search

murphyqm / pytesimal

Model the conductive cooling of small planetary bodies with temperature-dependent material properties
MIT License
1 stars 0 forks source link

Update numpy to 1.23.5 #79

Closed pyup-bot closed 1 year ago

pyup-bot commented 1 year ago

This PR updates numpy from 1.20.3 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 numpy-1.23.5-cp311-cp311-macosx_11_0_arm64.whl 6b7319f66bf7ac01b49e2a32470baf28 numpy-1.23.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3c60928ddb1f55163801f06ac2229eb0 numpy-1.23.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6936b6bcfd6474acc7a8c162a9393b3c numpy-1.23.5-cp311-cp311-win32.whl 6c9af68b7b56c12c913678cafbdc44d6 numpy-1.23.5-cp311-cp311-win_amd64.whl 699daeac883260d3f182ae4bbbd9bbd2 numpy-1.23.5-cp38-cp38-macosx_10_9_x86_64.whl 6c233a36339de0652139e78ef91504d4 numpy-1.23.5-cp38-cp38-macosx_11_0_arm64.whl 57d5439556ab5078c91bdeffd9c0036e numpy-1.23.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a8045b59187f2e0ccd4294851adbbb8a numpy-1.23.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 7f38f7e560e4bf41490372ab84aa7a38 numpy-1.23.5-cp38-cp38-win32.whl 76095726ba459d7f761b44acf2e56bd1 numpy-1.23.5-cp38-cp38-win_amd64.whl 174befd584bc1b03ed87c8f0d149a58e numpy-1.23.5-cp39-cp39-macosx_10_9_x86_64.whl 9cbac793d77278f5d27a7979b64f6b5b numpy-1.23.5-cp39-cp39-macosx_11_0_arm64.whl 6e417b087044e90562183b33f3049b09 numpy-1.23.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 54fa63341eaa6da346d824399e8237f6 numpy-1.23.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cc14d62a158e99c57f925c86551e45f0 numpy-1.23.5-cp39-cp39-win32.whl bad36b81e7e84bd7a028affa0659d235 numpy-1.23.5-cp39-cp39-win_amd64.whl b4d17d6b79a8354a2834047669651963 numpy-1.23.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 89f6dc4a4ff63fca6af1223111cd888d numpy-1.23.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 633d574a35b8592bab502ef569b0731e numpy-1.23.5-pp38-pypy38_pp73-win_amd64.whl 8b2692a511a3795f3af8af2cd7566a15 numpy-1.23.5.tar.gz SHA256 9c88793f78fca17da0145455f0d7826bcb9f37da4764af27ac945488116efe63 numpy-1.23.5-cp310-cp310-macosx_10_9_x86_64.whl e9f4c4e51567b616be64e05d517c79a8a22f3606499941d97bb76f2ca59f982d numpy-1.23.5-cp310-cp310-macosx_11_0_arm64.whl 7903ba8ab592b82014713c491f6c5d3a1cde5b4a3bf116404e08f5b52f6daf43 numpy-1.23.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 5e05b1c973a9f858c74367553e236f287e749465f773328c8ef31abe18f691e1 numpy-1.23.5-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 522e26bbf6377e4d76403826ed689c295b0b238f46c28a7251ab94716da0b280 numpy-1.23.5-cp310-cp310-win32.whl dbee87b469018961d1ad79b1a5d50c0ae850000b639bcb1b694e9981083243b6 numpy-1.23.5-cp310-cp310-win_amd64.whl ce571367b6dfe60af04e04a1834ca2dc5f46004ac1cc756fb95319f64c095a96 numpy-1.23.5-cp311-cp311-macosx_10_9_x86_64.whl 56e454c7833e94ec9769fa0f86e6ff8e42ee38ce0ce1fa4cbb747ea7e06d56aa numpy-1.23.5-cp311-cp311-macosx_11_0_arm64.whl 5039f55555e1eab31124a5768898c9e22c25a65c1e0037f4d7c495a45778c9f2 numpy-1.23.