Changelog
### 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#variance-of-generic-types)
for more information.
([gh-26081](https://github.com/numpy/numpy/pull/26081))
`np.quantile` with method `closest_observation` chooses nearest even order statistic
This changes the definition of nearest for border cases from the nearest
odd order statistic to nearest even order statistic. The numpy
implementation now matches other reference implementations.
([gh-26656](https://github.com/numpy/numpy/pull/26656))
`lapack_lite` is now thread safe
NumPy provides a minimal low-performance version of LAPACK named
`lapack_lite` that can be used if no BLAS/LAPACK system is detected at
build time.
Until now, `lapack_lite` was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.
We have added a global lock, serializing access to `lapack_lite` in
multiple threads.
([gh-26750](https://github.com/numpy/numpy/pull/26750))
The `numpy.printoptions` context manager is now thread and async-safe
In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python `ContextVar`, making the context manager
thread and async-safe.
([gh-26846](https://github.com/numpy/numpy/pull/26846))
Type hinting `numpy.polynomial`
Starting from the 2.1 release, PEP 484 type annotations have been
included for the functions and convenience classes in `numpy.polynomial`
and its sub-packages.
([gh-26897](https://github.com/numpy/numpy/pull/26897))
Improved `numpy.dtypes` type hints
The type annotations for `numpy.dtypes` are now a better reflection of
the runtime: The `numpy.dtype` type-aliases have been replaced with
specialized `dtype` *subtypes*, and the previously missing annotations
for `numpy.dtypes.StringDType` have been added.
([gh-27008](https://github.com/numpy/numpy/pull/27008))
Performance improvements and changes
- `numpy.save` now uses pickle protocol version 4 for saving arrays
with object dtype, which allows for pickle objects larger than 4GB
and improves saving speed by about 5% for large arrays.
([gh-26388](https://github.com/numpy/numpy/pull/26388))
- 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`.
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- OpenBLAS on windows is linked without quadmath, simplifying
licensing
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- 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))
`ma.cov` and `ma.corrcoef` are now significantly faster
The private function has been refactored along with `ma.cov` and
`ma.corrcoef`. They are now significantly faster, particularly on large,
masked arrays.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Changes
- As `numpy.vecdot` is now a ufunc it has a less precise signature.
This is due to the limitations of ufunc\'s typing stub.
([gh-26313](https://github.com/numpy/numpy/pull/26313))
- `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform
casting to a floating dtype for integer and boolean dtype input
arrays.
([gh-26766](https://github.com/numpy/numpy/pull/26766))
`ma.corrcoef` may return a slightly different result
A pairwise observation approach is currently used in `ma.corrcoef` to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using `ma.cov`, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as `corrcoef`
when using a masked array with no masked values.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Cast-safety fixes in `copyto` and `full`
`copyto` now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered \"safe\" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:
- `np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast
of the Python integer. It will now always raise, to achieve an
unsafe cast you must pass an array or NumPy scalar.
- `np.copyto(uint8_arr, 1000, casting="safe")` will raise an
OverflowError rather than a TypeError due to same-kind casting.
- `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to
`inf` (float32 cannot hold `1e300`) rather raising a TypeError.
Further, only the dtype is used when assigning NumPy scalars (or 0-d
arrays), meaning that the following behaves differently:
- `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises.
- `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises.
Previously, NumPy checked whether the 100 fits the `int8_arr`.
This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2
behavior.
([gh-27091](https://github.com/numpy/numpy/pull/27091))
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```
### 2.1.0
```
NumPy 2.1.0 provides support for the upcoming Python 3.13 release and
drops support for Python 3.9. In addition to the usual bug fixes and
updated Python support, it helps get us back into our usual release
cycle after the extended development of 2.0. The highlights for this
release are:
- Support for the array-api 2023.12 standard.
- Support for Python 3.13.
- Preliminary support for free threaded Python 3.13.
Python versions 3.10-3.13 are supported in this release.
