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cggh / scikit-allel

A Python package for exploring and analysing genetic variation data
MIT License
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Scheduled monthly dependency update for December #389

Closed pyup-bot closed 1 year ago

pyup-bot commented 1 year ago

Update cython from 0.29.24 to 0.29.32.

Changelog ### 0.29.32 ``` ==================== Bugs fixed ---------- * Revert "Using memoryview typed arguments in inner functions is now rejected as unsupported." Patch by David Woods. (Github issue :issue:`4798`) * ``from module import *`` failed in 0.29.31 when using memoryviews. Patch by David Woods. (Github issue :issue:`4927`) ``` ### 0.29.31 ``` ==================== Features added -------------- * A new argument ``--module-name`` was added to the ``cython`` command to provide the (one) exact target module name from the command line. Patch by Matthew Brett and h-vetinari. (Github issue :issue:`4906`) Bugs fixed ---------- * Use ``importlib.util.find_spec()`` instead of the deprecated ``importlib.find_loader()`` function when setting up the package path at import-time. Patch by Matti Picus. (Github issue :issue:`4764`) * Require the C compiler to support the two-arg form of ``va_start`` on Python 3.10 and higher. Patch by Thomas Caswell. (Github issue :issue:`4820`) * Make ``fused_type`` subscriptable in Shadow.py. Patch by Pfebrer. (Github issue :issue:`4842`) * Fix the incorrect code generation of the target type in ``bytearray`` loops. Patch by Kenrick Everett. (Github issue :issue:`4108`) * Atomic refcounts for memoryviews were not used on some GCC versions by accident. Patch by Sam Gross. (Github issue :issue:`4915`) * Silence some GCC ``-Wconversion`` warnings in C utility code. Patch by Lisandro Dalcin. (Github issue :issue:`4854`) * Tuple multiplication was ignored in expressions such as ``[*(1,) * 2]``. Patch by David Woods. (Github issue :issue:`4864`) * Calling ``append`` methods on extension types could fail to find the method in some cases. Patch by David Woods. (Github issue :issue:`4828`) * Ensure that object buffers (e.g. ``ndarray[object, ndim=1]``) containing ``NULL`` pointers are safe to use, returning ``None`` instead of the ``NULL`` pointer. Patch by Sebastian Berg. (Github issue :issue:`4859`) * Using memoryview typed arguments in inner functions is now rejected as unsupported. Patch by David Woods. (Github issue :issue:`4798`) * Compilation could fail on systems (e.g. FIPS) that block MD5 checksums at runtime. (Github issue :issue:`4909`) * Experimental adaptations for the CPython "nogil" fork was added. Note that there is no official support for this in Cython 0.x. Patch by Sam Gross. (Github issue :issue:`4912`) ``` ### 0.29.30 ``` ==================== Bugs fixed ---------- * The GIL handling changes in 0.29.29 introduced a regression where objects could be deallocated without holding the GIL. (Github issue :issue:`4796`) ``` ### 0.29.29 ``` ==================== Features added -------------- * Avoid acquiring the GIL at the end of nogil functions. This change was backported in order to avoid generating wrong C code that would trigger C compiler warnings with tracing support enabled. Backport by Oleksandr Pavlyk. (Github issue :issue:`4637`) Bugs fixed ---------- * Function definitions in ``finally:`` clauses were not correctly generated. Patch by David Woods. (Github issue :issue:`4651`) * A case where C-API functions could be called with a live exception set was fixed. Patch by Jakub Kulík. (Github issue :issue:`4722`) * Pickles can now be exchanged again with those generated from Cython 3.0 modules. (Github issue :issue:`4680`) * Cython now correctly generates Python methods for both the provided regular and reversed special numeric methods of extension types. Patch by David Woods. (Github issue :issue:`4750`) * Calling unbound extension type methods without arguments could raise an ``IndexError`` instead of a ``TypeError``. Patch by David Woods. (Github issue :issue:`4779`) * Calling unbound ``.__contains__()`` super class methods on some builtin base types could trigger an infinite recursion. Patch by David Woods. (Github issue :issue:`4785`) * The C union type in pure Python mode mishandled some field names. Patch by Jordan Brière. (Github issue :issue:`4727`) * Allow users to overwrite the C macro ``_USE_MATH_DEFINES``. Patch by Yuriy Chernyshov. (Github issue :issue:`4690`) * Improved compatibility with CPython 3.10/11. Patches by Thomas Caswell, David Woods. (Github issues :issue:`4609`, :issue:`4667`, :issue:`4721`, :issue:`4730`, :issue:`4777`) * Docstrings of descriptors are now provided in PyPy 7.3.9. Patch by Matti Picus. (Github issue :issue:`4701`) ``` ### 0.29.28 ``` ==================== Bugs fixed ---------- * Due to backwards incompatible changes in CPython 