[Numpy-discussion] Matching 0-d arrays and NumPy scalars

Lisandro Dalcin dalcinl@gmail....
Fri Feb 22 16:05:42 CST 2008


Travis, after reading all the post on this thread, my comments

Fist of all, I'm definitelly +1 on your suggestion. Below my rationale.

* I believe numpy scalars should provide all possible features needed
to smooth the difference between mutable, indexable 0-d arrays and
inmutable, non-indexable builtin Python numeric types.

* Given that in the context of generic multi-dimensional array
processing a 0-d array are more natural and useful concept that a
Python 'int' and 'float', I really think that numpy scalars shoud
follow as much as possible the behavior of 0-d arrays (of course,
retaining inmutability).

* Numpy scalars already have (thanks for that!) a very, very similar
API to ndarrays. You can as for 'size', 'shape', etc ( BTW, why
scalar.fill(x) does not generate any error????). Why do not add
indexing as well?

* However, I'm not sure about the proposal of supporting len(), I'm -0
on this point. Anyway, if this is added, then 0-d arrays should also
have to support it. And then... len(scalar) or len(0-d-array) is going
to return 0 (zero)?


Regards.


On 2/21/08, Travis E. Oliphant <oliphant@enthought.com> wrote:
>  In writing some generic code, I've encountered situations where it would
>  reduce code complexity to allow NumPy scalars to be "indexed" in the
>  same number of limited ways, that 0-d arrays support.
>
>  For example, 0-d arrays can be indexed with
>
>     * Boolean masks
>     * Ellipses x[...]  and x[..., newaxis]
>     * Empty tuple x[()]
>
>  I think that numpy scalars should also be indexable in these particular
>  cases as well (read-only of course,  i.e. no setting of the value would
>  be possible).
>
>  This is an easy change to implement, and I don't think it would cause
>  any backward compatibility issues.
>
>  Any opinions from the list?
>
>
>  Best regards,
>
>  -Travis O.
>
>
>
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-- 
Lisandro Dalcín
---------------
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