lmfit_gaussian_model_fitting_with_parameter_estimation.py

1import matplotlib.pyplot as plt
2import numpy as np
3
4from lmfit.models import GaussianModel
5
6# create data from a Gaussian and add noise
7def gaussian(x, amp, cen, wid):
8    return amp * np.exp(-(x-cen)**2 / wid)
9
10x = np.linspace(-10, 10, 101)
11y = gaussian(x, 2.33, 0.21, 1.51) + np.random.normal(0, 0.2, len(x))
12
13# setup the model
14mod = GaussianModel()
15
16# initial parameter estimates
17pars = mod.guess(y, x=x)
18
19# perform the fit
20out = mod.fit(y, pars, x=x)
21
22# print the results
23print(out.fit_report())
24
25# plot the results
26plt.plot(x, y, 'o')
27plt.plot(x, out.init_fit, '--', label='initial fit')
28plt.plot(x, out.best_fit, '-', label='best fit')
29plt.legend()
30plt.show()