1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
|
import numpy as np
from lmfit.old_models1d import GaussianModel, VoigtModel
import matplotlib.pyplot as plt
data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]
model = VoigtModel(background='linear')
# get default starting values, but then alter them
model.guess_starting_values(y, x=x)
model.params['amplitude'].value = 2.0
init_fit = model.model(x=x)
# the actual fit
model.fit(y, x=x)
print model.fit_report(min_correl=0.25)
vfit = model.model(x=x)
mod2 = GaussianModel(background='linear')
mod2.fit(y, x=x)
gfit = mod2.model(x=x)
print mod2.fit_report(min_correl=0.25)
print 'Voigt Sum of Squares: ', ((vfit - y)**2).sum()
print 'Gaussian Sum of Squares: ', ((gfit - y)**2).sum()
plt.plot(x, vfit, 'r-')
plt.plot(x, gfit, 'b-')
plt.plot(x, y, 'bo')
plt.show()
|
