On Sun, Oct 19, 2008 at 4:28 PM, Maximilian Fabricius <mxhf@gmx.net> wrote:
Robin,
great, nice idea! As this may be of use to others, my solution now is:
def fit_function(p_fit, fit, p_fixed, ... ): # assemble p_fit and p_fixed into a single array # while restoring the correct order of the original p0 array. p = zeros(fit.size) p[ where(fit == True) ] = p_fit p[ where(fit == False) ] = p_fixed # calls the original residuals routine return residuals(p, ... )
main(): # best guesses p0 = zeros(10) p0[0] = vrot_d p0[1] = vsig_d ....
# choose which parameters to fit fit = zeros(len(p0)) fit[0:7] = True fit[8] = False fit[9] = False
# store parameters which should be fit in p_fit # all the others in p_fixed p_fit = p0[ where(fit == True) ] p_fixed = p0[ where(fit == False) ] plsq = leastsq(fit_function, p_fit, args=(fit, p_fixed, ...) )
Thanks!
Just want to point out that if fit is a boolean array (ie fit = array([True,False,False,True]) etc. then you can index with it directly p0[fit] instead of p0[where(fit==True)] and p0[~fit] instead of p0[where(fit==False)] which is likely to be a bit quicker. Doesn't matter outside, but in the fit_function you probably want to save any time you can since it will be run many times. Robin