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#!/usr/bin/env python
#<examples/models_doc2.py>
from numpy import loadtxt
from lmfit import fit_report
from lmfit.models import GaussianModel, LinearModel
import matplotlib.pyplot as plt
data = loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1] + x * 0.1 - 3.0
gauss = GaussianModel()
gauss.guess_starting_values(y, x=x)
line = LinearModel()
line.params['slope'].value = 0
line.params['intercept'].value = -1.0
total = gauss + line
result = total.fit(y, x=x)
print fit_report(result.params, min_correl=0.25)
plt.plot(x, y, 'bo')
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')
plt.show()
#<end examples/models_doc2.py>
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