doc_model_savemodelresult.py

Out:

[[Model]]
    Model(gaussian)
[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 29
    # data points      = 101
    # variables        = 3
    chi-square         = 3.40883599
    reduced chi-square = 0.03478404
    Akaike info crit   = -336.263713
    Bayesian info crit = -328.418352
[[Variables]]
    amplitude:  8.88022277 +/- 0.11359552 (1.28%) (init = 5)
    center:     5.65866081 +/- 0.01030506 (0.18%) (init = 5)
    sigma:      0.69765538 +/- 0.01030503 (1.48%) (init = 1)
    fwhm:       1.64285285 +/- 0.02426649 (1.48%) == '2.3548200*sigma'
    height:     5.07800352 +/- 0.06495781 (1.28%) == '0.3989423*amplitude/max(2.220446049250313e-16, sigma)'
[[Correlations]] (unreported correlations are < 0.100)
    C(amplitude, sigma) =  0.577

##
import warnings
warnings.filterwarnings("ignore")
##
# <examples/doc_model_savemodelresult.py>
import numpy as np

from lmfit.model import save_modelresult
from lmfit.models import GaussianModel

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

gmodel = GaussianModel()
result = gmodel.fit(y, x=x, amplitude=5, center=5, sigma=1)

save_modelresult(result, 'gauss_modelresult.sav')

print(result.fit_report())
# <end examples/doc_model_savemodelresult.py>