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Update numpy to 1.26.1 #87

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This PR updates numpy from 1.20.3 to 1.26.1.

Changelog

1.26.1

discovered after the 1.26.0 release. In addition, it adds new
functionality for detecting BLAS and LAPACK when building from source.
Highlights are:

-   Improved detection of BLAS and LAPACK libraries for meson builds
-   Pickle compatibility with the upcoming NumPy 2.0.

The 1.26.release series is the last planned minor release series before
NumPy 2.0. The Python versions supported by this release are 3.9-3.12.

Build system changes

Improved BLAS/LAPACK detection and control

Auto-detection for a number of BLAS and LAPACK is now implemented for
Meson. By default, the build system will try to detect MKL, Accelerate
(on macOS \>=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK.
Support for MKL was significantly improved, and support for FlexiBLAS
was added.

New command-line flags are available to further control the selection of
the BLAS and LAPACK libraries to build against.

To select a specific library, use the config-settings interface via
`pip` or `pypa/build`. E.g., to select `libblas`/`liblapack`, use:

 $ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack
 $  OR
 $ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack

This works not only for the libraries named above, but for any library
that Meson is able to detect with the given name through `pkg-config` or
CMake.

Besides `-Dblas` and `-Dlapack`, a number of other new flags are
available to control BLAS/LAPACK selection and behavior:

-   `-Dblas-order` and `-Dlapack-order`: a list of library names to
 search for in order, overriding the default search order.
-   `-Duse-ilp64`: if set to `true`, use ILP64 (64-bit integer) BLAS and
 LAPACK. Note that with this release, ILP64 support has been extended
 to include MKL and FlexiBLAS. OpenBLAS and Accelerate were supported
 in previous releases.
-   `-Dallow-noblas`: if set to `true`, allow NumPy to build with its
 internal (very slow) fallback routines instead of linking against an
 external BLAS/LAPACK library. *The default for this flag may be
 changed to \`\`true\`\` in a future 1.26.x release, however for
 1.26.1 we\'d prefer to keep it as \`\`false\`\` because if failures
 to detect an installed library are happening, we\'d like a bug
 report for that, so we can quickly assess whether the new
 auto-detection machinery needs further improvements.*
-   `-Dmkl-threading`: to select the threading layer for MKL. There are
 four options: `seq`, `iomp`, `gomp` and `tbb`. The default is
 `auto`, which selects from those four as appropriate given the
 version of MKL selected.
-   `-Dblas-symbol-suffix`: manually select the symbol suffix to use for
 the library - should only be needed for linking against libraries
 built in a non-standard way.

New features

`numpy._core` submodule stubs

`numpy._core` submodule stubs were added to provide compatibility with
pickled arrays created using NumPy 2.0 when running Numpy 1.26.

Contributors

A total of 13 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Anton Prosekin +
-   Charles Harris
-   Chongyun Lee +
-   Ivan A. Melnikov +
-   Jake Lishman +
-   Mahder Gebremedhin +
-   Mateusz Sokół
-   Matti Picus
-   Munira Alduraibi +
-   Ralf Gommers
-   Rohit Goswami
-   Sayed Adel

Pull requests merged

A total of 20 pull requests were merged for this release.

