Closed platform-engineering-bot closed 3 months ago
This PR contains the following updates:
==2.0.0
==2.0.1
📅 Schedule: Branch creation - "before 4am on Monday" (UTC), Automerge - At any time (no schedule defined).
🚦 Automerge: Enabled.
â™» 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 Renovate Bot.
This PR contains the following updates:
==2.0.0->==2.0.1Release Notes
numpy/numpy (numpy)
### [`v2.0.1`](https://togithub.com/numpy/numpy/releases/tag/v2.0.1) [Compare Source](https://togithub.com/numpy/numpy/compare/v2.0.0...v2.0.1) ### NumPy 2.0.1 Release Notes NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly. The Python versions supported by this release are 3.9-3.12. ***NOTE:*** Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage. #### Improvements ##### `np.quantile` with method `closest_observation` chooses nearest even order statistic This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations. ([gh-26656](https://togithub.com/numpy/numpy/pull/26656)) #### Contributors A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - [@vahidmech](https://togithub.com/vahidmech) + - Alex Herbert + - Charles Harris - Giovanni Del Monte + - Leo Singer - Lysandros Nikolaou - Matti Picus - Nathan Goldbaum - Patrick J. Roddy + - Raghuveer Devulapalli - Ralf Gommers - Rostan Tabet + - Sebastian Berg - Tyler Reddy - Yannik Wicke + #### Pull requests merged A total of 24 pull requests were merged for this release. - [#26711](https://togithub.com/numpy/numpy/pull/26711): MAINT: prepare 2.0.x for further development - [#26792](https://togithub.com/numpy/numpy/pull/26792): TYP: fix incorrect import in `ma/extras.pyi` stub - [#26793](https://togithub.com/numpy/numpy/pull/26793): DOC: Mention '1.25' legacy printing mode in `set_printoptions` - [#26794](https://togithub.com/numpy/numpy/pull/26794): DOC: Remove mention of NaN and NAN aliases from constants - [#26821](https://togithub.com/numpy/numpy/pull/26821): BLD: Fix x86-simd-sort build failure on openBSD - [#26822](https://togithub.com/numpy/numpy/pull/26822): BUG: Ensure output order follows input in numpy.fft - [#26823](https://togithub.com/numpy/numpy/pull/26823): TYP: fix missing sys import in numeric.pyi - [#26832](https://togithub.com/numpy/numpy/pull/26832): DOC: remove hack to override \_add_newdocs_scalars - [#26835](https://togithub.com/numpy/numpy/pull/26835): BUG: avoid side-effect of 'include complex.h' - [#26836](https://togithub.com/numpy/numpy/pull/26836): BUG: fix max_rows and chunked string/datetime reading in `loadtxt` - [#26837](https://togithub.com/numpy/numpy/pull/26837): BUG: fix PyArray_ImportNumPyAPI under -Werror=strict-prototypes - [#26856](https://togithub.com/numpy/numpy/pull/26856): DOC: Update some documentation - [#26868](https://togithub.com/numpy/numpy/pull/26868): BUG: fancy indexing copy - [#26869](https://togithub.com/numpy/numpy/pull/26869): BUG: Mismatched allocation domains in `PyArray_FillWithScalar` - [#26870](https://togithub.com/numpy/numpy/pull/26870): BUG: Handle --f77flags and --f90flags for meson \[wheel build] - [#26887](https://togithub.com/numpy/numpy/pull/26887): BUG: Fix new DTypes and new string promotion when signature is... - [#26888](https://togithub.com/numpy/numpy/pull/26888): BUG: remove