UGentBiomath / wwdata

Python package to analyse, validate, fill and visualise data acquired in the context of (waste) water treatment
GNU General Public License v3.0
13 stars 12 forks source link

Bug #12345 Scheduled daily dependency update on Saturday #2302

Closed pyup-bot closed 5 months ago

pyup-bot commented 5 months ago

Update pandas from 0.24.2 to 2.2.2.

The bot wasn't able to find a changelog for this release. Got an idea?

Links - PyPI: https://pypi.org/project/pandas - Homepage: https://pandas.pydata.org

Update numpy from 1.16.2 to 1.26.4.

Changelog ### 1.26.4 ``` 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://github.com/numpy/numpy/pull/25323): BUG: Restore missing asstr import - [25523](https://github.com/numpy/numpy/pull/25523): MAINT: prepare 1.26.x for further development - [25539](https://github.com/numpy/numpy/pull/25539): BUG: `numpy.array_api`: fix `linalg.cholesky` upper decomp\... - [25584](https://github.com/numpy/numpy/pull/25584): CI: Bump azure pipeline timeout to 120 minutes - [25585](https://github.com/numpy/numpy/pull/25585): MAINT, BLD: Fix unused inline functions warnings on clang - [25599](https://github.com/numpy/numpy/pull/25599): BLD: include fix for MinGW platform detection - [25618](https://github.com/numpy/numpy/pull/25618): TST: Fix test_numeric on riscv64 - [25619](https://github.com/numpy/numpy/pull/25619): BLD: fix building for windows ARM64 - [25620](https://github.com/numpy/numpy/pull/25620): MAINT: add `newaxis` to `__all__` in `numpy.array_api` - [25630](https://github.com/numpy/numpy/pull/25630): BUG: Use large file fallocate on 32 bit linux platforms - [25643](https://github.com/numpy/numpy/pull/25643): TST: Fix test_warning_calls on Python 3.12 - [25645](https://github.com/numpy/numpy/pull/25645): TST: Bump pytz to 2023.3.post1 - [25658](https://github.com/numpy/numpy/pull/25658): BUG: Fix AVX512 build flags on Intel Classic Compiler - [25670](https://github.com/numpy/numpy/pull/25670): BLD: fix potential issue with escape sequences in `__config__.py` - [25718](https://github.com/numpy/numpy/pull/25718): CI: pin cygwin python to 3.9.16-1 and fix typing tests \[skip\... - [25720](https://github.com/numpy/numpy/pull/25720): MAINT: Bump cibuildwheel to v2.16.4 - [25748](https://github.com/numpy/numpy/pull/25748): BLD: unvendor meson-python on 1.26.x and upgrade to meson-python\... - [25755](https://github.com/numpy/numpy/pull/25755): MAINT: Include header defining backtrace - [25756](https://github.com/numpy/numpy/pull/25756): BUG: Fix np.quantile(\[Fraction(2,1)\], 0.5) (#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 ``` ### 1.26.3 ``` discovered after the 1.26.2 release. The most notable changes are the f2py bug fixes. The Python versions supported by this release are 3.9-3.12. Compatibility `f2py` will no longer accept ambiguous `-m` and `.pyf` CLI combinations. When more than one `.pyf` file is passed, an error is raised. When both `-m` and a `.pyf` is passed, a warning is emitted and the `-m` provided name is ignored. Improvements `f2py` now handles `common` blocks which have `kind` specifications from modules. This further expands the usability of intrinsics like `iso_fortran_env` and `iso_c_binding`. Contributors A total of 18 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - \DWesl - \Illviljan - Alexander Grund - Andrea Bianchi + - Charles Harris - Daniel Vanzo - Johann Rohwer + - Matti Picus - Nathan Goldbaum - Peter Hawkins - Raghuveer Devulapalli - Ralf Gommers - Rohit Goswami - Sayed Adel - Sebastian Berg - Stefano Rivera + - Thomas A Caswell - matoro Pull requests merged A total of 42 pull requests were merged for this release. - [25130](https://github.com/numpy/numpy/pull/25130): MAINT: prepare 1.26.x for further development - [25188](https://github.com/numpy/numpy/pull/25188): TYP: add None to `__getitem__` in `numpy.array_api` - [25189](https://github.com/numpy/numpy/pull/25189): BLD,BUG: quadmath required where available \[f2py\] - [25190](https://github.com/numpy/numpy/pull/25190): BUG: alpha doesn\'t use REAL(10) - [25191](https://github.com/numpy/numpy/pull/25191): BUG: Fix FP overflow error in division when the divisor is scalar - [25192](https://github.com/numpy/numpy/pull/25192): MAINT: Pin scipy-openblas version. - [25201](https://github.com/numpy/numpy/pull/25201): BUG: Fix f2py to enable use of string optional inout argument - [25202](https://github.com/numpy/numpy/pull/25202): BUG: Fix -fsanitize=alignment issue in numpy/\_core/src/multiarray/arraytypes.c.src - [25203](https://github.com/numpy/numpy/pull/25203): TST: Explicitly pass NumPy path to cython during tests (also\... - [25204](https://github.com/numpy/numpy/pull/25204): BUG: fix issues with `newaxis` and `linalg.solve` in `numpy.array_api` - [25205](https://github.com/numpy/numpy/pull/25205): BUG: Disallow shadowed modulenames - [25217](https://github.com/numpy/numpy/pull/25217): BUG: Handle common blocks with kind specifications from modules - [25218](https://github.com/numpy/numpy/pull/25218): BUG: Fix moving compiled executable to root with f2py -c on Windows - [25219](https://github.com/numpy/numpy/pull/25219): BUG: Fix single to half-precision conversion on PPC64/VSX3 - [25227](https://github.com/numpy/numpy/pull/25227): TST: f2py: fix issue in test skip condition - [25240](https://github.com/numpy/numpy/pull/25240): Revert \"MAINT: Pin scipy-openblas version.\" - [25249](https://github.com/numpy/numpy/pull/25249): MAINT: do not use `long` type - [25377](https://github.com/numpy/numpy/pull/25377): TST: PyPy needs another gc.collect on latest versions - [25378](https://github.com/numpy/numpy/pull/25378): CI: Install Lapack runtime on Cygwin. - [25379](https://github.com/numpy/numpy/pull/25379): MAINT: Bump conda-incubator/setup-miniconda from 2.2.0 to 3.0.1 - [25380](https://github.com/numpy/numpy/pull/25380): BLD: update vendored Meson for AIX shared library fix - [25419](https://github.com/numpy/numpy/pull/25419): MAINT: Init `base` in cpu_avx512_kn - [25420](https://github.com/numpy/numpy/pull/25420): BUG: Fix failing test_features on SapphireRapids - [25422](https://github.com/numpy/numpy/pull/25422): BUG: Fix non-contiguous memory load when ARM/Neon is enabled - [25428](https://github.com/numpy/numpy/pull/25428): MAINT,BUG: Never import distutils above 3.12 \[f2py\] - [25452](https://github.com/numpy/numpy/pull/25452): MAINT: make the import-time check for old Accelerate more specific - [25458](https://github.com/numpy/numpy/pull/25458): BUG: fix macOS version checks for Accelerate support - [25465](https://github.com/numpy/numpy/pull/25465): MAINT: Bump actions/setup-node and larsoner/circleci-artifacts-redirector-action - [25466](https://github.com/numpy/numpy/pull/25466): BUG: avoid seg