Closed renovate[bot] closed 2 months ago
Renovate failed to update an artifact related to this branch. You probably do not want to merge this PR as-is.
♻ Renovate will retry this branch, including artifacts, only when one of the following happens:
The artifact failure details are included below:
Command failed: pipenv lock
Creating a virtualenv for this project...
Pipfile: /tmp/renovate/repos/github/ut-issl/s2e-core/scripts/Plot/Pipfile
Using /opt/containerbase/tools/python/3.8.19/bin/python3.8 (3.8.19) to create virtualenv...
created virtual environment CPython3.8.19.final.0-64 in 594ms
creator CPython3Posix(dest=/tmp/renovate/cache/others/virtualenvs/Plot-Rq2hs_8E, clear=False, no_vcs_ignore=False, global=False)
seeder FromAppData(download=False, pip=bundle, setuptools=bundle, wheel=bundle, via=copy, app_data_dir=/home/ubuntu/.local/share/virtualenv)
added seed packages: pip==24.1, setuptools==70.1.0, wheel==0.43.0
activators BashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator
✔ Successfully created virtual environment!
Virtualenv location: /tmp/renovate/cache/others/virtualenvs/Plot-Rq2hs_8E
Locking [packages] dependencies...
False
CRITICAL:pipenv.patched.pip._internal.resolution.resolvelib.factory:Ignored the following versions that require a different python version: 1.25.0 Requires-Python >=3.9; 1.25.0rc1 Requires-Python >=3.9; 1.25.1 Requires-Python >=3.9; 1.25.2 Requires-Python >=3.9; 1.26.0 Requires-Python <3.13,>=3.9; 1.26.0b1 Requires-Python <3.13,>=3.9; 1.26.0rc1 Requires-Python <3.13,>=3.9; 1.26.1 Requires-Python <3.13,>=3.9; 1.26.2 Requires-Python >=3.9; 1.26.3 Requires-Python >=3.9; 1.26.4 Requires-Python >=3.9; 2.0.0 Requires-Python >=3.9; 2.0.0b1 Requires-Python >=3.9; 2.0.0rc1 Requires-Python >=3.9; 2.0.0rc2 Requires-Python >=3.9
CRITICAL:pipenv.patched.pip._internal.resolution.resolvelib.factory:Could not find a version that satisfies the requirement numpy==2.0.0 (from versions: 1.3.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.6.1, 1.6.2, 1.7.0, 1.7.1, 1.7.2, 1.8.0, 1.8.1, 1.8.2, 1.9.0, 1.9.1, 1.9.2, 1.9.3, 1.10.0.post2, 1.10.1, 1.10.2, 1.10.4, 1.11.0, 1.11.1, 1.11.2, 1.11.3, 1.12.0, 1.12.1, 1.13.0, 1.13.1, 1.13.3, 1.14.0, 1.14.1, 1.14.2, 1.14.3, 1.14.4, 1.14.5, 1.14.6, 1.15.0, 1.15.1, 1.15.2, 1.15.3, 1.15.4, 1.16.0, 1.16.1, 1.16.2, 1.16.3, 1.16.4, 1.16.5, 1.16.6, 1.17.0, 1.17.1, 1.17.2, 1.17.3, 1.17.4, 1.17.5, 1.18.0, 1.18.1, 1.18.2, 1.18.3, 1.18.4, 1.18.5, 1.19.0, 1.19.1, 1.19.2, 1.19.3, 1.19.4, 1.19.5, 1.20.0, 1.20.1, 1.20.2, 1.20.3, 1.21.0, 1.21.1, 1.21.2, 1.21.3, 1.21.4, 1.21.5, 1.21.6, 1.22.0, 1.22.1, 1.22.2, 1.22.3, 1.22.4, 1.23.0rc1, 1.23.0rc2, 1.23.0rc3, 1.23.0, 1.23.1, 1.23.2, 1.23.3, 1.23.4, 1.23.5, 1.24.0rc1, 1.24.0rc2, 1.24.0, 1.24.1, 1.24.2, 1.24.3, 1.24.4)
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/resolver.py", line 645, in _main
[ResolutionFailure]: resolve_packages(
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/resolver.py", line 612, in resolve_packages
[ResolutionFailure]: results, resolver = resolve(
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/resolver.py", line 592, in resolve
[ResolutionFailure]: return resolve_deps(
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/utils/resolver.py", line 932, in resolve_deps
[ResolutionFailure]: results, hashes, internal_resolver = actually_resolve_deps(
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/utils/resolver.py", line 700, in actually_resolve_deps
[ResolutionFailure]: resolver.resolve()
[ResolutionFailure]: File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/utils/resolver.py", line 457, in resolve
[ResolutionFailure]: raise ResolutionFailure(message=str(e))
[pipenv.exceptions.ResolutionFailure]: Warning: Your dependencies could not be resolved. You likely have a mismatch in your sub-dependencies.
You can use $ pipenv run pip install <requirement_name> to bypass this mechanism, then run $ pipenv graph to inspect the versions actually installed in the virtualenv.
Hint: try $ pipenv lock --pre if it is a pre-release dependency.
ERROR: No matching distribution found for numpy==2.0.0
Traceback (most recent call last):
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/bin/pipenv", line 8, in <module>
sys.exit(cli())
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 1157, in __call__
return self.main(*args, **kwargs)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/cli/options.py", line 58, in main
return super().main(*args, **kwargs, windows_expand_args=False)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 1078, in main
rv = self.invoke(ctx)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 1688, in invoke
return _process_result(sub_ctx.command.invoke(sub_ctx))
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 1434, in invoke
return ctx.invoke(self.callback, **ctx.params)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/decorators.py", line 92, in new_func
return ctx.invoke(f, obj, *args, **kwargs)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/core.py", line 783, in invoke
return __callback(*args, **kwargs)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/vendor/click/decorators.py", line 33, in new_func
return f(get_current_context(), *args, **kwargs)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/cli/command.py", line 344, in lock
do_lock(
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/routines/lock.py", line 66, in do_lock
venv_resolve_deps(
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/utils/resolver.py", line 873, in venv_resolve_deps
c = resolve(cmd, st, project=project)
File "/opt/containerbase/tools/pipenv/2024.0.1/3.8.19/lib/python3.8/site-packages/pipenv/utils/resolver.py", line 737, in resolve
raise RuntimeError("Failed to lock Pipfile.lock!")
RuntimeError: Failed to lock Pipfile.lock!
Because you closed this PR without merging, Renovate will ignore this update. You will not get PRs for any future 2.x
releases. But if you manually upgrade to 2.x
then Renovate will re-enable minor
and patch
updates automatically.
If you accidentally closed this PR, or if you changed your mind: rename this PR to get a fresh replacement PR.
