search

APLA-Toolbox / pymapf

📍🗺️ A Python library for Multi-Agents Planning and Pathfinding (Centralized and Decentralized)
MIT License
71 stars 11 forks source link

Update dependency numpy to v1.22.4 - autoclosed #57

Closed renovate[bot] closed 2 years ago

renovate[bot] commented 2 years ago

Mend Renovate

This PR contains the following updates:

Package Change Age Adoption Passing Confidence
numpy (source) ==1.21.4 -> ==1.22.4 age adoption passing confidence

Release Notes

numpy/numpy ### [`v1.22.4`](https://togithub.com/numpy/numpy/releases/tag/v1.22.4) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.3...v1.22.4) ### NumPy 1.22.4 Release Notes NumPy 1.22.4 is a maintenance release that fixes bugs discovered after the 1.22.3 release. 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://togithub.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://togithub.com/numpy/numpy/pull/21191): TYP, BUG: Fix `np.lib.stride_tricks` re-exported under the... - [#​21192](https://togithub.com/numpy/numpy/pull/21192): TST: Bump mypy from 0.931 to 0.940 - [#​21243](https://togithub.com/numpy/numpy/pull/21243): MAINT: Explicitly re-export the types in `numpy._typing` - [#​21245](https://togithub.com/numpy/numpy/pull/21245): MAINT: Specify sphinx, numpydoc versions for CI doc builds - [#​21275](https://togithub.com/numpy/numpy/pull/21275): BUG: Fix typos - [#​21277](https://togithub.com/numpy/numpy/pull/21277): ENH, BLD: Fix math feature detection for wasm - [#​21350](https://togithub.com/numpy/numpy/pull/21350): MAINT: Fix failing simd and cygwin tests. - [#​21438](https://togithub.com/numpy/numpy/pull/21438): MAINT: Fix failing Python 3.8 32-bit Windows test. - [#​21444](https://togithub.com/numpy/numpy/pull/21444): BUG: add linux guard per [#​21386](https://togithub.com/numpy/numpy/issues/21386) - [#​21445](https://togithub.com/numpy/numpy/pull/21445): BUG: Allow legacy dtypes to cast to datetime again - [#​21446](https://togithub.com/numpy/numpy/pull/21446): BUG: Make mmap handling safer in frombuffer - [#​21447](https://togithub.com/numpy/numpy/pull/21447): BUG: Stop using PyBytesObject.ob_shash deprecated in Python 3.11. - [#​21448](https://togithub.com/numpy/numpy/pull/21448): ENH: Introduce numpy.core.setup_common.NPY_CXX_FLAGS - [#​21472](https://togithub.com/numpy/numpy/pull/21472): BUG: Ensure compile errors are raised correclty - [#​21473](https://togithub.com/numpy/numpy/pull/21473): BUG: Fix segmentation fault - [#​21474](https://togithub.com/numpy/numpy/pull/21474): MAINT: Update doc requirements - [#​21475](https://togithub.com/numpy/numpy/pull/21475): MAINT: Mark `npy_memchr` with `no_sanitize("alignment")` on clang - [#​21512](https://togithub.com/numpy/numpy/pull/21512): DOC: Proposal - make the doc landing page cards more similar... - [#​21525](https://togithub.com/numpy/numpy/pull/21525): MAINT: Update Cython version to 0.29.30. - [#​21536](https://togithub.com/numpy/numpy/pull/21536): BUG: Fix GCC error during build configuration - [#​21541](https://togithub.com/numpy/numpy/pull/21541): REL: Prepare for the NumPy 1.22.4 release. - [#​21547](https://togithub.com/numpy/numpy/pull/21547): MAINT: Skip tests that fail on PyPy. #### Checksums ##### MD5 a19351fd3dc0b3bbc733495ed18b8f24 numpy-1.22.4-cp310-cp310-macosx_10_14_x86_64.whl 0730f9e196f70ad89f246bf95ccf05d5 numpy-1.22.4-cp310-cp310-macosx_10_15_x86_64.whl 63c74e5395a2b31d8adc5b1aa0c62471 numpy-1.22.4-cp310-cp310-macosx_11_0_arm64.whl f99778023770c12f896768c90f7712e5 numpy-1.22.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 757d68b0cdb4e28ffce8574b6a2f3c5e numpy-1.22.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 50becf2e048e54dc5227dfe8378aae1e numpy-1.22.4-cp310-cp310-win32.whl 79dfdc29a4730e44d6df33dbea5b35b0 numpy-1.22.4-cp310-cp310-win_amd64.whl 8fd8f04d71ead55c2773d1b46668ca67 numpy-1.22.4-cp38-cp38-macosx_10_15_x86_64.whl 41a7c6240081010824cc0d5c02900fe6 numpy-1.22.4-cp38-cp38-macosx_11_0_arm64.whl 6bc066d3f61da3304c82d92f3f900a4f numpy-1.22.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 86d959605c66ccba11c6504f25fff0d7 numpy-1.22.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ae0405894c065349a511e4575b919e2a numpy-1.22.4-cp38-cp38-win32.whl c9a731d08081396b7a1b66977734d2ac numpy-1.22.4-cp38-cp38-win_amd64.whl 4d9b97d74799e5fc48860f0b4a3b255a numpy-1.22.4-cp39-cp39-macosx_10_14_x86_64.whl c99fa7e04cb7cc23f1713f2023b4e489 numpy-1.22.4-cp39-cp39-macosx_10_15_x86_64.whl dda3815df12b8a99c6c3069f69997521 numpy-1.22.4-cp39-cp39-macosx_11_0_arm64.whl 9b7c5b39d5611d92b66eb545d44b25db numpy-1.22.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 90fc45eaf8b8c4fac3f3ebd105a5a856 numpy-1.22.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9562153d4a83d773c20eb626cbd65cde numpy-1.22.4-cp39-cp39-win32.whl 711b23acce54a18ce74fc80f48f48062 numpy-1.22.4-cp39-cp39-win_amd64.whl ab803b24ea557452e828adba1b986af3 numpy-1.22.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 09b3a41ea0b9bc20bd1691cf88f0b0d3 numpy-1.22.4.tar.gz b44849506fbb54cdef9dbb435b2b1987 numpy-1.22.4.zip ##### SHA256 ba9ead61dfb5d971d77b6c131a9dbee62294a932bf6a356e48c75ae684e635b3 numpy-1.22.4-cp310-cp310-macosx_10_14_x86_64.whl 1ce7ab2053e36c0a71e7a13a7475bd3b1f54750b4b433adc96313e127b870887 numpy-1.22.4-cp310-cp310-macosx_10_15_x86_64.whl 7228ad13744f63575b3a972d7ee4fd61815b2879998e70930d4ccf9ec721dce0 numpy-1.22.4-cp310-cp310-macosx_11_0_arm64.whl 43a8ca7391b626b4c4fe20aefe79fec683279e31e7c79716863b4b25021e0e74 numpy-1.22.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a911e317e8c826ea632205e63ed8507e0dc877dcdc49744584dfc363df9ca08c numpy-1.22.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9ce7df0abeabe7fbd8ccbf343dc0db72f68549856b863ae3dd580255d009648e numpy-1.22.4-cp310-cp310-win32.whl 3e1ffa4748168e1cc8d3cde93f006fe92b5421396221a02f2274aab6ac83b077 numpy-1.22.4-cp310-cp310-win_amd64.whl 59d55e634968b8f77d3fd674a3cf0b96e85147cd6556ec64ade018f27e9479e1 numpy-1.22.4-cp38-cp38-macosx_10_15_x86_64.whl c1d937820db6e43bec43e8d016b9b3165dcb42892ea9f106c70fb13d430ffe72 numpy-1.22.4-cp38-cp38-macosx_11_0_arm64.whl d4c5d5eb2ec8da0b4f50c9a843393971f31f1d60be87e0fb0917a49133d257d6 numpy-1.22.