Tle Spé - Dosage conductimétrie (Monte Carlo).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ce programme permet d'estimer la valeur moyenne et l'incertitude-type sur la valeur obtenue lors d'un dosage par étalonnage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dans cet exercice il faudra :\n",
    "###     1. Compléter les listes des concentrations et conductivités\n",
    "###     2. Préciser la valeur de la conductivité de la solution de sérum\n",
    "###     3. Préciser les incertitudes-types sur les concentrations et les conductivités"
   ]
  },
  {
   "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 $\\color {red} {\"A\\: VOUS\\: DE\\: JOUER\\,!\"}$ dans le titre de la partie."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etape 0 : On importe les bibliothèques nécessaires"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # Bibliothèque permettant le calcul scientifique\n",
    "import matplotlib.pyplot as plt # Bibliothèque permettant de tracer des graphiques\n",
    "from scipy.optimize import curve_fit # Bibliothèque permettant d'optimiser un modèle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etape 1 : On entre les listes de mesures et les incertitudes associées à ces mesures\n",
    "### $\\color {red} {\"A\\: VOUS\\: DE\\: JOUER\\,!\"}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#déclaration des listes de concentration C et conductivité S et de la conductivité S_inc de la solution inconnue\n",
    "C = np.array(['A modifier']) # Liste contenant les valeurs des concentrations\n",
    "S = np.array(['A modifier']) # Liste contenant les valeurs de conductivités\n",
    "S_inc = 'A modifier' # Valeur de la conductivité de la solution de sérum\n",
    "\n",
    "#déclaration des incertitudes sur les mesures de C et S\n",
    "uC = 'A modifier' # Incertitude-type sur les concentrations\n",
    "uS = 'A modifier' # Incertitudes-types sur les conductivités"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etape 2 : On initialise les variables et la fonction nécessaires aux calculs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Nombre de fois où on fait les calculs (nécessairement supérieur à 1 !)\n",
    "iterations = 1000\n",
    "\n",
    "#Création des listes contenant les valeurs aléatoires calculées pour C, S et C_inc\n",
    "C_alea=[]\n",
    "S_alea=[]\n",
    "LC_inc=[]\n",
    "\n",
    "# Définition de la fonction de modélisation, elle est linéaire ici y = a*x\n",
    "def modele(x, a):\n",
    "    return a * x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etape 3 : On simule \"iterations\" fois un dosage par étalonnage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#On fait \"iterations\" fois les opérations suivantes\n",
    "for i in range(iterations):    \n",
    "    \n",
    "    #On crée une liste aléatoire de chaque grandeur C et S\n",
    "    for j in range(len(C)):\n",
    "\n",
    "        VC = C[j]+uC*np.random.normal()\n",
    "        C_alea.append(VC)\n",
    "    \n",
    "        VS = S[j]+uS*np.random.normal()\n",
    "        S_alea.append(VS)\n",
    "    \n",
    "    #On fait la régression linéaire de la répartition aléatoire de C et S, S = k*C\n",
    "    k, _ = curve_fit(modele, C_alea, S_alea) # On calcule la meilleure valeur de k pour l'ajustement linéaire\n",
    "    \n",
    "    #On calcule une valeur de C_inc de la concentration inconnue\n",
    "    C_inc = (S_inc+uS*np.random.normal()) / k[0]\n",
    "    \n",
    "    #On Multiplie la valeur obtenue par 20 pour obtenir la valeur réelle\n",
    "    C_inc = C_inc*20\n",
    "    \n",
    "    #On stocke la valeur de C_inc dans une liste regroupant l'ensemble des valeurs obtenues\n",
    "    LC_inc.append(C_inc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Etape 4 : On affiche les résultats obtenus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Concentration inconnue (Valeur moyenne) :  664.6155539765283  g/L\n",
      "u(C_inc):  10.614752199348013  g/L\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#On calcule et on affiche la moyenne des valeurs C_inc calculées et l'incertitude-type\n",
    "MoyCinc = sum(LC_inc)/iterations\n",
    "uCinc = (1/(iterations-1)*sum((np.array(LC_inc)-MoyCinc)**2))**0.5\n",
    "\n",
    "print('Concentration inconnue (Valeur moyenne) : ', MoyCinc, ' g/L')\n",
    "print('u(C_inc): ', uCinc, ' g/L')\n",
    "\n",
    "#On affiche l'histogramme de répartition des valeurs C_inc obtenues\n",
    "plt.hist(LC_inc, range = (MoyCinc-4*uCinc, MoyCinc+4*uCinc), bins = 50, color = 'blue', edgecolor = 'black')\n",
    "plt.axvline(MoyCinc, color=\"orange\", linestyle=\"-.\", label='Valeur moyenne (Concentration inconnue)')\n",
    "plt.title('Pour ' + str(iterations) + ' iterations')\n",
    "plt.xlabel('C (g/L)')\n",
    "plt.ylabel('Nombre de valeurs calculées')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}