3.11a4, the feature flags ``CYTHON_FAST_THREAD_STATE`` and ``CYTHON_USE_EXC_INFO_STACK`` are now disabled in Python 3.11 and later. They are enabled again in Cython 3.0. Patch by David Woods. (Github issue :issue:`4610`) * A C compiler warning in older PyPy versions was resolved. Patch by Matti Picus. (Github issue :issue:`4236`) ``` ### 0.29.27 ``` ==================== Features added -------------- * The ``cythonize`` command has a new option ``-M`` to generate ``.dep`` dependency files for the compilation unit. This can be used by external build tools to track these dependencies. Patch by Evgeni Burovski. (Github issue :issue:`1214`) Bugs fixed ---------- * Compilation failures on PyPy were resolved. Patches by Matti Picus. (Github issues :issue:`4509`, :issue:`4517`) * Calls to ``range()`` with more than three arguments did not fail. Original patch by Max Bachmann. (Github issue :issue:`4550`) * Some C compiler warnings about missing type struct initialisers in Py3.10 were resolved. * Cython no longer warns about using OpenMP 3.0 features since they are now considered generally available. ``` ### 0.29.26 ``` ==================== Bugs fixed ---------- * An incompatibility with CPython 3.11.0a3 was resolved. (Github issue :issue:`4499`) * The ``in`` operator failed on literal lists with starred expressions. Patch by Arvind Natarajan. (Github issue :issue:`3938`) * A C compiler warning in PyPy about a missing struct field initialisation was resolved. ``` ### 0.29.25 ``` ==================== Bugs fixed ---------- * Several incompatibilities with CPython 3.11 were resolved. Patches by David Woods, Victor Stinner, Thomas Caswell. (Github issues :issue:`4411`, :issue:`4414`, :issue:`4415`, :issue:`4416`, :issue:`4420`, :issue:`4428`, :issue:`4473`, :issue:`4479`, :issue:`4480`) * Some C compiler warnings were resolved. Patches by Lisandro Dalcin and others. (Github issue :issue:`4439`) * C++ ``std::move()`` should only be used automatically in MSVC versions that support it. Patch by Max Bachmann. (Github issue :issue:`4191`) * The ``Py_hash_t`` type failed to accept arbitrary "index" values. (Github issue :issue:`2752`) * Avoid copying unaligned 16-bit values since some platforms require them to be aligned. Use memcpy() instead to let the C compiler decide how to do it. (Github issue :issue:`4343`) * Cython crashed on invalid truthiness tests on C++ types without ``operator bool``. Patch by David Woods. (Github issue :issue:`4348`) * The declaration of ``PyUnicode_CompareWithASCIIString()`` in ``cpython.unicode`` was incorrect. Patch by Max Bachmann. (Github issue :issue:`4344`) ```
Links - PyPI: https://pypi.org/project/cython - Changelog: https://pyup.io/changelogs/cython/ - Homepage: http://cython.org/

Update numpy from 1.21.2 to 1.23.5.

Changelog ### 1.23.5 ``` the 1.23.4 release and keeps the build infrastructure current. The Python versions supported for this release are 3.8-3.11. Contributors A total of 7 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - \DWesl - Aayush Agrawal + - Adam Knapp + - Charles Harris - Navpreet Singh + - Sebastian Berg - Tania Allard Pull requests merged A total of 10 pull requests were merged for this release. - [22489](https://github.com/numpy/numpy/pull/22489): TST, MAINT: Replace most setup with setup_method (also teardown) - [22490](https://github.com/numpy/numpy/pull/22490): MAINT, CI: Switch to cygwin/cygwin-install-actionv2 - [22494](https://github.com/numpy/numpy/pull/22494): TST: Make test_partial_iteration_cleanup robust but require leak\... - [22592](https://github.com/numpy/numpy/pull/22592): MAINT: Ensure graceful handling of large header sizes - [22593](https://github.com/numpy/numpy/pull/22593): TYP: Spelling alignment for array flag literal - [22594](https://github.com/numpy/numpy/pull/22594): BUG: Fix bounds checking for `random.logseries` - [22595](https://github.com/numpy/numpy/pull/22595): DEV: Update GH actions and Dockerfile for Gitpod - [22596](https://github.com/numpy/numpy/pull/22596): CI: Only fetch in actions/checkout - [22597](https://github.com/numpy/numpy/pull/22597): BUG: Decrement ref count in gentype_reduce if allocated memory\... - [22625](https://github.com/numpy/numpy/pull/22625): BUG: Histogramdd breaks on big arrays in Windows Checksums MD5 8a412b79d975199cefadb465279fd569 numpy-1.23.5-cp310-cp310-macosx_10_9_x86_64.whl 1b56e8e6a0516c78473657abf0710538 numpy-1.23.5-cp310-cp310-macosx_11_0_arm64.whl c787f4763c9a5876e86a17f1651ba458 numpy-1.23.