How to correctly guess the initial points in LogLog plot linear regression?

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I have 5 sets of data represented in 5 distinct colored errorbars in the following code (I have not shown caps). errorbar plot is shown in logarithmic scale in both axes. Using curvefit, I am trying to find the best linear regression passing through these errorbars. However, it seems the power-law equation I have defined to fit is not easily able to find the best-fit slope of the 5 lines. My expectation is that all 5 colored lines should be straight with negative slopes. I had hard time figuring out which starting point p0 should I specify in curve fitting process. Even with my initial hard-to-guess values, I still don't get all straight lines and some of them are too off from my points. What is the issue here?

my plot

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

x_mean = [2.81838293e+20, 5.62341325e+20, 1.12201845e+21, 2.23872114e+21, 4.46683592e+21, 8.91250938e+21, 1.77827941e+22]

mean_1 = [52., 21.33333333, 4., 1., 0., 0.,  0.]
mean_2 = [57., 16.66666667, 5.66666667, 2.33333333, 0.66666667, 0., 0.33333333]
mean_3 = [67.33333333, 20., 8.66666667, 3., 0.66666667, 1., 0.33333333]
mean_4 = [79.66666667, 25., 8.33333333, 3., 1., 0., 0.]
mean_5 = [54.66666667, 16.66666667, 8.33333333, 2., 2., 1., 0.]

error_1 = [4.163332, 2.66666667, 1.15470054, 0.57735027, 0., 0., 0.]
error_2 = [4.35889894, 2.3570226, 1.37436854, 0.8819171, 0.47140452, 0., 0.33333333]
error_3 = [4.7375568, 2.5819889, 1.69967317, 1., 0.47140452, 0.57735027, 0.33333333]
error_4 = [5.15320828, 2.88675135, 1.66666667, 1., 0.57735027, 0., 0.]
error_5 = [4.26874949, 2.3570226, 1.66666667, 0.81649658, 0.81649658, 0.57735027, 0.]


newX = np.logspace(20, 22.3)
def myExpFunc(x, a, b):
    return a*np.power(x, b)

popt_1, pcov_1 = curve_fit(myExpFunc, x_mean, mean_1, sigma=error_1, absolute_sigma=True, p0=(4e31,-1.5))
popt_2, pcov_2 = curve_fit(myExpFunc, x_mean, mean_2, sigma=error_2, absolute_sigma=True, p0=(4e31,-1.5))
popt_3, pcov_3 = curve_fit(myExpFunc, x_mean, mean_3, sigma=error_3, absolute_sigma=True, p0=(4e31,-1.5))
popt_4, pcov_4 = curve_fit(myExpFunc, x_mean, mean_4, sigma=error_4, absolute_sigma=True, p0=(4e31,-1.5))
popt_5, pcov_5 = curve_fit(myExpFunc, x_mean, mean_5, sigma=error_5, absolute_sigma=True, p0=(4e31,-1.5))


fig, ax1 = plt.subplots(figsize=(3,5))
ax1.errorbar(x_mean, mean_1, yerr=error_1, ecolor = 'magenta', fmt= 'mo', ms=0, elinewidth = 1, capsize = 0, capthick=0)
ax1.errorbar(x_mean, mean_2, yerr=error_2, ecolor = 'red', fmt= 'ro', ms=0, elinewidth = 1, capsize = 0, capthick=0)
ax1.errorbar(x_mean, mean_3, yerr=error_3, ecolor = 'orange', fmt= 'yo', ms=0, elinewidth = 1, capsize = 0, capthick=0)
ax1.errorbar(x_mean, mean_4, yerr=error_4, ecolor = 'green', fmt= 'go', ms=0, elinewidth = 1, capsize = 0, capthick=0)
ax1.errorbar(x_mean, mean_5, yerr=error_5, ecolor = 'blue', fmt= 'bo', ms=0, elinewidth = 1, capsize = 0, capthick=0)

ax1.plot(newX, myExpFunc(newX, *popt_1), 'm-', label='{:.2f} \u00B1 {:.2f}'.format(popt_1[1], pcov_1[1,1]**0.5))
ax1.plot(newX, myExpFunc(newX, *popt_2), 'r-', label='{:.2f} \u00B1 {:.2f}'.format(popt_2[1], pcov_2[1,1]**0.5))
ax1.plot(newX, myExpFunc(newX, *popt_3), 'y-', label='{:.2f} \u00B1 {:.2f}'.format(popt_3[1], pcov_3[1,1]**0.5))
ax1.plot(newX, myExpFunc(newX, *popt_4), 'g-', label='{:.2f} \u00B1 {:.2f}'.format(popt_4[1], pcov_4[1,1]**0.5))
ax1.plot(newX, myExpFunc(newX, *popt_5), 'b-', label='{:.2f} \u00B1 {:.2f}'.format(popt_5[1], pcov_5[1,1]**0.5))
ax1.legend(handlelength=0, loc='upper right', ncol=1, fontsize=10)

ax1.set_xlim([2e20, 3e22])
ax1.set_ylim([2e-1, 1e2])
ax1.set_xscale("log")
ax1.set_yscale("log")
plt.show()
curve-fitting scipy python-3.x optimization
1 Answers

