Mailman 3 June 2025 - NumPy-Discussion

next NumPy triage meeting - Wednesday, May 28th, 2025 at 5 pm UTC
by Inessa Pawson Nov. 8, 2025

Nov. 8, 2025

The next NumPy triage meeting will be held this Wednesday, May 28th at 17:00 (5 pm) UTC. This is a meeting where we synchronously triage prioritized PRs and issues. Everyone is welcome to attend and contribute to a conversation. Join us via Zoom: https://numfocus-org.zoom.us/j/82096749952?pwd=MW9oUmtKQ1c3a2gydGk1RTdYUUVX… 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 - Monday, January 20th, 2025 at 5 pm UTC
by Inessa Pawson Nov. 7, 2025

Nov. 7, 2025

Hi, all! The next NumPy Optimization Team meeting will be held on Monday, January 20th at 17:00 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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next NumPy community meeting - Wednesday, April 9th, 2025 at 5 pm UTC - now meeting ONE HOUR EARLIER
by Inessa Pawson Nov. 3, 2025

Nov. 3, 2025

The next NumPy community meeting will be held this Wednesday, April 9th at 17:00 (5 pm) UTC. Please note the TIME CHANGE! 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 Sept. 12, 2025

Sept. 12, 2025

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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NumPy security roadmap proposal
by Ralf Gommers July 29, 2025

July 29, 2025

Hi all, In https://github.com/numpy/numpy/issues/29178 I posted a proposal for steps to take to improve supply chain security. The most important proposed change is: - Move building release artifacts that get uploaded to PyPI and anaconda.org to a new repository. Other proposed changes: - Further tighten 2FA requirements for everyone with any permissions beyond read-only on the repository. - Move GITHUB_TOKEN to read-only default permissions. - Start shipping SBOMs, in the way outlined by PEP 770 - Start verifying that our builds are fully reproducible It looks like everyone is happy with the proposal so far. Posting here for visibility. Please comment if you have any concerns or other feedback. We'll start implementation next week if there are no blocking concerns. For 2FA and repository/PyPI access, we'll start making changes soon. Note that GitHub has recently made changes to its 2FA settings that ask for action from many people: on https://github.com/orgs/numpy/people you can see that under "Two-factor authentication" the options increased; there is now a Secure/Insecure distinction instead of only Enabled/Disabled. If you want to move yourself from Insecure to Secure, you have to disable the SMS/mobile recovery option in your personal settings under "Password and authentication". A large majority of the 94 people with permissions are currently marked as Insecure. Cheers, Ralf

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Tricky ufunc implementation question
by Benjamin Root July 3, 2025

July 3, 2025

I'm looking at a situation where I like to wrap a C++ function that takes two doubles as inputs, and returns an error code, a position vector, and a velocity vector so that I essentially would have a function signature of (N), (N) -> (N), (N, 3), (N, 3). When I try to use np.vectorize() or np.frompyfunc() on the python version of this function, I keep running into issues where it wants to make the outputs into object arrays of tuples. And looking at utilizing PyUFunc_FromFuncAndData, it isn't clear to me how I can tell it to expect those two output arrays to have a size 3 outer dimension. Are ufuncs the wrong thing here? How should I go about this? Is it even possible? Thanks in advance, Ben Root

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Can't launch Jupyter
by Kwaku Oppong June 28, 2025

June 28, 2025

can someone helps me. Whenever i ytried to launch Jupyter from Navigator, this is the message I gets [I 2025-06-28 02:56:24.163 ServerApp] Extension package panel.io.jupyter_server_extension took 1.1084s to import [I 2025-06-28 02:56:24.164 ServerApp] jupyter_lsp | extension was successfully linked. [I 2025-06-28 02:56:24.169 ServerApp] jupyter_server_terminals | extension was successfully linked. [I 2025-06-28 02:56:24.247 ServerApp] jupyterlab | extension was successfully linked. [I 2025-06-28 02:56:24.253 ServerApp] notebook | extension was successfully linked. [I 2025-06-28 02:56:24.643 ServerApp] notebook_shim | extension was successfully linked. [I 2025-06-28 02:56:24.643 ServerApp] panel.io.jupyter_server_extension | extension was successfully linked. [I 2025-06-28 02:56:24.710 ServerApp] notebook_shim | extension was successfully loaded. [I 2025-06-28 02:56:24.713 ServerApp] jupyter_lsp | extension was successfully loaded. [I 2025-06-28 02:56:24.713 ServerApp] jupyter_server_terminals | extension was successfully loaded. [I 2025-06-28 02:56:24.717 LabApp] JupyterLab extension loaded from C:\ProgramData\anaconda3\Lib\site-packages\jupyterlab [I 2025-06-28 02:56:24.717 LabApp] JupyterLab application directory is C:\ProgramData\anaconda3\share\jupyter\lab [I 2025-06-28 02:56:24.718 LabApp] Extension Manager is 'pypi'. [I 2025-06-28 02:56:25.074 ServerApp] jupyterlab | extension was successfully loaded. [I 2025-06-28 02:56:25.081 ServerApp] notebook | extension was successfully loaded. [I 2025-06-28 02:56:25.082 ServerApp] panel.io.jupyter_server_extension | extension was successfully loaded. [I 2025-06-28 02:56:25.083 ServerApp] The port 8888 is already in use, trying another port. [I 2025-06-28 02:56:25.083 ServerApp] The port 8889 is already in use, trying another port. [I 2025-06-28 02:56:25.084 ServerApp] Serving notebooks from local directory: C:\Users\HP [I 2025-06-28 02:56:25.084 ServerApp] Jupyter Server 2.15.0 is running at: [I 2025-06-28 02:56:25.085 ServerApp] http://localhost:8890/tree?token=ec1318d4aaae449c0f15a43efe0c4fe9e9f5bca851… [I 2025-06-28 02:56:25.085 ServerApp] http://127.0.0.1:8890/tree?token=ec1318d4aaae449c0f15a43efe0c4fe9e9f5bca851… [I 2025-06-28 02:56:25.085 ServerApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). [E 2025-06-28 02:56:25.085 ServerApp] Failed to write server-info to C:\Users\HP\AppData\Roaming\jupyter\runtime\jpserver-11204.json: PermissionError(13, 'Permission denied') Traceback (most recent call last): File "C:\ProgramData\anaconda3\Scripts\jupyter-notebook-script.py", line 10, in sys.exit(main()) ~~~~^^ File "C:\ProgramData\anaconda3\Lib\site-packages\jupyter_server\extension\application.py", line 623, in launch_instance serverapp.start() ~~~~~~~~~~~~~~~^^ File "C:\ProgramData\anaconda3\Lib\site-packages\jupyter_server\serverapp.py", line 3179, in start self.start_app() ~~~~~~~~~~~~~~^^ File "C:\ProgramData\anaconda3\Lib\site-packages\jupyter_server\serverapp.py", line 3083, in start_app self.write_browser_open_files() ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^ File "C:\ProgramData\anaconda3\Lib\site-packages\jupyter_server\serverapp.py", line 2950, in write_browser_open_files self.write_browser_open_file() ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^ File "C:\ProgramData\anaconda3\Lib\site-packages\jupyter_server\serverapp.py", line 2973, in write_browser_open_file with open(self.browser_open_file, "w", encoding="utf-8") as f: ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ PermissionError: [Errno 13] Permission denied: 'C:\\Users\\HP\\AppData\\Roaming\\jupyter\\runtime\\jpserver-11204-open.html'

