NumPy 1.21.1 Release Notes

The NumPy 1.21.1 is maintenance release that fixes bugs discovered after
the 1.21.0 release and updates OpenBLAS to v0.3.17 to deal with problems
on arm64.

The Python versions supported for this release are 3.7-3.9. The 1.21.x
series is compatible with development Python 3.10. Python 3.10 will be
officially supported after it is released.

⚠️ There are unresolved problems compiling NumPy 1.20.0 with gcc-11.1.

• Optimization level -O3 results in many incorrect
  warnings when running the tests.
• On some hardware NumPY will hang in an infinite loop.

Contributors

A total of 11 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Bas van Beek
• Charles Harris
• Ganesh Kathiresan
• Gregory R. Lee
• Hugo Defois +
• Kevin Sheppard
• Matti Picus
• Ralf Gommers
• Sayed Adel
• Sebastian Berg
• Thomas J. Fan

Pull requests merged

A total of 26 pull requests were merged for this release.

• #19311: REV,BUG: Replace NotImplemented with typing.Any
• #19324: MAINT: Fixed the return-dtype of ndarray.real and imag
• #19330: MAINT: Replace "dtype[Any]" with dtype in the definiton of...
• #19342: DOC: Fix some docstrings that crash pdf generation.
• #19343: MAINT: bump scipy-mathjax
• #19347: BUG: Fix arr.flat.index for large arrays and big-endian machines
• #19348: ENH: add numpy.f2py.get_include function
• #19349: BUG: Fix reference count leak in ufunc dtype handling
• #19350: MAINT: Annotate missing attributes of np.number subclasses
• #19351: BUG: Fix cast safety and comparisons for zero sized voids
• #19352: BUG: Correct Cython declaration in random
• #19353: BUG: protect against accessing base attribute of a NULL subarray
• #19365: BUG, SIMD: Fix detecting AVX512 features on Darwin
• #19366: MAINT: remove print()'s in distutils template handling
• #19390: ENH: SIMD architectures to show_config
• #19391: BUG: Do not raise deprecation warning for all nans in unique...
• #19392: BUG: Fix NULL special case in object-to-any cast code
• #19430: MAINT: Use arm64-graviton2 for testing on travis
• #19495: BUILD: update OpenBLAS to v0.3.17
• #19496: MAINT: Avoid unicode characters in division SIMD code comments
• #19499: BUG, SIMD: Fix infinite loop during count non-zero on GCC-11
• #19500: BUG: fix a numpy.npiter leak in npyiter_multi_index_set
• #19501: TST: Fix a GenericAlias test failure for python 3.9.0
• #19502: MAINT: Start testing with Python 3.10.0b3.
• #19503: MAINT: Add missing dtype overloads for object- and ctypes-based...
• #19510: REL: Prepare for NumPy 1.21.1 release.

Checksums

MD5

d88af78c155cb92ce5535724ed13ed73  numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl
946e54ec9d174ec90db8ae07a4c4ae2f  numpy-1.21.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
84d7f8534fa3ce1a8c2e2eab18e514de  numpy-1.21.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
2e256d7862047967f2a7dbff8b8e9d6c  numpy-1.21.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
4887ff09cc0652f3f1d9e0f40d1add63  numpy-1.21.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
bbe00679ce0ae484bb46776f64e00e32  numpy-1.21.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
b8eff5ba6bb920f3e65409abcfe7a55e  numpy-1.21.1-cp37-cp37m-win32.whl
d6ab781ad4537a818663a37392bdf647  numpy-1.21.1-cp37-cp37m-win_amd64.whl
f974f7a90567e082b16817e1218eb059  numpy-1.21.1-cp38-cp38-macosx_10_9_universal2.whl
37fb814042195516db4c5eedc23f65ef  numpy-1.21.1-cp38-cp38-macosx_10_9_x86_64.whl
2840e0ed51c8ebfb6fded7f1acfed810  numpy-1.21.1-cp38-cp38-macosx_11_0_arm64.whl
d87ed548450f324a3a6a3a230991e90a  numpy-1.21.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
e5e0e271fb18986887920f24b9ad8ec3  numpy-1.21.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f060727f195388df3f3c1e2c43a8d247  numpy-1.21.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
26b0cc05d6f59241f401c16a6fe9300e  numpy-1.21.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
dac4489fdaeffd24d402a555e61b4087  numpy-1.21.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
c248a8f07bb458660274eab769dcc1e2  numpy-1.21.1-cp38-cp38-win32.whl
52386872b66b108de80b5447d0e3f6b1  numpy-1.21.1-cp38-cp38-win_amd64.whl
1a730aa7303421f31c2bca5a343010bb  numpy-1.21.1-cp39-cp39-macosx_10_9_universal2.whl
141701393752d472456d4a15f9a554e4  numpy-1.21.1-cp39-cp39-macosx_10_9_x86_64.whl
33a9c001675f708aebc06f0a653378c1  numpy-1.21.1-cp39-cp39-macosx_11_0_arm64.whl
6b9482c5090f532285313ad2cf48d319  numpy-1.21.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
94fa7591ad4e51a85cb17bcec170b986  numpy-1.21.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f580b2ce2fb9cead163bab3f1d88fba7  numpy-1.21.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
238930d877b5d8a012b5b1bbc994ebb1  numpy-1.21.1-cp39-cp39-win32.whl
4014c63ac2a1c3e1df95f76feb14816e  numpy-1.21.1-cp39-cp39-win_amd64.whl
7cff22c1a04fdee710d38bd9468edbf1  numpy-1.21.1-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
033726e7ec59eea6b23307dcec35a37b  numpy-1.21.1.tar.gz
1d016e05851a4ba85307f3246eb569aa  numpy-1.21.1.zip

