Mailman 3 January 2020 - NumPy-Discussion

Interest to port Numpy to Haskell, with Accelerate/Repa to do the crunching?
by YueCompl 22 Jan '20

22 Jan '20

Hello folks, Myself a long time Numpy user, and recently done some Haskell stuff, Please have a look at https://github.com/e-wrks/edh/tree/master/Tour#defining-more-magic-methods <https://github.com/e-wrks/edh/tree/master/Tour#defining-more-magic-methods> and https://github.com/e-wrks/edh/tree/master/Tour#indexing <https://github.com/e-wrks/edh/tree/master/Tour#indexing> , any interest to port Numpy to Haskell, with http://hackage.haskell.org/package/accelerate <http://hackage.haskell.org/package/accelerate> and/or http://hackage.haskell.org/package/repa <http://hackage.haskell.org/package/repa> doing the number crunching? Best regards, Compl

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pip 20 release broke wheels builds.
by Charles R Harris 22 Jan '20

22 Jan '20

Hi All, Just a note that latest pip release broke the numpy wheel builds. The fix is to update multibuild to the latest version. I'm sending this out in case others are affected. Chuck

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Re-enable deprecation warning for "ragged arrays"
by Matti Picus 21 Jan '20

21 Jan '20

A few months ago we merged an implementation of "NEP 34 - Disallow inferring dtype=object from sequences" https://numpy.org/neps/nep-0034.html but it broke downstream packages like scipy, pandas, matplotlib, astropy (those are the ones we know about). Since then, the downstream projects seem to have fixed the problematic code. We have re-merged the implementation. Please check your package against the latest NumPy HEAD and let us know if there are problems. Matti

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NumPy Community Meeting Wednesday, Jan. 22
by Sebastian Berg 21 Jan '20

21 Jan '20

Hi all, There will be a NumPy Community meeting Wednesday January 22 at 11 am Pacific Time. Everyone is invited to join in and edit the work-in- progress meeting topics and notes: https://hackmd.io/76o-IxCjQX2mOXO_wwkcpg?both Best wishes Sebastian

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ANN: SciPy 1.2.3 (LTS)
by Tyler Reddy 21 Jan '20

21 Jan '20

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA256 Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.2.3, which is a bug fix release. This is part of the long-term support (LTS) branch that includes Python 2.7. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.2.3 One of a few ways to install this release with pip: pip install scipy==1.2.3 ========================== SciPy 1.2.3 Release Notes ========================== SciPy 1.2.3 is a bug-fix release with no new features compared to 1.2.2. It is part of the long-term support (LTS) release series for Python 2.7. Authors ======= * Geordie McBain * Matt Haberland * David Hagen * Tyler Reddy * Pauli Virtanen * Eric Larson * Yu Feng * ananyashreyjain * Nikolay Mayorov * Evgeni Burovski * Warren Weckesser Issues closed for 1.2.3 -------------------------------- * `#4915 <https://github.com/scipy/scipy/issues/4915>`__: Bug in unique_roots in scipy.signal.signaltools.py for roots with same magnitude * `#5546 <https://github.com/scipy/scipy/issues/5546>`__: ValueError raised if scipy.sparse.linalg.expm recieves array larger than 200x200 * `#7117 <https://github.com/scipy/scipy/issues/7117>`__: Warn users when using float32 input data to curve_fit and friends * `#7906 <https://github.com/scipy/scipy/issues/7906>`__: Wrong result from scipy.interpolate.UnivariateSpline.integral for out-of-bounds * `#9581 <https://github.com/scipy/scipy/issues/9581>`__: Least-squares minimization fails silently when x and y data are different types * `#9901 <https://github.com/scipy/scipy/issues/9901>`__: lsoda fails to detect stiff problem when called from solve_ivp * `#9988 <https://github.com/scipy/scipy/issues/9988>`__: doc build broken with Sphinx 2.0.0 * `#10303 <https://github.com/scipy/scipy/issues/10303>`__: BUG: optimize: `linprog` failing TestLinprogSimplexBland::test_unbounded_below_no_presolve_corrected * `#10376 <https://github.com/scipy/scipy/issues/10376>`__: TST: Travis CI fails (with pytest 5.0 ?) * `#10384 <https://github.com/scipy/scipy/issues/10384>`__: CircleCI doc build failing on new warnings * `#10535 <https://github.com/scipy/scipy/issues/10535>`__: TST: master branch CI failures * `#11121 <https://github.com/scipy/scipy/issues/11121>`__: Calls to `scipy.interpolate.splprep` increase RAM usage. * `#11198 <https://github.com/scipy/scipy/issues/11198>`__: BUG: sparse eigs (arpack) shift-invert drops the smallest eigenvalue for some k * `#11266 <https://github.com/scipy/scipy/issues/11266>`__: Sparse matrix constructor data type detection changes on Numpy 1.18.0 Pull requests for 1.2.3 -------------------------------- * `#9992 <https://github.com/scipy/scipy/pull/9992>`__: MAINT: Undo Sphinx pin * `#10071 <https://github.com/scipy/scipy/pull/10071>`__: DOC: reconstruct SuperLU permutation matrices avoiding SparseEfficiencyWarning * `#10076 <https://github.com/scipy/scipy/pull/10076>`__: BUG: optimize: fix curve_fit for mixed float32/float64 input * `#10138 <https://github.com/scipy/scipy/pull/10138>`__: BUG: special: Invalid arguments to ellip_harm can crash Python. * `#10306 <https://github.com/scipy/scipy/pull/10306>`__: BUG: optimize: Fix for 10303 * `#10309 <https://github.com/scipy/scipy/pull/10309>`__: BUG: Pass jac=None directly to lsoda * `#10377 <https://github.com/scipy/scipy/pull/10377>`__: TST, MAINT: adjustments for pytest 5.0 * `#10379 <https://github.com/scipy/scipy/pull/10379>`__: BUG: sparse: set writeability to be forward-compatible with numpy>=1.17 * `#10426 <https://github.com/scipy/scipy/pull/10426>`__: MAINT: Fix doc build bugs * `#10540 <https://github.com/scipy/scipy/pull/10540>`__: MAINT: Fix Travis and Circle * `#10633 <https://github.com/scipy/scipy/pull/10633>`__: BUG: interpolate: integral(a, b) should be zero when both limits are outside of the interpolation range * `#10833 <https://github.com/scipy/scipy/pull/10833>`__: BUG: Fix subspace_angles for complex values * `#10882 <https://github.com/scipy/scipy/pull/10882>`__: BUG: sparse/arpack: fix incorrect code for complex hermitian M * `#10906 <https://github.com/scipy/scipy/pull/10906>`__: BUG: sparse/linalg: fix expm for np.matrix inputs * `#10961 <https://github.com/scipy/scipy/pull/10961>`__: BUG: Fix signal.unique_roots * `#11126 <https://github.com/scipy/scipy/pull/11126>`__: BUG: interpolate/fitpack: fix memory leak in splprep * `#11199 <https://github.com/scipy/scipy/pull/11199>`__: BUG: sparse.linalg: mistake in unsymm. real shift-invert ARPACK eigenvalue selection * `#11269 <https://github.com/scipy/scipy/pull/11269>`__: Fix: Sparse matrix 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[ANN] pvlib python v0.7.1: predicting power for solar energy
by Dr. Mark Alexander Mikofski PhD 21 Jan '20