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 64f56fc53a2d18b1924abd15745e30d82a5782b2cab3429aceecc6875bd5add0 numpy-1.22.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl fb7a980c81dd932381f8228a426df8aeb70d59bbcda2af075b627bbc50207cba numpy-1.22.4-cp38-cp38-win32.whl e96d7f3096a36c8754207ab89d4b3282ba7b49ea140e4973591852c77d09eb76 numpy-1.22.4-cp38-cp38-win_amd64.whl 4c6036521f11a731ce0648f10c18ae66d7143865f19f7299943c985cdc95afb5 numpy-1.22.4-cp39-cp39-macosx_10_14_x86_64.whl b89bf9b94b3d624e7bb480344e91f68c1c6c75f026ed6755955117de00917a7c numpy-1.22.4-cp39-cp39-macosx_10_15_x86_64.whl 2d487e06ecbf1dc2f18e7efce82ded4f705f4bd0cd02677ffccfb39e5c284c7e numpy-1.22.4-cp39-cp39-macosx_11_0_arm64.whl f3eb268dbd5cfaffd9448113539e44e2dd1c5ca9ce25576f7c04a5453edc26fa numpy-1.22.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 37431a77ceb9307c28382c9773da9f306435135fae6b80b62a11c53cfedd8802 numpy-1.22.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cc7f00008eb7d3f2489fca6f334ec19ca63e31371be28fd5dad955b16ec285bd numpy-1.22.4-cp39-cp39-win32.whl f0725df166cf4785c0bc4cbfb320203182b1ecd30fee6e541c8752a92df6aa32 numpy-1.22.4-cp39-cp39-win_amd64.whl 0791fbd1e43bf74b3502133207e378901272f3c156c4df4954cad833b1380207 numpy-1.22.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b4308198d0e41efaa108e57d69973398439c7299a9d551680cdd603cf6d20709 numpy-1.22.4.tar.gz 425b390e4619f58d8526b3dcf656dde069133ae5c240229821f01b5f44ea07af numpy-1.22.4.zip ### [`v1.22.3`](https://togithub.com/numpy/numpy/releases/tag/v1.22.3) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.2...v1.22.3) ### NumPy 1.22.3 Release Notes NumPy 1.22.3 is a maintenance release that fixes bugs discovered after the 1.22.2 release. The most noticeable fixes may be those for DLPack. One that may cause some problems is disallowing strings as inputs to logical ufuncs. It is still undecided how strings should be treated in those functions and it was thought best to simply disallow them until a decision was reached. That should not cause problems with older code. The Python versions supported for this release are 3.8-3.10. Note that the Mac wheels are now based on OS X 10.14 rather than 10.9 that was used in previous NumPy release cycles. 10.14 is the oldest release supported by Apple. #### Contributors A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@​GalaxySnail](https://togithub.com/GalaxySnail) + - Alexandre de Siqueira - Bas van Beek - Charles Harris - Melissa Weber Mendonça - Ross Barnowski - Sebastian Berg - Tirth Patel - Matthieu Darbois #### Pull requests merged A total of 10 pull requests were merged for this release. - [#​21048](https://togithub.com/numpy/numpy/pull/21048): MAINT: Use "3.10" instead of "3.10-dev" on travis. - [#​21106](https://togithub.com/numpy/numpy/pull/21106): TYP,MAINT: Explicitly allow sequences of array-likes in `np.concatenate` - [#​21137](https://togithub.com/numpy/numpy/pull/21137): BLD,DOC: skip broken ipython 8.1.0 - [#​21138](https://togithub.com/numpy/numpy/pull/21138): BUG, ENH: np.\_from_dlpack: export correct device information - [#​21139](https://togithub.com/numpy/numpy/pull/21139): BUG: Fix numba DUFuncs added loops getting picked up - [#​21140](https://togithub.com/numpy/numpy/pull/21140): BUG: Fix unpickling an empty ndarray with a non-zero dimension... - [#​21141](https://togithub.com/numpy/numpy/pull/21141): BUG: use ThreadPoolExecutor instead of ThreadPool - [#​21142](https://togithub.com/numpy/numpy/pull/21142): API: Disallow strings in logical ufuncs - [#​21143](https://togithub.com/numpy/numpy/pull/21143): MAINT, DOC: Fix SciPy intersphinx link - [#​21148](https://togithub.com/numpy/numpy/pull/21148): BUG,ENH: np.\_from_dlpack: export arrays with any strided size-1... #### Checksums ##### MD5 14f1872bbab050b0579e5fcd8b341b81 numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl c673faa3ac8745ad10ed0428a21a77aa numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl d925fff720561673fd7ee8ead0e94935 numpy-1.22.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 319f97f5ee26b9c3c06f7a2a3df412a3 numpy-1.22.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 866eae5dba934cad50eb38c8505c8449 numpy-1.22.3-cp310-cp310-win32.whl e4c512437a6d4eb4a384225861067ad8 numpy-1.22.3-cp310-cp310-win_amd64.whl a28052af37037f0d5c3b47f4a7040135 numpy-1.22.3-cp38-cp38-macosx_10_14_x86_64.whl d22dc074bde64f6e91a2d1990345f821 numpy-1.22.3-cp38-cp38-macosx_11_0_arm64.whl e8a01c2ca1474aff142366a0a2fe0812 numpy-1.22.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4fe6e71e7871cb31ffc4122aa5707be7 numpy-1.22.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1273fb3c77383ab28f2fb05192751340 numpy-1.22.3-cp38-cp38-win32.whl 001244a6bafa640d7509c85661a4e98e numpy-1.22.3-cp38-cp38-win_amd64.whl b8694b880a1a68d1716f60a9c9e82b38 numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl ba122eaa0988801e250f8674e3dd612e numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl 3641825aca07cb26732425e52d034daf numpy-1.22.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f92412e4273c2580abcc1b75c56e9651 numpy-1.22.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b38604778ffd0a17931c06738c3ce9ed numpy-1.22.3-cp39-cp39-win32.whl 644e0b141fa36a1baf0338032254cc9a numpy-1.22.3-cp39-cp39-win_amd64.whl 99d2dfb943327b108b2c3b923bd42000 numpy-1.22.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3305c27e5bdf7f19247a7eee00ac053e numpy-1.22.3.tar.gz b56530be068796a50bf5a09105c8011e numpy-1.22.3.zip ##### SHA256 92bfa69cfbdf7dfc3040978ad09a48091143cffb778ec3b03fa170c494118d75 numpy-1.22.3-cp310-cp310-macosx_10_14_x86_64.whl 8251ed96f38b47b4295b1ae51631de7ffa8260b5b087808ef09a39a9d66c97ab numpy-1.22.3-cp310-cp310-macosx_11_0_arm64.whl 48a3aecd3b997bf452a2dedb11f4e79bc5bfd21a1d4cc760e703c31d57c84b3e numpy-1.22.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a3bae1a2ed00e90b3ba5f7bd0a7c7999b55d609e0c54ceb2b076a25e345fa9f4 numpy-1.22.