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cetteup / BF2AutoSpectator

An automated spectator for Battlefield 2 written in Python
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chore(deps): update dependency numpy to v1.26.1 #56

Closed renovate[bot] closed 10 months ago

renovate[bot] commented 1 year ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source, changelog) ==1.24.3 -> ==1.26.1 age adoption passing confidence

Release Notes

numpy/numpy (numpy) ### [`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 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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 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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 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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-23403](https://togithub.com/numpy/numpy/pull/23403)) - `np.clip` will now propagate `np.nan` values passed as `min` or `max`. Previously, a scalar NaN was usually ignored. This was deprecated in NumPy 1.17. ([gh-23403](https://togithub.com/numpy/numpy/pull/23403)) - The `np.dual` submodule has been removed. ([gh-23480](https://togithub.com/numpy/numpy/pull/23480)) - NumPy now always ignores sequence behavior for an array-like (defining one of the array protocols). (Deprecation started NumPy 1.20) ([gh-23660](https://togithub.com/numpy/numpy/pull/23660)) - The niche `FutureWarning` when casting to a subarray dtype in `astype` or the array creation functions such as `asarray` is now finalized. The behavior is now always the same as if the subarray dtype was wrapped into a single field (which was the workaround, previously). (FutureWarning since NumPy 1.20) ([gh-23666](https://togithub.com/numpy/numpy/pull/23666)) - `==` and `!=` warnings have been finalized. The `==` and `!=` operators on arrays now always: - raise errors that occur during comparisons such as when the arrays have incompatible shapes (`np.array([1, 2]) == np.array([1, 2, 3])`). - return an array of all `True` or all `False` when values are fundamentally not comparable (e.g. have different dtypes). An example is `np.array(["a"]) == np.array([1])`. This mimics the Python behavior of returning `False` and `True` when comparing incompatible types like `"a" == 1` and `"a" != 1`. For a long time these gave `DeprecationWarning` or `FutureWarning`. ([gh-22707](https://togithub.com/numpy/numpy/pull/22707)) - Nose support has been removed. NumPy switched to using pytest in 2018 and nose has been unmaintained for many years. We have kept NumPy's nose support to avoid breaking downstream projects who might have been using it and not yet switched to pytest or some other testing framework. With the arrival of Python 3.12, unpatched nose will raise an error. It is time to move on. *Decorators removed*: - raises - slow - setastest - skipif - knownfailif - deprecated - parametrize - \_needs_refcount These are not to be confused with pytest versions with similar names, e.g., pytest.mark.slow, pytest.mark.skipif, pytest.mark.parametrize. *Functions removed*: - Tester - import_nose - run_module_suite ([gh-23041](https://togithub.com/numpy/numpy/pull/23041)) - The `numpy.testing.utils` shim has been removed. Importing from the `numpy.testing.utils` shim has been deprecated since 2019, the shim has now been removed. All imports should be made directly from `numpy.testing`. ([gh-23060](https://togithub.com/numpy/numpy/pull/23060)) - The environment variable to disable dispatching has been removed. Support for the `NUMPY_EXPERIMENTAL_ARRAY_FUNCTION` environment variable has been removed. This variable disabled dispatching with `__array_function__`. ([gh-23376](https://togithub.com/numpy/numpy/pull/23376)) - Support for `y=` as an alias of `out=` has been removed. The `fix`, `isposinf` and `isneginf` functions allowed using `y=` as a (deprecated) alias for `out=`. This is no longer supported. ([gh-23376](https://togithub.com/numpy/numpy/pull/23376)) #### Compatibility notes - The `busday_count` method now correctly handles cases where the `begindates` is later in time than the `enddates`. Previously, the `enddates` was included, even though the documentation states it is always excluded. ([gh-23229](https://togithub.com/numpy/numpy/pull/23229)) - When comparing datetimes and timedelta using `np.equal` or `np.not_equal` numpy previously allowed the comparison with `casting="unsafe"`. This operation now fails. Forcing the output dtype using the `dtype` kwarg can make the operation succeed, but we do not recommend it. ([gh-22707](https://togithub.com/numpy/numpy/pull/22707)) - When loading data from a file handle using `np.load`, if the handle is at the end of file, as can happen when reading multiple arrays by calling `np.load` repeatedly, numpy previously raised `ValueError` if `allow_pickle=False`, and `OSError` if `allow_pickle=True`. Now it raises `EOFError` instead, in both cases. ([gh-23105](https://togithub.com/numpy/numpy/pull/23105)) ##### `np.pad` with `mode=wrap` pads with strict multiples of original data Code based on earlier version of `pad` that uses `mode="wrap"` will return different results when the padding size is larger than initial array. `np.pad` with `mode=wrap` now always fills the space with strict multiples of original data even if the padding size is larger than the initial array. ([gh-22575](https://togithub.com/numpy/numpy/pull/22575)) ##### Cython `long_t` and `ulong_t` removed `long_t` and `ulong_t` were aliases for `longlong_t` and `ulonglong_t` and confusing (a remainder from of Python 2). This change may lead to the errors: 'long_t' is not a type identifier 'ulong_t' is not a type identifier We recommend use of bit-sized types such as `cnp.int64_t` or the use of `cnp.intp_t` which is 32 bits on 32 bit systems and 64 bits on 64 bit systems (this is most compatible with indexing). If C `long` is desired, use plain `long` or `npy_long`. `cnp.int_t` is also `long` (NumPy's default integer). However, `long` is 32 bit on 64 bit windows and we may wish to adjust this even in NumPy. (Please do not hesitate to contact NumPy developers if you are curious about this.) ([gh-22637](https://togithub.com/numpy/numpy/pull/22637)) ##### Changed error message and type for bad `axes` argument to `ufunc` The error message and type when a wrong `axes` value is passed to `ufunc(..., axes=[...])