What does linearize do in this case?

Question

def linearize(p, x):
    return p[0] * x**p[1]
def error(p, x, y):    
    return (np.log10(y) - np.log10(linearize(p, x)))


from scipy import optimize

args = freq_log[1:9063], np.abs(spec_log[1:9063])
qout, success = optimize.leastsq(error,
                             [1e5,-0.8],
                             args=args,
                             maxfev=3000)

fig,ax1=plt.subplots(figsize=(5,5))

spec = ax1.plot(freq_log,np.abs(spec_log), 'o', alpha=0.3)
approx = ax1.plot(freq_log[1:], linearize(qout, freq_log[1:]), linewidth=3)

I am trying to replicate a code, and I am trying to understand what linearize does in this case, what arguments it takes, and what the return does in order to remedy the following errors

Currently it returns runtime errors and a ValueError

RuntimeWarning: divide by zero encountered in power return p[0] * x**p[1]
RuntimeWarning: invalid value encountered in power return p[0] * x**p[1]
ValueError: object too deep for desired array

My primary concern is the first four lines, can anyone help me understand what is causing this error and how to remedy it?

Full code is @ https://github.com/seg/tutorials-2017/blob/master/1710_Colored_inversion/Colored_inversion_notebook.ipynb

Answer 1

A detailed description would be too long, nonetheless, it is worth knowing.

I found this good and short example online.

Leaving a link instead of copying and pasting here.

http://www.scipy-lectures.org/intro/summary-exercises/optimize-fit.html

The linearize here is trying to return the overall noise in passed data which would then be used to evaluate your error, and then the leastsq as it sounds, minimizes the sum of squares of the initial waveform.