[Numpy-discussion] Creating a subclass that never propagates

[Ralf Gommers]

On Tue, Jul 16, 2019 at 5:58 AM Charles R Harris <charlesr.harris at gmail.com>
wrote:

>
>
> On Tue, Jul 16, 2019 at 3:44 AM Kevin Sheppard <kevin.k.sheppard at gmail.com>
> wrote:
>
>> I am trying to make a subclass that never propagates so that when
>> interacted with another ndarray, or even itself so that the return type is
>> always ndarray.  Is this possible?
>>
>> I got pretty far with
>>
>> def __array_wrap__(self, out_arr, context=None):
>>     if out_arr.shape == ():
>>         return out_arr.item()  # if ufunc output is scalar, return it
>>     else:
>>         out = super(ArrayLike, self).__array_wrap__(out_arr, context)
>>         # Never return ArrayLike
>>         if isinstance(out, ArrayLike):
>>             out = out.view(np.ndarray)
>>         return out
>>
>> Which works well for ufuncs.  However, when I try other functions like
>> `dot` I get my subclass type returned.
>>
>> If there a reasonable way to ensure that my subclass doesn't propagate? I
>> think I would need some way to override the behavior when .view(MySubClass)
>> is called.
>>
>
I think you need to implement __array_finalize__ for this (see e.g.
https://docs.scipy.org/doc/numpy-1.13.0/user/basics.subclassing.html#implications-for-subclassing
)


>>
> I think you will be able to do that with `__array_function__` in the
> upcoming 1.17 release. It is also in 1.16, but you need an environmental
> variable to activate it. Some documentation can be found at
> https://www.numpy.org/devdocs/reference/arrays.classes.html#special-attributes-and-methods
> .
>

That's kind of an orthogonal thing: __array_function__ is for providing
your own implementation of functions, which you don't necessarily want to
do if you're just building a small subclass.

Cheers,
Ralf
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