NumPy-Discussion August 2020

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27 participants
26 discussions

Building Numpy Documentation
by cooperrc 07 Aug '20

07 Aug '20

I'm trying to build NumPy and its documentation from the current git repo, but I'm hitting a snag. I keep getting a RuntimeError: I'm trying to build NumPy inside the cloned repository from my fork. I'm running Arch (kernel 5.7.12) with gcc and gcc-libs installed. I'm using a fresh conda environment that has only installed Python 3.8 and Cython. Any way I can troubleshoot this issue? -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Add Chebyshev (cosine) transforms implemented via FFTs
by Chris Vavaliaris 05 Aug '20

05 Aug '20

PR #16999: https://github.com/numpy/numpy/pull/16999 Hello all, this PR adds the two 1D Chebyshev transform functions `chebyfft` and `ichebyfft` into the `numpy.fft` module, utilizing the real FFTs `rfft` and `irfft`, respectively. As far as I understand, `pockefft` does not support cosine transforms natively; for this reason, an even extension of the input vector is constructed, whose real FFT corresponds to a cosine transform. The motivation behind these two additions is the ability to quickly perform direct and inverse Chebyshev transforms with `numpy`, without the need to write scripts that do the necessary (although minor) modifications. Chebyshev transforms are used often e.g. in the spectral integration of PDE problems; thus, I believe having them implemented in `numpy` would be useful to many people in the community. I'm happy to get comments/feedback on this feature, and on whether it's something more people would be interested in. Also, I'm not entirely sure what part of this functionality is/isn't present in `scipy`, so that the two `fft` modules remain consistent with one another. Best, Chris -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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New random.Generator method: permuted
by Warren Weckesser 05 Aug '20

05 Aug '20

In one of the previous weekly zoom meetings, it was suggested to ping the mailing list about an updated PR that implements the `permuted` method for the Generator class in numpy.random. The relevant issue is https://github.com/numpy/numpy/issues/5173 and the PR is https://github.com/numpy/numpy/pull/15121 The new method (as it would be called from Python) is permuted(x, axis=None, out=None) The CircleCI rendering of the docstring from the pull request is https://14745-908607-gh.circle-artifacts.com/0/doc/build/html/reference/ran… The new method is an alternative to the existing `shuffle` and `permutation` methods. It handles the `axis` parameter similar to how the sort methods do, i.e. when `axis` is given, the slices along the axis are shuffled independently. This new documentation (added as part of the pull request) explains the API of the various related methods: https://14745-908607-gh.circle-artifacts.com/0/doc/build/html/reference/ran… Additional feedback on the implementation of `permuted` in the pull request is welcome. Further discussion of the API should be held in the issue gh-5173 (but please familiarize yourself with the discussion of the API in gh-5173--there has already been quite a long discussion of several different APIs). Thanks, Warren

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NumPy Community Meeting Wednesday
by Sebastian Berg 04 Aug '20

04 Aug '20

Hi all, There will be a NumPy Community meeting Wednesday Agust 5th at 1pm Pacific Time (20:00 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

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participating in AI Code-In?
by Ralf Gommers 02 Aug '20

02 Aug '20

Hi all, We got an invitation to participate in AI Code-In ( https://aicode-in.github.io/AICode-In/). It's a new initiative, seems a bit GSoC like, but created by and for middle/high schoolers. We'd have to create tasks to work on (more like tagging/creation actionable issues than a full project), and provide some mentoring bandwidth. It seems well-organized and because it's a new initiative it may be smaller and more "early adopter" than GSoC. Would anyone be interested to participate as a mentor and/or lead the NumPy organization participation? Cheers, Ralf

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a summary function to get a quick glimpse on the contents of a numpy array
by Peter Steinbach 01 Aug '20

01 Aug '20

Dear numpy devs and interested readers, as a day-to-day user, it occurred to me that having a quick look into the contents and extents of arrays is well doable with numpy. numpy offers a rich set of methods for this. However, very often I oversee myself and others that one just wants to see if the values of an array have a certain min/max or mean or how wide the range of values are. I hence sat down to write a summary function that returns a string of hand-packed summary statistics for a quick inspection. I propose to include it into numpy and would love to have your feedback on this idea before I submit a PR. Here is the core functionality: Examples -------- >>> a = np.random.normal(size=20) >>> print(summary(a)) min 25perc mean stdev median 75perc max -2.289870 -2.265757 -0.083213 1.115033 -0.162885 -2.217532 1.639802 >>> a = np.reshape(a, newshape=(4,5)) >>> print(summary(a,axis=1)) min 25perc mean stdev median 75perc max 0 -0.976279 -0.974090 0.293003 1.009383 0.466814 -0.969712 1.519695 1 -0.468854 -0.467739 0.184139 0.649378 -0.036762 -0.465510 1.303144 2 -2.289870 -2.276455 -0.324450 1.230031 -0.289008 -2.249625 1.111107 3 -1.782239 -1.777304 -0.485546 1.259598 -1.236190 -1.767434 1.639802 So you see, it is merely a tiny helper function that can aid practitioners and data scientists to get a quick insight on what an array contains. first off, here is the code: https://github.com/psteinb/numpy/blob/summary-function/numpy/lib/utils.py#L… I put it there as I am not sure at this point, if the community would appreciate such a function or not. Judging from the tests, lib/utils.py appears to a be place for undocumented functions. So to resolve this and prepare a proper PR, please let me know where this summary function could reside! Second, please give me your thoughts on the summary function's output? Should the number of digits be configurable? Should the columns be configurable? Is is ok to honor the axis parameter which is found in so many numpy functions? Last but not least, let me stress that this is my first time contribution to numpy. I love the library and would like to contribute something back. So bear with me, if my code violates best practices in your community for now. I'll bite my teeth into the formalities of a github PR once I get support from the community and the core devs. I think that a summary function would be a valuable addition to numpy! Best, Peter

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NumPy-Discussion August 2020