squeezie

# Créé par profsos, le 21/11/2025 en Python 3.7
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
kc=42e-3


from scipy.optimize import curve_fit

Lu=[5,5.5,6,6.5,7,7.5]
Li=[0.87,0.95,1.03,1.12,1.21,1.28]
Lw=[31.8,123.1,180.2,263.6,329.9,380.8]
Lc=[kc*e for e in Li]

print(Lc)
'''plt.plot(Lw,Lc,"ro")
plt.xlabel('Wm en rad/s')
plt.ylabel('Cm en Nm')
'''


a,b,r,p,std_err = stats.linregress(Lw,Lc)
Lc_th=[a*e+b for e in Lw]
#plt.plot(Lw,Lc_th,"g*")

print('f= ',a,'\n','Cfs = ' ,b)

#plt.show()
F=a
#fmesure= open("RobotEriccMesureTemp_30.csv","r")
Lt,L30=np.loadtxt('RobotEriccMesureTemp_30.csv',skiprows=1,unpack=True)
plt.plot(Lt,L30)

def f(x,K,Tau):
    return 3*K*(1-np.exp(-x/Tau))


(K,Tau), cov = curve_fit(f,Lt, L30)

print (K,Tau)
print(cov)
err = np.sqrt (np.diag (cov))
print(err)

L__8=[f(x,K,Tau) for x in Lt]

plt.plot(Lt,L__8)
plt.show()

Jeq= Tau*F
print('Jeq: ', Jeq)
Créé il y a 1 mois.