---------- Forwarded message ---------- From: Ralf Gommers <ralf.gommers@gmail.com> Date: Wed, Feb 10, 2016 at 11:02 PM Subject: Re: [Numpy-discussion] GSoC? To: Discussion of Numerical Python <numpy-discussion@scipy.org> On Wed, Feb 10, 2016 at 11:55 PM, Ralf Gommers <ralf.gommers@gmail.com> wrote:

On Wed, Feb 10, 2016 at 11:48 PM, Chris Barker <chris.barker@noaa.gov> wrote:

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Thanks Ralf,

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Note that we have always done a combined numpy/scipy ideas page and submission. For really good students numpy may be the right challenge, but in general scipy is easier to get started on.

yup -- good idea. Is there a page ready to go, or do we need to get one up? (I don't even know where to put it...)

This is last year's page: https://github.com/scipy/scipy/wiki/GSoC-2015-project-ideas

Some ideas have been worked on, others are still relevant. Let's copy this page to -2016- and start editing it and adding new ideas. I'll start right now actually.

OK first version: https://github.com/scipy/scipy/wiki/GSoC-2016-project-ideas I kept some of the ideas from last year, but removed all potential mentors as the same people may not be available this year - please re-add yourselves where needed. And to everyone who has a good idea, and preferably is willing to mentor for that idea: please add it to that page. Here are a couple of vague ideas which I'd like to hash out on the list first. Both are mostly in the request-for-comment, feedback-welcome stage. (I am going to write them out even though I cannot guarantee that I'll be available to mentor either of them.) 1. Numeric differentiation. The current proposal focuses on bundling numdifftools. I think there's room for a complementary set of routines for numeric differentiation with fixed step, or limited intelligence w.r.t. the step selection. In fact, there is a fairly comprehensive implementation in scipy already, a by-product of last year's GSoC. It's not exposed publicly, and is hidden as scipy.optimize._numdiff.approx_derivative. I suspect that what's already there covers the bulk of the implementation, and what's left is a only a bit of work to spec out the user-facing API, especially for the broadcasting behavior, and then replacing semi-broken bits of scipy.misc.derivative, scipy.optimize.approx_fprime etc. The original PR has quite extensive discussion, https://github.com/scipy/scipy/pull/4884 And here's an unfinished attempt at following up on that discussion: https://gist.github.com/ev-br/7a1edb3f250bd375c46a 2. Optimization benchmarks. There are two quite large bodies of work on benchmarking various optimization algorithms: - Andrew Nelson's global optimizers benchmarks, https://github.com/scipy/scipy/pull/4191. - Nikolay Mayorov's nonliear LSQ benchmarks, some of which are available in scipy/benchmarks, and the most recent version is at https://github.com/nmayorov/lsq-benchmarks I think both can be very valuable for a broader scipy ecosystem, and both of these herculean efforts are currently blocked on the question of how to actually run them. It seems to me that it would be a useful project to come up with a standardized way of running these sorts of benchmarks and publishing the results. Ideally, it would include, * a runner. Maybe an ASV-based one, but then the project might include some enhancements to asv itself. * a way of pushlishing the results (again, asv has one, is it sufficient?) * a self-contained environment for running and publishing (a VM image? a docker container?) So that by the end of the project we would have a web page with the reference results, some automation for refreshing when needed, and a documented way of reproducing them. Thoughts? Evgeni