-   [24742](https://github.com/numpy/numpy/pull/24742): MAINT: Update cibuildwheel version
-   [24748](https://github.com/numpy/numpy/pull/24748): MAINT: fix version string in wheels built with setup.py
-   [24771](https://github.com/numpy/numpy/pull/24771): BLD, BUG: Fix build failure for host flags e.g. `-march=native`\...
-   [24773](https://github.com/numpy/numpy/pull/24773): DOC: Updated the f2py docs to remove a note on -fimplicit-none
-   [24776](https://github.com/numpy/numpy/pull/24776): BUG: Fix SIMD f32 trunc test on s390x when baseline is none
-   [24785](https://github.com/numpy/numpy/pull/24785): BLD: add libquadmath to licences and other tweaks (#24753)
-   [24786](https://github.com/numpy/numpy/pull/24786): MAINT: Activate `use-compute-credits` for Cirrus.
-   [24803](https://github.com/numpy/numpy/pull/24803): BLD: updated vendored-meson/meson for mips64 fix
-   [24804](https://github.com/numpy/numpy/pull/24804): MAINT: fix licence path win
-   [24813](https://github.com/numpy/numpy/pull/24813): BUG: Fix order of Windows OS detection macros.
-   [24831](https://github.com/numpy/numpy/pull/24831): BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828)
-   [24840](https://github.com/numpy/numpy/pull/24840): BUG: Fix DATA statements for f2py
-   [24870](https://github.com/numpy/numpy/pull/24870): API: Add `NumpyUnpickler` for backporting
-   [24872](https://github.com/numpy/numpy/pull/24872): MAINT: Xfail test failing on PyPy.
-   [24879](https://github.com/numpy/numpy/pull/24879): BLD: fix math func feature checks, fix FreeBSD build, add CI\...
-   [24899](https://github.com/numpy/numpy/pull/24899): ENH: meson: implement BLAS/LAPACK auto-detection and many CI\...
-   [24902](https://github.com/numpy/numpy/pull/24902): DOC: add a 1.26.1 release notes section for BLAS/LAPACK build\...
-   [24906](https://github.com/numpy/numpy/pull/24906): MAINT: Backport `numpy._core` stubs. Remove `NumpyUnpickler`
-   [24911](https://github.com/numpy/numpy/pull/24911): MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2
-   [24912](https://github.com/numpy/numpy/pull/24912): BUG: loongarch doesn\'t use REAL(10)

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1.26.0

The NumPy 1.26.0 release is a continuation of the 1.25.x release cycle
with the addition of Python 3.12.0 support. Python 3.12 dropped
distutils, consequently supporting it required finding a replacement for
the setup.py/distutils based build system NumPy was using. We have
chosen to use the Meson build system instead, and this is the first
NumPy release supporting it. This is also the first release that
supports Cython 3.0 in addition to retaining 0.29.X compatibility.
Supporting those two upgrades was a large project, over 100 files have
been touched in this release. The changelog doesn\'t capture the full
extent of the work, special thanks to Ralf Gommers, Sayed Adel, Stéfan
van der Walt, and Matti Picus who did much of the work in the main
development branch.

The highlights of this release are:

-   Python 3.12.0 support.
-   Cython 3.0.0 compatibility.
-   Use of the Meson build system
-   Updated SIMD support

The Python versions supported in this release are 3.9-3.12.

Build system changes

In this release, NumPy has switched to Meson as the build system and
meson-python as the build backend. Installing NumPy or building a wheel
can be done with standard tools like `pip` and `pypa/build`. The
following are supported:

-   Regular installs: `pip install numpy` or (in a cloned repo)
 `pip install .`
-   Building a wheel: `python -m build` (preferred), or `pip wheel .`
-   Editable installs: `pip install -e . --no-build-isolation`
-   Development builds through the custom CLI implemented with
 [spin](https://github.com/scientific-python/spin): `spin build`.

All the regular `pip` and `pypa/build` flags (e.g.,
`--no-build-isolation`) should work as expected.

NumPy-specific build customization

Many of the NumPy-specific ways of customizing builds have changed. The
`NPY_*` environment variables which control BLAS/LAPACK, SIMD,
threading, and other such options are no longer supported, nor is a
`site.cfg` file to select BLAS and LAPACK. Instead, there are
command-line flags that can be passed to the build via `pip`/`build`\'s
config-settings interface. These flags are all listed in the
`meson_options.txt` file in the root of the repo. Detailed documented
will be available before the final 1.26.0 release; for now please see
[the SciPy \"building from source\"docs](http://scipy.github.io/devdocs/building/index.html) since most
build customization works in an almost identical way in SciPy as it does
in NumPy.

