v1.23.1

NumPy 1.23.1 Release Notes

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: BUG: Fix discovered MachAr (still used within valgrind)
• #21867: BUG: Handle NaNs correctly for float16 during sorting
• #21868: BUG: Use keepdims during normalization in np.average and...
• #21869: DOC: mention changes to max_rows behaviour in np.loadtxt
• #21870: BUG: Reject non integer array-likes with size 1 in delete
• #21949: BLD: Make can_link_svml return False for 32bit builds on x86_64
• #21951: BUG: Reorder extern "C" to only apply to function declarations...
• #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