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Update numpy to 2.2.3 #100

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This PR updates numpy from 1.20.3 to 2.2.3.

Changelog

2.2.3

release. The majority of the changes are typing improvements and fixes
for free threaded Python. Both of those areas are still under
development, so if you discover new problems, please report them.

This release supports Python versions 3.10-3.13.

Contributors

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

-   !amotzop
-   Charles Harris
-   Chris Sidebottom
-   Joren Hammudoglu
-   Matthew Brett
-   Nathan Goldbaum
-   Raghuveer Devulapalli
-   Sebastian Berg
-   Yakov Danishevsky +

Pull requests merged

A total of 21 pull requests were merged for this release.

-   [28185](https://github.com/numpy/numpy/pull/28185): MAINT: Prepare 2.2.x for further development
-   [28201](https://github.com/numpy/numpy/pull/28201): BUG: fix data race in a more minimal way on stable branch
-   [28208](https://github.com/numpy/numpy/pull/28208): BUG: Fix `from_float_positional` errors for huge pads
-   [28209](https://github.com/numpy/numpy/pull/28209): BUG: fix data race in np.repeat
-   [28212](https://github.com/numpy/numpy/pull/28212): MAINT: Use VQSORT_COMPILER_COMPATIBLE to determine if we should\...
-   [28224](https://github.com/numpy/numpy/pull/28224): MAINT: update highway to latest
-   [28236](https://github.com/numpy/numpy/pull/28236): BUG: Add cpp atomic support (#28234)
-   [28237](https://github.com/numpy/numpy/pull/28237): BLD: Compile fix for clang-cl on WoA
-   [28243](https://github.com/numpy/numpy/pull/28243): TYP: Avoid upcasting `float64` in the set-ops
-   [28249](https://github.com/numpy/numpy/pull/28249): BLD: better fix for clang / ARM compiles
-   [28266](https://github.com/numpy/numpy/pull/28266): TYP: Fix `timedelta64.__divmod__` and `timedelta64.__mod__`\...
-   [28274](https://github.com/numpy/numpy/pull/28274): TYP: Fixed missing typing information of set_printoptions
-   [28278](https://github.com/numpy/numpy/pull/28278): BUG: backport resource cleanup bugfix from gh-28273
-   [28282](https://github.com/numpy/numpy/pull/28282): BUG: fix incorrect bytes to stringdtype coercion
-   [28283](https://github.com/numpy/numpy/pull/28283): TYP: Fix scalar constructors
-   [28284](https://github.com/numpy/numpy/pull/28284): TYP: stub `numpy.matlib`
-   [28285](https://github.com/numpy/numpy/pull/28285): TYP: stub the missing `numpy.testing` modules
-   [28286](https://github.com/numpy/numpy/pull/28286): CI: Fix the github label for `TYP:` PR\'s and issues
-   [28305](https://github.com/numpy/numpy/pull/28305): TYP: Backport typing updates from main
-   [28321](https://github.com/numpy/numpy/pull/28321): BUG: fix race initializing legacy dtype casts
-   [28324](https://github.com/numpy/numpy/pull/28324): CI: update test_moderately_small_alpha

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2.2.2

release. The number of typing fixes/updates is notable. This release
supports Python versions 3.10-3.13.

Contributors

A total of 8 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Alicia Boya García +
-   Charles Harris
-   Joren Hammudoglu
-   Kai Germaschewski +
-   Nathan Goldbaum
-   PTUsumit +
-   Rohit Goswami
-   Sebastian Berg

Pull requests merged

A total of 16 pull requests were merged for this release.

