13-Act3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Activit&eacute; 3 - Vérification de la 3ème loi de Kepler \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Le programme va permettre de vérifier la 3ème loi de Kepler dans le cas des satellites de Jupiter puis d'évaluer la masse de Jupiter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import des bibliothèques nécessaires au fonctionnement du programme\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Valeurs des demi-grands axes a en milliers de km et périodes en jours pour les planètes du systèmes solaire  \n",
    "LIST_a = [181,221,421,671,1070,1882] #liste des demi-grands axes\n",
    "LIST_T = [0.498,0.674,1.769,3.551,7.155,16.689] # liste périodes de révolution\n",
    "LIST_P = ['Amalthée','Thébé','Io','Europe','Ganymède','Callisto'] # nom des satellites de Jupiter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Création des valeurs à tracer sur l'axe des ordonnées et des abscisses\n",
    "for i in range(0, len(LIST_a)):\n",
    "   LIST_a[i] = (LIST_a[i]*1e6)**3 \n",
    "   LIST_T[i] = (LIST_T[i]*24*3600)**2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pente --> 3.1190857441966125e-16\n",
      "ordonnée à l origine --> 13474077.96661377\n",
      "coefficient de corrélation -->0.9999999968990072\n"
     ]
    }
   ],
   "source": [
    "# Régression linéaire\n",
    "regression = stat.linregress(LIST_a,LIST_T) # regression est une liste des trois données : pente, ordonnée à l'origine et coefficient directeur\n",
    "pente =regression[0] # récupération dans la variable pente du coefficient directeur\n",
    "print('pente --> '+str(pente)) # Affichage du coefficient directeur\n",
    "ordorigine =regression[1] #  récupération dans la variable ordorigine de l'ordonnée à l'origine\n",
    "print('ordonnée à l origine --> '+str(ordorigine)) # Affichage de l'ordonnée à l'origine\n",
    "coeffcorel = regression[2] #récupération dans la variable coeffcorel du coefficient de correlation r\n",
    "print('coefficient de corrélation -->'+str(coeffcorel)) # Affichage du coefficient de correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calcul des coordonnées d'un point appartenant à la droite de régression\n",
    "# ce point est utilisé pour tracer la droite sur le graphe\n",
    "# la droite est tracé entre les points de coordonnées (0,0).et (a_3_max , T_2_max)\n",
    "a_3_max = LIST_a[len(LIST_a)-1]\n",
    "T_2_max =pente *a_3_max + ordorigine "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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 graphe\n",
    "plt.grid(True)\n",
    "plt.xlabel(\"a au cube (m3)\" ) \n",
    "plt.ylabel(\"periode au carré (s2)\")\n",
    "plt.scatter(LIST_a,LIST_T,s = 100,c ='red',marker ='+') # affichage des points\n",
    "\n",
    "# Affichage des noms et de la droite de regression\n",
    "for i in range (0,len(LIST_a)): \n",
    "   plt.text(LIST_a[i],LIST_T[i], LIST_P[i],fontsize =8)\n",
    "   plt.plot ([0,a_3_max],[ordorigine,T_2_max],c ='blue')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "cell_metadata_filter": "-all",
   "encoding": "# -*- coding: utf-8 -*-",
   "notebook_metadata_filter": "-all",
   "text_representation": {
    "extension": ".py",
    "format_name": "light"
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
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  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
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   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
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 },
 "nbformat": 4,
 "nbformat_minor": 4
}