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Pin numpy to latest version 2.2.0 #737

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This PR pins numpy to the latest release 2.2.0.

Changelog

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

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2.1.2

discovered after the 2.1.1 release.

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

Contributors

A total of 11 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Charles Harris
-   Chris Sidebottom
-   Ishan Koradia +
-   João Eiras +
-   Katie Rust +
-   Marten van Kerkwijk
-   Matti Picus
-   Nathan Goldbaum
-   Peter Hawkins
-   Pieter Eendebak
-   Slava Gorloff +

Pull requests merged

A total of 14 pull requests were merged for this release.

-   [27333](https://github.com/numpy/numpy/pull/27333): MAINT: prepare 2.1.x for further development
-   [27400](https://github.com/numpy/numpy/pull/27400): BUG: apply critical sections around populating the dispatch cache
-   [27406](https://github.com/numpy/numpy/pull/27406): BUG: Stub out get_build_msvc_version if distutils.msvccompiler\...
-   [27416](https://github.com/numpy/numpy/pull/27416): BUILD: fix missing include for std::ptrdiff_t for C++23 language\...
-   [27433](https://github.com/numpy/numpy/pull/27433): BLD: pin setuptools to avoid breaking numpy.distutils
-   [27437](https://github.com/numpy/numpy/pull/27437): BUG: Allow unsigned shift argument for np.roll
-   [27439](https://github.com/numpy/numpy/pull/27439): BUG: Disable SVE VQSort
-   [27471](https://github.com/numpy/numpy/pull/27471): BUG: rfftn axis bug
-   [27479](https://github.com/numpy/numpy/pull/27479): BUG: Fix extra decref of PyArray_UInt8DType.
-   [27480](https://github.com/numpy/numpy/pull/27480): CI: use PyPI not scientific-python-nightly-wheels for CI doc\...
-   [27481](https://github.com/numpy/numpy/pull/27481): MAINT: Check for SVE support on demand
-   [27484](https://github.com/numpy/numpy/pull/27484): BUG: initialize the promotion state to be weak
-   [27501](https://github.com/numpy/numpy/pull/27501): MAINT: Bump pypa/cibuildwheel from 2.20.0 to 2.21.2
-   [27506](https://github.com/numpy/numpy/pull/27506): BUG: avoid segfault on bad arguments in ndarray.\_\_array_function\_\_

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2.1.1

discovered after the 2.1.0 release.

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

Contributors

A total of 7 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Charles Harris
-   Mateusz Sokół
-   Maximilian Weigand +
-   Nathan Goldbaum
-   Pieter Eendebak
-   Sebastian Berg

Pull requests merged

A total of 10 pull requests were merged for this release.

-   [27236](https://github.com/numpy/numpy/pull/27236): REL: Prepare for the NumPy 2.1.0 release \[wheel build\]
-   [27252](https://github.com/numpy/numpy/pull/27252): MAINT: prepare 2.1.x for further development
-   [27259](https://github.com/numpy/numpy/pull/27259): BUG: revert unintended change in the return value of set_printoptions
-   [27266](https://github.com/numpy/numpy/pull/27266): BUG: fix reference counting bug in \_\_array_interface\_\_ implementation...
-   [27267](https://github.com/numpy/numpy/pull/27267): TST: Add regression test for missing descr in array-interface
-   [27276](https://github.com/numpy/numpy/pull/27276): BUG: Fix #27256 and 27257
-   [27278](https://github.com/numpy/numpy/pull/27278): BUG: Fix array_equal for numeric and non-numeric scalar types
-   [27287](https://github.com/numpy/numpy/pull/27287): MAINT: Update maintenance/2.1.x after the 2.0.2 release
-   [27303](https://github.com/numpy/numpy/pull/27303): BLD: cp311- macosx_arm64 wheels \[wheel build\]
-   [27304](https://github.com/numpy/numpy/pull/27304): BUG: f2py: better handle filtering of public/private subroutines

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2.1

3.13. This support was enabled by fixing a number of C thread-safety
issues in NumPy. Before NumPy 2.1, NumPy used a large number of C global
static variables to store runtime caches and other state. We have either
refactored to avoid the need for global state, converted the global
state to thread-local state, or added locking.