This PR contains the following updates:
==1.24.3
->==2.0.0
Release Notes
numpy/numpy (numpy)
### [`v2.0.0`](https://togithub.com/numpy/numpy/compare/v1.26.4...v2.0.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.26.4...v2.0.0) ### [`v1.26.4`](https://togithub.com/numpy/numpy/releases/tag/v1.26.4) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.26.3...v1.26.4) ### NumPy 1.26.4 Release Notes NumPy 1.26.4 is a maintenance release that fixes bugs and regressions discovered after the 1.26.3 release. The Python versions supported by this release are 3.9-3.12. This is the last planned release in the 1.26.x series. #### Contributors A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Elliott Sales de Andrade - Lucas Colley + - Mark Ryan + - Matti Picus - Nathan Goldbaum - Ola x Nilsson + - Pieter Eendebak - Ralf Gommers - Sayed Adel - Sebastian Berg - Stefan van der Walt - Stefano Rivera #### Pull requests merged A total of 19 pull requests were merged for this release. - [#25323](https://togithub.com/numpy/numpy/pull/25323): BUG: Restore missing asstr import - [#25523](https://togithub.com/numpy/numpy/pull/25523): MAINT: prepare 1.26.x for further development - [#25539](https://togithub.com/numpy/numpy/pull/25539): BUG: `numpy.array_api`: fix `linalg.cholesky` upper decomp... - [#25584](https://togithub.com/numpy/numpy/pull/25584): CI: Bump azure pipeline timeout to 120 minutes - [#25585](https://togithub.com/numpy/numpy/pull/25585): MAINT, BLD: Fix unused inline functions warnings on clang - [#25599](https://togithub.com/numpy/numpy/pull/25599): BLD: include fix for MinGW platform detection - [#25618](https://togithub.com/numpy/numpy/pull/25618): TST: Fix test_numeric on riscv64 - [#25619](https://togithub.com/numpy/numpy/pull/25619): BLD: fix building for windows ARM64 - [#25620](https://togithub.com/numpy/numpy/pull/25620): MAINT: add `newaxis` to `__all__` in `numpy.array_api` - [#25630](https://togithub.com/numpy/numpy/pull/25630): BUG: Use large file fallocate on 32 bit linux platforms - [#25643](https://togithub.com/numpy/numpy/pull/25643): TST: Fix test_warning_calls on Python 3.12 - [#25645](https://togithub.com/numpy/numpy/pull/25645): TST: Bump pytz to 2023.3.post1 - [#25658](https://togithub.com/numpy/numpy/pull/25658): BUG: Fix AVX512 build flags on Intel Classic Compiler - [#25670](https://togithub.com/numpy/numpy/pull/25670): BLD: fix potential issue with escape sequences in `__config__.py` - [#25718](https://togithub.com/numpy/numpy/pull/25718): CI: pin cygwin python to 3.9.16-1 and fix typing tests \[skip... - [#25720](https://togithub.com/numpy/numpy/pull/25720): MAINT: Bump cibuildwheel to v2.16.4 - [#25748](https://togithub.com/numpy/numpy/pull/25748): BLD: unvendor meson-python on 1.26.x and upgrade to meson-python... - [#25755](https://togithub.com/numpy/numpy/pull/25755): MAINT: Include header defining backtrace - [#25756](https://togithub.com/numpy/numpy/pull/25756): BUG: Fix np.quantile(\[Fraction(2,1)], 0.5) ([#24711](https://togithub.com/numpy/numpy/issues/24711)) #### Checksums ##### MD5 90f33cdd8934cd07192d6ede114d8d4d numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl 63ac60767f6724490e587f6010bd6839 numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl ad4e82b225aaaf5898ea9798b50978d8 numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl d428e3da2df4fa359313348302cf003a numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 89937c3bb596193f8ca9eae2ff84181e numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl de4f9da0a4e6dfd4cec39c7ad5139803 numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl 2c1f73fd9b3acf4b9b0c23e985cdd38f numpy-1.26.4-cp310-cp310-win32.whl 920ad1f50e478b1a877fe7b7a46cc520 numpy-1.26.4-cp310-cp310-win_amd64.whl 719d1ff12db38903dcfd6749078fb11d numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl eb601e80194d2e1c00d8daedd8dc68c4 numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl 71a7ab11996fa370dc28e28731bd5c32 numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl eb0cdd03e1ee2eb45c57c7340c98cf48 numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9d4ae1b0b27a625400f81ed1846a5667 numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl 1b6771350d2f496157430437a895ba4b numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl 1e4a18612ee4d0e54e0833574ebc6d25 numpy-1.26.4-cp311-cp311-win32.whl 5fd325dd8704023c1110835d7a1b095a numpy-1.26.4-cp311-cp311-win_amd64.whl d95ce582923d24dbddbc108aa5fd2128 numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl 6f16f3d70e0d95ce2b032167c546cc95 numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl 5369536d4c45fbe384147ff23185b48a numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1ceb224096686831ad731e472b65e96a numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cd8d3c00bbc89f9bc07e2df762f9e2ae numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl 5bd81ce840bb2e42befe01efb0402b79 numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl 2cc3b0757228078395da3efa3dc99f23 numpy-1.26.4-cp312-cp312-win32.whl 305155bd5ae879344c58968879584ed1 numpy-1.26.4-cp312-cp312-win_amd64.whl ec2310f67215743e9c5d16b6c9fb87b6 numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl 406aea6081c1affbebdb6ad56b5deaf4 numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl fee12f0a3cbac7bbf1a1c2d82d3b02a9 numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl baf4b7143c7b9ce170e62b33380fb573 numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 376ff29f90b7840ae19ecd59ad1ddf53 numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl 86785b3a7cd156c08c2ebc26f7816fb3 numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl ab8a9ab69f16b7005f238cda76bc0bac numpy-1.26.4-cp39-cp39-win32.whl fafa4453e820c7ff40907e5dc79d8199 numpy-1.26.4-cp39-cp39-win_amd64.whl 7f13e2f07bd3e4a439ade0e4d27905c6 numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 928954b41c1cd0e856f1a31d41722661 numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 57bbd5c0b3848d804c416cbcab4a0ae8 numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl 19550cbe7bedd96a928da9d4ad69509d numpy-1.26.4.tar.gz ##### SHA256 9ff0f4f29c51e2803569d7a51c2304de5554655a60c5d776e35b4a41413830d0 numpy-1.26.4-cp310-cp310-macosx_10_9_x86_64.whl 2e4ee3380d6de9c9ec04745830fd9e2eccb3e6cf790d39d7b98ffd19b0dd754a numpy-1.26.4-cp310-cp310-macosx_11_0_arm64.whl d209d8969599b27ad20994c8e41936ee0964e6da07478d6c35016bc386b66ad4 numpy-1.26.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ffa75af20b44f8dba823498024771d5ac50620e6915abac414251bd971b4529f numpy-1.26.