Wsrtjtyk

Making my question a bit clearer:

I have determined a minimised chi-squared value, let's call it def chisq(), for some data given a fit involving 5 parameters: a,b,c,d and e.

I would like to know of a function, or a way, where I can minimise my chi-squared function keeping 1 parameter constant whilst varying the others so as to tend to a minimum value... AND

A way in which I can keep 4 of the 5 parameters constant whilst minimising to a specified value (the value will be chisq() + 1, so as to tend to the extreme of a chi squared contour plot, but that's just some added context).

So I'm stuck... Anyway, I hope that's more coherent than my last attempt

Update: This is where I'm at now. I borrowed the main code from another page and I'm trying to adapt it to my needs. And I never intended of doing confidence area plots but they look very nice.

However, I'm getting the error message " 'numpy.ndarray' object is not callable ". I've looked into this and I think it's something to do with me referncing an array as though it were a function. This leads me to the minimiser / mini section, which I think is where the problem lies.

So any help on how I can correct and adapt this code would be much appreciated.
Thank you so far @mikuszefski

import numpy as np

import matplotlib.pyplot as plt

import lmfit





# Data



xval1 = np.array([0.11914471, 0.230193321, 0.346926427, 0.460501481, 0.575512013, 0.689201904, 0.804958885, 0.918017166, 1.034635434, 1.149990481, 1.266264234]) 

xval2 = np.array([0.116503429, 0.230078483, 0.348247067, 0.461420187, 0.577751359, 0.690120611, 0.80398276, 0.918878453, 1.033716728, 1.150794349, 1.266149395]) 

xval3 = np.array([0.115240208, 0.230710093, 0.346007721, 0.458032458, 0.576028785, 0.692359957, 0.80725565, 0.920888123, 1.035037368, 1.147521458, 1.267871969])

xval_av = (xval1 + xval2 + xval3 )/3



yval1 = np.array([2173.446136, 2400.969972, 2547.130603, 2658.022565, 2723.098769, 2760.481961, 2761.881166, 2734.638209, 2671.839502, 2559.251885, 2313.774753])

yval2 = np.array([2185.207051, 2441.547714, 2587.120886, 2701.697704, 2765.897692, 2792.190609, 2793.030024, 2761.191402, 2697.078931, 2583.572776, 2329.860358])

yval3 = np.array([2178.269249, 2438.81575, 2601.305985, 2704.228832, 2765.493933, 2802.801105, 2796.873214, 2760.426641, 2690.92674, 2575.961498, 2330.099396])

yval_av = (yval1 + yval2 + yval3 )/3



yerr1 = np.array([28.0041333, 17.22884875, 11.77504404, 12.0784658, 11.43116392, 10.17734322, 9.839420192, 7.879393332, 8.586145049, 8.129879332, 6.543378761])

yerr2 = np.array([28.84371257, 22.30391049, 16.3503791, 12.32503283, 10.6931908, 8.909895306, 9.310031644, 9.619524491, 7.725528299, 6.64584248, 5.483917187])

yerr3 = np.array([25.22103486, 17.58553765, 16.56186149, 14.93545788, 9.884470694, 11.15591573, 8.696907113, 9.602409632, 8.156617355, 6.966615987, 5.706426531])

yerr_av = (yerr1 + yerr2 + yerr3 )/3



# Parameters



a_original = 1772.73301389 

b_original = 4137.16888269 

c_original = -6903.58620042 

d_original = 5845.15206084 



#-----------------------------------------------------------------------------------------



x = xval_av

y = yval_av



p = lmfit.Parameters()

p.add_many(('a', a_original), ('b', b_original), ('c', c_original), ('d', d_original))



def residual(x,y,p):

    return p['a'] + p['b']*x + p['c']*x**2 + p['d']*x**3 - y 



# Minimiser

mini = lmfit.Minimizer(residual(x,y,p), p, nan_policy='omit')



# Nelder-Mead

out1 = mini.minimize(method='Nelder')



# Levenberg-Marquardt

# Nelder-Mead as initial guess

out2 = mini.minimize(method='leastsq', params=out1.params)



lmfit.report_fit(out2.params, min_correl=0.5)



ci, trace = lmfit.conf_interval(mini, out2, sigmas=[1, 2],

                                trace=True, verbose=False)

lmfit.printfuncs.report_ci(ci)



plot_type = 2



if plot_type == 0:

    plt.plot(x, y)

    plt.plot(x, residual(out2.params) + y)



elif plot_type == 1:

    cx, cy, grid = lmfit.conf_interval2d(mini, out2, 'b', 'd' )

    plt.contourf(cx, cy, grid, np.linspace(0, 1, 11))

    plt.xlabel('b')

    plt.colorbar()

    plt.ylabel('d')



elif plot_type == 2:

    cx, cy, grid = lmfit.conf_interval2d(mini, out2, 'a', 'd', 30, 30)

    plt.contourf(cx, cy, grid, np.linspace(0, 1, 11))

    plt.xlabel('a')

    plt.colorbar()

    plt.ylabel('d')



elif plot_type == 3:

    cx1, cy1, prob = trace['a']['a'], trace['a']['d'], trace['a']['prob']

    cx2, cy2, prob2 = trace['d']['d'], trace['d']['a'], trace['d']['prob']

    plt.scatter(cx1, cy1, c=prob, s=30)

    plt.scatter(cx2, cy2, c=prob2, s=30)

    plt.gca().set_xlim((2.5, 3.5))

    plt.gca().set_ylim((11, 13))

    plt.xlabel('a')

    plt.ylabel('d')



if plot_type > 0:

    plt.show()