Mailman 3 November 2021 - NumPy-Discussion

NumPy 1.21.4 release.
by Charles R Harris 05 Nov '21

05 Nov '21

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.4. NumPy 1.21.4 is a maintenance release that fixes a few bugs discovered after 1.21.3. The most important fix here is for the NumPy header files to make them work for both x86_64 and M1 hardware when included in the Mac universal2 wheels. Previously, the header files only worked for M1 and this caused problems for folks building x86_64 extensions. This problem was not seen before Python 3.10 because there were thin wheels for x86_64 that had precedence. This release also provides thin x86_64 Mac wheels for Python 3.10. Note that there are *no* manylinux1 wheels, the needed CentOS 5 repos have gone missing. Make sure you upgrade your pip version if you have problems downloading the correct wheels. The Python versions supported in this release are 3.7-3.10. If you want to compile your own version using gcc-11 you will need to use gcc-11.2+ to avoid problems. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.4/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.4>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. A recent version of pip is needed to install the universal2 macos wheels. *Contributors* A total of 7 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Bas van Beek - Charles Harris - Isuru Fernando - Matthew Brett - Sayed Adel - Sebastian Berg - 傅立业（Chris Fu） + *Pull requests merged* A total of 9 pull requests were merged for this release. - #20278: BUG: Fix shadowed reference of `dtype` in type stub - #20293: BUG: Fix headers for universal2 builds - #20294: BUG: ``VOID_nonzero`` could sometimes mutate alignment flag - #20295: BUG: Do not use nonzero fastpath on unaligned arrays - #20296: BUG: Distutils patch to allow for 2 as a minor version (!) - #20297: BUG, SIMD: Fix 64-bit/8-bit integer division by a scalar - #20298: BUG, SIMD: Workaround broadcasting SIMD 64-bit integers on MSVC... - #20300: REL: Prepare for the NumPy 1.21.4 release. - #20302: TST: Fix a ``Arrayterator`` typing test failure Cheers, Charles Harris

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg 02 Nov '21

02 Nov '21

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, November 3rd at 16:30 UTC (9:30am Pacific Time). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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Consistency of random number functions APIs.
by Todd Anderson 01 Nov '21

01 Nov '21

Some NumPy random number generation functions take a dtype parameter whereas others don't. Some of them take an out parameter whereas others don't. Just glancing at it, there seems to be no rhyme or reason why this would be the case but is there some hidden consistency underneath the hood to explain why some have these params and others don't? Is there any reason that things like random.randn and numpy.random.Generator.normal don't take a dtype and out parameters? If I need to create a huge array of random numbers whose dtype is float16 or float32 then what is the BKM to do this when the routine I would like to use generates an array of float64 and with the 64-bit data type the array won't fit in memory?

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