[Numpy-discussion] numarray rank-0 decisions, rationale, and summary
perry at stsci.edu
Tue Sep 24 14:33:03 CDT 2002
Konrad Hinsen writes:
> "Perry Greenfield" <perry at stsci.edu> writes:
> > Questions:
> > 1) given 2, is there still a desire for .reduce() to return
> > rank-0 arrays (if not, we have .areduce() which is intented to return
> > arrays always).
> > 2) whichever is the "returns arrays always" reduce method, should the
> > endpoint be rank-0 arrays or rank-1 len-1 arrays?
> I don't really see an application where a reduction operation yielding
> rank-1 or higher arrays would be useful. It would be a special case,
> not useful for generic programming. So my answer to 2) is rank-0.
What I am wondering is what behavior suits "generic" programming more.
Eric Jones and Paul Dubois have given examples where having to
deal with entities that may be scalars or arrays is a pain. But,
having said that, the behavior that Eric wanted was not consistent
with what many thought rank-0 arrays should have (i.e., len(rank-0)=1,
rank-0 = value). The proposal to generate rank-1 len-1 arrays was
made since len() of these arrays is = 1, and indexing with 
does work. So for the kinds of examples he gave, rank-1 len-1 arrays
appear to allow for more generic code.
But I'm not trying to speak for everyone; that's why I'm asking
for opinions. Do you have examples where you find rank-0 arrays
make for more generic code in your cases when len() and indexing
on these does not have the behavior Eric wanted? May I see a couple?
> As for 1), if indexing doesn't return rank-0 arrays, then standard
> reduction shouldn't either. We would then have a system in which
> rank-0 arrays are "expert only" stuff, most users would never see
> them, and they could safely be ignored in tutorials.
That's my inclination, but I think that the question of whether there
should be some reduce mechanism that returns arrays always is still
a valid one. I can see that there are good uses for that (or at least
a function to cast a scalar to a rank-1 len-1 array if it isn't already
an array, like what array() does except that it now generates rank-0
More information about the Numpy-discussion