Hey everyone, Over in numpystubs we've been working on typing "array like": https://github.com/numpy/numpystubs/pull/66 It would be nice if the type were public so that downstream projects could use it (e.g. it would be very helpful in SciPy). Originally the plan was to only make it publicly available at typing time and not runtime, which would mean that no changes to NumPy are necessary; see https://github.com/numpy/numpystubs/pull/66#issuecomment618784833 for more information on how that works. 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 toplevel namespace. The module would initially contain the types  ArrayLike  DtypeLike  (maybe) ShapeLike Note that we would not need to make changes to NumPy right away; instead it would probably be done when numpystubs is merged into NumPy itself. What do people think?  Josh
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 toplevel 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. Stéfan
On Fri, 20200424 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 toplevel 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? Out of curiousity, I guess `ArrayLike` would be an ABC that a downstream project can register with?  Sebastian
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On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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
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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@gmail.com> wrote:
On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg < sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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 _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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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@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@gmail.com> wrote:
On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg < sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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 _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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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/numpystubs/pull/66#issuecomment619131274 Related to that, Ralf was opposed to numpy.typing because it would shadow a stdlib module name: https://github.com/numpy/numpystubs/pull/66#issuecomment619123629 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@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@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@gmail.com> wrote:
On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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
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Interestingly this was proposed independently here:
Wow apologies for missing the entire thread about it and the noise. On Sun, Apr 26, 2020 at 11:19 PM Joshua Wilson <josh.craig.wilson@gmail.com> wrote:
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/numpystubs/pull/66#issuecomment619131274
Related to that, Ralf was opposed to numpy.typing because it would shadow a stdlib module name:
https://github.com/numpy/numpystubs/pull/66#issuecomment619123629
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@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 <
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@gmail.com> wrote:
On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg <
sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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
Type annotations are becoming an increasingly core part of modern
Python code. We should make it easy to appropriately type check functions
kevin.k.sheppard@gmail.com> wrote: three suggested so far), I would lean towards adding ArrayLike to the top level NumPy namespace as np.ArrayLike. 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 _______________________________________________ NumPyDiscussion mailing list NumPyDiscussion@python.org https://mail.python.org/mailman/listinfo/numpydiscussion
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Following some additional discussion on the PR (see comments after https://github.com/numpy/numpystubs/pull/66#issuecomment620139434), the proposed path forward is:  Add the module `numpy.typing` to the type stubs only for now  Initially it will contain types for ArrayLike and DtypeLike  When the stubs are merged into NumPy, add the `numpy.typing` module to NumPy itself. Any further objections? On Mon, Apr 27, 2020 at 10:50 AM Ilhan Polat <ilhanpolat@gmail.com> wrote:
Interestingly this was proposed independently here:
Wow apologies for missing the entire thread about it and the noise.
On Sun, Apr 26, 2020 at 11:19 PM Joshua Wilson <josh.craig.wilson@gmail.com> wrote:
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/numpystubs/pull/66#issuecomment619131274
Related to that, Ralf was opposed to numpy.typing because it would shadow a stdlib module name:
https://github.com/numpy/numpystubs/pull/66#issuecomment619123629
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@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@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@gmail.com> wrote:
On Fri, Apr 24, 2020 at 11:31 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
On Fri, 20200424 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 toplevel 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 > _______________________________________________ > NumPyDiscussion mailing list > NumPyDiscussion@python.org > https://mail.python.org/mailman/listinfo/numpydiscussion
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participants (6)

Ilhan Polat

Joshua Wilson

Kevin Sheppard

Sebastian Berg

Stefan van der Walt

Stephan Hoyer