import sys
from numpy import linspace, exp, random

from lmfit import Parameters, minimize
from lmfit.lineshapes import gaussian, lorentzian, pvoigt

try:
    import matplotlib
    import pylab
    HASPYLAB = True
except ImportError:
    HASPYLAB = False

def per_iteration(pars, i, resid, x, *args, **kws):
    if i < 10 or i % 10 == 0:
        print( '====== Iteration ', i)
        for p in pars.values():
            print( p.name , p.value)

def residual(pars, x, sigma=None, data=None):
    yg = gaussian(x, pars['amp_g'].value,
                  pars['cen_g'].value, pars['wid_g'].value)
    yl = lorentzian(x, pars['amp_l'].value,
                    pars['cen_l'].value, pars['wid_l'].value)

    frac = pars['frac'].value
    slope = pars['line_slope'].value
    offset = pars['line_off'].value
    model = (1-frac) * yg + frac * yl + offset + x * slope
    if data is None:
        return model
    if sigma is  None:
        return (model - data)
    return (model - data)/sigma


n = 601
xmin = 0.
xmax = 20.0
x = linspace(xmin, xmax, n)

p_true = Parameters()
p_true.add('amp_g', value=21.0)
p_true.add('cen_g', value=8.1)
p_true.add('wid_g', value=1.6)
p_true.add('frac', value=0.37)
p_true.add('line_off', value=-1.023)
p_true.add('line_slope', value=0.62)

data = (pvoigt(x, p_true['amp_g'].value, p_true['cen_g'].value,
              p_true['wid_g'].value, p_true['frac'].value) +
        random.normal(scale=0.23,  size=n) +
        x*p_true['line_slope'].value + p_true['line_off'].value )


pfit = Parameters()
pfit.add('amp_g', value=10)
pfit.add('amp_g', value=10.0)
pfit.add('cen_g', value=9)
pfit.add('wid_g', value=1)
pfit.add('frac', value=0.50)
pfit.add('amp_l', expr='amp_g')
pfit.add('cen_l', expr='cen_g')
pfit.add('wid_l', expr='wid_g')
pfit.add('line_slope', value=0.0)
pfit.add('line_off', value=0.0)

sigma = 0.021

myfit = minimize(residual, pfit, method='nelder',
                 args=(x,), kws={'sigma':sigma, 'data':data})

print(' Nfev = ', myfit.nfev)

fit = residual(myfit.params, x)

if HASPYLAB:
    pylab.plot(x, data, 'r+')
    pylab.plot(x, fit, 'k-')
    pylab.show()