[Numpy-discussion] __numpy_ufunc__

[Charles R Harris]

On Mon, Nov 7, 2016 at 1:08 AM, Ralf Gommers <ralf.gommers at gmail.com> wrote:

>
>
> On Mon, Nov 7, 2016 at 9:10 AM, Charles R Harris <
> charlesr.harris at gmail.com> wrote:
>
>>
>>
>> On Sun, Nov 6, 2016 at 11:44 AM, Charles R Harris <
>> charlesr.harris at gmail.com> wrote:
>>
>>> Hi All,
>>>
>>> For those interested in continuing the __numpy_ufunc__ saga, there is a pull
>>> request enabling it <https://github.com/numpy/numpy/pull/8247>. Likely
>>> we will want to make some changes up front before merging that, so some
>>> discussion is in order.
>>>
>>>
>> As a first order of business, let's decide whether to remove the index
>> and rename `__numpy_ufunc__`. The motivation for this is discussed in issue
>> #5986. <https://github.com/numpy/numpy/issues/5986>
>> If we decide positive on that (I'm in favor),
>>
>
> It seems like everyone on that issue is in favor or at least +0. So +1
> from me too.
>
>
>> I would be happy with the proposed name `__array_ufunc__`, although
>> something more descriptive like `__handle_ufunc__` might be better.
>>
>
>
>
>> This is a wonderful opportunity for bike shedding for those so inclined ;)
>>
>
Let me try to break things down an bit.


*Uncontroversial*


   - Rename __numpy_ufunc__ to __array_ufunc__
   - Remove index
   - Out is always a tuple


I think this much is useful even if nothing else is done.

*Goals*


   - Deprecate __array_priority__
   - Ufuncs should succeed or error, never return NotImplemented
   - Add __array_ufunc__ stub to ndarray.


I don't think these are controversial either, but they are longer term
except possibly the last. Note that never returning NotImplemented
disentangles ufuncs from ndarray binops, which I think is a good thing.

*Binops*


Here we come to the crux of the last arguments. The functions used for
binops can currently be set dynamically, the method that is currently used
to set them when the ufunc module is loaded. I think we want to do away
with that at some point and fix a set of ufuncs with which they are
implemented. This allows folks to overide the binop behavior using
__array_ufunc__. I think that is mostly of interest to people who are
subclassing ndarray and with that restriction doesn't bother me except that
it entangles ufuncs with binops. However, what I'd like to see as well is
an opt out for objects that don't care about ufuncs, but want to override
the python numeric operators, something simple like `__me_me_me__`, or,
more seriously, `__array_opt_out__`  that will only come into play if the
defining object is on the right hand side of an instance of ndarray. In
that case the binop would return NotImplemented so as to defer to the
Python machinery.  Note that  __array_priority__ is currently (ab)used for
this.

*Numpy scalars*

Numpy scalars default to the corresponding PyArray_Type or
PyGenericArrType_Type unless both arguments can be converted to the same c
type as the calling scalar, so I don't think there is a problem there. Note
that they do check _array_priority_ before trying to convert unknown
objects to array scalars in a fallback case.

Chuck
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