Mailman 3 November 2024 - NumPy-Discussion

next NumPy Newcomers' Hour - June 27th, 2024 at 10 pm UTC
by Inessa Pawson Sept. 12, 2025

Sept. 12, 2025

Our next Newcomers' Hour will be held TOMORROW, June 27th at 10 pm UTC. Stop by to ask questions, share your progress, celebrate success, or just to say hi. To add to the meeting agenda the topics you’d like to discuss, follow the link: https://hackmd.io/3f3otyyuTte3FU9y3QzsLg?both. Join the meeting via Zoom: https://us06web.zoom.us/j/82563808729?pwd=ZFU3Z2dMcXBGb05YemRsaGE1OW5nQT09. -- Cheers, Inessa Inessa Pawson GitHub: inessapawson

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next Documentation team meeting
by Mukulika Pahari May 5, 2025

May 5, 2025

Hi all, Our next Documentation Team meeting will happen on *Monday, July 15* at *7PM UTC*. All are welcome - you don't need to already be a contributor to join. If you have questions or are curious about what we're doing, we'll be happy to meet you! If you wish to join on Zoom, use this (updated) link: https://numfocus-org.zoom.us/j/85016474448?pwd=TWEvaWJ1SklyVEpwNXUrcHV1YmFJQ... Here's the permanent hackmd document with the meeting notes (still being updated): https://hackmd.io/oB_boakvRqKR-_2jRV-Qjg Hope to see you around! Best wishes, Mukulika

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next NumPy community meeting - June 5th, 2024 at 6 pm UTC
by Inessa Pawson March 22, 2025

March 22, 2025

The next NumPy community meeting will be held this Wednesday, June 5th at 6 pm UTC. 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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next NumPy triage meeting - May 29th, 2024 at 7 pm UTC
by Inessa Pawson Jan. 5, 2025

Jan. 5, 2025

The next NumPy triage meeting will be held this Wednesday, May 29th at 7pm UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… . Everyone is welcome to attend and contribute to a conversation. 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 Optimization Team meeting - October 28th, 2024 at 5 pm UTC
by Inessa Pawson Dec. 8, 2024

Dec. 8, 2024

Hi, all! The next NumPy Optimization Team meeting will be held TODAY, October 28th 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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ANN: Python-Blosc2 3.0.0 release candidate 1 is out!
by Francesc Alted Nov. 28, 2024

Nov. 28, 2024

Announcing Python-Blosc2 3.0.0-rc1 ================================== The Blosc development team is pleased to announce the first release release of Python-Blosc2 3.0.0. In this release, we have focused on the making of a compute engine that can work with compressed data in a NumPy-like fashion. You can think of Python-Blosc2 3.0 as a replacement of numexpr, but better :-) As always, we would like to get feedback from the community before the final release. We are providing binary wheels that you can easily install from PyPI with: pip install blosc2==3.0.0rc1 For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc2/releases Docs and examples are available in the documentation site: https://www.blosc.org/python-blosc2 Code example:: import blosc2 from time import time # Create some data operands N = 10_000 a = blosc2.linspace(0, 1, N * N, dtype="float32", shape=(N, N)) b = blosc2.linspace(1, 2, N * N, shape=(N, N)) c = blosc2.linspace(-10, 10, N * N, shape=(N, N)) # Expression t0 = time() expr = ((a**3 + blosc2.sin(c * 2)) < b) & (c > 0) print(f"Time to create expression: {time()-t0:.5f}") # Evaluate and get a NDArray as result t0 = time() out = expr.compute() print(f"Time to compute: {time()-t0:.5f}") This will output something like:: Time to create expression: 0.00041 Time to compute: 0.56215 Note that the expression is computed lazily, only when the `compute` method is called. BTW, I'll be teaching about Python-Blosc2 3.0 in my forthcoming tutorial at PyData Global 2024 (https://pydata.org/global2024/schedule). Be sure to be there for an exciting introduction to the bells and whistles of the shiny new computation engine and its enhanced capabilities to deal with large datasets. Thanks and see you there! -- Francesc Alted

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numpy defaults to free-threaded version on conda-forge
by george trojan Nov. 26, 2024

Nov. 26, 2024

My apologies if this is a wrong venue. Is this a conda-forge problem? When I run mamba create -n foo meson-python numpy 'python>=3.13' the free-threaded python is selected: + python 3.13.0 h6355ac2_0_cp313t conda-forge Cached + numpy 2.1.3 py313hb01392b_0 conda-forge Cached When python spec is moved to the front, I do get the "usual" version. However, when I build a python extension with conda-build, I do not control the order. The extension is created for the free-threaded version. Here is my requirements section in recipe/meta.yaml: requirements: build: - {{ compiler('c') }} - {{ compiler('fortran') }} host: - python >=3.13 - python_abi =3.13.* *_cp313 # needed to prevent selection of freethreaded python - meson-python - numpy >=2.0 - pip run: - python >=3.13 - numpy >=2.0 I did manage to find a workaround, which is the python_abi line. Am I doing it right?

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Fixing definition of reduceat for Numpy 2.0?
by Martin Ling Nov. 24, 2024

Nov. 24, 2024

Hi folks, I don't follow numpy development in much detail these days but I see that there is a 2.0 release planned soon. Would this be an opportunity to change the behaviour of 'reduceat'? This issue has been open in some form since 2006! https://github.com/numpy/numpy/issues/834 The current behaviour was originally inherited from Numeric, and makes reduceat often unusable in practice, even where it should be the perfect, concise, efficient solution. But it has been impossible to change it without breaking compatibіlity with existing code. As a result, horrible hacks are needed instead, e.g. my answer here: https://stackoverflow.com/questions/57694003 Is this something that could finally be fixed in 2.0? Martin

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NumPy 2.2.x has been branched
by Charles R Harris Nov. 23, 2024

Nov. 23, 2024

Hi All, NumPy 2.2.x has been branched and main is now open for 2.3.0 development. Chuck

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New matvec and vecmat functions
by Marten van Kerkwijk Nov. 22, 2024

Nov. 22, 2024

Hi All, I have a PR [1] that adds `np.matvec` and `np.vecmat` gufuncs for matrix-vector and vector-matrix calculations, to add to plain matrix-matrix multiplication with `np.matmul` and the inner vector product with `np.vecdot`. They call BLAS where possible for speed. I'd like to hear whether these are good additions. I also note that for complex numbers, `vecmat` is defined as `x†A`, i.e., the complex conjugate of the vector is taken. This seems to be the standard and is what we used for `vecdot` too (`x†x`). However, it is *not* what `matmul` does for vector-matrix or indeed vector-vector products (remember that those are possible only if the vector is one-dimensional, i.e., not with a stack of vectors). I think this is a bug in matmul, which I'm happy to fix. But I'm posting here in part to get feedback on that. Thanks! Marten [1] https://github.com/numpy/numpy/pull/25675 p.s. Separately, with these functions available, in principle these could be used in `__matmul__` (and thus for `@`) and the specializations in `np.matmul` removed. But that can be a separate PR (if it is wanted at all).

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