Mailman 3 October 2021 - NumPy-Discussion

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.

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01 Oct '21

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