Fit with Algebraic ConstraintΒΆ

example fit with algebraic constraint

Out:

[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 53
    # data points      = 601
    # variables        = 6
    chi-square         = 71878.3055
    reduced chi-square = 120.803875
    Akaike info crit   = 2887.26503
    Bayesian info crit = 2913.65660
[[Variables]]
    amp_g:       21.1877703 +/- 0.32191821 (1.52%) (init = 10)
    cen_g:       8.11125922 +/- 0.01162984 (0.14%) (init = 9)
    wid_g:       1.20925846 +/- 0.01170853 (0.97%) (init = 1)
    amp_tot:     30.6003740 +/- 0.36481391 (1.19%) (init = 20)
    amp_l:       9.41260369 +/- 0.61672675 (6.55%) == 'amp_tot - amp_g'
    cen_l:       9.61125922 +/- 0.01162984 (0.12%) == '1.5+cen_g'
    wid_l:       2.41851692 +/- 0.02341706 (0.97%) == '2*wid_g'
    line_slope:  0.49615727 +/- 0.00170178 (0.34%) (init = 0)
    line_off:    0.04128588 +/- 0.02448064 (59.30%) (init = 0)
[[Correlations]] (unreported correlations are < 0.100)
    C(amp_g, wid_g)         =  0.866
    C(amp_g, cen_g)         =  0.750
    C(line_slope, line_off) = -0.714
    C(cen_g, amp_tot)       = -0.695
    C(cen_g, wid_g)         =  0.623
    C(amp_g, amp_tot)       = -0.612
    C(amp_tot, line_off)    = -0.588
    C(wid_g, amp_tot)       = -0.412
    C(cen_g, line_off)      =  0.387
    C(amp_g, line_off)      =  0.183
    C(amp_g, line_slope)    =  0.183
    C(wid_g, line_slope)    =  0.174

import matplotlib.pyplot as plt
from numpy import linspace, random

from lmfit import Minimizer, Parameters
from lmfit.lineshapes import gaussian, lorentzian
from lmfit.printfuncs import report_fit


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

    slope = pars['line_slope']
    offset = pars['line_off']
    model = yg + yl + offset + x*slope

    if data is None:
        return model
    if sigma is None:
        return model - data
    return (model - data) / sigma


random.seed(0)
x = linspace(0.0, 20.0, 601)

data = (gaussian(x, 21, 8.1, 1.2) +
        lorentzian(x, 10, 9.6, 2.4) +
        random.normal(scale=0.23, size=x.size) +
        x*0.5)


pfit = Parameters()
pfit.add(name='amp_g', value=10)
pfit.add(name='cen_g', value=9)
pfit.add(name='wid_g', value=1)
pfit.add(name='amp_tot', value=20)
pfit.add(name='amp_l', expr='amp_tot - amp_g')
pfit.add(name='cen_l', expr='1.5+cen_g')
pfit.add(name='wid_l', expr='2*wid_g')
pfit.add(name='line_slope', value=0.0)
pfit.add(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)

result = myfit.leastsq()
init = residual(pfit, x)
fit = residual(result.params, x)

report_fit(result)

plt.plot(x, data, 'r+')
plt.plot(x, init, 'b--', label='initial fit')
plt.plot(x, fit, 'k-', label='best fit')
plt.legend(loc='best')
plt.show()

Total running time of the script: ( 0 minutes 0.211 seconds)

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