8000 ENH: make typing module available at runtime by person142 · Pull Request #16558 · numpy/numpy · GitHub
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ENH: make typing module available at runtime #16558

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Merged
merged 7 commits into from
Jun 17, 2020
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ENH: make typing module available at runtime #16558

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8e8a8f1
MAINT: make typing module available at runtime
person142 Jun 10, 2020
70130f8
DOC: add documentation for the numpy.typing module
person142 Jun 11, 2020
4a120f0
DOC: add warning about typing-extensions module to numpy.typing docs
person142 Jun 11, 2020
c88f5a2
DOC: add release note about `np.typing` being available at runtime
person142 Jun 14, 2020
c63f233
DOC: clarify `ArrayLike` example in typing docs
person142 Jun 16, 2020
347a368
DOC: add note about supporting buffer protocols in `ArrayLike`
person142 Jun 16, 2020
d985e8c
DOC: add reference to Python issue about buffer protocols
person142 Jun 17, 2020
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DOC: clarify ArrayLike example in typing docs
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@person142
person142 committed Jun 16, 2020
commit c63f2333288772defcd84627986b035b6e7018ef
26 changes: 21 additions & 5 deletions numpy/typing/__init__.py
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Expand Up @@ -39,10 +39,26 @@

.. code-block:: python

np.array(x**2 for x in range(10))
>>> np.array(x**2 for x in range(10))
array(<generator object <genexpr> at 0x10c004cd0>, dtype=object)

is valid NumPy code which will create an object array. The types will
complain about this usage however.
is valid NumPy code which will create a 0-dimensional object
array. Type checkers will complain about the above example when using
the NumPy types however. If you really intended to do the above, then
you can either use a ``# type: ignore`` comment:

.. code-block:: python

>>> np.array(x**2 for x in range(10)) # type: ignore

or explicitly type the array like object as ``Any``:

.. code-block:: python

>>> from typing import Any
>>> array_like: Any = (x**2 for x in range(10))
>>> np.array(array_like)
array(<generator object <genexpr> at 0x1192741d0>, dtype=object)

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Can we add a brief note on the suggested work-around?

The obvious way would be to add a comment disabling typing:

  np.array(x**2 for x in range(10))  # type: ignore

Are there other recommended options?

I think we've also discussed making checks less strict if dtype=object is specified, e.g.,

  np.array(x**2 for x in range(10), dtype=object)

I don't know if that works yet. If it does, perhaps we should mention it, too.

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Are there other recommended options?

The other way we test for:

https://github.com/numpy/numpy/blob/master/numpy/tests/typing/pass/array_like.py#L43)

is adding an explicit Any annotation. I've added examples of both methods to the docs.

I don't know if that works yet. If it does, perhaps we should mention it, too.

Seems like @seberg would probably know the answer to that?

ndarray
~~~~~~~
Expand All @@ -52,8 +68,8 @@

.. code-block:: python

x = np.array([1, 2])
x.dtype = np.bool_
x = np.array([1, 2])
x.dtype = np.bool_

This sort of mutation is not allowed by the types. Users who want to
write statically typed code should insted use the `numpy.ndarray.view`
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