[SciPy-dev] Porting SciPy to Py3k GSOC project

Charles R Harris charlesr.harris@gmail....
Tue Mar 24 09:17:14 CDT 2009

On Tue, Mar 24, 2009 at 2:21 AM, David Cournapeau <
david@ar.media.kyoto-u.ac.jp> wrote:

> Stéfan van der Walt wrote:
> > 2009/3/24 David Cournapeau <david@ar.media.kyoto-u.ac.jp>:
> >
> >> You would need to port numpy itself before starting scipy as well. I
> >> think it is fair to say that most work will be inside numpy/core - which
> >> is ~ 30 000 LOC according to sloccount (wo counting comments, empty
> >> lines and the like); IOW, it is massive, and there is no chance to do
> >> the conversion in a couple of months unless you are very familiar with
> >> numpy. I am not sure I can see a meaningful subpart of numpy/core which
> >> could be ported for python 3 for a SoC.
> >>
> >
> > Is the situation really that dire?  How much has the C API changed
> > between 2 and 3, and are these changes difficult to propagate?
> >
> Talking only about the C code, here are some things which changed:
>    - the buffer API is changed -> I don't know how significant this is
>    - the basic C types/objects structures have changed a bit -> again,
> no idea how significant this is
>    - the Unicode/String unification
>    - long/int unification
> But I would think the main problem is that numpy simply is a big,
> complicated set of C code, whose parts can't simply be done separately.
> You would need to do quite a bit of changes for the code to only
> compile, making bugs hard to track - and that's assuming numpy.distutils
> itself won't get in the way. Then, there is the problem on how to deal
> with two codebases - if we can't handle things with a few #ifdef, the
> situation will be really bad. That's why I am not convinced that it
> would be a good project for a GSoC. You can't try things easily.
> Using cython for the C code is the advice given by the python doc itself
> - although again, numpy is not the usual extension. I think almost
> everything outside numpy/core should be relatively easy to convert to
> cython (where easy means would take time, but could be done gradually
> without impact everywhere). I don't know how usable cython would be to
> define C-accessible, public extension types. But if it is, then things
> can be done gradually - this can be tested by many people, etc...

A while back I took a shot at interfacing lapack_lite using cython just to
see what it looked like, but decided that the current interface was actually
pretty clean. There is also fftpack. Random is already done. Are there any
separate bits folks can think of?

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