[SciPy-dev] adding mpfit to scipy optimize (Please respond)

[Charles R Harris]

On Sat, May 9, 2009 at 9:55 AM, Pauli Virtanen <pav@iki.fi> wrote:

> Fri, 08 May 2009 16:09:35 -0400, william ratcliff wrote:
>
> > Hi!  For a long time, there has been a standard package used by IDL
> > users for fitting functions to data called MPFIT:
> > http://cow.physics.wisc.edu/~craigm/idl/fitting.html<http://cow.physics.wisc.edu/%7Ecraigm/idl/fitting.html>
> > <http://cow.physics.wisc.edu/%7Ecraigm/idl/fitting.html>
> [clip]
> > http://drop.io/mpfitdrop
>
> Nice!
>
> However, I see some points where the code could be improved for better
> reusability and maintainability.
>
> If we lived in an ideal world with infinite time to polish everything,
> I'd like to see all of the points below addressed before landing this to
> Scipy. But since this would be lots of error-prone work, it's probably
> best to try to reach some compromise.
>
> Given these constraints, I'd still like to see at least the coding style
> and error handling fixed (which probably are not too difficult to
> change), in addition to having better test coverage. The rest could come
> later, even if we accrue yet more technical debt with this...
>
>
> First, broad maintenance concerns:
>
> - We already have `leastsq` from MINPACK. Having two MINPACK-derived
>  least squares fitting routines is not good.
>
>  So, I'd perhaps like to see the `leastsq` core part extracted out of
>  MPFIT, and the MPFIT interface implemented on top of it as a thin
>  wrapper, or the other way around.
>
>  Maybe, if the modifications made on MINPACK are small, they can be
>  backported to the Fortran code and MPFIT can be reimplemented on top
>  of `leastsq`.
>
>  Any thoughts on this?
>
> - What is the performance of the Python implementation as compared to the
>  Fortran code? For small data sets, the Python code is probably much
>  slower, but for large datasets is the performance is comparable?
>
> - Much of the code is Fortran written in Python: long routines,
>  goto-equivalents, 6-letter variable names.
>
>  Good commenting offset this, though.
>
>
> Then more specific points of refactoring:
>
> - The code implements QR factorization with column pivoting from scratch.
>
>  Perhaps some of this could be replaced with suitable LAPACK routines,
>  or with stuff from scipy.linalg. (Cf. DGEQPF, DGEQP3)
>
>  I'm not sure whether there's something suitable for qrsolve in LAPACK;
>  the triangular system solver could be replaced with DTRTRS.
>
>  Anyway, it might be useful to refactor qrfac and qrsolve out of MPFIT;
>  there may be other applications when it's useful to be able to solve
>  ||(A + I lambda) x - b||_2 = min! efficiently for multiple different
>  `lambda` in sequence.
>

This looks like Levenberg-Marquardt. There is a version already in MINPACK.

<snip>

Chuck
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