Python / Scipy - implementing optimize.curve_fit 's sigma into optimize.leastsq

I just found that it is possible to combine the best of both worlds, and to have the full leastsq() output also from curve_fit(), using the option full_output:

popt, pcov, infodict, errmsg, ier = curve_fit(func, xdata, ydata, sigma = SD, full_output = True)

This gives me infodict that I can use to calculate all my Goodness of Fit stuff, and lets me use curve_fit's sigma option at the same time...

Assuming your data are in arrays x, y with yerr, and the model is f(p, x), just define the error function to be minimized as (y-f(p,x))/yerr.