Mailman 3 April 2023 - NumPy-Discussion

Managing integer overflow
by jmsachs＠gmail.com May 3, 2023

May 3, 2023

Hi there! I'm looking at options for fixed-point math, and none of the existing Python modules (https://github.com/smlgit/fpbinary, https://github.com/francof2a/fxpmath, etc.) seem to check the right boxes, so I'm thinking about creating my own, and maybe contributing it to numpy. Are there any facilities in numpy to manage integer overflow in addition/subtraction/multiplication of values with identical dtypes? The default behavior with integer types like np.uint8 appears to be twos-complement wrapping syntax with an optional warning (or exception or whatever via np.seterr), and that's well-designed, but not as helpful as what I'd like to see in some cases where I'm willing to sacrifice some speed to provide feedback. Ideally if there is an overflow in an array operation I'd like to produce an "overflow array" that's the same size as the result array, but with the values +1 if it's a positive overflow or -1 if it's a negative overflow. That way I can either raise an exception or saturate the output. If there are not such facilities already --- and again, I don't want to burden the default operations with this kind of behavior, but would like to see some way of facilitating this behavior through some mechanism --- let me know and I will open up a feature request. --Jason

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Google Season of Docs 2023 Project Roadmap
by Mars Lee May 2, 2023

May 2, 2023

Hi everyone, I'm Mars, the selected technical writer for NumPy’s Google Summer of Docs project proposal, the ‘NumPy Contributor Comics'. The project roadmap and timeline can be read here: https://medium.com/@marsbarlee/gsod-numpy-contributor-comics-project-roadma…. In a few weeks, I'll host short brainstorming sessions to gather community input during the NumPy Community and Docs meeting. It will also be open for review soon. Looking forward to shaping and sharing these comics with everyone! Best, Mars

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Google Season of Docs 2023 participation announcement
by Mukulika Pahari April 28, 2023

April 28, 2023

Hi all, I am pleased to announce that NumPy has been selected as a participant for the Google Season of Docs program. This year, the grant will support Mars (@MarsBarLee) as our technical writer on the "NumPy Contributor Journey Comics" project. She has been instrumental in improving the NumPy documentation experience through design and conceived the idea for this project here- https://github.com/numpy/numpy.org/pull/600. She will share her detailed implementation plan with us soon. The project proposal can be read here- https://github.com/numpy/numpy/wiki/Google-Season-of-Docs-2023:-Project-Pro…. And the full list of selected participants can be seen at https://developers.google.com/season-of-docs/docs/participants. Also, we are always happy to welcome other documentation contributions on a volunteer basis. Please check out our community page (https://numpy.org/community/) if you wish to get involved. Best wishes, Mukulika

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Align Community & Triage meeting times
by Ross Barnowski April 26, 2023

April 26, 2023

Hi All, Currently, the numpy community and triage meetings alternate between 7PM UTC (community) and 5PM UTC (triage) on a biweekly basis. At the most recent community meeting, there was a strong preference among attendees to do away with the alternating times and have both meetings at 5PM UTC. Therefore, I plan to update the meeting times so that both community & triage meetings are held at 5PM UTC (10AM PST, 8PM EEST). This time was determined to work better for regular meeting attendees, but if there are any strong objections please comment on this pull request <https://github.com/scientific-python/scientific-python.org/pull/383>. ~Ross

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structured_to_unstructured: return a view more often
by philip.holzmann＠aurivus.com April 25, 2023