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 62b8e4b1e28009ef2846b4c7852046736bab361f7aeadeb6a5b89ebec3c7055a numpy-1.26.4-cp310-cp310-musllinux_1_1_aarch64.whl a4abb4f9001ad2858e7ac189089c42178fcce737e4169dc61321660f1a96c7d2 numpy-1.26.4-cp310-cp310-musllinux_1_1_x86_64.whl bfe25acf8b437eb2a8b2d49d443800a5f18508cd811fea3181723922a8a82b07 numpy-1.26.4-cp310-cp310-win32.whl b97fe8060236edf3662adfc2c633f56a08ae30560c56310562cb4f95500022d5 numpy-1.26.4-cp310-cp310-win_amd64.whl 4c66707fabe114439db9068ee468c26bbdf909cac0fb58686a42a24de1760c71 numpy-1.26.4-cp311-cp311-macosx_10_9_x86_64.whl edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef numpy-1.26.4-cp311-cp311-macosx_11_0_arm64.whl 7ab55401287bfec946ced39700c053796e7cc0e3acbef09993a9ad2adba6ca6e numpy-1.26.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 666dbfb6ec68962c033a450943ded891bed2d54e6755e35e5835d63f4f6931d5 numpy-1.26.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 96ff0b2ad353d8f990b63294c8986f1ec3cb19d749234014f4e7eb0112ceba5a numpy-1.26.4-cp311-cp311-musllinux_1_1_aarch64.whl 60dedbb91afcbfdc9bc0b1f3f402804070deed7392c23eb7a7f07fa857868e8a numpy-1.26.4-cp311-cp311-musllinux_1_1_x86_64.whl 1af303d6b2210eb850fcf03064d364652b7120803a0b872f5211f5234b399f20 numpy-1.26.4-cp311-cp311-win32.whl cd25bcecc4974d09257ffcd1f098ee778f7834c3ad767fe5db785be9a4aa9cb2 numpy-1.26.4-cp311-cp311-win_amd64.whl b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218 numpy-1.26.4-cp312-cp312-macosx_10_9_x86_64.whl 03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b numpy-1.26.4-cp312-cp312-macosx_11_0_arm64.whl 9fad7dcb1aac3c7f0584a5a8133e3a43eeb2fe127f47e3632d43d677c66c102b numpy-1.26.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 675d61ffbfa78604709862923189bad94014bef562cc35cf61d3a07bba02a7ed numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ab47dbe5cc8210f55aa58e4805fe224dac469cde56b9f731a4c098b91917159a numpy-1.26.4-cp312-cp312-musllinux_1_1_aarch64.whl 1dda2e7b4ec9dd512f84935c5f126c8bd8b9f2fc001e9f54af255e8c5f16b0e0 numpy-1.26.4-cp312-cp312-musllinux_1_1_x86_64.whl 50193e430acfc1346175fcbdaa28ffec49947a06918b7b92130744e81e640110 numpy-1.26.4-cp312-cp312-win32.whl 08beddf13648eb95f8d867350f6a018a4be2e5ad54c8d8caed89ebca558b2818 numpy-1.26.4-cp312-cp312-win_amd64.whl 7349ab0fa0c429c82442a27a9673fc802ffdb7c7775fad780226cb234965e53c numpy-1.26.4-cp39-cp39-macosx_10_9_x86_64.whl 52b8b60467cd7dd1e9ed082188b4e6bb35aa5cdd01777621a1658910745b90be numpy-1.26.4-cp39-cp39-macosx_11_0_arm64.whl d5241e0a80d808d70546c697135da2c613f30e28251ff8307eb72ba696945764 numpy-1.26.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3 numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 679b0076f67ecc0138fd2ede3a8fd196dddc2ad3254069bcb9faf9a79b1cebcd numpy-1.26.4-cp39-cp39-musllinux_1_1_aarch64.whl 47711010ad8555514b434df65f7d7b076bb8261df1ca9bb78f53d3b2db02e95c numpy-1.26.4-cp39-cp39-musllinux_1_1_x86_64.whl a354325ee03388678242a4d7ebcd08b5c727033fcff3b2f536aea978e15ee9e6 numpy-1.26.4-cp39-cp39-win32.whl 3373d5d70a5fe74a2c1bb6d2cfd9609ecf686d47a2d7b1d37a8f3b6bf6003aea numpy-1.26.4-cp39-cp39-win_amd64.whl afedb719a9dcfc7eaf2287b839d8198e06dcd4cb5d276a3df279231138e83d30 numpy-1.26.4-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 95a7476c59002f2f6c590b9b7b998306fba6a5aa646b1e22ddfeaf8f78c3a29c numpy-1.26.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7e50d0a0cc3189f9cb0aeb3a6a6af18c16f59f004b866cd2be1c14b36134a4a0 numpy-1.26.4-pp39-pypy39_pp73-win_amd64.whl 2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010 numpy-1.26.4.tar.gz ### [`v1.26.3`](https://togithub.com/numpy/numpy/compare/v1.26.2...v1.26.3) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.26.2...v1.26.3) ### [`v1.26.2`](https://togithub.com/numpy/numpy/releases/tag/v1.26.2): 1.26.2 release [Compare Source](https://togithub.com/numpy/numpy/compare/v1.26.1...v1.26.2) ### NumPy 1.26.2 Release Notes NumPy 1.26.2 is a maintenance release that fixes bugs and regressions discovered after the 1.26.1 release. 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. #### Contributors A total of 13 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@stefan6419846](https://togithub.com/stefan6419846) - [@thalassemia](https://togithub.com/thalassemia) + - Andrew Nelson - Charles Bousseau + - Charles Harris - Marcel Bargull + - Mark Mentovai + - Matti Picus - Nathan Goldbaum - Ralf Gommers - Sayed Adel - Sebastian Berg - William Ayd + #### Pull requests merged A total of 25 pull requests were merged for this release. - [#24814](https://togithub.com/numpy/numpy/pull/24814): MAINT: align test_dispatcher s390x targets with \_umath_tests_mtargets - [#24929](https://togithub.com/numpy/numpy/pull/24929): MAINT: prepare 1.26.x for further development - [#24955](https://togithub.com/numpy/numpy/pull/24955): ENH: Add Cython enumeration for NPY_FR_GENERIC - [#24962](https://togithub.com/numpy/numpy/pull/24962): REL: Remove Python upper version from the release branch - [#24971](https://togithub.com/numpy/numpy/pull/24971): BLD: Use the correct Python interpreter when running tempita.py - [#24972](https://togithub.com/numpy/numpy/pull/24972): MAINT: Remove unhelpful error replacements from `import_array()` - [#24977](https://togithub.com/numpy/numpy/pull/24977): BLD: use classic linker on macOS, the new one in XCode 15 has... - [#25003](https://togithub.com/numpy/numpy/pull/25003): BLD: musllinux_aarch64 \[wheel build] - [#25043](https://togithub.com/numpy/numpy/pull/25043): MAINT: Update mailmap - [#25049](https://togithub.com/numpy/numpy/pull/25049): MAINT: Update meson build infrastructure. - [#25071](https://togithub.com/numpy/numpy/pull/25071): MAINT: Split up .github/workflows to match main - [#25083](https://togithub.com/numpy/numpy/pull/25083): BUG: Backport fix build on ppc64 when the baseline set to Power9... - [#25093](https://togithub.com/numpy/numpy/pull/25093): BLD: Fix features.h detection for Meson builds \[1.26.x Backport] - [#25095](https://togithub.com/numpy/numpy/pull/25095): BUG: Avoid intp conversion regression in Cython 3 (backport) - [#25107](https://togithub.com/numpy/numpy/pull/25107): CI: remove obsolete jobs, and move macOS and conda Azure jobs... - [#25108](https://togithub.com/numpy/numpy/pull/25108): CI: Add linux_qemu action and remove travis testing. - [#25112](https://togithub.com/numpy/numpy/pull/25112): MAINT: Update .spin/cmds.py from main. - [#25113](https://togithub.com/numpy/numpy/pull/25113): DOC: Visually divide main license and bundled licenses in wheels - [#25115](https://togithub.com/numpy/numpy/pull/25115): MAINT: Add missing `noexcept` to shuffle helpers - [#25116](https://togithub.com/numpy/numpy/pull/25116): DOC: Fix license identifier for OpenBLAS - [#25117](https://togithub.com/numpy/numpy/pull/25117): BLD: improve detection of Netlib libblas/libcblas/liblapack - [#25118](https://togithub.com/numpy/numpy/pull/25118): MAINT: Make bitfield integers unsigned - [#25119](https://togithub.com/numpy/numpy/pull/25119): BUG: Make n a long int for np.random.multinomial - [#25120](https://togithub.com/numpy/numpy/pull/25120): BLD: change default of the `allow-noblas` option to true. - [#25121](https://togithub.com/numpy/numpy/pull/25121): BUG: ensure passing `np.dtype` to itself doesn't crash #### Checksums ##### MD5 1a5dc6b5b3bf11ad40a59eedb3b69fa1 numpy-1.26.2-cp310-cp310-macosx_10_9_x86_64.whl 4b741c6dfe4e6e22e34e9c5c788d4f04 numpy-1.26.2-cp310-cp310-macosx_11_0_arm64.whl 2953687fb26e1dd8a2d1bb7109551fcd numpy-1.26.