Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update numpy to 1.26.0 #86

Closed
wants to merge 2 commits into from
Closed

Update numpy to 1.26.0 #86

wants to merge 2 commits into from

Conversation

pyup-bot
Copy link
Collaborator

This PR updates numpy from 1.20.3 to 1.26.0.

Changelog

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

Checksums

MD5

 875d02016f215f8ce2513453393f0089  numpy-1.26.0b1-cp310-cp310-macosx_10_9_x86_64.whl
 7df1856729096fbbbbb82b58c1695810  numpy-1.26.0b1-cp310-cp310-macosx_11_0_arm64.whl
 928037510906572ecadb154b8089853f  numpy-1.26.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 93fb7c8a0e7af169c9bf42d8bfa17c2c  numpy-1.26.0b1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 a865069d224bf3830671de8e1f374344  numpy-1.26.0b1-cp310-cp310-musllinux_1_1_x86_64.whl
 c53d1d8cb653fc08bd3f931e4c965430  numpy-1.26.0b1-cp310-cp310-win_amd64.whl
 c7e212fbb7e64231747c6c8aac0f8678  numpy-1.26.0b1-cp311-cp311-macosx_10_9_x86_64.whl
 f2df03cdaee283c1f7486d2f66e497dd  numpy-1.26.0b1-cp311-cp311-macosx_11_0_arm64.whl
 8af359b78166474b7a621a482f3073fd  numpy-1.26.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 4eec2761b87ccd43028697410ed8909d  numpy-1.26.0b1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 d9f0b03e455e9e99bdbe69e2e729c197  numpy-1.26.0b1-cp311-cp311-musllinux_1_1_x86_64.whl
 dd1c5e4492988e2b3641602b295e7de3  numpy-1.26.0b1-cp311-cp311-win_amd64.whl
 88e35ab901c8315ccdb172abc0d2350c  numpy-1.26.0b1-cp312-cp312-macosx_10_9_x86_64.whl
 ad426a4203844eaa8de6b519e94dc2c0  numpy-1.26.0b1-cp312-cp312-macosx_11_0_arm64.whl
 2e0e7a297de88cfe930c205b1ab8fdb0  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 5d4ea12ab53e506a9887ab8a587f68f6  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 1b3c3a80d2fb928b753545ded60312f3  numpy-1.26.0b1-cp312-cp312-musllinux_1_1_x86_64.whl
 e27356122ee42d84f6965ac802792bc3  numpy-1.26.0b1-cp312-cp312-win_amd64.whl
 1cc0d71476548fa30c27a542e3c3f9bf  numpy-1.26.0b1-cp39-cp39-macosx_10_9_x86_64.whl
 ec4882af449c1754cc7af84a82305aed  numpy-1.26.0b1-cp39-cp39-macosx_11_0_arm64.whl
 142493180019de1ec22c4510bf650366  numpy-1.26.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 4a0c76b75fa36c54c0d2a9107c838910  numpy-1.26.0b1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 cb4d1c3b95e3a2662f94475b4b525da0  numpy-1.26.0b1-cp39-cp39-musllinux_1_1_x86_64.whl
 afa3f60467530e022eb1a584a8c48f84  numpy-1.26.0b1-cp39-cp39-win_amd64.whl
 35c77e2f2b25225ae62354f91c26a693  numpy-1.26.0b1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 1986181def7286ae37ced5df7c0ca312  numpy-1.26.0b1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e013942d0d71cb6a680afa89c9aa5259  numpy-1.26.0b1-pp39-pypy39_pp73-win_amd64.whl
 3268568cee06327fa34175aa3805829d  numpy-1.26.0b1.tar.gz

