{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Etape 8 : Importer les données numériques d’un tableur scientifique dans un programme python\n",
    "\n",
    "**Notions abordées**\n",
    "\n",
    "- Import de données numériques à partir d’un tableur\n",
    "\n",
    "**Référence pyspc**\n",
    "\n",
    "- [Import de données numériques](https://pyspc.readthedocs.io/fr/latest/05-bases/09-fichiers-csv.html)\n",
    "\n",
    "**Consigne** : \n",
    "\n",
    "Télécharger, faire une copie du notebook « Importer les données numériques d’un tableur scientifique dans un programme python » puis répondre aux questions posées dans le notebook. Enfin effectuer la mise en situation présentée dans la dernière cellule.\n",
    "\n",
    "-----------"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ":download:`Télécharger le pdf <./mvt_parabolique_import_donnees.pdf>` |\n",
    ":download:`Télécharger le notebook <./mvt_parabolique_import_donnees-download.ipynb>` |\n",
    ":download:`Télécharger le csv <./parabole.csv>` |\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc-formation/master?filepath=08_mvt_parabolique%2Fmvt_parabolique_import_donnees.ipynb>`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le programme présenté ci-dessous est adapté à des fichiers .csv (type tableur) obtenus lors de pointages vidéo. Il devra évidemment être adapté pour des fichiers obtenus lors d'autres expériences."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.\tEnregistrer ou exporter le fichier contenant votre tableau de données sous format .csv (ou .txt pour Avistep) dans le dossier contenant votre notebook (fichier .ipynb) ou votre programme python (fichier.py). Attention, pour l'utilisation avec l'ENT Nero version 2018, petite subtilité à la fin.\n",
    "\n",
    "    -\tDans Regressi, enregistrer le fichier sous le format (type) OpenOffice, CSV (choisir « Vrai CSV » dans la fenêtre qui s’affiche alors).\n",
    "    -\tDans Loggerpro, exporter le fichier comme CSV…\n",
    "    -\tDans Aviméca, exporter les données dans Regressi puis vous reporter à la ligne ci-dessus.\n",
    "    -\tDans Avistep, exporter/enregistrer le fichier sous le format .txt\n",
    "    -\tDans Excel, enregistrer votre fichier sour le format CSV (séparateur:point-virgule). \n",
    "    -\tDans OpenCalc, enregistrer votre fichier sour le format CSV (texte CSV ; séparateur:point-virgule, **jeu de caractères : Unicode utf-8**)\n",
    "    \n",
    "    \n",
    "Attention : les logiciels de pointage retournent des tableaux de colonnes avec des entêtes (une à deux lignes) qu'il faudra par la suite retranscrire sous forme de listes (une liste par colonne) sans tenir compte des entêtes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Voici une capture d'écran du fichier parabole.csv obtenu à l'aide de Regavi/Regressi ouvert sous Excel\n",
    "\n",
    "![Capture fichier excel](capture-excel.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le même fichier ouvert sous Jupiter Notebook\n",
    "\n",
    "![Capture jupyter](capture-jupyter.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Les cellules suivantes contiennent les lignes de code qui vous permettront d'afficher votre tableau de données sous forme de listes (une liste par colonne de votre tableau csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chargement de la bibliothèque csv afin de \n",
    "# pouvoir lire par la suite le fichier csv\n",
    "\n",
    "import csv\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ceci est une fonction (nommée charge_fichier_csv) \n",
    "# qui sera appelée si besoin est, par le programme principal \n",
    "# situé dans une autre cellule, plus loin\n",
    "\n",
    "# Cette fonction aura pour arguments un fichier qui lui est \n",
    "# fourni par le programme principal (paramètre écrit entre \n",
    "# parenthèses et nommé ici cheminfichier), le délimiteur \n",
