Mailman 3 June 2024 - NumPy-Discussion

next NumPy community meeting - June 5th, 2024 at 6 pm UTC
by Inessa Pawson 23 Sep '24

23 Sep '24

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 Newcomers' Hour - June 27th, 2024 at 10 pm UTC
by Inessa Pawson 17 Sep '24

17 Sep '24

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 triage meeting - May 29th, 2024 at 7 pm UTC
by Inessa Pawson 15 Sep '24

15 Sep '24

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 - June 24th, 2024 at 5 pm UTC
by Inessa Pawson 13 Sep '24

13 Sep '24

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. 23 Jul '24

23 Jul '24

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 01 Jul '24

01 Jul '24

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 30 Jun '24

30 Jun '24

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 21 Jun '24

21 Jun '24

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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ANN: numexpr 2.10.1 released!
by Francesc Alted 20 Jun '24

20 Jun '24

========================= Announcing NumExpr 2.10.1 ========================= Hi everyone, NumExpr 2.10.1 continues to stabilize the support for NumPy 2.0.0. Also, the default number of 'safe' threads has been upgraded to 16 (instead of previous 8). Finally, preliminary support for Python 3.13; thanks to Karolina Surma. Project documentation is available at: http://numexpr.readthedocs.io/ Changes from 2.10.0 to 2.10.1 ----------------------------- - The default number of 'safe' threads has been upgraded to 16 (instead of previous 8). That means that if your CPU has > 16 cores, the default is to use 16. You can always override this with the "NUMEXPR_MAX_THREADS" environment variable. - NumPy 1.23 is now the minimum supported. - Preliminary support for Python 3.13. Thanks to Karolina Surma. - Fix tests on nthreads detection (closes: #479). Thanks to @avalentino. - The build process has been modernized and now uses the `pyproject.toml` file for more of the configuration options. What's Numexpr? --------------- Numexpr is a fast numerical expression evaluator for NumPy. With it, expressions that operate on arrays (like "3*a+4*b") are accelerated and use less memory than doing the same calculation in Python. It has multi-threaded capabilities, as well as support for Intel's MKL (Math Kernel Library), which allows an extremely fast evaluation of transcendental functions (sin, cos, tan, exp, log...) while squeezing the last drop of performance out of your multi-core processors. Look here for a some benchmarks of numexpr using MKL: https://github.com/pydata/numexpr/wiki/NumexprMKL Its only dependency is NumPy (MKL is optional), so it works well as an easy-to-deploy, easy-to-use, computational engine for projects that don't want to adopt other solutions requiring more heavy dependencies. Where I can find Numexpr? ------------------------- The project is hosted at GitHub in: https://github.com/pydata/numexpr You can get the packages from PyPI as well (but not for RC releases): http://pypi.python.org/pypi/numexpr Documentation is hosted at: http://numexpr.readthedocs.io/en/latest/ Share your experience --------------------- Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data!

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NumPy 2.0.0 released
by Charles R Harris 18 Jun '24

18 Jun '24

Hi All On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.0.0rc2. NumPy 2.0.0 is the first major release since 2006. It is the result of 11 months of development since the last feature release and is the work of 198 contributors spread over 1041 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/v2.0.0> - 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/2.0.0>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.0.0>. *Contributors* A total of 212 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @Algorithmist-Girl + - @DWesl - @Illviljan - @Searchingdays - @ellaella12 + - @liang3zy22 + - @matoro + - @mcp292 + - @mgunyho + - @msavinash + - @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 - Arun Kannawadi + - Bas van Beek - Ben Woodruff + - 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 - Cobalt Yang + - Gonçalo Bárias + - 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 - Eric Xie + - Even Rouault + - Evgeni Burovski - Filipe Laíns + - Francisco Sousa + - Ganesh Kathiresan - Gonçalo Bárias + - Gonzalo Tornaría + - Hans Meine - Heberto Mayorquin + - Heinz-Alexander Fuetterer + - Hood Chatham - Hugo van Kemenade - Ivan A. Melnikov + - Jacob M. Casey + - Jake Lishman + - Jake VanderPlas - James Oliver + - Jan Wassenberg + - Janukan Sivajeyan + - Johann Rohwer + - Johannes Kaisinger + - John Muradeli + - Joris Van den Bossche - Justus Magin - Jyn Spring 琴春 - Kai Striega - Kevin Sheppard - Kevin Wu + - Khawaja Junaid + - Kit Lee + - Kristian Minchev + - Kristoffer Pedersen + - Kuan-Wei Chiu + - Lane Votapka + - Larry Bradley - Leo Singer - Liang Yan + - Linus Sommer + - Logan Thomas - Lucas Colley + - Luiz Eduardo Amaral + - Lukas Geiger - Lysandros Nikolaou + - Maanas Arora + - Maharshi Basu + - Mahder Gebremedhin + - Marcel Bargull + - Marcel Loose + - Mark Mentovai + - Mark Ryan + - Marten van Kerkwijk - Mateusz Sokół - Matt Haberland - Matt Thompson + - Matthew Barber - Matthew Thompson + - 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 + - Olivier Grisel - 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 - Romain Geissler + - 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 + - Timo Röhling + - Trey Woodlief + - Tyler Reddy - Victor Tang + - Vladimir Fokow + - Warren Weckesser - Warrick Ball + - Will Ayd - William Andrea + - William Ayd + - Xiangyi Wang + - Yash Pethe + - Yuki K - Zach Brugh + - Zach Rottman + - Zolisa Bleki Cheers, Charles Harris

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