[Numpy-discussion] ndarray.T2 for 2D transpose

[josef.pktd at gmail.com]

On Fri, Apr 8, 2016 at 5:11 PM, Charles R Harris <charlesr.harris at gmail.com>
wrote:

>
>
> On Fri, Apr 8, 2016 at 2:52 PM, <josef.pktd at gmail.com> wrote:
>
>>
>>
>> On Fri, Apr 8, 2016 at 3:55 PM, Charles R Harris <
>> charlesr.harris at gmail.com> wrote:
>>
>>>
>>>
>>> On Fri, Apr 8, 2016 at 12:17 PM, Chris Barker <chris.barker at noaa.gov>
>>> wrote:
>>>
>>>> On Fri, Apr 8, 2016 at 9:59 AM, Charles R Harris <
>>>> charlesr.harris at gmail.com> wrote:
>>>>
>>>>> Apropos column/row vectors, I've toyed a bit with the idea of adding a
>>>>> flag to numpy arrays to indicate that the last index is one or the other,
>>>>> and maybe neither.
>>>>>
>>>>
>>>> I don't follow this. wouldn't it ony be an issue for 1D arrays, rather
>>>> than the "last index". Or maybe I'm totally missing the point.
>>>>
>>>> But anyway, are (N,1) and (1, N) arrays insufficient for representing
>>>> column and row vectors for some reason? If not -- then we have a way to
>>>> express a column or row vector, we just need an easier and more obvious way
>>>> to create them.
>>>>
>>>> *maybe* we could have actual column and row vector classes -- they
>>>> would BE regular arrays, with (1,N) or (N,1) dimensions, and act the same
>>>> in every way except their __repr__. and we're provide handy factor
>>>> functions for them.
>>>>
>>>> These were needed to complete the old Matrix class -- which is no
>>>> longer needed now that we have @ (i.e. a 2D array IS a matrix)
>>>>
>>>
>>> One problem with that approach is that `vrow @ vcol` has dimension 1 x
>>> 1, which is not a scalar.
>>>
>>
>> I think it's not supposed to be a scalar, if @ breaks on scalars
>>
>> `vrow @ vcol @ a
>>
>
> It's supposed to be a scalar and the expression should be written `vrow @
> vcol * a`, although parens are probably desireable for clarity `(vrow @
> vcol) * a`.
>


if a is 1d or twod vcol, and vrow and vcol could also be 2d arrays (not a
single row or col)
this is just a part of a long linear algebra expression

1d dot 1d is different from vrow dot vcol

A dot 1d is different from A dot vcol.

There intentional differences in the linear algebra behavior of 1d versus a
col or row vector. One of those is dropping the extra dimension.
We are using this a lot to switch between 1-d and 2-d cases.

And another great thing about numpy is that often code immediately
generalizes from 1-d to 2d with just some tiny adjustments.

(I haven't played with @ yet)


I worry that making the 1-d arrays suddenly behave ambiguously as weird
1-d/2-d mixture will make code more inconsistent and more difficult to
follow.

shortcuts and variations of atleast_2d sound fine, but not implicitly

Josef




>
> Chuck
>
>
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