[Numpy-discussion] matrices in 1.1

[Alan G Isaac]

> On Sat, Mar 22, 2008 at 5:49 PM, Alan G Isaac 
> <aisaac at american.edu> wrote:
>>  Are you trying to suggest that in most matrix programming
>>  languages if you extract a row you will then need to use two
>>  indices to extract an element of that row?  This does not
>>  match my experience.  I would ask you to justify that by
>>  listing the languages you have in mind.

On Sat, 22 Mar 2008, Stéfan van der Walt apparently wrote:
> No, I agree with you that that is unintuitive -- but it can be solved 
> by introducing Row and ColumnVectors, which are still 2-dimensional. 

To me, this seems to be adding a needless level of 
complexity.  I am not necessarily opposing it;
I just do not see a commensurate payoff.
In contrast, I see great payoff to keeping as much
ndarray behavior as possible.


> One important result you don't want is:
> In [9]: x = np.array([[1,2,3],[4,5,6],[7,8,9]])
> In [10]: x[:,0] 
> Out[10]: array([1, 4, 7])

Agreed.  I would hope it has been clear from earlier 
discussion that the proposal retains that any use
of multiple indexes will produce a 2d submatrix.
That offers a simple way to say how matrix indexing
will differ from ndarray indexing.


> Do I understand correctly that you want M[0,:] and M[0] to 
> behave differently?

Yes.  Again, I think that I have been consistent on this point.
Any use of multiple indexes such as M[0,:] will produce a 2d submatrix.
Any use of scalar indexes such as M[0] behave as with an ndarray.


> Would you like M[0] to return the first element of the 
> matrix as in Octave?

No!
Deviations from ndarray behavior should be minimized.
They should be:

1. Multiplication is redefined to matrix multiplication.
2. Powers are redefined accordingly.
3. The ``A`` and ``I`` attributes.
4. Any use of multiple indexes will produce a 2d submatrix.

I think that is it.


> If I'm the only one who is not completely satisfied, then 
> please, submit a patch and have it applied. 

Always a reasonable request, but with respect to NumPy, I'm 
a user not a developer.  That said, it looks to be simple:
perhaps no more than adding to __getitem__ after the 
existing lines::

        if not isinstance(out, N.ndarray):
            return out

two new lines::

        if isscalar(index):
            return out

(Not that I like multiple points of return from a function.)

Cheers,
Alan Isaac