Build dependencies

While the runtime dependencies of NumPy have not changed, the build
dependencies have. Because we temporarily vendor Meson and meson-python,
there are several new dependencies - please see the `[build-system]`
section of `pyproject.toml` for details.

Troubleshooting

This build system change is quite large. In case of unexpected issues,
it is still possible to use a `setup.py`-based build as a temporary
workaround (on Python 3.9-3.11, not 3.12), by copying
`pyproject.toml.setuppy` to `pyproject.toml`. However, please open an
issue with details on the NumPy issue tracker. We aim to phase out
`setup.py` builds as soon as possible, and therefore would like to see
all potential blockers surfaced early on in the 1.26.0 release cycle.

Contributors

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

-   Bas van Beek
-   Charles Harris
-   Matti Picus
-   Melissa Weber Mendonça
-   Ralf Gommers
-   Sayed Adel
-   Sebastian Berg
-   Stefan van der Walt
-   Tyler Reddy
-   Warren Weckesser

Pull requests merged

A total of 18 pull requests were merged for this release.

-   [24305](https://github.com/numpy/numpy/pull/24305): MAINT: Prepare 1.26.x branch for development
-   [24308](https://github.com/numpy/numpy/pull/24308): MAINT: Massive update of files from main for numpy 1.26
-   [24322](https://github.com/numpy/numpy/pull/24322): CI: fix wheel builds on the 1.26.x branch
-   [24326](https://github.com/numpy/numpy/pull/24326): BLD: update openblas to newer version
-   [24327](https://github.com/numpy/numpy/pull/24327): TYP: Trim down the `_NestedSequence.__getitem__` signature
-   [24328](https://github.com/numpy/numpy/pull/24328): BUG: fix choose refcount leak
-   [24337](https://github.com/numpy/numpy/pull/24337): TST: fix running the test suite in builds without BLAS/LAPACK
-   [24338](https://github.com/numpy/numpy/pull/24338): BUG: random: Fix generation of nan by dirichlet.
-   [24340](https://github.com/numpy/numpy/pull/24340): MAINT: Dependabot updates from main
-   [24342](https://github.com/numpy/numpy/pull/24342): MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1
-   [24353](https://github.com/numpy/numpy/pull/24353): MAINT: Update `extbuild.py` from main.
-   [24356](https://github.com/numpy/numpy/pull/24356): TST: fix distutils tests for deprecations in recent setuptools\...
-   [24375](https://github.com/numpy/numpy/pull/24375): MAINT: Update cibuildwheel to version 2.15.0
-   [24381](https://github.com/numpy/numpy/pull/24381): MAINT: Fix codespaces setup.sh script
-   [24403](https://github.com/numpy/numpy/pull/24403): ENH: Vendor meson for multi-target build support
-   [24404](https://github.com/numpy/numpy/pull/24404): BLD: vendor meson-python to make the Windows builds with SIMD\...
-   [24405](https://github.com/numpy/numpy/pull/24405): BLD, SIMD: The meson CPU dispatcher implementation
-   [24406](https://github.com/numpy/numpy/pull/24406): MAINT: Remove versioneer

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SHA256

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 c67eea90827e1e9aa220a3fc380ce8776428deba8ac9e7c931ce7b69e8dce115  numpy-1.26.0b1.tar.gz

1.25.2

discovered after the 1.25.1 release. This is the last planned release in
the 1.25.x series, the next release will be 1.26.0, which will use the
meson build system and support Python 3.12. The Python versions
supported by this release are 3.9-3.11.

Contributors

A total of 13 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Aaron Meurer
-   Andrew Nelson
-   Charles Harris
-   Kevin Sheppard
-   Matti Picus
-   Nathan Goldbaum
-   Peter Hawkins
-   Ralf Gommers
-   Randy Eckenrode +
-   Sam James +
-   Sebastian Berg
-   Tyler Reddy
-   dependabot\[bot\]

Pull requests merged

A total of 19 pull requests were merged for this release.

