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#!/usr/bin/env python
#<examples/doc_nistgauss.py>
import numpy as np
from lmfit.models import GaussianModel, ExponentialModel
import sys
import matplotlib.pyplot as plt
dat = np.loadtxt('NIST_Gauss2.dat')
x = dat[:, 1]
y = dat[:, 0]
exp_mod = ExponentialModel(prefix='exp_')
pars = exp_mod.guess(y, x=x)
gauss1 = GaussianModel(prefix='g1_')
pars.update( gauss1.make_params())
pars['g1_center'].set(105, min=75, max=125)
pars['g1_sigma'].set(15, min=3)
pars['g1_amplitude'].set(2000, min=10)
gauss2 = GaussianModel(prefix='g2_')
pars.update(gauss2.make_params())
pars['g2_center'].set(155, min=125, max=175)
pars['g2_sigma'].set(15, min=3)
pars['g2_amplitude'].set(2000, min=10)
mod = gauss1 + gauss2 + exp_mod
init = mod.eval(pars, x=x)
plt.plot(x, y)
plt.plot(x, init, 'k--')
out = mod.fit(y, pars, x=x)
print(out.fit_report(min_correl=0.5))
plt.plot(x, out.best_fit, 'r-')
plt.show()
#<end examples/doc_nistgauss.py>
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