New functions
New function `numpy.unstack`
A new function `np.unstack(array, axis=...)` was added, which splits an
array into a tuple of arrays along an axis. It serves as the inverse of
[numpy.stack]{.title-ref}.
([gh-26579](https://github.com/numpy/numpy/pull/26579))
Deprecations
- The `fix_imports` keyword argument in `numpy.save` is deprecated.
Since NumPy 1.17, `numpy.save` uses a pickle protocol that no longer
supports Python 2, and ignored `fix_imports` keyword. This keyword
is kept only for backward compatibility. It is now deprecated.
([gh-26452](https://github.com/numpy/numpy/pull/26452))
- Passing non-integer inputs as the first argument of
[bincount]{.title-ref} is now deprecated, because such inputs are
silently cast to integers with no warning about loss of precision.
([gh-27076](https://github.com/numpy/numpy/pull/27076))
Expired deprecations
- Scalars and 0D arrays are disallowed for `numpy.nonzero` and
`numpy.ndarray.nonzero`.
([gh-26268](https://github.com/numpy/numpy/pull/26268))
- `set_string_function` internal function was removed and
`PyArray_SetStringFunction` was stubbed out.
([gh-26611](https://github.com/numpy/numpy/pull/26611))
C API changes
API symbols now hidden but customizable
NumPy now defaults to hide the API symbols it adds to allow all NumPy
API usage. This means that by default you cannot dynamically fetch the
NumPy API from another library (this was never possible on windows).
If you are experiencing linking errors related to `PyArray_API` or
`PyArray_RUNTIME_VERSION`, you can define the `NPY_API_SYMBOL_ATTRIBUTE`
to opt-out of this change.
If you are experiencing problems due to an upstream header including
NumPy, the solution is to make sure you
`include "numpy/ndarrayobject.h"` before their header and import NumPy
yourself based on `including-the-c-api`.
([gh-26103](https://github.com/numpy/numpy/pull/26103))
Many shims removed from npy_3kcompat.h
Many of the old shims and helper functions were removed from
`npy_3kcompat.h`. If you find yourself in need of these, vendor the
previous version of the file into your codebase.
([gh-26842](https://github.com/numpy/numpy/pull/26842))
New `PyUFuncObject` field `process_core_dims_func`
The field `process_core_dims_func` was added to the structure
`PyUFuncObject`. For generalized ufuncs, this field can be set to a
function of type `PyUFunc_ProcessCoreDimsFunc` that will be called when
the ufunc is called. It allows the ufunc author to check that core
dimensions satisfy additional constraints, and to set output core
dimension sizes if they have not been provided.
([gh-26908](https://github.com/numpy/numpy/pull/26908))
New Features
- `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))
`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))
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#variance-of-generic-types)
for more information.
([gh-26081](https://github.com/numpy/numpy/pull/26081))
`np.quantile` with method `closest_observation` chooses nearest even order statistic
This changes the definition of nearest for border cases from the nearest
odd order statistic to nearest even order statistic. The numpy
implementation now matches other reference implementations.
([gh-26656](https://github.com/numpy/numpy/pull/26656))
`lapack_lite` is now thread safe
NumPy provides a minimal low-performance version of LAPACK named
`lapack_lite` that can be used if no BLAS/LAPACK system is detected at
build time.
Until now, `lapack_lite` was not thread safe. Single-threaded use cases
did not hit any issues, but running linear algebra operations in
multiple threads could lead to errors, incorrect results, or segfaults
due to data races.
We have added a global lock, serializing access to `lapack_lite` in
multiple threads.
([gh-26750](https://github.com/numpy/numpy/pull/26750))
The `numpy.printoptions` context manager is now thread and async-safe
In prior versions of NumPy, the printoptions were defined using a
combination of Python and C global variables. We have refactored so the
state is stored in a python `ContextVar`, making the context manager
thread and async-safe.
([gh-26846](https://github.com/numpy/numpy/pull/26846))
Performance improvements and changes
- `numpy.save` now uses pickle protocol version 4 for saving arrays
with object dtype, which allows for pickle objects larger than 4GB
and improves saving speed by about 5% for large arrays.
