[SciPy-Dev] GSOC 2015 projects

[Ian Henriksen]

Hi all,

I'm putting together an application for GSOC 2015, and, although the
deadline is approaching fast, I would still appreciate your feedback on a
few of my ideas.

I am a masters student studying mathematics at Brigham Young University.
Thus far my primary contribution to SciPy has been the Cython API for BLAS
and LAPACK (see https://github.com/scipy/scipy/pull/4021).

My research is in Isogeometric Analysis, i.e. finite element analysis on
spline curves. My open source work and research at BYU have given me a
great deal of experience with Python and Cython, but I am also familiar
with C, C++, and Fortran. As such, I have been reflecting on projects that
would be best suited to my skill set, as well as most beneficial to SciPy.

I'm curious to know which of the following projects would be of greatest
interest to the SciPy community:

1. Wrapping the PROPACK library for sparse linear algebra and using it for
sparse SVD computation in scipy.sparse. There has been some initial work on
f2py wrappers for PROPACK at https://github.com/jakevdp/pypropack, though
it appears the wrappers are still incomplete.
2. Implementing an improved library for spline operations in SciPy. I am
very familiar with the different refinement techniques used in CAD (knot
insertion, degree elevation, etc.) and could implement a library that would
be able to perform them all. My ideal here would be to write a C++ or
Fortran library to do this and then wrap it via Cython. The emphasis would
be primarily on writing code for refinement and evaluation that is both
fast and general. I could include code for spline subdivision methods as
well.
3. Adding support for Cython to both f2py and numpy.distutils. The goal
here would be to allow f2py to generate cython-compatible wrappers from
existing pyf files. I would also modify numpy.distutils so it could compile
Cython files.
4. Wrap ffts (https://github.com/anthonix/ffts) and use it as an
alternative to FITPACK in scipy.fft for use cases where it is faster.

Which of these projects would be most appreciated? I certainly want to be
able to make a valid and, more importantly, useful contribution.

Thanks!

- Ian Henriksen
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