C04-EX20_professeur-explications.ipynb

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    "![Titre](C04.jpg)"
   ]
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    "## <span style='color:#AC1620'> Explication du programme en langage Python</span>\n",
    "\n",
    "### <span style='color:#163538'> Exercice 20 p 89  : How to program a temporal evolution </span>\n",
    "\n",
    "<span style='color:Green' > Remarque </span>  : Cet exercice permet de travailler la capacité numérique \"_À l’aide d’un langage de programmation et à partir de données expérimentales, tracer l’évolution temporelle d’une concentration, d’une vitesse volumique d’apparition ou de disparition et tester une relation donnée entre la vitesse volumique de disparition et la concentration d’un réactif.\"_\n",
    "\n",
    "**Pour faire fonctionner le programme à l'aide de Jupyter Notebook, il faut exécuter les cellules contenant du code les unes après les autres** \n",
    "\n",
    "La première étape consiste à importer :\n",
    "\n",
    "* la bibliothèque <span style='color:red' > matplotlib.pyplot </span> avec l'alias *plt*. Elle sert à réaliser le graphique ;\n",
    "* la bibliothèque <span style='color:red' > numpy </span> avec l'alias *np* . Elle est utile pour de nombreux outils mathématiques."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L'exercice demande à l'élève de compléter les listes de la durée _t_ et de la concentration *C* avec les données de l'exercice 13."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compléter les listes suivantes et les décommenter\n",
    "t = [0,200,400,600,800,1000,2000]\n",
    "C = [200,100,50,25,12.5,6.3,3.1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On calcule alors la vitesse de disparition $v_d$ à l'instant $t$. Pour cela, on utilise la relation : \n",
    "\n",
    "$$v_d(i)=\\frac{\\displaystyle |C(i+1)-C(i)|}{\\displaystyle t(i+1)-t(i) }$$\n",
    "\n",
    "On rentre dans une boucle et on calcule les valeurs de $v_d$ pour toutes les valeurs de la liste _t_ moins une pour éviter d'avoir un message d'erreur."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################################################\n",
    "# calcul des vitesses de disparition\n",
    "###################################################\n",
    "vd = []\n",
    "for i in range (len(t)-1) :\n",
    "    vd=vd+[abs((C[i+1]-C[i])/(t[i+1]-t[i]))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On utilise alors la bibliothèque <span style='color:red' > matplotlib.pyplot </span> pour tracer les graphiques.\n",
    "\n",
    "Dans un premier temps, on cherche la valeurs maximales de la concentration _C_, du temps *t* et de la vitesse de disparition $v_d$. Ces valeurs sont utiles ultérieurement pour fixer des limites au graphique à l'aide des commandes ``plt.xlim(min,max)`` et ``plt.ylim(min,max)``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################################################\n",
    "### Représentation graphique\n",
    "###################################################\n",
    "Cmax=max(C)\n",
    "tmax=max(t)\n",
    "vdmax=max(vd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On crée une figure de 1600 pixels de large sur 800 pixels de hauteur à l'aide de la commande ``plt.figure(figsize=(16,8)``.\n",
    "On crée trois sous-figures à l'aide de la commande ``plt.subplot()``.\n",
    "\n",
    "Pour chaque sous-figure : \n",
    "* ``plt.grid`` permet d'ajouter une grille au graphique ;\n",
    "* ``plt.title`` permet d'ajouter un titre ; \n",
    "* ``plt.ylabel`` permet d'ajouter un titre à l'axe des ordonnées ;\n",
    "* ``plt.xlabel`` permet d'ajouter un titre à l'axe des abscisses ; \n",
    "* ``plt.scatter`` permet de placer les points de coordonnées ($x;y$) correspondants de chaque liste.\n",
    "Notons que pour la représentation $v_d=f(C)$, on a copié la liste de concentration _C_ (``C2=list(C)``), puis on a supprimé la dernière valeur ``del C2[-1]``, afin d'avoir autant de valeur pour la vitesse que pour la concentration, et ainsi ne pas avoir de message d'erreur.\n",
    "\n",
    "Dans la troisième sous-figure, on indique les valeurs de la vitesse de disparition pour différents instants à l'aide de la commande ``plt.text(x,y, \"texte\")``.\n",
    "\n",
    "Enfin,  ``plt.show()`` permet d'afficher le graphique ainsi obtenu."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "\n",
    "#sous-figure 1 - Graphique donnant l'évolution de la concentration en fonction du temps\n",
    "plt.subplot(221) \n",
    "plt.grid()\n",
    "plt.title(\"Évolution de la concentration en fonction du temps\")\n",
    "plt.xlim(0,1.15*tmax)\n",
    "plt.ylim(0,1.15*Cmax)\n",
    "plt.scatter(t,C)        \n",
    "plt.xlabel(\"Temps (min)\")\n",
    "plt.ylabel(\"Concentration ($\\mathrm{mmol \\cdot L^{-1}}$)\")\n",
    "\n",
    "#sous-figure 2- Graphique donnant l'évolution de la vitesse de disparition en fonction du temps\n",
    "plt.subplot(222)\n",
    "C2=list(C)\n",
    "C2max=max(C2)\n",
    "del C2[-1]\n",
    "plt.grid()\n",
    "plt.title(\"Évolution de la vitesse de disparition en fonction de la concentration\")\n",
    "plt.xlim(0,1.15*C2max)\n",
    "plt.ylim(0,1.15*vdmax)\n",
    "plt.scatter(C2,vd,color='orange')\n",
    "plt.ylabel(\"Vitesse ($\\mathrm{mmol\\cdot L^{-1} \\cdot min^{-1}}$)\")\n",
    "plt.xlabel(\"Concentration ($\\mathrm{mmol\\cdot L^{-1}}$)\")\n",
    "\n",
    "#sous-figure 3- texte donnant les vitesses de disparition en fonction du temps.\n",
    "plt.subplot(212, frameon=False)\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "for i in range (len(t)-1) :\n",
    "    plt.text(0.1,((len(t)-1)-i)/len(t),\"La vitesse de disparition à \"+ str(t[i]) +\\\n",
    "             \" min est v(\"+str(t[i])+\")=\"+ str(round(vd[i],3)) +\" $\\mathrm{mmol\\cdot L^{-1} \\cdot min^{-1}}$\",\\\n",
    "             fontsize = 16 )\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}