processus concerné.\n",
    "Pour quitter top, appuyer simplement sur la touche \"q\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Archivage de données (tar):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La commande \"tar\" gère des archives, contenant chacune au moins un répertoire ou fichier.\n",
    "\n",
    "Vous aurez souvent besoin de \"tar xzf nom_du_fichier.tar.gz\" , qui décompacte une archive au format .tar.gz ou .tgz.\n",
    "L'extension .tar.gz ou .tgz indique que le fichier est une archive tar et qu'il est compacté.\n",
    "Les arguments (aussi appelés options) employés dans la commande précédente (\"xzf\") peuvent être ainsi compris:\n",
    "\n",
    "x (extraction) déclenche l'extraction de certains fichiers d'une archive (lorsque l'on ne spécifie pas les noms des fichiers que l'on souhaite extraire de l'archive, tar les extrait tous)\n",
    "\n",
    "z décompacte l'archive grâce à la commande gzip\n",
    "\n",
    "f traite un fichier-archive dont le nom suit (ici: \"nom_du_fichier.tar.gz\")\n",
    "\n",
    "Si je me trouve dans le répertoire \"/home/delcros/\" la commande suivante créera une archive du répertoire \"/home/delcros/personnel\" : \n",
    "\n",
    "\"tar cvzf personnel.tgz personnel\"\n",
    "\n",
    "l'option \"c\" permet de créer une archive\n",
    "\n",
    "L'option \"z\" compacte l'archive grâce à gzip."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questions  : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Ouvrir une consolle Linux et créer un répertoire TP-PYTHON dans le répertoire courant. Comment vérifier qu'il a bien été créé ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Aller dans le répertoire que vous venez de créer, puis lister ce qu'il contient. Quelle commande vous permet de savoir si vous êtes bien dans le répertoire TP-PYTHON ?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Créer un fichier appelé README.txt dans le répertoire TP-PYTHON avec la commande \"emacs\", taper quelques lignes dedans. Enregistrer le ficher. Vérifier qu'il a bien été créé."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Créer un fichier-archive du repertoire TP-PYTHON."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5) Supprimer le fichier que vous venez de créer, comment vérifier qu'il a bien été supprimé ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6) Supprimer le répertoire TP-PYTHON. Vérifier qu'il a bien été supprimé.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7) Lancer l'interprète du langage Python (écrire \"python\" dans la consolle de Linux).\n",
    "Supposons maintenant que, pour une raison quelconque, le programme est bloqué. Tuez l'interprète Python  en utilisant la commande \"top\". "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction à la programmation numérique en Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour lancer l'interprète du langage Python écrire \"python\" dans la consolle de Linux.\n",
    "\n",
    "Saisir votre série d'instructions Python après l'invite de la ligne de commande \">>>\" (à noter que chaque instruction doit être validée avec la touche \"return\").\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une deuxième possibilité d'utilisation beaucoup plus pratique est de créer un fichier (appelé script), par exemple avec l'éditeur de text emacs : \"emacs -nw script.py\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Une fois que les instructions ont été écrites dans ce fichier, le script peut être exécuté avec la commande \"python script.py\".\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "Le site le plus utile pour trouver des informations sur python est :\n",
    "https://docs.python.org/2/  \n",
    "voir notamment dans les sections \"Tutorial\" et \"Language reference\".\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Modalités pour accomplir le TP et compte rendu "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Pendant ce TP vous aurez à écrire plusieurs scripts (nous vous suggérons de les nommer script1.py, script2.py, ...). \n",
    "Les scripts doivent être accompagnés par un document descriptif unique (README.txt). \n",
    "Dans ce fichier, vous devrez décrire le mode de fonctionnement des scripts et, si besoin, mettre vos commentaires.\n",
    "Merci d'y écrires aussi vos nomes et prénoms complets. Tous les fichiers doivent être mis dans un dossier appelé TP1-nom1-nom2 et ensuite être compressés dans un fichier TP1-nom1-nom2.tgz .\n",
    "\n",
    "Enfin vous allez envoyer ce fichier par email à l'enseignant : soit Enrico (enrico.calzavarini@polytech-lille.fr) soit Stefano (stefano.berti@polytech-lille.fr).\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Vous avez une semaine de temps pour compléter le TP, c'est-à-dire que la date limite pour envoyer vos travaux c'est dans 7 jours à partir d'aujourd'hui. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script 1 :  entrer ou afficher du texte et  des données "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Nous voulons écrire un script qui tout d'abord affiche simplement une message de bienvenue :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bienvenue au premier TP d'informatique\n"
