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Geodels / gospl

Global Scalable Paleo Landscape Evolution Model
https://gospl.readthedocs.io
GNU General Public License v3.0
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PyUP - Dependency Update Scheduled daily dependency update on Sunday #245

Closed pyup-bot closed 6 months ago

pyup-bot commented 6 months ago

Update numpy-indexed from 0.3.5 to 0.3.7.

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

Links - PyPI: https://pypi.org/project/numpy-indexed - Repo: https://github.com/EelcoHoogendoorn/Numpy_arraysetops_EP

Update numpy from 1.19.5 to 1.26.2.

Changelog ### 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. 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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 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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 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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 ``` ### 1.25.1 ``` discovered after the 1.25.0 release. The Python versions supported by this release are 3.9-3.11. Contributors A total of 10 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Andrew Nelson - Charles Harris - Developer-Ecosystem-Engineering - Hood Chatham - Nathan Goldbaum - Rohit Goswami - Sebastian Berg - Tim Paine + - dependabot\[bot\] - matoro + Pull requests merged A total of 14 pull requests were merged for this release. - [23968](https://github.com/numpy/numpy/pull/23968): MAINT: prepare 1.25.x for further development - [24036](https://github.com/numpy/numpy/pull/24036): BLD: Port long double identification to C for meson - [24037](https://github.com/numpy/numpy/pull/24037): BUG: Fix reduction `return NULL` to be `goto fail` - [24038](https://github.com/numpy/numpy/pull/24038): BUG: Avoid undefined behavior in array.astype() - [24039](https://github.com/numpy/numpy/pull/24039): BUG: Ensure `__array_ufunc__` works without any kwargs passed - [24117](https://github.com/numpy/numpy/pull/24117): MAINT: Pin urllib3 to avoid anaconda-client bug. - [24118](https://github.com/numpy/numpy/pull/24118): TST: Pin pydantic\<2 in Pyodide workflow - [24119](https://github.com/numpy/numpy/pull/24119): MAINT: Bump pypa/cibuildwheel from 2.13.0 to 2.13.1 - [24120](https://github.com/numpy/numpy/pull/24120): MAINT: Bump actions/checkout from 3.5.2 to 3.5.3 - [24122](https://github.com/numpy/numpy/pull/24122): BUG: Multiply or Divides using SIMD without a full vector can\... - [24127](https://github.com/numpy/numpy/pull/24127): MAINT: testing for IS_MUSL closes #24074 - [24128](https://github.com/numpy/numpy/pull/24128): BUG: Only replace dtype temporarily if dimensions changed - [24129](https://github.com/numpy/numpy/pull/24129): MAINT: Bump actions/setup-node from 3.6.0 to 3.7.0 - [24134](https://github.com/numpy/numpy/pull/24134): BUG: Fix private procedures in f2py modules Checksums MD5 d09d98643db31e892fad11b8c2b7af22 numpy-1.25.1-cp310-cp310-macosx_10_9_x86_64.whl d5b8d3b0424e2af41018f35a087c4500 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numpy-1.25.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 20e1266411120a4f16fad8efa8e0454d21d00b8c7cee5b5ccad7565d95eb42dd numpy-1.25.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f76aebc3358ade9eacf9bc2bb8ae589863a4f911611694103af05346637df1b7 numpy-1.25.1-cp39-cp39-musllinux_1_1_x86_64.whl 247d3ffdd7775bdf191f848be8d49100495114c82c2bd134e8d5d075fb386a1c numpy-1.25.1-cp39-cp39-win32.whl 1d5d3c68e443c90b38fdf8ef40e60e2538a27548b39b12b73132456847f4b631 numpy-1.25.1-cp39-cp39-win_amd64.whl 35a9527c977b924042170a0887de727cd84ff179e478481404c5dc66b4170009 numpy-1.25.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 0d3fe3dd0506a28493d82dc3cf254be8cd0d26f4008a417385cbf1ae95b54004 numpy-1.25.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 012097b5b0d00a11070e8f2e261128c44157a8689f7dedcf35576e525893f4fe numpy-1.25.1-pp39-pypy39_pp73-win_amd64.whl 9a3a9f3a61480cc086117b426a8bd86869c213fc4072e606f01c4e4b66eb92bf numpy-1.25.1.tar.gz ``` ### 1.25.0 ``` 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://github.com/numpy/numpy/pull/22638)) - `np.finfo(None)` is deprecated. ([gh-23011](https://github.com/numpy/numpy/pull/23011)) - `np.round_` is deprecated. Use `np.round` instead. ([gh-23302](https://github.com/numpy/numpy/pull/23302)) - `np.product` is deprecated. Use `np.prod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.cumproduct` is deprecated. Use `np.cumprod` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.sometrue` is deprecated. Use `np.any` instead. ([gh-23314](https://github.com/numpy/numpy/pull/23314)) - `np.alltrue` is deprecated. Use `np.all` instead. ([gh-23314](https://github.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://github.