SHA256

 9a74361204dc604ba53916ed55aef0ca73e7aa3d0b7e47e1c28aece8c2ad4f59  numpy-1.26.0b1-cp310-cp310-macosx_10_9_x86_64.whl
 ab9e86bb7c9d3e009945b24a92318ff5d8c245e0e0aaaa765825c4561c292d53  numpy-1.26.0b1-cp310-cp310-macosx_11_0_arm64.whl
 b0b73599c80b29dfa7f812cb2e8738ce3f058b413e9f2f478e3cc4e038bb8f8e  numpy-1.26.0b1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 4a6d4c99396c57e02b0181f01ba42b482f327774057e51fb7fb390a130c95cff  numpy-1.26.0b1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 02af7482f34aeb9658ece615c922942f1a3908c449a9a6cd9f33fa233ce486d4  numpy-1.26.0b1-cp310-cp310-musllinux_1_1_x86_64.whl
 5a8f04e957259ef93a1e4a29da0b64d49ee842af456257bbb7253925cfe2f7bd  numpy-1.26.0b1-cp310-cp310-win_amd64.whl
 f71e10402e705aaa5908464e489d38e6583c48e40a4721f83195772178c7da9f  numpy-1.26.0b1-cp311-cp311-macosx_10_9_x86_64.whl
 94d5572fea8dca0fa929da9d17fa49e525ceee1e59b04372dfa5bd8a5f688f5f  numpy-1.26.0b1-cp311-cp311-macosx_11_0_arm64.whl
 1f88e6fe42b0d6418e53332e525b299762dbd9e33055d2e0398e6298da5b0cc9  numpy-1.26.0b1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 c466707e5ce5a44caadb85fd672a5ce0bfc060012df465771e7b10506e1e5dad  numpy-1.26.0b1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 16313a28cf703ae722b3ac139809360ffef81a45e758f196e538be3bcbee85c9  numpy-1.26.0b1-cp311-cp311-musllinux_1_1_x86_64.whl
 ea85e8e297af49d30830177ecb0c54d1cbca051e4306161f3ceabfa66560b17c  numpy-1.26.0b1-cp311-cp311-win_amd64.whl
 321a063fabc302931029f831f284cf43c301fdeead1b15df2f8aa87673294d4d  numpy-1.26.0b1-cp312-cp312-macosx_10_9_x86_64.whl
 dc36a9e8df48b72dad668d6f4036ed477d8bc2cb1f7a23b688e8e8057afdfee3  numpy-1.26.0b1-cp312-cp312-macosx_11_0_arm64.whl
 3c6c5804671fa1697e3d0cbc608a65c55794fb6682f4e04e9f6d65d0ddfc47c7  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 3aa806da215e9c10ba89e9037a69c7a56367e059615679ef1a5cf937eedfbf61  numpy-1.26.0b1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 b66135c02ee55f9113dce3c8c5130b5feaead8767cd2c7ad36547a3d5e264230  numpy-1.26.0b1-cp312-cp312-musllinux_1_1_x86_64.whl
 87f2799f475e9e7aee69254dfe357975b163d409550d4641a0bca4cb4f64b725  numpy-1.26.0b1-cp312-cp312-win_amd64.whl
 2b258f67ca4a8245c74470da66a87684ddb3f06dde98760efc7ca792a44ee254  numpy-1.26.0b1-cp39-cp39-macosx_10_9_x86_64.whl
 a31d9109ffed9fc5566e73346a076fffbc7db00e626579ae4d5dfec933b29bfc  numpy-1.26.0b1-cp39-cp39-macosx_11_0_arm64.whl
 18e29ab806ec5e0b05df900d44b3b257a5901c32fc3ddaeb818c520cd9279b4e  numpy-1.26.0b1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 216b47882877ea5272f279c08bf7e42935728f35c6db2e4843b37db7b29ce016  numpy-1.26.0b1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 eea337d6d5ab2b6eb657b3f18e8b57a280f16fb5f94df484d9c1a8d3450d9ae9  numpy-1.26.0b1-cp39-cp39-musllinux_1_1_x86_64.whl
 db698c9008217c54a8005ea58bd5836241d7b519c8bb16a698a1b4ec4ca296a8  numpy-1.26.0b1-cp39-cp39-win_amd64.whl
 f250b3099649137f1021f8f95a9404273bcb7539f0bef6d6cf2c91260285edc4  numpy-1.26.0b1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 22584a41b1be30543dd8c030affc90d8cb7ec19a56fda7f27fc33f64f8b0fbaa  numpy-1.26.0b1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 8aefe8ab1228e00146e5ae88290c7fdb8221aef45b357aed7f3dff6ac3b3b25a  numpy-1.26.0b1-pp39-pypy39_pp73-win_amd64.whl
 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

 33518ccb4da8ee11f1dee4b9fef1e468  numpy-1.25.2-cp310-cp310-macosx_10_9_x86_64.whl
 b5cb0c3b33ef6d93ec2888f25b065636  numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl
 ae027dd38bd73f09c07220b2f516f148  numpy-1.25.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 88cf69dc3c0d293492c4c7e75dccf3d8  numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 3e4e3ad02375ba71ae2cd05ccd97aba4  numpy-1.25.2-cp310-cp310-musllinux_1_1_x86_64.whl
 f52bb644682deb26c35ddec77198b65c  numpy-1.25.2-cp310-cp310-win32.whl
 4944cf36652be7560a6bcd0d5d56e8ea  numpy-1.25.2-cp310-cp310-win_amd64.whl
 5a56e639defebb7b871c8c5613960ca3  numpy-1.25.2-cp311-cp311-macosx_10_9_x86_64.whl
 3988b96944e7218e629255214f2598bd  numpy-1.25.2-cp311-cp311-macosx_11_0_arm64.whl
 302d65015ddd908a862fb3761a2a0363  numpy-1.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 e54a2e23272d1c5e5b278bd7e304c948  numpy-1.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 961d390e8ccaf11b1b0d6200d2c8b1c0  numpy-1.25.2-cp311-cp311-musllinux_1_1_x86_64.whl
 e113865b90f97079d344100c41226fbe  numpy-1.25.2-cp311-cp311-win32.whl
 834a147aa1adaec97655018b882232bd  numpy-1.25.2-cp311-cp311-win_amd64.whl
 fb55f93a8033bde854c8a2b994045686  numpy-1.25.2-cp39-cp39-macosx_10_9_x86_64.whl
 d96e754217d29bf045e082b695667e62  numpy-1.25.2-cp39-cp39-macosx_11_0_arm64.whl
 beab540edebecbb257e482dd9e498b44  numpy-1.25.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 e0d608c9e09cd8feba48567586cfefc0  numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 fe1fc32c8bb005ca04b8f10ebdcff6dd  numpy-1.25.2-cp39-cp39-musllinux_1_1_x86_64.whl
 41df58a9935c8ed869c92307c95f02eb  numpy-1.25.2-cp39-cp39-win32.whl
 a4371272c64493beb8b04ac46c4c1521  numpy-1.25.2-cp39-cp39-win_amd64.whl
 bbe051cbd5f8661dd054277f0b0f0c3d  numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 3f68e6b4af6922989dc0133e37db34ee  numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 fc89421b79e8800240999d3a1d06a4d2  numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl
 cee1996a80032d47bdf1d9d17249c34e  numpy-1.25.2.tar.gz