fault from OOB access in RandomState.set_state() - [25467](https://github.com/numpy/numpy/pull/25467): BUG: Fix two errors related to not checking for failed allocations - [25468](https://github.com/numpy/numpy/pull/25468): BUG: Fix regression with `f2py` wrappers when modules and subroutines\... - [25475](https://github.com/numpy/numpy/pull/25475): BUG: Fix build issues on SPR - [25478](https://github.com/numpy/numpy/pull/25478): BLD: fix uninitialized variable warnings from simd/neon/memory.h - [25480](https://github.com/numpy/numpy/pull/25480): BUG: Handle `iso_c_type` mappings more consistently - [25481](https://github.com/numpy/numpy/pull/25481): BUG: Fix module name bug in signature files \[urgent\] \[f2py\] - [25482](https://github.com/numpy/numpy/pull/25482): BUG: Handle .pyf.src and fix SciPy \[urgent\] - [25483](https://github.com/numpy/numpy/pull/25483): DOC: `f2py` rewrite with `meson` details - [25485](https://github.com/numpy/numpy/pull/25485): BUG: Add external library handling for meson \[f2py\] - [25486](https://github.com/numpy/numpy/pull/25486): MAINT: Run f2py\'s meson backend with the same python that ran\... - [25489](https://github.com/numpy/numpy/pull/25489): MAINT: Update `numpy/f2py/_backends` from main. - [25490](https://github.com/numpy/numpy/pull/25490): MAINT: Easy updates of `f2py/*.py` from main. - [25491](https://github.com/numpy/numpy/pull/25491): MAINT: Update crackfortran.py and f2py2e.py from main Checksums MD5 7660db27715df261948e7f0f13634f16 numpy-1.26.3-cp310-cp310-macosx_10_9_x86_64.whl 98d5b98c822de4bed0cf1b0b8f367192 numpy-1.26.3-cp310-cp310-macosx_11_0_arm64.whl b71cd0710cec5460292a97a02fa349cd numpy-1.26.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 0f98a05c92598f849b1be2595f4a52a8 numpy-1.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b866c6aea8070c0753b776d2b521e875 numpy-1.26.3-cp310-cp310-musllinux_1_1_aarch64.whl cfdde5868e469fb27655ea73b0b9593b numpy-1.26.3-cp310-cp310-musllinux_1_1_x86_64.whl 2655440d61671b5e32b049d30397c58f numpy-1.26.3-cp310-cp310-win32.whl 7718a5d33344784ca7821f3bdd467550 numpy-1.26.3-cp310-cp310-win_amd64.whl 28e4b2ed9192c392f792d88b3c246d1c numpy-1.26.3-cp311-cp311-macosx_10_9_x86_64.whl fb1ae72749463e2c82f0127699728364 numpy-1.26.3-cp311-cp311-macosx_11_0_arm64.whl 304dec822b508a1d495917610e7562bf numpy-1.26.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 2cc0d8b073dfd55946a60ba8ed4369f6 numpy-1.26.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c99962375c599501820899c8ccab6960 numpy-1.26.3-cp311-cp311-musllinux_1_1_aarch64.whl 47ed42d067ce4863bbf1f40da61ba7d1 numpy-1.26.3-cp311-cp311-musllinux_1_1_x86_64.whl 3ab3757255feb54ca3793fb9db226586 numpy-1.26.3-cp311-cp311-win32.whl c33f2a4518bae535645357a08a93be1a numpy-1.26.3-cp311-cp311-win_amd64.whl bea43600aaff3a4d9978611ccfa44198 numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl c678d909ebe737fdabf215d8622ce2a3 numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl 9f21f1875c92425cec1060564b3abb1c numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl c44a1998965d45ec136078ee09d880f2 numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9274f5c51fa4f3c8fac5efa3d78acd63 numpy-1.26.3-cp312-cp312-musllinux_1_1_aarch64.whl 07c9f8f86f45077febc46c87ebc0b644 numpy-1.26.3-cp312-cp312-musllinux_1_1_x86_64.whl a4857b2f7b6a23bca41178bd344bb28a numpy-1.26.3-cp312-cp312-win32.whl 495d9534961d7b10f16fec4515a3d72b numpy-1.26.3-cp312-cp312-win_amd64.whl 6494f2d94fd1f184923a33e634692b5e numpy-1.26.3-cp39-cp39-macosx_10_9_x86_64.whl 515a7314a0ff6aaba8d53a7a1aaa73ab numpy-1.26.3-cp39-cp39-macosx_11_0_arm64.whl c856adc6a6a78773c43e9c738d662ed5 numpy-1.26.