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f950f8845b480cffe522913d35567e29dd381b0dc7e4ce6a4a9f9156417d2430 numpy-1.22.3-cp310-cp310-win32.whl 08d9b008d0156c70dc392bb3ab3abb6e7a711383c3247b410b39962263576cd4 numpy-1.22.3-cp310-cp310-win_amd64.whl 201b4d0552831f7250a08d3b38de0d989d6f6e4658b709a02a73c524ccc6ffce numpy-1.22.3-cp38-cp38-macosx_10_14_x86_64.whl f8c1f39caad2c896bc0018f699882b345b2a63708008be29b1f355ebf6f933fe numpy-1.22.3-cp38-cp38-macosx_11_0_arm64.whl 568dfd16224abddafb1cbcce2ff14f522abe037268514dd7e42c6776a1c3f8e5 numpy-1.22.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3ca688e1b9b95d80250bca34b11a05e389b1420d00e87a0d12dc45f131f704a1 numpy-1.22.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e7927a589df200c5e23c57970bafbd0cd322459aa7b1ff73b7c2e84d6e3eae62 numpy-1.22.3-cp38-cp38-win32.whl 07a8c89a04997625236c5ecb7afe35a02af3896c8aa01890a849913a2309c676 numpy-1.22.3-cp38-cp38-win_amd64.whl 2c10a93606e0b4b95c9b04b77dc349b398fdfbda382d2a39ba5a822f669a0123 numpy-1.22.3-cp39-cp39-macosx_10_14_x86_64.whl fade0d4f4d292b6f39951b6836d7a3c7ef5b2347f3c420cd9820a1d90d794802 numpy-1.22.3-cp39-cp39-macosx_11_0_arm64.whl 5bfb1bb598e8229c2d5d48db1860bcf4311337864ea3efdbe1171fb0c5da515d numpy-1.22.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 97098b95aa4e418529099c26558eeb8486e66bd1e53a6b606d684d0c3616b168 numpy-1.22.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl fdf3c08bce27132395d3c3ba1503cac12e17282358cb4bddc25cc46b0aca07aa numpy-1.22.3-cp39-cp39-win32.whl 639b54cdf6aa4f82fe37ebf70401bbb74b8508fddcf4797f9fe59615b8c5813a numpy-1.22.3-cp39-cp39-win_amd64.whl c34ea7e9d13a70bf2ab64a2532fe149a9aced424cd05a2c4ba662fd989e3e45f numpy-1.22.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a906c0b4301a3d62ccf66d058fe779a65c1c34f6719ef2058f96e1856f48bca5 numpy-1.22.3.tar.gz dbc7601a3b7472d559dc7b933b18b4b66f9aa7452c120e87dfb33d02008c8a18 numpy-1.22.3.zip ### [`v1.22.2`](https://togithub.com/numpy/numpy/releases/tag/v1.22.2) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.1...v1.22.2) ### NumPy 1.22.2 Release Notes The NumPy 1.22.2 is maintenance release that fixes bugs discovered after the 1.22.1 release. Notable fixes are: - Several build related fixes for downstream projects and other platforms. - Various Annotation fixes/additions. - Numpy wheels for Windows will use the 1.41 tool chain, fixing downstream link problems for projects using NumPy provided libraries on Windows. - Deal with CVE-2021-41495 complaint. The Python versions supported for this release are 3.8-3.10. #### Contributors A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Andrew J. Hesford + - Bas van Beek - Brénainn Woodsend + - Charles Harris - Hood Chatham - Janus Heide + - Leo Singer - Matti Picus - Mukulika Pahari - Niyas Sait - Pearu Peterson - Ralf Gommers - Sebastian Berg - Serge Guelton #### Pull requests merged A total of 21 pull requests were merged for this release. - [#​20842](https://togithub.com/numpy/numpy/pull/20842): BLD: Add NPY_DISABLE_SVML env var to opt out of SVML - [#​20843](https://togithub.com/numpy/numpy/pull/20843): BUG: Fix build of third party extensions with Py_LIMITED_API - [#​20844](https://togithub.com/numpy/numpy/pull/20844): TYP: Fix pyright being unable to infer the `real` and `imag`... - [#​20845](https://togithub.com/numpy/numpy/pull/20845): BUG: Fix comparator function signatures - [#​20906](https://togithub.com/numpy/numpy/pull/20906): BUG: Avoid importing `numpy.distutils` on import numpy.testing - [#​20907](https://togithub.com/numpy/numpy/pull/20907): MAINT: remove outdated mingw32 fseek support - [#​20908](https://togithub.com/numpy/numpy/pull/20908): TYP: Relax the return type of `np.vectorize` - [#​20909](https://togithub.com/numpy/numpy/pull/20909): BUG: fix f2py's define for threading when building with Mingw - [#​20910](https://togithub.com/numpy/numpy/pull/20910): BUG: distutils: fix building mixed C/Fortran extensions - [#​20912](https://togithub.com/numpy/numpy/pull/20912): DOC,TST: Fix Pandas code example as per new release - [#​20935](https://togithub.com/numpy/numpy/pull/20935): TYP, MAINT: Add annotations for `flatiter.__setitem__` - [#​20936](https://togithub.com/numpy/numpy/pull/20936): MAINT, TYP: Added missing where typehints in `fromnumeric.pyi` - [#​20937](https://togithub.com/numpy/numpy/pull/20937): BUG: Fix build_ext interaction with non numpy extensions - [#​20938](https://togithub.com/numpy/numpy/pull/20938): BUG: Fix missing intrinsics for windows/arm64 target - [#​20945](https://togithub.com/numpy/numpy/pull/20945): REL: Prepare for the NumPy 1.22.2 release. - [#​20982](https://togithub.com/numpy/numpy/pull/20982): MAINT: f2py: don't generate code that triggers `-Wsometimes-uninitialized`. - [#​20983](https://togithub.com/numpy/numpy/pull/20983): BUG: Fix incorrect return type in reduce without initial value - [#​20984](https://togithub.com/numpy/numpy/pull/20984): ENH: review return values for PyArray_DescrNew - [#​20985](https://togithub.com/numpy/numpy/pull/20985): MAINT: be more tolerant of setuptools >= 60 - [#​20986](https://togithub.com/numpy/numpy/pull/20986): BUG: Fix misplaced return. - [#​20992](https://togithub.com/numpy/numpy/pull/20992): MAINT: Further small return value validation fixes #### Checksums ##### MD5 2319f8d7c629d0ba3d3d3b1d5605d494 numpy-1.22.2-cp310-cp310-macosx_10_14_x86_64.whl 023c01a6d3aa528f8e88b0837dcab7ed numpy-1.22.2-cp310-cp310-macosx_11_0_arm64.whl 84b36e8893b811d17a19404c68db7ce6 numpy-1.22.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 744da9614e8272a384b542d129cd17a9 numpy-1.22.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ee012ed5e7c98c6f48026dfa818b2274 numpy-1.22.2-cp310-cp310-win_amd64.whl 73e4fdcf398327bc4241dc38b6d10211 numpy-1.22.2-cp38-cp38-macosx_10_14_x86_64.whl 9fcbca2a614af3b9a37456643ab1c99d numpy-1.22.2-cp38-cp38-macosx_11_0_arm64.whl b7e0d4a19867d33765c7187d1390eef4 numpy-1.22.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl dc8d79d75588737ea77fe85a4f05365a numpy-1.22.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 05906141c095148c53c043c381e6fabe numpy-1.22.2-cp38-cp38-win32.whl 05d3b6d34c0fa031e69ec0476e8d4c9c numpy-1.22.2-cp38-cp38-win_amd64.whl 1449889d856de0e88437fa76d3284e00 numpy-1.22.2-cp39-cp39-macosx_10_14_x86_64.whl e25666ab6ec0692368f328b7b98c27a3 numpy-1.22.2-cp39-cp39-macosx_11_0_arm64.whl 59e3013894bcc6267054c746d9339cf8 numpy-1.22.