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl db07645022e56747ba3f00c2d742232e numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c63a6fb7cc16a13aabc82ec57ac6bb4d numpy-1.23.5-cp310-cp310-win32.whl 3fea9247e1d812600015641941fa273f numpy-1.23.5-cp310-cp310-win_amd64.whl 4222cfb36e5ac9aec348c81b075e2c05 numpy-1.23.5-cp311-cp311-macosx_10_9_x86_64.whl 6c7102f185b310ac70a62c13d46f04e6 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1b1766d6f397c18153d40015ddfc79ddb715cabadc04d2d228d4e5a8bc4ded1a numpy-1.23.5.tar.gz ``` ### 1.23.4 ``` the 1.23.3 release and keeps the build infrastructure current. The main improvements are fixes for some annotation corner cases, a fix for a long time `nested_iters` memory leak, and a fix of complex vector dot for very large arrays. The Python versions supported for this release are 3.8-3.11. Note that the mypy version needs to be 0.981+ if you test using Python 3.10.7, otherwise the typing tests will fail. Contributors A total of 8 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Matthew Barber - Matti Picus - Ralf Gommers - Ross Barnowski - Sebastian Berg - Sicheng Zeng + Pull requests merged A total of 13 pull requests were merged for this release. - [22368](https://github.com/numpy/numpy/pull/22368): BUG: Add `__array_api_version__` to `numpy.array_api` namespace - [22370](https://github.com/numpy/numpy/pull/22370): MAINT: update sde toolkit to 9.0, fix download link - [22382](https://github.com/numpy/numpy/pull/22382): BLD: use macos-11 image on azure, macos-1015 is deprecated - [22383](https://github.com/numpy/numpy/pull/22383): MAINT: random: remove `get_info` from \"extending with Cython\"\... - [22384](https://github.com/numpy/numpy/pull/22384): BUG: Fix complex vector dot with more than NPY_CBLAS_CHUNK elements - [22387](https://github.com/numpy/numpy/pull/22387): REV: Loosen `lookfor`\'s import try/except again - [22388](https://github.com/numpy/numpy/pull/22388): TYP,ENH: Mark `numpy.typing` protocols as runtime checkable - [22389](https://github.com/numpy/numpy/pull/22389): TYP,MAINT: Change more overloads to play nice with pyright - [22390](https://github.com/numpy/numpy/pull/22390): TST,TYP: Bump mypy to 0.981 - [22391](https://github.com/numpy/numpy/pull/22391): DOC: Update delimiter param description. - [22392](https://github.com/numpy/numpy/pull/22392): BUG: Memory leaks in numpy.nested_iters - [22413](https://github.com/numpy/numpy/pull/22413): REL: Prepare for the NumPy 1.23.4 release. - [22424](https://github.com/numpy/numpy/pull/22424): TST: Fix failing aarch64 wheel builds. 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95de7dc7dc47a312f6feddd3da2500826defdccbc41608d0031276a24181a2c0 numpy-1.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2f390aa4da44454db40a1f0201401f9036e8d578a25f01a6e237cea238337ef numpy-1.23.4-cp39-cp39-win32.whl f260da502d7441a45695199b4e7fd8ca87db659ba1c78f2bbf31f934fe76ae0e numpy-1.23.4-cp39-cp39-win_amd64.whl 61be02e3bf810b60ab74e81d6d0d36246dbfb644a462458bb53b595791251911 numpy-1.23.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 296d17aed51161dbad3c67ed6d164e51fcd18dbcd5dd4f9d0a9c6055dce30810 numpy-1.23.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4d52914c88b4930dafb6c48ba5115a96cbab40f45740239d9f4159c4ba779962 numpy-1.23.4-pp38-pypy38_pp73-win_amd64.whl ed2cc92af0efad20198638c69bb0fc2870a58dabfba6eb722c933b48556c686c numpy-1.23.4.tar.gz ``` ### 1.23.3 ``` the 1.23.2 release. There is no major theme for this release, the main improvements are for some downstream builds and some annotation corner cases. The Python versions supported for this release are 3.8-3.11. Note that we will move to MacOS 11 for the NumPy 1.23.4 release, the ``` ### 1.23.2 ``` the 1.23.1 release. Notable features are: - Typing changes needed for Python 3.11 - Wheels for Python 3.11.0rc1 The Python versions supported for this release are 3.8-3.11. Contributors A total of 9 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Alexander Grund + - Bas van Beek - Charles Harris - Jon Cusick + - Matti Picus - Michael Osthege + - Pal Barta + - Ross Barnowski - Sebastian Berg Pull requests merged A total of 15 pull requests were merged for this release. - [22030](https://github.com/numpy/numpy/pull/22030): ENH: Add `__array_ufunc__` typing support to the `nin=1` ufuncs - [22031](https://github.com/numpy/numpy/pull/22031): MAINT, TYP: Fix `np.angle` dtype-overloads - [22032](https://github.com/numpy/numpy/pull/22032): MAINT: Do not let `_GenericAlias` wrap the underlying classes\'\... - [22033](https://github.com/numpy/numpy/pull/22033): TYP,MAINT: Allow `einsum` subscripts to be passed via integer\... - [22034](https://github.com/numpy/numpy/pull/22034): MAINT,TYP: Add object-overloads for the `np.generic` rich comparisons - [22035](https://github.com/numpy/numpy/pull/22035): MAINT,TYP: Allow the `squeeze` and `transpose` method to\... - [22036](https://github.com/numpy/numpy/pull/22036): BUG: Fix subarray to object cast ownership details - [22037](https://github.com/numpy/numpy/pull/22037): BUG: Use `Popen` to silently invoke f77 -v - [22038](https://github.com/numpy/numpy/pull/22038): BUG: Avoid errors on NULL during deepcopy - [22039](https://github.com/numpy/numpy/pull/22039): DOC: Add versionchanged for converter callable behavior. - [22057](https://github.com/numpy/numpy/pull/22057): MAINT: Quiet the anaconda uploads. - [22078](https://github.com/numpy/numpy/pull/22078): ENH: reorder includes for testing on top of system installations\... - [22106](https://github.com/numpy/numpy/pull/22106): TST: fix test_linear_interpolation_formula_symmetric - [22107](https://github.com/numpy/numpy/pull/22107): BUG: Fix skip condition for test_loss_of_precision\[complex256\] - [22115](https://github.com/numpy/numpy/pull/22115): BLD: Build python3.11.0rc1 wheels. 