` has changed. The message is now more indicative of the problem, and if the value is mismatched an `AxisError` will be raised. A `TypeError` will still be raised for invalidinput types. ([gh-22675](https://togithub.com/numpy/numpy/pull/22675)) ##### Array-likes that define `__array_ufunc__` can now override ufuncs if used as `where` If the `where` keyword argument of a `numpy.ufunc`{.interpreted-text role="class"} is a subclass of `numpy.ndarray`{.interpreted-text role="class"} or is a duck type that defines `numpy.class.__array_ufunc__`{.interpreted-text role="func"} it can override the behavior of the ufunc using the same mechanism as the input and output arguments. Note that for this to work properly, the `where.__array_ufunc__` implementation will have to unwrap the `where` argument to pass it into the default implementation of the `ufunc` or, for `numpy.ndarray`{.interpreted-text role="class"} subclasses before using `super().__array_ufunc__`. ([gh-23240](https://togithub.com/numpy/numpy/pull/23240)) ##### Compiling against the NumPy C API is now backwards compatible by default NumPy now defaults to exposing a backwards compatible subset of the C-API. This makes the use of `oldest-supported-numpy` unnecessary. Libraries can override the default minimal version to be compatible with using: #define NPY_TARGET_VERSION NPY_1_22_API_VERSION before including NumPy or by passing the equivalent `-D` option to the compiler. The NumPy 1.25 default is `NPY_1_19_API_VERSION`. Because the NumPy 1.19 C API was identical to the NumPy 1.16 one resulting programs will be compatible with NumPy 1.16 (from a C-API perspective). This default will be increased in future non-bugfix releases. You can still compile against an older NumPy version and run on a newer one. For more details please see `for-downstream-package-authors`{.interpreted-text role="ref"}. ([gh-23528](https://togithub.com/numpy/numpy/pull/23528)) #### New Features ##### `np.einsum` now accepts arrays with `object` dtype The code path will call python operators on object dtype arrays, much like `np.dot` and `np.matmul`. ([gh-18053](https://togithub.com/numpy/numpy/pull/18053)) ##### Add support for inplace matrix multiplication It is now possible to perform inplace matrix multiplication via the `@=` operator. ```python >>> import numpy as np >>> a = np.arange(6).reshape(3, 2) >>> print(a) [[0 1] [2 3] [4 5]] >>> b = np.ones((2, 2), dtype=int) >>> a @​= b >>> print(a) [[1 1] [5 5] [9 9]] ``` ([gh-21120](https://togithub.com/numpy/numpy/pull/21120)) ##### Added `NPY_ENABLE_CPU_FEATURES` environment variable Users may now choose to enable only a subset of the built CPU features at runtime by specifying the `NPY_ENABLE_CPU_FEATURES` environment variable. Note that these specified features must be outside the baseline, since those are always assumed. Errors will be raised if attempting to enable a feature that is either not supported by your CPU, or that NumPy was not built with. ([gh-22137](https://togithub.com/numpy/numpy/pull/22137)) ##### NumPy now has an `np.exceptions` namespace NumPy now has a dedicated namespace making most exceptions and warnings available. All of these remain available in the main namespace, although some may be moved slowly in the future. The main reason for this is to increase discoverability and add future exceptions. ([gh-22644](https://togithub.com/numpy/numpy/pull/22644)) ##### `np.linalg` functions return NamedTuples `np.linalg` functions that return tuples now return namedtuples. These functions are `eig()`, `eigh()`, `qr()`, `slogdet()`, and `svd()`. The return type is unchanged in instances where these functions return non-tuples with certain keyword arguments (like `svd(compute_uv=False)`). ([gh-22786](https://togithub.com/numpy/numpy/pull/22786)) ##### String functions in `np.char` are compatible with NEP 42 custom dtypes Custom dtypes that represent unicode strings or byte strings can now be passed to the string functions in `np.char`. ([gh-22863](https://togithub.com/numpy/numpy/pull/22863)) ##### String dtype instances can be created from the string abstract dtype classes It is now possible to create a string dtype instance with a size without using the string name of the dtype. For example, `type(np.dtype('U'))(8)` will create a dtype that is equivalent to `np.dtype('U8')`. This feature is most useful when writing generic code dealing with string dtype classes. ([gh-22963](https://togithub.com/numpy/numpy/pull/22963)) ##### Fujitsu C/C++ compiler is now supported Support for Fujitsu compiler has been added. To build with Fujitsu compiler, run: > python setup.py build -c fujitsu ##### SSL2 is now supported Support for SSL2 has been added. SSL2 is a library that provides OpenBLAS compatible GEMM functions. To enable SSL2, it need to edit site.cfg and build with Fujitsu compiler. See site.cfg.example. ([gh-22982](https://togithub.com/numpy/numpy/pull/22982)) #### Improvements ##### `NDArrayOperatorsMixin` specifies that it has no `__slots__` The `NDArrayOperatorsMixin` class now specifies that it contains no `__slots__`, ensuring that subclasses can now make use of this feature in Python. ([gh-23113](https://togithub.com/numpy/numpy/pull/23113)) ##### Fix power of complex zero `np.power` now returns a different result for `0^{non-zero}` for complex numbers. Note that the value is only defined when the real part of the exponent is larger than zero. Previously, NaN was returned unless the imaginary part was strictly zero. The return value is either `0+0j` or `0-0j`. ([gh-18535](https://togithub.com/numpy/numpy/pull/18535)) ##### New `DTypePromotionError` NumPy now has a new `DTypePromotionError` which is used when two dtypes cannot be promoted

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