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#!/usr/bin/env python
#<examples/doc_with_itercb.py>
from numpy import linspace, random
import matplotlib.pylab as pylab
from lmfit.models import LinearModel, GaussianModel
from lmfit.lineshapes import gaussian
def per_iteration(pars, iter, resid, *args, **kws):
if iter < 3 or iter % 10 == 0:
out = ['== %i ' % iter]
for key, val in pars.valuesdict().items():
out.append('%s=%.3f' % (key, val))
print ', '.join(out)
print args, kws
x = linspace(0., 20, 401)
y = gaussian(x, amplitude=24.56, center=7.6543, sigma=1.23)
y = y - .20*x + 3.333 + random.normal(scale=0.23, size=len(x))
mod = GaussianModel(prefix='peak_') + LinearModel(prefix='bkg_')
pars = mod.make_params()
pars['peak_amplitude'].value = 3.0
pars['peak_center'].value = 6.0
pars['peak_sigma'].value = 2.0
pars['bkg_intercept'].value = 0.0
pars['bkg_slope'].value = 0.0
out = mod.fit(y, pars, x=x, iter_cb=per_iteration)
pylab.plot(x, y, 'b--')
# print(' Nfev = ', out.nfev)
print( out.fit_report())
pylab.plot(x, out.best_fit, 'k-')
pylab.show()
#<end examples/doc_with_itercb.py>
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