{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# La loi d’Ohm (version linregress et avec fonctions)"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "raw_mimetype": "text/restructuredtext"
   },
   "source": [
    ":download:`Télécharger le pdf <./loi_ohm_prof_linregress_avec_fonctions.pdf>`\n",
    "\n",
    ":download:`Télécharger le notebook <./loi_ohm_prof_linregress_avec_fonctions-download.ipynb>`\n",
    "\n",
    ":download:`Lancer le notebook sur binder (lent) <https://mybinder.org/v2/gl/pyspc%2Fpyspc/master?filepath=07-activites%2F02-seconde%2Floi_d_ohm%2Floi_ohm_prof_linregress_avec_fonctions.ipynb>`"
   ]
  },
  {
   "attachments": {
    "tableau.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![tableau.png](attachment:tableau.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# création de la fonction modelisation \n",
    "# modélisation par une droite d'équation \n",
    "# y=ax+b (polynôme de degré 1)\n",
    "\n",
    "def modelisation(x,y):\n",
    "    slope, intercept, r_value, p_value, std_error = stats.linregress(x,y)\n",
    "    ymodel = slope*x+intercept\n",
    "    print ('U= {0:.1f}'.format(slope),'x I')\n",
    "    print ('Le coefficient de corrélation r vaut {0:.4f}'.format(r_value))\n",
    "    print('Les valeurs de la tension modélisée sont',ymodel)\n",
    "    return (ymodel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fonction permettant de tracer le graphique avec les points \n",
    "# expérimentaux et la courbe obtenue après modélisation\n",
    "\n",
    "def courbemodelisee (x,y,ymodel) :\n",
    "    fig = plt.figure(figsize=(12,10))\n",
    "    plt.plot(x,y,'r+',label='U=f(I)')\n",
    "    plt.plot(x,ymodel,'b',label='modèle linéaire')\n",
    "    plt.legend()\n",
    "    plt.xlabel(\"intensité I (A)\")\n",
    "    plt.ylabel(\"tension U (V)\")\n",
    "    plt.grid()\n",
    "    plt.title(\"Caractéristique Intensité-Tension d’un \"\n",
    "              \"dipôle ohmique\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "U= 67.9 x I\n",
      "Le coefficient de corrélation r vaut 0.9997\n",
      "Les valeurs de la tension modélisée sont [0.02380952 1.72095238 3.41809524 5.1152381  6.81238095 8.50952381]\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": [
    "# tableaux numpy obligatoires à cause de l'opération vectorisée\n",
    "# permettant de créer Umodel\n",
    "\n",
    "I=np.array([0,25e-3,50e-3,75e-3,100e-3,125e-3])  \n",
    "U=np.array([0,1.8,3.3,5.2,6.8,8.5])                  \n",
    "Umodel=modelisation(I,U)\n",
    "courbemodelisee(I,U,Umodel)"
   ]
  }
 ],
 "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
}