SHA256

 db3ccc4e37a6873045580d413fe79b68e47a681af8db2e046f1dacfa11f86eb3  numpy-1.25.2-cp310-cp310-macosx_10_9_x86_64.whl
 90319e4f002795ccfc9050110bbbaa16c944b1c37c0baeea43c5fb881693ae1f  numpy-1.25.2-cp310-cp310-macosx_11_0_arm64.whl
 dfe4a913e29b418d096e696ddd422d8a5d13ffba4ea91f9f60440a3b759b0187  numpy-1.25.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 f08f2e037bba04e707eebf4bc934f1972a315c883a9e0ebfa8a7756eabf9e357  numpy-1.25.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 bec1e7213c7cb00d67093247f8c4db156fd03075f49876957dca4711306d39c9  numpy-1.25.2-cp310-cp310-musllinux_1_1_x86_64.whl
 7dc869c0c75988e1c693d0e2d5b26034644399dd929bc049db55395b1379e044  numpy-1.25.2-cp310-cp310-win32.whl
 834b386f2b8210dca38c71a6e0f4fd6922f7d3fcff935dbe3a570945acb1b545  numpy-1.25.2-cp310-cp310-win_amd64.whl
 c5462d19336db4560041517dbb7759c21d181a67cb01b36ca109b2ae37d32418  numpy-1.25.2-cp311-cp311-macosx_10_9_x86_64.whl
 c5652ea24d33585ea39eb6a6a15dac87a1206a692719ff45d53c5282e66d4a8f  numpy-1.25.2-cp311-cp311-macosx_11_0_arm64.whl
 0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88ab4f14655a2  numpy-1.25.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 60e7f0f7f6d0eee8364b9a6304c2845b9c491ac706048c7e8cf47b83123b8dbf  numpy-1.25.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 bb33d5a1cf360304754913a350edda36d5b8c5331a8237268c48f91253c3a364  numpy-1.25.2-cp311-cp311-musllinux_1_1_x86_64.whl
 5883c06bb92f2e6c8181df7b39971a5fb436288db58b5a1c3967702d4278691d  numpy-1.25.2-cp311-cp311-win32.whl
 5c97325a0ba6f9d041feb9390924614b60b99209a71a69c876f71052521d42a4  numpy-1.25.2-cp311-cp311-win_amd64.whl
 b79e513d7aac42ae918db3ad1341a015488530d0bb2a6abcbdd10a3a829ccfd3  numpy-1.25.2-cp39-cp39-macosx_10_9_x86_64.whl
 eb942bfb6f84df5ce05dbf4b46673ffed0d3da59f13635ea9b926af3deb76926  numpy-1.25.2-cp39-cp39-macosx_11_0_arm64.whl
 3e0746410e73384e70d286f93abf2520035250aad8c5714240b0492a7302fdca  numpy-1.25.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 d7806500e4f5bdd04095e849265e55de20d8cc4b661b038957354327f6d9b295  numpy-1.25.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 8b77775f4b7df768967a7c8b3567e309f617dd5e99aeb886fa14dc1a0791141f  numpy-1.25.2-cp39-cp39-musllinux_1_1_x86_64.whl
 2792d23d62ec51e50ce4d4b7d73de8f67a2fd3ea710dcbc8563a51a03fb07b01  numpy-1.25.2-cp39-cp39-win32.whl
 76b4115d42a7dfc5d485d358728cdd8719be33cc5ec6ec08632a5d6fca2ed380  numpy-1.25.2-cp39-cp39-win_amd64.whl
 1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55  numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901  numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf  numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl
 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

 d09d98643db31e892fad11b8c2b7af22  numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl
 d5b8d3b0424e2af41018f35a087c4500  numpy-1.25.1-cp310-cp310-macosx_11_0_arm64.whl
 1007893b1a8bfd97d445a63d29d33642  numpy-1.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 6a62d7a6cee310b41dc872aa7f3d7e8b  numpy-1.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e81f6264aecfa2269c5d29d10c362cbc  numpy-1.25.1-cp310-cp310-musllinux_1_1_x86_64.whl
 ab8ecd125ca86eac0b3ada67ab66dad6  numpy-1.25.1-cp310-cp310-win32.whl
 5466bebeaafcc3d6e1b98858d77ff945  numpy-1.25.1-cp310-cp310-win_amd64.whl
 f31b059256ae09b7b83df63f52d8371e  numpy-1.25.1-cp311-cp311-macosx_10_9_x86_64.whl
 099f74d654888869704469c321af845d  numpy-1.25.1-cp311-cp311-macosx_11_0_arm64.whl
 20d04dccd2bfca5cfd88780d1dc9a3f8  numpy-1.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 61dfd7c00638e83a7af59b86615ee9d2  numpy-1.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4eb459c3d9479c4da2fdf20e4c4085d0  numpy-1.25.1-cp311-cp311-musllinux_1_1_x86_64.whl
 5e84e797866c68ba65fa89a4bf4ba8ce  numpy-1.25.1-cp311-cp311-win32.whl
 87bb1633b2e8029dbfa1e59f7ab22625  numpy-1.25.1-cp311-cp311-win_amd64.whl
 3fcf2eb5970d848a26abdff1b10228e7  numpy-1.25.1-cp39-cp39-macosx_10_9_x86_64.whl
 d71e1cbe18fe05944219e5a5be1796bf  numpy-1.25.1-cp39-cp39-macosx_11_0_arm64.whl
 5b457e10834c991bca84aae7eaa49f34  numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 5cbb4c2f2892fafdf6f34fcb37c9e743  numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 7d9d1ae23cf5420652088bfe8e048d89  numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl
 7e5bed491b85f0d7c718d6809f9b3ed2  numpy-1.25.1-cp39-cp39-win32.whl
 838e97b751bebadf47e2196b2c88ffa2  numpy-1.25.1-cp39-cp39-win_amd64.whl
 9ba95d8d6004d9659d7728fe93f67be9  numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 fbccb20254a2dc85bdec549a03b8eb56  numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 95e36689e6dd078caf11e7e2a2d5f5f1  numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl
 768d0ebf15e2242f4c7ca7565bb5dd3e  numpy-1.25.1.tar.gz