-   [24148](https://github.com/numpy/numpy/pull/24148): MAINT: prepare 1.25.x for further development
-   [24174](https://github.com/numpy/numpy/pull/24174): ENH: Improve clang-cl compliance
-   [24179](https://github.com/numpy/numpy/pull/24179): MAINT: Upgrade various build dependencies.
-   [24182](https://github.com/numpy/numpy/pull/24182): BLD: use `-ftrapping-math` with Clang on macOS
-   [24183](https://github.com/numpy/numpy/pull/24183): BUG: properly handle negative indexes in ufunc_at fast path
-   [24184](https://github.com/numpy/numpy/pull/24184): BUG: PyObject_IsTrue and PyObject_Not error handling in setflags
-   [24185](https://github.com/numpy/numpy/pull/24185): BUG: histogram small range robust
-   [24186](https://github.com/numpy/numpy/pull/24186): MAINT: Update meson.build files from main branch
-   [24234](https://github.com/numpy/numpy/pull/24234): MAINT: exclude min, max and round from `np.__all__`
-   [24241](https://github.com/numpy/numpy/pull/24241): MAINT: Dependabot updates
-   [24242](https://github.com/numpy/numpy/pull/24242): BUG: Fix the signature for np.array_api.take
-   [24243](https://github.com/numpy/numpy/pull/24243): BLD: update OpenBLAS to an intermeidate commit
-   [24244](https://github.com/numpy/numpy/pull/24244): BUG: Fix reference count leak in str(scalar).
-   [24245](https://github.com/numpy/numpy/pull/24245): BUG: fix invalid function pointer conversion error
-   [24255](https://github.com/numpy/numpy/pull/24255): BUG: Factor out slow `getenv` call used for memory policy warning
-   [24292](https://github.com/numpy/numpy/pull/24292): CI: correct URL in cirrus.star
-   [24293](https://github.com/numpy/numpy/pull/24293): BUG: Fix C types in scalartypes
-   [24294](https://github.com/numpy/numpy/pull/24294): BUG: do not modify the input to ufunc_at
-   [24295](https://github.com/numpy/numpy/pull/24295): BUG: Further fixes to indexing loop and added tests

Checksums

MD5

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 cee1996a80032d47bdf1d9d17249c34e  numpy-1.25.2.tar.gz

SHA256

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 fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760  numpy-1.25.2.tar.gz

1.25.1

discovered after the 1.25.0 release. The Python versions supported by
this release are 3.9-3.11.

Contributors

A total of 10 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Andrew Nelson
-   Charles Harris
-   Developer-Ecosystem-Engineering
-   Hood Chatham
-   Nathan Goldbaum
-   Rohit Goswami
-   Sebastian Berg
-   Tim Paine +
-   dependabot\[bot\]
-   matoro +

Pull requests merged

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

-   [23968](https://github.com/numpy/numpy/pull/23968): MAINT: prepare 1.25.x for further development
-   [24036](https://github.com/numpy/numpy/pull/24036): BLD: Port long double identification to C for meson
-   [24037](https://github.com/numpy/numpy/pull/24037): BUG: Fix reduction `return NULL` to be `goto fail`
-   [24038](https://github.com/numpy/numpy/pull/24038): BUG: Avoid undefined behavior in array.astype()
-   [24039](https://github.com/numpy/numpy/pull/24039): BUG: Ensure `__array_ufunc__` works without any kwargs passed
-   [24117](https://github.com/numpy/numpy/pull/24117): MAINT: Pin urllib3 to avoid anaconda-client bug.
-   [24118](https://github.com/numpy/numpy/pull/24118): TST: Pin pydantic\<2 in Pyodide workflow
-   [24119](https://github.com/numpy/numpy/pull/24119): MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1
-   [24120](https://github.com/numpy/numpy/pull/24120): MAINT: Bump actions/checkout from 3.5.2 to 3.5.3
-   [24122](https://github.com/numpy/numpy/pull/24122): BUG: Multiply or Divides using SIMD without a full vector can\...
-   [24127](https://github.com/numpy/numpy/pull/24127): MAINT: testing for IS_MUSL closes #24074
-   [24128](https://github.com/numpy/numpy/pull/24128): BUG: Only replace dtype temporarily if dimensions changed
-   [24129](https://github.com/numpy/numpy/pull/24129): MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0
-   [24134](https://github.com/numpy/numpy/pull/24134): BUG: Fix private procedures in f2py modules