April 25, 2023

Hello dear numpy community. I would like to propose an enhancement to the numpy.lib.recfunctions.structured_to_unstructured() function. I have submitted a PR already: <https://github.com/numpy/numpy/pull/23652> The PR also contains a short example. The structured_to_unstructured() function already returns views, if all elements in the structured array have the same dtype (or have sub-arrays of the same dtype) and are contiguous. I propose to relax the requirement of being contiguous, and instead only require that the offset between the fields is constant (so that it can be turned into a stride). This will for example allow performing structured_to_unstructured when selecting a subset of fields, e.g. from a vertex containing x, y, z, red, green, and blue (which would formerly return a copy): `structured_to_unstructured(vertex[['x', 'y', 'z']])` Additional changes: a) For views, ndarray (not an array subclass) is always returned: structured_to_unstructured() adds a new dimension to the array, but some subclasses behave strangely if that is done. For example, the numpy.matrix class always tries to keep 2 dimensions, which causes issues here. Maybe we want to change structured_to_unstructured() to always return an ndarray, even in the case of a copy? So far, the documentation does not specify if subclasses are preserved. b) The `base` attribute of the array will be more deeply nested than before. The only way I can think of to work around this, is to use the __array_interface__ directly.

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Documentation team meeting - Monday 24 April at 12 PM UTC
by Mukulika Pahari April 23, 2023

April 23, 2023

Hi all! Our next Documentation Team meeting will happen on *Monday, April 24* at ***12PM UTC***. We now alternate the meeting times to be a bit more inclusive. This means that we'll have a meeting at 12pm UTC every 28 days, and a meeting at 4pm UTC every 28 days. 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! * You can also visit https://scientific-python.org/calendars to add the NumPy community calendar as an .ics file to your preferred calendar manager. * Best wishes, Mukulika

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NumPy 1.24.3 released
by Charles R Harris April 22, 2023

April 22, 2023

Hi All, On behalf of the NumPy team, I'm pleased to announce the release of NumPy 1.24.3. NumPy 1.24.3 is a maintenance release that fixes bugs and regressions discovered after the 1.24.2 release. The Python versions supported by this release are 3.8-3.11 Note that 32 bit wheels are only provided for Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.24.3/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.24.3>. *Contributors* A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Aleksei Nikiforov + - Alexander Heger - Bas van Beek - Bob Eldering - Brock Mendel - Charles Harris - Kyle Sunden - Peter Hawkins - Rohit Goswami - Sebastian Berg - Warren Weckesser - dependabot[bot] *Pull requests merged* A total of 17 pull requests were merged for this release. - #23206: BUG: fix for f2py string scalars (#23194) - #23207: BUG: datetime64/timedelta64 comparisons return NotImplemented - #23208: MAINT: Pin matplotlib to version 3.6.3 for refguide checks - #23221: DOC: Fix matplotlib error in documentation - #23226: CI: Ensure submodules are initialized in gitpod. - #23341: TYP: Replace duplicate reduce in ufunc type signature with reduceat. - #23342: TYP: Remove duplicate CLIP/WRAP/RAISE in ``__init__.pyi``. - #23343: TYP: Mark ``d`` argument to fftfreq and rfftfreq as optional... - #23344: TYP: Add type annotations for comparison operators to MaskedArray. - #23345: TYP: Remove some stray type-check-only imports of ``msort`` - #23370: BUG: Ensure like is only stripped for ``like=`` dispatched functions - #23543: BUG: fix loading and storing big arrays on s390x - #23544: MAINT: Bump larsoner/circleci-artifacts-redirector-action - #23634: BUG: Ignore invalid and overflow warnings in masked setitem - #23635: BUG: Fix masked array raveling when ``order="A"`` or ``order="K"`` - #23636: MAINT: Update conftest for newer hypothesis versions - #23637: BUG: Fix bug in parsing F77 style string arrays. Cheers, Charles Harris

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Error from "import numpy": undefined symbol: _ZSt21__glibcxx_assert_failPKciS0_S0_
by Chiara Marmo April 16, 2023

