[SciPy-dev] SciPy Foundation

Sebastian Walter sebastian.walter@gmail....
Tue Aug 4 04:25:55 CDT 2009


On Tue, Aug 4, 2009 at 10:35 AM, David
Cournapeau<david@ar.media.kyoto-u.ac.jp> wrote:
> Sebastian Walter wrote:
>> 2 cents from an outsider who thought about contributing to
>> scipy/scikits (but didn't (yet)):
>>
>> I think it is a good idea to make scipy easy to use for beginners.
>> However, after reading this thread, I have the impression that it is
>> not the goal to provide state of the art algorithms but rather making
>> Scipy as popular as possible by putting money and effort into the
>> "marketing" of Scipy.
>> Don't get me wrong, I think there are some good reasons why a project
>> should thrive for a large user base. Some of the best projects are
>> popular.
>> Alas, correlation does not imply causality.
>>
>> Me for instance, would rather like to see more efforts to get state of
>> the art algorithms to be implemented in Scipy because that's something
>> that would make a real difference in my research work. Of course,
>> targeting the "clueless Matlab" users is quite pointless if it is that
>> what you are after.
>>
>
> One point which has not been mentioned concerning matlab-like
> environment - maybe it is obvious and everyone implicitly acknowledges
> it, but Mathworks is a 30 years old company, with > 1000 people today.
>
> Building something like matlab, with a good GUI and top notch
> documentation takes a huge amount of resources, of which the 'useful'
> code is only a fraction. I of course don't know the details of matlab
> implementation, but I know that for music oriented softwares (which need
> good UI to sell well, and have non trivial computational requirements,
> so the comparison is not totally stupid), the graphical code is 80 % of
> the code. This ratio is consistent with the big open source audio
> softwares as well (ardour, rosegarden). Worse, being cross platform
> makes the problem much more difficult. For music softwares market, mac
> os x is rarely ignored (~ 40-50% of the market I believe), so people
> need to support two platforms, and that's really a lot of work. For
> scientific software, I think you can go the non native route for the
> graphical toolkit, though.
>
> Also, very few open source software are successful as far as good GUI
> are concerned (I don't want to enter into a debate here, but there are
> good documents/studies on this topic). You need financial incentive for
> this, so only projects backed up by big companies managed to pull it of.
>
> IOW, I am pretty pessimistic about being a 'matlab' clone. We should
> rather shoot for what makes numpy/scipy better (extensibility, cross
> platform, actual language, etc...), because really, matlab will always
> be a much better matlab than us. Price and licensing are not good enough
> to justify migration - if what you want is a free matlab clone, why not
> using octave or scilab anyway.
>
> That does NOT mean that we should not aim at making the software more
> accessible. I (and I guess other developers) are definitely interested
> in a more product-like, integrated stack, to make the barrier of entry
> lower. I for example am really tired of the installation problems
> consistently reported. I feel like we cover mac os x and windows pretty
> well now, but the linux situation is still dreadful. I have a few ideas
> on how to improve the situation, but they all requires quite a bit of
> work/infrastructure. I hope that soon, the scenario "I see this cool
> python script on the internet, it requires this numpy/scipy thing, can I
> try it in 2 minutes ?" will be a reality.
>
>> Then you really get some "killer applications". I could name a few
>> people who are coding some cool state of the art algorithms but waste
>> so much time because they started coding directly in C++. In the
>> meantime, they could have implemented the algorithms in Python _and_
>> in C++. If scipy had something really good that Matlab etc. do not
>> have: guess what ppl would do....
>>
>
> Yes, there are a lot of people who still don't know that there are
> languages outside Fortran, C and C++. In my field, I still see some
> people who implement parsers in C...
>
>> 1) an easy, modular and flexible build system (fortran, c, c++, D,
>> swig, boost:python, cython,...)
>>
>
> you mean like numscons :) ? Adding D support to numscons should be easy.
> For example, I added initial cython support in a couple of minutes
> during the cython talk at SciPy08, adding new languages is relatively
> easy thanks to scons.
>
>> 2) very low entry barrier for possible contributors:
>>   a simple checkout, then  ./manage.py startapp  mycoolmodule
>>   and everything is ready to go ( "Start coding in 5 minutes!")
>>
>
> there are various pieces to enable this (in place build, develop command
> of setuptools, virtualenv/pip/easy_install), but yes, the situation is
> kind of messy. For scikits, that's not so difficult  - you should be
> able to implement a trivial scikit by copying the scikits.example
> package and starting from there.
>
> One problem is that it is technically impossible to build in place and
> test in one go because of a nose limitation ATM (for some reason, nose
> fails to import a package if it is in the current directory).
>
>> 3) a distributed version control system (e.g. git). SVN really scares me off...
>>
>
> That's a sensitive issue, I think we should avoid starting this one here
> :) Needless to say, you can use git-svn - several core developers use it
> for numpy/scipy dev, and we distribute an official import:
>
> http://projects.scipy.org/numpy/browse_git
>
> At least I have not touched svn for numpy/scipy development for > 6
> months now, except to check releases when I tag them.
>
>> 4) standardized unit tests
>>
>
> What do you mean exactly here ? We use nose for testing, what do you
> consider "non standard".
>
>> 5) automated documentation generation
>>
>
> It is almost automated now - but an example for scikits is missing in
> the example package :)
>

Just enumerating what I think would be useful to attract high quality
contributors.  I'm aware that scipy has already  a lot of the features
(which is nice).
But it would be even nicer to have a really low entry barrier and have
a framework that guides you to write good (and documented) code with
extensive unit tests, just like the big web frameworks (Django, RoR,
...)
It has to be a win-win situation for both the community and the developer.


> cheers,
>
> David
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