([gh-26388](https://github.com/numpy/numpy/pull/26388))
- 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`.
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- OpenBLAS on windows is linked without quadmath, simplifying
licensing
([gh-27147](https://github.com/numpy/numpy/pull/27147))
- 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))
`ma.cov` and `ma.corrcoef` are now significantly faster
The private function has been refactored along with `ma.cov` and
`ma.corrcoef`. They are now significantly faster, particularly on large,
masked arrays.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Changes
- As `numpy.vecdot` is now a ufunc it has a less precise signature.
This is due to the limitations of ufunc\'s typing stub.
([gh-26313](https://github.com/numpy/numpy/pull/26313))
- `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform
casting to a floating dtype for integer and boolean dtype input
arrays.
([gh-26766](https://github.com/numpy/numpy/pull/26766))
`ma.corrcoef` may return a slightly different result
A pairwise observation approach is currently used in `ma.corrcoef` to
calculate the standard deviations for each pair of variables. This has
been changed as it is being used to normalise the covariance, estimated
using `ma.cov`, which does not consider the observations for each
variable in a pairwise manner, rendering it unnecessary. The
normalisation has been replaced by the more appropriate standard
deviation for each variable, which significantly reduces the wall time,
but will return slightly different estimates of the correlation
coefficients in cases where the observations between a pair of variables
are not aligned. However, it will return the same estimates in all other
cases, including returning the same correlation matrix as `corrcoef`
when using a masked array with no masked values.
([gh-26285](https://github.com/numpy/numpy/pull/26285))
Cast-safety fixes in `copyto` and `full`
`copyto` now uses NEP 50 correctly and applies this to its cast safety.
Python integer to NumPy integer casts and Python float to NumPy float
casts are now considered \"safe\" even if assignment may fail or
precision may be lost. This means the following examples change
slightly:
-
`np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast
: of the Python integer. It will now always raise, to achieve an
unsafe cast you must pass an array or NumPy scalar.
- `np.copyto(uint8_arr, 1000, casting="safe")` will raise an
OverflowError rather than a TypeError due to same-kind casting.
- `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to
`inf` (float32 cannot hold `1e300`) rather raising a TypeError.
Further, only the dtype is used when assigning NumPy scalars (or 0-d
arrays), meaning that the following behaves differently:
- `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises.
- `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises.
Previously, NumPy checked whether the 100 fits the `int8_arr`.
This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2
behavior.
([gh-27091](https://github.com/numpy/numpy/pull/27091))
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```
### 2.0.2
```
discovered after the 2.0.1 release.
The Python versions supported by this release are 3.9-3.12.
Contributors
A total of 13 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.
- Bruno Oliveira +
- Charles Harris
- Chris Sidebottom
- Christian Heimes +
- Christopher Sidebottom
- Mateusz Sokół
- Matti Picus
- Nathan Goldbaum
- Pieter Eendebak
- Raghuveer Devulapalli
- Ralf Gommers
- Sebastian Berg
- Yair Chuchem +
Pull requests merged
A total of 19 pull requests were merged for this release.
- [27000](https://github.com/numpy/numpy/pull/27000): REL: Prepare for the NumPy 2.0.1 release \[wheel build\]
- [27001](https://github.com/numpy/numpy/pull/27001): MAINT: prepare 2.0.x for further development
- [27021](https://github.com/numpy/numpy/pull/27021): BUG: cfuncs.py: fix crash when sys.stderr is not available
- [27022](https://github.com/numpy/numpy/pull/27022): DOC: Fix migration note for `alltrue` and `sometrue`
- [27061](https://github.com/numpy/numpy/pull/27061): BUG: use proper input and output descriptor in array_assign_subscript\...
