[SciPy-user] Constrained least-squares fitting routine?
Fri May 1 23:43:16 CDT 2009
Some time back, there was a python port of mpfit, using Numeric. I talked
to that author and he is happy with a BSD license. Since, several people
have patched it to use numpy (see http://code.google.com/p/astrolibpy/) and
the author there is happy with a BSD license--though there is room for
cosmetic improvement. Does anyone know the original license for minpack?
I plan to talk to the author of mpfit next week and see if he is amenable to
a BSD license. If so, would this fit into numpy? It takes care of the
annoyance of making a wrapper to leastsq for the simple case of fixed
parameters and has an ansatz for deaing with limits. It relies on a qr
factorization, but we could either switch out the python chunks of code
which do that for their minpack equivalents, or use the Numerical Recipes
suggestion (not the code!!!!) to use SVD instead of QR--but as a stop-gap,
could one of the developers tell me if we do manage to get BSD licensing
agreements, can this go into scipy, or do we have to implement from scratch?
Also, for the BSD agreements, are emails sufficient, or do I need to try to
For openopt, there seems to be a way to fix variables only for the ralg
algorithm, and I don't see where you get a covariance matrix out at the end
so that you have a fighting chance of getting errorbars....
On Fri, May 1, 2009 at 11:05 PM, Alan G Isaac <email@example.com> wrote:
> On 5/1/2009 8:19 PM Adam Ginsburg apparently wrote:
> > Is there a constrained least squares fitting routine available, or
> > can anyone offer me tips on implementing such a beast?
> Are any of these helpful to you?
> (e.g., http://openopt.org/LLSP)
> Alan Isaac
> SciPy-user mailing list
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