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ea9127a3a03f27fd101c62425c661d8d numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7a6be7c6c1cc3e1ff73f64052fe30677 numpy-1.26.2-cp310-cp310-musllinux_1_1_aarch64.whl 4f45d3f69f54fd1638609fde34c33a5c numpy-1.26.2-cp310-cp310-musllinux_1_1_x86_64.whl f22f5ea26c86eb126ff502fff75d6c21 numpy-1.26.2-cp310-cp310-win32.whl 49871452488e1a55d15ab54c6f3e546e numpy-1.26.2-cp310-cp310-win_amd64.whl 676740bf60fb1c8f5a6b31e00b9a4e9b numpy-1.26.2-cp311-cp311-macosx_10_9_x86_64.whl 7170545dcc2a38a1c2386a6081043b64 numpy-1.26.2-cp311-cp311-macosx_11_0_arm64.whl feae1190c73d811e2e7ebcad4baf6edf numpy-1.26.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 03131896abade61b77e0f6e53abb988a numpy-1.26.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f160632f128a3fd46787aa02d8731fbb numpy-1.26.2-cp311-cp311-musllinux_1_1_aarch64.whl 014250db593d589b5533ef7127839c46 numpy-1.26.2-cp311-cp311-musllinux_1_1_x86_64.whl fb437346dac24d0cb23f5314db043c8b numpy-1.26.2-cp311-cp311-win32.whl 7359adc233874898ea768cd4aec28bb3 numpy-1.26.2-cp311-cp311-win_amd64.whl 207a678bea75227428e7fb84d4dc457a numpy-1.26.2-cp312-cp312-macosx_10_9_x86_64.whl 302ff6cc047a408cdf21981bd7b26056 numpy-1.26.2-cp312-cp312-macosx_11_0_arm64.whl 7526faaea58c76aed395c7128dd6e14d numpy-1.26.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 28d3b1943d3a8ad4bbb2ae9da0a77cb9 numpy-1.26.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d91f5b2bb2c931e41ae7c80ec7509a31 numpy-1.26.2-cp312-cp312-musllinux_1_1_aarch64.whl b2504d4239419f012c08fa1eab12f940 numpy-1.26.2-cp312-cp312-musllinux_1_1_x86_64.whl 57944ba30adc07f33e83a9b45f5c625a numpy-1.26.2-cp312-cp312-win32.whl fe38cd95bbee405ce0cf51c8753a2676 numpy-1.26.2-cp312-cp312-win_amd64.whl 28e1bc3efaf89cf6f0a2b616c0e16401 numpy-1.26.2-cp39-cp39-macosx_10_9_x86_64.whl 9932ccff54855f12ee24f60528279bf1 numpy-1.26.2-cp39-cp39-macosx_11_0_arm64.whl b52c1e987074dad100ad234122a397b9 numpy-1.26.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1d1bd7e0d2a89ce795a9566a38ed9bb5 numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 01d2abfe8e9b35415efb791ac6c5865e numpy-1.26.2-cp39-cp39-musllinux_1_1_aarch64.whl 5a6d6ac287ebd93a221e59590329e202 numpy-1.26.2-cp39-cp39-musllinux_1_1_x86_64.whl 4e4e4d8cf661a8d2838ee700fabae87e numpy-1.26.2-cp39-cp39-win32.whl b8e52ecac110471502686abbdf774b78 numpy-1.26.2-cp39-cp39-win_amd64.whl aed2d2914be293f60fedda360b64abf8 numpy-1.26.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 6bd88e0f33933445d0e18c1a850f60e0 numpy-1.26.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 010aeb2a50af0af1f7ef56f76f8cf463 numpy-1.26.2-pp39-pypy39_pp73-win_amd64.whl 8f6446a32e47953a03f8fe8533e21e98 numpy-1.26.2.tar.gz ##### SHA256 3703fc9258a4a122d17043e57b35e5ef1c5a5837c3db8be396c82e04c1cf9b0f numpy-1.26.2-cp310-cp310-macosx_10_9_x86_64.whl cc392fdcbd21d4be6ae1bb4475a03ce3b025cd49a9be5345d76d7585aea69440 numpy-1.26.2-cp310-cp310-macosx_11_0_arm64.whl 36340109af8da8805d8851ef1d74761b3b88e81a9bd80b290bbfed61bd2b4f75 numpy-1.26.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl bcc008217145b3d77abd3e4d5ef586e3bdfba8fe17940769f8aa09b99e856c00 numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3ced40d4e9e18242f70dd02d739e44698df3dcb010d31f495ff00a31ef6014fe numpy-1.26.2-cp310-cp310-musllinux_1_1_aarch64.whl b272d4cecc32c9e19911891446b72e986157e6a1809b7b56518b4f3755267523 numpy-1.26.2-cp310-cp310-musllinux_1_1_x86_64.whl 22f8fc02fdbc829e7a8c578dd8d2e15a9074b630d4da29cda483337e300e3ee9 numpy-1.26.2-cp310-cp310-win32.whl 26c9d33f8e8b846d5a65dd068c14e04018d05533b348d9eaeef6c1bd787f9919 numpy-1.26.2-cp310-cp310-win_amd64.whl b96e7b9c624ef3ae2ae0e04fa9b460f6b9f17ad8b4bec6d7756510f1f6c0c841 numpy-1.26.2-cp311-cp311-macosx_10_9_x86_64.whl aa18428111fb9a591d7a9cc1b48150097ba6a7e8299fb56bdf574df650e7d1f1 numpy-1.26.2-cp311-cp311-macosx_11_0_arm64.whl 06fa1ed84aa60ea6ef9f91ba57b5ed963c3729534e6e54055fc151fad0423f0a numpy-1.26.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 96ca5482c3dbdd051bcd1fce8034603d6ebfc125a7bd59f55b40d8f5d246832b numpy-1.26.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 854ab91a2906ef29dc3925a064fcd365c7b4da743f84b123002f6139bcb3f8a7 numpy-1.26.2-cp311-cp311-musllinux_1_1_aarch64.whl f43740ab089277d403aa07567be138fc2a89d4d9892d113b76153e0e412409f8 numpy-1.26.2-cp311-cp311-musllinux_1_1_x86_64.whl a2bbc29fcb1771cd7b7425f98b05307776a6baf43035d3b80c4b0f29e9545186 numpy-1.26.2-cp311-cp311-win32.whl 2b3fca8a5b00184828d12b073af4d0fc5fdd94b1632c2477526f6bd7842d700d numpy-1.26.2-cp311-cp311-win_amd64.whl a4cd6ed4a339c21f1d1b0fdf13426cb3b284555c27ac2f156dfdaaa7e16bfab0 numpy-1.26.2-cp312-cp312-macosx_10_9_x86_64.whl 5d5244aabd6ed7f312268b9247be47343a654ebea52a60f002dc70c769048e75 numpy-1.26.2-cp312-cp312-macosx_11_0_arm64.whl 6a3cdb4d9c70e6b8c0814239ead47da00934666f668426fc6e94cce869e13fd7 numpy-1.26.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl aa317b2325f7aa0a9471663e6093c210cb2ae9c0ad824732b307d2c51983d5b6 numpy-1.26.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 174a8880739c16c925799c018f3f55b8130c1f7c8e75ab0a6fa9d41cab092fd6 numpy-1.26.2-cp312-cp312-musllinux_1_1_aarch64.whl f79b231bf5c16b1f39c7f4875e1ded36abee1591e98742b05d8a0fb55d8a3eec numpy-1.26.2-cp312-cp312-musllinux_1_1_x86_64.whl 4a06263321dfd3598cacb252f51e521a8cb4b6df471bb12a7ee5cbab20ea9167 numpy-1.26.2-cp312-cp312-win32.whl b04f5dc6b3efdaab541f7857351aac359e6ae3c126e2edb376929bd3b7f92d7e numpy-1.26.2-cp312-cp312-win_amd64.whl 4eb8df4bf8d3d90d091e0146f6c28492b0be84da3e409ebef54349f71ed271ef numpy-1.26.2-cp39-cp39-macosx_10_9_x86_64.whl 1a13860fdcd95de7cf58bd6f8bc5a5ef81c0b0625eb2c9a783948847abbef2c2 numpy-1.26.2-cp39-cp39-macosx_11_0_arm64.whl 64308ebc366a8ed63fd0bf426b6a9468060962f1a4339ab1074c228fa6ade8e3 numpy-1.26.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl baf8aab04a2c0e859da118f0b38617e5ee65d75b83795055fb66c0d5e9e9b818 numpy-1.26.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d73a3abcac238250091b11caef9ad12413dab01669511779bc9b29261dd50210 numpy-1.26.2-cp39-cp39-musllinux_1_1_aarch64.whl b361d369fc7e5e1714cf827b731ca32bff8d411212fccd29ad98ad622449cc36 numpy-1.26.2-cp39-cp39-musllinux_1_1_x86_64.whl bd3f0091e845164a20bd5a326860c840fe2af79fa12e0469a12768a3ec578d80 numpy-1.26.2-cp39-cp39-win32.whl 2beef57fb031dcc0dc8fa4fe297a742027b954949cabb52a2a376c144e5e6060 numpy-1.26.2-cp39-cp39-win_amd64.whl 1cc3d5029a30fb5f06704ad6b23b35e11309491c999838c31f124fee32107c79 numpy-1.26.