Mailman 3 June 2023 - NumPy-Discussion

next NumPy triage meeting - now starting at 5 pm UTC
by Inessa Pawson 17 Sep '23

17 Sep '23

The next NumPy triage meeting will be held this Wednesday, January 11th at 5 pm UTC. (Please note the new time!) 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 Contributor Experience Lead | NumPy https://numpy.org/ GitHub: inessapawson

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Next Documentation team meeting
by Mukulika Pahari 11 Sep '23

11 Sep '23

Hi all! Our next Documentation Team meeting will happen on *Monday, June 5* at ***4PM 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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next NumPy community meeting
by Inessa Pawson 10 Sep '23

10 Sep '23

The next NumPy community meeting will be held this Wednesday, August 17th at 18:00 (6 pm) UTC. Join us via Zoom: https://us06web.zoom.us/j/83278611437?pwd=ekhoLzlHRjdWc0NOY2FQM0NPemdkZz09 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 meeting, visit: https://github.com/numpy/archive/blob/main/community_meetings/community-202… Cheers, Inessa Inessa Pawson Contributor Experience Lead | NumPy https://numpy.org/ Twitter: @inessapawson

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reminder: put an upper bound on setuptools if you use numpy.distutils!
by Ralf Gommers 31 Aug '23

31 Aug '23

Hi all, After setuptools 65.0 was released a few days ago, all users of numpy.distutils had their builds broken. This is already fixed in setuptools 65.0.2 because the breakage was particularly bad. However, the next breakage may not be fixed anymore (and more breakages *are* expected). So this is a good time to remind you all that you should put an upper bound on the setuptools version you allow in the releases of your package - to the last version that is known to work with your package. Our official stance here is that setuptools versions >=60 are not supported - see the "deprecating numpy.distutils" thread: https://mail.python.org/archives/list/numpy-discussion@python.org/message/P…. Newer versions may work for you, depending on what features you use. They don't for NumPy and for SciPy; both projects pin to 59.X to avoid problems. For the recent issue with setuptools 65.0.0, see https://github.com/numpy/numpy/issues/22135. We have also made the warnings about this topic in our docs more explicit, see https://github.com/numpy/numpy/pull/22154. Cheers, Ralf

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Adding bit_count ufunc
by Doug Turnbull 13 Aug '23

13 Aug '23

👋Long time numpy user, and big fan of all your work TL; DR - there's a `bit_count` method on numpy scalars, and Python ints, I'm advocating for a `ufunc` `bit_count` as has been implemented in this PR: https://github.com/numpy/numpy/pull/21429 A number of people have requested this as a numpy feature and there's a lot of great discussion at this issue https://github.com/numpy/numpy/issues/16325 A bit count comes up in certain similarity situations (like hamming distance). This involves an xor with numpy arrays and a bit count, where the bit count is currently the bottleneck by a few orders of magnitude in my local benchmarking. Currently there's a number of not particularly fast, workarounds for doing this with numpy arrays, like the bit-twiddling solutions here https://stackoverflow.com/a/68943135/8123 https://stackoverflow.com/a/109025/8123 So I'd love to be able to continue the work of bit_count -> numpy array ufunc to get performance gains at the array level 🙏

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mixed mode arithmetic
by Neal Becker 11 Jul '23

11 Jul '23

I've been browsing the numpy source. I'm wondering about mixed-mode arithmetic on arrays. I believe the way numpy handles this is that it never does mixed arithmetic, but instead converts arrays to a common type. Arguably, that might be efficient for a mix of say, double and float. Maybe not. But for a mix of complex and a scalar type (say, CDouble * Double), it's clearly suboptimal in efficiency. So, do I understand this correctly? If so, is that something we should improve?

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mean_std function returning both mean and std
by Ronald van Elburg 06 Jul '23

06 Jul '23

I created a solution for ENH: Computing std/var and mean at the same time, issue #23741. The solution can be found here: https://github.com/soundappraisal/numpy/tree/stdmean-dev-001 I still need to add tests and the solution does touch the implementation of var. But before starting a pull request I like to check whether mean_std is a welcome addition. Also I was struggling with the internally needed format of the arrays containing mean and std and the format produced as a result. Which makes me uncertain whether the chosen solution is correct for all cases.