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 7606b9898c20d2b2aa7fc7018bc9c5cd numpy-1.22.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2686a1495c620e85842967bf8a5f1b2f numpy-1.22.2-cp39-cp39-win32.whl 54432a84807ab69ac3432e6090d5a169 numpy-1.22.2-cp39-cp39-win_amd64.whl 4dbecace42595742485b854b213341b6 numpy-1.22.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 5b506b01ef454f39272ca75de1c7f61c numpy-1.22.2.tar.gz a903008d992b77cb68129173c0f61f60 numpy-1.22.2.zip ##### SHA256 515a8b6edbb904594685da6e176ac9fbea8f73a5ebae947281de6613e27f1956 numpy-1.22.2-cp310-cp310-macosx_10_14_x86_64.whl 76a4f9bce0278becc2da7da3b8ef854bed41a991f4226911a24a9711baad672c numpy-1.22.2-cp310-cp310-macosx_11_0_arm64.whl 168259b1b184aa83a514f307352c25c56af111c269ffc109d9704e81f72e764b numpy-1.22.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3556c5550de40027d3121ebbb170f61bbe19eb639c7ad0c7b482cd9b560cd23b numpy-1.22.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl aafa46b5a39a27aca566198d3312fb3bde95ce9677085efd02c86f7ef6be4ec7 numpy-1.22.2-cp310-cp310-win_amd64.whl 55535c7c2f61e2b2fc817c5cbe1af7cb907c7f011e46ae0a52caa4be1f19afe2 numpy-1.22.2-cp38-cp38-macosx_10_14_x86_64.whl 60cb8e5933193a3cc2912ee29ca331e9c15b2da034f76159b7abc520b3d1233a numpy-1.22.2-cp38-cp38-macosx_11_0_arm64.whl 0b536b6840e84c1c6a410f3a5aa727821e6108f3454d81a5cd5900999ef04f89 numpy-1.22.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 2638389562bda1635b564490d76713695ff497242a83d9b684d27bb4a6cc9d7a numpy-1.22.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6767ad399e9327bfdbaa40871be4254d1995f4a3ca3806127f10cec778bd9896 numpy-1.22.2-cp38-cp38-win32.whl 03ae5850619abb34a879d5f2d4bb4dcd025d6d8fb72f5e461dae84edccfe129f numpy-1.22.2-cp38-cp38-win_amd64.whl d76a26c5118c4d96e264acc9e3242d72e1a2b92e739807b3b69d8d47684b6677 numpy-1.22.2-cp39-cp39-macosx_10_14_x86_64.whl 15efb7b93806d438e3bc590ca8ef2f953b0ce4f86f337ef4559d31ec6cf9d7dd numpy-1.22.2-cp39-cp39-macosx_11_0_arm64.whl badca914580eb46385e7f7e4e426fea6de0a37b9e06bec252e481ae7ec287082 numpy-1.22.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 94dd11d9f13ea1be17bac39c1942f527cbf7065f94953cf62dfe805653da2f8f numpy-1.22.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 8cf33634b60c9cef346663a222d9841d3bbbc0a2f00221d6bcfd0d993d5543f6 numpy-1.22.2-cp39-cp39-win32.whl 59153979d60f5bfe9e4c00e401e24dfe0469ef8da6d68247439d3278f30a180f numpy-1.22.2-cp39-cp39-win_amd64.whl 4a176959b6e7e00b5a0d6f549a479f869829bfd8150282c590deee6d099bbb6e numpy-1.22.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 093d513a460fd94f94c16193c3ef29b2d69a33e482071e3d6d6e561a700587a6 numpy-1.22.2.tar.gz 076aee5a3763d41da6bef9565fdf3cb987606f567cd8b104aded2b38b7b47abf numpy-1.22.2.zip ### [`v1.22.1`](https://togithub.com/numpy/numpy/releases/tag/v1.22.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.22.0...v1.22.1) ### NumPy 1.22.1 Release Notes The NumPy 1.22.1 is maintenance release that fixes bugs discovered after the 1.22.0 release. Notable fixes are: - Fix f2PY docstring problems (SciPy) - Fix reduction type problems (AstroPy) - Fix various typing bugs. The Python versions supported for this release are 3.8-3.10. #### Contributors A total of 14 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Arryan Singh - Bas van Beek - Charles Harris - Denis Laxalde - Isuru Fernando - Kevin Sheppard - Matthew Barber - Matti Picus - Melissa Weber Mendonça - Mukulika Pahari - Omid Rajaei + - Pearu Peterson - Ralf Gommers - Sebastian Berg #### Pull requests merged A total of 20 pull requests were merged for this release. - [#​20702](https://togithub.com/numpy/numpy/pull/20702): MAINT, DOC: Post 1.22.0 release fixes. - [#​20703](https://togithub.com/numpy/numpy/pull/20703): DOC, BUG: Use pngs instead of svgs. - [#​20704](https://togithub.com/numpy/numpy/pull/20704): DOC: Fixed the link on user-guide landing page - [#​20714](https://togithub.com/numpy/numpy/pull/20714): BUG: Restore vc141 support - [#​20724](https://togithub.com/numpy/numpy/pull/20724): BUG: Fix array dimensions solver for multidimensional arguments... - [#​20725](https://togithub.com/numpy/numpy/pull/20725): TYP: change type annotation for `__array_namespace__` to ModuleType - [#​20726](https://togithub.com/numpy/numpy/pull/20726): TYP, MAINT: Allow `ndindex` to accept integer tuples - [#​20757](https://togithub.com/numpy/numpy/pull/20757): BUG: Relax dtype identity check in reductions - [#​20763](https://togithub.com/numpy/numpy/pull/20763): TYP: Allow time manipulation functions to accept `date` and `timedelta`... - [#​20768](https://togithub.com/numpy/numpy/pull/20768): TYP: Relax the type of `ndarray.__array_finalize__` - [#​20795](https://togithub.com/numpy/numpy/pull/20795): MAINT: Raise RuntimeError if setuptools version is too recent. - [#​20796](https://togithub.com/numpy/numpy/pull/20796): BUG, DOC: Fixes SciPy docs build warnings - [#​20797](https://togithub.com/numpy/numpy/pull/20797): DOC: fix OpenBLAS version in release note - [#​20798](https://togithub.com/numpy/numpy/pull/20798): PERF: Optimize array check for bounded 0,1 values - [#​20805](https://togithub.com/numpy/numpy/pull/20805): BUG: Fix that reduce-likes honor out always (and live in the... - [#​20806](https://togithub.com/numpy/numpy/pull/20806): BUG: `array_api.argsort(descending=True)` respects relative... - [#​20807](https://togithub.com/numpy/numpy/pull/20807): BUG: Allow integer inputs for pow-related functions in `array_api` - [#​20814](https://togithub.com/numpy/numpy/pull/20814): DOC: Refer to NumPy, not pandas, in main page - [#​20815](https://togithub.com/numpy/numpy/pull/20815): DOC: Update Copyright to 2022 \[License] - [#​20819](https://togithub.com/numpy/numpy/pull/20819): BUG: Return correctly shaped inverse indices in array_api set... #### Checksums ##### MD5 8edd68c8998cb694e244ce793b2d088c numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl e4858aafd41cdba76cd14161bfc512c3 numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl 96f4fc3f321625278ca3807c7c8c789c numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl 2ddc25b9c9d7b517610689055f9f553a numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8d40c6fd64389c05646b5ef95cded6e5 numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1a8359c6436d1bcfe84a094337903a48 numpy-1.22.1-cp310-cp310-win_amd64.whl 033f9aa72a732646f3fb4563226320ee numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl 59e13abecdf4194f75b654f1d853b244 numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl 3ce885a0c10e95f5756d7c1878eaa246 numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl 546b2a0866561673d5b7eadcc086af24 numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 200c0a7bc3a24cfa6f4358d7274b5535 numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl defe48b3b5f44c3991e830f7cde0a79c numpy-1.22.1-cp38-cp38-win32.whl 15557a847a78bcbf651ca6689ae37935 numpy-1.22.1-cp38-cp38-win_amd64.whl 067e734594c67d8141190b7eabb979ee numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl 1458d42b26da341baaee134d85e3fd70 numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl 463b365c80efffd807194c78b4796235 numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl 58d8dc02dd884898c1b7ee1bee1dd216 numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 48e2d2905822f78a96d400c78bd16cbb numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c5059bd82d8f2c509c889fba09251307 numpy-1.22.1-cp39-cp39-win32.whl eb9a0655d16897f0adf6ea53b9f3bda4 numpy-1.22.1-cp39-cp39-win_amd64.whl 74cb5dba2f37dc445ffd3068eb1d58fe numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 90fff1ee7c7f843fc7a234addc70c71c numpy-1.22.1.tar.gz c25dad73053350dd0278605d8ed8a5c7 numpy-1.22.1.zip ##### SHA256 3d62d6b0870b53799204515145935608cdeb4cebb95a26800b6750e48884cc5b numpy-1.22.1-cp310-cp310-macosx_10_9_universal2.whl 831f2df87bd3afdfc77829bc94bd997a7c212663889d56518359c827d7113b1f numpy-1.22.1-cp310-cp310-macosx_10_9_x86_64.whl 8d1563060e77096367952fb44fca595f2b2f477156de389ce7c0ade3aef29e21 numpy-1.22.1-cp310-cp310-macosx_11_0_arm64.whl 69958735d5e01f7b38226a6c6e7187d72b7e4d42b6b496aca5860b611ca0c193 numpy-1.22.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 45a7dfbf9ed8d68fd39763940591db7637cf8817c5bce1a44f7b56c97cbe211e numpy-1.22.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7e957ca8112c689b728037cea9c9567c27cf912741fabda9efc2c7d33d29dfa1 numpy-1.22.1-cp310-cp310-win_amd64.whl 800dfeaffb2219d49377da1371d710d7952c9533b57f3d51b15e61c4269a1b5b numpy-1.22.1-cp38-cp38-macosx_10_9_universal2.whl 65f5e257987601fdfc63f1d02fca4d1c44a2b85b802f03bd6abc2b0b14648dd2 numpy-1.22.1-cp38-cp38-macosx_10_9_x86_64.whl 632e062569b0fe05654b15ef0e91a53c0a95d08ffe698b66f6ba0f927ad267c2 numpy-1.22.1-cp38-cp38-macosx_11_0_arm64.whl 0d245a2bf79188d3f361137608c3cd12ed79076badd743dc660750a9f3074f7c numpy-1.22.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 26b4018a19d2ad9606ce9089f3d52206a41b23de5dfe8dc947d2ec49ce45d015 numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f8ad59e6e341f38266f1549c7c2ec70ea0e3d1effb62a44e5c3dba41c55f0187 numpy-1.22.1-cp38-cp38-win32.whl 60f19c61b589d44fbbab8ff126640ae712e163299c2dd422bfe4edc7ec51aa9b numpy-1.22.1-cp38-cp38-win_amd64.whl 2db01d9838a497ba2aa9a87515aeaf458f42351d72d4e7f3b8ddbd1eba9479f2 numpy-1.22.1-cp39-cp39-macosx_10_9_universal2.whl bcd19dab43b852b03868796f533b5f5561e6c0e3048415e675bec8d2e9d286c1 numpy-1.22.1-cp39-cp39-macosx_10_9_x86_64.whl 78bfbdf809fc236490e7e65715bbd98377b122f329457fffde206299e163e7f3 numpy-1.22.1-cp39-cp39-macosx_11_0_arm64.whl c51124df17f012c3b757380782ae46eee85213a3215e51477e559739f57d9bf6 numpy-1.22.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 88d54b7b516f0ca38a69590557814de2dd638d7d4ed04864826acaac5ebb8f01 numpy-1.22.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b5ec9a5eaf391761c61fd873363ef3560a3614e9b4ead17347e4deda4358bca4 numpy-1.22.1-cp39-cp39-win32.whl 4ac4d7c9f8ea2a79d721ebfcce81705fc3cd61a10b731354f1049eb8c99521e8 numpy-1.22.1-cp39-cp39-win_amd64.whl e60ef82c358ded965fdd3132b5738eade055f48067ac8a5a8ac75acc00cad31f numpy-1.22.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl dd1968402ae20dfd59b34acd799b494be340c774f6295e9bf1c2b9842a5e416d numpy-1.22.1.tar.gz e348ccf5bc5235fc405ab19d53bec215bb373300e5523c7b476cc0da8a5e9973 numpy-1.22.1.zip ### [`v1.22.0`](https://togithub.com/numpy/numpy/releases/tag/v1.22.0) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.21.6...v1.22.0) # NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are: - Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release. - A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX. - NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data. - New methods for `quantile`, `percentile`, and related functions. The new methods provide a complete set of the methods commonly found in the literature. - A new configurable allocator for use by downstream projects. These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation. The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays. ## Expired deprecations ##### Deprecated numeric style dtype strings have been removed Using the strings `"Bytes0"`, `"Datetime64"`, `"Str0"`, `"Uint32"`, and `"Uint64"` as a dtype will now raise a `TypeError`. ([gh-19539](https://togithub.com/numpy/numpy/pull/19539)) ##### Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio `numpy.loads` was deprecated in v1.15, with the recommendation that users use `pickle.loads` instead. `ndfromtxt` and `mafromtxt` were both deprecated in v1.17 - users should use `numpy.genfromtxt` instead with the appropriate value for the `usemask` parameter. ([gh-19615](https://togithub.com/numpy/numpy/pull/19615)) ## Deprecations ##### Use delimiter rather than delimitor as kwarg in mrecords The misspelled keyword argument `delimitor` of `numpy.ma.mrecords.fromtextfile()` has been changed to `delimiter`, using it will emit a deprecation warning. ([gh-19921](https://togithub.com/numpy/numpy/pull/19921)) ##### Passing boolean `kth` values to (arg-)partition has been deprecated `numpy.partition` and `numpy.argpartition` would previously accept boolean values for the `kth` parameter, which would subsequently be converted into integers. This behavior has now been