Programme python pour déterminé une concentration inconnue par dosage spectrophotométrique, plus simulation Monte-Carlo
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Course
Informatique/Chimie
Institution
Lycée Blaise-Pascal
Le programme définit les valeurs d'absorbance et de concentration, ainsi que les incertitudes associées, pour créer un modèle de régression linéaire en utilisant la fonction "polyfit" de NumPy. Il affiche ensuite les paramètres de la droite de régression linéaire, notamment la pente, l'ord...
#Calcule de C inconnue :
import numpy as np
import numpy.random as rd
import matplotlib as plt
import matplotlib.pyplot as plt
data=np.loadtxt('C:/Users/basti/Documents/PCSI2/TIPE/Classeur1.txt') #prend les
valeur dans le document joint en txt
C=[1,2,3,4,5] #Abscisse:concentration
absorbance=[0.1925,0.3883,0.5846,0.7948,0.9761] #Absorbance:ordonnée
Ab_c_inconnue=0.5705 #Absorbance de la concentration inconnue
uC=np.array([0.1,0.1,0.1,0.1,0.1]) #incertitude de la concentration
uab=np.array([0.02,0.02,0.02,0.02,0.02]) #incertitude de l'absorbance
p=np.polyfit(C,absorbance,1)
plt.errorbar(C,absorbance,xerr=5*uC,yerr=5*uab,fmt='o')
C_modele=np.linspace(0,13,1000) #remplire avec la valeur maximal de la
concentration au milieu
absorbance_modele=p[0]*C_modele+p[1]
r=np.corrcoef(C,absorbance)
k=p[0]
def C_inconnue(ab_c_inconnue):
return Ab_c_inconnue/k #détermination de la concentration inconnue
print("pente k = ",p[0]," / ordonnée à l'origine = ",p[1]," / coefficient de
corélation r =",r[0,1]," / concentration iconnue = ",C_inconnue)
plt.plot(C,absorbance,'+')
plt.plot(C_modele,absorbance_modele, color='red')
plt.xlabel('Concentration en mol/L')
plt.ylabel('Absorbance')
plt.title('Absorbance en fonction de la concentration')
plt.text(7.5,3,"• Droite d'équation A=k*C+b : ")
plt.text(7.75,2.5,"k = " + "{:.4}".format(p[0]) + " L/mol")
plt.text(7.75,2,"b = " + "{:.4}".format(p[1]))
plt.text(7.75,1.5,"r = " + "{:.4}".format(r[0,1]))
plt.text(7.5,1,"• Concentration inconnue : ")
plt.text(7.75,0.5,"C_inconnue = " + "{:.4}".format(C_inconnue(Ab_c_inconnue)) +
"mol/L")
plt.show()
#__________________________________________________________________________________
_______________________________________________________________
#simulation Monte-Carlo pour l'absorbance:
import numpy as np
import matplotlib.pyplot as plt
# Concentration et incertitude associée
c=0.001
u_c=0.0001
k=0.1974
# Nombre de simulations Monte-Carlo
n_simulations=3000
# Simulations Monte-Carlo de l'absorbance
absorbances=[]
for i in range(n_simulations):
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