[Numpy-discussion] next NumPy triage meeting - Wednesday, June 11th, 2025 at 5 pm UTC

[Inessa Pawson via NumPy-Discussion]

The next NumPy triage meeting will be held this Wednesday, June 11th 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=MW9oUmtKQ1c3a2gydGk1RTdYUUVXZz09
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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[Numpy-discussion] NumPy 2.3.0 released

[Charles R Harris via NumPy-Discussion]

Hi All,

On behalf of the NumPy team, I'm pleased to announce the release of
NumPy 2.3.0.  The NumPy 2.3.0 release improves free threaded Python support
and annotations together with the usual set of bug fixes. It is unusual in
the number of expired deprecations, 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. Highlights 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 3.11-3.13, Python 3.14 will be supported when
it is released. Wheels can be downloaded from PyPI
; source archives, release notes, and
wheel hashes are available on Github
.

Cheers,

Charles Harris
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[Numpy-discussion] ENH: Add reverse argument to ufunc.accumulate, cumsum, nancumsum, cumprod, nancumprod

[Carlos Martin]

Add a reverse argument to accumulating functions that, when true, causes the 
accumulation to be performed in reverse. Examples:

- ufunc.accumulate: 
https://numpy.org/doc/stable/reference/generated/numpy.ufunc.accumulate.html
- cumsum: https://numpy.org/doc/stable/reference/generated/numpy.cumsum.html
- nancumsum: 
https://numpy.org/doc/stable/reference/generated/numpy.nancumsum.html
- cumprod: https://numpy.org/doc/stable/reference/generated/numpy.cumprod.html
- nancumprod: 
https://numpy.org/doc/stable/reference/generated/numpy.nancumprod.html

Currently, this can be done via two flips 
(https://numpy.org/doc/stable/reference/generated/numpy.flip.html) before and 
after the operation. But it might be more efficient to do it directly at the 
low level.
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