- [27073](https://github.com/numpy/numpy/pull/27073): BUG: Mirror VQSORT_ENABLED logic in Quicksort
- [27074](https://github.com/numpy/numpy/pull/27074): BUG: Bump Highway to latest master
- [27077](https://github.com/numpy/numpy/pull/27077): BUG: Off by one in memory overlap check
- [27122](https://github.com/numpy/numpy/pull/27122): BUG: Use the new `npyv_loadable_stride_` functions for ldexp and\...
- [27126](https://github.com/numpy/numpy/pull/27126): BUG: Bump Highway to latest
- [27128](https://github.com/numpy/numpy/pull/27128): BUG: add missing error handling in public_dtype_api.c
- [27129](https://github.com/numpy/numpy/pull/27129): BUG: fix another cast setup in array_assign_subscript
- [27130](https://github.com/numpy/numpy/pull/27130): BUG: Fix building NumPy in FIPS mode
- [27131](https://github.com/numpy/numpy/pull/27131): BLD: update vendored Meson for cross-compilation patches
- [27146](https://github.com/numpy/numpy/pull/27146): MAINT: Scipy openblas 0.3.27.44.4
- [27151](https://github.com/numpy/numpy/pull/27151): BUG: Do not accidentally store dtype metadata in `np.save`
- [27195](https://github.com/numpy/numpy/pull/27195): REV: Revert undef I and document it
- [27213](https://github.com/numpy/numpy/pull/27213): BUG: Fix NPY_RAVEL_AXIS on backwards compatible NumPy 2 builds
- [27279](https://github.com/numpy/numpy/pull/27279): BUG: Fix array_equal for numeric and non-numeric scalar types
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8cafab480740e22f8d833
This PR pins numpy to the latest release 2.1.1.
Changelog
### 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 Checksums MD5 3053a97400db800b7377749e691eb39e numpy-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl 84b752a2220dce7c96ff89eef4f4aec3 numpy-2.1.1-cp310-cp310-macosx_11_0_arm64.whl 47ed4f704a64261f07ca24ef2e674524 numpy-2.1.1-cp310-cp310-macosx_14_0_arm64.whl b8a45caa870aee980c298053cf064d28 numpy-2.1.1-cp310-cp310-macosx_14_0_x86_64.whl e097ad5eee572b791b4a25eedad6df4a numpy-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ae502c99315884cda7f0236a07c035c4 numpy-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 841a859d975c55090c0b60b72aab93a3 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numpy-2.1.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 99f4a9ee60eed1385a86e82288971a51e71df052ed0b2900ed30bc840c0f2e39 numpy-2.1.1-pp310-pypy310_pp73-win_amd64.whl d0cf7d55b1051387807405b3898efafa862997b4cba8aa5dbe657be794afeafd numpy-2.1.1.tar.gz ``` ### 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#variance-of-generic-types) for more information. ([gh-26081](https://github.com/numpy/numpy/pull/26081)) `np.quantile` with method `closest_observation` chooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations. ([gh-26656](https://github.com/numpy/numpy/pull/26656)) `lapack_lite` is now thread safe NumPy provides a minimal low-performance version of LAPACK named `lapack_lite` that can be used if no BLAS/LAPACK system is detected at build time. Until now, `lapack_lite` was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or segfaults due to data races. We have added a global lock, serializing access to `lapack_lite` in multiple threads. ([gh-26750](https://github.com/numpy/numpy/pull/26750)) The `numpy.printoptions` context manager is now thread and async-safe In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python `ContextVar`, making the context manager thread and async-safe. ([gh-26846](https://github.com/numpy/numpy/pull/26846)) Type hinting `numpy.polynomial` Starting from the 2.1 release, PEP 484 type annotations have been included for the functions and convenience classes in `numpy.polynomial` and its sub-packages. ([gh-26897](https://github.com/numpy/numpy/pull/26897)) Improved `numpy.dtypes` type hints The type annotations for `numpy.dtypes` are now a better reflection of the runtime: The `numpy.dtype` type-aliases have been replaced with specialized `dtype` *subtypes*, and the previously missing annotations for `numpy.dtypes.StringDType` have been added. ([gh-27008](https://github.com/numpy/numpy/pull/27008)) Performance improvements and changes - `numpy.save` now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays. ([gh-26388](https://github.com/numpy/numpy/pull/26388)) - 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`. ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - OpenBLAS on windows is linked without quadmath, simplifying licensing ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - 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)) `ma.cov` and `ma.corrcoef` are now significantly faster The private function has been refactored along with `ma.cov` and `ma.corrcoef`. They are now significantly faster, particularly on large, masked arrays. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Changes - As `numpy.vecdot` is now a ufunc it has a less precise signature. This is due to the limitations of ufunc\'s typing stub. ([gh-26313](https://github.com/numpy/numpy/pull/26313)) - `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform casting to a floating dtype for integer and boolean dtype input arrays. ([gh-26766](https://github.com/numpy/numpy/pull/26766)) `ma.corrcoef` may return a slightly different result A pairwise observation approach is currently used in `ma.corrcoef` to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using `ma.cov`, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as `corrcoef` when using a masked array with no masked values. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Cast-safety fixes in `copyto` and `full` `copyto` now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered \"safe\" even if assignment may fail or precision may be lost. This means the following examples change slightly: - `np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar. - `np.copyto(uint8_arr, 1000, casting="safe")` will raise an OverflowError rather than a TypeError due to same-kind casting. - `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to `inf` (float32 cannot hold `1e300`) rather raising a TypeError. Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently: - `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises. - `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises. Previously, NumPy checked whether the 100 fits the `int8_arr`. This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2 behavior. 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624884b572dff8ca8f60fab591413f077471de64e376b17d291b19f56504b2bb numpy-2.1.0-cp313-cp313t-musllinux_1_2_aarch64.whl 15ef8b2177eeb7e37dd5ef4016f30b7659c57c2c0b57a779f1d537ff33a72c7b numpy-2.1.0-pp310-pypy310_pp73-macosx_10_15_x86_64.whl e5f0642cdf4636198a4990de7a71b693d824c56a757862230454629cf62e323d numpy-2.1.0-pp310-pypy310_pp73-macosx_14_0_x86_64.whl f15976718c004466406342789f31b6673776360f3b1e3c575f25302d7e789575 numpy-2.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6c1de77ded79fef664d5098a66810d4d27ca0224e9051906e634b3f7ead134c2 numpy-2.1.0-pp310-pypy310_pp73-win_amd64.whl 7dc90da0081f7e1da49ec4e398ede6a8e9cc4f5ebe5f9e06b443ed889ee9aaa2 numpy-2.1.0.tar.gz ``` ### 2.1.0 ``` NumPy 2.1.0 provides support for the upcoming Python 3.13 release and drops support for Python 3.9. In addition to the usual bug fixes and updated Python support, it helps get us back into our usual release cycle after the extended development of 2.0. The highlights for this release are: - Support for the array-api 2023.12 standard. - Support for Python 3.13. - Preliminary support for free threaded Python 3.13. Python versions 3.10-3.13 are supported in this release. New functions New function `numpy.unstack` A new function `np.unstack(array, axis=...)` was added, which splits an array into a tuple of arrays along an axis. It serves as the inverse of [numpy.stack]{.title-ref}. ([gh-26579](https://github.com/numpy/numpy/pull/26579)) Deprecations - The `fix_imports` keyword argument in `numpy.save` is deprecated. Since NumPy 1.17, `numpy.save` uses a pickle protocol that no longer supports Python 2, and ignored `fix_imports` keyword. This keyword is kept only for backward compatibility. It is now deprecated. ([gh-26452](https://github.com/numpy/numpy/pull/26452)) - Passing non-integer inputs as the first argument of [bincount]{.title-ref} is now deprecated, because such inputs are silently cast to integers with no warning about loss of precision. ([gh-27076](https://github.com/numpy/numpy/pull/27076)) Expired deprecations - Scalars and 0D arrays are disallowed for `numpy.nonzero` and `numpy.ndarray.nonzero`. ([gh-26268](https://github.com/numpy/numpy/pull/26268)) - `set_string_function` internal function was removed and `PyArray_SetStringFunction` was stubbed out. ([gh-26611](https://github.com/numpy/numpy/pull/26611)) C API changes API symbols now hidden but customizable NumPy now defaults to hide the API symbols it adds to allow all NumPy API usage. This means that by default you cannot dynamically fetch the NumPy API from another library (this was never possible on windows). If you are experiencing linking errors related to `PyArray_API` or `PyArray_RUNTIME_VERSION`, you can define the `NPY_API_SYMBOL_ATTRIBUTE` to opt-out of this change. If you are experiencing problems due to an upstream header including NumPy, the solution is to make sure you `include "numpy/ndarrayobject.h"` before their header and import NumPy yourself based on `including-the-c-api`. ([gh-26103](https://github.com/numpy/numpy/pull/26103)) Many shims removed from npy_3kcompat.h Many of the old shims and helper functions were removed from `npy_3kcompat.h`. If you find yourself in need of these, vendor the previous version of the file into your codebase. ([gh-26842](https://github.com/numpy/numpy/pull/26842)) New `PyUFuncObject` field `process_core_dims_func` The field `process_core_dims_func` was added to the structure `PyUFuncObject`. For generalized ufuncs, this field can be set to a function of type `PyUFunc_ProcessCoreDimsFunc` that will be called when the ufunc is called. It allows the ufunc author to check that core dimensions satisfy additional constraints, and to set output core dimension sizes if they have not been provided. ([gh-26908](https://github.com/numpy/numpy/pull/26908)) New Features - `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)) `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)) 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#variance-of-generic-types) for more information. ([gh-26081](https://github.com/numpy/numpy/pull/26081)) `np.quantile` with method `closest_observation` chooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations. ([gh-26656](https://github.com/numpy/numpy/pull/26656)) `lapack_lite` is now thread safe NumPy provides a minimal low-performance version of LAPACK named `lapack_lite` that can be used if no BLAS/LAPACK system is detected at build time. Until now, `lapack_lite` was not thread safe. Single-threaded use cases did not hit any issues, but running linear algebra operations in multiple threads could lead to errors, incorrect results, or segfaults due to data races. We have added a global lock, serializing access to `lapack_lite` in multiple threads. ([gh-26750](https://github.com/numpy/numpy/pull/26750)) The `numpy.printoptions` context manager is now thread and async-safe In prior versions of NumPy, the printoptions were defined using a combination of Python and C global variables. We have refactored so the state is stored in a python `ContextVar`, making the context manager thread and async-safe. ([gh-26846](https://github.com/numpy/numpy/pull/26846)) Performance improvements and changes - `numpy.save` now uses pickle protocol version 4 for saving arrays with object dtype, which allows for pickle objects larger than 4GB and improves saving speed by about 5% for large arrays. ([gh-26388](https://github.com/numpy/numpy/pull/26388)) - 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`. ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - OpenBLAS on windows is linked without quadmath, simplifying licensing ([gh-27147](https://github.com/numpy/numpy/pull/27147)) - 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)) `ma.cov` and `ma.corrcoef` are now significantly faster The private function has been refactored along with `ma.cov` and `ma.corrcoef`. They are now significantly faster, particularly on large, masked arrays. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Changes - As `numpy.vecdot` is now a ufunc it has a less precise signature. This is due to the limitations of ufunc\'s typing stub. ([gh-26313](https://github.com/numpy/numpy/pull/26313)) - `numpy.floor`, `numpy.ceil`, and `numpy.trunc` now won\'t perform casting to a floating dtype for integer and boolean dtype input arrays. ([gh-26766](https://github.com/numpy/numpy/pull/26766)) `ma.corrcoef` may return a slightly different result A pairwise observation approach is currently used in `ma.corrcoef` to calculate the standard deviations for each pair of variables. This has been changed as it is being used to normalise the covariance, estimated using `ma.cov`, which does not consider the observations for each variable in a pairwise manner, rendering it unnecessary. The normalisation has been replaced by the more appropriate standard deviation for each variable, which significantly reduces the wall time, but will return slightly different estimates of the correlation coefficients in cases where the observations between a pair of variables are not aligned. However, it will return the same estimates in all other cases, including returning the same correlation matrix as `corrcoef` when using a masked array with no masked values. ([gh-26285](https://github.com/numpy/numpy/pull/26285)) Cast-safety fixes in `copyto` and `full` `copyto` now uses NEP 50 correctly and applies this to its cast safety. Python integer to NumPy integer casts and Python float to NumPy float casts are now considered \"safe\" even if assignment may fail or precision may be lost. This means the following examples change slightly: - `np.copyto(int8_arr, 1000)` previously performed an unsafe/same-kind cast : of the Python integer. It will now always raise, to achieve an unsafe cast you must pass an array or NumPy scalar. - `np.copyto(uint8_arr, 1000, casting="safe")` will raise an OverflowError rather than a TypeError due to same-kind casting. - `np.copyto(float32_arr, 1e300, casting="safe")` will overflow to `inf` (float32 cannot hold `1e300`) rather raising a TypeError. Further, only the dtype is used when assigning NumPy scalars (or 0-d arrays), meaning that the following behaves differently: - `np.copyto(float32_arr, np.float64(3.0), casting="safe")` raises. - `np.coptyo(int8_arr, np.int64(100), casting="safe")` raises. Previously, NumPy checked whether the 100 fits the `int8_arr`. This aligns `copyto`, `full`, and `full_like` with the correct NumPy 2 behavior. 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dc7ce867d277aa74555c67b93ef2a6f78bd7bd73e6c2bbafeb96f8bccd05b9d9 numpy-2.1.0rc1.tar.gz ``` ### 2.0.2 ``` discovered after the 2.0.1 release. The Python versions supported by this release are 3.9-3.12. Contributors A total of 13 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bruno Oliveira + - Charles Harris - Chris Sidebottom - Christian Heimes + - Christopher Sidebottom - Mateusz Sokół - Matti Picus - Nathan Goldbaum - Pieter Eendebak - Raghuveer Devulapalli - Ralf Gommers - Sebastian Berg - Yair Chuchem + Pull requests merged A total of 19 pull requests were merged for this release. - [27000](https://github.com/numpy/numpy/pull/27000): REL: Prepare for the NumPy 2.0.1 release \[wheel build\] - [27001](https://github.com/numpy/numpy/pull/27001): MAINT: prepare 2.0.x for further development - [27021](https://github.com/numpy/numpy/pull/27021): BUG: cfuncs.py: fix crash when sys.stderr is not available - [27022](https://github.com/numpy/numpy/pull/27022): DOC: Fix migration note for `alltrue` and `sometrue` - [27061](https://github.com/numpy/numpy/pull/27061): BUG: use proper input and output descriptor in array_assign_subscript\... - [27073](https://github.com/numpy/numpy/pull/27073): BUG: Mirror VQSORT_ENABLED logic in Quicksort - [27074](https://github.com/numpy/numpy/pull/27074): BUG: Bump Highway to latest master - [27077](https://github.com/numpy/numpy/pull/27077): BUG: Off by one in memory overlap check - [27122](https://github.com/numpy/numpy/pull/27122): BUG: Use the new `npyv_loadable_stride_` functions for ldexp and\... - [27126](https://github.com/numpy/numpy/pull/27126): BUG: Bump Highway to latest - [27128](https://github.com/numpy/numpy/pull/27128): BUG: add missing error handling in public_dtype_api.c - [27129](https://github.com/numpy/numpy/pull/27129): BUG: fix another cast setup in array_assign_subscript - [27130](https://github.com/numpy/numpy/pull/27130): BUG: Fix building NumPy in FIPS mode - [27131](https://github.com/numpy/numpy/pull/27131): BLD: update vendored Meson for cross-compilation patches - [27146](https://github.com/numpy/numpy/pull/27146): MAINT: Scipy openblas 0.3.27.44.4 - 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