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 58f545efd1108e647604a1b5aa809591ccd2540f468a880bedb97247e72db387 numpy-1.23.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b2a9ab7c279c91974f756c84c365a669a887efa287365a8e2c418f8b3ba73fb0 numpy-1.23.5-cp311-cp311-win32.whl 0cbe9848fad08baf71de1a39e12d1b6310f1d5b2d0ea4de051058e6e1076852d numpy-1.23.5-cp311-cp311-win_amd64.whl f063b69b090c9d918f9df0a12116029e274daf0181df392839661c4c7ec9018a numpy-1.23.5-cp38-cp38-macosx_10_9_x86_64.whl 0aaee12d8883552fadfc41e96b4c82ee7d794949e2a7c3b3a7201e968c7ecab9 numpy-1.23.5-cp38-cp38-macosx_11_0_arm64.whl 92c8c1e89a1f5028a4c6d9e3ccbe311b6ba53694811269b992c0b224269e2398 numpy-1.23.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl d208a0f8729f3fb790ed18a003f3a57895b989b40ea4dce4717e9cf4af62c6bb numpy-1.23.5-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 06005a2ef6014e9956c09ba07654f9837d9e26696a0470e42beedadb78c11b07 numpy-1.23.5-cp38-cp38-win32.whl ca51fcfcc5f9354c45f400059e88bc09215fb71a48d3768fb80e357f3b457e1e numpy-1.23.5-cp38-cp38-win_amd64.whl 8969bfd28e85c81f3f94eb4a66bc2cf1dbdc5c18efc320af34bffc54d6b1e38f numpy-1.23.5-cp39-cp39-macosx_10_9_x86_64.whl a7ac231a08bb37f852849bbb387a20a57574a97cfc7b6cabb488a4fc8be176de numpy-1.23.5-cp39-cp39-macosx_11_0_arm64.whl bf837dc63ba5c06dc8797c398db1e223a466c7ece27a1f7b5232ba3466aafe3d numpy-1.23.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 33161613d2269025873025b33e879825ec7b1d831317e68f4f2f0f84ed14c719 numpy-1.23.5-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl af1da88f6bc3d2338ebbf0e22fe487821ea4d8e89053e25fa59d1d79786e7481 numpy-1.23.5-cp39-cp39-win32.whl 09b7847f7e83ca37c6e627682f145856de331049013853f344f37b0c9690e3df numpy-1.23.5-cp39-cp39-win_amd64.whl abdde9f795cf292fb9651ed48185503a2ff29be87770c3b8e2a14b0cd7aa16f8 numpy-1.23.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl f9a909a8bae284d46bbfdefbdd4a262ba19d3bc9921b1e76126b1d21c3c34135 numpy-1.23.5-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 01dd17cbb340bf0fc23981e52e1d18a9d4050792e8fb8363cecbf066a84b827d numpy-1.23.5-pp38-pypy38_pp73-win_amd64.whl 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. Checksums MD5 90a3d95982490cfeeef22c0f7cbd874f numpy-1.23.4-cp310-cp310-macosx_10_9_x86_64.whl c3cae63394db6c82fd2cb5700fc5917d numpy-1.23.4-cp310-cp310-macosx_11_0_arm64.whl b3ff0878de205f56c38fd7dcab80081f numpy-1.23.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl e2b086ca2229209f2f996c2f9a38bf9c numpy-1.23.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 44cc8bb112ca737520cf986fff92dfb0 numpy-1.23.4-cp310-cp310-win32.whl 21c8e5fdfba2ff953e446189379cf0c9 numpy-1.23.4-cp310-cp310-win_amd64.whl 27445a9c85977cb8efa682a4b993347f numpy-1.23.4-cp311-cp311-macosx_10_9_x86_64.whl 11ef4b7dfdaa37604cb881f3ca4459db numpy-1.23.4-cp311-cp311-macosx_11_0_arm64.whl b3c77344274f91514f728a454fd471fa numpy-1.23.