NumPy v1.16.5 Release Notes

Release Date: 2019-08-28 // over 4 years ago
  • ๐Ÿš€ NumPy 1.16.5 Release Notes

    ๐Ÿš€ The NumPy 1.16.5 release fixes bugs reported against the 1.16.4 release, and
    ๐Ÿ‘€ also backports several enhancements from master that seem appropriate for a
    ๐Ÿš€ release series that is the last to support Python 2.7. The wheels on PyPI are
    ๐Ÿ”— linked with OpenBLAS v0.3.7-dev, which should fix errors on Skylake series
    cpus.

    ๐Ÿš€ Downstream developers building this release should use Cython >= 0.29.2 and, if
    ๐Ÿ‘ using OpenBLAS, OpenBLAS >= v0.3.7. The supported Python versions are 2.7 and
    3.5-3.7.

    Contributors

    ๐Ÿš€ A total of 18 people contributed to this release. People with a "+" by their
    names contributed a patch for the first time.

    • Alexander Shadchin
    • Allan Haldane
    • Bruce Merry +
    • Charles Harris
    • Colin Snyder +
    • Dan Allan +
    • Emile +
    • Eric Wieser
    • Grey Baker +
    • Maksim Shabunin +
    • Marten van Kerkwijk
    • Matti Picus
    • Peter Andreas Entschev +
    • Ralf Gommers
    • Richard Harris +
    • Sebastian Berg
    • Sergei Lebedev +
    • Stephan Hoyer

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 23 pull requests were merged for this release.

    • #13742: ENH: Add project URLs to setup.py
    • โœ… #13823: TEST, ENH: fix tests and ctypes code for PyPy
    • #13845: BUG: use npy_intp instead of int for indexing array
    • ๐Ÿ—„ #13867: TST: Ignore DeprecationWarning during nose imports
    • ๐Ÿ†“ #13905: BUG: Fix use-after-free in boolean indexing
    • โž• #13933: MAINT/BUG/DOC: Fix errors in _add_newdocs
    • #13984: BUG: fix byte order reversal for datetime64[ns]
    • #13994: MAINT,BUG: Use nbytes to also catch empty descr during allocation
    • #14042: BUG: np.array cleared errors occured in PyMemoryView_FromObject
    • ๐Ÿ›  #14043: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
    • #14044: BUG: ensure that casting to/from structured is properly checked.
    • #14045: MAINT: fix histogram*d dispatchers
    • #14046: BUG: further fixup to histogram2d dispatcher.
    • #14052: BUG: Replace contextlib.suppress for Python 2.7
    • #14056: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
    • #14057: BUG: Fix memory leak in dtype from dict contructor
    • #14058: DOC: Document array_function at a higher level.
    • #14084: BUG, DOC: add new recfunctions to __all__
    • ๐Ÿšš #14162: BUG: Remove stray print that causes a SystemError on python 3.7
    • โœ… #14297: TST: Pin pytest version to 5.0.1.
    • ๐Ÿง #14322: ENH: Enable huge pages in all Linux builds
    • #14346: BUG: fix behavior of structured_to_unstructured on non-trivial...
    • ๐Ÿš€ #14382: REL: Prepare for the NumPy 1.16.5 release.

    Checksums

    MD5

    cf7ff97464eb044cb49618be5fe29aee numpy-1.16.5-cp27-cp27m-macosx_10_9_x86_64.whl
    6fbf51644f8722fa90276c04fe3d031f numpy-1.16.5-cp27-cp27m-manylinux1_i686.whl
    df4ab8600495131e44ad1b173f6cc9fc numpy-1.16.5-cp27-cp27m-manylinux1_x86_64.whl
    2f6fd50a02da9d56e3d950a6b738337e numpy-1.16.5-cp27-cp27m-win32.whl
    d36b67522ee102b7865a83b26a1d97aa numpy-1.16.5-cp27-cp27m-win_amd64.whl
    5b4f83c092257f6c98bedd44505e7b6d numpy-1.16.5-cp27-cp27mu-manylinux1_i686.whl
    d6fd33607099abdea62752cf303a1763 numpy-1.16.5-cp27-cp27mu-manylinux1_x86_64.whl
    fa48e45bd3e5dbac923296b039e70706 numpy-1.16.5-cp35-cp35m-macosx_10_9_x86_64.whl
    85a7db0c597037cced7ab82c0f0cdcc8 numpy-1.16.5-cp35-cp35m-manylinux1_i686.whl
    401e053e98faada4bc8cdcc9b04d619f numpy-1.16.5-cp35-cp35m-manylinux1_x86_64.whl
    2912ba9109dca60115dba59606cac27b numpy-1.16.5-cp35-cp35m-win32.whl
    756b7ff320ef821f2cd279c5df7c9f46 numpy-1.16.5-cp35-cp35m-win_amd64.whl
    2ae22b506a07575a4bc6a91d2db25df5 numpy-1.16.5-cp36-cp36m-macosx_10_9_x86_64.whl
    12cbf61ed2abec3f77cfa3a46b7e4bdc numpy-1.16.5-cp36-cp36m-manylinux1_i686.whl
    ab726a4244e9e070cde814d8415cff4c numpy-1.16.5-cp36-cp36m-manylinux1_x86_64.whl
    752e461d193b7049e25c7e20f7d4808a numpy-1.16.5-cp36-cp36m-win32.whl
    2712434cdfb27a301c49cf97eee656d5 numpy-1.16.5-cp36-cp36m-win_amd64.whl
    394fee86faa235dea6d2bb6270961266 numpy-1.16.5-cp37-cp37m-macosx_10_9_x86_64.whl
    0713da36acc884897f76bc8117ca7a42 numpy-1.16.5-cp37-cp37m-manylinux1_i686.whl
    7856a32b3b2d93d018d2ba5dce941ffa numpy-1.16.5-cp37-cp37m-manylinux1_x86_64.whl
    33b7fd0d727c9f09d61879afde8096f6 numpy-1.16.5-cp37-cp37m-win32.whl
    5287ce297cd8093463bb29bef42db103 numpy-1.16.5-cp37-cp37m-win_amd64.whl
    f9c22f53f17e81b25af8e53b026a9831 numpy-1.16.5.tar.gz
    adaad8c166cf0344af3ca1a664dd4a38 numpy-1.16.5.zip
    

