Mailman 3 June 2024 - NumPy-Discussion

next NumPy Newcomers' Hour - June 27th, 2024 at 10 pm UTC
by Inessa Pawson March 31, 2025

March 31, 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 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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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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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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next NumPy Optimization Team meeting - June 24th, 2024 at 5 pm UTC
by Inessa Pawson Oct. 4, 2024

Oct. 4, 2024

The next NumPy Optimization Team meeting will be held this Monday, June 24th at 5 pm 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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PR - can I get a new review?
by Jake S. July 23, 2024

July 23, 2024

Hi community, PR 26081 <https://github.com/numpy/numpy/pull/26081> is about making numpy's ShapeType covariant and bound to a tuple of ints. The community has requested this occasionally in issue 16544 <https://github.com/numpy/numpy/issues/16544>. I'm reaching out via the listserv because it's been a few months, and I don't want it to get too stale. I could really use some help pushing it over the finish line. Summary: Two numpy reviewers and one interested community member reviewed the PR and asked for a type alias akin to npt.NDArray that allowed shape. I worked through the issues with TypeVarTuple and made npt.Array, and it was fragile, but passing CI. After a few months passed, I returned to fix the fragility in the hopes of getting some more attention, but now it fails CI in some odd builds (passes the mypy bit). I have no idea how to get these to pass, as they appear unrelated to anything I've worked on (OpenBLAS on windows, freeBSD...?). Thanks, Jake

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next Documentation team meeting at 7 PM UTC - New Time!
by Mukulika Pahari July 1, 2024

July 1, 2024

Hi all, Our next Documentation Team meeting will happen on *Monday, June 17* at *7PM UTC*. ** I picked the new time trying my best to accommodate my and other's schedules (thanks to those who responded to the poll). Sorry to those who can't make the new time- hopefully we can collaborate through the other communication channels. ** 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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Comparison between numpy scalars returns numpy bool class and not native python bool class
by Stefano Miccoli June 30, 2024

June 30, 2024

It is well known that ‘np.bool' is not interchangeable with python ‘bool’, and in fact 'issubclass(np.bool, bool)’ is false. On the contrary, numpy floats are subclassing python floats—'issubclass(np.float64, float) is true—so I’m wondering if the fact that scalar comparison returns a np.bool breaks the Liskov substitution principle. In fact ’(np.float64(1) > 0) is True’ is unexpectedly false. I was hit by this behaviour because in python structural pattern matching, the ‘a > 1’ subject will not match neither ’True’ or ‘False’ if ‘a' is a numpy scalar: In this short example import numpy as np a = np.float64(1) assert isinstance(a, float) match a > 1: case True | False: print('python float') case _: print('Huh?: numpy float’) the default clause is matched. If we set instead ‘a = float(1)’, the first clause will be matched. The surprise factor is quite high here, in my opinion. (Let me add that ‘True', ‘False', ‘None' are special in python structural pattern matching, because they are matched by identity and not by equality.) I’m not sure if this behaviour can be avoided, or if we have to live with the fact that numpy floats are to be kept well contained and never mixed with python floats. Stefano

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ANN: First beta of Python-Blosc2 3.0.0
by Francesc Alted June 21, 2024

June 21, 2024

Hi, The Blosc development team is pleased to announce the first beta release of Python-Blosc2 3.0.0. We have been working hard to provide a new evaluation engine (based on numexpr) for NDArray instances, and we would like to get feedback from the community before the final release. Now, you can evaluate expressions like `a + sin(b) + 1` where `a` and `b` are NDArray instances. This is a powerful feature that allows for efficient computations on compressed data, and supports advanced features like reductions, filters, user-defined functions and broadcasting (still in beta). See this `example <https://github.com/Blosc/python-blosc2/blob/main/examples/ndarray/eval_expr…>`_. Also, we have added support for memory mapping in `SChunk` and `NDArray` instances. This allows to map super-chunks stored in disk and access them as if they were in memory. When combined with the evaluation engine, this feature allows for very good performance when working with large datasets. See this `benchmark <https://github.com/Blosc/python-blosc2/blob/main/bench/ndarray/lazyarray-ex…>`_ (as it is a Jupyter notebook, you can easily run it in your own computer). Last, but not least, we are using NumPy 2.x as the default for testing procedures and builds. This means that our wheels are built against NumPy 2, so in case you want to use NumPy 1.x, you will need to use NumPy 1.23.0 or later. We are providing binary wheels that you can easily install from PyPI with:: pip install blosc2==3.0.0b1 For more info, you can have a look at the release notes in: https://github.com/Blosc/python-blosc2/releases More docs and examples are available in the documentation site: https://www.blosc.org/python-blosc2/python-blosc2.html What is it? ----------- `C-Blosc2 <https://github.com/Blosc/c-blosc2>`_ is the new major version of `C-Blosc <https://github.com/Blosc/c-blosc>`_, and is backward compatible with both the C-Blosc1 API and its in-memory format. Python-Blosc2 is a Python package that wraps C-Blosc2, the newest version of the Blosc compressor. Starting with version 3.0.0, Python-Blosc2 is including a powerful computing engine that can operate on compressed data that can be either in-memory, on-disk or on the network. This engine also supports advanced features like reductions, filters, user-defined functions and broadcasting. You can read some of our tutorials on how to perform advanced computations at: * https://github.com/Blosc/python-blosc2/blob/main/doc/getting_started/tutori… * https://github.com/Blosc/python-blosc2/blob/main/doc/getting_started/tutori… In addition, Python-Blosc2 aims to leverage the full C-Blosc2 functionality to support super-chunks (`SChunk <https://www.blosc.org/python-blosc2/reference/schunk_api.html>`_), multi-dimensional arrays (`NDArray <https://www.blosc.org/python-blosc2/reference/ndarray_api.html>`_), metadata, serialization and other bells and whistles introduced in C-Blosc2. **Note:** Blosc2 is meant to be backward compatible with Blosc(1) data. That means that it can read data generated with Blosc, but the opposite is not true (i.e. there is no *forward* compatibility). Sources repository ------------------ The sources and documentation are managed through github services at: https://github.com/Blosc/python-blosc2 Python-Blosc2 is distributed using the BSD license, see https://github.com/Blosc/python-blosc2/blob/main/LICENSE.txt for details. Mastodon feed ------------- Please follow https://fosstodon.org/@Blosc2 to get informed about the latest developments. - Blosc Development Team Make compression better

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