#%%Bibliotheques
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
plt.rcParams[’font.size’]=20
plt.rcParams[’lines.markersize’]=10
#%%Grandeurs
epsilon=900#L/mol/cm
l=1#cm
K=1/(epsilon∗l)∗1000#mmol/L:C=A∗K
#%%Donnees
l_i,l_f=50,110#nbrdelignesskipletempsdemettrelacuve\
puisnbrdelignesgardees
tabx=(np.arange(l_f∗4)∗3+50)/60#abscisse:1pt/3secondes
tabx=tabx[5:−5]#Onraccourcitcarladeriveediminuelenbrdepts.
data=np.loadtxt("Nom_fichier.txt",delimiter="␣␣␣",usecols=(1,2,3,4),\
skiprows=l_i,max_rows=l_f)
data=np.reshape(data,(4∗l_f,))∗K#donneessur1seuleligne
kernel_size=10
kernel=np.ones(kernel_size)/kernel_size
data=np.convolve(data,kernel,mode=’same’)[5:−5]#lissage
C=np.amax(data)−data#conversionabsorbanceto[H2O2]
V=−(C[1:]−C[:−1])/(3)#derivation
#%%Ajustementavecnumpy
a,b=np.polyfit(C[:−1],V,1)#fitlineairedev=f(c)
#%%Ajustementavecscipy
a,b,r_value,p_value,std_err=stats.linregress(C[:−1],V)
print("Coefficient␣de␣correlation␣r^2=",r_value∗∗2)
print("valeur−p␣p=",p_value)
print("Ecart−type␣sigma=",std_err)
#%%Courbes
plt.figure()#c=f(t)
plt.plot(tabx[:−1],C[:−1]∗10∗∗3,"b+",label="$[I^−]_0=1.0\,10^{−1}$␣mol/L")
12
LC08:Cin tiqueetcatalyseLouisUsalaetJulieLimonet
plt.plot(tabx,122∗np.ones_like(tabx),"k")
plt.xlim(0,np.amax(tabx))
plt.ylim(0,10∗∗3∗np.amax(data)/1.5)
plt.xlabel("temps␣(min)")
plt.ylabel("concentration␣(mmol/L)")
plt.title("$[H_2O_2]=f(t)$")
plt.legend()
plt.show()
plt.figure()#v=f(t)
plt.plot(tabx[:−1],V∗10∗∗6,"b",marker="+",linestyle="",\
label="$[I^−]_0=1.0\,10^{−1}$␣mol/L")
plt.xlabel("temps␣(min)")
plt.ylabel("vitesse␣($\\mu$mol/L/s)")
plt.title("$\\frac{d[H_2O_2]}{dt}=f(t)$")
plt.legend()
plt.show()
plt.figure()#v=f(c)
plt.plot(C[:−1]∗10∗∗3,V∗10∗∗6,"b",marker="+",linestyle="",\
label="$[I^−]_0=1.0\,10^{−1}$␣mol/L")
plt.plot(C[:−1]∗10∗∗3,(a∗C[:−1]+b)∗10∗∗6,"b")
plt.xlabel("$[H_2O_2]$␣(mmol/L)")
plt.ylabel("$\\frac{d[H_2O_2]}{dt}$␣($\\mu$mol/L)")
plt.title("$\\frac{d[H_2O_2]}{dt}=[H_2O_2]$")
plt.legend()
plt.show()