[SciPy-dev] Ideas for scipy.sparse?
Mon Apr 14 03:18:44 CDT 2008
On Mon, Apr 14, 2008 at 10:11 AM, Robert Cimrman <email@example.com> wrote:
> Hi Brian,
> Brian Granger wrote:
> > So, I am currently implementing a distributed memory array package for python:
> > http://projects.scipy.org/ipython/ipython/browser/ipythondistarray
> > The goal is to have distributed/parallel arrays that look and feel
> > just like numpy arrays. Here is an example:
> Very nice!
> > Obviously, my first though was scipy.sparse. I am _really_ excited
> > about the massive improvements that have been happening in this area
> > recently. Here are the problems I am running into:
> > 1) I need N-dimensional sparse arrays. Some of the storage formats in
> > scipy.sparse (dok, coo, maybe lil) could be generalized to
> > N-dimensions, but some work would have to be done.
> > 2) I need these things to be in numpy. I hate to start another
> > "should this go into numpy or scipy" thread, but I actually do think
> > there is a decent case for moving the core sparse arrays into numpy
> > (not the solvers though). Please hear me out:
> > a) Numpy at its core is about arrays. Conceptually, sparse arrays fit
> > into this narrow vision of Numpy.
> > b) Sparse arrays are just as foundational as dense arrays in many
> > areas of computing/science (I would argue, that they are more
> > foundational than ffts and random numbers).
> > c) Moving the core sparse arrays into numpy would increase their
> > visibility and encourage other projects to rely on them.
> > d) It would not make numpy more difficult to build.
> > e) It is currently somewhat confusing that they are not in numpy
> > (remember Numpy = arrays).
> You can add f) Having sparse arrays in numpy would greatly improve
> unifying the two worlds. There have been tons of questions why this or
> that numpy function do not work for a sparse matrix, when in <plug in a
> popular commercial system> it works seamlessly.
I think we should first get them working at least in scipy + scikits first.
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