-   [28050](https://github.com/numpy/numpy/pull/28050): MAINT: Prepare 2.2.x for further development
-   [28055](https://github.com/numpy/numpy/pull/28055): TYP: fix `void` arrays not accepting `str` keys in `__setitem__`
-   [28066](https://github.com/numpy/numpy/pull/28066): TYP: fix unnecessarily broad `integer` binop return types (#28065)
-   [28112](https://github.com/numpy/numpy/pull/28112): TYP: Better `ndarray` binop return types for `float64` &\...
-   [28113](https://github.com/numpy/numpy/pull/28113): TYP: Return the correct `bool` from `issubdtype`
-   [28114](https://github.com/numpy/numpy/pull/28114): TYP: Always accept `date[time]` in the `datetime64` constructor
-   [28120](https://github.com/numpy/numpy/pull/28120): BUG: Fix auxdata initialization in ufunc slow path
-   [28131](https://github.com/numpy/numpy/pull/28131): BUG: move reduction initialization to ufunc initialization
-   [28132](https://github.com/numpy/numpy/pull/28132): TYP: Fix `interp` to accept and return scalars
-   [28137](https://github.com/numpy/numpy/pull/28137): BUG: call PyType_Ready in f2py to avoid data races
-   [28145](https://github.com/numpy/numpy/pull/28145): BUG: remove unnecessary call to PyArray_UpdateFlags
-   [28160](https://github.com/numpy/numpy/pull/28160): BUG: Avoid data race in PyArray_CheckFromAny_int
-   [28175](https://github.com/numpy/numpy/pull/28175): BUG: Fix f2py directives and \--lower casing
-   [28176](https://github.com/numpy/numpy/pull/28176): TYP: Fix overlapping overloads issue in 2-\>1 ufuncs
-   [28177](https://github.com/numpy/numpy/pull/28177): TYP: preserve shape-type in ndarray.astype()
-   [28178](https://github.com/numpy/numpy/pull/28178): TYP: Fix missing and spurious top-level exports

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2.2.1

after the 2.2.0 release and has several maintenance pins to work around
upstream changes.

There was some breakage in downstream projects following the 2.2.0
release due to updates to NumPy typing. Because of problems due to MyPy
defects, we recommend using basedpyright for type checking, it can be
installed from PyPI. The Pylance extension for Visual Studio Code is
also based on Pyright. Problems that persist when using basedpyright
should be reported as issues on the NumPy github site.

This release supports Python 3.10-3.13.

Contributors

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

-   Charles Harris
-   Joren Hammudoglu
-   Matti Picus
-   Nathan Goldbaum
-   Peter Hawkins
-   Simon Altrogge
-   Thomas A Caswell
-   Warren Weckesser
-   Yang Wang +

Pull requests merged

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

-   [27935](https://github.com/numpy/numpy/pull/27935): MAINT: Prepare 2.2.x for further development
-   [27950](https://github.com/numpy/numpy/pull/27950): TEST: cleanups
-   [27958](https://github.com/numpy/numpy/pull/27958): BUG: fix use-after-free error in npy_hashtable.cpp (#27955)
-   [27959](https://github.com/numpy/numpy/pull/27959): BLD: add missing include
-   [27982](https://github.com/numpy/numpy/pull/27982): BUG:fix compile error libatomic link test to meson.build
-   [27990](https://github.com/numpy/numpy/pull/27990): TYP: Fix falsely rejected value types in `ndarray.__setitem__`
-   [27991](https://github.com/numpy/numpy/pull/27991): MAINT: Don\'t wrap `#include <Python.h>` with `extern "C"`
-   [27993](https://github.com/numpy/numpy/pull/27993): BUG: Fix segfault in stringdtype lexsort
-   [28006](https://github.com/numpy/numpy/pull/28006): MAINT: random: Tweak module code in mtrand.pyx to fix a Cython\...
-   [28007](https://github.com/numpy/numpy/pull/28007): BUG: Cython API was missing NPY_UINTP.
-   [28021](https://github.com/numpy/numpy/pull/28021): CI: pin scipy-doctest to 1.5.1
-   [28044](https://github.com/numpy/numpy/pull/28044): TYP: allow `None` in operand sequence of nditer

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2.2.0

The NumPy 2.2.0 release is a quick release that brings us back into sync
with the usual twice yearly release cycle. There have been an number of
small cleanups, as well as work bringing the new StringDType to
completion and improving support for free threaded Python. Highlights
are:

-   New functions `matvec` and `vecmat`, see below.
-   Many improved annotations.
-   Improved support for the new StringDType.
-   Improved support for free threaded Python
-   Fixes for f2py

This release supports Python versions 3.10-3.13.

