JJ wrote:
> Hello.
> I haven't been following development too closely
> lately, but I did just download and reinstall the
> current svn version. For what its worth, I would like
> to again suggest two changes:
>
Suggestions are nice. Working code is better. Many ideas are just too
difficult to implement (and still work with the system as it exists) and
so never get done. I'm not saying these ideas fit into this category,
but generally if a suggestion is not taken it's very likely seen in that
light.
> -- If M is a nxm matrix and P and Z are nx1 (or 1xn)
> matrices, then it would be nice if we could write
> M[P==Z,:] to obtain all columns and only those rows
> where P==Z.
This works already if p and z are 1-d arrays. That seems to be the
only issue. you want this to work with p and z being 2-d arrays (i.e.
matrices). The problem is there is already a defined behavior for this
case that would have to be ignored (i.e. special-cased to get what you
want). This could be done within the special matrix sub-class of
course, but I'm not sure it is wise. Too many special cases make life
difficult down the road.
It is better to just un-think the ambiguity between 1-d and 2-d arrays
that was inspired by Matlab and recognize a 1-d situation when you have
it. But, that's just my opinion. I'm not dead-set against
special-casing in the matrix object if enough matrix-oriented people are
in favor of it. But, it would be a feature for a later NumPy (not 1.0).
> Likewise, for 1xm (or mx1) matrices U and
> V, it would be nice to be able to use M[P==Z,U==V].
>
Same issue as before + cross-product versus element-by-element.
> Also, it would be nice to use M[P==Z,U==2], for
> example, to obtain selected rows where matrix U is
> equal to a constant.
>
Again. Form the cross-product using ix_().
> -- It would be nice to slice a matrix by using
> M[[1,2,3],[3,5,7]], for example.
>
You can get the cross-product using M[ix_([1,2,3],[3,5,7])]. This was a
design choice and I think a good one. It's been discussed before.
> I believe this would help make indexing more user
> friendly. In my humble opinion, I think indexing is a
> weak spot in numpy.
I'm sorry you see it that way. I think indexing is a strength of
numpy. It's a little different then what you are used to with Matlab,
perhaps, but it is much more general-purpose and capable (there is one
weak-spot in that a certain boolean indexing operations uses more memory
than it needs to, but that is a separate issue...). The Matlab behavior
can always be created in a sub-class.
Best regards,
-Travis
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