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ANN: SciPy 1.11.1
by Tyler Reddy 28 Jun '23

28 Jun '23

Hi all, On behalf of the SciPy development team, I'm pleased to announce the release of SciPy 1.11.1, which addresses a few bugs and in particular a licensing issue discovered shortly after the release of 1.11.0, which has now been yanked from PyPI. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.11.1 One of a few ways to install this release with pip: pip install scipy==1.11.1 ====================== SciPy 1.11.1 Release Notes ====================== SciPy 1.11.1 is a bug-fix release with no new features compared to 1.11.0. In particular, a licensing issue discovered after the release of 1.11.0 has been addressed. Authors ======= * Name (commits) * h-vetinari (1) * Robert Kern (1) * Ilhan Polat (4) * Tyler Reddy (8) A total of 4 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 1.11.1 -------------------------------- * `#18739 <https://github.com/scipy/scipy/issues/18739>`__: BUG: run method of scipy.odr.ODR class fails when delta0 parameter... * `#18751 <https://github.com/scipy/scipy/issues/18751>`__: BUG: segfault in \`scipy.linalg.lu\` on x86_64 windows and macos... * `#18753 <https://github.com/scipy/scipy/issues/18753>`__: BUG: factorial return type inconsistent for 0-dim arrays * `#18759 <https://github.com/scipy/scipy/issues/18759>`__: determinant of a 1x1 matrix returns an array, not a scalar * `#18765 <https://github.com/scipy/scipy/issues/18765>`__: Licensing concern Pull requests for 1.11.1 -------------------------------- * `#18741 <https://github.com/scipy/scipy/pull/18741>`__: BUG: Fix work array construction for various weight shapes. * `#18747 <https://github.com/scipy/scipy/pull/18747>`__: REL, MAINT: prep for 1.11.1 * `#18754 <https://github.com/scipy/scipy/pull/18754>`__: BUG: fix handling for \`factorial(..., exact=False)\` for 0-dim... * `#18762 <https://github.com/scipy/scipy/pull/18762>`__: FIX:linalg.lu:Guard against permute_l out of bound behavior * `#18763 <https://github.com/scipy/scipy/pull/18763>`__: MAINT:linalg.det:Return scalars for singleton inputs * `#18778 <https://github.com/scipy/scipy/pull/18778>`__: MAINT: fix unuran licensing Checksums ========= MD5 ~~~ 0d8ef8d4b3a484a1edb5b1027c635175 Changelog 9c279f2e8c4bfc18c08dee388f0e3937 README.txt aca7e7047ecb15a0a8fbdc6704ba88ab scipy-1.11.1-cp310-cp310-macosx_10_9_x86_64.whl 9619336484eb1f39799a771456882cb0 scipy-1.11.1-cp310-cp310-macosx_12_0_arm64.whl e3943627bc04c4b2cd5bbc8c46928199 scipy-1.11.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl e48d61b72e9af960387b5249a7c3fd37 scipy-1.11.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 508a6c71a1d8c040646fdc91d7b2067f scipy-1.11.1-cp310-cp310-musllinux_1_1_x86_64.whl 8fb76fe2d1b7e45ec16f39a3d50748bb scipy-1.11.1-cp310-cp310-win_amd64.whl 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Example gufuncs
by Stefan van der Walt 28 Jun '23

28 Jun '23

Hi all, To teach gufuncs, we used to rely on the example implementations in `numpy.core.umath_tests`. That module has since been deprecated. Is there another, more suitable location for such examples? I know it's been on SciPy's roadmap to convert some of `sp.linalg` to gufuncs, but that hasn't yet happened. Stéfan

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Moving busday functionality to numpy-financial
by kaistriega＠gmail.com 28 Jun '23

28 Jun '23

TLDR: There has been some discussion regarding moving busday and related functions to numpy--financial. The sentiment for this has generally been positive, this thread is to assess the amount of work required to move these functions. Dear NumPy Maintainers, There has been some discussion about moving np.busday* to numpy-financial first on GitHub [1] and more recently on Slack. The sentiment has largely been positive, however one concern is that the amount of work required is not justified as few people currently use these functions and the move could be messy. I have recently been contributing to NumPy and am currently the sole maintainer of numpy-financial, so it feels like I am the right person to do this. In my opinion, these functions conceptually fit the scope of numpy-financial. However, I have some hesitations about this work. Namely, I am struggling to estimate the scope of this work and I have only some experience with C, and the NumPy C API. I am having trouble estimating the amount of work this would require, but suspect that it will not be easy. The functions use some of the internal C-API (I think, how do I find out the differences between the internal/external API?) and depends on `np.datetime`. Further, the busday* functions are implemented in C. I only have some experience with C and the C aspects of NumPy—I am happy to learn, but this seems like a pretty steep learning curve. Finally, numpy-financial is currently a pure Python module and does not build C, again I will either need help with this or to learn how to do this. To finish, I have a few questions / requests for advice: * Is this change supported by the NumPy community? * Does anyone have advice on how to estimate the amount of work required? * If I start working on this, would anyone be willing to help me learn the C components? Kind regards Kai Striega (@Kai-Striega) [1]_ https://github.com/numpy/numpy/pull/23229#issuecomment-1467084886

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