April 16, 2023

Dear list, I am compiling numpy from source (main 1.25.0.dev0+1173.g7f682bca5) on Fedora 37. - Python 3.11 - GNU C Library (GNU libc) stable release version 2.36. - g++ (GCC) 12.2.1 20220819 (Red Hat 12.2.1-2) I am compiling in a python venv, the C and C++ libraries are from the Fedora distribution. The compilation went well, but when importing: >>> import numpy gives Traceback (most recent call last): File "/home/cmarmo/software/numpy/numpy/core/__init__.py", line 23, in <module> from . import multiarray File "/home/cmarmo/software/numpy/numpy/core/multiarray.py", line 10, in <module> from . import overrides File "/home/cmarmo/software/numpy/numpy/core/overrides.py", line 8, in <module> from numpy.core._multiarray_umath import ( ImportError: /home/cmarmo/software/numpy/numpy/core/_ multiarray_umath.cpython-311-x86_64-linux-gnu.so: undefined symbol: _ZSt21__glibcxx_assert_failPKciS0_S0_ During handling of the above exception, another exception occurred: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/cmarmo/software/numpy/numpy/__init__.py", line 139, in <module> from . import core File "/home/cmarmo/software/numpy/numpy/core/__init__.py", line 49, in <module> raise ImportError(msg) ImportError: IMPORTANT: PLEASE READ THIS FOR ADVICE ON HOW TO SOLVE THIS ISSUE! Importing the numpy C-extensions failed. This error can happen for many reasons, often due to issues with your setup or how NumPy was installed. We have compiled some common reasons and troubleshooting tips at: https://numpy.org/devdocs/user/troubleshooting-importerror.html Please note and check the following: * The Python version is: Python3.11 from "/home/cmarmo/numpydevenv/bin/python" * The NumPy version is: "1.25.0.dev0+1173.g7f682bca5" and make sure that they are the versions you expect. Please carefully study the documentation linked above for further help. Original error was: /home/cmarmo/software/numpy/numpy/core/_ multiarray_umath.cpython-311-x86_64-linux-gnu.so: undefined symbol: _ZSt21__glibcxx_assert_failPKciS0_S0_ I have upgraded and downgraded the libstdc++ as far as possible in fedora37, but the error is still there. I have compiled with meson too and the result is the same. I can provide both the buildlogs for setuptools and meson, they are vvverbose and big (17M), just let me know where I can upload them. I don't know if the issue is from numpy or the fedora packaging. I hope some fedora packagers related to python are also in the list, perhaps they may provide some hint? Thanks for your attention! Regards, Chiara -- GPG Key ID: ed25519/7611FA19766900CD Fingerprint FD45 A232 211E DE9F 23E9 DDD5 7611 FA19 7669 00CD

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NumPy mentored sprint at PyCon US’23
by Inessa Pawson April 15, 2023

April 15, 2023

Next Saturday, April 22nd, I’m hosting a NumPy mentored sprint at PyCon US’23 in person. A newcomer or not, if you are at the conference, feel free to stop by and say hi. For details about the event, visit: https://us.pycon.org/2023/events/mentored-sprints/. -- Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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update / revision suggest around f2py
by nbehrnd＠yahoo.com April 14, 2023

April 14, 2023

Dear maintainers of the documentation, Python numpy includes f2py to access functionality (and performance) of Fortran modules, which https://numpy.org/doc/stable/f2py/f2py.getting-started.html#the-smart-way aims to present. However, I was not able to successfully replicate fully either one of the three approaches (the quick way, the smart way, the quick and smart way) in an instance of Linux Debian 12/bookworm with gcc and gfortran (12.2.0), Python (3.11.2), and f2py (1.24.2) as provided from the repositories of Debian's branch testing. I would like to suggest an update and a revision of the page in question. For one, the snippets of Fortran adhere the (fixed format) standard of FORTRAN77, which became "old" with the major revision of Fortran90 (by 1991/1992) and the introduction of the free format. With current standard Fortran2018 (and Fortran2023 anticipated for July this year) an update of them would be very welcome - one still can state (as by now) that multiple standards of Fortran are supported. For two and in addition to this, it would be nice if the instructions given could be cross checked if they still provide the functionality claimed. If helpful, I can send the captured terminal input/output as ASCII text to one of you maintainers in a direct message, too. Regards

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