Mailman 3 March 2024 - NumPy-Discussion

next NumPy community meeting
by Inessa Pawson 19 May '24

19 May '24

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 06 May '24

06 May '24

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 28 Apr '24

28 Apr '24

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 17 Apr '24

17 Apr '24

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 15 Apr '24

15 Apr '24

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 08 Apr '24

08 Apr '24

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Numpy wheels: who maintains the builds and who pays for it?
by sean.gillies＠gmail.com 04 Apr '24

04 Apr '24

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 02 Apr '24

02 Apr '24

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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NumPy 2.0.0rc1 released
by Charles R Harris 30 Mar '24

30 Mar '24

Hi All On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.0.0rc1. NumPy 2.0.0 is the first major release since 2006. It is the result of 10 months of development since the last feature release and is the work of 193 contributors spread over 1005 pull requests. It contains a large number of exciting new features as well as changes to both the Python and C APIs. This major release includes breaking changes that could not happen in a regular minor (feature) release - including an ABI break, changes to type promotion rules, and API changes which may not have been emitting deprecation warnings in 1.26.x. Key documents related to how to adapt to changes in NumPy 2.0 include: - The release notes on Github <https://github.com/numpy/numpy/releases/tag/v2.0.0rc1> - The numpy-2-migration-guide <https://numpy.org/devdocs/numpy_2_0_migration_guide.html> - The numpy 2.0-specific advice in for downstream package authors <https://numpy.org/devdocs/dev/depending_on_numpy.html> Highlights include: - A new variable-length string dtype, StringDType, and a new `numpy.strings` namespace with performant ufuncs for string operations. - Support for float32 and long double in all `numpy.fft` functions. - Support for the array API standard in the main numpy namespace. - MacOS Accelerate support and binary wheels for macOS >=14. - A new tracing and introspection API. - Performance improvements. - Python API improvements. - C API improvements. This release supports Python 3.9-3.12. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.0.0rc1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.0.0rc1>. *Contributors* - @Algorithmist-Girl + - @DWesl - @Illviljan - @ellaella12 + - @liang3zy22 + - @matoro + - @mcp292 + - @mykykh + - @pojaghi + - @pratiklp00 + - @stefan6419846 + - @undermyumbrella1 + - Aaron Meurer - Aditi Saluja + - Adrin Jalali + - Agriya Khetarpal + - Albert Steppi + - Alex Cabrera + - Alexander Grund - Andrea Bianchi + - Andreas Florath + - Andrew Ardill + - Andrew Ho + - Andrew Nelson - Andrey Rybakov + - Ankur Singh + - Anton Prosekin + - Antony Lee - Bas van Beek - Bharat Raghunathan - Bhavya Alekhya + - Brandon Smith + - Brian Walshe + - Brigitta Sipőcz - Brock Mendel - Carl Meyer + - Charles Bousseau + - Charles Harris - Chris Sidebottom - Christian Lorentzen - Christian Veenhuis - Christoph Reiter - Christopher Sidebottom - Clément Robert - Cédric Hannotier - D.J. Ramones + - DanShatford + - Daniel Li + - Daniel Vanzo - Daval Parmar - Developer-Ecosystem-Engineering - Dhruv Rawat + - Dimitri Papadopoulos Orfanos - Edward E - Edward Yang + - Eisuke Kawashima + - Eliah Kagan + - Élie Goudout + - Elliott Sales de Andrade - Emil Olszewski + - Emily Hunt + - Éric Piel + - Eric Wieser - Even Rouault + - Evgeni