     ]
    }
   ],
   "source": [
    "print(\"Bienvenue au premier TP d'informatique\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vérifiez que le programme ci-dessus fonctionne quand il est écrit dans un script (appelé par exemple script1.py) et interprété par python avec la commande \"python script1.py\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous souhaitons maintenant que ce programme nous demande aussi combien d'étudiants sont présents dans la salle et qu'il affiche ce numéro sur l'écran."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A noter que la lecture de données peut se faire par interrogation de l'utilisateur. Par exemple :  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entrer la valeur de la precision p, p =0.1\n"
     ]
    }
   ],
   "source": [
    "p = input(\"Entrer la valeur de la precision p, p =\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "l'écriture des données enregistrées dans une variable peut se faire tout simplement encore avec la fonction \"print\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la valeure de la precision est :0.1\n"
     ]
    }
   ],
   "source": [
    "print(\"la valeure de la precision est :\" + str(p) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ou de façon équivalente :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la valeure de la precision est : 1.000000e-01\n"
     ]
    }
   ],
   "source": [
    "print(\"la valeure de la precision est : %e\" %p )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Commentaires en python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une bonne habitude à prendre est d'écrire des commentaires dans le srtipts. \n",
    "Nous pouvons le faire avec le symbole \" # \" , par exemple :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "la valeure de p est : 1.000000e-01\n"
     ]
    }
   ],
   "source": [
    "print(\"la valeure de p est : %e\" %p ) # ici nous imprimons le résultat final"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Écrivez donc votre premier script!  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script 2 : effectuer des opérations simples sur les variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous souhaiton écrire un script qui fait la conversion d'unité pour des mesures de pression. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Ce script devra demander à l'utilisateur d'introduire une valeur de pression et une unité de mesure (choisi parmi: Pascal (Pa) , bar (bar) , atmosphère (atm) , Torricelli (torr) et pounds per square inch (psi)), puis demander à l'utilisateur quelle unité il souhaite pour la conversion et enfin afficher la valeur de pression dans l'unité demandée.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour mener  à bien ce script les informations suivantes peuvent être utiles :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La table de conversion des unités de pression peut être repèrée ici :\n",
    "\n",
    "http://en.wikipedia.org/wiki/Pressure_measurement#Units"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L'affectation de données dans des variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = 5.0 \n",
    "b , c = 10 , 3\n",
    "name = \"Mark\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ici \"a\" , \"b\" , \"c\" et \"name\" sont des identificateurs (ou simplement des variables). \n",
    "\n",
    "Python ne nécessite pas de déclarer explicitement les types de variables, différemment qu'en Fortran, C et d'autres langages. Juste affectez une variable et Python comprendera automatiquement, ce n'est pas nécessaire de préciser le type (réel, entier, character, etc...) \n",
    "\n",
    "Vous pouvez demander à Python de vous dire quel type il a attribué à vos variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Portez une attention particulière à l'attribution de valeurs à virgule flottante à des variables ou vous risquez d'obtenir des valeurs que vous ne vous attendez pas dans vos programmes. Par exemple ,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "print(b/c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vous voyez , si vous divisez un nombre entier par un nombre entier , Python retourne une réponse arrondie à l'entier le plus proche . Mais si vous voulez une résultat à virgule flottante (type float), l'un des numéros doit être de type float. Le simple ajout d'un point décimal fera l'affaire :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.33333333333\n"
     ]
    }
   ],
   "source": [
    "b , c = 10. , 3.\n",
    "print (b/c)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utilisation des listes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une liste est une collections d’objets :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4\n",
      "4\n",
      "[0, 5, 6]\n",
      "[0, 6]\n"
     ]
    }
   ],
   "source": [
    "# Definition et affectation d'une liste\n",
    "l = [0, 5, 6, 4]  \n",
    "\n",
    "# Les indices commencent à 0, et non à 1\n",
    "print(l[0])      \n",
    "\n",
    "# Le dernier indice est le 3ème\n",
    "print(l[3])      \n",
    "\n",
    "# On peut compter à partir de la fin, en utilisant des indices negatifs\n",