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 43aef7f984cd63d95c11fb74dd59ef0b numpy-1.23.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 637fe21b585228c9670d6e002bf8047f numpy-1.23.4-cp311-cp311-win32.whl f529edf9b849d6e3b8cdb5120ae5b81a numpy-1.23.4-cp311-cp311-win_amd64.whl 76c61ce36317a7e509663829c6844fd9 numpy-1.23.4-cp38-cp38-macosx_10_9_x86_64.whl 2133f6893eef41cd9331c7d0271044c4 numpy-1.23.4-cp38-cp38-macosx_11_0_arm64.whl 5ccb3aa6fb8cb9e20ec336e315d01dec numpy-1.23.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl da71f34a4df0b98e4d9e17906dd57b07 numpy-1.23.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a318978f51fb80a17c2381e39194e906 numpy-1.23.4-cp38-cp38-win32.whl eac810d6bc43830bf151ea55cd0ded93 numpy-1.23.4-cp38-cp38-win_amd64.whl 4cf0a6007abe42564c7380dbf92a26ce numpy-1.23.4-cp39-cp39-macosx_10_9_x86_64.whl 2e005bedf129ce8bafa6f550537f3740 numpy-1.23.4-cp39-cp39-macosx_11_0_arm64.whl 10aa210311fcd19a03f6c5495824a306 numpy-1.23.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6301298a67999657a0878b64eeed09f2 numpy-1.23.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 76144e575a3c3863ea22e03cdf022d8a numpy-1.23.4-cp39-cp39-win32.whl 8291dd66ef5451b4db2da55c21535757 numpy-1.23.4-cp39-cp39-win_amd64.whl 7cc095b18690071828b5b620d5ec40e7 numpy-1.23.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 63742f15e8bfa215c893136bbfc6444f numpy-1.23.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4ed382e55abc09c89a34db047692f6a6 numpy-1.23.4-pp38-pypy38_pp73-win_amd64.whl d9ffd2c189633486ec246e61d4b947a0 numpy-1.23.4.tar.gz SHA256 95d79ada05005f6f4f337d3bb9de8a7774f259341c70bc88047a1f7b96a4bcb2 numpy-1.23.4-cp310-cp310-macosx_10_9_x86_64.whl 926db372bc4ac1edf81cfb6c59e2a881606b409ddc0d0920b988174b2e2a767f numpy-1.23.4-cp310-cp310-macosx_11_0_arm64.whl c237129f0e732885c9a6076a537e974160482eab8f10db6292e92154d4c67d71 numpy-1.23.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a8365b942f9c1a7d0f0dc974747d99dd0a0cdfc5949a33119caf05cb314682d3 numpy-1.23.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2341f4ab6dba0834b685cce16dad5f9b6606ea8a00e6da154f5dbded70fdc4dd numpy-1.23.4-cp310-cp310-win32.whl d331afac87c92373826af83d2b2b435f57b17a5c74e6268b79355b970626e329 numpy-1.23.4-cp310-cp310-win_amd64.whl 488a66cb667359534bc70028d653ba1cf307bae88eab5929cd707c761ff037db numpy-1.23.4-cp311-cp311-macosx_10_9_x86_64.whl ce03305dd694c4873b9429274fd41fc7eb4e0e4dea07e0af97a933b079a5814f numpy-1.23.4-cp311-cp311-macosx_11_0_arm64.whl 8981d9b5619569899666170c7c9748920f4a5005bf79c72c07d08c8a035757b0 numpy-1.23.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 7a70a7d3ce4c0e9284e92285cba91a4a3f5214d87ee0e95928f3614a256a1488 numpy-1.23.