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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}],"source":["import numpy as np #Pour faire divers calculs\n","import matplotlib.pyplot as plt #Pour tracer des graphiques\n","\n","## Données pour la réaction étudiée\n","pKs=9.8 \t\t#valeur de pKs\n","C_0=0.01\t\t#concentration initiale de Ag(+) en mol/L\n","\n","## Définition des fonctions\n","def Ag(C):\t\t# % de Ag(+)\n","\treturn 100*(10**(-pKs))/C/C_0\n","\n","## Graphe\n","n=10000\t\t#nombre de points de l'intervalle pCl\n","x_min= 5\t#pCl_min\n","x_max=10\t #pCl_max\n","x = np.linspace(x_min,x_max,n) \t#liste des pCl\n","ion_Ag =[]\t\t\t#initialisation du np.array des %Ag(+)\n","Cl=10**(-x)\t\t\t#concentration de Cl(-)\n","for Cl in Cl: \t\t#boucle de calcul des pourcentages de Ag(+) sur l'intervale\n","\tif Cl*C_0<10**(-pKs):\t#test de condition de précipitation\n","\t\tion_Ag.append(100)\t#pourcentages de Ag(+) si aucun précipité\n","\telse:\n","\t\tion_Ag.append(Ag(Cl))\t#pourcentages de Ag(+) si AgCl existe\n","\n","plt.figure(1)\n","plt.xlabel('pCl') \t#légende de l’axe des abscisses\n","plt.ylabel('%Ag(+)') \t#légende de l’axe des ordonnées\n","plt.plot(x,ion_Ag,'b-',label='%Ag(+)')\n","#représente %Ag(+) en fonction du pCl en bleu de manière continue avec une étiquette pour la courbe\n","plt.legend() #affiche l’étiquette de la courbe\n","plt.grid() #affiche le quadrillage\n","plt.show() #affiche le graphique\n"]}]}