SHA256

38e8648f9449a549a7dfe8d8755a5979b45b3538520d1e735637ef28e8c2dc50  numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl
fd7d7409fa643a91d0a05c7554dd68aa9c9bb16e186f6ccfe40d6e003156e33a  numpy-1.21.1-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl
a75b4498b1e93d8b700282dc8e655b8bd559c0904b3910b144646dbbbc03e062  numpy-1.21.1-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
1412aa0aec3e00bc23fbb8664d76552b4efde98fb71f60737c83efbac24112f1  numpy-1.21.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
e46ceaff65609b5399163de5893d8f2a82d3c77d5e56d976c8b5fb01faa6b671  numpy-1.21.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl
c6a2324085dd52f96498419ba95b5777e40b6bcbc20088fddb9e8cbb58885e8e  numpy-1.21.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl
73101b2a1fef16602696d133db402a7e7586654682244344b8329cdcbbb82172  numpy-1.21.1-cp37-cp37m-win32.whl
7a708a79c9a9d26904d1cca8d383bf869edf6f8e7650d85dbc77b041e8c5a0f8  numpy-1.21.1-cp37-cp37m-win_amd64.whl
95b995d0c413f5d0428b3f880e8fe1660ff9396dcd1f9eedbc311f37b5652e16  numpy-1.21.1-cp38-cp38-macosx_10_9_universal2.whl
635e6bd31c9fb3d475c8f44a089569070d10a9ef18ed13738b03049280281267  numpy-1.21.1-cp38-cp38-macosx_10_9_x86_64.whl
4a3d5fb89bfe21be2ef47c0614b9c9c707b7362386c9a3ff1feae63e0267ccb6  numpy-1.21.1-cp38-cp38-macosx_11_0_arm64.whl
8a326af80e86d0e9ce92bcc1e65c8ff88297de4fa14ee936cb2293d414c9ec63  numpy-1.21.1-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl
791492091744b0fe390a6ce85cc1bf5149968ac7d5f0477288f78c89b385d9af  numpy-1.21.1-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
0318c465786c1f63ac05d7c4dbcecd4d2d7e13f0959b01b534ea1e92202235c5  numpy-1.21.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
9a513bd9c1551894ee3d31369f9b07460ef223694098cf27d399513415855b68  numpy-1.21.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl
91c6f5fc58df1e0a3cc0c3a717bb3308ff850abdaa6d2d802573ee2b11f674a8  numpy-1.21.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl
978010b68e17150db8765355d1ccdd450f9fc916824e8c4e35ee620590e234cd  numpy-1.21.1-cp38-cp38-win32.whl
9749a40a5b22333467f02fe11edc98f022133ee1bfa8ab99bda5e5437b831214  numpy-1.21.1-cp38-cp38-win_amd64.whl
d7a4aeac3b94af92a9373d6e77b37691b86411f9745190d2c351f410ab3a791f  numpy-1.21.1-cp39-cp39-macosx_10_9_universal2.whl
d9e7912a56108aba9b31df688a4c4f5cb0d9d3787386b87d504762b6754fbb1b  numpy-1.21.1-cp39-cp39-macosx_10_9_x86_64.whl
25b40b98ebdd272bc3020935427a4530b7d60dfbe1ab9381a39147834e985eac  numpy-1.21.1-cp39-cp39-macosx_11_0_arm64.whl
8a92c5aea763d14ba9d6475803fc7904bda7decc2a0a68153f587ad82941fec1  numpy-1.21.1-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl
05a0f648eb28bae4bcb204e6fd14603de2908de982e761a2fc78efe0f19e96e1  numpy-1.21.1-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
f01f28075a92eede918b965e86e8f0ba7b7797a95aa8d35e1cc8821f5fc3ad6a  numpy-1.21.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
88c0b89ad1cc24a5efbb99ff9ab5db0f9a86e9cc50240177a571fbe9c2860ac2  numpy-1.21.1-cp39-cp39-win32.whl
01721eefe70544d548425a07c80be8377096a54118070b8a62476866d5208e33  numpy-1.21.1-cp39-cp39-win_amd64.whl
2d4d1de6e6fb3d28781c73fbde702ac97f03d79e4ffd6598b880b2d95d62ead4  numpy-1.21.1-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl
504ced5d900fd5724c74ebf5dbb03572c04074bec9baa24b5646c66a2450e654  numpy-1.21.1.tar.gz
dff4af63638afcc57a3dfb9e4b26d434a7a602d225b42d746ea7fe2edf1342fd  numpy-1.21.1.zip