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 5e13030f8793e9ee42f9c7d5777465a560eb78fa7e11b1c053427f2ccab90c79 numpy-1.23.4-cp311-cp311-win32.whl 7607b598217745cc40f751da38ffd03512d33ec06f3523fb0b5f82e09f6f676d numpy-1.23.4-cp311-cp311-win_amd64.whl 7ab46e4e7ec63c8a5e6dbf5c1b9e1c92ba23a7ebecc86c336cb7bf3bd2fb10e5 numpy-1.23.4-cp38-cp38-macosx_10_9_x86_64.whl a8aae2fb3180940011b4862b2dd3756616841c53db9734b27bb93813cd79fce6 numpy-1.23.4-cp38-cp38-macosx_11_0_arm64.whl 8c053d7557a8f022ec823196d242464b6955a7e7e5015b719e76003f63f82d0f numpy-1.23.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a0882323e0ca4245eb0a3d0a74f88ce581cc33aedcfa396e415e5bba7bf05f68 numpy-1.23.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl dada341ebb79619fe00a291185bba370c9803b1e1d7051610e01ed809ef3a4ba numpy-1.23.4-cp38-cp38-win32.whl 0fe563fc8ed9dc4474cbf70742673fc4391d70f4363f917599a7fa99f042d5a8 numpy-1.23.4-cp38-cp38-win_amd64.whl c67b833dbccefe97cdd3f52798d430b9d3430396af7cdb2a0c32954c3ef73894 numpy-1.23.4-cp39-cp39-macosx_10_9_x86_64.whl f76025acc8e2114bb664294a07ede0727aa75d63a06d2fae96bf29a81747e4a7 numpy-1.23.4-cp39-cp39-macosx_11_0_arm64.whl 12ac457b63ec8ded85d85c1e17d85efd3c2b0967ca39560b307a35a6703a4735 numpy-1.23.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 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. Checksums MD5 fe1e3480ea8c417c8f7b05f543c1448d numpy-1.23.2-cp310-cp310-macosx_10_9_x86_64.whl 0ab14b1afd0a55a374ca69b3b39cab3c numpy-1.23.2-cp310-cp310-macosx_11_0_arm64.whl df059e5405bfe75c0ac77b01abbdb237 numpy-1.23.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4ed412c4c078e96edf11ca3b11eef76b numpy-1.23.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 0caad53d9a5e3c5e8cd29f19a9f0c014 numpy-1.23.2-cp310-cp310-win32.whl 01e508b8b4f591daff128da1cfde8e1f numpy-1.23.2-cp310-cp310-win_amd64.whl 8ecdb7e2a87255878b748550d91cfbe0 numpy-1.23.2-cp311-cp311-macosx_10_9_x86_64.whl e3004aae46cec9e234f78eaf473272e0 numpy-1.23.2-cp311-cp311-macosx_11_0_arm64.whl ec23c73caf581867d5ca9255b802f144 numpy-1.23.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9b8389f528fe113247954248f0b78ce1 numpy-1.23.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl a54b136daa2fbb483909f08eecbfa3c5 numpy-1.23.2-cp311-cp311-win32.whl ead32e141857c5ef33b1a6cd88aefc0f numpy-1.23.2-cp311-cp311-win_amd64.whl df1f18e52d0a2840d101fdc9c2c6af84 numpy-1.23.2-cp38-cp38-macosx_10_9_x86_64.whl 04c986880bb24fac2f44face75eab914 numpy-1.23.2-cp38-cp38-macosx_11_0_arm64.whl edeba58edb214390112810f7ead903a8 numpy-1.23.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl c26ea699d94d7f1009c976c66cc4def3 numpy-1.23.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl c246a78b09f8893d998d449dcab0fac3 numpy-1.23.2-cp38-cp38-win32.whl b5c5a2f961402259e301c49b8b05de55 numpy-1.23.2-cp38-cp38-win_amd64.whl d156dfae94d33eeff7fb9c6e5187e049 numpy-1.23.2-cp39-cp39-macosx_10_9_x86_64.whl 7f2ad7867c577eab925a31de76486765 numpy-1.23.2-cp39-cp39-macosx_11_0_arm64.whl 76262a8e5d7a4d945446467467300a10 numpy-1.23.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8ee105f4574d61a2d494418b55f63fcb numpy-1.23.