BUG, TYP: add the remaining ``_core.multiarray`` function runtime signatures by jorenham · Pull Request #30147 · numpy/numpy

jorenham · 2025-11-04T22:01:37Z

This fixes inspect.signature from raising a ValueError for the following functions:

numpy.arange
numpy.char.compare_chararrays
numpy.busdaycalendar
numpy.datetime_data
numpy.from_dlpack
numpy.frombuffer
numpy.fromfile
numpy.fromiter
numpy.frompyfunc
numpy.fromstring
numpy.nested_iters
numpy.promote_types

The tests were getting a bit messy, so I cleaned them up a bit, and grouped the signature-related onces into a class-bsaed test.

~~I'll try and improve the arange signature in a follow-up, as that'll require quite a few changes in the stubs.~~

Related to #30104, #30114, #30121, #30124, #30126, #30137, #30138, #30140, #30143, and #30146.

FYI, still on my inspect.signature to-do list are the classes in numpy.random, ndarray.to_device (which is missing docs), and 53 of the np.generic methods.

jorenham · 2025-11-04T22:19:31Z

Hmm apparently the array api testsuite doesn't understand that *args and **kwargs are (always) compatible with Liskov's substitution principle. I guess I'll have to put the planned arange changes in this PR then.

jorenham · 2025-11-04T23:53:05Z

Ah woops, step can also be None apparently.

jorenham · 2025-11-05T00:14:10Z

In order to be compatible with the Array API spec for arange, the first start parameter had to be made positional-only. I've tried making it positional-or-keyword-only, but I now think that's not possible to do in a type-safe manner.

This won't cause any backwards-incompatibily issues at runtime, as you can still call arange(start=..., stop=...) just fine. However, static type-checkers will no longer accept this. But then again, I don't see any way around it.

But as mypy_primer shows, calling arange this way isn't as uncommon as I'd have hoped. ~~So I think I'll include a release note explaining why this change was needed. That'll have to wait until tomorrow though.~~

jorenham · 2025-11-05T00:18:47Z

re: the primer diff (#30147 (comment)):

The errors for Jax and Colour look correct to me; arange does not support ndarray input, so mypy rightly rejects it.

The xarray errors are a consequence of the start parameter no longer being usable as keyword-argument, as I've explained in #30147 (comment). Luckily, it's very easy to fix these errors; i.e. pass start positionally instead of by keyword.

github-actions · 2025-11-06T10:48:05Z

Diff from mypy_primer, showing the effect of this PR on type check results on a corpus of open source code:

jax (https://github.com/google/jax)
+ jax/_src/numpy/lax_numpy.py:5994: error: Argument 1 to "arange" has incompatible type "Array | ndarray[tuple[Any, ...], dtype[Any]] | numpy.bool[builtins.bool] | number[Any, int | float | complex] | builtins.bool | int | float | complex | TypedNdArray | int | Any"; expected "integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float"  [arg-type]
+ jax/_src/scipy/special.py:1211: error: Incompatible return value type (got "signedinteger[_64Bit]", expected "ndarray[tuple[Any, ...], dtype[Any]]")  [return-value]

xarray (https://github.com/pydata/xarray)
+ xarray/tests/test_plot.py: note: In member "setUp" of class "TestDiscreteColorMap":
+ xarray/tests/test_plot.py:1167: error: No overload variant of "arange" matches argument types "int", "int", "int"  [call-overload]
+ xarray/tests/test_plot.py:1167: note: Possible overload variants:
+ xarray/tests/test_plot.py:1167: note:     def [_ArangeScalarT: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]] arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[_ArangeScalarT] | dtype[_ArangeScalarT] | _SupportsDType[dtype[_ArangeScalarT]], device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[_ArangeScalarT]]
+ xarray/tests/test_plot.py:1167: note:     def arange(int | integer[Any] | numpy.bool[builtins.bool], /, stop: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[int] | type[signedinteger[_32Bit | _64Bit]] | dtype[signedinteger[_32Bit | _64Bit]] | _SupportsDType[dtype[signedinteger[_32Bit | _64Bit]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]
+ xarray/tests/test_plot.py:1167: note:     def arange(float | floating[Any], /, stop: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _SupportsDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
+ xarray/tests/test_plot.py:1167: note:     def arange(float | floating[Any] | integer[Any] | numpy.bool[builtins.bool], /, stop: float | floating[Any], step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _SupportsDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
+ xarray/tests/test_plot.py:1167: note:     def arange(timedelta64[timedelta | int | None], /, stop: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _SupportsDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
+ xarray/tests/test_plot.py:1167: note:     def arange(int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool], /, stop: timedelta64[timedelta | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _SupportsDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
+ xarray/tests/test_plot.py:1167: note:     def arange(datetime64[date | int | None], /, stop: datetime64[date | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[datetime64[date | int | None]] | dtype[datetime64[date | int | None]] | _SupportsDType[dtype[datetime64[date | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[datetime64[Any]]]
+ xarray/tests/test_plot.py:1167: note:     def arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[Any] | dtype[Any] | _SupportsDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict | str | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[Any]]
+ xarray/tests/test_plot.py:1168: error: No overload variant of "arange" matches argument types "int", "int", "int"  [call-overload]
+ xarray/tests/test_plot.py:1168: note: Possible overload variants:
+ xarray/tests/test_plot.py:1168: note:     def [_ArangeScalarT: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]] arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[_ArangeScalarT] | dtype[_ArangeScalarT] | _SupportsDType[dtype[_ArangeScalarT]], device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[_ArangeScalarT]]
+ xarray/tests/test_plot.py:1168: note:     def arange(int | integer[Any] | numpy.bool[builtins.bool], /, stop: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[int] | type[signedinteger[_32Bit | _64Bit]] | dtype[signedinteger[_32Bit | _64Bit]] | _SupportsDType[dtype[signedinteger[_32Bit | _64Bit]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]
+ xarray/tests/test_plot.py:1168: note:     def arange(float | floating[Any], /, stop: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _SupportsDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
+ xarray/tests/test_plot.py:1168: note:     def arange(float | floating[Any] | integer[Any] | numpy.bool[builtins.bool], /, stop: float | floating[Any], step: float | floating[Any] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[float] | type[float64] | dtype[float64] | _SupportsDType[dtype[float64]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[float64 | Any]]
+ xarray/tests/test_plot.py:1168: note:     def arange(timedelta64[timedelta | int | None], /, stop: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _SupportsDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
+ xarray/tests/test_plot.py:1168: note:     def arange(int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool], /, stop: timedelta64[timedelta | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[timedelta64[timedelta | int | None]] | dtype[timedelta64[timedelta | int | None]] | _SupportsDType[dtype[timedelta64[timedelta | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[timedelta64[Any]]]
+ xarray/tests/test_plot.py:1168: note:     def arange(datetime64[date | int | None], /, stop: datetime64[date | int | None], step: int | timedelta64[timedelta | int | None] | integer[Any] | numpy.bool[builtins.bool] | None = ..., *, dtype: type[datetime64[date | int | None]] | dtype[datetime64[date | int | None]] | _SupportsDType[dtype[datetime64[date | int | None]]] | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[datetime64[Any]]]
+ xarray/tests/test_plot.py:1168: note:     def arange(integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float, /, stop: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., step: integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None] | float | None = ..., *, dtype: type[Any] | dtype[Any] | _SupportsDType[dtype[Any]] | tuple[Any, Any] | list[Any] | _DTypeDict | str | None = ..., device: Literal['cpu'] | None = ..., like: _SupportsArrayFunc | None = ...) -> ndarray[tuple[int], dtype[Any]]
+ xarray/tests/test_plot.py: note: At top level:

colour (https://github.com/colour-science/colour)
+ colour/plotting/temperature.py:150: error: Incompatible types in assignment (expression has type "ndarray[tuple[Any, ...], dtype[floating[_16Bit] | floating[_32Bit] | float64]]", variable has type "ndarray[tuple[Any, ...], dtype[float64]]")  [assignment]
- colour/plotting/quality.py:214: note:     def __sub__(self, int | numpy.bool[builtins.bool], /) -> ndarray[tuple[int], dtype[generic[Any]]]
+ colour/plotting/quality.py:214: note:     def __sub__(self, int | numpy.bool[builtins.bool], /) -> ndarray[tuple[int], dtype[integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]]]
- colour/plotting/quality.py:214: note:     def __sub__(self, _SupportsArray[dtype[numpy.bool[builtins.bool]]] | _NestedSequence[_SupportsArray[dtype[numpy.bool[builtins.bool]]]] | builtins.bool | _NestedSequence[builtins.bool], /) -> ndarray[tuple[Any, ...], dtype[generic[Any]]]
+ colour/plotting/quality.py:214: note:     def __sub__(self, _SupportsArray[dtype[numpy.bool[builtins.bool]]] | _NestedSequence[_SupportsArray[dtype[numpy.bool[builtins.bool]]]] | builtins.bool | _NestedSequence[builtins.bool], /) -> ndarray[tuple[Any, ...], dtype[integer[Any] | floating[Any] | datetime64[date | int | None] | timedelta64[timedelta | int | None]]]
- colour/utilities/tests/test_array.py:1604: error: Incompatible types in assignment (expression has type "ndarray[tuple[int], dtype[signedinteger[Any]]]", variable has type "int")  [assignment]
+ colour/utilities/tests/test_array.py:1604: error: Incompatible types in assignment (expression has type "ndarray[tuple[int], dtype[signedinteger[_32Bit | _64Bit]]]", variable has type "int")  [assignment]

charris · 2025-11-06T17:27:59Z

Thanks Joren.

This fixes `inspect.signature` for - `np.random.BitGenerator` - `np.random.Generator` - `np.random.MT19937` - `np.random.PCG64` - `np.random.PCG64DXSM` - `np.random.Philox` - `np.random.RandomState` - `np.random.SFC64` - `np.random.SeedSequence` - `np.random.bit_generator.SeedlessSeedSequence` This also fixes a typo in `bit_generator.pxd` that accidentally defined an empty unused class `np.random.bit_generator.SeedlessSequence`. Related to #30104, #30114, #30121, #30124, #30126, #30137, #30138, #30140, #30143, #30146, #30147, and #30155

rgommers · 2025-11-11T09:52:00Z

The blast radius for this PR, and the whole "add more signatures" effort in general, seems quite large (the mypy_primer output and incoming links show JAX, AstroPy, SciPy, scikit-image and Colour from just this PR) so this seems to be in need of a prominent entry in the release notes at least.

jorenham · 2025-11-11T14:57:59Z

The blast radius for this PR, and the whole "add more signatures" effort in general, seems quite large

So far, the reactions have been mostly positive. But the impact that this seems to have is pretty surprising to me.

(the mypy_primer output and incoming links show JAX, AstroPy, SciPy, scikit-image and Colour from just this PR)

The mypy_primer output looks pretty scary at first glance, but I'm pretty sure that the new mypy_errors that this will introduce for jax and colour, are justified; i.e. that they're either true positives. And in case of xarray, related to the arange(start=) change. See #30147 (comment) for some details.

If you consider the reported line numbers in the mypy_primer diff, you'll see that we're talking about 5 lines of code here; so most what you're seeing is just irrelevant noise :)

so this seems to be in need of a prominent entry in the release notes at least.

That's also what I was thinking, yea. I wasn't sure yet what the best way to go about that was. Open a separate PR for the release note (highlight?) and link that PR, or use the PR number of one these actual PR's?

jorenham · 2025-11-13T14:09:32Z

so this seems to be in need of a prominent entry in the release notes at least.

#30208 :)

This reverts a very small part of numpygh-30147, because it broken Pythran (see pythran#2368). While a new Pythran 0.18.1 release is available, that doesn't help for the SciPy 1.15.x releases which allow numpy<2.5 and pythran<0.18, see https://github.com/scipy/scipy/blob/maintenance/1.15.x/pyproject.toml#L30-L41 So introducing this signature change only for 2.5.0 is much better. In addition, doing so doesn't leave us only a very narrow window with compatible NumPy and Pythran releases which would otherwise bite distro packagers as well.