    "print(l[-1])     \n",
    "\n",
    "# On peut selectioner une tranche (slice) d'elements dans la liste\n",
    "print(l[0:3])    \n",
    "\n",
    "# La syntaxe du “slicing” est [start:stop:step]\n",
    "print(l[0:3:2])  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Une liste peut être modifiée:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 5, 13, 4]\n"
     ]
    }
   ],
   "source": [
    "l[2] = l[2]*2 + 1\n",
    "\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si nous voulons connaître le nombre d'éléments dans une liste :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous pouvons  créer  de la même manière une liste de mots  :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avion\n"
     ]
    }
   ],
   "source": [
    "mots = [\"arbre\" , \"avion\" , \"nuage\"]\n",
    "\n",
    "print(mots[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Structure alternative \"if\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardez l'exemple ci-dessous concernant le calcul des racines réelles d'une équation algébrique du second ordre, pour comprendre le fonctionnement d'une structure \"if\" :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RESULTATS :\n",
      "Deux racines distinctes : x1 = -1.63245553203 et x2 = -0.367544467966\n"
     ]
    }
   ],
   "source": [
    "d = b**2-4*a*c\n",
    "\n",
    "if d > 0 :\n",
    "    x1 = (-b - sqrt(d))/(2*a)\n",
    "    x2 = (-b + sqrt(d))/(2*a)\n",
    "    print(\"RESULTATS :\")\n",
    "    print(\"Deux racines distinctes : x1 = \" + str(x1) + \" et x2 = \" + str(x2))\n",
    "elif d == 0 :\n",
    "    x1 = -b/(2*a)\n",
    "    print(\"RESULTATS :\")\n",
    "    print(\"Une racine double : x1 = \" + str(x1))\n",
    "else :\n",
    "    print(\"RESULTATS :\")\n",
    "    print(\"Aucune solution!\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### à noter : indentation nécessaire en langage Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour les structures de controle du langage Python, comme \"if\"(et nous verros ensuite \"for\"), il n'y a pas de mot clé \"end\" pour signaler la fin du bloc de lignes concernées par les structures \"if\",\"while\",\"for\".\n",
    "Il faut par contre indenter les lignes (c'est à dire créer des décalages à l'aide de la touche tabulation \"tab\" du clavier) afin de définir une dépendance d'un bloc de lignes par rapport à un autre."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script 3 : lire des données à partir d'un fichier et les élaborer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous disposons des données de pression enregistrées  (à la fréquence d'une mesure par seconde) par un tube de Pitot d'un avion en vol à une altitude de 10000 mètres :\n",
    "\n",
    "https://github.com/ecalzavarini/python-at-polytech-lille/blob/master/pressure.txt\n",
    "\n",
    "\n",
    "Vous pouvez télécharger ce fichier avec la commande de Linux \" wget \". Ecrivez sur votre console :  \"wget    adresse_internet_du_fichier \". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('http://upload.wikimedia.org/wikipedia/commons/0/00/Pitot_tube_wings-fr.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On est interessé à écrire un script qui lit ces données et qui les transforme dans des mesures de vitesse. La relation entre la pression et la vitesse est donnée par la loi de Bernoulli: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$V(t) = \\sqrt{\\frac{2\\left(p(t)-p_s\\right)}{\\rho}}.$$ \n",
    "Ici:\n",
    "\n",
    "$p(t)$ est la pression mesurée au cours du temps ($t$) en Pascal,\n",
    "\n",
    "$p_s$ est la pression statique, $p_s=4 \\, kPa$,\n",
    "\n",
    "$\\rho$ est la masse volumique de l'air, $\\rho=0.91875 \\, kg/m^3$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour la réalisation de ce script les informations suivantes vous seront utiles :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La lecture des données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'str'>\n",
      "53274.300000\n"
     ]
    }
   ],
   "source": [
    "# Ouverture d'un fichier\n",
    "f = open(\"pressure.txt\", \"r\")\n",
    "# définition d'une liste vide\n",
    "data=[]\n",
    "# lecture de fichier complet et affectation des éléments de la liste \n",
    "data=f.read().split()\n",
    "# fermeture du fichier f\n",
    "f.close()\n",
    "\n",
    "#contrôle de type\n",
    "print( type(data[0]) )\n",
    "#impression de la première donnée \n",
    "print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'float'>\n",
      "53274.3\n"
     ]
    }
   ],
   "source": [
    "#conversion au type désiré \n",
    "data[0] = float(data[0])\n",
    "#contrôle de type\n",
    "print ( type(data[0]) )\n",
    "#impression\n",
    "print data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "à noter que lorsque les données sont lues de la manière ci-dessus, elles sont stockées dans une liste de type chaîne de caractères (string). \n",
    "Si nous devons effectuer des opérations mathématiques sur elles, nous devons les convertir en un type numérique ( par exemple \"float \" ou \"int\" )."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ecriture dans un fichier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "val1 = 5.0  # affectation d'une variable réelle\n",
