Mailman 3 - NumPy-Discussion

Exact representable difference between for two arrays.
by Andrew Nelson Dec. 20, 2024

Dec. 20, 2024

Hi all, Let's assume we have two float arrays, `x0` and `h`, both of which have the same shape. Let's calculate the sum of those two arrays: ``` x1 = x0 + h ``` The true representable difference between `x1` and `x0` is: ``` dx = x1 - x0 ``` Is it also possible to get that exact representation by doing the following: ``` dx = (x0 + h) - x0 ``` Or is there a risk that the Python interpreter would prematurely optimize that to give: ``` dx = h ``` -- _____________________________________ Dr. Andrew Nelson _____________________________________

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Build wheels for manylinux_2_24 instead of manylinux2014
by Matti Picus Dec. 17, 2024

Dec. 17, 2024

We are starting to discuss moving up from manylinux2104_manylinux_2_17 for the next NumPy release in June 2025. It seems, looking at https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-d… <https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-d…>, that the next step would be manylinux_2_24. Is there a demand to continue supporting EOL linux operating systems? Matti

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next NumPy community meeting - Wednesday, December 18th, 2024 at 6 pm UTC
by Inessa Pawson Dec. 14, 2024

Dec. 14, 2024

The next NumPy community meeting will be held this Wednesday, December 18th at 18:00 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 Please note that this will be the last NumPy community event in 2024. -- 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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NumPy 2.2.0 Released
by Charles R Harris Dec. 8, 2024

Dec. 8, 2024

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.2.0. The NumPy 2.2.0 release is a short release that brings us back into sync with the usual twice yearly release cycle. There have been a number of small cleanups, as well as work bringing the new StringDType to completion and improving support for free threaded Python. Highlights are: - New functions `matvec` and `vecmat`, see below. - Many improved annotations. - Improved support for the new StringDType. - Improved support for free threaded Python - Fixes for f2py This release supports Python 3.10-3.13. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.2.0>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.2.0>. Cheers, Charles Harris

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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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