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 94cc3c222bb9fb5a12e334d0479b97bb2df446fbe622b470928f5284ffca3f8d numpy-1.26.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl fe6b44fb8fcdf7eda4ef4461b97b3f63c466b27ab151bec2366db8b197387841 numpy-1.26.2-pp39-pypy39_pp73-win_amd64.whl f65738447676ab5777f11e6bbbdb8ce11b785e105f690bc45966574816b6d3ea numpy-1.26.2.tar.gz ### [`v1.26.1`](https://togithub.com/numpy/numpy/releases/tag/v1.26.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.26.0...v1.26.1) ##### NumPy 1.26.1 Release Notes NumPy 1.26.1 is a maintenance release that fixes bugs and regressions 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://togithub.com/numpy/numpy/pull/24742): MAINT: Update cibuildwheel version - [#24748](https://togithub.com/numpy/numpy/pull/24748): MAINT: fix version string in wheels built with setup.py - [#24771](https://togithub.com/numpy/numpy/pull/24771): BLD, BUG: Fix build failure for host flags e.g. `-march=native`... - [#24773](https://togithub.com/numpy/numpy/pull/24773): DOC: Updated the f2py docs to remove a note on -fimplicit-none - [#24776](https://togithub.com/numpy/numpy/pull/24776): BUG: Fix SIMD f32 trunc test on s390x when baseline is none - [#24785](https://togithub.com/numpy/numpy/pull/24785): BLD: add libquadmath to licences and other tweaks ([#24753](https://togithub.com/numpy/numpy/issues/24753)) - [#24786](https://togithub.com/numpy/numpy/pull/24786): MAINT: Activate `use-compute-credits` for Cirrus. - [#24803](https://togithub.com/numpy/numpy/pull/24803): BLD: updated vendored-meson/meson for mips64 fix - [#24804](https://togithub.com/numpy/numpy/pull/24804): MAINT: fix licence path win - [#24813](https://togithub.com/numpy/numpy/pull/24813): BUG: Fix order of Windows OS detection macros. - [#24831](https://togithub.com/numpy/numpy/pull/24831): BUG, SIMD: use scalar cmul on bad Apple clang x86\_64 ([#24828](https://togithub.com/numpy/numpy/issues/24828)) - [#24840](https://togithub.com/numpy/numpy/pull/24840): BUG: Fix DATA statements for f2py - [#24870](https://togithub.com/numpy/numpy/pull/24870): API: Add `NumpyUnpickler` for backporting - [#24872](https://togithub.com/numpy/numpy/pull/24872): MAINT: Xfail test failing on PyPy. - [#24879](https://togithub.com/numpy/numpy/pull/24879): BLD: fix math func feature checks, fix FreeBSD build, add CI... - [#24899](https://togithub.com/numpy/numpy/pull/24899): ENH: meson: implement BLAS/LAPACK auto-detection and many CI... - [#24902](https://togithub.com/numpy/numpy/pull/24902): DOC: add a 1.26.1 release notes section for BLAS/LAPACK build... - [#24906](https://togithub.com/numpy/numpy/pull/24906): MAINT: Backport `numpy._core` stubs. Remove `NumpyUnpickler` - [#24911](https://togithub.com/numpy/numpy/pull/24911): MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2 - [#24912](https://togithub.com/numpy/numpy/pull/24912): BUG: loongarch doesn't use REAL(10) ##### Checksums ##### MD5 bda38de1a047dd9fdddae16c0d9fb358 numpy-1.26.1-cp310-cp310-macosx_10_9_x86_64.whl 196d2e39047da64ab28e177760c95461 numpy-1.26.1-cp310-cp310-macosx_11_0_arm64.whl 9d25010a7bf50e624d2fed742790afbd numpy-1.26.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9b22fa3d030807f0708007d9c0659f65 numpy-1.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl eea626b8b930acb4b32302a9e95714f5 numpy-1.26.1-cp310-cp310-musllinux_1_1_x86_64.whl 3c40ef068f50d2ac2913c5b9fa1233fa numpy-1.26.1-cp310-cp310-win32.whl 315c251d2f284af25761a37ce6dd4d10 numpy-1.26.1-cp310-cp310-win_amd64.whl ebdd5046937df50e9f54a6d38c5775dd numpy-1.26.1-cp311-cp311-macosx_10_9_x86_64.whl 682f9beebe8547f205d6cdc8ff96a984 numpy-1.26.1-cp311-cp311-macosx_11_0_arm64.whl e86da9b6040ea88b3835c4d8f8578658 numpy-1.26.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ebcb6cf7f64454215e29d8a89829c8e1 numpy-1.26.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a8c89e13dc9a63712104e2fb06fb63a6 numpy-1.26.1-cp311-cp311-musllinux_1_1_x86_64.whl 339795930404988dbc664ff4cc72b399 numpy-1.26.1-cp311-cp311-win32.whl 4ef5e1bdd7726c19615843f5ac72e618 numpy-1.26.1-cp311-cp311-win_amd64.whl 3aad6bc72db50e9cc88aa5813e8f35bd numpy-1.26.1-cp312-cp312-macosx_10_9_x86_64.whl fd62f65ae7798dbda9a3f7af7aa5c8db numpy-1.26.1-cp312-cp312-macosx_11_0_arm64.whl 104d939e080f1baf0a56aed1de0e79e3 numpy-1.26.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl c44b56c96097f910bbec1420abcf3db5 numpy-1.26.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1dce230368ae5fc47dd0fe8de8ff771d numpy-1.26.1-cp312-cp312-musllinux_1_1_x86_64.whl d93338e7d60e1d294ca326450e99806b numpy-1.26.1-cp312-cp312-win32.whl a1832f46521335c1ee4c56dbf12e600b numpy-1.26.1-cp312-cp312-win_amd64.whl 946fbb0b6caca9258985495532d3f9ab numpy-1.26.1-cp39-cp39-macosx_10_9_x86_64.whl 78c2ab13d395d67d90bcd6583a6f61a8 numpy-1.26.1-cp39-cp39-macosx_11_0_arm64.whl 0a9d80d8b646abf4ffe51fff3e075d10 numpy-1.26.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0229ba8145d4f58500873b540a55d60e numpy-1.26.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9179fc57c03260374c86e18867c24463 numpy-1.26.1-cp39-cp39-musllinux_1_1_x86_64.whl 246a3103fdbe5d891d7a8aee28875a26 numpy-1.26.1-cp39-cp39-win32.whl 4589dcb7f754fade6ea3946416bee638 numpy-1.26.1-cp39-cp39-win_amd64.whl 3af340d5487a6c045f00fe5eb889957c numpy-1.26.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 28aece4f1ceb92ec463aa353d4a91c8b numpy-1.26.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl bbd0461a1e31017b05509e9971b3478e numpy-1.26.1-pp39-pypy39_pp73-win_amd64.whl 2d770f4c281d405b690c4bcb3dbe99e2 numpy-1.26.1.tar.gz ##### SHA256 82e871307a6331b5f09efda3c22e03c095d957f04bf6bc1804f30048d0e5e7af numpy-1.26.1-cp310-cp310-macosx_10_9_x86_64.whl cdd9ec98f0063d93baeb01aad472a1a0840dee302842a2746a7a8e92968f9575 numpy-1.26.1-cp310-cp310-macosx_11_0_arm64.whl d78f269e0c4fd365fc2992c00353e4530d274ba68f15e968d8bc3c69ce5f5244 numpy-1.26.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8ab9163ca8aeb7fd32fe93866490654d2f7dda4e61bc6297bf72ce07fdc02f67 numpy-1.26.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 78ca54b2f9daffa5f323f34cdf21e1d9779a54073f0018a3094ab907938331a2 numpy-1.26.1-cp310-cp310-musllinux_1_1_x86_64.whl d1cfc92db6af1fd37a7bb58e55c8383b4aa1ba23d012bdbba26b4bcca45ac297 numpy-1.26.1-cp310-cp310-win32.whl d2984cb6caaf05294b8466966627e80bf6c7afd273279077679cb010acb0e5ab numpy-1.26.1-cp310-cp310-win_amd64.whl cd7837b2b734ca72959a1caf3309457a318c934abef7a43a14bb984e574bbb9a numpy-1.26.1-cp311-cp311-macosx_10_9_x86_64.whl 1c59c046c31a43310ad0199d6299e59f57a289e22f0f36951ced1c9eac3665b9 numpy-1.26.1-cp311-cp311-macosx_11_0_arm64.whl d58e8c51a7cf43090d124d5073bc29ab2755822181fcad978b12e144e5e5a4b3 numpy-1.26.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6081aed64714a18c72b168a9276095ef9155dd7888b9e74b5987808f0dd0a974 numpy-1.26.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 97e5d6a9f0702c2863aaabf19f0d1b6c2628fbe476438ce0b5ce06e83085064c numpy-1.26.1-cp311-cp311-musllinux_1_1_x86_64.whl b9d45d1dbb9de84894cc50efece5b09939752a2d75aab3a8b0cef6f3a35ecd6b numpy-1.26.1-cp311-cp311-win32.whl 3649d566e2fc067597125428db15d60eb42a4e0897fc48d28cb75dc2e0454e53 numpy-1.26.1-cp311-cp311-win_amd64.whl 1d1bd82d539607951cac963388534da3b7ea0e18b149a53cf883d8f699178c0f numpy-1.26.1-cp312-cp312-macosx_10_9_x86_64.whl afd5ced4e5a96dac6725daeb5242a35494243f2239244fad10a90ce58b071d24 numpy-1.26.1-cp312-cp312-macosx_11_0_arm64.whl a03fb25610ef560a6201ff06df4f8105292ba56e7cdd196ea350d123fc32e24e numpy-1.26.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl dcfaf015b79d1f9f9c9fd0731a907407dc3e45769262d657d754c3a028586124 numpy-1.26.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e509cbc488c735b43b5ffea175235cec24bbc57b227ef1acc691725beb230d1c numpy-1.26.1-cp312-cp312-musllinux_1_1_x86_64.whl af22f3d8e228d84d1c0c44c1fbdeb80f97a15a0abe4f080960393a00db733b66 numpy-1.26.1-cp312-cp312-win32.whl 9f42284ebf91bdf32fafac29d29d4c07e5e9d1af862ea73686581773ef9e73a7 numpy-1.26.1-cp312-cp312-win_amd64.whl bb894accfd16b867d8643fc2ba6c8617c78ba2828051e9a69511644ce86ce83e numpy-1.26.1-cp39-cp39-macosx_10_9_x86_64.whl e44ccb93f30c75dfc0c3aa3ce38f33486a75ec9abadabd4e59f114994a9c4617 numpy-1.26.1-cp39-cp39-macosx_11_0_arm64.whl 9696aa2e35cc41e398a6d42d147cf326f8f9d81befcb399bc1ed7ffea339b64e numpy-1.26.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a5b411040beead47a228bde3b2241100454a6abde9df139ed087bd73fc0a4908 numpy-1.26.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1e11668d6f756ca5ef534b5be8653d16c5352cbb210a5c2a79ff288e937010d5 numpy-1.26.1-cp39-cp39-musllinux_1_1_x86_64.whl d1d2c6b7dd618c41e202c59c1413ef9b2c8e8a15f5039e344af64195459e3104 numpy-1.26.1-cp39-cp39-win32.whl 59227c981d43425ca5e5c01094d59eb14e8772ce6975d4b2fc1e106a833d5ae2 numpy-1.26.1-cp39-cp39-win_amd64.whl 06934e1a22c54636a059215d6da99e23286424f316fddd979f5071093b648668 numpy-1.26.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 76ff661a867d9272cd2a99eed002470f46dbe0943a5ffd140f49be84f68ffc42 numpy-1.26.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6965888d65d2848e8768824ca8288db0a81263c1efccec881cb35a0d805fcd2f numpy-1.26.1-pp39-pypy39_pp73-win_amd64.whl c8c6c72d4a9f831f328efb1312642a1cafafaa88981d9ab76368d50d07d93cbe numpy-1.26.1.tar.gz ### [`v1.26.0`](https://togithub.com/numpy/numpy/releases/tag/v1.26.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.25.2...v1.26.0) ### NumPy 1.26.0 Release Notes 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 - f2py fixes, meson and bind(x) support - Support for the updated Accelerate BLAS/LAPACK library The Python versions supported in this release are 3.9-3.12. #### New Features ##### Array API v2022.12 support in `numpy.array_api` `numpy.array_api` now full supports the [v2022.12 version](https://data-apis.org/array-api/2022.12) of the array API standard. Note that this does not yet include the optional `fft` extension in the standard. ([gh-23789](https://togithub.com/numpy/numpy/pull/23789)) ##### Support for the updated Accelerate BLAS/LAPACK library Support for the updated Accelerate BLAS/LAPACK library, including ILP64 (64-bit integer) support, in macOS 13.3 has been added. This brings arm64 support, and significant performance improvements of up to 10x for commonly used linear algebra operations. When Accelerate is selected at build time, the 13.3+ version will automatically be used if available. ([gh-24053](https://togithub.com/numpy/numpy/pull/24053)) ##### `meson` backend for `f2py` `f2py` in compile mode (i.e. `f2py -c`) now accepts the `--backend meson` option. This is the default option for Python `3.12` on-wards. Older versions will still default to `--backend distutils`. To support this in realistic use-cases, in compile mode `f2py` takes a `--dep` flag one or many times which maps to `dependency()` calls in the `meson` backend, and does nothing in the `distutils` backend. There are no changes for users of `f2py` only as a code generator, i.e. without `-c`. ([gh-24532](https://togithub.com/numpy/numpy/pull/24532)) ##### `bind(c)` support for `f2py` Both functions and subroutines can be annotated with `bind(c)`. `f2py` will handle both the correct type mapping, and preserve the unique label for other `C` interfaces. **Note:** `bind(c, name = 'routine_name_other_than_fortran_routine')` is not honored by the `f2py` bindings by design, since `bind(c)` with the `name` is meant to guarantee only the same name in `C` and `Fortran`, not in `Python` and `Fortran`. ([gh-24555](https://togithub.com/numpy/numpy/pull/24555)) #### Improvements ##### `iso_c_binding` support for `f2py` Previously, users would have to define their own custom `f2cmap` file to use type mappings defined by the Fortran2003 `iso_c_binding` intrinsic module. These type maps are now natively supported by `f2py` ([gh-24555](https://togithub.com/numpy/numpy/pull/24555)) #### 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://togithub.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 20 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@DWesl](https://togithub.com/DWesl) - Albert Steppi + - Bas van Beek - Charles Harris - Developer-Ecosystem-Engineering - Filipe Laíns + - Jake Vanderplas - Liang Yan + - Marten van Kerkwijk - Matti Picus - Melissa Weber Mendonça - Namami Shanker - Nathan Goldbaum - Ralf Gommers - Rohit Goswami - Sayed Adel - Sebastian Berg - Stefan van der Walt - Tyler Reddy - Warren Weckesser #### Pull requests merged A total of 59 pull requests were merged for this release. - [#24305](https://togithub.com/numpy/numpy/pull/24305): MAINT: Prepare 1.26.x branch for development - [#24308](https://togithub.com/numpy/numpy/pull/24308): MAINT: Massive update of files from main for numpy 1.26 - [#24322](https://togithub.com/numpy/numpy/pull/24322): CI: fix wheel builds on the 1.26.x branch - [#24326](https://togithub.com/numpy/numpy/pull/24326): BLD: update openblas to newer version - [#24327](https://togithub.com/numpy/numpy/pull/24327): TYP: Trim down the `_NestedSequence.