Deprecations

-   `_add_newdoc_ufunc` is now deprecated. `ufunc.__doc__ = newdoc`
 should be used instead.

 ([gh-27735](https://github.com/numpy/numpy/pull/27735))

Expired deprecations

-   `bool(np.array([]))` and other empty arrays will now raise an error.
 Use `arr.size > 0` instead to check whether an array has no
 elements.

 ([gh-27160](https://github.com/numpy/numpy/pull/27160))

Compatibility notes

-   `numpy.cov` now properly transposes single-row (2d array) design matrices
 when `rowvar=False`. Previously, single-row design matrices would return a
 scalar in this scenario, which is not correct, so this is a behavior change
 and an array of the appropriate shape will now be returned.

 ([gh-27661](https://github.com/numpy/numpy/pull/27661))

New Features

-   New functions for matrix-vector and vector-matrix products

 Two new generalized ufuncs were defined:

 -   `numpy.matvec` - matrix-vector product, treating the
     arguments as stacks of matrices and column vectors,
     respectively.
 -   `numpy.vecmat` - vector-matrix product, treating the
     arguments as stacks of column vectors and matrices,
     respectively. For complex vectors, the conjugate is taken.

 These add to the existing `numpy.matmul` as well as to
 `numpy.vecdot`, which was added in numpy 2.0.

 Note that `numpy.matmul` never takes a complex conjugate, also not when its
 left input is a vector, while both `numpy.vecdot` and `numpy.vecmat` do
 take the conjugate for complex vectors on the left-hand side (which are
 taken to be the ones that are transposed, following the physics
 convention).

 ([gh-25675](https://github.com/numpy/numpy/pull/25675))

-   `np.complexfloating[T, T]` can now also be written as
 `np.complexfloating[T]`

 ([gh-27420](https://github.com/numpy/numpy/pull/27420))

-   UFuncs now support `__dict__` attribute and allow overriding
 `__doc__` (either directly or via `ufunc.__dict__["__doc__"]`).
 `__dict__` can be used to also override other properties, such as
 `__module__` or `__qualname__`.

 ([gh-27735](https://github.com/numpy/numpy/pull/27735))

-   The \"nbit\" type parameter of `np.number` and its subtypes now
 defaults to `typing.Any`. This way, type-checkers will infer
 annotations such as `x: np.floating` as `x: np.floating[Any]`, even
 in strict mode.

 ([gh-27736](https://github.com/numpy/numpy/pull/27736))

Improvements

-   The `datetime64` and `timedelta64` hashes now correctly match the
 Pythons builtin `datetime` and `timedelta` ones. The hashes now
 evaluated equal even for equal values with different time units.

 ([gh-14622](https://github.com/numpy/numpy/pull/14622))

-   Fixed a number of issues around promotion for string ufuncs with
 StringDType arguments. Mixing StringDType and the fixed-width DTypes
 using the string ufuncs should now generate much more uniform
 results.

 ([gh-27636](https://github.com/numpy/numpy/pull/27636))

-   Improved support for empty `memmap`. Previously an empty `memmap` would
 fail unless a non-zero `offset` was set.  Now a zero-size `memmap` is
 supported even if `offset=0`. To achieve this, if a `memmap` is mapped to
 an empty file that file is padded with a single byte.

 ([gh-27723](https://github.com/numpy/numpy/pull/27723))

-   `f2py` handles multiple modules and exposes variables again.  A regression
 has been fixed which allows F2PY users to expose variables to Python in
 modules with only assignments, and also fixes situations where multiple
 modules are present within a single source file.