Your numbers for X are way too enormous. Maybe you can try taking the log of both sides and fit that? Such as:

log Y = log(a) + b*log(X)

You won’t even need curve_fit at that point, it’s a standard linear regression.

EDIT

Please see my rough and not very well checked implementation (NOTE: I only have Python 2, so adjust to fit):

import numpy as np
import matplotlib.pyplot as plt
import scipy.optimize as optimize

x_mean = [2.81838293e+20, 5.62341325e+20, 1.12201845e+21, 2.23872114e+21, 4.46683592e+21, 8.91250938e+21, 1.77827941e+22]

mean_1 = [52., 21.33333333, 4., 1., 0., 0.,  0.]
mean_2 = [57., 16.66666667, 5.66666667, 2.33333333, 0.66666667, 0., 0.33333333]
mean_3 = [67.33333333, 20., 8.66666667, 3., 0.66666667, 1., 0.33333333]
mean_4 = [79.66666667, 25., 8.33333333, 3., 1., 0., 0.]
mean_5 = [54.66666667, 16.66666667, 8.33333333, 2., 2., 1., 0.]

error_1 = [4.163332, 2.66666667, 1.15470054, 0.57735027, 0., 0., 0.]
error_2 = [4.35889894, 2.3570226, 1.37436854, 0.8819171, 0.47140452, 0., 0.33333333]
error_3 = [4.7375568, 2.5819889, 1.69967317, 1., 0.47140452, 0.57735027, 0.33333333]
error_4 = [5.15320828, 2.88675135, 1.66666667, 1., 0.57735027, 0., 0.]
error_5 = [4.26874949, 2.3570226, 1.66666667, 0.81649658, 0.81649658, 0.57735027, 0.]


def powerlaw(x, amp, index):
    return amp * (x**index)
    
# define our (line) fitting function
def fitfunc(p, x):
    return p[0] + p[1] * x   

def errfunc(p, x, y, err):
    out = (y - fitfunc(p, x)) / err
    out[~np.isfinite(out)] = 0.0
    return out

pinit = [1.0, -1.0]
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)

for indx in range(1, 6):

    mean = eval('mean_%d'%indx)
    error = eval('error_%d'%indx)
    logx = np.log10(x_mean)
    logy = np.log10(mean)
    logy[~np.isfinite(logy)] = 0.0
    logyerr = np.array(error) / np.array(mean)
    logyerr[~np.isfinite(logyerr)] = 0.0

    out = optimize.leastsq(errfunc, pinit, args=(logx, logy, logyerr), full_output=1)

    pfinal = out[0]
    covar = out[1]
    
    index = pfinal[1]
    amp = 10.0**pfinal[0]

    indexErr = np.sqrt(covar[0][0] ) 
    ampErr = np.sqrt(covar[1][1] ) * amp

    ##########
    # Plotting data
    ##########

    ax1.plot(x_mean, powerlaw(x_mean, amp, index), label=u'{:.2f} \u00B1 {:.2f}'.format(pfinal[1], covar[1,1]**0.5))     # Fit
    ax1.errorbar(x_mean, mean, yerr=error, fmt='k.', label='__no_legend__')  # Data
    ax1.set_title('Best Fit Power Law', fontsize=18, fontweight='bold')
    ax1.set_xlabel('X', fontsize=14, fontweight='bold')
    ax1.set_ylabel('Y', fontsize=14, fontweight='bold')
    ax1.grid()

    ax2.loglog(x_mean, powerlaw(x_mean, amp, index), label=u'{:.2f} \u00B1 {:.2f}'.format(pfinal[1], covar[1,1]**0.5))
    ax2.errorbar(x_mean, mean, yerr=error, fmt='k.', label='__no_legend__')  # Data
    ax2.set_xlabel('X (log scale)', fontsize=14, fontweight='bold')
    ax2.set_ylabel('Y (log scale)', fontsize=14, fontweight='bold')
    ax2.grid(b=True, which='major', linestyle='--', color='darkgrey')
    ax2.grid(b=True, which='minor', linestyle=':', color='grey')


ax1.legend()
ax2.legend()
plt.show()

Picture:

enter image description here

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