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


fwhm_in = 3                               # set FWHM for the artificial data - What does that it mean actually?
sigma = fwhm_in/2/np.sqrt(2*np.log(2))    # calculate sigma - is my function normally distributed?

xval, yval = np.genfromtxt("G3.txt", unpack=True) # I'll read datas from file so I made it by taking values as a np.array

def fitFunc(x, x0, sigm):                 # this defines the fit-function
    return (sigm*np.sqrt(2*np.pi))**(-1)*np.exp(-(x-x0)**2/(2*sigm**2)) # Will I also find a fit-function for my own distribution?

guess = (35, 60)           # Corresponding degree value for high intensity is around 40 degree, so it might be between these values

fitParams, fitCovariance = curve_fit(fitFunc, xval, yval, guess) # do the actual fit
print(fitParams)

print('FWHM_calc = {:.3f}'.format(fwhm_in))
fwhm_fit = 2*fitParams[1]*np.sqrt(2*np.log(2))  # calculate the FWHM from the fitted sigma ( = fitParams[1], since fitParams[0] is the offset x0)
print('FWHM_fit = {:.3f}'.format(fwhm_fit))

plt.plot(xval,yval, 'r.', label='data')
plt.plot(xval, fitFunc(xval, fitParams[0], fitParams[1]), 'k-', label='fit', linewidth = 3)

plt.grid(True)
plt.legend()
ax = plt.gca()
ax.axvline(fwhm_fit/2, color='b')
ax.axvline(-fwhm_fit/2, color='b')