__getitem__` signature - [#24328](https://togithub.com/numpy/numpy/pull/24328): BUG: fix choose refcount leak - [#24337](https://togithub.com/numpy/numpy/pull/24337): TST: fix running the test suite in builds without BLAS/LAPACK - [#24338](https://togithub.com/numpy/numpy/pull/24338): BUG: random: Fix generation of nan by dirichlet. - [#24340](https://togithub.com/numpy/numpy/pull/24340): MAINT: Dependabot updates from main - [#24342](https://togithub.com/numpy/numpy/pull/24342): MAINT: Add back NPY_RUN_MYPY_IN_TESTSUITE=1 - [#24353](https://togithub.com/numpy/numpy/pull/24353): MAINT: Update `extbuild.py` from main. - [#24356](https://togithub.com/numpy/numpy/pull/24356): TST: fix distutils tests for deprecations in recent setuptools... - [#24375](https://togithub.com/numpy/numpy/pull/24375): MAINT: Update cibuildwheel to version 2.15.0 - [#24381](https://togithub.com/numpy/numpy/pull/24381): MAINT: Fix codespaces setup.sh script - [#24403](https://togithub.com/numpy/numpy/pull/24403): ENH: Vendor meson for multi-target build support - [#24404](https://togithub.com/numpy/numpy/pull/24404): BLD: vendor meson-python to make the Windows builds with SIMD... - [#24405](https://togithub.com/numpy/numpy/pull/24405): BLD, SIMD: The meson CPU dispatcher implementation - [#24406](https://togithub.com/numpy/numpy/pull/24406): MAINT: Remove versioneer - [#24409](https://togithub.com/numpy/numpy/pull/24409): REL: Prepare for the NumPy 1.26.0b1 release. - [#24453](https://togithub.com/numpy/numpy/pull/24453): MAINT: Pin upper version of sphinx. - [#24455](https://togithub.com/numpy/numpy/pull/24455): ENH: Add prefix to \_ALIGN Macro - [#24456](https://togithub.com/numpy/numpy/pull/24456): BUG: cleanup warnings - [#24460](https://togithub.com/numpy/numpy/pull/24460): MAINT: Upgrade to spin 0.5 - [#24495](https://togithub.com/numpy/numpy/pull/24495): BUG: `asv dev` has been removed, use `asv run`. - [#24496](https://togithub.com/numpy/numpy/pull/24496): BUG: Fix meson build failure due to unchanged inplace auto-generated... - [#24521](https://togithub.com/numpy/numpy/pull/24521): BUG: fix issue with git-version script, needs a shebang to run - [#24522](https://togithub.com/numpy/numpy/pull/24522): BUG: Use a default assignment for git_hash - [#24524](https://togithub.com/numpy/numpy/pull/24524): BUG: fix NPY_cast_info error handling in choose - [#24526](https://togithub.com/numpy/numpy/pull/24526): BUG: Fix common block handling in f2py - [#24541](https://togithub.com/numpy/numpy/pull/24541): CI,TYP: Bump mypy to 1.4.1 - [#24542](https://togithub.com/numpy/numpy/pull/24542): BUG: Fix assumed length f2py regression - [#24544](https://togithub.com/numpy/numpy/pull/24544): MAINT: Harmonize fortranobject - [#24545](https://togithub.com/numpy/numpy/pull/24545): TYP: add kind argument to numpy.isin type specification - [#24561](https://togithub.com/numpy/numpy/pull/24561): BUG: fix comparisons between masked and unmasked structured arrays - [#24590](https://togithub.com/numpy/numpy/pull/24590): CI: Exclude import libraries from list of DLLs on Cygwin. - [#24591](https://togithub.com/numpy/numpy/pull/24591): BLD: fix `_umath_linalg` dependencies - [#24594](https://togithub.com/numpy/numpy/pull/24594): MAINT: Stop testing on ppc64le. - [#24602](https://togithub.com/numpy/numpy/pull/24602): BLD: meson-cpu: fix SIMD support on platforms with no features - [#24606](https://togithub.com/numpy/numpy/pull/24606): BUG: Change Cython `binding` directive to "False". - [#24613](https://togithub.com/numpy/numpy/pull/24613): ENH: Adopt new macOS Accelerate BLAS/LAPACK Interfaces, including... - [#24614](https://togithub.com/numpy/numpy/pull/24614): DOC: Update building docs to use Meson - [#24615](https://togithub.com/numpy/numpy/pull/24615): TYP: Add the missing `casting` keyword to `np.clip` - [#24616](https://togithub.com/numpy/numpy/pull/24616): TST: convert cython test from setup.py to meson - [#24617](https://togithub.com/numpy/numpy/pull/24617): MAINT: Fixup `fromnumeric.pyi` - [#24622](https://togithub.com/numpy/numpy/pull/24622): BUG, ENH: Fix `iso_c_binding` type maps and fix `bind(c)`... - [#24629](https://togithub.com/numpy/numpy/pull/24629): TYP: Allow `binary_repr` to accept any object implementing... - [#24630](https://togithub.com/numpy/numpy/pull/24630): TYP: Explicitly declare `dtype` and `generic` hashable - [#24637](https://togithub.com/numpy/numpy/pull/24637): ENH: Refactor the typing "reveal" tests using `typing.assert_type` - [#24638](https://togithub.com/numpy/numpy/pull/24638): MAINT: Bump actions/checkout from 3.6.0 to 4.0.0 - [#24647](https://togithub.com/numpy/numpy/pull/24647): ENH: `meson` backend for `f2py` - [#24648](https://togithub.com/numpy/numpy/pull/24648): MAINT: Refactor partial load Workaround for Clang - [#24653](https://togithub.com/numpy/numpy/pull/24653): REL: Prepare for the NumPy 1.26.0rc1 release. - [#24659](https://togithub.com/numpy/numpy/pull/24659): BLD: allow specifying the long double format to avoid the runtime... - [#24665](https://togithub.com/numpy/numpy/pull/24665): BLD: fix bug in random.mtrand extension, don't link libnpyrandom - [#24675](https://togithub.com/numpy/numpy/pull/24675): BLD: build wheels for 32-bit Python on Windows, using MSVC - [#24700](https://togithub.com/numpy/numpy/pull/24700): BLD: fix issue with compiler selection during cross compilation - [#24701](https://togithub.com/numpy/numpy/pull/24701): BUG: Fix data stmt handling for complex values in f2py - [#24707](https://togithub.com/numpy/numpy/pull/24707): TYP: Add annotations for the py3.12 buffer protocol - [#24718](https://togithub.com/numpy/numpy/pull/24718): DOC: fix a few doc build issues on 1.26.x and update `spin docs`... #### Checksums ##### MD5 052d84a2aaad4d5a455b64f5ff3f160b numpy-1.26.0-cp310-cp310-macosx_10_9_x86_64.whl 874567083be194080e97bea39ea7befd numpy-1.26.0-cp310-cp310-macosx_11_0_arm64.whl 1a5fa023e05e050b95549d355890fbb6 numpy-1.26.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 2af03fbadd96360b26b993975709d072 numpy-1.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 32717dd51a915e9aee4dcca72acb00d0 numpy-1.26.0-cp310-cp310-musllinux_1_1_x86_64.whl 3f101e51b3b5f8c3f01256da645a1962 numpy-1.26.0-cp310-cp310-win32.whl d523a40f0a5f5ba94f09679adbabf825 numpy-1.26.0-cp310-cp310-win_amd64.whl 6115698fdf5fb8cf895540a57d12bfb9 numpy-1.26.0-cp311-cp311-macosx_10_9_x86_64.whl 207603ee822d8af4542f239b8c0a7a67 numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl 0cc5f95c4aebab0ca4f9f66463981016 numpy-1.26.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a4654b46bc10738825f37a1797e1eba5 numpy-1.26.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3b037dc746499f2a19bb58b55fdd0bfb numpy-1.26.0-cp311-cp311-musllinux_1_1_x86_64.whl 7bfb0c44e95f765e7fc5a7a86968a56c numpy-1.26.0-cp311-cp311-win32.whl 3355b510410cb20bacfb3c87632a731a numpy-1.26.0-cp311-cp311-win_amd64.whl 9624a97f1df9f64054409d274c1502f3 numpy-1.26.0-cp312-cp312-macosx_10_9_x86_64.whl 53429b1349542c38b2f3822c7f2904d5 numpy-1.26.0-cp312-cp312-macosx_11_0_arm64.whl 66a21bf4d8a6372cc3c4c89a67b96279 numpy-1.26.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl cb9abc312090046563eae619c0b68210 numpy-1.26.