 ([gh-27695](https://github.com/numpy/numpy/pull/27695))

Performance improvements and changes

-   NumPy now uses fast-on-failure attribute lookups for protocols. This
 can greatly reduce overheads of function calls or array creation
 especially with custom Python objects. The largest improvements will
 be seen on Python 3.12 or newer.

 ([gh-27119](https://github.com/numpy/numpy/pull/27119))

-   OpenBLAS on x86_64 and i686 is built with fewer kernels. Based on
 benchmarking, there are 5 clusters of performance around these
 kernels: `PRESCOTT NEHALEM SANDYBRIDGE HASWELL SKYLAKEX`.

-   OpenBLAS on windows is linked without quadmath, simplifying
 licensing

-   Due to a regression in OpenBLAS on windows, the performance
 improvements when using multiple threads for OpenBLAS 0.3.26 were
 reverted.

 ([gh-27147](https://github.com/numpy/numpy/pull/27147))

-   NumPy now indicates hugepages also for large `np.zeros` allocations
 on linux. Thus should generally improve performance.

 ([gh-27808](https://github.com/numpy/numpy/pull/27808))

Changes

-   `numpy.fix` now won\'t perform casting to a floating
 data-type for integer and boolean data-type input arrays.

 ([gh-26766](https://github.com/numpy/numpy/pull/26766))

-   The type annotations of `numpy.float64` and `numpy.complex128` now reflect
 that they are also subtypes of the built-in `float` and `complex` types,
 respectively. This update prevents static type-checkers from reporting
 errors in cases such as:

  python
 x: float = numpy.float64(6.28)   valid
 z: complex = numpy.complex128(-1j)   valid
 

 ([gh-27334](https://github.com/numpy/numpy/pull/27334))

-   The `repr` of arrays large enough to be summarized (i.e., where
 elements are replaced with `...`) now includes the `shape` of the
 array, similar to what already was the case for arrays with zero
 size and non-obvious shape. With this change, the shape is always
 given when it cannot be inferred from the values. Note that while
 written as `shape=...`, this argument cannot actually be passed in
 to the `np.array` constructor. If you encounter problems, e.g., due
 to failing doctests, you can use the print option `legacy=2.1` to
 get the old behaviour.

 ([gh-27482](https://github.com/numpy/numpy/pull/27482))

-   Calling `__array_wrap__` directly on NumPy arrays or scalars now
 does the right thing when `return_scalar` is passed (Added in NumPy
 2). It is further safe now to call the scalar `__array_wrap__` on a
 non-scalar result.

 ([gh-27807](https://github.com/numpy/numpy/pull/27807))

-   Bump the musllinux CI image and wheels to 1_2 from 1_1. This is because
 1_1 is [end of life](https://github.com/pypa/manylinux/issues/1629).

 ([gh-27088](https://github.com/numpy/numpy/pull/27088))

-   NEP 50 promotion state option removed

 The NEP 50 promotion state settings are now removed. They were always meant as
 temporary means for testing. A warning will be given if the environment
 variable is set to anything but `NPY_PROMOTION_STATE=weak` while
 `_set_promotion_state` and `_get_promotion_state` are removed. In case code
 used `_no_nep50_warning`, a `contextlib.nullcontext` could be used to replace
 it when not available.

 ([gh-27156](https://github.com/numpy/numpy/pull/27156))

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2.1.3

discovered after the 2.1.2 release. This release also adds support
for free threaded Python 3.13 on Windows.

The Python versions supported by this release are 3.10-3.13.

Improvements

-   Fixed a number of issues around promotion for string ufuncs with
 StringDType arguments. Mixing StringDType and the fixed-width DTypes
 using the string ufuncs should now generate much more uniform
 results.

 ([gh-27636](https://github.com/numpy/numpy/pull/27636))

Changes

-   `numpy.fix` now won\'t perform casting to a floating
 data-type for integer and boolean data-type input arrays.