Checksums

MD5

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 9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf  numpy-1.25.1.tar.gz

1.25.0

The NumPy 1.25.0 release continues the ongoing work to improve the
handling and promotion of dtypes, increase the execution speed, and
clarify the documentation. There has also been work to prepare for the
future NumPy 2.0.0 release, resulting in a large number of new and
expired deprecation. Highlights are:

-   Support for MUSL, there are now MUSL wheels.
-   Support the Fujitsu C/C++ compiler.
-   Object arrays are now supported in einsum
-   Support for inplace matrix multiplication (`=`).

We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is
needed because distutils has been dropped by Python 3.12 and we will be
switching to using meson for future builds. The next mainline release
will be NumPy 2.0.0. We plan that the 2.0 series will still support
downstream projects built against earlier versions of NumPy.

The Python versions supported in this release are 3.9-3.11.

Deprecations

-   `np.core.MachAr` is deprecated. It is private API. In names defined
 in `np.core` should generally be considered private.

 ([gh-22638](https://github.com/numpy/numpy/pull/22638))

-   `np.finfo(None)` is deprecated.

 ([gh-23011](https://github.com/numpy/numpy/pull/23011))

-   `np.round_` is deprecated. Use `np.round` instead.

 ([gh-23302](https://github.com/numpy/numpy/pull/23302))

-   `np.product` is deprecated. Use `np.prod` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.cumproduct` is deprecated. Use `np.cumprod` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.sometrue` is deprecated. Use `np.any` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   `np.alltrue` is deprecated. Use `np.all` instead.

 ([gh-23314](https://github.com/numpy/numpy/pull/23314))

-   Only ndim-0 arrays are treated as scalars. NumPy used to treat all
 arrays of size 1 (e.g., `np.array([3.14])`) as scalars. In the
 future, this will be limited to arrays of ndim 0 (e.g.,
 `np.array(3.14)`). The following expressions will report a
 deprecation warning:

  python
 a = np.array([3.14])
 float(a)   better: a[0] to get the numpy.float or a.item()

 b = np.array([[3.14]])
 c = numpy.random.rand(10)
 c[0] = b   better: c[0] = b[0, 0]
 

 ([gh-10615](https://github.com/numpy/numpy/pull/10615))

-   `numpy.find_common_type` is now deprecated and its use
 should be replaced with either `numpy.result_type` or
 `numpy.promote_types`. Most users leave the second
 `scalar_types` argument to `find_common_type` as `[]` in which case
 `np.result_type` and `np.promote_types` are both faster and more
 robust. When not using `scalar_types` the main difference is that
 the replacement intentionally converts non-native byte-order to
 native byte order. Further, `find_common_type` returns `object`
 dtype rather than failing promotion. This leads to differences when
 the inputs are not all numeric. Importantly, this also happens for
 e.g. timedelta/datetime for which NumPy promotion rules are
 currently sometimes surprising.

 When the `scalar_types` argument is not `[]` things are more
 complicated. In most cases, using `np.result_type` and passing the
 Python values `0`, `0.0`, or `0j` has the same result as using
 `int`, `float`, or `complex` in `scalar_types`.

 When `scalar_types` is constructed, `np.result_type` is the correct
 replacement and it may be passed scalar values like
 `np.float32(0.0)`. Passing values other than 0, may lead to
 value-inspecting behavior (which `np.find_common_type` never used
 and NEP 50 may change in the future). The main possible change in
 behavior in this case, is when the array types are signed integers
 and scalar types are unsigned.

 If you are unsure about how to replace a use of `scalar_types` or
 when non-numeric dtypes are likely, please do not hesitate to open a
 NumPy issue to ask for help.