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c403c81bb8ffb1c993d0165a11493fd4bf1353d258f6997b3ee288b0a48fce77 numpy-1.23.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cf8c6aed12a935abf2e290860af8e77b26a042eb7f2582ff83dc7ed5f963340c numpy-1.23.2-cp39-cp39-win32.whl 5e28cd64624dc2354a349152599e55308eb6ca95a13ce6a7d5679ebff2962913 numpy-1.23.2-cp39-cp39-win_amd64.whl 806970e69106556d1dd200e26647e9bee5e2b3f1814f9da104a943e8d548ca38 numpy-1.23.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 2bd879d3ca4b6f39b7770829f73278b7c5e248c91d538aab1e506c628353e47f numpy-1.23.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl be6b350dfbc7f708d9d853663772a9310783ea58f6035eec649fb9c4371b5389 numpy-1.23.2-pp38-pypy38_pp73-win_amd64.whl b78d00e48261fbbd04aa0d7427cf78d18401ee0abd89c7559bbf422e5b1c7d01 numpy-1.23.2.tar.gz ``` ### 1.23.1 ``` The NumPy 1.23.1 is a maintenance release that fixes bugs discovered after the 1.23.0 release. Notable fixes are: - Fix searchsorted for float16 NaNs - Fix compilation on Apple M1 - Fix KeyError in crackfortran operator support (Slycot) The Python version supported for this release are 3.8-3.10. Contributors A total of 7 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Charles Harris - Matthias Koeppe + - Pranab Das + - Rohit Goswami - Sebastian Berg - Serge Guelton - Srimukh Sripada + Pull requests merged A total of 8 pull requests were merged for this release. - [21866](https://github.com/numpy/numpy/pull/21866): BUG: Fix discovered MachAr (still used within valgrind) - [21867](https://github.com/numpy/numpy/pull/21867): BUG: Handle NaNs correctly for float16 during sorting - [21868](https://github.com/numpy/numpy/pull/21868): BUG: Use `keepdims` during normalization in `np.average` and\... - [21869](https://github.com/numpy/numpy/pull/21869): DOC: mention changes to `max_rows` behaviour in `np.loadtxt` - [21870](https://github.com/numpy/numpy/pull/21870): BUG: Reject non integer array-likes with size 1 in delete - [21949](https://github.com/numpy/numpy/pull/21949): BLD: Make can_link_svml return False for 32bit builds on x86_64 - [21951](https://github.com/numpy/numpy/pull/21951): BUG: Reorder extern \"C\" to only apply to function declarations\... - [21952](https://github.com/numpy/numpy/pull/21952): BUG: Fix KeyError in crackfortran operator support Checksums MD5 79f0d8c114f282b834b49209d6955f98 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 42a89a88ef26b768e8933ce46b1cc2bd numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl 1c1d68b3483eaf99b9a3583c8ac8bf47 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9d3e9f7f9b3dce6cf15209e4f25f346e numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a9afb7c34b48d08fc50427ae6516b42d numpy-1.23.1-cp310-cp310-win32.whl a0e02823883bdfcec49309e108f65e13 numpy-1.23.1-cp310-cp310-win_amd64.whl f40cdf4ec7bb0cf31a90a4fa294323c2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 80115a959f0fe30d6c401b2650a61c70 numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 1cf199b3a93960c4f269853a56a8d8eb numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl aa6f0f192312c79cd770c2c395e9982a numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d07bee0ea3142a96cb5e4e16aca273ca numpy-1.23.1-cp38-cp38-win32.whl 02d0734ae8ad5e18a40c6c6de18486a0 numpy-1.23.1-cp38-cp38-win_amd64.whl e1ca14acd7d83bc74bdf6ab0bb4bd195 numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl c9152c62b2f31e742e24bfdc97b28666 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 05b0b37c92f7a7e7c01afac0a5322b40 numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl d9810bb71a0ef9837e87ea5c44fcab5e numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4255577f857e838f7a94e3a614ddc5eb numpy-1.23.1-cp39-cp39-win32.whl 787486e3cd87b98024ffe1c969c4db7a numpy-1.23.1-cp39-cp39-win_amd64.whl 5c7b2d1471b1b9ec6ff1cb3fe1f8ac14 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 40d5b2ff869707b0d97325ce44631135 numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 44ce1e07927cc09415df9898857792da numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl 4f8636a9c1a77ca0fb923ba55378891f numpy-1.23.1.tar.gz SHA256 b15c3f1ed08df4980e02cc79ee058b788a3d0bef2fb3c9ca90bb8cbd5b8a3a04 numpy-1.23.1-cp310-cp310-macosx_10_9_x86_64.whl 9ce242162015b7e88092dccd0e854548c0926b75c7924a3495e02c6067aba1f5 numpy-1.23.1-cp310-cp310-macosx_11_0_arm64.whl e0d7447679ae9a7124385ccf0ea990bb85bb869cef217e2ea6c844b6a6855073 numpy-1.23.