SHA256

 77d339465dff3eb33c701430bcb9c325b60354698340229e1dff97745e6b3efa  numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl
 d736b75c3f2cb96843a5c7f8d8ccc414768d34b0a75f466c05f3a739b406f10b  numpy-1.25.1-cp310-cp310-macosx_11_0_arm64.whl
 4a90725800caeaa160732d6b31f3f843ebd45d6b5f3eec9e8cc287e30f2805bf  numpy-1.25.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 6c6c9261d21e617c6dc5eacba35cb68ec36bb72adcff0dee63f8fbc899362588  numpy-1.25.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 0def91f8af6ec4bb94c370e38c575855bf1d0be8a8fbfba42ef9c073faf2cf19  numpy-1.25.1-cp310-cp310-musllinux_1_1_x86_64.whl
 fd67b306320dcadea700a8f79b9e671e607f8696e98ec255915c0c6d6b818503  numpy-1.25.1-cp310-cp310-win32.whl
 c1516db588987450b85595586605742879e50dcce923e8973f79529651545b57  numpy-1.25.1-cp310-cp310-win_amd64.whl
 6b82655dd8efeea69dbf85d00fca40013d7f503212bc5259056244961268b66e  numpy-1.25.1-cp311-cp311-macosx_10_9_x86_64.whl
 e8f6049c4878cb16960fbbfb22105e49d13d752d4d8371b55110941fb3b17800  numpy-1.25.1-cp311-cp311-macosx_11_0_arm64.whl
 41a56b70e8139884eccb2f733c2f7378af06c82304959e174f8e7370af112e09  numpy-1.25.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 d5154b1a25ec796b1aee12ac1b22f414f94752c5f94832f14d8d6c9ac40bcca6  numpy-1.25.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 38eb6548bb91c421261b4805dc44def9ca1a6eef6444ce35ad1669c0f1a3fc5d  numpy-1.25.1-cp311-cp311-musllinux_1_1_x86_64.whl
 791f409064d0a69dd20579345d852c59822c6aa087f23b07b1b4e28ff5880fcb  numpy-1.25.1-cp311-cp311-win32.whl
 c40571fe966393b212689aa17e32ed905924120737194b5d5c1b20b9ed0fb171  numpy-1.25.1-cp311-cp311-win_amd64.whl
 3d7abcdd85aea3e6cdddb59af2350c7ab1ed764397f8eec97a038ad244d2d105  numpy-1.25.1-cp39-cp39-macosx_10_9_x86_64.whl
 1a180429394f81c7933634ae49b37b472d343cccb5bb0c4a575ac8bbc433722f  numpy-1.25.1-cp39-cp39-macosx_11_0_arm64.whl
 d412c1697c3853c6fc3cb9751b4915859c7afe6a277c2bf00acf287d56c4e625  numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 20e1266411120a4f16fad8efa8e0454d21d00b8c7cee5b5ccad7565d95eb42dd  numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 f76aebc3358ade9eacf9bc2bb8ae589863a4f911611694103af05346637df1b7  numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl
 247d3ffdd7775bdf191f848be8d49100495114c82c2bd134e8d5d075fb386a1c  numpy-1.25.1-cp39-cp39-win32.whl
 1d5d3c68e443c90b38fdf8ef40e60e2538a27548b39b12b73132456847f4b631  numpy-1.25.1-cp39-cp39-win_amd64.whl
 35a9527c977b924042170a0887de727cd84ff179e478481404c5dc66b4170009  numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
 0d3fe3dd0506a28493d82dc3cf254be8cd0d26f4008a417385cbf1ae95b54004  numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 012097b5b0d00a11070e8f2e261128c44157a8689f7dedcf35576e525893f4fe  numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl
 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