numpy.f2py from excludedimports - [#26959](https://togithub.com/numpy/numpy/pull/26959): BUG: Quantile closest_observation to round to nearest even order - [#26960](https://togithub.com/numpy/numpy/pull/26960): BUG: Fix off-by-one error in amount of characters in strip - [#26961](https://togithub.com/numpy/numpy/pull/26961): API: Partially revert unique with return_inverse - [#26962](https://togithub.com/numpy/numpy/pull/26962): BUG,MAINT: Fix utf-8 character stripping memory access - [#26963](https://togithub.com/numpy/numpy/pull/26963): BUG: Fix out-of-bound minimum offset for in1d table method - [#26971](https://togithub.com/numpy/numpy/pull/26971): BUG: fix f2py tests to work with v2 API - [#26995](https://togithub.com/numpy/numpy/pull/26995): BUG: Add object cast to avoid warning with limited API #### Checksums ##### MD5 a3e7d0f361ee7302448cae3c10844dd3 numpy-2.0.1-cp310-cp310-macosx_10_9_x86_64.whl cff8546b69e43ae7b5050f05bdc25df2 numpy-2.0.1-cp310-cp310-macosx_11_0_arm64.whl 1713d23342528f4f8f4027970f010068 numpy-2.0.1-cp310-cp310-macosx_14_0_arm64.whl 20020d28606ea58f986a262daa6018f1 numpy-2.0.1-cp310-cp310-macosx_14_0_x86_64.whl db22154ea943a707917aebc79e449bc5 numpy-2.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl fe86cd85f240216f64eb076a62a229d2 numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e0ca08f85150af3cc6050d64e8c0bd27 numpy-2.0.1-cp310-cp310-musllinux_1_1_x86_64.whl b76f432906f62e31f0e09c41f3f08b4c numpy-2.0.1-cp310-cp310-musllinux_1_2_aarch64.whl 28e8109e4ef524fa5c272d6faec870ae numpy-2.0.1-cp310-cp310-win32.whl 874beffaefdc73da42300ce691c2419c numpy-2.0.1-cp310-cp310-win_amd64.whl 7bbe029f650c924e952da117842d456d numpy-2.0.1-cp311-cp311-macosx_10_9_x86_64.whl 6d3d6ae26c520e93cef7f11ba3951f57 numpy-2.0.1-cp311-cp311-macosx_11_0_arm64.whl de6082d719437eb7468ae31c407c503e numpy-2.0.1-cp311-cp311-macosx_14_0_arm64.whl d15a8d95661f8a1dfcc4eb089f9b46e8 numpy-2.0.1-cp311-cp311-macosx_14_0_x86_64.whl c181105e074ee575ccf2c992e40f947a numpy-2.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 00d22b299343fcdc78fbb0716ead6243 numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl d9c4f49dbedb3f3d0158f00db459bd25 numpy-2.0.1-cp311-cp311-musllinux_1_1_x86_64.whl 63caa03e0625327ad3a756e01c83a6ca numpy-2.0.1-cp311-cp311-musllinux_1_2_aarch64.whl 99d01d768a115d448ca2b4680de15191 numpy-2.0.1-cp311-cp311-win32.whl 8d1a31eccc8b9f077312095b11f62cb2 numpy-2.0.1-cp311-cp311-win_amd64.whl 6cc86f7761a33941d8c1c552186e774b numpy-2.0.1-cp312-cp312-macosx_10_9_x86_64.whl 67c48f352afff5f41108f1b9561d1d5c numpy-2.0.1-cp312-cp312-macosx_11_0_arm64.whl 1068d4eadcac6a869e0e457853b7e611 numpy-2.0.1-cp312-cp312-macosx_14_0_arm64.whl dfb667450315fddcf84381fc8ef16892 numpy-2.0.1-cp312-cp312-macosx_14_0_x86_64.whl 69822bbbbb65d8a7d00ae32b435f61cc numpy-2.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 883ed6c41395fb2def6cc0d64dcb817f numpy-2.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 4b1e9fd464821a7d1de3a8ddf911311e numpy-2.0.1-cp312-cp312-musllinux_1_1_x86_64.whl 79e6557f40b8ed8f5973b404d98eab3d numpy-2.0.1-cp312-cp312-musllinux_1_2_aarch64.whl 85596f15d4cf85c2f78b4cc12c2cad1e numpy-2.0.1-cp312-cp312-win32.whl 487c7c2944306f62b3770576ce903a91 numpy-2.0.1-cp312-cp312-win_amd64.whl 491093641afa21e65d6e629eb70571fc numpy-2.0.1-cp39-cp39-macosx_10_9_x86_64.whl 5008b16c20f3d7e5a0c7764712f8908e numpy-2.0.1-cp39-cp39-macosx_11_0_arm64.whl 14633b898f863ea797c40ba1cf226c29 numpy-2.0.1-cp39-cp39-macosx_14_0_arm64.whl 9054ecb69d21b364e59e94aab24247cb numpy-2.0.1-cp39-cp39-macosx_14_0_x86_64.whl be028cf4bb691921943939de17593dd7 numpy-2.