Mailman 3 October 2021 - NumPy-Discussion

spam on the mailing lists
by Andras Deak 18 Oct '21

18 Oct '21

Hi All, Today both of the python.org mailing lists I'm subscribed to (numpy and scipy-dev) got the same kind of link shortener spam. I assume all the mailing lists started getting these, and that these won't go away for a while. Is there any way to prevent these, short of moderating emails from new list members? Assuming the engine even supports that. There aren't many emails, especially from new members, and I can't think of other ways that ensure no false positives in filtering. Since maintainer time is precious, I can volunteer to moderate such emails if needed. Cheers, András

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Fwd: [numpy/numpy] BUG: fix nanpercentile returns incorrect shape when input empty array (#18562)
by Max Chen 15 Oct '21

15 Oct '21

Dear pals, It has been a while since the last feedback from the reviewer, could someone help to review this PR again? --> https://github.com/numpy/numpy/pull/18562 P.S. this is my first time contributing to numpy : ) ---------- Forwarded message --------- 寄件者： Sayantika Banik <notifications(a)github.com> Date: 2021年10月15日週五下午4:30 Subject: Re: [numpy/numpy] BUG: fix nanpercentile returns incorrect shape when input empty array (#18562) To: numpy/numpy <numpy(a)noreply.github.com> Cc: Max Chen <immaxchen(a)gmail.com>, Author <author(a)noreply.github.com> I was attempting to go for 18158 <https://github.com/numpy/numpy/issues/18158>, with the additional tests and added components, the issue seems addressed. Would be happy to chime in if anything is left to be covered. — You are receiving this because you authored the thread. Reply to this email directly, view it on GitHub <https://github.com/numpy/numpy/pull/18562#issuecomment-944105611>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AGKLBWABWGXSGRE6GBWSCN3UG…> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&m…> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=u…>.

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What happened to the numpy.random documentation?
by Paul M. 15 Oct '21

15 Oct '21

Hi All, The documentation of Numpy's submodules used to have a fairly standard structure as shown here in the 1.16 documentation: https://docs.scipy.org/doc/numpy-1.16.1/reference/routines.random.html Now the same page in the API documentation looks like this: https://numpy.org/doc/stable/reference/random/index.html While I appreciate the expository text in the new documentation about how the generators work, this new version is much less useful as a reference to the API. It seems like it might fit better in the user manual rather than the API reference. From my perspective it seems like the new version of the documentation is harder to navigate in terms of finding information quickly (more scrolling, harder to get a bird's eye view of functions in various submodules, etc). Has anyone else had a similar reaction to the changes? I teach a couple of courses in scientific computing and bioinformatics and my students seem to also struggle to get a sense of what the different modules offer based on the new version of the documentation. For now, I'm referring them to the old (1.70) reference manuals as a better way to get acquainted with the libraries. Cheers, Paul Magwene

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg 14 Oct '21

14 Oct '21

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, September 8th at 9 am Pacific Time (16:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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NumPy Community Meeting Wednesday (today @16:30 UTC!)
by Sebastian Berg 13 Oct '21

13 Oct '21

Hi all, There will be a NumPy Community meeting Wednesday September 29th at 16:30 UTC. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian PS: Sorry for the late reminder!

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Contributing to Numpy (non coder)
by andresguzman26222014＠gmail.com 13 Oct '21

13 Oct '21

Hello everyone, I´m an student of Data Science and a technical writer, and I would like to contribute to NumPy. The problem is that even though I know the basics of Github, I´m not a Developer. I would like to know about these listed options of contributing: https://numpy.org/doc/stable/dev/index.html Among the mentioned in your doc, I have experience in Developing educational content, Project Management and Writing technical documentation. Hope is still possible to contribute. Thanks for reading.

