1re Spé - Loi de Beer-Lambert.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# On détaille ici un programme python affichant les points expérimentaux des mesures de l'absorbance A en fonction de la concentration C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Chaque partie de code est précédée d'un titre.\n",
    "### Certaines parties de code sont à modifier par vous, cela est indiqué par la mention \"A VOUS DE JOUER\" dans le titre de la partie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAPE 0 : Initialisation des bibliothèques python nécessaires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "plt.close() #On ferme les fenêtres graphiques éventuellement ouvertes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAPE 1 : Entrée des valeurs expérimentales de l'absorbance A et de la concentration C."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A VOUS DE JOUER : compléter les listes de valeurs d'absorbance A et de concentration C."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = [] #liste des valeurs des absorbances\n",
    "C = [] #liste des valeurs des concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAPE 2 : Construction de la droite qui modélisera la répartition des points (Loi de Beer-Lambert)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def modele(x, a, b): # Définition de la fonction de modélisation\n",
    "    y = a * x + b\n",
    "    return y\n",
    "\n",
    "unpeu_x = (max(C) - min(C)) / 10\n",
    "unpeu_y = (max(A) - min(A)) / 10\n",
    "\n",
    "x_mod_min = 0 #Abscisse de début de la courbe modèle\n",
    "x_mod_max = max(C) + unpeu_x #Abscisse de fin de la courbe modèle\n",
    "\n",
    "X_modele = np.linspace(x_mod_min, x_mod_max, 100) #On génère les abscisses du modèle, unpeu permet d'obtenir une courbe qui dépasse à droite et à gauche\n",
    "\n",
    "valeurs, covar = curve_fit(modele, C, A) # On calcule les meilleurs valeurs de a et b pour l'ajustement\n",
    "\n",
    "y_mod_min = 0\n",
    "y_mod_max = valeurs[0]*x_mod_max + valeurs[1]\n",
    "\n",
    "Y_modele = modele(X_modele, valeurs[0], valeurs[1]) # On génère les ordonnées du modèle avec les valeurs de a et b adaptées"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAPE 3 : On définit les limites des valeurs minimales et maximales de la fenêtre graphique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_graph_min = 0 # abscisse minimale de la fenêtre graphique\n",
    "x_graph_max = x_mod_max + unpeu_x # abscisse maximale de la fenêtre graphique\n",
    "\n",
    "y_graph_min = 0 # ordonnée minimale de la fenêtre graphique\n",
    "y_graph_max = y_mod_max + unpeu_y # ordonnée maximale de la fenêtre graphique"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAPE 4 : On trace le graphique A = f(C)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A VOUS DE JOUER : donner un titre au graphique et un nom aux axes avec leurs unités."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "plt.title(\"Titre\", size=18) # Affiche le titre du graphique\n",
    "plt.ylabel(\"Nom de l'axe des ordonnées\",size=14) # Affiche le nom de l’axe des ordonnées (vertical)\n",
    "plt.xlabel(\"Nom de l'axe des abscisses\", size=14) # Affiche le nom de l’axe des abscisses (horizontal)\n",
    "\n",
    "plt.grid(linestyle='--') # Le cadrillage doit être affiché sous forme de tirets\n",
    "plt.xlim(x_graph_min, x_graph_max) # On précise les limites en abscisses de la fenêtre graphique (horizontal)\n",
    "plt.ylim(y_graph_min, y_graph_max)# On précise les limites en ordonnées de la fenêtre graphique (vertical)\n",
    "plt.plot(C, A, 'o', color ='red', label = 'Points issus de la mesure') # Affiche les points expérimentaux\n",
    "plt.plot(X_modele, Y_modele, label = 'Modèle') # Affiche le modèle mathématique\n",
    "plt.legend(fontsize=18) # Affiche la légende du graphique\n",
    "plt.show() # Affiche le graphique"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}