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 09848456158a01feff28f88c6106aef1 numpy-1.26.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl adec00ea2bc98580a436f82e188c0e2f numpy-1.26.3-cp39-cp39-musllinux_1_1_aarch64.whl 718bd35dd0431a6434bb30bf8d91d77d numpy-1.26.3-cp39-cp39-musllinux_1_1_x86_64.whl e813aa59cb807efb4a8fee52a6dd41ba numpy-1.26.3-cp39-cp39-win32.whl 08e1b0973d0ae5976b38563eaec1253f numpy-1.26.3-cp39-cp39-win_amd64.whl e8887a14750161709636e9fb87df4f36 numpy-1.26.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 0bdb19040525451553fb5758b65caf4c numpy-1.26.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b931c14d06cc37d85d63ed1ddd88e875 numpy-1.26.3-pp39-pypy39_pp73-win_amd64.whl 1c915dc6c36dd4c674d9379e9470ff8b numpy-1.26.3.tar.gz SHA256 806dd64230dbbfaca8a27faa64e2f414bf1c6622ab78cc4264f7f5f028fee3bf numpy-1.26.3-cp310-cp310-macosx_10_9_x86_64.whl 02f98011ba4ab17f46f80f7f8f1c291ee7d855fcef0a5a98db80767a468c85cd numpy-1.26.3-cp310-cp310-macosx_11_0_arm64.whl 6d45b3ec2faed4baca41c76617fcdcfa4f684ff7a151ce6fc78ad3b6e85af0a6 numpy-1.26.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl bdd2b45bf079d9ad90377048e2747a0c82351989a2165821f0c96831b4a2a54b numpy-1.26.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 211ddd1e94817ed2d175b60b6374120244a4dd2287f4ece45d49228b4d529178 numpy-1.26.3-cp310-cp310-musllinux_1_1_aarch64.whl b1240f767f69d7c4c8a29adde2310b871153df9b26b5cb2b54a561ac85146485 numpy-1.26.3-cp310-cp310-musllinux_1_1_x86_64.whl 21a9484e75ad018974a2fdaa216524d64ed4212e418e0a551a2d83403b0531d3 numpy-1.26.3-cp310-cp310-win32.whl 9e1591f6ae98bcfac2a4bbf9221c0b92ab49762228f38287f6eeb5f3f55905ce numpy-1.26.3-cp310-cp310-win_amd64.whl b831295e5472954104ecb46cd98c08b98b49c69fdb7040483aff799a755a7374 numpy-1.26.3-cp311-cp311-macosx_10_9_x86_64.whl 9e87562b91f68dd8b1c39149d0323b42e0082db7ddb8e934ab4c292094d575d6 numpy-1.26.3-cp311-cp311-macosx_11_0_arm64.whl 8c66d6fec467e8c0f975818c1796d25c53521124b7cfb760114be0abad53a0a2 numpy-1.26.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f25e2811a9c932e43943a2615e65fc487a0b6b49218899e62e426e7f0a57eeda numpy-1.26.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl af36e0aa45e25c9f57bf684b1175e59ea05d9a7d3e8e87b7ae1a1da246f2767e numpy-1.26.3-cp311-cp311-musllinux_1_1_aarch64.whl 51c7f1b344f302067b02e0f5b5d2daa9ed4a721cf49f070280ac202738ea7f00 numpy-1.26.3-cp311-cp311-musllinux_1_1_x86_64.whl 7ca4f24341df071877849eb2034948459ce3a07915c2734f1abb4018d9c49d7b numpy-1.26.3-cp311-cp311-win32.whl 39763aee6dfdd4878032361b30b2b12593fb445ddb66bbac802e2113eb8a6ac4 numpy-1.26.3-cp311-cp311-win_amd64.whl a7081fd19a6d573e1a05e600c82a1c421011db7935ed0d5c483e9dd96b99cf13 numpy-1.26.3-cp312-cp312-macosx_10_9_x86_64.whl 12c70ac274b32bc00c7f61b515126c9205323703abb99cd41836e8125ea0043e numpy-1.26.3-cp312-cp312-macosx_11_0_arm64.whl 7f784e13e598e9594750b2ef6729bcd5a47f6cfe4a12cca13def35e06d8163e3 numpy-1.26.