Mailman 3 March 2024 - NumPy-Discussion

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 community meeting
by Inessa Pawson May 19, 2024

May 19, 2024

The next NumPy community meeting will be held this Wednesday, November 8th at 5pm UTC. Join us via Zoom: https://numfocus-org.zoom.us/s/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 Documentation team meeting
by Mukulika Pahari May 6, 2024

May 6, 2024

Hi all, Our next Documentation Team meeting will happen on *Monday, December 4* at *11PM UTC*. If this time slot is inconvenient for you to join, please let me know in the replies or Slack and we will try to add another time slot. 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 triage meeting - April 3rd, 2024 at 6pm UTC
by Inessa Pawson April 28, 2024

April 28, 2024

The next NumPy triage meeting will be held this Wednesday, April 3rd at 6pm 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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JSON format for multi-dimensional data
by philippe＠loco-labs.io April 17, 2024

April 17, 2024

Hi community, This memo is a proposal to implement a compact and reversible (lossless round-trip) JSON interface for multi-dimensional data and in particular for Numpy (see issue #12481). The links to the documents are at the end of the memo. The JSON-NTV (Named and Typed value) format is a JSON format which integrates a notion of type. This format has also been implemented for tabular data (see NTV-pandas package available in the pandas ecosystem and the PDEP12 specification). . The use of this format has the following advantages: - Taking into account data types not known to Numpy, - Reversible format (lossless round-trip) - Interoperability with other tools for tabular or multi-dimensional data (e.g. pandas, Xarray) - Ease of sharing Json format - Binary coding possible (e.g. CBOR format) - Format integrating data of different nature The associated Jupyter Notebook presents some key points of this proposal (first draft): Summary: - introduction - benefits - multi-dimensionnal data - Multi-dimensional types - Format JSON - Using the NTV format - Equivalence of tabular format and multidimensional format - Astropy specific points - Units and quantities - Coordinates - Tables - Other structures This subject seems important to me (in particular for interoperability issues) and I would like to have your feedback before working on the implementation. Especially, - do you think this “semantic” format is interesting to use? - do you have any particular expectations or subjects that I need to study beforehand? - do you have any examples or test cases to offer me? And of course, any type of remark and comment is welcome. Thanks in advance ! links: - Jupyter notebook : https://nbviewer.org/github/loco-philippe/Environmental-Sensing/blob/main/p… - JSON-NTV format : https://www.ietf.org/archive/id/draft-thomy-json-ntv-02.html - JSON-NTV overview : https://nbviewer.org/github/loco-philippe/NTV/blob/main/example/example_ntv… - NTV tabular format : https://www.ietf.org/archive/id/draft-thomy-ntv-tab-00.html#name-tabular-st… - NTV-pandas package : https://github.com/loco-philippe/ntv-pandas/blob/main/README.md - NTV-pandas examples : https://nbviewer.org/github/loco-philippe/ntv-pandas/blob/main/example/exam… - Pandas specification - PDEP12 : https://pandas.pydata.org/pdeps/0012-compact-and-reversible-JSON-interface.…

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NumPy Optimization Team meeting - March 25th, 5pm UTC
by Inessa Pawson April 15, 2024

April 15, 2024

The next NumPy Optimization Team meeting will be held this Monday, March 25th at 5pm 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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ENH: Add "where" argument to reduction functions that are missing it
by Carlos Martin April 8, 2024

April 8, 2024

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Numpy wheels: who maintains the builds and who pays for it?
by sean.gillies＠gmail.com April 4, 2024

April 4, 2024

Hi folks, My name is Sean and I'm the author of several GIS packages using Numpy: Fiona, Rasterio, and Shapely. I've followed Numpy's trail when it comes to wheel building for many years and now I'm seeking advice on how to prioritize platforms to support and how to pay for the labor and computing that it takes to build wheels and maintain the infrastructure over time. Fiona and Rasterio have an order of magnitude more C library dependencies than Numpy, via GDAL (https://gdal.org/), which is almost more of an OS than a library. I found a thread in the archive about adding musllinux wheels, but it wasn't clear to me how the work gets done, who does, and how it gets paid for. Does NumFOCUS support pay a maintainer to do it? Are Numpy maintainers adding new platform builds as part of their day jobs? Are they donating their own time to the effort? Does the Numpy project aspire to provide wheels for all of the top N platforms? Is it more than an aspiration? Is support for one or more platforms part of any sponsorship agreement? Maybe users and sponsorship cover the cost of building wheels and maintenance entirely for Numpy, but it's not so in my case. I've got so many questions in this vein, and I'm grateful for any answers or insights or more discussion. Thanks!

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Which generation of s390x CPU and how to change it?
by Matěj Cepl April 2, 2024

April 2, 2024

Hello, As a maintainer of Python packages for openSUSE/SUSE, I would like to ask for help with our bug https://bugzilla.suse.com/1221902. It seems to us that the latest version of NumPy suddenly requires z15 CPU generation, although it used to be OK with z13+ before, and unfortunately that is the level of the CPU support we standardize in openSUSE. Is it true? If so, when this change happened and why? How could we push numpy to work with older processors? Is there anything more to do than just switch of particular features in `--cpu-dispatch`? Thank you for any reply, Matěj Cepl -- http://matej.ceplovi.cz/blog/, @mcepl@floss.social GPG Finger: 3C76 A027 CA45 AD70 98B5 BC1D 7920 5802 880B C9D8 I was quite depressed two weeks ago when I spent an afternoon at Brentano's Bookshop in New York and was looking at the kind of books most people read. Once you see that you lose all hope. -- Friedrich August von Hayek

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