    "# de cellules et le nombre de lignes d'en-tête N. Il renvoie un \n",
    "# résultat au programme principal grâce à l'instruction return \n",
    "# (ici un tableau de valeurs nommé tab)\n",
    "\n",
    "def charge_fichier_csv(cheminfichier, delimiter=\";\",N=0):\n",
    "    with open(cheminfichier, 'r', encoding='utf-8') as f :\n",
    "        rfichier = csv.reader(f, delimiter=delimiter)\n",
    "        tab=[]\n",
    "        index_row=0\n",
    "        for row in rfichier:            \n",
    "            if index_row < N:\n",
    "                index_row = index_row+1\n",
    "            else : \n",
    "                for i in range (len(row)): \n",
    "                    if len(tab) <= i:\n",
    "                        X = []       \n",
    "                        tab.append(X) \n",
    "                    try:\n",
    "                        tab[i].append(float(row[i].replace(\",\",'.')))    \n",
    "                    except ValueError:\n",
    "                        print('erreur:contenu de cellule non numérique')\n",
    "                        continue\n",
    "            \n",
    "    return tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 0.04, 0.08, 0.12, 0.16, 0.2, 0.24, 0.28, 0.32, 0.36, 0.4, 0.44, 0.48, 0.52, 0.56, 0.6, 0.64, 0.68, 0.72]\n",
      "[-0.002808944, 0.064605722, 0.140447222, 0.213479777, 0.286512333, 0.362353832, 0.435386388, 0.514036832, 0.584260443, 0.662910887, 0.738752387, 0.814593887, 0.890435386, 0.966276886, 1.039309442, 1.115150941, 1.190992441, 1.269642885, 1.339866496]\n",
      "[0.0, 0.143256166, 0.266849722, 0.376398555, 0.471902665, 0.553362054, 0.617967776, 0.665719832, 0.693809276, 0.713471887, 0.713471887, 0.69661822, 0.660101943, 0.617967776, 0.553362054, 0.469093721, 0.37358961, 0.261231833, 0.134829333]\n"
     ]
    }
   ],
   "source": [
    "# Programme principal\n",
    "# ouverture, lecture et affichage du fichier \"parabole. csv\" . \n",
    "# Le début du chemin n'a pas besoin d'être spécifié si le \n",
    "# fichier .csv se trouve dans le même dossier que ce fichier \n",
    "# notebook\n",
    "   \n",
    "\n",
    "table = charge_fichier_csv(\"parabole.csv\",delimiter=\";\",N=2)\n",
    "t=table[0]\n",
    "print(t)\n",
    "x=table[1]\n",
    "print(x)\n",
    "y=table[2]\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Répondre aux questions suivantes :\n",
    "\n",
    "   - Quelle ligne de code dans le programme principal permet d'appeler la fonction ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comment s'appelle le fichier envoyé à la fonction par le programme principal ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Que doit-on écrire entre parenthèses lors de l'appel de la fonction pour pouvoir accéder au fichier?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comment s'appelle les paramètres correspondant dans la fonction ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Comment s'appelle la variable contenant le tableau renvoyé par la fonction dans le programme principal ?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "La suite du notebook vous donne un aperçu de ce qu'il est possible de faire avec les données importées."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage des courbes x=f(t) et y=g(t)\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "plt.plot(t,x,'.b',label='x=f(t)')\n",
    "plt.plot(t,y,'r+',label='y=g(t)')\n",
    "plt.legend()\n",
    "plt.xlabel(\"t (s)\")\n",
    "plt.ylabel(\"x(m) et y(m)\")\n",
    "plt.grid()\n",
    "plt.title(\"Evolution des coordonnées au cours d'un mouvement parabolique\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients de Xmodel: [ 1.8766952  -0.01107315]\n",
      "coeffx[0]= 1.9 \n",
      " coeffx[1]= -0.0\n",
      "Le tableau contenant les valeurs de Xmodel est:\n",
      " [-0.01107315  0.06399465  0.13906246  0.21413027  0.28919808  0.36426589\n",
