Mailman 3 May 2025 - NumPy-Discussion

next NumPy triage meeting - Wednesday, May 28th, 2025 at 5 pm UTC
by Inessa Pawson June 21, 2025

June 21, 2025

The next NumPy triage meeting will be held this Wednesday, May 28th at 17:00 (5 pm) UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Everyone is welcome to attend and contribute to a conversation. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… Please notify us of issues or PRs that you’d like to have reviewed by adding a GitHub link to them in the meeting agenda: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next NumPy community meeting - Wednesday, April 9th, 2025 at 5 pm UTC - now meeting ONE HOUR EARLIER
by Inessa Pawson June 15, 2025

June 15, 2025

The next NumPy community meeting will be held this Wednesday, April 9th at 17:00 (5 pm) UTC. Please note the TIME CHANGE! Join us via Zoom: https://numfocus-org.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdk… Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/community_meetings -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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Bumping CPU baseline to x86-64-v2
by Sayed Adel June 15, 2025

June 15, 2025

Hi All, I wanted to bring your attention to an important change to NumPy that will affect CPU compatibility requirements on x86. PR #28896 (https://github.com/numpy/numpy/pull/28896) is reorganizing how NumPy handles x86 CPU features by moving from individual instruction set extensions to standardized microarchitecture levels. As part of this change, *the minimum CPU requirement for x86 systems will be raised to x86-64-v2*If your x86 CPU was manufactured in 2009 or later, you likely won't be affected, unless you're using Intel Atom CPUs released before 2013. We're making this change in 2025, when CPUs that don't support x86-64-v2 are now over 15 years old. Supporting these antiquated processors increases maintenance burden and binary size. Bumping the default baseline can also increase the performance since not all code paths are dispatched. -- Cheers, Sayed Sayed Adel GitHub: seiko2plus

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next NumPy Optimization Team meeting - Monday, January 20th, 2025 at 5 pm UTC
by Inessa Pawson June 12, 2025

June 12, 2025

Hi, all! The next NumPy Optimization Team meeting will be held on Monday, January 20th at 17:00 UTC. Join us via Zoom: https://numfocus-org.zoom.us/j/81261288210?pwd=iwV99tGSjR61RTGEERKM4QKxe46g… Everyone is welcome and encouraged to attend. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/dVdSlQ0TThWkOk0OkmGsmw?both For the notes from the previous meetings, visit: https://github.com/numpy/archive/tree/main/optim_team_meetings -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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Interesting discussion of ``fast-math``
by Charles R Harris May 30, 2025

May 30, 2025

Hi All, Thought some might find this discussion <https://simonbyrne.github.io/notes/fastmath/> interesting. Chuck

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NumPy 2.3.0rc1 Released
by Charles R Harris May 25, 2025

May 25, 2025

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.3.0rc1. The NumPy 2.3.0rc1 release continues the work to improve free threaded Python support and annotations together with the usual set of bug fixes. It is unusual in the number of expired deprecations and the number of code modernizations and style cleanups. The latter may not be visible to users, but is important for code maintenance over the long term. Note that we have also upgraded from manylinux2014 to manylinux_2_28. There are known test failures in the rc1 release involving MyPy and PyPy. The cause of both has been determined and fixes will be applied before the final release. The current Windows on ARM wheels also lack OpenBLAS, but they should suffice for initial downstream testing. OpenBLAS will be incorporated in those wheels when it becomes available. Highlights for this release are: - Interactive examples in the NumPy documentation. - Building NumPy with OpenMP Parallelization. - Preliminary support for Windows on ARM. - Improved support for free threaded Python. - Improved annotations. This release supports Python 3.10-3.13. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.3.0rc1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.3.0rc1>. Cheers, Charles Harris

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ANN: Python-Blosc2 3.3.4 released
by Francesc Alted May 22, 2025