Checksums

MD5

 25049e3aee79dde29e7a498d3ad13379  numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl
 579b5c357c918feaef4af03af8afb721  numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl
 c873a14fa4f0210884db9c05e2904286  numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 110a13ac016286059f0658b52b3646c0  numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 fa67218966c0aef4094867cad7703648  numpy-1.24.4-cp310-cp310-win32.whl
 6ee768803d8ebac43ee0a04e628a69f9  numpy-1.24.4-cp310-cp310-win_amd64.whl
 0c918c16b58cb7f6773ea7d76e0bdaff  numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl
 20506ae8003faf097c6b3a8915b4140e  numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl
 902df9d5963e89d88a1939d94207857f  numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2543611d802c141c8276e4868b4d9619  numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 37b23a4e4e148d61dd3a515ac5dbf7ec  numpy-1.24.4-cp311-cp311-win32.whl
 25e9f6bee2b65ff2a87588e717f15165  numpy-1.24.4-cp311-cp311-win_amd64.whl
 f39a0cc3655a482af7d300bcaff5978e  numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl
 9ed27941388fdb392e8969169f3fc600  numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl
 dee3f0c7482f1dc8bd1cd27b9b028a2c  numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2cc0967af29df3caef9fb3520f14e071  numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 8572a3a0973fa78355bcb5f737745b47  numpy-1.24.4-cp38-cp38-win32.whl
 771c63f2ef0d31466bbb12362a532265  numpy-1.24.4-cp38-cp38-win_amd64.whl
 5713d9dc3dff287fb72121fe1960c48d  numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl
 4e6718e3b655219a2a733b4fa242ca32  numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl
 31487f9a52ef81f8f88ec7fce8738dad  numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 ea597b30187e55eb16ee31631e66f60d  numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 98adbf30c67154056474001c125f6188  numpy-1.24.4-cp39-cp39-win32.whl
 49c444b0e572ef45f1d92c106a36004e  numpy-1.24.4-cp39-cp39-win_amd64.whl
 cdddfdeac437b0f20b4e366f00b5c42e  numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 3778338c15628caa3abd61e6f7bd46ec  numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e16bd49d5295dc1b01ed50d76229fb54  numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl
 3f3995540a17854a29dc79f8eeecd832  numpy-1.24.4.tar.gz

SHA256

 c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64  numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl
 ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1  numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl
 79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4  numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6  numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc  numpy-1.24.4-cp310-cp310-win32.whl
 b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e  numpy-1.24.4-cp310-cp310-win_amd64.whl
 f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810  numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl
 e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254  numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl
 222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7  numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5  numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d  numpy-1.24.4-cp311-cp311-win32.whl
 b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694  numpy-1.24.4-cp311-cp311-win_amd64.whl
 1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61  numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl
 04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f  numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl
 a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e  numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc  numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2  numpy-1.24.4-cp38-cp38-win32.whl
 692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706  numpy-1.24.4-cp38-cp38-win_amd64.whl
 2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400  numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl
 9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f  numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl
 f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9  numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d  numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835  numpy-1.24.4-cp39-cp39-win32.whl
 befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8  numpy-1.24.4-cp39-cp39-win_amd64.whl
 31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef  numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a  numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2  numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl
 80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463  numpy-1.24.4.tar.gz

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

 93a3ce07e3773842c54d831f18e3eb8d  numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl
 39691ff3d1612438dfcd3266c9765aab  numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl
 a99234799a239e7e9c6fa15c212996df  numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 3673aa638746851dd19d5199e1eb3a91  numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 3c72962360bcd0938a6bddee6cdca766  numpy-1.24.3-cp310-cp310-win32.whl
 a3329efa646012fa4ee06ce5e08eadaf  numpy-1.24.3-cp310-cp310-win_amd64.whl
 5323fb0323d1ec10ee3c35a2fa79cbcd  numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl
 cfa001dcd07cdf6414ced433e88959d4  numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
 d75bbfb06ed00d04232dce0e865eb42c  numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 fe18b810bcf284572467ce585dbc533b  numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e97699a4ef96a81e0916bdf15440abe0  numpy-1.24.3-cp311-cp311-win32.whl
 e6de5b7d77dc43ed47f516eb10bbe8b6  numpy-1.24.3-cp311-cp311-win_amd64.whl
 dd04ebf441a8913f4900b56e7a33a75e  numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl
 e47ac5521b0bfc3effb040072d8a7902  numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl
 7b7dae3309e7ca8a8859633a5d337431  numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 8cc87b88163ed84e70c48fd0f5f8f20e  numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 350934bae971d0ebe231a59b640069db  numpy-1.24.3-cp38-cp38-win32.whl
 c4708ef009bb5d427ea94a4fc4a10e12  numpy-1.24.3-cp38-cp38-win_amd64.whl
 44b08a293a4e12d62c27b8f15ba5664e  numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl
 3ae7ac30f86c720e42b2324a0ae1adf5  numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl
 065464a8d918c670c7863d1e72e3e6dd  numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 1f163b9ea417c253e84480aa8d99dee6  numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 c86e648389e333e062bea11c749b9a32  numpy-1.24.3-cp39-cp39-win32.whl
 bfe332e577c604d6d62a57381e6aa0a6  numpy-1.24.3-cp39-cp39-win_amd64.whl
 374695eeef5aca32a5b7f2f518dd3ba1  numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 6abd9dba54405182e6e7bb32dbe377bb  numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 0848bd41c08dd5ebbc5a7f0788678e0e  numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
 89e5e2e78407032290ae6acf6dcaea46  numpy-1.24.3.tar.gz

