NumPy 1.19.2 Release Notes

NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x
release. and pins setuptools to keep distutils working while upstream
modifications are ongoing. The aarch64 wheels are built with the latest
manylinux2014 release that fixes the problem of differing page sizes
used by different linux distros.

This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
used when building with Python 3.9 for testing purposes.

There is a known problem with Windows 10 version=2004 and OpenBLAS svd
that we are trying to debug. If you are running that Windows version you
should use a NumPy version that links to the MKL library, earlier
Windows versions are fine.

Improvements

Add NumPy declarations for Cython 3.0 and later

The pxd declarations for Cython 3.0 were improved to avoid using
deprecated NumPy C-API features. Extension modules built with Cython
3.0+ that use NumPy can now set the C macro
NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings
about deprecated API usage.

Contributors

A total of 8 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Charles Harris
• Matti Picus
• Pauli Virtanen
• Philippe Ombredanne +
• Sebastian Berg
• Stefan Behnel +
• Stephan Loyd +
• Zac Hatfield-Dodds

Pull requests merged

A total of 9 pull requests were merged for this release.

• #16959: TST: Change aarch64 to arm64 in travis.yml.
• #16998: MAINT: Configure hypothesis in np.test() for determinism,...
• #17000: BLD: pin setuptools < 49.2.0
• #17015: ENH: Add NumPy declarations to be used by Cython 3.0+
• #17125: BUG: Remove non-threadsafe sigint handling from fft calculation
• #17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
• #17244: DOC: Use SPDX license expressions with correct license
• #17245: DOC: Fix the link to the quick-start in the old API functions
• #17272: BUG: fix pickling of arrays larger than 2GiB

Checksums

MD5

b74295cbb5b1c98f46f26e13c0fca0ea  numpy-1.19.2-cp36-cp36m-macosx_10_9_x86_64.whl
3e307eca6c448bbe30e4c1dc99824642  numpy-1.19.2-cp36-cp36m-manylinux1_i686.whl
bfe6c2053a7a792097df912d1175ef7e  numpy-1.19.2-cp36-cp36m-manylinux1_x86_64.whl
3b61953b421460abc7d2ecb4df4060bc  numpy-1.19.2-cp36-cp36m-manylinux2010_i686.whl
7c442b7c5af62bd5be669bf6c360e114  numpy-1.19.2-cp36-cp36m-manylinux2010_x86_64.whl
f6eaf46804f0d66c123fa7ff728b178e  numpy-1.19.2-cp36-cp36m-manylinux2014_aarch64.whl
30bbe0bcd774ab483c7494d1cf827199  numpy-1.19.2-cp36-cp36m-win32.whl
cf54372ccde7de333d7b69cd16abfa70  numpy-1.19.2-cp36-cp36m-win_amd64.whl
285d0fc2986bf4a050523d98f47f2175  numpy-1.19.2-cp37-cp37m-macosx_10_9_x86_64.whl
a0901b44347ba39154058a26a9fc8e77  numpy-1.19.2-cp37-cp37m-manylinux1_i686.whl
21bfe38bdb317ad4af4959279dd90fde  numpy-1.19.2-cp37-cp37m-manylinux1_x86_64.whl
ec32c124ace9c08399e88b8eca6d7475  numpy-1.19.2-cp37-cp37m-manylinux2010_i686.whl
0d5cae15043a8172a1b8a478b7c98119  numpy-1.19.2-cp37-cp37m-manylinux2010_x86_64.whl
c7e9905e721dc31a666f59e30e37aa0d  numpy-1.19.2-cp37-cp37m-manylinux2014_aarch64.whl
ad32d083e641f2cf1a50fe821f3673a7  numpy-1.19.2-cp37-cp37m-win32.whl
a243b3e844507e424e828430010612c1  numpy-1.19.2-cp37-cp37m-win_amd64.whl
8f4d5df29d4fbf21bf8c4c976595214f  numpy-1.19.2-cp38-cp38-macosx_10_9_x86_64.whl
7b003b2fd18125f3956eb3a182ab0d7f  numpy-1.19.2-cp38-cp38-manylinux1_i686.whl
e7b8242ee7a79778c6df64772fde5885  numpy-1.19.2-cp38-cp38-manylinux1_x86_64.whl
e89e05d24b6f898005e03ba3f01c0641  numpy-1.19.2-cp38-cp38-manylinux2010_i686.whl
4cffe85a99bfe08d47d7f1f655142be4  numpy-1.19.2-cp38-cp38-manylinux2010_x86_64.whl
39e363f10f0a9af0a8506699118d3aaf  numpy-1.19.2-cp38-cp38-manylinux2014_aarch64.whl
13ccd230fefdd56a1679fd72fd0d8a55  numpy-1.19.2-cp38-cp38-win32.whl
a3d85f244058882b90140468b86f2e2e  numpy-1.19.2-cp38-cp38-win_amd64.whl
ef4cf0675f801a4bf339348fc1843f50  numpy-1.19.2-pp36-pypy36_pp73-manylinux2010_x86_64.whl
471156268abd8686e39e811003726ab1  numpy-1.19.2.tar.gz
2d011c5422596d742784ba5c2204bc5d  numpy-1.19.2.zip