    "\n",
    "val2 = str(val1) # conversion en chaîne de caractères\n",
    "\n",
    "# Ouverture d'un nouveau fichier\n",
    "f = open(\"output.txt\", \"w\")\n",
    "# écriture dans le fichier \n",
    "f.write(val2)  \n",
    "# fermeture du fichier \n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "à noter que la commande \"write\" ne peut écrire que des chaînes de caractères (string)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### La structure repetitive \"for\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Regardez l'exemple ci-dessous concernant tri par sélection de données, pour comprendre le fonctionnement d'une structure  boucle \"for\" :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Liste triée :\n",
      "[0.0, 1.0, 2.0, 2.0, 3.0, 5.0, 8.0, 40.0, 65.0, 98.0, 115.0]\n"
     ]
    }
   ],
   "source": [
    "# Tri par sélection\n",
    "# Données initiales \n",
    "M = [1. , 2. , 5. , 40. , 65. , 2.,8. , 98. ,115. ,0. , 3.]\n",
    "\n",
    "# Détermination de la longueur  de la liste\n",
    "N = len(M) \n",
    "\n",
    "for i in range(0,N-1):   \n",
    "    for j in range(i+1,N):\n",
    "        if M[j] < M[i]:\n",
    "            val_temp = M[i]\n",
    "            M[i] = M[j]\n",
    "            M[j] = val_temp\n",
    "print(\"Liste triée :\")\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "à noter que la commande \" range(a,b) \" produit une liste de nombres entiers progressifs  compris entre a et b-1 , par exemple :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(0,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utiliser les fonctions mathématiques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En Python afin de pouvoir utiliser les fonctions mathématiques (par exemple, exp, sqrt , sin , cos , tan), vous devez les importer par avance à partir de la bibliothèque mathématique \"math\" :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# importing all functions from the math library\n",
    "from math import *      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous vous demandons donc de lire la serie des données de pression dans le fichier et de calculer la vitesse de l' avion en utilisant la relation donnée.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On demande ensuite de calculer la valeur moyenne de la vitesse de l'avion (en Km/heure) et de l'imprimer.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question bonus : Seriez-vous capable de calculer l'accélération de l'avion au cours du temps, et de l'enregistrer dans un fichier?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Script 4 : représentation graphique des données"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous souhaitons analyser graphiquement les données , par exemple en les traçant ou en faisant leur histogramme ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pour la réalisation de ce script les informations suivantes vous seront utiles :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliothèques de Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python dispose de nombreuses bibliothèques qui fournissent des fonctionnalités avancées comme la possibilté d'effectuer des opérations sur des matrices, des fonctions graphiques, et bien plus encore. Nous pouvons donc importer ces bibliothèques de fonctions pour étendre les capacités de Python dans nos programmes.\n",
    "\n",
    "Nous allons commencer par l'importation de quelques bibliothèques pour nous aider.\n",
    "\n",
    "Premièrement, nous l'avons déjà vu, nous utilisons Math, la bibliothèque mathématique standard qui comprend les fonctions trigonométriques et transcendantales, ainsi que  d'autres fonctions spéciales.\n",
    "\n",
    "Pour voir la liste complète des fonctions de la bibliothèque Math:\n",
    "https://docs.python.org/2/library/math.html#module-math\n",
    "\n",
    "Notre deuxième bibliothèque préférée est NumPy (Numerical Python), fournissant un grande nombre d'opérations matricielles utiles (cette bibliothèque est similaire à MATLAB ). Nous allons l'utiliser beaucoup! \n",
    "\n",
    "La documentation de numpy se trouve ici : http://docs.scipy.org/doc/numpy/reference/\n",
    "\n",
    "La troisième bibliothèque dont nous avons besoin est Matplotlib, une bibliothèque de graphique 2D que nous allons utiliser pour tracer nos résultats.\n",
    "\n",
    "La documentation de référence est disponible ici : http://matplotlib.org/contents.html\n",
    "\n",
    "Le code suivant sera au sommet de la plupart de vos programmes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# importing all functions from the math library\n",
    "from math import *            \n",
    "# we import the array library, and call it 'np'\n",
    "import numpy as np                 \n",
    "# import plotting library, and call it 'plt'\n",
    "import matplotlib.pyplot as plt    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nous importons donc toutes les fonctions de la bibliothèque \"math\" et nous importons aussi\n",
