Mailman 3 August 2020 - NumPy-Discussion

NumPy Development Meeting Today - Triage Focus
by Sebastian Berg Aug. 11, 2020

Aug. 11, 2020

Hi all, Our bi-weekly triage-focused NumPy development meeting is tomorrow (Wednesday, August 12th) at 11 am Pacific Time (18:00 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 or simply discussed briefly. Just comment on it so we can label it, or add your PR/issue to this weeks topics for discussion. Best regards Sebastian

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Type declaration to include all valid numerical NumPy types for Cython
by Ilhan Polat Aug. 10, 2020

Aug. 10, 2020

Hi all, As you might have seen my recent mails in Cython list, I'm trying to cook up an input validator for the linalg.solve() function. The machinery of SciPy linalg is as follows: Some input comes in passes through np.asarray() then depending on the resulting dtype of the numpy array we choose a LAPACK flavor (s,d,c,z) and off it goes through f2py to lalaland and comes back with some result. For the backslash polyalgorithm I need the arrays to be contiguous (C- or F- doesn't matter) and any of the four (possibly via making new copies) float, double, float complex, double complex after the intake because we are using wrapped fortran code (LAPACK) in SciPy. So my difficulty is how to type such function input, say, ctypedef fused numeric_numpy_t: bint cnp.npy_bool cnp.int_t cnp.intp_t cnp.int8_t cnp.int16_t cnp.int32_t cnp.int64_t cnp.uint8_t cnp.uint16_t cnp.uint32_t cnp.uint64_t cnp.float32_t cnp.float64_t cnp.complex64_t cnp.complex128_t Is this acceptable or something else needs to be used? Then there is the storyof np.complex256 and mysterious np.float16. Then there is the Linux vs Windows platform dependence issue and possibly some more that I can't comprehend. Then there are datetime, str, unicode etc. that need to be rejected. So this is quickly getting out of hand for my small brain. To be honest, I am a bit running out of steam working with this issue even though I managed to finish the actual difficult algorithmic part but got stuck here. I am quite surprised how fantastically complicated and confusing both NumPy and Cython docs about this stuff. Shouldn't we keep a generic fused type for such usage? Or maybe there already exists but I don't know and would be really grateful for pointers. Here I wrote a dummy typed Cython function just for type checking: cpdef inline bint ncc( numeric_numpy_t[:, :] a): print(a.is_f_contig()) print(a.is_c_contig()) return a.is_f_contig() or a.is_c_contig() And this is a dummy loop (with aliases) just to check whether fused type is working or not (on windows I couldn't make it work for float16). for x in (np.uint, np.uintc, np.uintp, np.uint0, np.uint8, np.uint16, np.uint32, np.uint64, np.int, np.intc, np.intp, np.int0, np.int8, np.int16, np.int32,np.int64, np.float, np.float32, np.float64, np.float_, np.complex, np.complex64, np.complex128, np.complex_): print(x) C = np.arange(25., dtype=x).reshape(5, 5) ncc(C) Thanks in advance, ilhan

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Building Numpy Documentation
by cooperrc Aug. 7, 2020

Aug. 7, 2020

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 Aug. 5, 2020

Aug. 5, 2020

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 Aug. 5, 2020

Aug. 5, 2020

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 Aug. 4, 2020

Aug. 4, 2020

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 Aug. 2, 2020

Aug. 2, 2020

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 Aug. 1, 2020

Aug. 1, 2020

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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