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 5f24750ef94d56ce6e33e4019a8a4d68cfdb1ef661a52cdaee628a56d2437419 numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 77810ef29e0fb1d289d225cabb9ee6cf4d11978a00bb99f7f8ec2132a84e0166 numpy-1.26.3-cp312-cp312-musllinux_1_1_aarch64.whl 8ed07a90f5450d99dad60d3799f9c03c6566709bd53b497eb9ccad9a55867f36 numpy-1.26.3-cp312-cp312-musllinux_1_1_x86_64.whl f73497e8c38295aaa4741bdfa4fda1a5aedda5473074369eca10626835445511 numpy-1.26.3-cp312-cp312-win32.whl da4b0c6c699a0ad73c810736303f7fbae483bcb012e38d7eb06a5e3b432c981b numpy-1.26.3-cp312-cp312-win_amd64.whl 1666f634cb3c80ccbd77ec97bc17337718f56d6658acf5d3b906ca03e90ce87f numpy-1.26.3-cp39-cp39-macosx_10_9_x86_64.whl 18c3319a7d39b2c6a9e3bb75aab2304ab79a811ac0168a671a62e6346c29b03f numpy-1.26.3-cp39-cp39-macosx_11_0_arm64.whl 0b7e807d6888da0db6e7e75838444d62495e2b588b99e90dd80c3459594e857b numpy-1.26.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b4d362e17bcb0011738c2d83e0a65ea8ce627057b2fdda37678f4374a382a137 numpy-1.26.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b8c275f0ae90069496068c714387b4a0eba5d531aace269559ff2b43655edd58 numpy-1.26.3-cp39-cp39-musllinux_1_1_aarch64.whl cc0743f0302b94f397a4a65a660d4cd24267439eb16493fb3caad2e4389bccbb numpy-1.26.3-cp39-cp39-musllinux_1_1_x86_64.whl 9bc6d1a7f8cedd519c4b7b1156d98e051b726bf160715b769106661d567b3f03 numpy-1.26.3-cp39-cp39-win32.whl 867e3644e208c8922a3be26fc6bbf112a035f50f0a86497f98f228c50c607bb2 numpy-1.26.3-cp39-cp39-win_amd64.whl 3c67423b3703f8fbd90f5adaa37f85b5794d3366948efe9a5190a5f3a83fc34e numpy-1.26.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 46f47ee566d98849323f01b349d58f2557f02167ee301e5e28809a8c0e27a2d0 numpy-1.26.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a8474703bffc65ca15853d5fd4d06b18138ae90c17c8d12169968e998e448bb5 numpy-1.26.3-pp39-pypy39_pp73-win_amd64.whl 697df43e2b6310ecc9d95f05d5ef20eacc09c7c4ecc9da3f235d39e71b7da1e4 numpy-1.26.3.tar.gz ``` ### 1.26.2 ``` 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 - \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://github.com/numpy/numpy/pull/24814): MAINT: align test_dispatcher s390x targets with \_umath_tests_mtargets - [24929](https://github.com/numpy/numpy/pull/24929): MAINT: prepare 1.26.x for further development - [24955](https://github.com/numpy/numpy/pull/24955): ENH: Add Cython enumeration for NPY_FR_GENERIC - [24962](https://github.com/numpy/numpy/pull/24962): REL: Remove Python upper version from the release branch - [24971](https://github.com/numpy/numpy/pull/24971): BLD: Use the correct Python interpreter when running tempita.py - [24972](https://github.com/numpy/numpy/pull/24972): MAINT: Remove unhelpful error replacements from `import_array()` - [24977](https://github.com/numpy/numpy/pull/24977): BLD: use classic linker on macOS, the new one in XCode 15 has\... - [25003](https://github.com/numpy/numpy/pull/25003): BLD: musllinux_aarch64 \[wheel build\] - [25043](https://github.com/numpy/numpy/pull/25043): MAINT: Update mailmap - [25049](https://github.com/numpy/numpy/pull/25049): MAINT: Update meson build infrastructure. - [25071](https://github.com/numpy/numpy/pull/25071): MAINT: Split up .github/workflows to match main - [25083](https://github.com/numpy/numpy/pull/25083): BUG: Backport fix build on ppc64 when the baseline set to Power9\... - [25093](https://github.com/numpy/numpy/pull/25093): BLD: Fix features.h detection for Meson builds \[1.26.x Backport\] - [25095](https://github.com/numpy/numpy/pull/25095): BUG: Avoid intp conversion regression in