    "une bibliothèque nommée \"numpy\". Enfin, nous importons un module appelé \"pyplot\" d'une grande bibliothèque appelée \"matplotlib\".  \n",
    "\n",
    "Les deux dernières lignes ci-dessus ont créé des raccourcis pour les bibliothèques tel que \"np\" et \"plt\" respectivement. La raison de la création des raccourcis est que nous avons besoin de taper les noms de bibliothèque assez souvent dans le code.\n",
    "Pour utiliser une fonction appartenant à l'une de ces bibliothèques, nous devons dire à Python où la chercher. Pour cela, chaque nom de fonction est écrit après le nom de la bibliothèque, avec un point entre les deux. Par exemple :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tracer des courbes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DwNNlvB8HXAC2OLffekyZAoSEQJs2Yhy9pnIW7F/A+MjxOBwO2VJMUdmoptKMjxzPggPq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\nNBqNRqPRaDQajUaj0Wg0tY3/B3Ux707PQ84+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6bf9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "#ignorez la ligne ci-dessus. c'est juste une commande de notre éditeur de texte\n",
    "\n",
    "x=np.linspace(-5,5,100)  # nous définissons une liste (array) avec Numpy \n",
    "plt.plot(x,np.sin(x))  # on utilise la fonction sinus de Numpy\n",
    "plt.plot(x,np.cos(x))  \n",
    "plt.ylabel('fonction sinus')\n",
    "plt.xlabel(\"l'axe des abcisses\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Changer le style des courbes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10ff47c50>,\n",
       " <matplotlib.lines.Line2D at 0x10ff47dd0>,\n",
       " <matplotlib.lines.Line2D at 0x10ff8c290>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nFCbPn8wr775C+cMtOLb5W7gQxDn8OOfYvmqH0Bz3qwOaxcjp03DunHNgNWkC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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e6bf7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline  \n",
    "\n",
    "t1=np.linspace(0,5,10)\n",
    "t2=np.linspace(0,5,20)\n",
    "plt.plot(t1, t1, 'r--', t1, t1**2, 'bs', t2, t2**3, 'g^-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Un histogramme et un affichage de texte sur le graphique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5bnkZdrQxEmgFVrntfwTWAKOKvGY+8In0u17dQq/hBh+fxlC8Fnp8SWSZUEYBGyPrm+hM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\nM2bQ0NAAQF1dHY2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffc2650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "mu, sigma = 100, 15\n",
    "#generation de 10000 nombres aleatoires\n",
    "x = mu + sigma * np.random.randn(10000)  \n",
    "\n",
    "bins = 30\n",
    "\n",
    "#  histogramme des donnees\n",
    "plt.hist(x, bins, normed=1, facecolor='r', alpha=0.8)\n",
    "plt.xlabel('Donnees')\n",
    "plt.ylabel('Probabilite')\n",
    "plt.title('Histogramme')\n",
    "plt.text(60, .025, r'$\\mu=100,\\ \\sigma=15$')\n",
    "plt.axis([40, 160, 0, 0.03])\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "D'abord, nous demandons d'écrire un script qui trace un graphique de la pression (en kPa) en fonction du temps (en heures). N'oubliez pas d'indiquer les étiquettes sur les axes.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Sur le même graphique tracer aussi trois lignes (en couleurs differentes) l'une représentant la valeur moyenne ( $\\mu$ ) de la pression et les deux autres la valeurs moyennes plus / moins l'écart type ( $\\sigma$ ).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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bbbZ9m18KfWhQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFABQAUAFAB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cXXzDBZBkuHWYcRZrTxdbA4Nt+xw2BwFF4jM87zWqnGOCyTKcOnXx+OxFXD4eH7ujPE0HV9rT+h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\nBQAUAFABQAUAFAH/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='mean-sigma.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "Deuxièmement, nous demandons également de construire un histogramme, mais  cette fois de la vitesse (en Km/h).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "IOError",
     "evalue": "[Errno 2] No such file or directory: 'custom.css'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIOError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-6e0370214c4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mstyles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'custom.css'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mcss_styling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-39-6e0370214c4d>\u001b[0m in \u001b[0;36mcss_styling\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcss_styling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mstyles\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'custom.css'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstyles\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mcss_styling\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIOError\u001b[0m: [Errno 2] No such file or directory: 'custom.css'"
     ]
    }
   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "def css_styling():\n",
    "    styles = open('custom.css', 'r').read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}