[Numpy-discussion] Reminder: weekly status meeting
ralf.gommers at gmail.com
Sat Oct 27 00:08:19 EDT 2018
On Sat, Oct 27, 2018 at 11:10 AM Stefan van der Walt <stefanv at berkeley.edu>
> On Sat, 27 Oct 2018 10:27:49 +1300, Ralf Gommers wrote:
> > Just to make sure we're talking about the same things here: Stefan, I
> > with "sparray" you mean "an n-D sparse array implementation that lives in
> > SciPy", nothing more specific? In that case pydata/sparse is the one
> > implementation, and including it in scipy.sparse would make it "sparray".
> > I'm currently indeed leaning towards depending on pydata/sparse rather
> > including it in scipy.
> I want to double check: when we last spoke, it seemed as though certain
> refactorings inside of SciPy (specifically, sparray was mentioned) would
> simplify the life of pydata/sparse devs. That no longer seems to be the
There's no such thing as `sparray` anywhere in SciPy. There's two inactive
projects to create an n-D sparse array implementation, one of which is
called sparray (https://github.com/perimosocordiae/sparray). And there's
one very active project to do that same thing which is
> If our recommended route is to tell users to use pydata/sparse instead
> of SciPy (for the sparse array object), we probably want to get rid of
> our own internal implementation, and deprecate spmatrix
Doc-deprecate I think; the sparse matrix classes in SciPy are very heavily
used, so it doesn't make sense to start emitting deprecation warnings for
them. But at some point we'll want to point users to pydata/sparse for new
> (or, build
> spmatrix on top of pydata/sparse)?
It's the matrix vs. array semantics that are the issue, so not sure that
building one on top of the other would be useful.
> Once we can define a clear API for sparse arrays, we can include some
> algorithms that ingest those objects in SciPy. But, I'm not sure we
> have an API in place that will allow handover of such objects to the
> existing C/FORTRAN-level code.
I don't think the constructors for sparse matrix/array care about C/F
order. pydata/sparse is pure Python (and uses Numba). For reusing
scipy.sparse.linalg and scipy.sparse.csgraph you're right I think that that
will need some careful design work. Not sure anyone has thought about that
in a lot of detail yet.
There are interesting API questions probably, such as how to treat explicit
zeros (that debate still isn't settled for the matrix classes IIRC). And
there's an interesting transition puzzle to figure out (which also includes
np.matrix). At the moment the discussion on that is spread out over many
mailing list threads and Github issues, at some point we'll need to
summarize that. Probably around the time that the CSR/CSC replacement that
Hameer mentioned is finished.
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion