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Update numpy to 2.3.1 #7856

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This PR updates numpy from 2.2.6 to 2.3.1.

Changelog

2.3.1

The NumPy 2.3.1 release is a patch release with several bug fixes,
annotation improvements, and better support for OpenBSD. Highlights are:

-   Fix bug in `matmul` for non-contiguous out kwarg parameter
-   Fix for Accelerate runtime warnings on M4 hardware
-   Fix new in NumPy 2.3.0 `np.vectorize` casting errors
-   Improved support of cpu features for FreeBSD and OpenBSD

This release supports Python versions 3.11-3.13, Python 3.14 will be
supported when it is released.

Contributors

A total of 9 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Brad Smith +
-   Charles Harris
-   Developer-Ecosystem-Engineering
-   François Rozet
-   Joren Hammudoglu
-   Matti Picus
-   Mugundan Selvanayagam
-   Nathan Goldbaum
-   Sebastian Berg

Pull requests merged

A total of 12 pull requests were merged for this release.

-   [29140](https://github.com/numpy/numpy/pull/29140): MAINT: Prepare 2.3.x for further development
-   [29191](https://github.com/numpy/numpy/pull/29191): BUG: fix matmul with transposed out arg (#29179)
-   [29192](https://github.com/numpy/numpy/pull/29192): TYP: Backport typing fixes and improvements.
-   [29205](https://github.com/numpy/numpy/pull/29205): BUG: Revert `np.vectorize` casting to legacy behavior (#29196)
-   [29222](https://github.com/numpy/numpy/pull/29222): TYP: Backport typing fixes
-   [29233](https://github.com/numpy/numpy/pull/29233): BUG: avoid negating unsigned integers in resize implementation\...
-   [29234](https://github.com/numpy/numpy/pull/29234): TST: Fix test that uses uninitialized memory (#29232)
-   [29235](https://github.com/numpy/numpy/pull/29235): BUG: Address interaction between SME and FPSR (#29223)
-   [29237](https://github.com/numpy/numpy/pull/29237): BUG: Enforce integer limitation in concatenate (#29231)
-   [29238](https://github.com/numpy/numpy/pull/29238): CI: Add support for building NumPy with LLVM for Win-ARM64
-   [29241](https://github.com/numpy/numpy/pull/29241): ENH: Detect CPU features on OpenBSD ARM and PowerPC64
-   [29242](https://github.com/numpy/numpy/pull/29242): ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64.

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2.3.0

The NumPy 2.3.0 release continues the work to improve free threaded
Python support and annotations together with the usual set of bug fixes.
It is unusual in the number of expired deprecations and the number of
code modernizations and style cleanups. The latter may not be visible to
users, but is important for code maintenance over the long term. Note
that we have also upgraded from manylinux2014 to manylinux_2_28.

There are known test failures in the rc1 release involving MyPy and
PyPy. The cause of both has been determined and fixes will be applied
before the final release. The current Windows on ARM wheels also lack
OpenBLAS, but they should suffice for initial downstream testing.
OpenBLAS will be incorporated in those wheels when it becomes available.

This release supports Python versions 3.11-3.13, Python 3.14 will be
supported when it is released.

Highlights

-   Interactive examples in the NumPy documentation.
-   Building NumPy with OpenMP Parallelization.
-   Preliminary support for Windows on ARM.
-   Improved support for free threaded Python.
-   Improved annotations.

New functions

New function `numpy.strings.slice`

The new function `numpy.strings.slice` was added, which implements fast
native slicing of string arrays. It supports the full slicing API
including negative slice offsets and steps.

([gh-27789](https://github.com/numpy/numpy/pull/27789))

Deprecations

-   The `numpy.typing.mypy_plugin` has been deprecated in favor of
 platform-agnostic static type inference. Please remove
 `numpy.typing.mypy_plugin` from the `plugins` section of your mypy
 configuration. If this change results in new errors being reported,
 kindly open an issue.

 ([gh-28129](https://github.com/numpy/numpy/pull/28129))

-   The `numpy.typing.NBitBase` type has been deprecated and will be
 removed in a future version.

 This type was previously intended to be used as a generic upper
 bound for type-parameters, for example:

  python
 import numpy as np
 import numpy.typing as npt

 def f[NT: npt.NBitBase](x: np.complexfloating[NT]) -> np.floating[NT]: ...
 

 But in NumPy 2.2.0, `float64` and `complex128` were changed to
 concrete subtypes, causing static type-checkers to reject
 `x: np.float64 = f(np.complex128(42j))`.

 So instead, the better approach is to use `typing.overload`:

  python
 import numpy as np
 from typing import overload

 overload
 def f(x: np.complex64) -> np.float32: ...
 overload
 def f(x: np.complex128) -> np.float64: ...
 overload
 def f(x: np.clongdouble) -> np.longdouble: ...
 

 ([gh-28884](https://github.com/numpy/numpy/pull/28884))

Expired deprecations

-   Remove deprecated macros like `NPY_OWNDATA` from Cython interfaces
 in favor of `NPY_ARRAY_OWNDATA` (deprecated since 1.7)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Remove `numpy/npy_1_7_deprecated_api.h` and C macros like
 `NPY_OWNDATA` in favor of `NPY_ARRAY_OWNDATA` (deprecated since 1.7)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Remove alias `generate_divbyzero_error` to
 `npy_set_floatstatus_divbyzero` and `generate_overflow_error` to
 `npy_set_floatstatus_overflow` (deprecated since 1.10)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Remove `np.tostring` (deprecated since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Raise on `np.conjugate` of non-numeric types (deprecated since 1.13)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Raise when using `np.bincount(...minlength=None)`, use 0 instead
 (deprecated since 1.14)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Passing `shape=None` to functions with a non-optional shape argument
 errors, use `()` instead (deprecated since 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Inexact matches for `mode` and `searchside` raise (deprecated since
 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Setting `__array_finalize__ = None` errors (deprecated since 1.23)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   `np.fromfile` and `np.fromstring` error on bad data, previously they
 would guess (deprecated since 1.18)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   `datetime64` and `timedelta64` construction with a tuple no longer
 accepts an `event` value, either use a two-tuple of (unit, num) or a
 4-tuple of (unit, num, den, 1) (deprecated since 1.14)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   When constructing a `dtype` from a class with a `dtype` attribute,
 that attribute must be a dtype-instance rather than a thing that can
 be parsed as a dtype instance (deprecated in 1.19). At some point
 the whole construct of using a dtype attribute will be deprecated
 (see 25306)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Passing booleans as partition index errors (deprecated since 1.23)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Out-of-bounds indexes error even on empty arrays (deprecated since
 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   `np.tostring` has been removed, use `tobytes` instead (deprecated
 since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Disallow make a non-writeable array writeable for arrays with a base
 that do not own their data (deprecated since 1.17)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   `concatenate()` with `axis=None` uses `same-kind` casting by
 default, not `unsafe` (deprecated since 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Unpickling a scalar with object dtype errors (deprecated since 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   The binary mode of `fromstring` now errors, use `frombuffer` instead
 (deprecated since 1.14)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Converting `np.inexact` or `np.floating` to a dtype errors
 (deprecated since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Converting `np.complex`, `np.integer`, `np.signedinteger`,
 `np.unsignedinteger`, `np.generic` to a dtype errors (deprecated
 since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   The Python built-in `round` errors for complex scalars. Use
 `np.round` or `scalar.round` instead (deprecated since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   \'np.bool\' scalars can no longer be interpreted as an index
 (deprecated since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Parsing an integer via a float string is no longer supported.
 (deprecated since 1.23) To avoid this error you can

 -   make sure the original data is stored as integers.
 -   use the `converters=float` keyword argument.
 -   Use `np.loadtxt(...).astype(np.int64)`

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   The use of a length 1 tuple for the ufunc `signature` errors. Use
 `dtype` or fill the tuple with `None` (deprecated since 1.19)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Special handling of matrix is in np.outer is removed. Convert to a
 ndarray via `matrix.A` (deprecated since 1.20)

 ([gh-28254](https://github.com/numpy/numpy/pull/28254))

-   Removed the `np.compat` package source code (removed in 2.0)

 ([gh-28961](https://github.com/numpy/numpy/pull/28961))

C API changes

-   `NpyIter_GetTransferFlags` is now available to check if the iterator
 needs the Python API or if casts may cause floating point errors
 (FPE). FPEs can for example be set when casting `float64(1e300)` to
 `float32` (overflow to infinity) or a NaN to an integer (invalid
 value).

 ([gh-27883](https://github.com/numpy/numpy/pull/27883))

-   `NpyIter` now has no limit on the number of operands it supports.

 ([gh-28080](https://github.com/numpy/numpy/pull/28080))

New `NpyIter_GetTransferFlags` and `NpyIter_IterationNeedsAPI` change

NumPy now has the new `NpyIter_GetTransferFlags` function as a more
precise way checking of iterator/buffering needs. I.e. whether the
Python API/GIL is required or floating point errors may occur. This
function is also faster if you already know your needs without
buffering.

The `NpyIter_IterationNeedsAPI` function now performs all the checks
that were previously performed at setup time. While it was never
necessary to call it multiple times, doing so will now have a larger
cost.

([gh-27998](https://github.com/numpy/numpy/pull/27998))

New Features

-   The type parameter of `np.dtype` now defaults to `typing.Any`. This
 way, static type-checkers will infer `dtype: np.dtype` as
 `dtype: np.dtype[Any]`, without reporting an error.

 ([gh-28669](https://github.com/numpy/numpy/pull/28669))

-   Static type-checkers now interpret:

 -   `_: np.ndarray` as `_: npt.NDArray[typing.Any]`.
 -   `_: np.flatiter` as `_: np.flatiter[np.ndarray]`.

 This is because their type parameters now have default values.

 ([gh-28940](https://github.com/numpy/numpy/pull/28940))

NumPy now registers its pkg-config paths with the [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) PyPI package

The [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) PyPI
package provides an interface for projects like NumPy to register their
own paths to be added to the pkg-config search path. This means that
when using [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi)
from PyPI, NumPy will be discoverable without needing for any custom
environment configuration.

> [!NOTE]
>This only applies when using the [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) package from [PyPI](https://pypi.org/),
or put another way, this only applies when installing [pkgconf](https://github.com/pypackaging-native/pkgconf-pypi) via a
Python package manager.
>
>If you are using `pkg-config` or `pkgconf` provided by your system,
or any other source that does not use the [pkgconf-pypi](https://github.com/pypackaging-native/pkgconf-pypi)
project, the NumPy pkg-config directory will not be automatically added
to the search path. In these situations, you might want to use `numpy-config`.

([gh-28214](https://github.com/numpy/numpy/pull/28214))

Allow `out=...` in ufuncs to ensure array result

NumPy has the sometimes difficult behavior that it currently usually
returns scalars rather than 0-D arrays (even if the inputs were 0-D
arrays). This is especially problematic for non-numerical dtypes (e.g.
`object`).

For ufuncs (i.e. most simple math functions) it is now possible to use
`out=...` (literally \`\...\`, e.g. `out=Ellipsis`) which is identical
in behavior to `out` not being passed, but will ensure a non-scalar
return. This spelling is borrowed from `arr1d[0, ...]` where the `...`
also ensures a non-scalar return.

Other functions with an `out=` kwarg should gain support eventually.
Downstream libraries that interoperate via `__array_ufunc__` or
`__array_function__` may need to adapt to support this.

([gh-28576](https://github.com/numpy/numpy/pull/28576))

Building NumPy with OpenMP Parallelization

NumPy now supports OpenMP parallel processing capabilities when built
with the `-Denable_openmp=true` Meson build flag. This feature is
disabled by default. When enabled, `np.sort` and `np.argsort` functions
can utilize OpenMP for parallel thread execution, improving performance
for these operations.

([gh-28619](https://github.com/numpy/numpy/pull/28619))

Interactive examples in the NumPy documentation

The NumPy documentation includes a number of examples that can now be
run interactively in your browser using WebAssembly and Pyodide.

Please note that the examples are currently experimental in nature and
may not work as expected for all methods in the public API.

([gh-26745](https://github.com/numpy/numpy/pull/26745))

Improvements

-   Scalar comparisons between non-comparable dtypes such as
 `np.array(1) == np.array('s')` now return a NumPy bool instead 
8000
of a
 Python bool.

 ([gh-27288](https://github.com/numpy/numpy/pull/27288))

-   `np.nditer` now has no limit on the number of supported operands
 (C-integer).

 ([gh-28080](https://github.com/numpy/numpy/pull/28080))

-   No-copy pickling is now supported for any array that can be
 transposed to a C-contiguous array.

 ([gh-28105](https://github.com/numpy/numpy/pull/28105))

-   The `__repr__` for user-defined dtypes now prefers the `__name__` of
 the custom dtype over a more generic name constructed from its
 `kind` and `itemsize`.

 ([gh-28250](https://github.com/numpy/numpy/pull/28250))

-   `np.dot` now reports floating point exceptions.

 ([gh-28442](https://github.com/numpy/numpy/pull/28442))

-   `np.dtypes.StringDType` is now a [generic
 type](https://typing.python.org/en/latest/spec/generics.html) which
 accepts a type argument for `na_object` that defaults to
 `typing.Never`. For example, `StringDType(na_object=None)` returns a
 `StringDType[None]`, and `StringDType()` returns a
 `StringDType[typing.Never]`.

 ([gh-28856](https://github.com/numpy/numpy/pull/28856))

Added warnings to `np.isclose`

Added warning messages if at least one of atol or rtol are either
`np.nan` or `np.inf` within `np.isclose`.

-   Warnings follow the user\'s `np.seterr` settings

([gh-28205](https://github.com/numpy/numpy/pull/28205))

Performance improvements and changes

Performance improvements to `np.unique`

`np.unique` now tries to use a hash table to find unique values instead
of sorting values before finding unique values. This is limited to
certain dtypes for now, and the function is now faster for those dtypes.
The function now also exposes a `sorted` parameter to allow returning
unique values as they were found, instead of sorting them afterwards.

([gh-26018](https://github.com/numpy/numpy/pull/26018))

Performance improvements to `np.sort` and `np.argsort`

`np.sort` and `np.argsort` functions now can leverage OpenMP for
parallel thread execution, resulting in up to 3.5x speedups on x86
architectures with AVX2 or AVX-512 instructions. This opt-in feature
requires NumPy to be built with the -Denable_openmp Meson flag. Users
can control the number of threads used by setting the OMP_NUM_THREADS
environment variable.

([gh-28619](https://github.com/numpy/numpy/pull/28619))

Performance improvements for `np.float16` casts

Earlier, floating point casts to and from `np.float16` types were
emulated in software on all platforms.

Now, on ARM devices that support Neon float16 intrinsics (such as recent
Apple Silicon), the native float16 path is used to achieve the best
performance.

([gh-28769](https://github.com/numpy/numpy/pull/28769))

Changes

-   The vector norm `ord=inf` and the matrix norms
 `ord={1, 2, inf, 'nuc'}` now always returns zero for empty arrays.
 Empty arrays have at least one axis of size zero. This affects
 `np.linalg.norm`, `np.linalg.vector_norm`, and
 `np.linalg.matrix_norm`. Previously, NumPy would raises errors or
 return zero depending on the shape of the array.

 ([gh-28343](https://github.com/numpy/numpy/pull/28343))

-   A spelling error in the error message returned when converting a
 string to a float with the method `np.format_float_positional` has
 been fixed.

 ([gh-28569](https://github.com/numpy/numpy/pull/28569))

-   NumPy\'s `__array_api_version__` was upgraded from `2023.12` to
 `2024.12`.

-   `numpy.count_nonzero` for `axis=None` (default) now returns a NumPy
 scalar instead of a Python integer.

-   The parameter `axis` in `numpy.take_along_axis` function has now a
 default value of `-1`.

 ([gh-28615](https://github.com/numpy/numpy/pull/28615))

-   Printing of `np.float16` and `np.float32` scalars and arrays have
 been improved by adjusting the transition to scientific notation
 based on the floating point precision. A new legacy
 `np.printoptions` mode `'2.2'` has been added for backwards
 compatibility.

 ([gh-28703](https://github.com/numpy/numpy/pull/28703))

`unique_values` may return unsorted data

The relatively new function (added in NumPy 2.0) `unique_values` may now
return unsorted results. Just as `unique_counts` and `unique_all` these
never guaranteed a sorted result, however, the result was sorted until
now. In cases where these do return a sorted result, this may change in
future releases to improve performance.

([gh-26018](https://github.com/numpy/numpy/pull/26018))

Changes to the main iterator and potential numerical changes

The main iterator, used in math functions and via `np.nditer` from
Python and `NpyIter` in C, now behaves differently for some buffered
iterations. This means that:

-   The buffer size used will often be smaller than the maximum buffer
 sized allowed by the `buffersize` parameter.
-   The \"growinner\" flag is now honored with buffered reductions when
 no operand requires buffering.

For `np.sum()` such changes in buffersize may slightly change numerical
results of floating point operations. Users who use \"growinner\" for
custom reductions could notice changes in precision (for example, in
NumPy we removed it from `einsum` to avoid most precision changes and
improve precision for some 64bit floating point inputs).

([gh-27883](https://github.com/numpy/numpy/pull/27883))

The minimum supported GCC version is now 9.3.0

The minimum supported version was updated from 8.4.0 to 9.3.0, primarily
in order to reduce the chance of platform-specific bugs in old GCC
versions from causing issues.

([gh-28102](https://github.com/numpy/numpy/pull/28102))

Changes to automatic bin selection in numpy.histogram

The automatic bin selection algorithm in `numpy.histogram` has been
modified to avoid out-of-memory errors for samples with low variation.
For full control over the selected bins the user can use set the `bin`
or `range` parameters of `numpy.histogram`.

([gh-28426](https://github.com/numpy/numpy/pull/28426))

Build manylinux_2_28 wheels

Wheels for linux systems will use the `manylinux_2_28` tag (instead of
the `manylinux2014` tag), which means dropping support for
redhat7/centos7, amazonlinux2, debian9, ubuntu18.04, and other
pre-glibc2.28 operating system versions, as per the [PEP 600 support
table](https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#pep600-compliance-check).

([gh-28436](https://github.com/numpy/numpy/pull/28436))

Remove use of -Wl,-ld_classic on macOS

Remove use of -Wl,-ld_classic on macOS. This hack is no longer needed by
Spack, and results in libraries that cannot link to other libraries
built with ld (new).

([gh-28713](https://github.com/numpy/numpy/pull/28713))

Re-enable overriding functions in the `numpy.strings`

Re-enable overriding functions in the `numpy.strings` module.

([gh-28741](https://github.com/numpy/numpy/pull/28741))

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