doc_builtinmodels_stepmodel.py

Out:

[[Model]]
    (Model(step, prefix='step_', form='erf') + Model(linear, prefix='line_'))
[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 49
    # data points      = 201
    # variables        = 5
    chi-square         = 593.709622
    reduced chi-square = 3.02913072
    Akaike info crit   = 227.700173
    Bayesian info crit = 244.216698
[[Variables]]
    line_slope:      1.87164656 +/- 0.09318713 (4.98%) (init = 0)
    line_intercept:  12.0964833 +/- 0.27606235 (2.28%) (init = 11.58574)
    step_amplitude:  112.858376 +/- 0.65392947 (0.58%) (init = 134.7378)
    step_center:     3.13494792 +/- 0.00516615 (0.16%) (init = 2.5)
    step_sigma:      0.67392841 +/- 0.01091168 (1.62%) (init = 1.428571)
[[Correlations]] (unreported correlations are < 0.100)
    C(line_slope, step_amplitude)     = -0.879
    C(step_amplitude, step_sigma)     =  0.564
    C(line_slope, step_sigma)         = -0.457
    C(line_intercept, step_center)    =  0.427
    C(line_slope, line_intercept)     = -0.309
    C(line_slope, step_center)        = -0.234
    C(line_intercept, step_sigma)     = -0.137
    C(line_intercept, step_amplitude) = -0.117
    C(step_amplitude, step_center)    =  0.109

##
import warnings
warnings.filterwarnings("ignore")
##
# <examples/doc_builtinmodels_stepmodel.py>
import matplotlib.pyplot as plt
import numpy as np

from lmfit.models import LinearModel, StepModel

x = np.linspace(0, 10, 201)
y = np.ones_like(x)
y[:48] = 0.0
y[48:77] = np.arange(77-48)/(77.0-48)
np.random.seed(0)
y = 110.2 * (y + 9e-3*np.random.randn(x.size)) + 12.0 + 2.22*x

step_mod = StepModel(form='erf', prefix='step_')
line_mod = LinearModel(prefix='line_')

pars = line_mod.make_params(intercept=y.min(), slope=0)
pars += step_mod.guess(y, x=x, center=2.5)

mod = step_mod + line_mod
out = mod.fit(y, pars, x=x)

print(out.fit_report())

plt.plot(x, y, 'b')
plt.plot(x, out.init_fit, 'k--', label='initial fit')
plt.plot(x, out.best_fit, 'r-', label='best fit')
plt.legend(loc='best')
plt.show()
# <end examples/doc_builtinmodels_stepmodel.py>