Mailman 3 November 2020 - NumPy-Discussion

Do not understand what f2py is reporting
by Samuel Dupree 03 Nov '20

03 Nov '20

I'm attempting to build wrappers around two Fortran routines. One is a Fortran 77 subroutine (see file gravity_derivs.f) that calls a Fortran 90 package that performs automatic differentiation (see file auto_deriv.f90). I'm running he Anaconda distribution for Python 3.7.6 on a Mac Pro (2019) under Mac OS X Catalina (ver. 10.15.6). The version of NumPy I'm running is 1.18.3. The commands I used to attempt the build are contained in the file auto_deriv_build. The messages output by f2py are captured in the file auto_derivs_build_report.txt. I don't understand the cause behind the error messages I got, so any advice would be welcomed. Sam Dupree.

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NumPy 1.19.4 release
by Charles R Harris 02 Nov '20

02 Nov '20

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.19.4. NumPy 1.19.4 is a quick release to revert the OpenBLAS library version. It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work around the Microsoft fmod bug, but problems in some docker environments turned up. Instead, 1.19.4 will use the older library and run a sanity check on import, raising an error if the problem is detected. Microsoft is aware of the problem and has promised a fix, users should upgrade when it becomes available. This release supports Python 3.6-3.9. NumPy Wheels for this release can be downloaded from PyPI <https://pypi.org/project/numpy/1.19.4/>, source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.19.4>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 1 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris *Pull requests merged* A total of 2 pull requests were merged for this release. - #17679: MAINT: Add check for Windows 10 version 2004 bug. - #17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4 Cheers, Charles Harris

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Efficient way to draw multinomial distribution random samples
by Currurant 02 Nov '20

02 Nov '20

I realized that neither numpy.random.multinomial nor rng.multinomial has the ability to draw from different multinomial distributions at the same time like what MATLAB mnrnd() does here: https://www.mathworks.com/help/stats/mnrnd.html Also, I have asked this question on StackOverFlow: https://stackoverflow.com/questions/64529620/is-there-an-efficient-way-to-g… It seems like this is something good to add to numpy.random, since it would be much more faster when you have many multinomial distributions to draw from---using loops. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Ndarray static typing: Order of generic types
by bas van beek 01 Nov '20

01 Nov '20

Hey all, With the recent merging of numpy/numpy#16759<https://github.com/numpy/numpy/pull/16759> we're at the point where `ndarray` can be made generic w.r.t. its dtype and shape. An open question which yet remains is to order in which these two parameters should appear (numpy/numpy#16547<https://github.com/numpy/numpy/issues/16547>): * `ndarray[Dtype, Shape]` * `ndarray[Shape, Dtype]` There has been a some discussion about this question in issue 16547, but a consensus has not yet to be reached. Most people seem to slightly preferring one option over the other. Are there any further thoughts on this subject? Regards, Bas van Beek

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