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3119daed207e9410eaf57dcf9591fdc68045f60483d94956bee0bfdcba790953 numpy-1.23.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3ab67966c8d45d55a2bdf40701536af6443763907086c0a6d1232688e27e5447 numpy-1.23.1-cp310-cp310-win32.whl 1865fdf51446839ca3fffaab172461f2b781163f6f395f1aed256b1ddc253622 numpy-1.23.1-cp310-cp310-win_amd64.whl aeba539285dcf0a1ba755945865ec61240ede5432df41d6e29fab305f4384db2 numpy-1.23.1-cp38-cp38-macosx_10_9_x86_64.whl 7e8229f3687cdadba2c4faef39204feb51ef7c1a9b669247d49a24f3e2e1617c numpy-1.23.1-cp38-cp38-macosx_11_0_arm64.whl 68b69f52e6545af010b76516f5daaef6173e73353e3295c5cb9f96c35d755641 numpy-1.23.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 1408c3527a74a0209c781ac82bde2182b0f0bf54dea6e6a363fe0cc4488a7ce7 numpy-1.23.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 47f10ab202fe4d8495ff484b5561c65dd59177949ca07975663f4494f7269e3e numpy-1.23.1-cp38-cp38-win32.whl 37e5ebebb0eb54c5b4a9b04e6f3018e16b8ef257d26c8945925ba8105008e645 numpy-1.23.1-cp38-cp38-win_amd64.whl 173f28921b15d341afadf6c3898a34f20a0569e4ad5435297ba262ee8941e77b numpy-1.23.1-cp39-cp39-macosx_10_9_x86_64.whl 876f60de09734fbcb4e27a97c9a286b51284df1326b1ac5f1bf0ad3678236b22 numpy-1.23.1-cp39-cp39-macosx_11_0_arm64.whl 35590b9c33c0f1c9732b3231bb6a72d1e4f77872390c47d50a615686ae7ed3fd numpy-1.23.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a35c4e64dfca659fe4d0f1421fc0f05b8ed1ca8c46fb73d9e5a7f175f85696bb numpy-1.23.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c2f91f88230042a130ceb1b496932aa717dcbd665350beb821534c5c7e15881c numpy-1.23.1-cp39-cp39-win32.whl 37ece2bd095e9781a7156852e43d18044fd0d742934833335599c583618181b9 numpy-1.23.1-cp39-cp39-win_amd64.whl 8002574a6b46ac3b5739a003b5233376aeac5163e5dcd43dd7ad062f3e186129 numpy-1.23.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 5d732d17b8a9061540a10fda5bfeabca5785700ab5469a5e9b93aca5e2d3a5fb numpy-1.23.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 55df0f7483b822855af67e38fb3a526e787adf189383b4934305565d71c4b148 numpy-1.23.1-pp38-pypy38_pp73-win_amd64.whl d748ef349bfef2e1194b59da37ed5a29c19ea8d7e6342019921ba2ba4fd8b624 numpy-1.23.1.tar.gz ``` ### 1.23.0 ``` The NumPy 1.23.0 release continues the ongoing work to improve the handling and promotion of dtypes, increase the execution speed, clarify the documentation, and expire old deprecations. The highlights are: - Implementation of `loadtxt` in C, greatly improving its performance. - Exposing DLPack at the Python level for easy data exchange. - Changes to the promotion and comparisons of structured dtypes. - Improvements to f2py. See below for the details, New functions - A masked array specialization of `ndenumerate` is now available as `numpy.ma.ndenumerate`. It provides an alternative to `numpy.ndenumerate` and skips masked values by default. ([gh-20020](https://github.com/numpy/numpy/pull/20020)) - `numpy.from_dlpack` has been added to allow easy exchange of data using the DLPack protocol. It accepts Python objects that implement the `__dlpack__` and `__dlpack_device__` methods and returns a ndarray object which is generally the view of the data of the input object. ([gh-21145](https://github.com/numpy/numpy/pull/21145)) Deprecations - Setting `__array_finalize__` to `None` is deprecated. It must now be a method and may wish to call `super().__array_finalize__(obj)` after checking for `None` or if the NumPy version is sufficiently new. ([gh-20766](https://github.com/numpy/numpy/pull/20766)) - Using `axis=32` (`axis=np.MAXDIMS`) in many cases had the same meaning as `axis=None`. This is deprecated and `axis=None` must be used instead. ([gh-20920](https://github.com/numpy/numpy/pull/20920)) - The hook function `PyDataMem_SetEventHook` has been deprecated and the demonstration of its use in tool/allocation_tracking has been removed. The ability to track allocations is now built-in to python via `tracemalloc`. ([gh-20394](https://github.com/numpy/numpy/pull/20394)) - `numpy.distutils` has been deprecated, as a result of `distutils` itself being deprecated. It will not be present in NumPy for Python >= 3.12, and will be removed completely 2 years after the release of Python 3.12 For more details, see `distutils-status-migration`{.interpreted-text role="ref"}. ([gh-20875](https://github.com/numpy/numpy/pull/20875)) Expired deprecations - The `NpzFile.iteritems()` and `NpzFile.iterkeys()` methods have been removed as part of the continued removal of Python 2 compatibility. This concludes the deprecation from 1.15. ([gh-16830](https://github.com/numpy/numpy/pull/16830)) - The `alen` and `asscalar` functions have been removed. ([gh-20414](https://github.com/numpy/numpy/pull/20414)) - The `UPDATEIFCOPY` array flag has been removed together with the enum `NPY_ARRAY_UPDATEIFCOPY`. The associated (and deprecated) `PyArray_XDECREF_ERR` was also removed. These were all deprecated in 1.14. They are replaced by `WRITEBACKIFCOPY`, that requires calling `PyArray_ResoveWritebackIfCopy` before the array is deallocated. ([gh-20589](https://github.com/numpy/numpy/pull/20589)) - Exceptions will be raised during array-like creation. When an object raised an exception during access of the special attributes `__array__` or `__array_interface__`, this exception was usually ignored. This behaviour was deprecated in 1.21, and the exception will now be raised. ([gh-20835](https://github.com/numpy/numpy/pull/20835)) - Multidimensional indexing with non-tuple values is not allowed. Previously, code such as `arr[ind]` where `ind = [[0, 1], [0, 1]]` produced a `FutureWarning` and was interpreted as a multidimensional index (i.e., `arr[tuple(ind)]`). Now this example is treated like an array index over a single dimension (`arr[array(ind)]`). Multidimensional indexing with anything but a tuple was deprecated in NumPy 1.15. ([gh-21029](https://github.com/numpy/numpy/pull/21029)) - Changing to a dtype of different size in F-contiguous arrays is no longer permitted. Deprecated since Numpy 1.11.0. See below for an extended explanation of the effects of this change. ([gh-20722](https://github.com/numpy/numpy/pull/20722)) New Features crackfortran has support for operator and assignment overloading `crackfortran` parser now understands operator and assignment definitions in a module. They are added in the `body` list of the module which contains a new key `implementedby` listing the names of the subroutines or functions implementing the operator or assignment. ([gh-15006](https://github.com/numpy/numpy/pull/15006)) f2py supports reading access type attributes from derived type statements As a result, one does not need to use `public` or `private` statements to specify derived type access properties. ([gh-15844](https://github.com/numpy/numpy/pull/15844)) New parameter `ndmin` added to `genfromtxt` This parameter behaves the same as `ndmin` from `numpy.loadtxt`. ([gh-20500](https://github.com/numpy/numpy/pull/20500)) `np.loadtxt` now supports quote character and single converter function `numpy.loadtxt` now supports an additional `quotechar` keyword argument which is not set by default. Using `quotechar='"'` will read quoted fields as used by the Excel CSV dialect. Further, it is now possible to pass a single callable rather than a dictionary for the `converters` argument. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Changing to dtype of a different size now requires contiguity of only the last axis Previously, viewing an array with a dtype of a different item size required that the entire array be C-contiguous. This limitation would unnecessarily force the user to make contiguous copies of non-contiguous arrays before being able to change the dtype. This change affects not only `ndarray.view`, but other construction mechanisms, including the discouraged direct assignment to `ndarray.dtype`. This change expires the deprecation regarding the viewing of F-contiguous arrays, described elsewhere in the release notes. ([gh-20722](https://github.com/numpy/numpy/pull/20722)) Deterministic output files for F2PY For F77 inputs, `f2py` will generate `modname-f2pywrappers.f` unconditionally, though these may be empty. For free-form inputs, `modname-f2pywrappers.f`, `modname-f2pywrappers2.f90` will both be generated unconditionally, and may be empty. This allows writing generic output rules in `cmake` or `meson` and other build systems. Older behavior can be restored by passing `--skip-empty-wrappers` to `f2py`. `f2py-meson`{.interpreted-text role="ref"} details usage. ([gh-21187](https://github.com/numpy/numpy/pull/21187)) `keepdims` parameter for `average` The parameter `keepdims` was added to the functions `numpy.average` and `numpy.ma.average`. The parameter has the same meaning as it does in reduction functions such as `numpy.sum` or `numpy.mean`. ([gh-21485](https://github.com/numpy/numpy/pull/21485)) Compatibility notes 1D `np.linalg.norm` preserves float input types, even for scalar results Previously, this would promote to `float64` when the `ord` argument was not one of the explicitly listed values, e.g. `ord=3`: >>> f32 = np.float32([1, 2]) >>> np.linalg.norm(f32, 2).dtype dtype('float32') >>> np.linalg.norm(f32, 3) dtype('float64') numpy 1.22 dtype('float32') numpy 1.23 This change affects only `float32` and `float16` vectors with `ord` other than `-Inf`, `0`, `1`, `2`, and `Inf`. ([gh-17709](https://github.com/numpy/numpy/pull/17709)) Changes to structured (void) dtype