 ([gh-22539](https://github.com/numpy/numpy/pull/22539))

Expired deprecations

-   `np.core.machar` and `np.finfo.machar` have been removed.

 ([gh-22638](https://github.com/numpy/numpy/pull/22638))

-   `+arr` will now raise an error when the dtype is not numeric (and
 positive is undefined).

 ([gh-22998](https://github.com/numpy/numpy/pull/22998))

-   A sequence must now be passed into the stacking family of functions
 (`stack`, `vstack`, `hstack`, `dstack` and `column_stack`).

 ([gh-23019](https://github.com/numpy/numpy/pull/23019))

-   `np.clip` now defaults to same-kind casting. Falling back to unsafe
 casting was deprecated in NumPy 1.17.

 ([gh-23403](https://github.com/numpy/numpy/pull/23403))

-   `np.clip` will now propagate `np.nan` values passed as `min` or
 `max`. Previously, a scalar NaN was usually ignored. This was
 deprecated in NumPy 1.17.

 ([gh-23403](https://github.com/numpy/numpy/pull/23403))

-   The `np.dual` submodule has been removed.

 ([gh-23480](https://github.com/numpy/numpy/pull/23480))

-   NumPy now always ignores sequence behavior for an array-like
 (defining one of the array protocols). (Deprecation started NumPy
 1.20)

 ([gh-23660](https://github.com/numpy/numpy/pull/23660))

-   The niche `FutureWarning` when casting to a subarray dtype in
 `astype` or the array creation functions such as `asarray` is now
 finalized. The behavior is now always the same as if the subarray
 dtype was wrapped into a single field (which was the workaround,
 previously). (FutureWarning since NumPy 1.20)

 ([gh-23666](https://github.com/numpy/numpy/pull/23666))

-   `==` and `!=` warnings have been finalized. The `==` and `!=`
 operators on arrays now always:

 -   raise errors that occur during comparisons such as when the
     arrays have incompatible shapes
     (`np.array([1, 2]) == np.array([1, 2, 3])`).

 -   return an array of all `True` or all `False` when values are
     fundamentally not comparable (e.g. have different dtypes). An
     example is `np.array(["a"]) == np.array([1])`.

     This mimics the Python behavior of returning `False` and `True`
     when comparing incompatible types like `"a" == 1` and
     `"a" != 1`. For a long time these gave `DeprecationWarning` or
     `FutureWarning`.

 ([gh-22707](https://github.com/numpy/numpy/pull/22707))

-   Nose support has been removed. NumPy switched to using pytest in
 2018 and nose has been unmaintained for many years. We have kept
 NumPy\'s nose support to avoid breaking downstream projects who
 might have been using it and not yet switched to pytest or some
 other testing framework. With the arrival of Python 3.12, unpatched
 nose will raise an error. It is time to move on.

 *Decorators removed*:

 -   raises
 -   slow
 -   setastest
 -   skipif
 -   knownfailif
 -   deprecated
 -   parametrize
 -   \_needs_refcount

 These are not to be confused with pytest versions with similar
 names, e.g., pytest.mark.slow, pytest.mark.skipif,
 pytest.mark.parametrize.

 *Functions removed*:

 -   Tester
 -   import_nose
 -   run_module_suite

 ([gh-23041](https://github.com/numpy/numpy/pull/23041))

-   The `numpy.testing.utils` shim has been removed. Importing from the
 `numpy.testing.utils` shim has been deprecated since 2019, the shim
 has now been removed. All imports should be made directly from
 `numpy.testing`.

 ([gh-23060](https://github.com/numpy/numpy/pull/23060))

-   The environment variable to disable dispatching has been removed.
 Support for the `NUMPY_EXPERIMENTAL_ARRAY_FUNCTION` environment
 variable has been removed. This variable disabled dispatching with
 `__array_function__`.

 ([gh-23376](https://github.com/numpy/numpy/pull/23376))

-   Support for `y=` as an alias of `out=` has been removed. The `fix`,
 `isposinf` and `isneginf` functions allowed using `y=` as a
 (deprecated) alias for `out=`. This is no longer supported.

