{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python3\n",
    "# -*- coding: utf-8 -*-"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2C16 Exercice 49 page 325 : CORRECTION\n",
    "# Programme permettant de représenter et de modéliser la caractéristique U=f(I) d'un dipole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question a : Compléter les deux cellules suivantes selon les consignes puis les exécuter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        \n",
    "from matplotlib import pyplot as plt      \n",
    "\n",
    "# Définition de 2 listes pour les 2 variables I et U\n",
    "I=[0.00,0.99,2.00,2.98,3.95,4.96,5.92] # I (en mA)\n",
    "U=[0.00,1.02,2.03,3.02,4.00,5.03,6.01] # U (en V)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Affichage du nuage de points expérimentaux de coordonnées (I,U): U=f(I)\n",
    "plt.title('Caractéristique U=f(I)')# Titre du graphe\n",
    "plt.xlabel('I (en mA)')            # Légende axe I\n",
    "plt.ylabel('U (en V)')             # Légende axe U\n",
    "plt.axis([min(I),max(I),min(U),max(U)])# Minimum et maximum des axes\n",
    "\n",
    "plt.plot(I,U,'r+',ms=10,label='U=f(I) exp')# Trace le nuage de points\n",
    "\n",
    "plt.grid()                         # Affiche une grille\n",
    "plt.legend()                       # Affiche la légende\n",
    "plt.show()                         # Affiche la figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Question b : Compléter les trois cellules suivantes selon les consignes puis les exécuter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Modélisation du nuage de points par la fonction np.polyfit()\n",
    "# Calcule les coefficients de la fonction modélisant le nuage de points et les range dans un tableau nommé Modele\n",
    "Modele = np.polyfit(I,U,1)\n",
    "a,b = [coef for coef in Modele] # Affecte les coefficients du modèle aux variables a,b,c...(à choisir)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Choix du modéle linéaire et affichage de la modélisation de la caractéristique \n",
    "\n",
    "U_mod = [a*i for i in I] # Liste des ordonnées de la modélisation\n",
    "\n",
    "plt.title('Caractéristique U=f(I)')# Titre du graphe\n",
    "plt.xlabel('I (en mA)')            # Légende axe I\n",
    "plt.ylabel('U (en V)')             # Légende axe U\n",
    "plt.axis([min(I),max(I),min(U),max(U)])# Minimum et maximum des axes\n",
    "\n",
    "plt.plot(I,U,'r+',ms=10,label='U=f(I) exp')    # Trace le nuage de points\n",
    "plt.plot(I,U_mod,'b-',label='U=f(I) modélisé') # Trace les points de coordonnées I et U_mod en bleu et reliés  \n",
    "\n",
    "plt.grid()                         # Affiche une grille\n",
    "plt.legend()                       # Affiche la légende\n",
    "plt.show()                         # Affiche la figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expression du modèle\n",
      "U(en V)= 1.0134 x I(en mA)\n"
     ]
    }
   ],
   "source": [
    "print('Expression du modèle') \n",
    "print('U(en V)=',round(a,4),'x I(en mA)')       # Affiche l’équation du modèle en précisant les unités."
   ]
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}