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
39
40
41
42
43
44
45
46
47
48
49
50
|
#!/usr/bin/env python
# <examples/doc_parameters_basic.py>
import numpy as np
from lmfit import Minimizer, Parameters, report_fit
# create data to be fitted
x = np.linspace(0, 15, 301)
data = (5. * np.sin(2*x - 0.1) * np.exp(-x*x*0.025) +
np.random.normal(size=len(x), scale=0.2))
# define objective function: returns the array to be minimized
def fcn2min(params, x, data):
"""Model a decaying sine wave and subtract data."""
amp = params['amp']
shift = params['shift']
omega = params['omega']
decay = params['decay']
model = amp * np.sin(x*omega + shift) * np.exp(-x*x*decay)
return model - data
# create a set of Parameters
params = Parameters()
params.add('amp', value=10, min=0)
params.add('decay', value=0.1)
params.add('shift', value=0.0, min=-np.pi/2., max=np.pi/2)
params.add('omega', value=3.0)
# do fit, here with leastsq model
minner = Minimizer(fcn2min, params, fcn_args=(x, data))
result = minner.minimize()
# calculate final result
final = data + result.residual
# write error report
report_fit(result)
# try to plot results
try:
import matplotlib.pyplot as plt
plt.plot(x, data, 'k+')
plt.plot(x, final, 'r')
plt.show()
except ImportError:
pass
# <end of examples/doc_parameters_basic.py>
|
