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Update numpy to 2.1.3 #604

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Update numpy to 2.1.3 #604

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@pyup-bot pyup-bot commented Nov 2, 2024

This PR updates numpy from 1.16.4 to 2.1.3.

Changelog

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#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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