[SciPy-dev] parameter control in scipy.optimize.leastsq

[Maximilian Fabricius]

On Sun, Oct 19, 2008 at 5:40 PM, Robin <robince@gmail.com> wrote:
> 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

Robin,

thank you. Indeed, I should know that. Python sometime is
overwhelmingly convenient.

Maximilian