Cython 3 (backport) - [25107](https://github.com/numpy/numpy/pull/25107): CI: remove obsolete jobs, and move macOS and conda Azure jobs\... - [25108](https://github.com/numpy/numpy/pull/25108): CI: Add linux_qemu action and remove travis testing. - [25112](https://github.com/numpy/numpy/pull/25112): MAINT: Update .spin/cmds.py from main. - [25113](https://github.com/numpy/numpy/pull/25113): DOC: Visually divide main license and bundled licenses in wheels - [25115](https://github.com/numpy/numpy/pull/25115): MAINT: Add missing `noexcept` to shuffle helpers - [25116](https://github.com/numpy/numpy/pull/25116): DOC: Fix license identifier for OpenBLAS - [25117](https://github.com/numpy/numpy/pull/25117): BLD: improve detection of Netlib libblas/libcblas/liblapack - [25118](https://github.com/numpy/numpy/pull/25118): MAINT: Make bitfield integers unsigned - [25119](https://github.com/numpy/numpy/pull/25119): BUG: Make n a long int for np.random.multinomial - [25120](https://github.com/numpy/numpy/pull/25120): BLD: change default of the `allow-noblas` option to true. - [25121](https://github.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 ``` ### 1.26.1 ``` discovered after the 1.26.0 release. In addition, it adds new functionality for detecting BLAS and LAPACK when building from source. Highlights are: - Improved detection of BLAS and LAPACK libraries for meson builds - Pickle compatibility with the upcoming NumPy 2.0. The 1.26.release series is the last planned minor release series before NumPy 2.0. The Python versions supported by this release are 3.9-3.12. Build system changes Improved BLAS/LAPACK detection and control Auto-detection for a number of BLAS and LAPACK is now implemented for Meson. By default, the build system will try to detect MKL, Accelerate (on macOS \>=13.3), OpenBLAS, FlexiBLAS, BLIS and reference BLAS/LAPACK. Support for MKL was significantly improved, and support for FlexiBLAS was added. New command-line flags are available to further control the selection of the BLAS and LAPACK libraries to build against. To select a specific library, use the config-settings interface via `pip` or `pypa/build`. E.g., to select `libblas`/`liblapack`, use: $ pip install numpy -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack $ OR $ python -m build . -Csetup-args=-Dblas=blas -Csetup-args=-Dlapack=lapack This works not only for the libraries named above, but for any library that Meson is able to detect with the given name through `pkg-config` or CMake. Besides `-Dblas` and `-Dlapack`, a number of other new flags are available to control BLAS/LAPACK selection and behavior: - `-Dblas-order` and `-Dlapack-order`: a list of library names to search for in order, overriding the default search order. - `-Duse-ilp64`: if set to `true`, use ILP64 (64-bit integer) BLAS and LAPACK. Note that with this release, ILP64 support has been extended to include MKL and FlexiBLAS. OpenBLAS and Accelerate were supported in previous releases. - `-Dallow-noblas`: if set to `true`, allow NumPy to build with its internal (very slow) fallback routines instead of linking against an external BLAS/LAPACK library. *The default for this flag may be changed to \`\`true\`\` in a future 1.26.x release, however for 1.26.1 we\'d prefer to keep it as \`\`false\`\` because if failures to detect an installed library are happening, we\'d like a bug report for that, so we can quickly assess whether the new auto-detection machinery needs further improvements.