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Musllinux wheels
by Laurie O 12 Oct '21

12 Oct '21

CI request: build PEP 656 (https://www.python.org/dev/peps/pep-0656/) compliant "musllinux" wheels. This will allow easy installation in Alpine Docker images. GitHub issue: https://github.com/numpy/numpy/issues/20089

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Intel's Short Vector Math Library has been merged as a git submodule
by Matti Picus 12 Oct '21

12 Oct '21

Yesterday we merged PR 19478 [0] to add a open source version of Intel's Short Vector Math library into NumPy. The original PR was by Raghuveer Devulapalli. There was a lot of discussion on the PR, but now that I look back it seems the mailing list may not have been involved. The code lives in https://github.com/numpy/SVML, and in order to pull it in you must now do `git submodule update --init` before building NumPy. The library provides AVX-512 implementations of the following math functions: exp, exp2, log, log2, log10, expm1, log1p, cbrt, pow, sin, cos, tan, asin, acos, atan, atan2, sinh, cosh, tanh, asinh, acosh and atanh, speeding them up significantly where AVX-512 is available. Before this PR was merged, Raghuveer added a set of tests for accuracy and compliance for these functions [1]. The accuracy is now defined as up to 4 ULP in the worst case for tan, cos, sin, asin, atan, expm1, but typically the error is up to 2 ULP. As SciPy already found out, some downstream libraries may need to tweak their tolerances for some functions as a result of this PR. We wanted to put it in early enough in the release cycle so that we can back it out fully or partially if the accuracy degradation is too large, so please speak up if you notice anything strange. Matti [0] https://github.com/numpy/numpy/pull/19478 [1] https://github.com/numpy/numpy/pull/19485

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Idea: adding `ndarray.isin` for consistency with pandas
by Max Ghenis 11 Oct '21

11 Oct '21

`numpy.isin` is equivalent to `pandas.{Series,DataFrame}.isin`. Adding `numpy.ndarray.isin` would produce consistency with `pandas` and save 4 characters (`np.isin(x, y)` vs. `x.isin(y)`). I'm adding this idea here in conjunction with filing https://github.com/numpy/numpy/issues/20092 per the instructions.

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Musllinux wheels
by Laurie O 11 Oct '21

11 Oct '21

CI request: build PEP 656 (https://www.python.org/dev/peps/pep-0656/) compliant "musllinux" wheels. This will allow easy installation in Alpine Docker images. GitHub issue: https://github.com/numpy/numpy/issues/20089

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A bite of C++
by Serge Guelton 08 Oct '21

08 Oct '21

Hi folks, https://github.com/numpy/numpy/pull/19713 showcases what *could* be a first step toward getting rid of generated C code within numpy, in favor of some C++ code, coupled with a single macro trick. Basically, templated code is an easy and robust way to replace generated code (the C++ compiler becomes the code generator when instantiating code), and a single X-macro takes care of the glue with the C world. Some changes in distutils were needed to cope with C++-specific flags, and extensions that consist in mixed C and C++ code. I've kept the change as minimal as possible to ease the (potential) transition and keep the C++ code close to the C code. This led to less idiomatic C++ code, but I value a "correct first" approach. There's an on-going effort by seiko2plus to remove that C layer, I acknowledge this would bring even more C++ code, but that looks orthogonal to me (and a very good second step!) All lights are green for the PR, let's assume it's a solid ground for discussion :-) So, well, what do you think? Should we go forward? Potential follow-ups : - do we want to use -nostdlib, to be sure we don't bring any C++ runtime dep? - what about -fno-exception, -fno-rtti? - coding style? - (I'm-not-a-farseer-I-don-t-know-all-topics)

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by Dr. Mark Alexander Mikofski PhD 07 Oct '21

07 Oct '21

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Moving community meeting to 16:00 UTC
by Sebastian Berg 07 Oct '21

07 Oct '21

Hi all, Melissa brought up that we should reschedule the bi-weekly community meeting to the same time slot at the triage meeting: Wednesdays 16:00 UTC (9 am Pacific time) [1] This will simplify attending for those in Europe and allow a more reasonable time including for India. As of now, all regular attendees are happy with this move. But the original idea was that we could also alternate between two times, the current time is e.g. impossible to make from east Asia. Please let me/us know if you prefer a different schedule now (or later!). The idea is that these meetings are accessible to anyone interested. Without any comments, I will announce and change the calendar starting with next weeks meeting. Cheers, Sebastian [1] I have to see whether we will stick with UTC or do any daylight savings.