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://github.com/numpy/numpy/pull/22539)) Expired deprecations - `np.core.machar` and `np.finfo.machar` have been removed. ([gh-22638](https://github.com/numpy/numpy/pull/22638)) - `+arr` will now raise an error when the dtype is not numeric (and positive is undefined). ([gh-22998](https://github.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://github.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://github.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://github.com/numpy/numpy/pull/23403)) - The `np.dual` submodule has been removed. ([gh-23480](https://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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://github.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 ``` ### 1.24.4 ``` discovered after the 1.24.3 release. It is the last planned release in the 1.24.x cycle. The Python versions supported by this release are 3.8-3.11. Contributors A total of 4 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Sebastian Berg - Hongyang Peng + Pull requests merged A total of 6 pull requests were merged for this release. - [23720](https://github.com/numpy/numpy/pull/23720): MAINT, BLD: Pin rtools to version 4.0 for Windows builds. - [23739](https://github.com/numpy/numpy/pull/23739): BUG: fix the method for checking local files for 1.24.x - [23760](https://github.com/numpy/numpy/pull/23760): MAINT: Copy rtools installation from install-rtools. - [23761](https://github.com/numpy/numpy/pull/23761): BUG: Fix masked array ravel order for A (and somewhat K) - [23890](https://github.com/numpy/numpy/pull/23890): TYP,DOC: Annotate and document the `metadata` parameter of\... - [23994](https://github.com/numpy/numpy/pull/23994): MAINT: Update rtools installation Checksums MD5 25049e3aee79dde29e7a498d3ad13379 numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl 579b5c357c918feaef4af03af8afb721 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6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835 numpy-1.24.4-cp39-cp39-win32.whl befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8 numpy-1.24.4-cp39-cp39-win_amd64.whl 31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2 numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl 80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463 numpy-1.24.4.tar.gz ``` ### 1.24.3 ``` discovered after the 1.24.2 release. The Python versions supported by this release are 3.8-3.11. Contributors A total of 12 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Aleksei Nikiforov + - Alexander Heger - Bas van Beek - Bob Eldering - Brock Mendel - Charles Harris - Kyle Sunden - Peter Hawkins - Rohit Goswami - Sebastian Berg - Warren Weckesser - dependabot\[bot\] Pull requests merged A total of 17 pull requests were merged for this release. - [23206](https://github.com/numpy/numpy/pull/23206): BUG: fix for f2py string scalars (#23194) - [23207](https://github.com/numpy/numpy/pull/23207): BUG: datetime64/timedelta64 comparisons return NotImplemented - [23208](https://github.com/numpy/numpy/pull/23208): MAINT: Pin matplotlib to version 3.6.3 for refguide checks - [23221](https://github.com/numpy/numpy/pull/23221): DOC: Fix matplotlib error in documentation - [23226](https://github.com/numpy/numpy/pull/23226): CI: Ensure submodules are initialized in gitpod. - [23341](https://github.com/numpy/numpy/pull/23341): TYP: Replace duplicate reduce in ufunc type signature with reduceat. - [23342](https://github.com/numpy/numpy/pull/23342): TYP: Remove duplicate CLIP/WRAP/RAISE in `__init__.pyi`. - [23343](https://github.com/numpy/numpy/pull/23343): TYP: Mark `d` argument to fftfreq and rfftfreq as optional\... - [23344](https://github.com/numpy/numpy/pull/23344): TYP: Add type annotations for comparison operators to MaskedArray. - [23345](https://github.com/numpy/numpy/pull/23345): TYP: Remove some stray type-check-only imports of `msort` - [23370](https://github.com/numpy/numpy/pull/23370): BUG: Ensure like is only stripped for `like=` dispatched functions - [23543](https://github.com/numpy/numpy/pull/23543): BUG: fix loading and storing big arrays on s390x - [23544](https://github.com/numpy/numpy/pull/23544): MAINT: Bump larsoner/circleci-artifacts-redirector-action - [23634](https://github.com/numpy/numpy/pull/23634): BUG: Ignore invalid and overflow warnings in masked setitem - [23635](https://github.com/numpy/numpy/pull/23635): BUG: Fix masked array raveling when `order="A"` or `order="K"` - [23636](https://github.com/numpy/numpy/pull/23636): MAINT: Update conftest for newer hypothesis versions - [23637](https://github.com/numpy/numpy/pull/23637): BUG: Fix bug in parsing F77 style string arrays. 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