SHA256

 3c1104d3c036fb81ab923f507536daedc718d0ad5a8707c6061cdfd6d184e570  numpy-1.24.3-cp310-cp310-macosx_10_9_x86_64.whl
 202de8f38fc4a45a3eea4b63e2f376e5f2dc64ef0fa692838e31a808520efaf7  numpy-1.24.3-cp310-cp310-macosx_11_0_arm64.whl
 8535303847b89aa6b0f00aa1dc62867b5a32923e4d1681a35b5eef2d9591a463  numpy-1.24.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2d926b52ba1367f9acb76b0df6ed21f0b16a1ad87c6720a1121674e5cf63e2b6  numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 f21c442fdd2805e91799fbe044a7b999b8571bb0ab0f7850d0cb9641a687092b  numpy-1.24.3-cp310-cp310-win32.whl
 ab5f23af8c16022663a652d3b25dcdc272ac3f83c3af4c02eb8b824e6b3ab9d7  numpy-1.24.3-cp310-cp310-win_amd64.whl
 9a7721ec204d3a237225db3e194c25268faf92e19338a35f3a224469cb6039a3  numpy-1.24.3-cp311-cp311-macosx_10_9_x86_64.whl
 d6cc757de514c00b24ae8cf5c876af2a7c3df189028d68c0cb4eaa9cd5afc2bf  numpy-1.24.3-cp311-cp311-macosx_11_0_arm64.whl
 76e3f4e85fc5d4fd311f6e9b794d0c00e7002ec122be271f2019d63376f1d385  numpy-1.24.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 a1d3c026f57ceaad42f8231305d4653d5f05dc6332a730ae5c0bea3513de0950  numpy-1.24.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 c91c4afd8abc3908e00a44b2672718905b8611503f7ff87390cc0ac3423fb096  numpy-1.24.3-cp311-cp311-win32.whl
 5342cf6aad47943286afa6f1609cad9b4266a05e7f2ec408e2cf7aea7ff69d80  numpy-1.24.3-cp311-cp311-win_amd64.whl
 7776ea65423ca6a15255ba1872d82d207bd1e09f6d0894ee4a64678dd2204078  numpy-1.24.3-cp38-cp38-macosx_10_9_x86_64.whl
 ae8d0be48d1b6ed82588934aaaa179875e7dc4f3d84da18d7eae6eb3f06c242c  numpy-1.24.3-cp38-cp38-macosx_11_0_arm64.whl
 ecde0f8adef7dfdec993fd54b0f78183051b6580f606111a6d789cd14c61ea0c  numpy-1.24.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 4749e053a29364d3452c034827102ee100986903263e89884922ef01a0a6fd2f  numpy-1.24.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 d933fabd8f6a319e8530d0de4fcc2e6a61917e0b0c271fded460032db42a0fe4  numpy-1.24.3-cp38-cp38-win32.whl
 56e48aec79ae238f6e4395886b5eaed058abb7231fb3361ddd7bfdf4eed54289  numpy-1.24.3-cp38-cp38-win_amd64.whl
 4719d5aefb5189f50887773699eaf94e7d1e02bf36c1a9d353d9f46703758ca4  numpy-1.24.3-cp39-cp39-macosx_10_9_x86_64.whl
 0ec87a7084caa559c36e0a2309e4ecb1baa03b687201d0a847c8b0ed476a7187  numpy-1.24.3-cp39-cp39-macosx_11_0_arm64.whl
 ea8282b9bcfe2b5e7d491d0bf7f3e2da29700cec05b49e64d6246923329f2b02  numpy-1.24.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 210461d87fb02a84ef243cac5e814aad2b7f4be953b32cb53327bb49fd77fbb4  numpy-1.24.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 784c6da1a07818491b0ffd63c6bbe5a33deaa0e25a20e1b3ea20cf0e43f8046c  numpy-1.24.3-cp39-cp39-win32.whl
 d5036197ecae68d7f491fcdb4df90082b0d4960ca6599ba2659957aafced7c17  numpy-1.24.3-cp39-cp39-win_amd64.whl
 352ee00c7f8387b44d19f4cada524586f07379c0d49270f87233983bc5087ca0  numpy-1.24.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 1a7d6acc2e7524c9955e5c903160aa4ea083736fde7e91276b0e5d98e6332812  numpy-1.24.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 35400e6a8d102fd07c71ed7dcadd9eb62ee9a6e84ec159bd48c28235bbb0f8e4  numpy-1.24.3-pp38-pypy38_pp73-win_amd64.whl
 ab344f1bf21f140adab8e47fdbc7c35a477dc01408791f8ba00d018dd0bc5155  numpy-1.24.3.tar.gz

1.24.2

discovered after the 1.24.1 release. The Python versions supported by
this release are 3.8-3.11.

Contributors

A total of 14 people contributed to this release. People with a \"+\" by
their names contributed a patch for the first time.

-   Bas van Beek
-   Charles Harris
-   Khem Raj +
-   Mark Harfouche
-   Matti Picus
-   Panagiotis Zestanakis +
-   Peter Hawkins
-   Pradipta Ghosh
-   Ross Barnowski
-   Sayed Adel
-   Sebastian Berg
-   Syam Gadde +
-   dmbelov +
-   pkubaj +

