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import sys
from numpy import linspace, zeros, sin, exp, random, sqrt, pi, sign
from scipy.optimize import leastsq
from lmfit import Parameters, Minimizer, report_fit
from lmfit.lineshapes import gaussian
try:
import pylab
HASPYLAB = True
except ImportError:
HASPYLAB = False
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)
slope = pars['line_slope'].value
offset = pars['line_off'].value
model = yg + offset + x * slope
if data is None:
return model
if sigma is None:
return (model - data)
return (model - data)/sigma
n = 201
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('line_off', value=-1.023)
p_true.add('line_slope', value=0.62)
data = (gaussian(x, p_true['amp_g'].value, p_true['cen_g'].value,
p_true['wid_g'].value) +
random.normal(scale=0.23, size=n) +
x*p_true['line_slope'].value + p_true['line_off'].value )
if HASPYLAB:
pylab.plot(x, data, 'r+')
p_fit = Parameters()
p_fit.add('amp_g', value=10.0)
p_fit.add('cen_g', value=9)
p_fit.add('wid_g', value=1)
p_fit.add('line_slope', value=0.0)
p_fit.add('line_off', value=0.0)
myfit = Minimizer(residual, p_fit,
fcn_args=(x,),
fcn_kws={'sigma':0.2, 'data':data})
myfit.prepare_fit()
#
for scale_covar in (True, False):
myfit.scale_covar = scale_covar
print ' ==== scale_covar = ', myfit.scale_covar, ' ==='
for sigma in (0.1, 0.2, 0.23, 0.5):
myfit.userkws['sigma'] = sigma
p_fit['amp_g'].value = 10
p_fit['cen_g'].value = 9
p_fit['wid_g'].value = 1
p_fit['line_slope'].value =0.0
p_fit['line_off'].value =0.0
myfit.leastsq()
print ' sigma = ', sigma
print ' chisqr = ', myfit.chisqr
print ' reduced_chisqr = ', myfit.redchi
report_fit(p_fit, modelpars=p_true, show_correl=False)
print ' =============================='
# if HASPYLAB:
# fit = residual(p_fit, x)
# pylab.plot(x, fit, 'k-')
# pylab.show()
#
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