Mailman 3 December 2024 - NumPy-Discussion

next NumPy community meeting - June 5th, 2024 at 6 pm UTC
by Inessa Pawson Jan. 26, 2025

Jan. 26, 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 Documentation team meeting
by Mukulika Pahari Jan. 13, 2025

Jan. 13, 2025

Hi all, Our next Documentation Team meeting will happen on *Monday, July 15* at *7PM UTC*. 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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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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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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Proposing a flattening functionality for deeply nested lists in NumPy
by Mark Dec. 31, 2024

Dec. 31, 2024

Hello all, Many people have asked how to flatten a nested list into a one-dimensional list (e.g., see this StackOverflow thread <https://stackoverflow.com/questions/952914/how-do-i-make-a-flat-list-out-of…> ). While flattening a 2D list is relatively straightforward, deeply nested lists can become cumbersome to handle. To address this challenge, I propose adding a built-in list-flattening functionality to NumPy. By adding this feature to NumPy, the library would not only simplify a frequently used task but also enhance its overall usability, making it an even more powerful tool for data manipulation and scientific computing. The code snippet below demonstrates how a nested list can be flattened, enabling conversion into a NumPy array. I believe this would be a valuable addition to the package. See also this issue <https://github.com/numpy/numpy/issues/28079>. from collections.abc import Iterable def flatten_list(iterable): """ Flatten a (nested) list into a one-dimensional list. Parameters ---------- iterable : iterable The input collection. Returns ------- flattened_list : list A one-dimensional list containing all the elements from the input, with any nested structures flattened. Examples -------- Flattening a list containing nested lists: >>> obj = [[1, 2, 3], [1, 2, 3]] >>> flatten_list(obj) [1, 2, 3, 1, 2, 3] Flattening a list with sublists of different lengths: >>> obj = [1, [7, 4], [8, 1, 5]] >>> flatten_list(obj) [1, 7, 4, 8, 1, 5] Flattening a deeply nested list. >>> obj = [1, [2], [[3]], [[[4]]],] >>> flatten_list(obj) [1, 2, 3, 4] Flattening a list with various types of elements: >>> obj = [1, [2], (3), (4,), {5}, np.array([1,2,3]), range(3), 'Hello'] >>> flatten_list(obj) [1, 2, 3, 4, 5, 1, 2, 3, 0, 1, 2, 'Hello'] """ if not isinstance(iterable, Iterable) or isinstance(iterable, str): return [iterable] def flatten_generator(iterable): for item in iterable: if isinstance(item, Iterable) and not isinstance(item, str): yield from flatten_generator(item) else: yield item return list(flatten_generator(iterable))

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surprising behavior from array indexing
by Mark Harfouche Dec. 30, 2024

Dec. 30, 2024

Happy new year everybody! I've been upgrading my code to start to support array indexing and in my tests I found something that was well documented, but surprising to me. I've tried to read through https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-a… and even after multiple passes, I still find it very terse... Consider a mutli dimensional dataset: import numpy as np shape = (10, 20, 30) original = np.arange(np.prod(shape)).reshape(shape) Let's consider we want to collapse dim 0 to a single entry Let's consider we want a subset from dim 1, with a slice Let's consider that we want want 3 elements from dim 2 i = 2 j = slice(1, 6) k = slice(7, 10) out_basic = original[i, j, k] assert out_basic.shape == (5, 3) Now consider we want to provide freedom to have instead of a slice for k, an arbitrary "array" k = [7, 11, 13] out_array = original[i, j, k] assert out_array.shape == (5, 3), f"shape is actually {out_array.shape}" AssertionError: shape is actually (3, 5) To get the result "Mark expects", one has to do it in two steps integer_types = (int, np.integer) integer_indexes = ( i if isinstance(i, integer_types) else slice(None), j if isinstance(j, integer_types) else slice(None), k if isinstance(k, integer_types) else slice(None), ) non_integer_indexes = ( ((i,) if not isinstance(i, integer_types) else ()) + ((j,) if not isinstance(j, integer_types) else ()) + ((k,) if not isinstance(k, integer_types) else ()) ) out_double_indexed = original [integer_indexes][non_integer_indexes] assert out_double_indexed.shape == (5, 3), f"shape is actually {out_double_indexed.shape}" This is somewhat very surprising to me. I totally understand that things won't change in terms of this kind of indexing in numpy, but is there a way I can adjust my indexing strategy to regain the ability to slice into my array in a "single shot". The main usecase is for arrays that are truly huge, but chucked in ways where slicing into them can be quite efficient. This multi-dimensional imaging data. Each chunk is quite "huge" so this kind of metadata manipulation is worthwhile to avoid unecessary IO. Perhaps there is a "simple" distinction I am missing, for example using a tuple for k instead of a list???? Thanks for your input! Mark (I tried to keep my code copy pastable)