deprecated. ([gh-20000](https://togithub.com/numpy/numpy/pull/20000)) ##### The `np.MachAr` class has been deprecated The `numpy.MachAr` class and `finfo.machar ` attribute have been deprecated. Users are encouraged to access the property if interest directly from the corresponding `numpy.finfo` attribute. ([gh-20201](https://togithub.com/numpy/numpy/pull/20201)) ## Compatibility notes ##### Distutils forces strict floating point model on clang NumPy now sets the `-ftrapping-math` option on clang to enforce correct floating point error handling for universal functions. Clang defaults to non-IEEE and C99 conform behaviour otherwise. This change (using the equivalent but newer `-ffp-exception-behavior=strict`) was attempted in NumPy 1.21, but was effectively never used. ([gh-19479](https://togithub.com/numpy/numpy/pull/19479)) ##### Removed floor division support for complex types Floor division of complex types will now result in a `TypeError` ```{.python} >>> a = np.arange(10) + 1j* np.arange(10) >>> a // 1 TypeError: ufunc 'floor_divide' not supported for the input types... ``` ([gh-19135](https://togithub.com/numpy/numpy/pull/19135)) ##### `numpy.vectorize` functions now produce the same output class as the base function When a function that respects `numpy.ndarray` subclasses is vectorized using `numpy.vectorize`, the vectorized function will now be subclass-safe also for cases that a signature is given (i.e., when creating a `gufunc`): the output class will be the same as that returned by the first call to the underlying function. ([gh-19356](https://togithub.com/numpy/numpy/pull/19356)) ##### Python 3.7 is no longer supported Python support has been dropped. This is rather strict, there are changes that require Python >= 3.8. ([gh-19665](https://togithub.com/numpy/numpy/pull/19665)) ##### str/repr of complex dtypes now include space after punctuation The repr of `np.dtype({"names": ["a"], "formats": [int], "offsets": [2]})` is now `dtype({'names': ['a'], 'formats': ['>> np.uint32(1023).bit_count() 10 >>> np.int32(-127).bit_count() 7 ``` ([gh-19355](https://togithub.com/numpy/numpy/pull/19355)) ##### The `ndim` and `axis` attributes have been added to `numpy.AxisError` The `ndim` and `axis` parameters are now also stored as attributes within each `numpy.AxisError` instance. ([gh-19459](https://togithub.com/numpy/numpy/pull/19459)) ##### Preliminary support for `windows/arm64` target `numpy` added support for windows/arm64 target. Please note `OpenBLAS` support is not yet available for windows/arm64 target. ([gh-19513](https://togithub.com/numpy/numpy/pull/19513)) ##### Added support for LoongArch LoongArch is a new instruction set, numpy compilation failure on LoongArch architecture, so add the commit. ([gh-19527](https://togithub.com/numpy/numpy/pull/19527)) ##### A `.clang-format` file has been added Clang-format is a C/C++ code formatter, together with the added `.clang-format` file, it produces code close enough to the NumPy C_STYLE_GUIDE for general use. Clang-format version 12+ is required due to the use of several new features, it is available in Fedora 34 and Ubuntu Focal among other distributions. ([gh-19754](https://togithub.com/numpy/numpy/pull/19754)) ##### `is_integer` is now available to `numpy.floating` and `numpy.integer` Based on its counterpart in Python `float` and `int`, the numpy floating point and integer types now support `float.is_integer`. Returns `True` if the number is finite with integral value, and `False` otherwise. ```{.python} >>> np.float32(-2.0).is_integer() True >>> np.float64(3.2).is_integer() False >>> np.int32(-2).is_integer() True ``` ([gh-19803](https://togithub.com/numpy/numpy/pull/19803)) ##### Symbolic parser for Fortran dimension specifications A new symbolic parser has been added to f2py in order to correctly parse dimension specifications. The parser is the basis for future improvements and provides compatibility with Draft Fortran 202x. ([gh-19805](https://togithub.com/numpy/numpy/pull/19805)) ##### `ndarray`, `dtype` and `number` are now runtime-subscriptable Mimicking PEP-585, the `numpy.ndarray`, `numpy.dtype` and `numpy.number` classes are now subscriptable for python 3.9 and later. Consequently, expressions that were previously only allowed in .pyi stub files or with the help of `from __future__ import annotations` are now also legal during runtime. ```{.python} >>> import numpy as np >>> from typing import Any >>> np.ndarray[Any, np.dtype[np.float64]] numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] ``` ([gh-19879](https://togithub.com/numpy/numpy/pull/19879)) ## Improvements ##### `ctypeslib.load_library` can now take any path-like object All parameters in the can now take any `python:path-like object`{.interpreted-text role="term"}. This includes the likes of strings, bytes and objects implementing the `__fspath__`{.interpreted-text role="meth"} protocol. ([gh-17530](https://togithub.com/numpy/numpy/pull/17530)) ##### Add `smallest_normal` and `smallest_subnormal` attributes to `finfo` The attributes `smallest_normal` and `smallest_subnormal` are available as an extension of `finfo` class for any floating-point data type. To use these new attributes, write `np.finfo(np.float64).smallest_normal` or `np.finfo(np.float64).smallest_subnormal`. ([gh-18536](https://togithub.com/numpy/numpy/pull/18536)) ##### `numpy.linalg.qr` accepts stacked matrices as inputs `numpy.linalg.qr` is able to produce results for stacked matrices as inputs. Moreover, the implementation of QR decomposition has been shifted to C from Python. ([gh-19151](https://togithub.com/numpy/numpy/pull/19151)) ##### `numpy.fromregex` now accepts `os.PathLike` implementations `numpy.fromregex` now accepts objects implementing the `__fspath__` protocol, *e.g.