Pull requests merged

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

-   [22965](https://github.com/numpy/numpy/pull/22965): MAINT: Update python 3.11-dev to 3.11.
-   [22966](https://github.com/numpy/numpy/pull/22966): DOC: Remove dangling deprecation warning
-   [22967](https://github.com/numpy/numpy/pull/22967): ENH: Detect CPU features on FreeBSD/powerpc64\*
-   [22968](https://github.com/numpy/numpy/pull/22968): BUG: np.loadtxt cannot load text file with quoted fields separated\...
-   [22969](https://github.com/numpy/numpy/pull/22969): TST: Add fixture to avoid issue with randomizing test order.
-   [22970](https://github.com/numpy/numpy/pull/22970): BUG: Fix fill violating read-only flag. (#22959)
-   [22971](https://github.com/numpy/numpy/pull/22971): MAINT: Add additional information to missing scalar AttributeError
-   [22972](https://github.com/numpy/numpy/pull/22972): MAINT: Move export for scipy arm64 helper into main module
-   [22976](https://github.com/numpy/numpy/pull/22976): BUG, SIMD: Fix spurious invalid exception for sin/cos on arm64/clang
-   [22989](https://github.com/numpy/numpy/pull/22989): BUG: Ensure correct loop order in sin, cos, and arctan2
-   [23030](https://github.com/numpy/numpy/pull/23030): DOC: Add version added information for the strict parameter in\...
-   [23031](https://github.com/numpy/numpy/pull/23031): BUG: use `_Alignof` rather than `offsetof()` on most compilers
-   [23147](https://github.com/numpy/numpy/pull/23147): BUG: Fix for npyv\_\_trunc_s32_f32 (VXE)
-   [23148](https://github.com/numpy/numpy/pull/23148): BUG: Fix integer / float scalar promotion
-   [23149](https://github.com/numpy/numpy/pull/23149): BUG: Add missing \<type_traits> header.
-   [23150](https://github.com/numpy/numpy/pull/23150): TYP, MAINT: Add a missing explicit `Any` parameter to the `npt.ArrayLike`\...
-   [23161](https://github.com/numpy/numpy/pull/23161): BLD: remove redundant definition of npy_nextafter \[wheel build\]

Checksums

MD5

 73fe0b507f56c0baf43171a76ad2003f  numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl
 2dbbe6f8a14e14978d24de9fcc8b49fe  numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl
 9ddadbf9cac2742318d8b292cb9ca579  numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 969f4f33baaff53dbbbaf1a146c43534  numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 6df575dff02feac835d22debb15d190e  numpy-1.24.2-cp310-cp310-win32.whl
 2f939228a8c33265f2a8a1fce349d6f1  numpy-1.24.2-cp310-cp310-win_amd64.whl
 c093e61421be01ffff435387839949f1  numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl
 03d71e3d9a086b56837c461fd7c9188b  numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl
 c0dc33697d156e2b9a029095efeb1b10  numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 13b57957a1f40e13f8826d14b031a6fe  numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 5afd966db0b59655618c1859d98d87f6  numpy-1.24.2-cp311-cp311-win32.whl
 e0b850f9c20871cd65ecb35235688f4d  numpy-1.24.2-cp311-cp311-win_amd64.whl
 9a30452135ab0387b8ea9007e94e9f81  numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl
 bdd6eede4524a230574b37e1f631f2c0  numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl
 4f930a9030d77d45a1cb6f374c91fb53  numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 e77155c010f9dd63ea2815579a28c503  numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 1a45f4373945eaeabeaa4020ce04e8fd  numpy-1.24.2-cp38-cp38-win32.whl
 66e93d70fad16b4ccb4531e31aad36e3  numpy-1.24.2-cp38-cp38-win_amd64.whl
 93a4984da83c6811367d3daf709ed25c  numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl
 e0281b96c490ba00f1382eb3984b4e51  numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl
 ce97d81e4ae6e10241d471492391b1be  numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 0c0ea440190705f98abeaa856e7da690  numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 c25f7fbb185f1b8f7761bc22082d9939  numpy-1.24.2-cp39-cp39-win32.whl
 7705c6b0bcf22b5e64cf248144b2f554  numpy-1.24.2-cp39-cp39-win_amd64.whl
 07b6361e36e0093b580dc05799b1f03d  numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 4c1466ae486b39d1a35aacb46256ec1e  numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 4fea9d95e0489d06c3a24a87697d2fc0  numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl
 c4212a8da1ecf17ece37e2afd0319806  numpy-1.24.2.tar.gz