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2b7c79cae66023f8e716150223201981 numpy-1.23.2-cp39-cp39-win32.whl d7af57dd070ccb165f3893412eb602e3 numpy-1.23.2-cp39-cp39-win_amd64.whl 355a231dbd87a0f2125cc23eb8f97075 numpy-1.23.2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 4ab13c35056f67981d03f9ceec41db42 numpy-1.23.2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3a6f1e1256ee9be10d8cdf6be578fe52 numpy-1.23.2-pp38-pypy38_pp73-win_amd64.whl 9bf2a361509797de14ceee607387fe0f numpy-1.23.2.tar.gz SHA256 e603ca1fb47b913942f3e660a15e55a9ebca906857edfea476ae5f0fe9b457d5 numpy-1.23.2-cp310-cp310-macosx_10_9_x86_64.whl 633679a472934b1c20a12ed0c9a6c9eb167fbb4cb89031939bfd03dd9dbc62b8 numpy-1.23.2-cp310-cp310-macosx_11_0_arm64.whl 17e5226674f6ea79e14e3b91bfbc153fdf3ac13f5cc54ee7bc8fdbe820a32da0 numpy-1.23.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl bdc02c0235b261925102b1bd586579b7158e9d0d07ecb61148a1799214a4afd5 numpy-1.23.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl df28dda02c9328e122661f399f7655cdcbcf22ea42daa3650a26bce08a187450 numpy-1.23.2-cp310-cp310-win32.whl 8ebf7e194b89bc66b78475bd3624d92980fca4e5bb86dda08d677d786fefc414 numpy-1.23.2-cp310-cp310-win_amd64.whl dc76bca1ca98f4b122114435f83f1fcf3c0fe48e4e6f660e07996abf2f53903c numpy-1.23.2-cp311-cp311-macosx_10_9_x86_64.whl ecfdd68d334a6b97472ed032b5b37a30d8217c097acfff15e8452c710e775524 numpy-1.23.2-cp311-cp311-macosx_11_0_arm64.whl 5593f67e66dea4e237f5af998d31a43e447786b2154ba1ad833676c788f37cde numpy-1.23.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl ac987b35df8c2a2eab495ee206658117e9ce867acf3ccb376a19e83070e69418 numpy-1.23.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d98addfd3c8728ee8b2c49126f3c44c703e2b005d4a95998e2167af176a9e722 numpy-1.23.2-cp311-cp311-win32.whl 8ecb818231afe5f0f568c81f12ce50f2b828ff2b27487520d85eb44c71313b9e numpy-1.23.2-cp311-cp311-win_amd64.whl 909c56c4d4341ec8315291a105169d8aae732cfb4c250fbc375a1efb7a844f8f numpy-1.23.2-cp38-cp38-macosx_10_9_x86_64.whl 8247f01c4721479e482cc2f9f7d973f3f47810cbc8c65e38fd1bbd3141cc9842 numpy-1.23.2-cp38-cp38-macosx_11_0_arm64.whl b8b97a8a87cadcd3f94659b4ef6ec056261fa1e1c3317f4193ac231d4df70215 numpy-1.23.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl bd5b7ccae24e3d8501ee5563e82febc1771e73bd268eef82a1e8d2b4d556ae66 numpy-1.23.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 9b83d48e464f393d46e8dd8171687394d39bc5abfe2978896b77dc2604e8635d numpy-1.23.2-cp38-cp38-win32.whl dec198619b7dbd6db58603cd256e092bcadef22a796f778bf87f8592b468441d numpy-1.23.2-cp38-cp38-win_amd64.whl 4f41f5bf20d9a521f8cab3a34557cd77b6f205ab2116651f12959714494268b0 numpy-1.23.2-cp39-cp39-macosx_10_9_x86_64.whl 806cc25d5c43e240db709875e947076b2826f47c2c340a5a2f36da5bb10c58d6 numpy-1.23.2-cp39-cp39-macosx_11_0_arm64.whl 8f9d84a24889ebb4c641a9b99e54adb8cab50972f0166a3abc14c3b93163f074 numpy-1.23.