[SciPy-User] Least-squares fittings with bounds: why is scipy not up to the task?
Thu Mar 8 14:29:22 CST 2012
I know the title looks a little provocative, but this was obviously done
on purpose. I am very impressed by the capabilities of scipy (et al.,
numpy etc) and have been a fan since years! But one thing (in my
opinion) seems to be missing (see below). If it exists, then great (and
What I didn't find in Scipy (or numpy or..) is *an efficient
least-squares fitting routine which can include bounded, or fixed
parameters*. This seems like something many people must be needing! I am
right now using mpfit.py (from minpack then Craig B. Markwardt for idl
and Mark Rivers for python), which I did integrate in the package I am
developing. It is much faster than many other routines in scipy although
Adam Ginsburg did mention some test-bench he conducted some time ago,
showing that leastsq was quite efficient. It can include bounds, fixed
parameters etc. And it works great! But this is probably not the best
way to have such a stand-alone routine... and it is far from being
optimised for the modern python.
is there ANY plan for having such a module in Scipy?? I think
(personally) that this is a MUST DO. This is typically the type of
routines that I hear people use in e.g., idl etc. If this could be an
optimised, fast (and easy to use) routine, all the better.
Any input is welcome!
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