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import sys
from numpy import linspace, zeros, sin, exp, random, sqrt, pi, sign
from lmfit import Parameters, Parameter, Minimizer
from lmfit.lineshapes import gaussian, lorentzian
from lmfit.printfuncs import report_fit
try:
import pylab
HASPYLAB = True
except ImportError:
HASPYLAB = False
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)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + 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)
data = (gaussian(x, 21, 8.1, 1.2) +
lorentzian(x, 10, 9.6, 2.4) +
random.normal(scale=0.23, size=n) +
x*0.5)
if HASPYLAB:
pylab.plot(x, data, 'r+')
pfit = [Parameter(name='amp_g', value=10),
Parameter(name='cen_g', value=9),
Parameter(name='wid_g', value=1),
Parameter(name='amp_tot', value=20),
Parameter(name='amp_l', expr='amp_tot - amp_g'),
Parameter(name='cen_l', expr='1.5+cen_g'),
Parameter(name='wid_l', expr='2*wid_g'),
Parameter(name='line_slope', value=0.0),
Parameter(name='line_off', value=0.0)]
sigma = 0.021 # estimate of data error (for all data points)
myfit = Minimizer(residual, pfit,
fcn_args=(x,), fcn_kws={'sigma':sigma, 'data':data},
scale_covar=True)
myfit.prepare_fit()
init = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, init, 'b--')
myfit.leastsq()
print(' Nfev = ', myfit.nfev)
print( myfit.chisqr, myfit.redchi, myfit.nfree)
report_fit(myfit.params)
fit = residual(myfit.params, x)
if HASPYLAB:
pylab.plot(x, fit, 'k-')
pylab.show()
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