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 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. ([gh-21354](https://github.com/numpy/numpy/pull/21354)) Checksums MD5 c67b4cc1de8a0753bc65765a508aa0e3 numpy-1.23.0rc1-cp310-cp310-macosx_10_9_x86_64.whl 9daceb162c46298986b5fceb13f10e54 numpy-1.23.0rc1-cp310-cp310-macosx_11_0_arm64.whl ac07046b70001710d8d3243d9b5d0389 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 14269d197cd6aac02655d43aa10ba108 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl ba5fc5cd776549afc353e0043f6c6f5a numpy-1.23.0rc1-cp310-cp310-win32.whl 0e1b00f156f32aabde1e29607c709a24 numpy-1.23.0rc1-cp310-cp310-win_amd64.whl 34b5a9f3abeb9f6e9c6fbd494305d53d numpy-1.23.0rc1-cp38-cp38-macosx_10_9_x86_64.whl bb825cf372822daad1e440577e324042 numpy-1.23.0rc1-cp38-cp38-macosx_11_0_arm64.whl 45296e6b41691c4265c90562c94254aa numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl b10b131e5c0576629ab99829301d6fba numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl bc6210254087b73715d8c6a79dafa3b8 numpy-1.23.0rc1-cp38-cp38-win32.whl d9b7fb5a539a738309a717051f13e41a numpy-1.23.0rc1-cp38-cp38-win_amd64.whl d2cec33489c96dfc489bb00353d351fe numpy-1.23.0rc1-cp39-cp39-macosx_10_9_x86_64.whl e4982f3bf3d4acab67cb61d3d0e2f85b numpy-1.23.0rc1-cp39-cp39-macosx_11_0_arm64.whl 781a79ea9f1683a579a1ef27d809a8e0 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8c03ea50c2baa172e8252d10dea73498 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 61829dec0785cf72b8f5bc92dc44d828 numpy-1.23.0rc1-cp39-cp39-win32.whl 94c7c971ed5eedb1b61a09bcfc123617 numpy-1.23.0rc1-cp39-cp39-win_amd64.whl b5c6f674b468e7fc513882563391efec numpy-1.23.0rc1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 41ab4b757478c8e244018c37bcb52bb3 numpy-1.23.0rc1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl f2082734772a6a7afbe3568e7b2ad458 numpy-1.23.0rc1-pp38-pypy38_pp73-win_amd64.whl faf6a08cda5696b96acb670c433495e5 numpy-1.23.0rc1.tar.gz SHA256 a72607e12891615a314a892f8d21301b930f211841f0084d269baa1eb31710b4 numpy-1.23.0rc1-cp310-cp310-macosx_10_9_x86_64.whl 4784a81089c75a941dcc013b09290dfb22768780a1f3525667328d09a3338116 numpy-1.23.0rc1-cp310-cp310-macosx_11_0_arm64.whl d4ebdeb0e2a57bbd28c3258a562ee011775127427eea833613744af1a66c3e11 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl a2dfb54cb1c6470918a3c02da77706f28977cb7eac4b76cc40b14942c8634615 numpy-1.23.0rc1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 1567c488f9ef97341c5937b4140a45ac37e0592c43ba2c59d3e49ff7d5da90b0 numpy-1.23.0rc1-cp310-cp310-win32.whl 78df1fc2ddf543508b5358dd24ac68ee693599e5df0d136062b9ec21ba7643cf numpy-1.23.0rc1-cp310-cp310-win_amd64.whl 020c6d8476fced48f42629f46996af8a07bc725cb821081205c4422eacaa2283 numpy-1.23.0rc1-cp38-cp38-macosx_10_9_x86_64.whl efd26eecd1ada0c8dadc5b221c52086ccc72e4cb0707e451889ef3b62c14163c numpy-1.23.0rc1-cp38-cp38-macosx_11_0_arm64.whl 8cf3f61984777a830eef452d8b04338795691949214e6cafc46c5236900cd1f5 numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 