Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
fix(deps): Update dependency numpy to v1.26.1 (#76)
This PR contains the following updates: | Package | Update | Change | |---|---|---| | [numpy](https://numpy.org) ([source](https://togithub.com/numpy/numpy), [changelog](https://numpy.org/doc/stable/release)) | patch | `==1.26.0` -> `==1.26.1` | --- ### Release Notes <details> <summary>numpy/numpy (numpy)</summary> ### [`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 </details> --- ### Configuration 📅 **Schedule**: Branch creation - "before 4am on the first day of the month" (UTC), 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. --- - [ ] <!-- rebase-check -->If you want to rebase/retry this PR, check this box --- This PR has been generated by [Renovate Bot](https://togithub.com/renovatebot/renovate). <!--renovate-debug:eyJjcmVhdGVkSW5WZXIiOiIzNi4xMDkuNCIsInVwZGF0ZWRJblZlciI6IjM2LjEwOS40IiwidGFyZ2V0QnJhbmNoIjoibWFpbiJ9-->
- Loading branch information
