[Numpy-discussion] Integers to integer powers, let's make a decision

[josef.pktd at gmail.com]

On Sat, Jun 4, 2016 at 8:07 PM, Charles R Harris <charlesr.harris at gmail.com>
wrote:

>
>
> On Sat, Jun 4, 2016 at 5:27 PM, <josef.pktd at gmail.com> wrote:
>
>>
>>
>> On Sat, Jun 4, 2016 at 6:10 PM, Nathaniel Smith <njs at pobox.com> wrote:
>>
>>> On Sat, Jun 4, 2016 at 2:07 PM, V. Armando Sole <sole at esrf.fr> wrote:
>>> > Also in favor of 2. Always return a float for '**'
>>>
>>> Even if we did want to switch to this, it's such a major
>>> backwards-incompatible change that I'm not sure how we could actually
>>> make the transition without first making it an error for a while.
>>>
>>
>> AFAIU, only the dtype for int**int would change. So, what would be the
>> problem with FutureWarnings as with other dtype changes that were done in
>> recent releases.
>>
>>
> The main problem I see with that is that numpy integers would behave
> differently than Python integers, and the difference would be silent. With
> option 1 it is possible to write code that behaves the same up to overflow
> and the error message would supply a warning when the exponent should be
> float. One could argue that numpy scalar integer types could be made to
> behave like python integers, but then their behavior would differ from
> numpy arrays and numpy scalar arrays.
>

I'm not sure I understand.

Do you mean

np.arange(5)**2 would behave differently than np.arange(5)**np.int_(2)

or 2**2 would behave differently than np.int_(2)**np.int(2)

?


AFAICS, there are many cases where numpy scalars don't behave like python
scalars. Also, does different behavior mean different type/dtype or
different numbers.  (The first I can live with, the second requires human
memory usage, which is a scarce resource.)

>>> 2**(-2)
0.25

Josef



>
> Chuck
>
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