SHA256

b594f76771bc7fc8a044c5ba303427ee67c17a09b36e1fa32bde82f5c419d17a  numpy-1.19.2-cp36-cp36m-macosx_10_9_x86_64.whl
e6ddbdc5113628f15de7e4911c02aed74a4ccff531842c583e5032f6e5a179bd  numpy-1.19.2-cp36-cp36m-manylinux1_i686.whl
3733640466733441295b0d6d3dcbf8e1ffa7e897d4d82903169529fd3386919a  numpy-1.19.2-cp36-cp36m-manylinux1_x86_64.whl
4339741994c775396e1a274dba3609c69ab0f16056c1077f18979bec2a2c2e6e  numpy-1.19.2-cp36-cp36m-manylinux2010_i686.whl
7c6646314291d8f5ea900a7ea9c4261f834b5b62159ba2abe3836f4fa6705526  numpy-1.19.2-cp36-cp36m-manylinux2010_x86_64.whl
7118f0a9f2f617f921ec7d278d981244ba83c85eea197be7c5a4f84af80a9c3c  numpy-1.19.2-cp36-cp36m-manylinux2014_aarch64.whl
9a3001248b9231ed73894c773142658bab914645261275f675d86c290c37f66d  numpy-1.19.2-cp36-cp36m-win32.whl
967c92435f0b3ba37a4257c48b8715b76741410467e2bdb1097e8391fccfae15  numpy-1.19.2-cp36-cp36m-win_amd64.whl
d526fa58ae4aead839161535d59ea9565863bb0b0bdb3cc63214613fb16aced4  numpy-1.19.2-cp37-cp37m-macosx_10_9_x86_64.whl
eb25c381d168daf351147713f49c626030dcff7a393d5caa62515d415a6071d8  numpy-1.19.2-cp37-cp37m-manylinux1_i686.whl
62139af94728d22350a571b7c82795b9d59be77fc162414ada6c8b6a10ef5d02  numpy-1.19.2-cp37-cp37m-manylinux1_x86_64.whl
0c66da1d202c52051625e55a249da35b31f65a81cb56e4c69af0dfb8fb0125bf  numpy-1.19.2-cp37-cp37m-manylinux2010_i686.whl
2117536e968abb7357d34d754e3733b0d7113d4c9f1d921f21a3d96dec5ff716  numpy-1.19.2-cp37-cp37m-manylinux2010_x86_64.whl
54045b198aebf41bf6bf4088012777c1d11703bf74461d70cd350c0af2182e45  numpy-1.19.2-cp37-cp37m-manylinux2014_aarch64.whl
aba1d5daf1144b956bc87ffb87966791f5e9f3e1f6fab3d7f581db1f5b598f7a  numpy-1.19.2-cp37-cp37m-win32.whl
addaa551b298052c16885fc70408d3848d4e2e7352de4e7a1e13e691abc734c1  numpy-1.19.2-cp37-cp37m-win_amd64.whl
58d66a6b3b55178a1f8a5fe98df26ace76260a70de694d99577ddeab7eaa9a9d  numpy-1.19.2-cp38-cp38-macosx_10_9_x86_64.whl
59f3d687faea7a4f7f93bd9665e5b102f32f3fa28514f15b126f099b7997203d  numpy-1.19.2-cp38-cp38-manylinux1_i686.whl
cebd4f4e64cfe87f2039e4725781f6326a61f095bc77b3716502bed812b385a9  numpy-1.19.2-cp38-cp38-manylinux1_x86_64.whl
c35a01777f81e7333bcf276b605f39c872e28295441c265cd0c860f4b40148c1  numpy-1.19.2-cp38-cp38-manylinux2010_i686.whl
d7ac33585e1f09e7345aa902c281bd777fdb792432d27fca857f39b70e5dd31c  numpy-1.19.2-cp38-cp38-manylinux2010_x86_64.whl
04c7d4ebc5ff93d9822075ddb1751ff392a4375e5885299445fcebf877f179d5  numpy-1.19.2-cp38-cp38-manylinux2014_aarch64.whl
51ee93e1fac3fe08ef54ff1c7f329db64d8a9c5557e6c8e908be9497ac76374b  numpy-1.19.2-cp38-cp38-win32.whl
1669ec8e42f169ff715a904c9b2105b6640f3f2a4c4c2cb4920ae8b2785dac65  numpy-1.19.2-cp38-cp38-win_amd64.whl
0bfd85053d1e9f60234f28f63d4a5147ada7f432943c113a11afcf3e65d9d4c8  numpy-1.19.2-pp36-pypy36_pp73-manylinux2010_x86_64.whl
74d0cf50aa28af81874aca3e67560945afd783b2a006913577d6cddc35a824a6  numpy-1.19.2.tar.gz
0d310730e1e793527065ad7dde736197b705d0e4c9999775f212b03c44a8484c  numpy-1.19.2.zip