SHA256

 eef70b4fc1e872ebddc38cddacc87c19a3709c0e3e5d20bf3954c147b1dd941d  numpy-1.24.2-cp310-cp310-macosx_10_9_x86_64.whl
 e8d2859428712785e8a8b7d2b3ef0a1d1565892367b32f915c4a4df44d0e64f5  numpy-1.24.2-cp310-cp310-macosx_11_0_arm64.whl
 6524630f71631be2dabe0c541e7675db82651eb998496bbe16bc4f77f0772253  numpy-1.24.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 a51725a815a6188c662fb66fb32077709a9ca38053f0274640293a14fdd22978  numpy-1.24.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 2620e8592136e073bd12ee4536149380695fbe9ebeae845b81237f986479ffc9  numpy-1.24.2-cp310-cp310-win32.whl
 97cf27e51fa078078c649a51d7ade3c92d9e709ba2bfb97493007103c741f1d0  numpy-1.24.2-cp310-cp310-win_amd64.whl
 7de8fdde0003f4294655aa5d5f0a89c26b9f22c0a58790c38fae1ed392d44a5a  numpy-1.24.2-cp311-cp311-macosx_10_9_x86_64.whl
 4173bde9fa2a005c2c6e2ea8ac1618e2ed2c1c6ec8a7657237854d42094123a0  numpy-1.24.2-cp311-cp311-macosx_11_0_arm64.whl
 4cecaed30dc14123020f77b03601559fff3e6cd0c048f8b5289f4eeabb0eb281  numpy-1.24.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 9a23f8440561a633204a67fb44617ce2a299beecf3295f0d13c495518908e910  numpy-1.24.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e428c4fbfa085f947b536706a2fc349245d7baa8334f0c5723c56a10595f9b95  numpy-1.24.2-cp311-cp311-win32.whl
 557d42778a6869c2162deb40ad82612645e21d79e11c1dc62c6e82a2220ffb04  numpy-1.24.2-cp311-cp311-win_amd64.whl
 d0a2db9d20117bf523dde15858398e7c0858aadca7c0f088ac0d6edd360e9ad2  numpy-1.24.2-cp38-cp38-macosx_10_9_x86_64.whl
 c72a6b2f4af1adfe193f7beb91ddf708ff867a3f977ef2ec53c0ffb8283ab9f5  numpy-1.24.2-cp38-cp38-macosx_11_0_arm64.whl
 c29e6bd0ec49a44d7690ecb623a8eac5ab8a923bce0bea6293953992edf3a76a  numpy-1.24.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 2eabd64ddb96a1239791da78fa5f4e1693ae2dadc82a76bc76a14cbb2b966e96  numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 e3ab5d32784e843fc0dd3ab6dcafc67ef806e6b6828dc6af2f689be0eb4d781d  numpy-1.24.2-cp38-cp38-win32.whl
 76807b4063f0002c8532cfeac47a3068a69561e9c8715efdad3c642eb27c0756  numpy-1.24.2-cp38-cp38-win_amd64.whl
 4199e7cfc307a778f72d293372736223e39ec9ac096ff0a2e64853b866a8e18a  numpy-1.24.2-cp39-cp39-macosx_10_9_x86_64.whl
 adbdce121896fd3a17a77ab0b0b5eedf05a9834a18699db6829a64e1dfccca7f  numpy-1.24.2-cp39-cp39-macosx_11_0_arm64.whl
 889b2cc88b837d86eda1b17008ebeb679d82875022200c6e8e4ce6cf549b7acb  numpy-1.24.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
 f64bb98ac59b3ea3bf74b02f13836eb2e24e48e0ab0145bbda646295769bd780  numpy-1.24.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 63e45511ee4d9d976637d11e6c9864eae50e12dc9598f531c035265991910468  numpy-1.24.2-cp39-cp39-win32.whl
 a77d3e1163a7770164404607b7ba3967fb49b24782a6ef85d9b5f54126cc39e5  numpy-1.24.2-cp39-cp39-win_amd64.whl
 92011118955724465fb6853def593cf397b4a1367495e0b59a7e69d40c4eb71d  numpy-1.24.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
 f9006288bcf4895917d02583cf3411f98631275bc67cce355a7f39f8c14338fa  numpy-1.24.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
 150947adbdfeceec4e5926d956a06865c1c690f2fd902efede4ca6fe2e657c3f  numpy-1.24.2-pp38-pypy38_pp73-win_amd64.whl
 003a9f530e880cb2cd177cba1af7220b9aa42def9c4afc2a2fc3ee6be7eb2b22  numpy-1.24.2.tar.gz

1.24.1

discovered after the 1.24.0 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.

-   Andrew Nelson
-   Ben Greiner +
-   Charles Harris
-   Clément Robert
-   Matteo Raso
-   Matti Picus
-   Melissa Weber Mendonça
-   Miles Cranmer
-   Ralf Gommers
-   Rohit Goswami
-   Sayed Adel
-   Sebastian Berg

Pull requests merged

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

-   [22820](https://github.com/numpy/numpy/pull/22820): BLD: add workaround in setup.py for newer setuptools
-   [22830](https://github.com/numpy/numpy/pull/22830): BLD: CIRRUS_TAG redux
-   [22831](https://github.com/numpy/numpy/pull/22831): DOC: fix a couple typos in 1.23 notes
-   [22832](https://github.com/numpy/numpy/pull/22832): BUG: Fix refcounting errors found using pytest-leaks
-   [22834](https://github.com/numpy/numpy/pull/22834): BUG, SIMD: Fix invalid value encountered in several ufuncs
-   [22837](https://github.com/numpy/numpy/pull/22837): TST: ignore more np.distutils.log imports
-   [22839](https://github.com/numpy/numpy/pull/22839): BUG: Do not use getdata() in np.ma.masked_invalid
-   [22847](https://github.com/numpy/numpy/pull/22847): BUG: Ensure correct behavior for rows ending in delimiter in\...
-   [22848](https://github.com/numpy/numpy/pull/22848): BUG, SIMD: Fix the bitmask of the boolean comparison
-   [22857](https://github.com/numpy/numpy/pull/22857): BLD: Help raspian arm + clang 13 about \_\_builtin_mul_overflow
-   [22858](https://github.com/numpy/numpy/pull/22858): API: Ensure a full mask is returned for masked_invalid
-   [22866](https://github.com/numpy/numpy/pull/22866): BUG: Polynomials now copy properly (#22669)
-   [22867](https://github.com/numpy/numpy/pull/22867): BUG, SIMD: Fix memory overlap in ufunc comparison loops
-   [22868](https://github.com/numpy/numpy/pull/22868): BUG: Fortify string casts against floating point warnings
-   [22875](https://github.com/numpy/nu

@pyup-bot pyup-bot mentioned this pull request Sep 17, 2023
Closed
@pyup-bot
Copy link
Collaborator Author

Closing this in favor of #87

@pyup-bot pyup-bot closed this Oct 15, 2023
@murphyqm murphyqm deleted the pyup-update-numpy-1.20.3-to-1.26.0 branch October 15, 2023 00:38
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers
No reviews
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

Successfully merging this pull request may close these issues.

None yet
1 participant
@pyup-bot