{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracé des vecteurs vitesse (version avec fonctions)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ":download:`Télécharger le pdf <./chute_balle_cycliste_avec_fonctions.pdf>`\n",
    "\n",
    ":download:`Télécharger le notebook <./chute_balle_cycliste_avec_fonctions.ipynb>`,\n",
    ":download:`le fichier csv <./chute_balle.csv>` et \n",
    ":download:`la vidéo <./cycliste.avi>`,\n",
    "\n",
    "\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc/master?filepath=07-activites%2F02-seconde%2Fmouvement_parabolique_vecteurs_vitesse%2Fchute_balle_cycliste_avec_fonctions.ipynb>`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def charge_fichier_csv(fichier, delimiter=\";\",N=0):\n",
    "    with open(fichier, 'r', encoding='utf-8') as f :\n",
    "        rfichier = csv.reader(f, delimiter=delimiter)\n",
    "        tableau=[]\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(tableau) <= i:\n",
    "                        X = []       \n",
    "                        tableau.append(X) \n",
    "                    try:\n",
    "                        tableau[i].append(float(row[i].replace(\",\",'.')))    \n",
    "                    except ValueError:\n",
    "                        print('erreur:contenu de cellule non numérique')\n",
    "                        continue\n",
    "            \n",
    "        return (tableau)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def vitesse(t,z):\n",
    "    vz=[]\n",
    "    for i in range (len(z)-1):\n",
    "        vzi=(z[i+1]-z[i])/(t[i+1]-t[i])\n",
    "        vz.append(vzi)\n",
    "    return vz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def graphvect(x,y,vx,vy):\n",
    "    fig = plt.figure(1,figsize=(12,10))\n",
    "    plt.plot(x,y,'ro-')\n",
    "    plt.xlim(0,max(x)+0.5)\n",
    "    plt.ylim(0,max(y)+0.5)\n",
    "    plt.grid()\n",
    "    plt.xlabel(\"x(m)\")\n",
    "    plt.ylabel(\"y(m)\")\n",
    "    for i in range (len (vx)) :\n",
    "        plt.arrow(x[i],y[i],0.03*vx[i],0.03*vy[i],fc='b',\n",
    "                  ec='b',head_width=0.02,\n",
    "                  length_includes_head=True)\n",
    "    plt.title(\"Tracé des vecteurs vitesse\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.76, 0.8, 0.84, 0.88, 0.92, 0.96, 1.0, 1.04, 1.08, 1.12, 1.16, 1.2, 1.24, 1.28, 1.32, 1.36]\n",
      "[0.00865710739046, 0.106770991149, 0.204884874908, 0.302998758666, 0.398226939961, 0.493455121256, 0.588683302551, 0.675254376456, 0.767596855287, 0.857053631656, 0.946510408024, 1.03019577946, 1.11676685337, 1.19756652235, 1.27836619132, 1.36205156277]\n",
      "[1.98247759242, 1.97093478256, 1.94496346039, 1.89590651851, 1.83530676678, 1.7545070978, 1.66216461897, 1.55827933028, 1.43707982682, 1.29568040611, 1.15139528293, 0.984024540049, 0.807996689776, 0.605997517332, 0.403998344888, 0.187570660127]\n"
     ]
    },
    {
     "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": [
    "tableau = charge_fichier_csv(\"chute_balle.csv\",N=1)\n",
    "t=tableau[0]\n",
    "print(t)\n",
    "x=tableau[1]\n",
    "print(x)\n",
    "y=tableau[2]\n",
    "print(y)\n",
    "vx=vitesse(t,x)\n",
    "vy=vitesse(t,y)\n",
    "graphvect(x,y,vx,vy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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
}