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# <examples/doc_with_itercb.py>
import matplotlib.pyplot as plt
from numpy import linspace, random
from lmfit.lineshapes import gaussian
from lmfit.models import GaussianModel, LinearModel
def per_iteration(pars, iteration, resid, *args, **kws):
print(" ITER ", iteration, ["%.5f" % p for p in pars.values()])
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=x.size)
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)
plt.plot(x, y, 'b--')
print('Nfev = ', out.nfev)
print(out.fit_report())
plt.plot(x, out.best_fit, 'k-', label='best fit')
plt.legend(loc='best')
plt.show()
# <end examples/doc_with_itercb.py>
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