 ([gh-26766](https://github.com/numpy/numpy/pull/26766))

Contributors

A total of 15 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Abhishek Kumar +
-   Austin +
-   Benjamin A. Beasley +
-   Charles Harris
-   Christian Lorentzen
-   Marcel Telka +
-   Matti Picus
-   Michael Davidsaver +
-   Nathan Goldbaum
-   Peter Hawkins
-   Raghuveer Devulapalli
-   Ralf Gommers
-   Sebastian Berg
-   dependabot\[bot\]
-   kp2pml30 +

Pull requests merged

A total of 21 pull requests were merged for this release.

-   [27512](https://github.com/numpy/numpy/pull/27512): MAINT: prepare 2.1.x for further development
-   [27537](https://github.com/numpy/numpy/pull/27537): MAINT: Bump actions/cache from 4.0.2 to 4.1.1
-   [27538](https://github.com/numpy/numpy/pull/27538): MAINT: Bump pypa/cibuildwheel from 2.21.2 to 2.21.3
-   [27539](https://github.com/numpy/numpy/pull/27539): MAINT: MSVC does not support #warning directive
-   [27543](https://github.com/numpy/numpy/pull/27543): BUG: Fix user dtype can-cast with python scalar during promotion
-   [27561](https://github.com/numpy/numpy/pull/27561): DEV: bump `python` to 3.12 in environment.yml
-   [27562](https://github.com/numpy/numpy/pull/27562): BLD: update vendored Meson to 1.5.2
-   [27563](https://github.com/numpy/numpy/pull/27563): BUG: weighted quantile for some zero weights (#27549)
-   [27565](https://github.com/numpy/numpy/pull/27565): MAINT: Use miniforge for macos conda test.
-   [27566](https://github.com/numpy/numpy/pull/27566): BUILD: satisfy gcc-13 pendantic errors
-   [27569](https://github.com/numpy/numpy/pull/27569): BUG: handle possible error for PyTraceMallocTrack
-   [27570](https://github.com/numpy/numpy/pull/27570): BLD: start building Windows free-threaded wheels \[wheel build\]
-   [27571](https://github.com/numpy/numpy/pull/27571): BUILD: vendor tempita from Cython
-   [27574](https://github.com/numpy/numpy/pull/27574): BUG: Fix warning \"differs in levels of indirection\" in npy_atomic.h\...
-   [27592](https://github.com/numpy/numpy/pull/27592): MAINT: Update Highway to latest
-   [27593](https://github.com/numpy/numpy/pull/27593): BUG: Adjust numpy.i for SWIG 4.3 compatibility
-   [27616](https://github.com/numpy/numpy/pull/27616): BUG: Fix Linux QEMU CI workflow
-   [27668](https://github.com/numpy/numpy/pull/27668): BLD: Do not set \_\_STDC_VERSION\_\_ to zero during build
-   [27669](https://github.com/numpy/numpy/pull/27669): ENH: fix wasm32 runtime type error in numpy.\_core
-   [27672](https://github.com/numpy/numpy/pull/27672): BUG: Fix a reference count leak in npy_find_descr_for_scalar.
-   [27673](https://github.com/numpy/numpy/pull/27673): BUG: fixes for StringDType/unicode promoters

Checksums

MD5

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 13da2761d1abe71731a2806537369115  numpy-2.1.3-cp310-cp310-macosx_11_0_arm64.whl
 5aef4a78b69cd90d0f6fff8f88817991  numpy-2.1.3-cp310-cp310-macosx_14_0_arm64.whl
 12da7f09cd5707634878f85845c9de10  numpy-2.1.3-cp310-cp310-macosx_14_0_x86_64.whl
 5b999693362815b56855533469aea0ca  numpy-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 8c49f457127bfb4f167c91583e5167af  numpy-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
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@pyup-bot pyup-bot mentioned this pull request Feb 13, 2025
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