Reputation: 688
I want to do a fit using lmfit but I am having some issues. Here is my code:
from lmfit import Model
import numpy as np
def fit_func(x,a,b,c):
return a*(b-x)**(5/8)+c
x = np.array([ 131.871 , 218.825 , 305.046 , 390.533 ,
475.128 , 558.959 , 642.001 , 724.307 ,
805.794 , 886.422 , 966.20900001, 1045.19300001,
1123.39300001, 1200.75800001, 1277.23700001, 1352.83300001,
1427.57800001, 1501.49800001, 1574.55300001, 1646.69500001,
1717.90800001, 1788.22100001, 1857.65100001, 1926.18300001,
1993.76400001, 2060.37000001, 2126.00900001, 2190.70600001,
2254.44800001, 2317.20000001, 2378.92000001, 2439.60300001,
2499.25800001, 2557.89000001, 2615.46600001, 2671.95000001,
2727.30900001, 2781.54300001, 2834.64700001, 2886.60600001,
2937.38000001, 2986.92900001])
y = np.array([ 0. , 3.14159265, 6.28318531, 9.42477796,
12.56637061, 15.70796327, 18.84955592, 21.99114858,
25.13274123, 28.27433388, 31.41592654, 34.55751919,
37.69911184, 40.8407045 , 43.98229715, 47.1238898 ,
50.26548246, 53.40707511, 56.54866776, 59.69026042,
62.83185307, 65.97344573, 69.11503838, 72.25663103,
75.39822369, 78.53981634, 81.68140899, 84.82300165,
87.9645943 , 91.10618695, 94.24777961, 97.38937226,
100.53096491, 103.67255757, 106.81415022, 109.95574288,
113.09733553, 116.23892818, 119.38052084, 122.52211349,
125.66370614, 128.8052988 ])
fit_model = Model(fit_func)
params = fit_model.make_params()
params['b'].set(5000, min=3500)
result = fit_model.fit(y, x=x)
But I am getting this error:
ValueError: The model function generated NaN values and the fit aborted! Please check your model function and/or set boundaries on parameters where applicable. In cases like this, using "nan_policy='omit'" will probably not work.
What am I doing wrong? I tried to adjust the a, b, c parameters by hand and a=-1.2, b=3600, c=196 give a pretty good fit, so the program should be able to find something similar to that.
Upvotes: 0
Views: 963
Reputation: 7862
Two things are missing:
a) you need to pass params
to fit_model.fit()
as with
result = fit_model.fit(y, params, x=x)
b) you need to give initial values for all parameters. Un-initialized parameters will have a value of -np.inf
, which is deliberately chosen because it will throw such errors.
You say you know reasonable values for a
, b
, and c
. Use that knowledge! Something like
fit_model = Model(fit_func)
params = fit_model.make_params(a=-1, b=4000, c=200)
params['b'].min = x.max() * (1.000001) # prevent (negative number)**fraction
result = fit_model.fit(y, params, x=x)
print(result.fit_report())
should work.
Upvotes: 1