 ([gh-23376](https://github.com/numpy/numpy/pull/23376))

Compatibility notes

-   The `busday_count` method now correctly handles cases where the
 `begindates` is later in time than the `enddates`. Previously, the
 `enddates` was included, even though the documentation states it is
 always excluded.

 ([gh-23229](https://github.com/numpy/numpy/pull/23229))

-   When comparing datetimes and timedelta using `np.equal` or
 `np.not_equal` numpy previously allowed the comparison with
 `casting="unsafe"`. This operation now fails. Forcing the output
 dtype using the `dtype` kwarg can make the operation succeed, but we
 do not recommend it.

 ([gh-22707](https://github.com/numpy/numpy/pull/22707))

-   When loading data from a file handle using `np.load`, if the handle
 is at the end of file, as can happen when reading multiple arrays by
 calling `np.load` repeatedly, numpy previously raised `ValueError`
 if `allow_pickle=False`, and `OSError` if `allow_pickle=True`. Now
 it raises `EOFError` instead, in both cases.

 ([gh-23105](https://github.com/numpy/numpy/pull/23105))

`np.pad` with `mode=wrap` pads with strict multiples of original data

Code based on earlier version of `pad` that uses `mode="wrap"` will
return different results when the padding size is larger than initial
array.

`np.pad` with `mode=wrap` now always fills the space with strict
multiples of original data even if the padding size is larger than the
initial array.

([gh-22575](https://github.com/numpy/numpy/pull/22575))

Cython `long_t` and `ulong_t` removed

`long_t` and `ulong_t` were aliases for `longlong_t` and `ulonglong_t`
and confusing (a remainder from of Python 2). This change may lead to
the errors:

 'long_t' is not a type identifier
 'ulong_t' is not a type identifier

We recommend use of bit-sized types such as `cnp.int64_t` or the use of
`cnp.intp_t` which is 32 bits on 32 bit systems and 64 bits on 64 bit
systems (this is most compatible with indexing). If C `long` is desired,
use plain `long` or `npy_long`. `cnp.int_t` is also `long` (NumPy\'s
default integer). However, `long` is 32 bit on 64 bit windows and we may
wish to adjust this even in NumPy. (Please do not hesitate to contact
NumPy developers if you are curious about this.)

([gh-22637](https://github.com/numpy/numpy/pull/22637))

Changed error message and type for bad `axes` argument to `ufunc`

The error message and type when a wrong `axes` value is passed to
`ufunc(..., axes=[...])` has changed. The message is now more
indicative of the problem, and if the value is mismatched an
`AxisError` will be raised. A `TypeError` will still be raised for
invalidinput types.

([gh-22675](https://github.com/numpy/numpy/pull/22675))

Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where`

If the `where` keyword argument of a `numpy.ufunc`{.interpreted-text
role="class"} is a subclass of `numpy.ndarray`{.interpreted-text
role="class"} or is a duck type that defines
`numpy.class.__array_ufunc__`{.interpreted-text role="func"} it can
override the behavior of the ufunc using the same mechanism as the input
and output arguments. Note that for this to work properly, the
`where.__array_ufunc__` implementation will have to unwrap the `where`
argument to pass it into the default implementation of the `ufunc` or,
for `numpy.ndarray`{.interpreted-text role="class"} subclasses before
using `super().__array_ufunc__`.

([gh-23240](https://github.com/numpy/numpy/pull/23240))

Compiling against the NumPy C API is now backwards compatible by default

NumPy now defaults to exposing a backwards compatible subset of the
C-API. This makes the use of `oldest-supported-numpy` unnecessary.
Libraries can override the default minimal version to be compatible with
using:

 define NPY_TARGET_VERSION NPY_1_22_API_VERSION

before including NumPy or by passing the equivalent `-D` option to the
compiler. The NumPy 1.25 default is `NPY_1_19_API_VERSION`. Because the

1.24.4

discovered after the 1.24.3 release. It is the last planned
release in the 1.24.x cycle. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 4 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Bas van Beek
-   Charles Harris
-   Sebastian Berg
-   Hongyang Peng +

Pull requests merged

A total of 6 pull requests were merged for this release.