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 9c440ad02ff0a954f696637de37aab2d numpy-2.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 27aec0d286eabe26d8e9149f4572dba1 numpy-2.0.1-cp39-cp39-musllinux_1_1_x86_64.whl b02eda82ee511ee27185c8a4073ea35c numpy-2.0.1-cp39-cp39-musllinux_1_2_aarch64.whl cf579b902325e023b2dc444692eb5991 numpy-2.0.1-cp39-cp39-win32.whl 302c8c3118a5f55d9ef35ed8e517f6b1 numpy-2.0.1-cp39-cp39-win_amd64.whl 34c17fe980accfb76c6f348f85b3cfef numpy-2.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 02676eb84379b0a223288d6fd9d76942 numpy-2.0.1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl b5300e6fe110bf69e1a8901c5c09e3f8 numpy-2.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 204a3ea7fb851e08d166c74f73f9b8a3 numpy-2.0.1-pp39-pypy39_pp73-win_amd64.whl 5df3c50fc124c3167404d396115898d0 numpy-2.0.1.tar.gz ##### SHA256 0fbb536eac80e27a2793ffd787895242b7f18ef792563d742c2d673bfcb75134 numpy-2.0.1-cp310-cp310-macosx_10_9_x86_64.whl 69ff563d43c69b1baba77af455dd0a839df8d25e8590e79c90fcbe1499ebde42 numpy-2.0.1-cp310-cp310-macosx_11_0_arm64.whl 1b902ce0e0a5bb7704556a217c4f63a7974f8f43e090aff03fcf262e0b135e02 numpy-2.0.1-cp310-cp310-macosx_14_0_arm64.whl f1659887361a7151f89e79b276ed8dff3d75877df906328f14d8bb40bb4f5101 numpy-2.0.1-cp310-cp310-macosx_14_0_x86_64.whl 4658c398d65d1b25e1760de3157011a80375da861709abd7cef3bad65d6543f9 numpy-2.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 4127d4303b9ac9f94ca0441138acead39928938660ca58329fe156f84b9f3015 numpy-2.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl e5eeca8067ad04bc8a2a8731183d51d7cbaac66d86085d5f4766ee6bf19c7f87 numpy-2.0.1-cp310-cp310-musllinux_1_1_x86_64.whl 9adbd9bb520c866e1bfd7e10e1880a1f7749f1f6e5017686a5fbb9b72cf69f82 numpy-2.0.1-cp310-cp310-musllinux_1_2_aarch64.whl 7b9853803278db3bdcc6cd5beca37815b133e9e77ff3d4733c247414e78eb8d1 numpy-2.0.1-cp310-cp310-win32.whl 81b0893a39bc5b865b8bf89e9ad7807e16717f19868e9d234bdaf9b1f1393868 numpy-2.0.1-cp310-cp310-win_amd64.whl 75b4e316c5902d8163ef9d423b1c3f2f6252226d1aa5cd8a0a03a7d01ffc6268 numpy-2.0.1-cp311-cp311-macosx_10_9_x86_64.whl 6e4eeb6eb2fced786e32e6d8df9e755ce5be920d17f7ce00bc38fcde8ccdbf9e numpy-2.0.1-cp311-cp311-macosx_11_0_arm64.whl a1e01dcaab205fbece13c1410253a9eea1b1c9b61d237b6fa59bcc46e8e89343 numpy-2.0.1-cp311-cp311-macosx_14_0_arm64.whl a8fc2de81ad835d999113ddf87d1ea2b0f4704cbd947c948d2f5513deafe5a7b numpy-2.0.1-cp311-cp311-macosx_14_0_x86_64.whl 5a3d94942c331dd4e0e1147f7a8699a4aa47dffc11bf8a1523c12af8b2e91bbe numpy-2.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 15eb4eca47d36ec3f78cde0a3a2ee24cf05ca7396ef808dda2c0ddad7c2bde67 numpy-2.