Mailman 3 - NumPy-Discussion

Functions for secant, cosecant, and cotangent
by Jayanth Tumuluri Feb. 13, 2025

Feb. 13, 2025

Hello, I noticed that current/past installations of Numpy do not have functions for secant, cosecant, and cotangent. Sometimes, there's a reason to use these over their less elegant forms of 1/cos, 1/sin, and 1/tan. Could this be something to be included in future installations of Numpy? I'd really appreciate it! Thank you, Jayanth kT

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Add diagonal offset argument to all functions that are missing it
by Carlos Martin Feb. 11, 2025

Feb. 11, 2025

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Proposal of a new feature - Vectorized Sampling Without Replacement
by Adly Thebaud Feb. 8, 2025

Feb. 8, 2025

Hello, This is my first time engaging with Numpy, so forgive me if I am emailing the wrong address. I am inquiring about a feature I'd like to propose. It is to address GitHub issue #28084 <https://github.com/numpy/numpy/issues/28084>. I saw that there was positive reception to the proposed signature and functionality proposed by Warren Weckesser <https://github.com/numpy/numpy/issues/28084#issuecomment-2573591622>, who is a member of Numpy team (according to GitHub). I will be pushing up a pull request soon, please let me know if this is a good feature to pursue or if I should hold off on it. Thanks, Adly Thebaud

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slice an array along specific axis
by Michael Mullen Feb. 1, 2025

Feb. 1, 2025

Hello, I am writing a class handling NumPy arrays, and basing it off some computations I have done in C++ using the Eigen library. For tensors whose length are only known at runtime, the Eigen chip method is useful for slicing a tensor along a specific axis while keeping all other axes the same. I have not found a similar method in NumPy, but a simple implementation is def chip(A, axis, vals): return np.swapaxes(np.swapaxes(A, axis, -1)[..., vals], -1, axis) Example usage would be (to slice the 3rd axis of a 4D array): A = np.random.randn(10,11,12,13) B = chip(A, axis=2, vals=np.arange(0,6)) B.shape #(10, 11, 6, 13) Since this may be useful for others, despite its simplicity, I thought it may be useful to have something similar in NumPy. Best, Mike

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Scientific Python Translations sprint - join us!
by Melissa Mendonça Jan. 30, 2025

Jan. 30, 2025

Hi all! As part of the Scientific Python Translations project [1], we are organizing a full-day sprint to answer questions and meet new volunteer contributors interested in working on Scientific Python project website translations. Currently, we are accepting translations for NumPy, SciPy, NetworkX, Zarr, pandas and Xarray, but more project may join in the future. The sprint will take place on **February 11th from 11AM UTC to 9PM UTC on the Scientific Python Discord server** [2]. You are free to join or leave at any time during those hours, and you can stay for any amount of time. You can choose to work on your own, or you can team up with other translators and discuss specific issues.This is a great opportunity for first-time contributors as the only requirement is a working knowledge of English and proficiency in the target translation language - although familiarity with the project being translated is useful. We encourage you to participate even if you are not sure you fulfill these requirements. Target languages include Spanish, Chinese (Simplified), Japanese, Portuguese (Brazilian), Arabic and Hindi. You can find more information on the translation process in our documentation [3] or join the #translation channel of the Scientific Python Discord server. You can also find this announcement in the Scientific Python discourse, if you want to have a link to share with your network: https://discuss.scientific-python.org/t/scientific-python-translations-spri… [1] https://blog.scientific-python.org/scientific-python/translations/ [2] https://discord.com/invite/vur45CbwMz (Scientific Python Discord server invite) [3] https://scientific-python-translations.github.io/ Cheers! - Melissa

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NumPy 2.2. 2 Release
by Charles R Harris Jan. 19, 2025

Jan. 19, 2025

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.2.2. NumPy 2.2.2 is a patch release that fixes bugs found after the 2.2.1 release. The number of typing fixes/updates is notable. This release supports Python 3.10-3.13. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.2.2>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.2.2>. *Contributors* A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Alicia Boya García + - Charles Harris - Joren Hammudoglu - Kai Germaschewski + - Nathan Goldbaum - PTUsumit + - Rohit Goswami - Sebastian Berg *Pull requests merged* A total of 16 pull requests were merged for this release. - #28050: MAINT: Prepare 2.2.x for further development - #28055: TYP: fix ``void`` arrays not accepting ``str`` keys in ``__setitem__`` - #28066: TYP: fix unnecessarily broad ``integer`` binop return types (#28065) - #28112: TYP: Better ``ndarray`` binop return types for ``float64`` &... - #28113: TYP: Return the correct ``bool`` from ``issubdtype`` - #28114: TYP: Always accept ``date[time]`` in the ``datetime64`` constructor - #28120: BUG: Fix auxdata initialization in ufunc slow path - #28131: BUG: move reduction initialization to ufunc initialization - #28132: TYP: Fix ``interp`` to accept and return scalars - #28137: BUG: call PyType_Ready in f2py to avoid data races - #28145: BUG: remove unnecessary call to PyArray_UpdateFlags - #28160: BUG: Avoid data race in PyArray_CheckFromAny_int - #28175: BUG: Fix f2py directives and --lower casing - #28176: TYP: Fix overlapping overloads issue in 2->1 ufuncs - #28177: TYP: preserve shape-type in ndarray.astype() - #28178: TYP: Fix missing and spurious top-level exports Cheers, Charles Harris

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New GitHub issue UI
by Nathan Jan. 15, 2025

Jan. 15, 2025

Hi all, GitHub is rolling out the new UI for issues, which includes a lot of new opportunities to reorganize our backlog. More detail on the changelog blog: https://github.blog/changelog/2025-01-13-evolving-github-issues-public-prev… In particular, there is now much richer support for tracking issues by marking issues as "sub-issues". We can also (finally) get rid of the issue category labels - GitHub now has support for "issue types". If someone with triage rights would like to take this on, it would be a nice project to try to go through the backlog and update things to use the new system, as well as the bot that auto-applies labels. You could probably use a script rather than doing it manually. -Nathan

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How to join IRC channel of numpy
by Diya Singh Jan. 14, 2025

Jan. 14, 2025

Please help me know how to join discussion channels for NumFocus organization so that I can meet the community

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Why ufunc out arg has to be an ndarray?
by Stefano Miccoli Jan. 8, 2025

Jan. 8, 2025

From time to time I find myself overwriting a python buffer with the output of a ufunc, for example like this: import array import numpy as np a = array.array('f', (1,1,1)) np.exp2(a, out=np.asarray(a)) assert a.tolist() == [2, 2, 2] Here I have to wrap `out=np.asarray(a)` because the more natural `np.exp2(a, out=a)` raises "TypeError: return arrays must be of ArrayType” In general, ufuncs are quite aggressive in utilizing the buffer protocol for input arguments. I was wondering, why are they so reluctant to do the same for the output argument? Is this a design choice? Efficiency? Legacy? Would be implementing `np.ufunc(a, out=a)` dangerous or cumbersome? Stefano

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Is a Python function a gufunc if it broadcasts its arguments appropriately?
by john.a.dawson＠proton.me Jan. 6, 2025

Jan. 6, 2025

For example, is the function `stack` below a gufunc with signature (),()->(2)? def stack(a, b): broadcasts = np.broadcast_arrays(a, b) return np.stack(broadcasts, axis=-1) Or must gufuncs be written in C? Or are other things required?

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