Hi Travis, On Tue, Apr 6, 2010 at 7:43 AM, Travis Oliphant <oliph...@enthought.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 blas/lapack operations) - 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), David _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