    SHA256

    37fdd3bb05caaaacac58015cfa38e38b006ee9cef1eaacdb70bb68c16ac7db1d numpy-1.16.5-cp27-cp27m-macosx_10_9_x86_64.whl
    f42e21d8db16315bc30b437bff63d6b143befb067b8cd396fa3ef17f1c21e1a0 numpy-1.16.5-cp27-cp27m-manylinux1_i686.whl
    4208b225ae049641a7a99ab92e84ce9d642ded8250d2b6c9fd61a7fa8c072561 numpy-1.16.5-cp27-cp27m-manylinux1_x86_64.whl
    4d790e2a37aa3350667d8bb8acc919010c7e46234c3d615738564ddc6d22026f numpy-1.16.5-cp27-cp27m-win32.whl
    1594aec94e4896e0688f4f405481fda50fb70547000ae71f2e894299a088a661 numpy-1.16.5-cp27-cp27m-win_amd64.whl
    2c5a556272c67566e8f4607d1c78ad98e954fa6c32802002a4a0b029ad8dd759 numpy-1.16.5-cp27-cp27mu-manylinux1_i686.whl
    3a96e59f61c7a8f8838d0f4d19daeba551c5f07c5cdd5c81e8e9d4089ade0042 numpy-1.16.5-cp27-cp27mu-manylinux1_x86_64.whl
    612297115bade249a118616c065597ff2e5e1f47ed220d7ba71f3e6c6ebcd814 numpy-1.16.5-cp35-cp35m-macosx_10_9_x86_64.whl
    dbc9e9a6a5e0c4f57498855d4e30ef8b599c0ce13fdf9d64299197508d67d9e8 numpy-1.16.5-cp35-cp35m-manylinux1_i686.whl
    fada0492dd35412cd96e0578677e9a4bdae8f102ef2b631301fcf19066b57119 numpy-1.16.5-cp35-cp35m-manylinux1_x86_64.whl
    ada1a1cd68b9874fa480bd287438f92bd7ce88ca0dd6e8d56c70f2b3dab97314 numpy-1.16.5-cp35-cp35m-win32.whl
    27aa457590268cb059c47daa8c55f48c610ce81da8a062ec117f74efa9124ec9 numpy-1.16.5-cp35-cp35m-win_amd64.whl
    03b28330253904d410c3c82d66329f29645eb54a7345cb7dd7a1529d61fa603f numpy-1.16.5-cp36-cp36m-macosx_10_9_x86_64.whl
    911d91ffc6688db0454d69318584415f7dfb0fc1b8ac9b549234e39495684230 numpy-1.16.5-cp36-cp36m-manylinux1_i686.whl
    ceb353e3ae840ce76256935b18c17236ca808509f231f41d5173d7b2680d5e77 numpy-1.16.5-cp36-cp36m-manylinux1_x86_64.whl
    e6ce7c0051ed5443f8343da2a14580aa438822ae6526900332c4564f371d2aaf numpy-1.16.5-cp36-cp36m-win32.whl
    9a2b950bca9faca0145491ae9fd214c432f2b1e36783399bc2c3732e7bcc94f4 numpy-1.16.5-cp36-cp36m-win_amd64.whl
    00836128feaf9a7c7fedeea05ad593e7965f523d23fe3ffbf20cfffd88e9f2b1 numpy-1.16.5-cp37-cp37m-macosx_10_9_x86_64.whl
    3d6a354bb1a1ce2cabd47e0bdcf25364322fb55a29efb59f76944d7ee546d8b6 numpy-1.16.5-cp37-cp37m-manylinux1_i686.whl
    f7fb27c0562206787011cf299c03f663c604b58a35a9c2b5218ba6485a17b145 numpy-1.16.5-cp37-cp37m-manylinux1_x86_64.whl
    46469e7fcb689036e72ce61c3d432ed35eb4c71b5119e894845b434b0fae5813 numpy-1.16.5-cp37-cp37m-win32.whl
    fb207362394567343d84c0462ec3ba203a21c78be9a0fdbb94982e76859ec37e numpy-1.16.5-cp37-cp37m-win_amd64.whl
    2b63c414fb43a4f0cb69b29b7e9d48275af0dbb5b1ffd2f2de99c4df9967e151 numpy-1.16.5.tar.gz
    8bb452d94e964b312205b0de1238dd7209da452343653ab214b5d681780e7a0c numpy-1.16.5.zip