# 2025-09-24 NumPy community meeting

- Time: 17:00 (5:00 pm) UTC
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- [Community meetings notes archive](https://github.com/numpy/archive/tree/main/community_meetings)
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**Present:**

## Follow-up from the last meeting / discussions

- Sebastian: Discussion about [sort PR](https://github.com/numpy/numpy/pull/28516)
- Discussion about python API, C-API and lower level struct of the context. Chuck suggests we start with the C-API, get it called from python, and then modify the other parts to support the new parameters. Maanas suggests shrinking the PR to special-case string dtype. It seems in the end Chuck will try to submit a PR to do the high-level things.
- 9 Sep discussion: Chuck's PR got merged, Maanas will make a new PR with some of the basic infrastructure, and then get input from Sebastian.

- [Same-value casting](https://github.com/numpy/numpy/pull/29129) in asarray
The PR is almost ready, plus/minus some last comments from Marten. What do people think?

- npymath: we should expose the float16 routines from the NumPy C-API itself, and not require downstream users to link to

- Inessa: make the numpy.org Plausible dashboard public?
- Sure! Unfortunately the stats do not include the docs. Could we get that info? Inessa willl follow up at the next meeting.

- RNG test thread safety and limitations of pytest-run-parallel. https://github.com/numpy/numpy/pull/29671
- this led to a general discussion about best practices and tests. We should update [the test recommendations](https://numpy.org/doc/stable/reference/testing.html#easier-setup-and-teardown-functions-methods) to avoid `setup_class` and `setup_method`
- Make RNGs local to the test to avoid threading problems.

- [name=rgommers] Touch on platform support (macOS x86-64, ppc64le, ...)
- Discussion about ppc64le wheels: CI is useful, but not too much enthusiasm from maintainers about releasing wheels for a platform that has perhaps (based on conda-forge download numbers) ~0.2% of the usage of x86-64 Linux. O(2,000) downloads for ppc64le vs. O(1,000,000) for x86-64. Another feeling was that most users are HPC users, and they'd get better performance when building from source (e.g., using Spack).

- Some discussion on 2.3.3 release process: there's an "unverified details" part in the left sidebar, would be nice to investigate.
- numpy-simd-routines: Dynamic/Precise SIMD Control, and testing unit based on buffer protocol.
* Currently static, 1~ulp(F64), 4~ulp(F32), https://github.com/numpy/numpy/pull/29699
* To enable independent testing, npsr intrinsics are mapped to support the buffer protocol, allowing Python projects to reuse them without low-level access. Could NumPy be one of the potential users?

## New topics

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