scipy
The scipy module contains a few convenience functions mostly designed to make fitting easier.
Fitting and Modeling
GaussResults(x, y[, sigma_y, p0, variance, ...]) |
Fits a gaussian to a curve specified by pairs x and y, with error on y of sigma_y. |
LinLsqFit(y_unweighted, X_unweighted[, y_error]) |
Gets the linear least squares for  of a problem given  . |
Statistics
chisquare(observe, expect, error, ddof[, ...]) |
Finds the reduced chi square difference of observe and expect with a given error and ddof degrees of freedom. |
curve_fit_unscaled(*args, **kwargs) |
Use the reduced chi square to unscale scipy‘s scaled scipy.optimize.curve_fit. |