May 22, 2025

Hi, This is a bugfix release, with some minor optimizations. We further improved the correct chaining of *string* lazy expressions (to allow operands with more diverse data types). In addition, both indexing and where expressions are now supported within *string* lazy expressions. Finally, casting rules have been improved to be more consistent with NumPy. You can think of Python-Blosc2 3.x as an extension of NumPy/numexpr that: - Can deal with ndarrays compressed using first-class codecs & filters. - Performs many kind of math expressions, including reductions, indexing... - Supports broadcasting operations. - Supports NumPy ufunc mechanism: mix and match NumPy and Blosc2 computations. - Integrates with Numba and Cython via UDFs (User Defined Functions). - Adheres to modern NumPy casting rules way better than numexpr. - Supports linear algebra operations (like ``blosc2.matmul()``). Install it with:: pip install blosc2 --update # if you prefer wheels conda install -c conda-forge python-blosc2 mkl # if you prefer conda and MKL For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc2/releases Have fun! -- Francesc Alted

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NumPy 2.3.x has been branched
by Charles R Harris May 21, 2025

May 21, 2025

Hi All, The main branch is now open for NumPy 2.4.0 development. Chuck

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ANN: PyData/Sparse 0.17.0 released
by Hameer Abbasi May 20, 2025

May 20, 2025

Hello all, PyData/Sparse <https://sparse.pydata.org <https://sparse.pydata.org/>> 0.17.0 is now available on GitHub <https://github.com/pydata/sparse/releases/tag/0.17.0>, pypi <https://pypi.org/project/sparse/0.17.0/> and conda-forge. PyData/Sparse provides sparse multi-dimensional arrays for the Python Ecosystem. The highlights of this release include support for the v2024 Array API standard. The full release notes can be found on GitHub. Best, Hameer

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Better compatibility of the Python scientific/data stack with fast Python interpreters
by PIERRE AUGIER May 18, 2025

May 18, 2025

Dear Numpy community members and Numpy developers, This email is to get the points of view of the Numpy community members and developers about a subject that I find very important. I'm going to introduce it just in few lines so I write few statements without backing them with proper arguments and without giving links and word definitions. I assume that most people registered to the numpy-discussion list are at least familiar to the subject. I think getting proper compatibility of the Python scientific/data stack with fast Python interpreters is very important for the long-term future of Python for science and data. Nowadays, fast Python implementations like PyPy and GraalPy are - complicated to use (need local builds of wheels) - slow as soon as the code involves Python extensions (because extensions work through an emulation layer, like cpyext in PyPy) Therefore, these fast Python implementations are not as popular as they should be. However, they are really great tools which should be used in particular for scientific/data applications. Unfortunately, if we just follow the pace of the CPython C API evolution, we will basically never have an ecosystem natively compatible with fast Python implementations. The more I think and learn about this subject, the more I think that Numpy has to stop using directly the CPython C API and to be ported to HPy (though there are other alternatives - in particular Nanobind and Cython - that could be discussed). Numpy 1 has been ported to HPy but unfortunately, there have been deep changes with Numpy 2 (and Meson) so it seems that one should restart the porting. Moreover, unfortunately, HPy does not currently receive as much care as it should. It seems to me that the project of fixing the roots of the Python ecosystem has to be relaunched. I think that the dynamics has to come from the Python scientific/data community and in particular Numpy. It is unfortunately outside of the C API working group's scope (see https://discuss.python.org/t/c-api-working-group-and-plan-to-get-a-python-c…). It seems to me that it is necessary (and possible) to get some founding for such an impactful project so that we can get people working on it. I'd like to write a long and serious text on this subject, and I first try to get the points of view of the different people and projects involved. I guess I should write explicit questions: - What do you think about the project of fixing the Python scientific/data stack so that it becomes natively compatible (hence fast and convenient, with easy installations) with alternative and fast Python interpreters? - Do you have points of view on how this should be done, technically (HPy?, something else?) and on other aspects (community, NEP?, founding, ...). - Anything else interesting on this subject? Best regards, Pierre -- Pierre Augier - CR CNRS http://www.legi.grenoble-inp.fr LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels BP53, 38041 Grenoble Cedex, France tel:+33.4.56.52.86.16

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