      "  0.43933369  0.5144015   0.58946931  0.66453712  0.73960493  0.81467273\n",
      "  0.88974054  0.96480835  1.03987616  1.11494397  1.19001177  1.26507958\n",
      "  1.34014739]\n",
      "coefficients de Ymodel: [-4.98167828e+00  3.77690491e+00 -2.15000401e-03]\n",
      "coeffy[0]= -5.0 \n",
      " coeffy[1]= 3.8 \n",
      " coeffy[2]= -0.0\n",
      "Le tableau contenant les valeurs de Ymodel est:\n",
      " [-0.00215     0.14095551  0.26811965  0.37934242  0.47462382  0.55396385\n",
      "  0.61736251  0.66481979  0.69633571  0.71191026  0.71154343  0.69523524\n",
      "  0.66298568  0.61479474  0.55066244  0.47058876  0.37457371  0.2626173\n",
      "  0.13471951]\n"
     ]
    }
   ],
   "source": [
    "# Modélisation de la courbe x=f(t)\n",
    "\n",
    "t=np.array(t)\n",
    "\n",
    "coeffx=np.polyfit(t, x,1)\n",
    "\n",
    "# Affichage du tableau coeffx\n",
    "print('coefficients de Xmodel:', coeffx)\n",
    "\n",
    "# Affichage des valeurs contenues dans la liste avec une décimale\n",
    "print ('coeffx[0]=','{0:.1f}'.format(coeffx[0]),'\\n', \n",
    "       'coeffx[1]=','{0:.1f}'.format(coeffx[1])) \n",
    "\n",
    "# Création puis affichage d'un tableau Xmodel regroupant les valeurs \n",
    "# de la coordonnée x modélisée par une fonction affine.  \n",
    "\n",
    "Xmodel = coeffx[0]*t+coeffx[1]\n",
    "print('Le tableau contenant les valeurs de Xmodel est:\\n',Xmodel)\n",
    "\n",
    "# Modélisation de la courbe y=g(t)\n",
    "\n",
    "coeffy=np.polyfit(t, y,2)\n",
    "\n",
    "# Affichage de la liste coeff\n",
    "print('coefficients de Ymodel:', coeffy)\n",
    "\n",
    "# Affichage des valeurs contenues dans la liste avec une décimale\n",
    "print ('coeffy[0]=','{0:.1f}'.format(coeffy[0]),'\\n', \n",
    "       'coeffy[1]=','{0:.1f}'.format(coeffy[1]),'\\n', \n",
    "       'coeffy[2]=','{0:.1f}'.format(coeffy[2]))\n",
    "\n",
    "# Création puis affichage d'un tableau Ymodel regroupant les valeurs \n",
    "# de la coordonnée y modélisée par une fonction polynome du second degré.  \n",
    "\n",
    "Ymodel = coeffy[0]*t**2+coeffy[1]*t+coeffy[2]\n",
    "print('Le tableau contenant les valeurs de Ymodel est:\\n',Ymodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage des courbes modélisées\n",
    "\n",
    "fig = plt.figure(figsize=(12,10))\n",
    "plt.plot(t,x,'g+',label='x=f(t)')\n",
    "plt.plot(t,Xmodel,'b-',label='modèle fonction affine')\n",
    "plt.plot(t,y,'r+',label='y=g(t)')\n",
    "plt.plot(t,Ymodel,'y-',label='modèle polynôme du second degré')\n",
    "plt.legend()\n",
    "plt.xlabel(\"t(s)\")\n",
    "plt.ylabel(\"x(m) et y(m)\")\n",
    "plt.grid()\n",
    "plt.title(\"Evolution des coordonnées au cours d'un mouvement parabolique\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Mise en situation**\n",
    "\n",
    "- Télécharger le programme complet niveau seconde : étude de la chute d'une balle lachée par un cycliste en mouvement.\n",
    "- Télécharger le fichier \"chute_balle.csv\" dans le même dossier.\n",
    "- Puis étudier ce programme. \n",
    "\n",
    "-----------------"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    "**Liens vers les documents**\n",
    "\n",
    ".. toctree::\n",
    "   :maxdepth: 1\n",
    "   \n",
    "   ./chute_balle_cycliste/chute_balle_cycliste.ipynb\n",
    "   "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ":download:`Télécharger le notebook <./chute_balle_cycliste/chute_balle_cycliste.ipynb>`\n",
    "\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc-formation/master?filepath=etape_08%2Fchute_balle_cycliste%2Fchute_balle_cycliste.ipynb>`\n",
    "\n",
    ":download:`Télécharger le csv <./chute_balle_cycliste/chute_balle.csv>`"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Format de la Cellule Texte Brut",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}