* `pathlib.Path`. ([gh-19680](https://togithub.com/numpy/numpy/pull/19680)) ##### Add new methods for `quantile` and `percentile` `quantile` and `percentile` now have have a `method=` keyword argument supporting 13 different methods. This replaces the `interpolation=` keyword argument. The methods are now aligned with nine methods which can be found in scientific literature and the R language. The remaining methods are the previous discontinuous variations of the default "linear" one. Please see the documentation of `numpy.percentile` for more information. ([gh-19857](https://togithub.com/numpy/numpy/pull/19857)) ##### Missing parameters have been added to the `nan` functions A number of the `nan` functions previously lacked parameters that were present in their ``-based counterpart, *e.g.* the `where` parameter was present in `numpy.mean` but absent from `numpy.nanmean`. The following parameters have now been added to the `nan` functions: - nanmin: `initial` & `where` - nanmax: `initial` & `where` - nanargmin: `keepdims` & `out` - nanargmax: `keepdims` & `out` - nansum: `initial` & `where` - nanprod: `initial` & `where` - nanmean: `where` - nanvar: `where` - nanstd: `where` ([gh-20027](https://togithub.com/numpy/numpy/pull/20027)) ##### Annotating the main Numpy namespace Starting from the 1.20 release, PEP 484 type annotations have been included for parts of the NumPy library; annotating the remaining functions being a work in progress. With the release of 1.22 this process has been completed for the main NumPy namespace, which is now fully annotated. Besides the main namespace, a limited number of sub-packages contain annotations as well. This includes, among others, `numpy.testing`, `numpy.linalg` and `numpy.random` (available since 1.21). ([gh-20217](https://togithub.com/numpy/numpy/pull/20217)) ##### Vectorize umath module using AVX-512 By leveraging Intel Short Vector Math Library (SVML), 18 umath functions (`exp2`, `log2`, `log10`, `expm1`, `log1p`, `cbrt`, `sin`, `cos`, `tan`, `arcsin`, `arccos`, `arctan`, `sinh`, `cosh`, `tanh`, `arcsinh`, `arccosh`, `arctanh`) are vectorized using AVX-512 instruction set for both single and double precision implementations. This change is currently enabled only for Linux users and on processors with AVX-512 instruction set. It provides an average speed up of 32x and 14x for single and double precision functions respectively. ([gh-19478](https://togithub.com/numpy/numpy/pull/19478)) ##### OpenBLAS v0.3.18 Update the OpenBLAS used in testing and in wheels to v0.3.18 ([gh-20058](https://togithub.com/numpy/numpy/pull/20058)) #### Checksums ##### MD5 66757b963ad5835038b9a2a9df852c84 numpy-1.22.0-cp310-cp310-macosx_10_9_universal2.whl 86b7f3a94c09dbd6869614c4d7f9ba5e numpy-1.22.0-cp310-cp310-macosx_10_9_x86_64.whl 5184db17d8e5e6ecdc53e2f0a6964c35 numpy-1.22.0-cp310-cp310-macosx_11_0_arm64.whl 6643e9a076cce736cfbe15face4db9db numpy-1.22.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6efef45bf63594703c094b2ad729e648 numpy-1.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7a1a21bb0958a3eb920deeef9e745935 numpy-1.22.0-cp310-cp310-win_amd64.whl 45241fb5f31ea46e2b6f1321a63c8e1c numpy-1.22.0-cp38-cp38-macosx_10_9_universal2.whl 472f24a5d35116634fcc57e9bda899bc numpy-1.22.0-cp38-cp38-macosx_10_9_x86_64.whl 6c15cf7847b20101ae281ade6121b79e numpy-1.22.0-cp38-cp38-macosx_11_0_arm64.whl 313f0fd99a899a7465511c1418e1031f numpy-1.22.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9ae6ecde0cbeadd2a9d7b8ae54285863 numpy-1.22.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 0f31a7b9e128b0cdafecf98cf1301fc0 numpy-1.22.0-cp38-cp38-win32.whl f4b45579cf532ea632b890b1df387081 numpy-1.22.0-cp38-cp38-win_amd64.whl 2cb27112b11c16f700e6019f5fd36408 numpy-1.22.0-cp39-cp39-macosx_10_9_universal2.whl 4554a5797a4cb787b5169a8f5482fb95 numpy-1.22.0-cp39-cp39-macosx_10_9_x86_64.whl 3780decd94837da6f0816f2feaace9c2 numpy-1.22.0-cp39-cp39-macosx_11_0_arm64.whl 6e519dd5205510dfebcadc6f7fdf9738 numpy-1.22.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 89d455bf290f459a70c57620f02d5b69 numpy-1.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6425f8d7dc779a54b8074e198cea43c9 numpy-1.22.0-cp39-cp39-win32.whl 1b5c670328146975b21b54fa5ef8ec4c numpy-1.22.0-cp39-cp39-win_amd64.whl 05d842127ca85cca12fed3a26b0f5177 numpy-1.22.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ab751b8d4195f91ae61a402184d16d18 numpy-1.22.0.tar.gz 252de134862a27bd66705d29622edbfe numpy-1.22.0.zip ##### SHA256 3d22662b4b10112c545c91a0741f2436f8ca979ab3d69d03d19322aa970f9695 numpy-1.22.0-cp310-cp310-macosx_10_9_universal2.whl 11a1f3816ea82eed4178102c56281782690ab5993251fdfd75039aad4d20385f numpy-1.22.0-cp310-cp310-macosx_10_9_x86_64.whl 5dc65644f75a4c2970f21394ad8bea1a844104f0fe01f278631be1c7eae27226 numpy-1.22.0-cp310-cp310-macosx_11_0_arm64.whl 42c16cec1c8cf2728f1d539bd55aaa9d6bb48a7de2f41eb944697293ef65a559 numpy-1.22.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a97e82c39d9856fe7d4f9b86d8a1e66eff99cf3a8b7ba48202f659703d27c46f numpy-1.22.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e41e8951749c4b5c9a2dc5fdbc1a4eec6ab2a140fdae9b460b0f557eed870f4d numpy-1.22.0-cp310-cp310-win_amd64.whl bece0a4a49e60e472a6d1f70ac6cdea00f9ab80ff01132f96bd970cdd8a9e5a9 numpy-1.22.0-cp38-cp38-macosx_10_9_universal2.whl 818b9be7900e8dc23e013a92779135623476f44a0de58b40c32a15368c01d471 numpy-1.22.0-cp38-cp38-macosx_10_9_x86_64.whl 47ee7a839f5885bc0c63a74aabb91f6f40d7d7b639253768c4199b37aede7982 numpy-1.22.0-cp38-cp38-macosx_11_0_arm64.whl a024181d7aef0004d76fb3bce2a4c9f2e67a609a9e2a6ff2571d30e9976aa383 numpy-1.22.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f71d57cc8645f14816ae249407d309be250ad8de93ef61d9709b45a0ddf4050c numpy-1.22.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 283d9de87c0133ef98f93dfc09fad3fb382f2a15580de75c02b5bb36a5a159a5 numpy-1.22.0-cp38-cp38-win32.whl 2762331de395739c91f1abb88041f94a080cb1143aeec791b3b223976228af3f numpy-1.22.0-cp38-cp38-win_amd64.whl 76ba7c40e80f9dc815c5e896330700fd6e20814e69da9c1267d65a4d051080f1 numpy-1.22.0-cp39-cp39-macosx_10_9_universal2.whl 0cfe07133fd00b27edee5e6385e333e9eeb010607e8a46e1cd673f05f8596595 numpy-1.22.0-cp39-cp39-macosx_10_9_x86_64.whl 6ed0d073a9c54ac40c41a9c2d53fcc3d4d4ed607670b9e7b0de1ba13b4cbfe6f numpy-1.22.0-cp39-cp39-macosx_11_0_arm64.whl 41388e32e40b41dd56eb37fcaa7488b2b47b0adf77c66154d6b89622c110dfe9 numpy-1.22.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b55b953a1bdb465f4dc181758570d321db4ac23005f90ffd2b434cc6609a63dd numpy-1.