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Copy on __setitem__
by Israel, Daniel M Dec. 27, 2024

Dec. 27, 2024

I was updating some code that uses a custom array container built with the mixin library. Specifically, I was trying to eliminate some warnings due to the change to the __array__ interface to add a copy argument. In doing so, I discovered that, for two objects u, v in my container class, the code: u[…] = v performs a copy on v. Specifically, it calls __array__() with copy=True. This seems unnecessary and wasteful of memory. Can someone explain to me what is happening here? Thanks. — Daniel Israel XCP-4: Continuum Models and Numerical Algorithms dmi1(a)lanl.gov

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unique_2D
by Mark Dec. 24, 2024

Dec. 24, 2024

Hello, I've made a contribution to Numpy: unique_2D() which determines unique values and counts of a multi-dimensional array for each row (last dimension). As this is my first contribution, I expect some feedback before being added to the final version. The request can be found here: https://github.com/numpy/numpy/pull/28064 I hope to get some constructive feedback such that I can be more effective in contributing to Numpy in the future. Kind regards, Mark

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a thank you for donations to NumPy
by Ralf Gommers Dec. 22, 2024

Dec. 22, 2024

Hi all, We just received two $1,000 donations on Open Collective ( https://opencollective.com/numpy), from MMIC and Joseph Jacks. Those are the largest individual/anonymous donations we've received over the last years I think, at least since the time we have a completely public record of our finances. There are no OC profiles or emails attached to those donations, so we have no way of sending MMIC and Joseph Jacks a direct thank you - hence doing so here, these donations are much appreciated. We'll aim to do something impactful with these funds in the near future! Happy holidays, Ralf

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NumPy 2.2.1 released
by Charles R Harris Dec. 21, 2024

Dec. 21, 2024

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.2.1. NumPy 2.2.1 is a patch release following 2.2.0. It fixes bugs found after the 2.2.0 release and has several maintenance pins to work around upstream changes. There was some breakage in downstream projects following the 2.2.0 release due to updates to NumPy typing. Because of problems due to MyPy defects, we recommend using basedpyright for type checking, it can be installed from PyPI. The Pylance extension for Visual Studio Code is also based on Pyright. Typing problems that persist when using basedpyright should be reported as issues on the NumPy github site. This release supports Python 3.10-3.13. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.2.1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.2.1>. *Contributors* A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Joren Hammudoglu - Matti Picus - Nathan Goldbaum - Peter Hawkins - Simon Altrogge - Thomas A Caswell - Warren Weckesser - Yang Wang + *Pull requests merged* A total of 12 pull requests were merged for this release. - #27935: MAINT: Prepare 2.2.x for further development - #27950: TEST: cleanups - #27958: BUG: fix use-after-free error in npy_hashtable.cpp (#27955) - #27959: BLD: add missing include - #27982: BUG:fix compile error libatomic link test to meson.build - #27990: TYP: Fix falsely rejected value types in ``ndarray.__setitem__`` - #27991: MAINT: Don't wrap ``#include <Python.h>`` with ``extern "C"`` - #27993: BUG: Fix segfault in stringdtype lexsort - #28006: MAINT: random: Tweak module code in mtrand.pyx to fix a Cython... - #28007: BUG: Cython API was missing NPY_UINTP. - #28021: CI: pin scipy-doctest to 1.5.1 - #28044: TYP: allow ``None`` in operand sequence of nditer Cheers, Charles Harris

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