CODEWITHSUNDEEP
pythonscipycurve-fitting
notAI
notAI

Reputation: 111

Curve fit in python using scipy.optimize.curve_fit

I am trying to curve fit my data with scipy.optimize.curve_fit. For some reason it doesn't like my equation. If I plot the equation using plausible numbers it looks right. So I trust my equation. If I try to fit to a simpler equation, it works, so I trust my code in general. Any advice as to why it doesn't work? The specific error return is "Residuals are not finite in the initial point." Changing p0 to anything reasonable is also unhelpful

import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
import numpy as np




Y=np.array([0.00001,1.421828697,2.553970883,3.340262844,3.931459808,
4.640896164,5.084293887,5.379892368,5.557251457,5.705050698,
5.823290091,5.911969636,5.971089332,6.059768876,6.118888573,6.178008269,
6.266687814])


X=np.array([0,2500,5000,7500,10000,15000,20000,25000,
30000,35000,40000,45000,50000,55000,60000,65000,70000])

plt.plot(X,Y,'g^')



def func(X,J,g):

    u=6.720*10**-5
    k=1.380*(10**-16)
    T=2
    N=1
    x=g*J*u*(X)/(k*T)
    return N*g*J*(((2*J+1)/(2*J))*(1/(np.tanh((2*J+1)*x/(2*J))))-(1/(2*J))*
    (1/(np.tanh(x/(2*J)))))




plt.figure(1)
popt, pcov = curve_fit(func, X,Y,p0=[3.5,2],bounds=([0.25,0.001],[10,4]))
plt.plot(X, func(X, *popt), 'r--', label='fit-with-bounds')

print(" J is %s " %(popt[0]),"\n","g is %s" %(popt[1]))

Upvotes: 0

Views: 2554

Answers (1)

James Phillips
James Phillips

Reputation: 4657

You include an X value of exactly zero, causing divide-by-zero errors. If I replace the value of zero with 0.01 your code works in my tests.

Upvotes: 1

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