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 5a311ee4d983c487a0ab546708edbdd759393a3dc9cd30305170149fedd23c88 numpy-1.22.0-cp39-cp39-win32.whl a97a954a8c2f046d3817c2bce16e3c7e9a9c2afffaf0400f5c16df5172a67c9c numpy-1.22.0-cp39-cp39-win_amd64.whl bb02929b0d6bfab4c48a79bd805bd7419114606947ec8284476167415171f55b numpy-1.22.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2be14ba396780a6f662b8ba1a24466c9cf18a6a386174f614668e58387a13d7 numpy-1.22.0.tar.gz a955e4128ac36797aaffd49ab44ec74a71c11d6938df83b1285492d277db5397 numpy-1.22.0.zip ### [`v1.21.6`](https://togithub.com/numpy/numpy/releases/tag/v1.21.6) [Compare Source](https://togithub.com/numpy/numpy/compare/v1.21.5...v1.21.6) ### NumPy 1.21.6 Release Notes NumPy 1.21.6 is a very small release that achieves two things: - Backs out the mistaken backport of C++ code into 1.21.5. - Provides a 32 bit Windows wheel for Python 3.10. The provision of the 32 bit wheel is intended to make life easier for oldest-supported-numpy. #### Checksums ##### MD5 5a3e5d7298056bcfbc3246597af474d4 numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl d981d2859842e7b62dc93e24808c7bac numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl 171313893c26529404d09fadb3537ed3 numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl 5a7a6dfdd43069f9b29d3fe6b7f3a2ce numpy-1.21.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a9e25375a72725c5d74442eda53af405 numpy-1.21.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6f9a782477380b2cdb7606f6f7634c00 numpy-1.21.6-cp310-cp310-win32.whl 32a73a348864700a3fa510d2fc4350b7 numpy-1.21.6-cp310-cp310-win_amd64.whl 0db8941ebeb0a02cd839d9cd3c5c20bb numpy-1.21.6-cp37-cp37m-macosx_10_9_x86_64.whl 67882155be9592850861f4ad8ba36623 numpy-1.21.6-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl c70e30e1ff9ab49f898c19e7a6492ae6 numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl e32dbd291032c7554a742f1bb9b2f7a3 numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 689bf804c2cd16cb241fd943e3833ffd numpy-1.21.6-cp37-cp37m-win32.whl 0062a7b0231a07cb5b9f3d7c495e6fe4 numpy-1.21.6-cp37-cp37m-win_amd64.whl 0d08809980ab497659e7aa0df9ce120e numpy-1.21.6-cp38-cp38-macosx_10_9_universal2.whl 3c67d14ea2009069844b27bfbf74304d numpy-1.21.6-cp38-cp38-macosx_10_9_x86_64.whl 5f0e773745cb817313232ac1bf4c7eee numpy-1.21.6-cp38-cp38-macosx_11_0_arm64.whl fa8011e065f1964d3eb870bb3926fc99 numpy-1.21.6-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl 486cf9d4daab59aad253aa5b84a5aa83 numpy-1.21.6-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl 88509abab303c076dfb26f00e455180d numpy-1.21.6-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl f7234e2ef837f5f6ddbde8db246fd05b numpy-1.21.6-cp38-cp38-win32.whl e1063e01fb44ea7a49adea0c33548217 numpy-1.21.6-cp38-cp38-win_amd64.whl 61c4caad729e3e0e688accbc1424ed45 numpy-1.21.6-cp39-cp39-macosx_10_9_universal2.whl 67488d8ccaeff798f2e314aae7c4c3d6 numpy-1.21.6-cp39-cp39-macosx_10_9_x86_64.whl 128c3713b5d1de45a0f522562bac5263 numpy-1.21.6-cp39-cp39-macosx_11_0_arm64.whl 50e79cd0610b4ed726b3bf08c3716dab numpy-1.21.6-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl bd0c9e3c0e488faac61daf3227fb95af numpy-1.21.6-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl aa5e9baf1dec16b15e481c23f8a23214 numpy-1.21.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a2405b0e5d3f775ad30177296a997092 numpy-1.21.6-cp39-cp39-win32.whl f0d20eda8c78f957ea70c5527954303e numpy-1.21.6-cp39-cp39-win_amd64.whl 9682abbcc38cccb7f56e48aacca7de23 numpy-1.21.6-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl 6aa3c2e8ea2886bf593bd8e0a1425c64 numpy-1.21.6.tar.gz 04aea95dcb1d256d13a45df42173aa1e numpy-1.21.6.zip ##### SHA256 8737609c3bbdd48e380d463134a35ffad3b22dc56295eff6f79fd85bd0eeeb25 numpy-1.21.6-cp310-cp310-macosx_10_9_universal2.whl fdffbfb6832cd0b300995a2b08b8f6fa9f6e856d562800fea9182316d99c4e8e numpy-1.21.6-cp310-cp310-macosx_10_9_x86_64.whl 3820724272f9913b597ccd13a467cc492a0da6b05df26ea09e78b171a0bb9da6 numpy-1.21.6-cp310-cp310-macosx_11_0_arm64.whl f17e562de9edf691a42ddb1eb4a5541c20dd3f9e65b09ded2beb0799c0cf29bb numpy-1.21.6-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 5f30427731561ce75d7048ac254dbe47a2ba576229250fb60f0fb74db96501a1 numpy-1.21.6-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d4bf4d43077db55589ffc9009c0ba0a94fa4908b9586d6ccce2e0b164c86303c numpy-1.21.6-cp310-cp310-win32.whl d136337ae3cc69aa5e447e78d8e1514be8c3ec9b54264e680cf0b4bd9011574f numpy-1.21.6-cp310-cp310-win_amd64.whl 6aaf96c7f8cebc220cdfc03f1d5a31952f027dda050e5a703a0d1c396075e3e7 numpy-1.21.6-cp37-cp37m-macosx_10_9_x86_64.whl 67c261d6c0a9981820c3a149d255a76918278a6b03b6a036800359aba1256d46 numpy-1.21.6-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl a6be4cb0ef3b8c9250c19cc122267263093eee7edd4e3fa75395dfda8c17a8e2 numpy-1.21.6-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl 7c4068a8c44014b2d55f3c3f574c376b2494ca9cc73d2f1bd692382b6dffe3db numpy-1.21.6-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 7c7e5fa88d9ff656e067876e4736379cc962d185d5cd808014a8a928d529ef4e numpy-1.21.6-cp37-cp37m-win32.whl bcb238c9c96c00d3085b264e5c1a1207672577b93fa666c3b14a45240b14123a numpy-1.21.6-cp37-cp37m-win_amd64.whl 82691fda7c3f77c90e62da69ae60b5ac08e87e775b09813559f8901a88266552 numpy-1.21.6-cp38-cp38-macosx_10_9_universal2.whl 643843bcc1c50526b3a71cd2ee561cf0d8773f062c8cbaf9ffac9fdf573f83ab numpy-1.21.6-cp38-cp38-macosx_10_9_x86_64.whl 357768c2e4451ac241465157a3e929b265dfac85d9214074985b1786244f2ef3 numpy-1.21.6-cp38-cp38-macosx_11_0_arm64.whl 9f411b2c3f3d76bba0865b35a425157c5dcf54937f82bbeb3d3c180789dd66a6 numpy-1.21.6-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl 4aa48afdce4660b0076a00d80afa54e8a97

Configuration

📅 Schedule: Branch creation - At any time (no schedule defined), Automerge - At any time (no schedule defined).

🚦 Automerge: Disabled by config. Please merge this manually once you are satisfied.

♻ Rebasing: Whenever PR becomes conflicted, or you tick the rebase/retry checkbox.

🔕 Ignore: Close this PR and you won't be reminded about this update again.

This PR has been generated by Mend Renovate. View repository job log here.