96738ae39db010502564325ce2f4aff4f42b75adf64f3ccb2b19214e9be1c01c numpy-1.23.0rc1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 33b233f59d9430a27c2a58a056f32259eadf9584f41c6ec02c493c3aeb90f844 numpy-1.23.0rc1-cp38-cp38-win32.whl edf0a82d285e18418e3915fece2cf0f4f31e84fe62271331fbaafbbc7d57e9ee numpy-1.23.0rc1-cp38-cp38-win_amd64.whl a71f1602bf84d0a2fb5d586a2d8c31f29fbca9253ae1eecf46b7059fa265eb79 numpy-1.23.0rc1-cp39-cp39-macosx_10_9_x86_64.whl 05000d27fd135dd0aab90acaf96652991c070dda688739097ac2dea92189f9f0 numpy-1.23.0rc1-cp39-cp39-macosx_11_0_arm64.whl ebe07758ac3e7402290f43d379f6d79d81a247488561743490cf2e5b64351ba6 numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 3cd05784cdcd09114c2f6186bb99af7f5ee65ffd720dae9990722a94309b17ea numpy-1.23.0rc1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 6ccb79435d4501b35ed3d807e1bf7345e42f68b25fbf720ade9c74c7196360f9 numpy-1.23.0rc1-cp39-cp39-win32.whl a8fbe61e09565fa2f7bca076627ea0efbf50ab689c35af5082c5d94fb24b30ee numpy-1.23.0rc1-cp39-cp39-win_amd64.whl 7a45352476e92c1958ce513fa84b508d59dd8e6ffe0e6f6cceebfc0f3c06d086 numpy-1.23.0rc1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl 4f15768493ecf23c5d82e5542642a36764e551c7744781268c7c221f26c7ffd6 numpy-1.23.0rc1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl edf0b720c8ba3d35b23c71c0cd13df34290be87b42f0e10d0ec2f1639cda2692 numpy-1.23.0rc1-pp38-pypy38_pp73-win_amd64.whl 3a09d0f564f59b6da54f592909d3fdbd50b492ef9fbe6d699043c992538ba0e0 numpy-1.23.0rc1.tar.gz ``` ### 1.22.4 ``` 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://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. 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 ``` ### 1.22.3 ``` 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 + - 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://github.com/numpy/numpy/pull/21048): MAINT: Use \"3.10\" instead of \"3.10-dev\" on travis. - [21106](https://github.com/numpy/numpy/pull/21106): TYP,MAINT: Explicitly allow sequences of array-likes in `np.concatenate` - [21137](https://github.com/numpy/numpy/pull/21137): BLD,DOC: skip broken ipython 8.1.0 - [21138](https://github.com/numpy/numpy/pull/21138): BUG, ENH: np.\_from_dlpack: export correct device information - [21139](https://github.com/numpy/numpy/pull/21139): BUG: Fix numba DUFuncs added loops getting picked up - [21140](https://github.com/numpy/numpy/pull/21140): BUG: Fix unpickling an empty ndarray with a non-zero dimension\... - [21141](https://github.com/numpy/numpy/pull/21141): BUG: use ThreadPoolExecutor instead of ThreadPool - [21142](https://github.com/numpy/numpy/pull/21142): API: Disallow strings in logical ufuncs - [21143](https://github.com/numpy/numpy/pull/21143): MAINT, DOC: Fix SciPy intersphinx link - [21148](https://github.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 ``` ### 1.22.2 ``` 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 - Mukuli
pyup-bot commented 1 year ago

Closing this in favor of #80