[SciPy-User] Google Summer of Code...
Mon Mar 8 03:09:10 CST 2010
On Mon, Mar 8, 2010 at 9:52 AM, Jarrod Millman <firstname.lastname@example.org> wrote:
> Hello Sebastian,
> Thanks for taking the time to outline a potential GSoC project. It is
> definitely worth taking the time to add it to the wiki:
> Unfortunately, though, it isn't likely that a project like this will
> be accepted this year. But it will be nice to have the proposal
> written down because someone might decide to work on it without a GSoC
> or someone might do the project next year.
will do that
> I forwarded an email from Titus Brown, which explains that the PSF
> will be looking for Py3K-related projects this year. So if we get any
> GSoC projects funded this year, they will probably be focused on
> porting numpy, scipy, matplotlib, ipython, etc. to Py3K.
I've just read that email about the focus on Py3K.
It's a pity that the focus is solely on porting code and not on adding
new features at all.
But of course I perfectly understand the priorities.
> On Wed, Mar 3, 2010 at 1:14 AM, Sebastian Walter
> <email@example.com> wrote:
>> I could mentor an algorithmic differentiation (AD) project, but I'm
>> still not quite sure how to proceed.
>> Should I write up a proposal on the wiki and after that propose the
>> project on the
>> http://mail.python.org/mailman/listinfo/soc2010-mentors mailing list?
>> I have already created a package that I believe would be a very good
>> addition to scipy/numpy (preferably numpy) when it is mature enough.
>> It is hosted on http://github.com/b45ch1/taylorpoly
>> The package is not an AD tool but makes it very easy to create AD
>> tools using those algorithms.
>> An illustrative example how to do (forward mode) AD is given in:
>> What is missing up to now are differentiated algorithms for tan, atan,
>> asin, sinh, etc. (in Taylor arithmetic)
>> as well as the core algorithms for reverse mode AD.
>> Implementing those algorithms should be doable in 4 months. Also,
>> since the task is to implement many small algorithms,
>> this project is unlikely to fail completely. The worst that can happen
>> is that not as many algorithms get implemented as one could wish for.
>> Could anyone give feedback if that project is ok?
>> best regards,
> SciPy-User mailing list
More information about the SciPy-User