[Numpy-discussion] Advanced indexing: "fancy" vs. orthogonal

[Jaime Fernández del Río]

On Wed, Apr 1, 2015 at 2:17 AM, R Hattersley <rhattersley at gmail.com> wrote:

> There are two different interpretations in common use of how to handle
> multi-valued (array/sequence) indexes. The numpy style is to consider all
> multi-valued indices together which allows arbitrary points to be
> extracted. The orthogonal style (e.g. as provided by netcdf4-python) is to
> consider each multi-valued index independently.
>
> For example:
>
> >>> type(v)<type 'netCDF4.Variable'>>>> v.shape
> (240, 37, 49)>>> v[(0, 1), (0, 2, 3)].shape
> (2, 3, 49)>>> np.array(v)[(0, 1), (0, 2, 3)].shape
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>IndexError: shape mismatch: indexing arrays could not be broadcast together with shapes (2,) (3,)
>
>
> In a netcdf4-python GitHub issue
> <https://github.com/Unidata/netcdf4-python/issues/385> the authors of
> various orthogonal indexing packages have been discussing how to
> distinguish the two behaviours and have currently settled on a boolean
> __orthogonal_indexing__ attribute.
>
> 1. Is there any appetite for adding that attribute (with the value
> `False`) to ndarray?
>
> 2. As suggested by shoyer
> <https://github.com/Unidata/netcdf4-python/issues/385#issuecomment-87775034>,
> is there any appetite for adding an alternative indexer to ndarray where
> __orthogonal_indexing__ = True? For example: myarray.ix_[(0,1), (0, 2, 3)]
>

Is there any other package implementing non-orthogonal indexing aside from
numpy? I understand that it would be nice to do:

if x.__orthogonal_indexing__:
    return x[idx]
else:
    return x.ix_[idx]

But I think you would get the exact same result doing:

if isinstance(x, np.ndarray):
    return x[np.ix_(*idx)]
else:
    return x[idx]

If `not x.__orthogonal_indexing__` is going to be a proxy for
`isinstance(x, ndarray)` I don't really see the point of disguising it,
explicit is better than implicit and all that.

If the functionality is lacking, e,g, use of slices in `np.ix_`, I'm all
for improving that to provide the full functionality of "orthogonal
indexing". I just need a little more convincing that those new
attributes/indexers are going to ever see any real use.

Jaime

>
> Richard
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion at scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>


-- 
(\__/)
( O.o)
( > <) Este es Conejo. Copia a Conejo en tu firma y ayúdale en sus planes
de dominación mundial.
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20150401/678715e0/attachment.html>