Okay, I found some answer myself: use scipy.optimize.curve_fit
However, I still find it strange that I have to define a gauss function myself
instead of it being readily available. I did this:
# Define model function to be used to fit to the data
def gauss(x, *p):
A, mu, sigma = p
return A*np.exp(-(x-mu)**2/(2.*sigma**2))
p0 = [1., 0., 1.]
# Fit the histogram -----------------------------
coeff, var_matrix = curve_fit(gauss, x_array, y_array, p0=p0)
amplitude, mu, sigma = coeff
print "amplitude, mu, sigma = ", amplitude, mu, sigma
# Get the fitted curve
hist_fit = gauss(x_array, *coeff)
plt.plot(x_array, hist_fit, color='red', linewidth=5, label='Fitted data')
plt.show()
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