[SciPy-dev] Status of sandbox.spline and my other code.

[John Travers]

Hi all,
This email is intended to clarify the status of various bits of code
I've committed/not committed so I don't leave too much of a mess after
me. I'm about to start writing my PhD thesis, so I'll be too busy to
work on scipy until September.

1. sandbox.spline stuff
The module here was supposed to be a tidy up of scipy.interpolate. All
of the dierkx functionality has been moved to f2py wrappers, which I
think makes maintenance easier. However, it does not add any new
functionality, and in retrospect appears to have been a waste of my
time. So I'll leave to you to decide if you want to integrate it into
scipy.interpolate or not; though it was originaly planned to be a
seperate module to clear up the ambiguity between interpolation and
smoothing spline functionality.
However I have added 20 unit tests to the code (most of which simply
check the wrapper against the pure fortran output, but still useful I
think) which could easily be moved over to the current
scipy.interpolate module.

2. Radial basis function module in sandbox (rbf)
This code has recently had attention from Robert Hetland and is quite
improved. I'm not sure if I will ever go into scipy though?? If not, I
will put it into a scikit at some point.
Related to this, the wiki page has been updated, however, it is at
http://www.scipy.org/RadialBasisFunctions but should probably be in
the Cookbook. I don't know how to move it over, so some pointers would
be helpful. In addition there are four unused attachments I uploaded
that could be deleted.

3. Boundary value ODE solver (BVP_SOLVER)
I have fairly functional code interfacing to some very good BVP solver
code (http://cs.smu.ca/~muir/BVP_SOLVER_Webpage.shtml). The license is
good for scipy (as confirmed by the authors), but the code if fortran
95 - therefore cannot apparently go into scipy. I'll make a scikit
when I get time (probably September).

4. An interface to the pikaia genetic algorithm routine
I have a python interface to pikaia fortran code for genetic algorithm
optimisation of real valued functions
(http://www.hao.ucar.edu/Public/models/pikaia/pikaia.html). The
fortran code itself is robust and highly optimised for numerical work,
though it is nowhere near as general as the sandbox.ga module. I think
it is useful to go in scipy.optimization alongside the annealing
module. If people disagree I'll make it into a scikit.

I hope  this clears a few things up.

Best regards,
John Travers