[Numpy-discussion] Feelings about type aliases in NumPy

Joshua Wilson josh.craig.wilson at gmail.com
Sun Apr 26 17:17:21 EDT 2020


To try and add some more data points to the conversation:

> Maybe we can go for a bit more distant name like "numpy.annotations" or whatever.

Interestingly this was proposed independently here:

https://github.com/numpy/numpy-stubs/pull/66#issuecomment-619131274

Related to that, Ralf was opposed to numpy.typing because it would
shadow a stdlib module name:

https://github.com/numpy/numpy-stubs/pull/66#issuecomment-619123629

But, types is _also_ a stdlib module name. Maybe the above points give
some extra weight to "numpy.annotations"?

> Unless we anticipate adding a long list of type aliases (more than the three suggested so far)

While working on some types in SciPy here:

https://github.com/scipy/scipy/pull/11936#discussion_r415280894

we ran into the issue of typing things that are "integer types" or
"floating types". For the time being we just inlined a definition like
Union[float, np.floating], but conceivably we would want to unify
those definitions somewhere instead of redefining them in every
project. (Note that existing types like SupportsInt etc. were not what
we wanted.) This perhaps suggests that the ultimate number of type
aliases might be larger than we initially thought.

On Sun, Apr 26, 2020 at 6:25 AM Ilhan Polat <ilhanpolat at gmail.com> wrote:
>
> I agree that parking all these in a secondary namespace sounds a better option, can't say that I feel for the word "typing" though. There are already too many type, dtype, ctypeslib etc. Maybe we can go for a bit more distant name like "numpy.annotations" or whatever.
>
> On Sat, Apr 25, 2020 at 8:51 AM Kevin Sheppard <kevin.k.sheppard at gmail.com> wrote:
>>
>> Typing is for library developers more than end users. I would also worry that putting it into the top level might discourage other typing classes since it is more difficult to add to the top level than to a lower level module. np.typing seems very clear to me.
>>
>> On Sat, Apr 25, 2020, 07:41 Stephan Hoyer <shoyer at gmail.com> wrote:
>>>
>>>
>>>
>>> On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg <sebastian at sipsolutions.net> wrote:
>>>>
>>>> On Fri, 2020-04-24 at 11:10 -0700, Stefan van der Walt wrote:
>>>> > On Fri, Apr 24, 2020, at 08:45, Joshua Wilson wrote:
>>>> > > But, Stephan pointed out that it might be confusing to users for
>>>> > > objects to only exist at typing time, so we came around to the
>>>> > > question of whether people are open to the idea of including the
>>>> > > type
>>>> > > aliases in NumPy itself. Ralf's concrete proposal was to make a
>>>> > > module
>>>> > > numpy.types (or maybe numpy.typing) to hold the aliases so that
>>>> > > they
>>>> > > don't pollute the top-level namespace. The module would initially
>>>> > > contain the types
>>>> >
>>>> > That sounds very sensible.  Having types available with NumPy should
>>>> > also encourage their use, especially if we can add some documentation
>>>> > around it.
>>>>
>>>> I agree, I might have a small tendency for `numpy.types` if we ever
>>>> find any usage other than direct typing that may be the better name?
>>>
>>>
>>> Unless we anticipate adding a long list of type aliases (more than the three suggested so far), I would lean towards adding ArrayLike to the top level NumPy namespace as np.ArrayLike.
>>>
>>> Type annotations are becoming an increasingly core part of modern Python code. We should make it easy to appropriately type check functions that act on NumPy arrays, and a top level np.ArrayLike is definitely more convenient than np.types.ArrayLike.
>>>
>>>> Out of curiousity, I guess `ArrayLike` would be an ABC that a
>>>> downstream project can register with?
>>>
>>>
>>> ArrayLike will be a typing Protocol, automatically recognizing attributes like __array__ to indicate that something can be cast to an array.
>>>
>>>>
>>>>
>>>> - Sebastian
>>>>
>>>>
>>>> >
>>>> > Stéfan
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