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ENH: More user-friendly way to pad a specific axis
by Carlos Martin June 26, 2025

June 26, 2025

It is common to want to pad an array along a *specific* axis. Examples: - https://stackoverflow.com/questions/72106542/how-to-pad-the-i-j-axes-of-a-3… - https://stackoverflow.com/questions/56076094/zero-pad-ndarray-along-axis - https://stackoverflow.com/questions/66225047/padding-a-3-dimensional-numpy-… - https://stackoverflow.com/questions/19349410/how-to-pad-with-zeros-a-tensor… - https://stackoverflow.com/questions/74966041/how-to-pad-a-specific-dimensio… Doing so with [`numpy.pad`](https://numpy.org/doc/stable/reference/generated/numpy.pad.html) requires constructing a list of pairs of length equal to the ndim of the array, with exactly one of those pairs at the right position containing the desired pad widths. This can be verbose and clumsy when there are several axes. I propose a new, more user-friendly way to pad along a specific axis (or axes): Let the `pad_width` argument accept a *dictionary* whose keys are axes and whose values are the `(before, after)` pair (or perhaps single number) for the corresponding axis. Example: ```python3 # before np.pad(array, [(0, 0), (0, 0), (1, 2), (0, 0), (0, 0)]) # after np.pad(array, {-3: (1, 2)}) ``` This should require only minor modification to the implementation of `numpy.pad`. If others like this idea, I can create a PR for it.

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NumPy 2.3.1 released
by Charles R Harris June 21, 2025

June 21, 2025

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 2.3.1. The NumPy 2.3.1 release is a patch release with several bug fixes, annotation improvements, and better support for OpenBSD. Highlights are: - Fix bug in ``matmul`` for non-contiguous out kwarg parameter - Fix for Accelerate runtime warnings on M4 hardware - Fix new in NumPy 2.3.0 ``np.vectorize`` casting errors - Improved support of cpu features for FreeBSD and OpenBSD This release supports Python 3.11-3.13, Python 3.14 will be supported when it is released. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/2.3.1>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v2.3.1>. *Contributors* A total of 9 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Brad Smith + - Charles Harris - Developer-Ecosystem-Engineering - François Rozet - Joren Hammudoglu - Matti Picus - Mugundan Selvanayagam - Nathan Goldbaum - Sebastian Berg *Pull requests merged* A total of 12 pull requests were merged for this release. - #29140: MAINT: Prepare 2.3.x for further development - #29191: BUG: fix matmul with transposed out arg (#29179) - #29192: TYP: Backport typing fixes and improvements. - #29205: BUG: Revert ``np.vectorize`` casting to legacy behavior (#29196) - #29222: TYP: Backport typing fixes - #29233: BUG: avoid negating unsigned integers in resize implementation... - #29234: TST: Fix test that uses uninitialized memory (#29232) - #29235: BUG: Address interaction between SME and FPSR (#29223) - #29237: BUG: Enforce integer limitation in concatenate (#29231) - #29238: CI: Add support for building NumPy with LLVM for Win-ARM64 - #29241: ENH: Detect CPU features on OpenBSD ARM and PowerPC64 - #29242: ENH: Detect CPU features on FreeBSD / OpenBSD RISC-V64. Cheers, Charles Harris

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ENH: Let numpy.size accept multiple axes
by Carlos Martin June 19, 2025

June 19, 2025

Let the `axis` argument of [`numpy.size`](https://numpy.org/doc/stable/reference/generated/numpy.size.html) accept a tuple of ints (like other functions that take an `axis` argument) to measure the size for multiple axes. This should be straightforward to implement with [`itertools.product`](https://docs.python.org/3/library/itertools.html#itertools.product).

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