bartvanandel
commented
9 months ago Holding off for now because of Python compatibility.
- Numpy v1.26.3 supports Python 3.9+
- Latest numpy that supports Python 3.8 (which we still support) is v1.24.4
Closed renovate[bot] closed 9 months ago
bartvanandel
commented
9 months ago Holding off for now because of Python compatibility.
renovate[bot]
commented
9 months ago Because you closed this PR without merging, Renovate will ignore this update (==1.26.3). You will get a PR once a newer version is released. To ignore this dependency forever, add it to the ignoreDeps array of your Renovate config.
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:
==1.24.4->==1.26.3Release Notes
numpy/numpy (numpy)
### [`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 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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 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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 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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 pointer conversion error - [#24255](https://togithub.com/numpy/numpy/pull/24255): BUG: Factor out slow `getenv` call used for memory policy warning - [#24292](https://togithub.com/numpy/numpy/pull/24292): CI: correct URL in cirrus.star - [#24293](https://togithub.com/numpy/numpy/pull/24293): BUG: Fix C types in scalartypes - [#24294](https://togithub.com/numpy/numpy/pull/24294): BUG: do not modify the input to ufunc_at - [#24295](https://togithub.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 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76b4115d42a7dfc5d485d358728cdd8719be33cc5ec6ec08632a5d6fca2ed380 numpy-1.25.2-cp39-cp39-win_amd64.whl 1a1329e26f46230bf77b02cc19e900db9b52f398d6722ca853349a782d4cff55 numpy-1.25.2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 4c3abc71e8b6edba80a01a52e66d83c5d14433cbcd26a40c329ec7ed09f37901 numpy-1.25.2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1b9735c27cea5d995496f46a8b1cd7b408b3f34b6d50459d9ac8fe3a20cc17bf numpy-1.25.2-pp39-pypy39_pp73-win_amd64.whl fd608e19c8d7c55021dffd43bfe5492fab8cc105cc8986f813f8c3c048b38760 numpy-1.25.2.tar.gz ### [`v1.25.1`](https://togithub.com/numpy/numpy/compare/v1.25.0...v1.25.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.25.0...v1.25.1) ### [`v1.25.0`](https://togithub.com/numpy/numpy/releases/tag/v1.25.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.24.4...v1.25.0) ### NumPy 1.25.0 Release Notes The NumPy 1.25.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, and clarify the documentation. There has also been work to prepare for the future NumPy 2.0.0 release, resulting in a large number of new and expired deprecation. Highlights are: - Support for MUSL, there are now MUSL wheels. - Support the Fujitsu C/C++ compiler. - Object arrays are now supported in einsum - Support for inplace matrix multiplication (`@=`). We will be releasing a NumPy 1.26 when Python 3.12 comes out. That is needed because distutils has been dropped by Python 3.12 and we will be switching to using meson for future builds. The next mainline release will be NumPy 2.0.0. We plan that the 2.0 series will still support downstream projects built against earlier versions of NumPy. The Python versions supported in this release are 3.9-3.11. #### Deprecations - `np.core.MachAr` is deprecated. It is private API. In names defined in `np.core` should generally be considered private. ([gh-22638](https://togithub.com/numpy/numpy/pull/22638)) - `np.finfo(None)` is deprecated. ([gh-23011](https://togithub.com/numpy/numpy/pull/23011)) - `np.round_` is deprecated. Use `np.round` instead. ([gh-23302](https://togithub.com/numpy/numpy/pull/23302)) - `np.product` is deprecated. Use `np.prod` instead. ([gh-23314](https://togithub.com/numpy/numpy/pull/23314)) - `np.cumproduct` is deprecated. Use `np.cumprod` instead. ([gh-23314](https://togithub.com/numpy/numpy/pull/23314)) - `np.sometrue` is deprecated. Use `np.any` instead. ([gh-23314](https://togithub.com/numpy/numpy/pull/23314)) - `np.alltrue` is deprecated. Use `np.all` instead. ([gh-23314](https://togithub.com/numpy/numpy/pull/23314)) - Only ndim-0 arrays are treated as scalars. NumPy used to treat all arrays of size 1 (e.g., `np.array([3.14])`) as scalars. In the future, this will be limited to arrays of ndim 0 (e.g., `np.array(3.14)`). The following expressions will report a deprecation warning: ```python a = np.array([3.14]) float(a) # better: a[0] to get the numpy.float or a.item() b = np.array([[3.14]]) c = numpy.random.rand(10) c[0] = b # better: c[0] = b[0, 0] ``` ([gh-10615](https://togithub.com/numpy/numpy/pull/10615)) - `numpy.find_common_type` is now deprecated and its use should be replaced with either `numpy.result_type` or `numpy.promote_types`. Most users leave the second `scalar_types` argument to `find_common_type` as `[]` in which case `np.result_type` and `np.promote_types` are both faster and more robust. When not using `scalar_types` the main difference is that the replacement intentionally converts non-native byte-order to native byte order. Further, `find_common_type` returns `object` dtype rather than failing promotion. This leads to differences when the inputs are not all numeric. Importantly, this also happens for e.g. timedelta/datetime for which NumPy promotion rules are currently sometimes surprising. When the `scalar_types` argument is not `[]` things are more complicated. In most cases, using `np.result_type` and passing the Python values `0`, `0.0`, or `0j` has the same result as using `int`, `float`, or `complex` in `scalar_types`. When `scalar_types` is constructed, `np.result_type` is the correct replacement and it may be passed scalar values like `np.float32(0.0)`. Passing values other than 0, may lead to value-inspecting behavior (which `np.find_common_type` never used and NEP 50 may change in the future). The main possible change in behavior in this case, is when the array types are signed integers and scalar types are unsigned. If you are unsure about how to replace a use of `scalar_types` or when non-numeric dtypes are likely, please do not hesitate to open a NumPy issue to ask for help. ([gh-22539](https://togithub.com/numpy/numpy/pull/22539)) #### Expired deprecations - `np.core.machar` and `np.finfo.machar` have been removed. ([gh-22638](https://togithub.com/numpy/numpy/pull/22638)) - `+arr` will now raise an error when the dtype is not numeric (and positive is undefined). ([gh-22998](https://togithub.com/numpy/numpy/pull/22998)) - A sequence must now be passed into the stacking family of functions (`stack`, `vstack`, `hstack`, `dstack` and `column_stack`). ([gh-23019](https://togithub.com/numpy/numpy/pull/23019)) - `np.clip` now defaults to same-kind casting. Falling back to unsafe casting was deprecated in NumPy 1.17. ([gh-Configuration
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