promotion and comparisons In general, NumPy now defines correct, but slightly limited, promotion for structured dtypes by promoting the subtypes of each field instead of raising an exception: >>> np.result_type(np.dtype("i,i"), np.dtype("i,d")) dtype([('f0', '<i4'), ('f1', '<f8')]) For promotion matching field names, order, and titles are enforced, however padding is ignored. Promotion involving structured dtypes now always ensures native byte-order for all fields (which may change the result of `np.concatenate`) and ensures that the result will be \"packed\", i.e. all fields are ordered contiguously and padding is removed. See `structured_dtype_comparison_and_promotion`{.interpreted-text role="ref"} for further details. The `repr` of aligned structures will now never print the long form including `offsets` and `itemsize` unless the structure includes padding not guaranteed by `align=True`. In alignment with the above changes to the promotion logic, the casting safety has been updated: - `"equiv"` enforces matching names and titles. The itemsize is allowed to differ due to padding. - `"safe"` allows mismatching field names and titles - The cast safety is limited by the cast safety of each included field. - The order of fields is used to decide cast safety of each individual field. Previously, the field names were used and only unsafe casts were possible when names mismatched. The main important change here is that name mismatches are now considered \"safe\" casts. ([gh-19226](https://github.com/numpy/numpy/pull/19226)) `NPY_RELAXED_STRIDES_CHECKING` has been removed NumPy cannot be compiled with `NPY_RELAXED_STRIDES_CHECKING=0` anymore. Relaxed strides have been the default for many years and the option was initially introduced to allow a smoother transition. ([gh-20220](https://github.com/numpy/numpy/pull/20220)) `np.loadtxt` has recieved several changes The row counting of `numpy.loadtxt` was fixed. `loadtxt` ignores fully empty lines in the file, but counted them towards `max_rows`. When `max_rows` is used and the file contains empty lines, these will now not be counted. Previously, it was possible that the result contained fewer than `max_rows` rows even though more data was available to be read. If the old behaviour is required, `itertools.islice` may be used: import itertools lines = itertools.islice(open("file"), 0, max_rows) result = np.loadtxt(lines, ...) While generally much faster and improved, `numpy.loadtxt` may now fail to converter certain strings to numbers that were previously successfully read. The most important cases for this are: - Parsing floating point values such as `1.0` into integers will now fail - Parsing hexadecimal floats such as `0x3p3` will fail - An `_` was previously accepted as a thousands delimiter `100_000`. This will now result in an error. If you experience these limitations, they can all be worked around by passing appropriate `converters=`. NumPy now supports passing a single converter to be used for all columns to make this more convenient. For example, `converters=float.fromhex` can read hexadecimal float numbers and `converters=int` will be able to read `100_000`. Further, the error messages have been generally improved. However, this means that error types may differ. In particularly, a `ValueError` is now always raised when parsing of a single entry fails. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Improvements `ndarray.__array_finalize__` is now callable This means subclasses can now use `super().__array_finalize__(obj)` without worrying whether `ndarray` is their superclass or not. The actual call remains a no-op. ([gh-20766](https://github.com/numpy/numpy/pull/20766)) Add support for VSX4/Power10 With VSX4/Power10 enablement, the new instructions available in Power ISA 3.1 can be used to accelerate some NumPy operations, e.g., floor_divide, modulo, etc. ([gh-20821](https://github.com/numpy/numpy/pull/20821)) `np.fromiter` now accepts objects and subarrays The `numpy.fromiter` function now supports object and subarray dtypes. Please see he function documentation for examples. ([gh-20993](https://github.com/numpy/numpy/pull/20993)) Math C library feature detection now uses correct signatures Compiling is preceded by a detection phase to determine whether the underlying libc supports certain math operations. Previously this code did not respect the proper signatures. Fixing this enables compilation for the `wasm-ld` backend (compilation for web assembly) and reduces the number of warnings. ([gh-21154](https://github.com/numpy/numpy/pull/21154)) `np.kron` now maintains subclass information `np.kron` maintains subclass information now such as masked arrays while computing the Kronecker product of the inputs python >>> x = ma.array([[1, 2], [3, 4]], mask=[[0, 1], [1, 0]]) >>> np.kron(x,x) masked_array( data=[[1, --, --, --], [--, 