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 49e3498e0e0ec5c1f6314fb86d7f006e numpy-1.26.0-cp312-cp312-musllinux_1_1_x86_64.whl f4a31765889478341597a7140044db85 numpy-1.26.0-cp312-cp312-win32.whl e7d7ded11f89baf760e5ba69249606e4 numpy-1.26.0-cp312-cp312-win_amd64.whl 19698f330ae322c4813eed6e790a04d5 numpy-1.26.0-cp39-cp39-macosx_10_9_x86_64.whl a3628f551d851fbcde6551adb8fcfe2b numpy-1.26.0-cp39-cp39-macosx_11_0_arm64.whl b34af2ddf43b28207ec7e2c837cbe35f numpy-1.26.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3d888129c86357ccfb779d9f0c1256f5 numpy-1.26.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e49d00c779df59a786d9f41e0d73c520 numpy-1.26.0-cp39-cp39-musllinux_1_1_x86_64.whl 69f6aa8a0f3919797cb28fab7069a578 numpy-1.26.0-cp39-cp39-win32.whl 8233224840dcdda49b08da1d5e91a730 numpy-1.26.0-cp39-cp39-win_amd64.whl c11b4d1181b825407b71a1ac8ec04a10 numpy-1.26.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 1515773d4f569d44c6a757cb5a636cb2 numpy-1.26.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 60dc766d863d8ab561b494a7a759d562 numpy-1.26.0-pp39-pypy39_pp73-win_amd64.whl 69bd28f07afbeed2bb6ecd467afcd469 numpy-1.26.0.tar.gz ##### SHA256 f8db2f125746e44dce707dd44d4f4efeea8d7e2b43aace3f8d1f235cfa2733dd numpy-1.26.0-cp310-cp310-macosx_10_9_x86_64.whl 0621f7daf973d34d18b4e4bafb210bbaf1ef5e0100b5fa750bd9cde84c7ac292 numpy-1.26.0-cp310-cp310-macosx_11_0_arm64.whl 51be5f8c349fdd1a5568e72713a21f518e7d6707bcf8503b528b88d33b57dc68 numpy-1.26.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 767254ad364991ccfc4d81b8152912e53e103ec192d1bb4ea6b1f5a7117040be numpy-1.26.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 436c8e9a4bdeeee84e3e59614d38c3dbd3235838a877af8c211cfcac8a80b8d3 numpy-1.26.0-cp310-cp310-musllinux_1_1_x86_64.whl c2e698cb0c6dda9372ea98a0344245ee65bdc1c9dd939cceed6bb91256837896 numpy-1.26.0-cp310-cp310-win32.whl 09aaee96c2cbdea95de76ecb8a586cb687d281c881f5f17bfc0fb7f5890f6b91 numpy-1.26.0-cp310-cp310-win_amd64.whl 637c58b468a69869258b8ae26f4a4c6ff8abffd4a8334c830ffb63e0feefe99a numpy-1.26.0-cp311-cp311-macosx_10_9_x86_64.whl 306545e234503a24fe9ae95ebf84d25cba1fdc27db971aa2d9f1ab6bba19a9dd numpy-1.26.0-cp311-cp311-macosx_11_0_arm64.whl 8c6adc33561bd1d46f81131d5352348350fc23df4d742bb246cdfca606ea1208 numpy-1.26.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl e062aa24638bb5018b7841977c360d2f5917268d125c833a686b7cbabbec496c numpy-1.26.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 546b7dd7e22f3c6861463bebb000646fa730e55df5ee4a0224408b5694cc6148 numpy-1.26.0-cp311-cp311-musllinux_1_1_x86_64.whl c0b45c8b65b79337dee5134d038346d30e109e9e2e9d43464a2970e5c0e93229 numpy-1.26.0-cp311-cp311-win32.whl eae430ecf5794cb7ae7fa3808740b015aa80747e5266153128ef055975a72b99 numpy-1.26.0-cp311-cp311-win_amd64.whl 166b36197e9debc4e384e9c652ba60c0bacc216d0fc89e78f973a9760b503388 numpy-1.26.0-cp312-cp312-macosx_10_9_x86_64.whl f042f66d0b4ae6d48e70e28d487376204d3cbf43b84c03bac57e28dac6151581 numpy-1.26.0-cp312-cp312-macosx_11_0_arm64.whl e5e18e5b14a7560d8acf1c596688f4dfd19b4f2945b245a71e5af4ddb7422feb numpy-1.26.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 7f6bad22a791226d0a5c7c27a80a20e11cfe09ad5ef9084d4d3fc4a299cca505 numpy-1.26.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4acc65dd65da28060e206c8f27a573455ed724e6179941edb19f97e58161bb69 numpy-1.26.0-cp312-cp312-musllinux_1_1_x86_64.whl bb0d9a1aaf5f1cb7967320e80690a1d7ff69f1d47ebc5a9bea013e3a21faec95 numpy-1.26.0-cp312-cp312-win32.whl ee84ca3c58fe48b8ddafdeb1db87388dce2c3c3f701bf447b05e4cfcc3679112 numpy-1.26.0-cp312-cp312-win_amd64.whl 4a873a8180479bc829313e8d9798d5234dfacfc2e8a7ac188418189bb8eafbd2 numpy-1.26.0-cp39-cp39-macosx_10_9_x86_64.whl 914b28d3215e0c721dc75db3ad6d62f51f630cb0c277e6b3bcb39519bed10bd8 numpy-1.26.0-cp39-cp39-macosx_11_0_arm64.whl c78a22e95182fb2e7874712433eaa610478a3caf86f28c621708d35fa4fd6e7f numpy-1.26.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 86f737708b366c36b76e953c46ba5827d8c27b7a8c9d0f471810728e5a2fe57c numpy-1.26.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b44e6a09afc12952a7d2a58ca0a2429ee0d49a4f89d83a0a11052da696440e49 numpy-1.26.0-cp39-cp39-musllinux_1_1_x86_64.whl 5671338034b820c8d58c81ad1dafc0ed5a00771a82fccc71d6438df00302094b numpy-1.26.0-cp39-cp39-win32.whl 020cdbee66ed46b671429c7265cf00d8ac91c046901c55684954c3958525dab2 numpy-1.26.0-cp39-cp39-win_amd64.whl 0792824ce2f7ea0c82ed2e4fecc29bb86bee0567a080dacaf2e0a01fe7654369 numpy-1.26.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 7d484292eaeb3e84a51432a94f53578689ffdea3f90e10c8b203a99be5af57d8 numpy-1.26.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 186ba67fad3c60dbe8a3abff3b67a91351100f2661c8e2a80364ae6279720299 numpy-1.26.0-pp39-pypy39_pp73-win_amd64.whl f93fc78fe8bf15afe2b8d6b6499f1c73953169fad1e9a8dd086cdff3190e7fdf numpy-1.26.0.tar.gz ### [`v1.25.2`](https://togithub.com/numpy/numpy/releases/tag/v1.25.2) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.25.1...v1.25.2) ### NumPy 1.25.2 Release Notes NumPy 1.25.2 is a maintenance release that fixes bugs and regressions 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://togithub.com/numpy/numpy/pull/24148): MAINT: prepare 1.25.x for further development - [#24174](https://togithub.com/numpy/numpy/pull/24174): ENH: Improve clang-cl compliance - [#24179](https://togithub.com/numpy/numpy/pull/24179): MAINT: Upgrade various build dependencies. - [#24182](https://togithub.com/numpy/numpy/pull/24182): BLD: use `-ftrapping-math` with Clang on macOS - [#24183](https://togithub.com/numpy/numpy/pull/24183): BUG: properly handle negative indexes in ufunc_at fast path - [#24184](https://togithub.com/numpy/numpy/pull/24184): BUG: PyObject_IsTrue and PyObject_Not error handling in setflags - [#24185](https://togithub.com/numpy/numpy/pull/24185): BUG: histogram small range robust - [#24186](https://togithub.com/numpy/numpy/pull/24186): MAINT: Update meson.build files from main branch - [#24234](https://togithub.com/numpy/numpy/pull/24234): MAINT: exclude min, max and round from `np.__all__` - [#24241](https://togithub.com/numpy/numpy/pull/24241): MAINT: Dependabot updates - [#24242](https://togithub.com/numpy/numpy/pull/24242): BUG: Fix the signature for np.array_api.take - [#24243](https://togithub.com/numpy/numpy/pull/24243): BLD: update OpenBLAS to an intermeidate commit - [#24244](https://togithub.com/numpy/numpy/pull/24244): BUG: Fix reference count leak in str(scalar). - [#24245](https://togithub.com/numpy/numpy/pull/24245): BUG: fix invalid function pointerConfiguration
📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).
🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.
♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.
🔕 Ignore: Close this PR and you won't be reminded about this update again.
This PR has been generated by Mend Renovate. View repository job log here.