Support for free-threaded Python does not mean that NumPy is thread
safe. Read-only shared access to ndarray should be safe. NumPy exposes
shared mutable state and we have not added any locking to the array
object itself to serialize access to shared state. Care must be taken in
user code to avoid races if you would like to mutate the same array in
multiple threads. It is certainly possible to crash NumPy by mutating an
array simultaneously in multiple threads, for example by calling a ufunc
and the `resize` method simultaneously. For now our guidance is:
\"don\'t do that\". In the future we would like to provide stronger
guarantees.

Object arrays in particular need special care, since the GIL previously
provided locking for object array access and no longer does. See
[Issue 27199](https://github.com/numpy/numpy/issues/27199) for more information about object
arrays in the free-threaded build.

If you are interested in free-threaded Python, for example because you
have a multiprocessing-based workflow that you are interested in running
with Python threads, we encourage testing and experimentation.

If you run into problems that you suspect are because of NumPy, please
[open an issue](https://github.com/numpy/numpy/issues/new/choose),
checking first if the bug also occurs in the \"regular\" non-free-threaded CPython 3.13 
build. Many threading bugs can also occur in code that releases
the GIL; disabling the GIL only makes it easier to hit threading bugs.

([gh-26157](https://github.com/numpy/numpy/issues/26157#issuecomment-2233864940))

`f2py` can generate freethreading-compatible C extensions

Pass `--freethreading-compatible` to the f2py CLI tool to produce a C
extension marked as compatible with the free threading CPython
interpreter. Doing so prevents the interpreter from re-enabling the GIL
at runtime when it imports the C extension. Note that `f2py` does not
analyze fortran code for thread safety, so you must verify that the
wrapped fortran code is thread safe before marking the extension as
compatible.

([gh-26981](https://github.com/numpy/numpy/pull/26981))

-   `numpy.reshape` and `numpy.ndarray.reshape` now support `shape` and
 `copy` arguments.

 ([gh-26292](https://github.com/numpy/numpy/pull/26292))

-   NumPy now supports DLPack v1, support for older versions will be
 deprecated in the future.

 ([gh-26501](https://github.com/numpy/numpy/pull/26501))

-   `numpy.asanyarray` now supports `copy` and `device` arguments,
 matching `numpy.asarray`.

 ([gh-26580](https://github.com/numpy/numpy/pull/26580))

-   `numpy.printoptions`, `numpy.get_printoptions`, and
 `numpy.set_printoptions` now support a new option, `override_repr`,
 for defining custom `repr(array)` behavior.

 ([gh-26611](https://github.com/numpy/numpy/pull/26611))

-   `numpy.cumulative_sum` and `numpy.cumulative_prod` were added as
 Array API compatible alternatives for `numpy.cumsum` and
 `numpy.cumprod`. The new functions can include a fixed initial
 (zeros for `sum` and ones for `prod`) in the result.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))

-   `numpy.clip` now supports `max` and `min` keyword arguments which
 are meant to replace `a_min` and `a_max`. Also, for `np.clip(a)` or
 `np.clip(a, None, None)` a copy of the input array will be returned
 instead of raising an error.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))

-   `numpy.astype` now supports `device` argument.

 ([gh-26724](https://github.com/numpy/numpy/pull/26724))


Improvements

`histogram` auto-binning now returns bin sizes \>=1 for integer input data

For integer input data, bin sizes smaller than 1 result in spurious
empty bins. This is now avoided when the number of bins is computed
using one of the algorithms provided by `histogram_bin_edges`.

([gh-12150](https://github.com/numpy/numpy/pull/12150))

`ndarray` shape-type parameter is now covariant and bound to `tuple[int, ...]`

Static typing for `ndarray` is a long-term effort that continues with
this change. It is a generic type with type parameters for the shape and
the data type. Previously, the shape type parameter could be any value.
This change restricts it to a tuple of ints, as one would expect from
using `ndarray.shape`. Further, the shape-type parameter has been
changed from invariant to covariant. This change also applies to the
subtypes of `ndarray`, e.g. `numpy.ma.MaskedArray`. See the
[typing docs](https://typing.readthedocs.io/en/latest/reference/generics.html#va

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