4, --, --], [--, --, 4, --], [--, --, --, 16]], mask=[[False, True, True, True], [ True, False, True, True], [ True, True, False, True], [ True, True, True, False]], fill_value=999999) :warning: Warning, `np.kron` output now follows `ufunc` ordering (`multiply`) to determine the output class type python >>> class myarr(np.ndarray): >>> __array_priority__ = -1 >>> a = np.ones([2, 2]) >>> ma = myarray(a.shape, a.dtype, a.data) >>> type(np.kron(a, ma)) == np.ndarray False Before it was True >>> type(np.kron(a, ma)) == myarr True ([gh-21262](https://github.com/numpy/numpy/pull/21262)) Performance improvements and changes Faster `np.loadtxt` `numpy.loadtxt` is now generally much faster than previously as most of it is now implemented in C. ([gh-20580](https://github.com/numpy/numpy/pull/20580)) Faster reduction operators Reduction operations like `numpy.sum`, `numpy.prod`, `numpy.add.reduce`, `numpy.logical_and.reduce` on contiguous integer-based arrays are now much faster. ([gh-21001](https://github.com/numpy/numpy/pull/21001)) Faster `np.where` `numpy.where` is now much faster than previously on unpredictable/random input data. ([gh-21130](https://github.com/numpy/numpy/pull/21130)) Faster operations on NumPy scalars Many operations on NumPy scalars are now significantly faster, although rare operations (e.g. with 0-D arrays rather than scalars) may be slower in some cases. However, even with these improvements users who want the best performance for their scalars, may want to convert a known NumPy scalar into a Python one using `scalar.item()`. ([gh-21188](https://github.com/numpy/numpy/pull/21188)) Faster `np.kron` `numpy.kron` is about 80% faster as the product is now computed using broadcasting. 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In addition, the wheels for this release are built using the recently released Cython 0.29.30, which should fix the reported problems with [debugging](https://github.com/numpy/numpy/issues/21008). The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.15 rather than 10.6 that was used in previous NumPy release cycles. Contributors A total of 12 people contributed to this release. People with a \"+\" by their names contributed a patch for the first time. - Alexander Shadchin - Bas van Beek - Charles Harris - Hood Chatham - Jarrod Millman - John-Mark Gurney + - Junyan Ou + - Mariusz Felisiak + - Ross Barnowski - Sebastian Berg - Serge Guelton - Stefan van der Walt Pull requests merged A total of 22 pull requests were merged for this release. - [21191](https://github.com/numpy/numpy/pull/21191): TYP, BUG: Fix `np.lib.stride_tricks` re-exported under the\... - [21192](https://github.com/numpy/numpy/pull/21192): TST: Bump mypy from 0.931 to 0.940 - [21243](https://github.com/numpy/numpy/pull/21243): MAINT: Explicitly re-export the types in `numpy._typing` - [21245](https://github.com/numpy/numpy/pull/21245): MAINT: Specify sphinx, numpydoc versions for CI doc builds - [21275](https://github.com/numpy/numpy/pull/21275): BUG: Fix typos - [21277](https://github.com/numpy/numpy/pull/21277): ENH, BLD: Fix math feature detection for wasm - [21350](https://github.com/numpy/numpy/pull/21350): MAINT: Fix failing simd and cygwin tests. - [21438](https://github.com/numpy/numpy/pull/21438): MAINT: Fix failing Python 3.8 32-bit Windows test. - [21444](https://github.com/numpy/numpy/pull/21444): BUG: add linux guard per #21386 - [21445](https://github.com/numpy/numpy/pull/21445): BUG: Allow legacy dtypes to cast to datetime again - [21446](https://github.com/numpy/numpy/pull/21446): BUG: Make mmap handling safer in frombuffer - [21447](https://github.com/numpy/numpy/pull/21447): BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11. - [21448](https://github.com/numpy/numpy/pull/21448): ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS - [21472](https://github.com/numpy/numpy/pull/21472): BUG: Ensure compile errors are raised correclty - [21473](https://github.com/numpy/numpy/pull/21473): BUG: Fix segmentation fault - [21474](https://github.com/numpy/numpy/pull/21474): MAINT: Update doc requirements - [21475](https://github.com/numpy/numpy/pull/21475): MAINT: Mark `npy_memchr` with `no_sanitize("alignment")` on clang - [21512](https://github.com/numpy/numpy/pull/21512): DOC: Proposal - make the doc landing page cards more similar\... - [21525](https://github.com/numpy/numpy/pull/21525): MAINT: Update Cython version to 0.29.30. - [21536](https://github.com/numpy/numpy/pull/21536): BUG: Fix GCC error during build configuration - [21541](https://github.com/numpy/numpy/pull/21541): REL: Prepare for the NumPy 1.22.4 release. - [21547](https://github.com/numpy/numpy/pull/21547): MAINT: Skip tests that fail on PyPy. 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pyup-bot commented 1 year ago

Closing this in favor of #390