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ENH: Extended choose -- add an implementation of the choose function which supports more than 32 choices
by Aaron Watters 07 Oct '21

07 Oct '21

Hi folks! The np.choose function raises a ValueError if called with more than 31 choices. This PR adds an alternate implementation np.extended_choose (which uses the base implementation) that supports any number of choices. https://github.com/numpy/numpy/pull/20001 FYI, I needed this functionality for a mouse embryo microscopy tool I'm building. I'm attempting to contribute it because I thought it might be generally useful. All comments, complaints, or suggestions or code reviews appreciated. thanks! -- Aaron Watters

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NumPy Development Meeting Wednesday - Triage Focus
by Sebastian Berg 06 Oct '21

06 Oct '21

Hi all, Our bi-weekly triage-focused NumPy development meeting is Wednesday, October 6th at 9 am Pacific Time (16:00 UTC). Everyone is invited to join in and edit the work-in-progress meeting topics and notes: https://hackmd.io/68i_JvOYQfy9ERiHgXMPvg I encourage everyone to notify us of issues or PRs that you feel should be prioritized, discussed, or reviewed. Best regards Sebastian

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Drop 32 bit manylinux wheels
by Charles R Harris 05 Oct '21

05 Oct '21

Hi All, Dropping 32 manylinux wheels was discussed in a triage meeting some time ago and the general consensus was to do so. However, it has never been run past the larger community on the discussion list, so this is that. Note that we have already dropped 32 bit manylinux wheels for the upcoming Python 3.10 as that Python version is only available on Ubuntu focal (20.04) and Ubuntu dropped 32 bit support in 19.10 while Fedora dropped it in 31. Rasbian is still largely 32 bit, but they have their own ppa source. We will continue to offer 32 wheels on Windows as it remains popular there. Thoughts? Chuck

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The source code corresponding to numpy.invert.
by hongyi.zhao＠gmail.com 05 Oct '21

05 Oct '21

I noticed the following documentation on `numpy.invert`: [1] ######## numpy.invert [...] Compute bit-wise inversion, or bit-wise NOT, element-wise. Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator ~. [...] The ~ operator can be used as a shorthand for np.invert on ndarrays. x1 = np.array([True, False]) ~x1 array([False, True]) ######## So, C/Python operator `~` has been overridden by the corresponding user function in numpy, but where is the corresponding source code implementation? [1] https://numpy.org/doc/stable/reference/generated/numpy.invert.html#numpy-in… Regards, HZ

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numpy.math docs
by Mark Bakker 03 Oct '21

03 Oct '21

Dear List, I just found out that there is a numpy.math subpackage with a bunch of extra mathematical functions (I needed the factorial function). I have been googling for 10 minutes, but can not find any documentation on the numpy.math subpackage (my poor google skills, I am sure). Can anybody point me to the documentation for this subpackage? Or maybe explain why it exists and what the advantages are over the regular math functions in numpy, scipy.special, etc? Thanks!

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Deprecating boolean indices in `partition` and disable negative time unit factors `M8[-1s]`
by Sebastian Berg 03 Oct '21

03 Oct '21

Hi all, A brief note that the PR: https://github.com/numpy/numpy/pull/20000 deprecated the use of boolean arrays as indices in argpartition (aligning its behaviour with normal indexing). Further: https://github.com/numpy/numpy/pull/19731 silently turns negative time factor indices into errors. Previously, it was apparently possible to create times with e.g. `- 1000s` as time unit: `np.dtype("M8[-1000s]")`. We assumed that those negative time units should never be allowed and nobody would use them on purpose. If anyone has concerns about either (or knows/thinks this may create a problem somewhere) please let us know so we can revert/reconsider the change! Cheers, Sebastian

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Contributing to Numpy
by andresguzman26222014＠gmail.com 01 Oct '21

01 Oct '21

Hello everyone, I´m an student of Data Science and a technical writer, and I would like to contribute to NumPy. The problem is that even though I know the basics of Github, I´m not a Developer. I would like to know about these listed options of contributing: https://numpy.org/doc/stable/dev/index.html Among the mentioned in your doc, I have experience in Developing educational content, Project Management and Writing technical documentation. Hope is still possible to contribute. Thanks for reading.

1 0

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01 Oct '21

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