Enlace al notebook:  https://yadi.sk/d/6rxNY7PfQTxp-g

En pdf:

Ajuste a un histograma

from scipy.optimize import curve_fit

hist, bin_edges = np.histogram(datos, density=False)

bin_centres = (bin_edges[:-1] + bin_edges[1:])/2

# Define model function to be used to fit to the data above:

def gauss(x, *p):

    A, mu, sigma = p

    return A*np.exp(-(x-mu)**2/(2.*sigma**2))

# p0 is the initial guess for the fitting coefficients (A, mu and sigma above)

p0 = [1., 0., 1.]

coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)

# Get the fitted curve

hist_fit = gauss(bin_centres, *coeff)

plt.bar(bin_centres, hist, label='Test data')

plt.plot(bin_centres, hist_fit, label='Fitted data', color='r')

# Finally, lets get the fitting parameters, i.e. the mean and standard deviation:

print('media = ', coeff[1])

print('desviación estandar = ', np.abs(coeff[2]))

plt.savefig('./test.png')

plt.show()