Burovski - Filipe Laíns + - Ganesh Kathiresan - Gonzalo Tornaría + - Hans Meine - Heberto Mayorquin + - Heinz-Alexander Fuetterer + - Hood Chatham - Ivan A. Melnikov + - Jacob M. Casey + - Jake Lishman + - Jake VanderPlas - Jake Vanderplas - James Oliver + - Jan Wassenberg + - Janukan Sivajeyan + - Johann Rohwer + - Johannes Kaisinger + - John Muradeli + - Kai Striega - Kevin Sheppard - Kevin Wu + - Khawaja Junaid + - Kit Lee + - Kristian Minchev + - Kuan-Wei Chiu + - Lane Votapka + - Larry Bradley - Leo Singer - Liang Yan + - Linus Sommer + - Logan Thomas - Lucas Colley + - Lukas Geiger - Lysandros Nikolaou + - Maanas Arora + - Maharshi Basu + - Mahder Gebremedhin + - Marcel Bargull + - Mark Mentovai + - Mark Ryan + - Marten Henric van Kerkwijk + - Marten van Kerkwijk - Mateusz Sokół - Matt Haberland - Matthew Barber - Matthias Bussonnier - Matthias Koeppe - Matthias Schaufelberger + - Matti Picus - Maxwell Aladago - Maya Anderson + - Melissa Weber Mendonça - Meng Xiangzhuo + - Michael Kiffer - Miki Watanabe (渡邉美希) - Milan Curcic + - Miles Cranmer - Miro Hrončok + - Mohamed E. BRIKI + - Mohaned Qunaibit + - Mohit Kumar + - Muhammed Muhsin + - Mukulika Pahari - Munira Alduraibi + - Namami Shanker - Nathan Goldbaum - Nyakku Shigure + - Ola x Nilsson + - Olivier Mattelaer + - Omid Rajaei - Pablo Losada + - Pamphile Roy - Paul Reece + - Pedro Kaj Kjellerup Nacht + - Peiyuan Liu + - Peter Hawkins - Pierre - Pieter Eendebak - Quentin Barthélemy + - Raghuveer Devulapalli - Ralf Gommers - Randy Eckenrode + - Raquel Braunschweig + - Richard Howe + - Robert Kern - Rohit Goswami - Ronald van Elburg + - Ross Barnowski - Sam James + - Sam Van Kooten + - Samuel Albanie + - Sarah Wang + - Sarah Zwiep + - Sarah-Yifei-Wang + - Sarthak Dawar + - Sayantika Banik - Sayed Adel - Sean Cheah + - Sebastian Berg - Serge Guelton - Shalini Roy + - Shen Zhou - Shubhal Gupta + - Stefan van der Walt - Stefano Rivera + - Takumasa N. + - Taras Tsugrii - Thomas A Caswell - Thomas Grainger + - Thomas Li - Tim Hoffmann - Tim Paine + - Trey Woodlief + - Tyler Reddy - Victor Tang + - Vladimir Fokow + - Warren Weckesser - Warrick Ball + - Will Ayd - William Andrea + - William Ayd + - Yash Pethe + - Yuki K - Zach Brugh + - Zach Rottman + - Zolisa Bleki - mgunyho + - msavinash + Cheers, Charles Harris

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NumPy-Financial: RFC: Dropping decimal.Decimal support
by kaistriega＠gmail.com 30 Mar '24

30 Mar '24

Hi all, I'm currently working NumPy-Financial (npf) and would like to ask for comments on dropping decimal.Decimal support in npf. I'm proposing to drop Decimal support for npf. There are a few reasons for this: 1. No one appears to use decimal types. Since it's release in 2019, not a single issue has been raised that uses decimal support. Furthermore, I have taken a quick survey of dependent projects, none of which seems to use decimals. 2. It's not actually supported that thoroughly. Npf claims that: “Functions support the :class:`decimal.Decimal` type unless otherwise stated.”. This isn't technically true. Many functions that claim to support decimal classes don't. I think it would be better to have a consistent interface that would either entierly support decimal or not support it at all. 3. I'm currently working on mimicking broadcasting behaviour for functions. This involves lots of for loops. To speed these up I will use either numba or Cython. Neither of these support decimal classes natively. If you have any comments on whether this seems to be the right option, I would be very interested in hearing it. Kind regards Kai

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