On 11/25/19 12:31 PM, Machiel Kolstein wrote: > 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() > That actually solves a slightly different problem. norm.fit basically computes the mean and standard deviation of the data (and if you know what the mean should be, you can provide that to get a better estimate of the standard deviation). If you problem really is a weighted data problem, then it isn't hard to compute a weighted average or weighted standard deviation, I don't know scipy to know if it has something like that built in. A quick scan shows that numpy.average allows a weighting array to be provided, so it shouldn't be hard to look at the code for sci[y.norm.fit and convert it to use weighted averages.
-- Richard Damon -- https://mail.python.org/mailman/listinfo/python-list