-   [23720](https://github.com/numpy/numpy/pull/23720): MAINT, BLD: Pin rtools to version 4.0 for Windows builds.
-   [23739](https://github.com/numpy/numpy/pull/23739): BUG: fix the method for checking local files for 1.24.x
-   [23760](https://github.com/numpy/numpy/pull/23760): MAINT: Copy rtools installation from install-rtools.
-   [23761](https://github.com/numpy/numpy/pull/23761): BUG: Fix masked array ravel order for A (and somewhat K)
-   [23890](https://github.com/numpy/numpy/pull/23890): TYP,DOC: Annotate and document the `metadata` parameter of\...
-   [23994](https://github.com/numpy/numpy/pull/23994): MAINT: Update rtools installation

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SHA256

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1.24.3

discovered after the 1.24.2 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 12 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Aleksei Nikiforov +
-   Alexander Heger
-   Bas van Beek
-   Bob Eldering
-   Brock Mendel
-   Charles Harris
-   Kyle Sunden
-   Peter Hawkins
-   Rohit Goswami
-   Sebastian Berg
-   Warren Weckesser
-   dependabot\[bot\]

Pull requests merged

A total of 17 pull requests were merged for this release.

-   [23206](https://github.com/numpy/numpy/pull/23206): BUG: fix for f2py string scalars (#23194)
-   [23207](https://github.com/numpy/numpy/pull/23207): BUG: datetime64/timedelta64 comparisons return NotImplemented
-   [23208](https://github.com/numpy/numpy/pull/23208): MAINT: Pin matplotlib to version 3.6.3 for refguide checks
-   [23221](https://github.com/numpy/numpy/pull/23221): DOC: Fix matplotlib error in documentation
-   [23226](https://github.com/numpy/numpy/pull/23226): CI: Ensure submodules are initialized in gitpod.
-   [23341](https://github.com/numpy/numpy/pull/23341): TYP: Replace duplicate reduce in ufunc type signature with reduceat.
-   [23342](https://github.com/numpy/numpy/pull/23342): TYP: Remove duplicate CLIP/WRAP/RAISE in `__init__.pyi`.
-   [23343](https://github.com/numpy/numpy/pull/23343): TYP: Mark `d` argument to fftfreq and rfftfreq as optional\...
-   [23344](https://github.com/numpy/numpy/pull/23344): TYP: Add type annotations for comparison operators to MaskedArray.
-   [23345](https://github.com/numpy/numpy/pull/23345): TYP: Remove some stray type-check-only imports of `msort`
-   [23370](https://github.com/numpy/numpy/pull/23370): BUG: Ensure like is only stripped for `like=` dispatched functions
-   [23543](https://github.com/numpy/numpy/pull/23543): BUG: fix loading and storing big arrays on s390x
-   [23544](https://github.com/numpy/numpy/pull/23544): MAINT: Bump larsoner/circleci-artifacts-redirector-action
-   [23634](https://github.com/numpy/numpy/pull/23634): BUG: Ignore invalid and overflow warnings in masked setitem
-   [23635](https://github.com/numpy/numpy/pull/23635): BUG: Fix masked array raveling when `order="A"` or `order="K"`
-   [23636](https://github.com/numpy/numpy/pull/23636): MAINT: Update conftest for newer hypothesis versions
-   [23637](https://github.com/numpy/numpy/pull/23637): BUG: Fix bug in parsing F77 style string arrays.

Checksums

MD5

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SHA256

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@pyup-bot pyup-bot mentioned this pull request Oct 15, 2023
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Closing this in favor of #88

@pyup-bot pyup-bot closed this Nov 13, 2023
@murphyqm murphyqm deleted the pyup-update-numpy-1.20.3-to-1.26.1 branch November 13, 2023 03:24
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