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl b83e16a5511d1b1f8a88cbabb1a6f6a499f82c062a4251892d9ad5d609863fb7 numpy-2.0.1-cp311-cp311-musllinux_1_1_x86_64.whl 1f87fec1f9bc1efd23f4227becff04bd0e979e23ca50cc92ec88b38489db3b55 numpy-2.0.1-cp311-cp311-musllinux_1_2_aarch64.whl 36d3a9405fd7c511804dc56fc32974fa5533bdeb3cd1604d6b8ff1d292b819c4 numpy-2.0.1-cp311-cp311-win32.whl 08458fbf403bff5e2b45f08eda195d4b0c9b35682311da5a5a0a0925b11b9bd8 numpy-2.0.1-cp311-cp311-win_amd64.whl 6bf4e6f4a2a2e26655717a1983ef6324f2664d7011f6ef7482e8c0b3d51e82ac numpy-2.0.1-cp312-cp312-macosx_10_9_x86_64.whl 7d6fddc5fe258d3328cd8e3d7d3e02234c5d70e01ebe377a6ab92adb14039cb4 numpy-2.0.1-cp312-cp312-macosx_11_0_arm64.whl 5daab361be6ddeb299a918a7c0864fa8618af66019138263247af405018b04e1 numpy-2.0.1-cp312-cp312-macosx_14_0_arm64.whl ea2326a4dca88e4a274ba3a4405eb6c6467d3ffbd8c7d38632502eaae3820587 numpy-2.0.1-cp312-cp312-macosx_14_0_x86_64.whl 529af13c5f4b7a932fb0e1911d3a75da204eff023ee5e0e79c1751564221a5c8 numpy-2.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 6790654cb13eab303d8402354fabd47472b24635700f631f041bd0b65e37298a numpy-2.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl cbab9fc9c391700e3e1287666dfd82d8666d10e69a6c4a09ab97574c0b7ee0a7 numpy-2.0.1-cp312-cp312-musllinux_1_1_x86_64.whl 99d0d92a5e3613c33a5f01db206a33f8fdf3d71f2912b0de1739894668b7a93b numpy-2.0.1-cp312-cp312-musllinux_1_2_aarch64.whl 173a00b9995f73b79eb0191129f2455f1e34c203f559dd118636858cc452a1bf numpy-2.0.1-cp312-cp312-win32.whl bb2124fdc6e62baae159ebcfa368708867eb56806804d005860b6007388df171 numpy-2.0.1-cp312-cp312-win_amd64.whl bfc085b28d62ff4009364e7ca34b80a9a080cbd97c2c0630bb5f7f770dae9414 numpy-2.0.1-cp39-cp39-macosx_10_9_x86_64.whl 8fae4ebbf95a179c1156fab0b142b74e4ba4204c87bde8d3d8b6f9c34c5825ef numpy-2.0.1-cp39-cp39-macosx_11_0_arm64.whl 72dc22e9ec8f6eaa206deb1b1355eb2e253899d7347f5e2fae5f0af613741d06 numpy-2.0.1-cp39-cp39-macosx_14_0_arm64.whl ec87f5f8aca726117a1c9b7083e7656a9d0d606eec7299cc067bb83d26f16e0c numpy-2.0.1-cp39-cp39-macosx_14_0_x86_64.whl 1f682ea61a88479d9498bf2091fdcd722b090724b08b31d63e022adc063bad59 numpy-2.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl 8efc84f01c1cd7e34b3fb310183e72fcdf55293ee736d679b6d35b35d80bba26 numpy-2.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 3fdabe3e2a52bc4eff8dc7a5044342f8bd9f11ef0934fcd3289a788c0eb10018 numpy-2.0.1-cp39-cp39-musllinux_1_1_x86_64.whl 24a0e1befbfa14615b49ba9659d3d8818a0f4d8a1c5822af8696706fbda7310c numpy-2.0.1-cp39-cp39-musllinux_1_2_aarch64.whl f9cf5ea551aec449206954b075db819f52adc1638d46a6738253a712d553c7b4 numpy-2.0.1-cp39-cp39-win32.whl e9e81fa9017eaa416c056e5d9e71be93d05e2c3c2ab308d23307a8bc4443c368 numpy-2.0.1-cp39-cp39-win_amd64.whl 61728fba1e464f789b11deb78a57805c70b2ed02343560456190d0501ba37b0f numpy-2.0.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl 12f5d865d60fb9734e60a60f1d5afa6d962d8d4467c120a1c0cda6eb2964437d numpy-2.0.1-pp39-pypy39_pp73-macosx_14_0_x86_64.whl eacf3291e263d5a67d8c1a581a8ebbcfd6447204ef58828caf69a5e3e8c75990 numpy-2.0.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 2c3a346ae20cfd80b6cfd3e60dc179963ef2ea58da5ec074fd3d9e7a1e7ba97f numpy-2.0.1-pp39-pypy39_pp73-win_amd64.whl 485b87235796410c3519a699cfe1faab097e509e90ebb05dcd098db2ae87e7b3 numpy-2.0.1.tar.gzConfiguration
📅 Schedule: Branch creation - "before 4am on Monday" (UTC), Automerge - At any time (no schedule defined).
🚦 Automerge: Enabled.
â™» 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 Renovate Bot.