This fixes `inspect.signature` for - `np.random.BitGenerator` - `np.random.Generator` - `np.random.MT19937` - `np.random.PCG64` - `np.random.PCG64DXSM` - `np.random.Philox` - `np.random.RandomState` - `np.random.SFC64` - `np.random.SeedSequence` - `np.random.bit_generator.SeedlessSeedSequence` This also fixes a typo in `bit_generator.pxd` that accidentally defined an empty unused class `np.random.bit_generator.SeedlessSequence`. Related to numpy#30104, numpy#30114, numpy#30121, numpy#30124, numpy#30126, numpy#30137, numpy#30138, numpy#30140, numpy#30143, numpy#30146, numpy#30147, and numpy#30155

This reverts a very small part of numpygh-30147, because it broken Pythran (see pythran#2368). While a new Pythran 0.18.1 release is available, that doesn't help for the SciPy 1.15.x releases which allow numpy<2.5 and pythran<0.18, see https://github.com/scipy/scipy/blob/maintenance/1.15.x/pyproject.toml#L30-L41 So introducing this signature change only for 2.5.0 is much better. In addition, doing so doesn't leave us only a very narrow window with compatible NumPy and Pythran releases which would otherwise bite distro packagers as well.

jorenham added the 00 - Bug label Nov 4, 2025

jorenham added this to the 2.4.0 release milestone Nov 4, 2025

jorenham changed the title ~~BUG: remaining _core.multiarray function runtime signatures~~ BUG: add the remaining _core.multiarray function runtime signatures Nov 4, 2025

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jorenham changed the title ~~BUG: add the remaining _core.multiarray function runtime signatures~~ BUG, TYP: add the remaining _core.multiarray function runtime signatures Nov 5, 2025

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jorenham added the 56 - Needs Release Note. Needs an entry in doc/release/upcoming_changes label Nov 5, 2025

neutrinoceros mentioned this pull request Nov 5, 2025

BUG: resolve incompatibilities in units and utils.masked with numpy 2.4 (dev) astropy/astropy#18821

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jorenham mentioned this pull request Nov 5, 2025

BUG, DOC: ndarray dunder method runtime signatures and missing docs #30155

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jorenham added 5 commits November 6, 2025 11:23

BUG: _core.multiarray function runtime signatures

32817cc

BUG: refine arange runtime signature for array-api compatibility

49de145

TYP: update and improve the arange stubs

25d7402

TYP: Allow passing step=None to arange

3b25928

DOC: add release note for numpy#30147

fa476b5

jorenham force-pushed the fix-remaining-top-level-function-signatures branch from 21d7edc to fa476b5 Compare November 6, 2025 10:37

jorenham removed the 56 - Needs Release Note. Needs an entry in doc/release/upcoming_changes label Nov 6, 2025

jorenham mentioned this pull request Nov 6, 2025

BUG: numpy.random.* class runtime signatures #30164

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charris merged commit 42cb3f9 into numpy:main Nov 6, 2025
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jorenham deleted the fix-remaining-top-level-function-signatures branch November 6, 2025 19:51

jorenham mentioned this pull request Nov 7, 2025

BUG: np.dtype and np.dtypes.* runtime signatures #30169

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BUG: fix forward compatibility for np.arange(<Quantity>, ...) against numpy 2.4 (dev) astropy/astropy#18849

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CI: Prerelease, mypy, and freethreaded jobs failing due to Pythran error: KeyError: 'sep' scipy/scipy#23958

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BUG, DOC: np.generic missing method runtime signatures and docs #30193

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Regression building scikit-image with latest NumPy serge-sans-paille/pythran#2368

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DOC: Release notes for the runtime signature changes #30208

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MAINT: undo change to fromstring text signature for 2.4.0 #30235

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[BUG]: Ensure type annotations are compatible with NumPy 2.4.0 rapidsai/cudf#20782

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[python-package] use positional args for np.arange in _InnerPredictor lightgbm-org/LightGBM#7119

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BUG, TYP: add the remaining `_core.multiarray` function runtime signatures#30147
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