[Numpy-discussion] Math Library
Tue Apr 6 09:08:39 CDT 2010
On Tue, Apr 6, 2010 at 7:43 AM, Travis Oliphant <email@example.com> wrote:
> I should have some time over the next couple of weeks, and I am very
> interested in refactoring the NumPy code to separate out the Python
> interface layer from the "library" layer as much as possible. I had
> some discussions with people at PyCon about making it easier for
> Jython, IronPython, and perhaps even other high-level languages to
> utilize NumPy.
> Is there a willingness to consider as part of this reorganization
> creating a clear boundary between the NumPy library code and the
> Python-specific interface to it? What other re-organization thoughts
> are you having David?
This is mainly it, reorganizing the code for clearer boundaries
between boilerplate (python C API) and actual compuational code.
Besides helping other python implementations, I think this would
benefit NumPy itself in the long run (code maintainability), as well
as scipy (and other C extensions). I think the npymath effort is a
good example: albeit simple in nature (the API and boundaries are
obvious), it has already helped a lot to solve numerous platform
specific issues in numpy and scipy, and I think the overall code
quality is better.
My own goals were:
- exposing core computational parts through an exported C API, so
that other C extensions may use it (for example, exposing basic
- dynamic loading of the code (for example depending on the CPU
capabilities - I have a git branch somewhere where I started exposing
a simple C API to query cpu capabilities like cache size or SSE
dynamically to that intent)
- more amenable codebase: I think multiarray in particular is too
big. I don't know the code well enough to know what can be split and
how, but I would have hoped that the scalartypes, the type descriptor
could be put out of multiarray proper. Also, exposing an API for
things like fancy indexing would be very useful, but I don't know if
it even makes sense - I think a pure python implementation of fancy
indexing as a reference would be very useful for array-like classes (I
am thinking about scipy.sparse, for example).
Unfortunately, I won't be able to help much in the near future (except
maybe for the fancy indexing as this could be useful for my job),
More information about the NumPy-Discussion