{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Etape 5 : Loi d’Ohm (épisode 2)\n", "\n", "**Notions abordées**\n", "\n", "- Création d’un tableau à l’aide de la bibliothèque numpy\n", "- Appel et utilisation de la fonction polyfit de la bibliothèque numpy\n", "- Création d’un tableau numpy à partir d’un calcul faisant intervenir \n", " d’autres tableaux numpy\n", "- Affichage d’un tableau numpy\n", "- Appel et affichage des valeurs d’un tableau numpy\n", "- Affichage de deux courbes sur un même graphique\n", "- Pointage et zoom sur une courbe\n", "\n", "**Référence pyspc**\n", "\n", "- [Les tableaux numpy](https://pyspc.readthedocs.io/fr/latest/05-bases/08-tableaux_numpy.html)\n", "- [Les modélisations](https://pyspc.readthedocs.io/fr/latest/05-bases/11-modelisation.html)\n", "- [Les fonctions](https://pyspc.readthedocs.io/fr/latest/05-bases/03-fonctions.html)\n", "- [Les graphiques - première partie](https://pyspc.readthedocs.io/fr/latest/05-bases/10-graphiques_partie_1.html)\n", "- [Les graphiques - zoom et pointeur](https://pyspc.readthedocs.io/fr/latest/05-bases/14-graphiques_zoom_pointeur.html)\n", "\n", "**Consigne** : \n", "\n", "Etudier le programme ci-dessous puis 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 <./ohm_episode_2.pdf>` |\n", ":download:`Télécharger le notebook <./ohm_episode_2.ipynb>` |\n", ":download:`Lancer le notebook sur binder (lent) `" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------------\n", "\n", "On souhaite modéliser la caractéristique d’un dipôle ohmique, c’est-à-dire la courbe donnant les valeurs de la tension aux bornes du dipôle ohmique en fonction des valeurs de l’intensité du courant qui le traverse." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Création des tableaux de valeurs avec la bibliothèque numpy\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])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Reprise du programme vu précédemment et permettant d'afficher \n", "# la caractéristique d'un dipole ohmique.\n", "\n", "fig = plt.figure(figsize=(12,10))\n", "plt.plot(I,U,'r+',label='U=f(I)')\n", "plt.legend()\n", "plt.xlabel(\"intensité I (A)\")\n", "plt.ylabel(\"tension U (V)\")\n", "plt.grid()\n", "plt.title(\"Caractéristique Intensité-Tension \"\n", " \"d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il s'agit maintenant de modéliser la courbe obtenue.\n", "\n", "Exécutez le programme ci-dessous permettant de modéliser la courbe obtenue par une droite." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6.78857143e+01 2.38095238e-02]\n", "67.9 0.0\n", "[0.02380952 1.72095238 3.41809524 5.1152381 6.81238095 8.50952381]\n", "U=67.9 x I\n" ] } ], "source": [ "coeff=np.polyfit(I, U,1)\n", "# La fonction polyfit (inclue dans la bibliothèque numpy) \n", "# retourne un tableau numpy a une dimension (nommée ici coeff)\n", "# contenant les coefficients d'un polynome U=f(I) de degré 1 (ici).\n", "\n", "# Affichage du tableau coeff\n", "print(coeff)\n", "\n", "# Affichage des valeurs contenues dans le tableau avec une décimale\n", "print ('{0:.1f}'.format(coeff[0]), \n", " '{0:.1f}'.format(coeff[1]))\n", "\n", "# Création puis affichage d'un tableau numpy Umodel regroupant \n", "# les valeurs de la tension modélisée par une fonction affine.\n", "# Cette opération dite vectorisée ne peut pas se faire sur des listes, \n", "# d'où la nécessité de créer des tableaux numpy.\n", "Umodel = coeff[0]*I+coeff[1]\n", "\n", "print(Umodel)\n", "\n", "# Affichage de l'équation de la droite modélisée\n", "print('U={0:.1f}'.format(coeff[0]),'x I')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Que représentent les trois paramètres ordonnés de la fonction polyfit ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Que représente coeff[0] ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Que représente coeff[1] ?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Affichez la droite modélisée grâce au programme ci-dessous." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(12,10))\n", "plt.plot(I,U,'r+',label='U=f(I)')\n", "plt.plot(I,Umodel,'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 \"\n", " \"d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si vous désirez faire un zoom ou utiliser un pointeur sur la courbe cela est possible (uniquement dans le logiciel Jupyter Notebook (ENT, Anaconda) mais pas dans le logiciel JupyterLab) en important la bibliothèque ipywidgets qui permet d'afficher une fenêtre interactive.\n", "\n", "Attention,ne pas écrire la ligne de code %matplotlib inline dans le programme pour voir s'afficher la fenêtre interactive. Ici, il sera nécessaire d'exécuter la cellule deux fois de suite afin de voir s'afficher cette fenêtre et la barre d'outil qui permet d'interagir avec la courbe." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e68f1ab3025641c7a957fd4b159ff569", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import ipywidgets as widgets\n", "%matplotlib widget\n", "\n", "fig = plt.figure(figsize=(6,5))\n", "plt.plot(I,U,'r+',label='U=f(I)')\n", "plt.plot(I,Umodel,'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 \"\n", " \"d’un dipôle ohmique\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Mise en situation**\n", "\n", "- Ouvrir le programme « Activité : Modélisation d’un mouvement parabolique » puis suivre les instructions pour compléter le programme.\n", "- Vérifier votre travail si besoin avec le notebook « Correction : Modélisation d’un mouvement parabolique »" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ "**Liens vers les documents**\n", "\n", ".. toctree::\n", " :maxdepth: 1\n", " \n", " ./mvt_parabolique_modelisation.ipynb\n", " ./mvt_parabolique_modelisation_correction.ipynb\n", " " ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ":download:`Télécharger le notebook <./mvt-parabolique_modelisation.ipynb>`\n", ":download:`Lancer le notebook sur binder (lent) `\n", "\n", ":download:`Télécharger le notebook de correction<./mvt-parabolique_modelisation_correction.ipynb>`\n", ":download:`Lancer le notebook sur binder (lent) `" ] } ], "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 }