python lmfit.minimizer not working

[Priya Singh]

Dear All,

I am trying to fit a spectrum using a power law model. I am using lmfit.minimizer. But the problem is that the value of parameter I am getting is
same as the initial value. Means minimizer is not working. Here is the part of my code:


p = Parameters()
p.add('b', value=10)
p.add('x_0', value=0.007, vary=False)
p.add('a', value=0.2)
p.add('tv', value=3.0, min=0.0)

def residual(p):
    v = p.valuesdict()
    return  (f_c_unmask - (v['b'] * (w_c_unmask/ v['x_0'] )**(-v['a']))*(np.exp(-v['tv']*kpa_smc)))

mi = minimize(residual, p, method='leastsq')
print(fit_report(mi))

The output I am getting:

[[Fit Statistics]]
    # fitting method   = leastsq
    # function evals   = 48
    # data points      = 1304
    # variables        = 3
    chi-square         = 232448.935
    reduced chi-square = 178.669435
    Akaike info crit   = 6764.93727
    Bayesian info crit = 6780.45685
[[Variables]]
    b:    10.0000000 +/- 0.00000000 (0.00%) (init = 10)
    x_0:  0.007 (fixed)
    a:    0.20000000 +/- 0.00000000 (0.00%) (init = 0.2)
    tv:   4.8215e-12 +/- 0.00000000 (0.00%) (init = 3)