* - `-Dmkl-threading`: to select the threading layer for MKL. There are four options: `seq`, `iomp`, `gomp` and `tbb`. The default is `auto`, which selects from those four as appropriate given the version of MKL selected. - `-Dblas-symbol-suffix`: manually select the symbol suffix to use for the library - should only be needed for linking against libraries built in a non-standard way. New features `numpy._core` submodule stubs `numpy._core` submodule stubs were added to provide compatibility with pickled arrays created using NumPy 2.0 when running Numpy 1.26. Contributors A total of 13 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Andrew Nelson - Anton Prosekin + - Charles Harris - Chongyun Lee + - Ivan A. Melnikov + - Jake Lishman + - Mahder Gebremedhin + - Mateusz Sokół - Matti Picus - Munira Alduraibi + - Ralf Gommers - Rohit Goswami - Sayed Adel Pull requests merged A total of 20 pull requests were merged for this release. - [24742](https://github.com/numpy/numpy/pull/24742): MAINT: Update cibuildwheel version - [24748](https://github.com/numpy/numpy/pull/24748): MAINT: fix version string in wheels built with setup.py - [24771](https://github.com/numpy/numpy/pull/24771): BLD, BUG: Fix build failure for host flags e.g. `-march=native`\... - [24773](https://github.com/numpy/numpy/pull/24773): DOC: Updated the f2py docs to remove a note on -fimplicit-none - [24776](https://github.com/numpy/numpy/pull/24776): BUG: Fix SIMD f32 trunc test on s390x when baseline is none - [24785](https://github.com/numpy/numpy/pull/24785): BLD: add libquadmath to licences and other tweaks (#24753) - [24786](https://github.com/numpy/numpy/pull/24786): MAINT: Activate `use-compute-credits` for Cirrus. - [24803](https://github.com/numpy/numpy/pull/24803): BLD: updated vendored-meson/meson for mips64 fix - [24804](https://github.com/numpy/numpy/pull/24804): MAINT: fix licence path win - [24813](https://github.com/numpy/numpy/pull/24813): BUG: Fix order of Windows OS detection macros. - [24831](https://github.com/numpy/numpy/pull/24831): BUG, SIMD: use scalar cmul on bad Apple clang x86_64 (#24828) - [24840](https://github.com/numpy/numpy/pull/24840): BUG: Fix DATA statements for f2py - [24870](https://github.com/numpy/numpy/pull/24870): API: Add `NumpyUnpickler` for backporting - [24872](https://github.com/numpy/numpy/pull/24872): MAINT: Xfail test failing on PyPy. - [24879](https://github.com/numpy/numpy/pull/24879): BLD: fix math func feature checks, fix FreeBSD build, add CI\... - [24899](https://github.com/numpy/numpy/pull/24899): ENH: meson: implement BLAS/LAPACK auto-detection and many CI\... - [24902](https://github.com/numpy/numpy/pull/24902): DOC: add a 1.26.1 release notes section for BLAS/LAPACK build\... - [24906](https://github.com/numpy/numpy/pull/24906): MAINT: Backport `numpy._core` stubs. Remove `NumpyUnpickler` - [24911](https://github.com/numpy/numpy/pull/24911): MAINT: Bump pypa/cibuildwheel from 2.16.1 to 2.16.2 - [24912](https://github.com/numpy/numpy/pull/24912): BUG: loongarch doesn\'t use REAL(10) 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 ``` ### 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 0d60fbae8e0019865fc4784745814cff1c421df5afee233db6d88
pyup-bot commented 5 months ago

Closing this in favor of #2303