[SciPy-Dev] GSoC 2019 Projects

[Peter Bell]

Hi Greg,

Thanks for your response.

>Ideally there should also be some way to provide additional arguments specific to those implementations

I see that pyFFTW adds additional keyword args to the fftpack functions. In your opinion is it okay to pass backend dependant arguments to SciPy? I think most of the same functionality could be done via global configuration and/or some generally applicable arguments could be incorporated into the fftpack API directly in a backwords compatible manner.
For example, pyFFTW’s ‘planner_effort’ is quite backend specific so would be global. Whereas ‘overwrite_input’ option might make sense for other FFT implementations and not just FFTW specifically; so could be added as an argument with a backwards compatible default value.

Peter

From: SciPy-Dev <scipy-dev-bounces+peterbell10=live.co.uk at python.org> On Behalf Of Gregory Lee
Sent: 04 April 2019 15:42
To: SciPy Developers List <scipy-dev at python.org>
Subject: Re: [SciPy-Dev] GSoC 2019 Projects

Hi Peter,

Yes, the application period is still open for GSOC, although the final deadline is the 9th, so time is becoming fairly short to get feedback on proposals.

A few other students have expressed interest in the FFT-based backends, but any interested candidates are welcome to apply. Please see some prior posts on this lists for some discussions related to that project. The primary intention is to allow 3rd party implementations to override the ones provided by SciPy when requested. Ideally there should also be some way to provide additional arguments specific to those implementations (e.g. specifying number of threads, planner effort, etc in the case of PyFFTW). We do not want any breaking changes to the existing scipy.fftpack interfaces as we need to maintain backwards compatibility with a lot of preexisting code in downstream libraries. Ideally downstream libraries should be able to benefit from the changes made in SciPy without any substantial refactoring on their end.

I am not personally familiar with the specifics of the ODE project and do not know if other candidates are currently pursuing that topic. Hopefully one of the mentors for it can respond here.

- Greg




On Wed, Apr 3, 2019 at 10:05 AM Peter Bell <peterbell10 at live.co.uk<mailto:peterbell10 at live.co.uk>> wrote:
Hello,

I am interested in applying for Google summer of code and was wondering if you
are still looking for students?

A bit about myself
-------------------
My name is Peter and I'm in my 4th (penultimate) year of an integrated masters
in computational physics. Programming and numerical methods forms a key part of
this degree and many of my courses make heavy use of python with cython, numpy,
scipy, matplotlib, etc. While my degree is primarily Physics, I have taken a
number of computer science courses covering software engineering, object
oriented design and high performance programming.

I also have an interest in open source collaboration and, when time has allowed,
have contributed to a few projects via github (@peterbell10). The majority of
my contributions have been to a minecraft server which I've been contributing to
since summer 2017. You can see my pull requests here:
https://github.com/cuberite/cuberite/pulls?q=is%3Apr+is%3Amerged+author%3Apeterbell10

Questions about the projects
-----------------------------
I have some questions regarding both of the proposed projects.

Revamp fftpack:
* Is the primary intention to keep the scipy.fftpack interface the same, but add
  some mechanism to inject custom implementations? Or will this require changes
  to the front-facing interface as well? (e.g. for agreement with numpy.fft)
* What should this look like from the user's point of view? e.g.

       import scipy.fftpack
       import pyFFTW.interfaces.scipy_fftpack as fftw_pack

       scipy.fftpack.set_backend(fftw_pack)

  where we just provide a module implementing a well-documented backend
  interface. Alternatively, fftw_pack could be an object, potentially with
  its own instances of caches and settings which might allow for more
  configuration from the user.

Improve ODE solvers (mainly the first 3 tasks):
* How much mathematical background is required? I have done a course covering
  the mathematics of ODE schemes including RK methods and some variable timestep
  methods, but am not familiar with control theory which is mentioned for the
  3rd task.
* Will developing the test set just involve binding existing implementations to
  python for use in a testing framework, or do you expect these to be rewritten
  entirely in python or cython?


Best regards,
Peter
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