21 Jan '20

pvlib has a new minor release, v0.7.1 Release Notes: https://pvlib-python.readthedocs.io/en/v0.7.1/whatsnew.html PyPI: https://pypi.org/project/pvlib/ Read the Docs: https://pvlib-python.readthedocs.io/en/latest/ GitHub: https://github.com/pvlib/pvlib-python New examples gallery: https://pvlib-python.readthedocs.io/en/stable/auto_examples/index.html -- Mark Mikofski, PhD (2005) *Fiat Lux*

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8 Real Distributions
by Michael Lance 20 Jan '20

20 Jan '20

TLDR; I think this could be a useful contribution to NumPy, but I want to get feedback on where it should go (either in NumPy or elsewhere). I have functions using numpy.random which invoke the 8 "Real" data sets as estimated by Ted Micceri in 1989. These can be useful in Monte Carlo simulations. Background info: Parametric inferential statistics generally assume normal distributions (though kurtosis presents less of an issue than skew). However, in "nature", distributions are often not normal. In 1989, Ted Micceri's study ( http://psycnet.apa.org/record/1989-14214-001) on real data sets resulted in the estimation of 8 "Real" distributions. Using these distributions in simulations help to produce more realistic types I and II error rate and power estimates, particularly for smaller samples. A similar module is currently available in Fortran called realpops.

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Adding an nd generalization of np.ma.mask_rowscols
by Eric Wieser 17 Jan '20

17 Jan '20

Today, numpy has a np.ma.mask_rowcols function, which stretches masks along the full length of an axis. For example, given the matrix:: >>> a2d = np.zeros((3, 3), dtype=int) >>> a2d[1, 1] = 1 >>> a2d = np.ma.masked_equal(a2d, 1) >>> print(a2d) [[0 0 0] [0 -- 0] [0 0 0]] The API allows:: >>> print(np.ma.mask_rowcols(a2d, axis=0)) [[0 0 0] [-- -- --] [0 0 0]] >>> print(np.ma.mask_rowcols(a2d, axis=1)) [[0 -- 0] [0 -- 0] [0 -- 0]] >>> print(np.ma.mask_rowcols(a2d, axis=None)) [[0 -- 0] [-- -- --] [0 -- 0]] However, this function only works for 2D arrays. It would be useful to generalize this to work on ND arrays as well. Unfortunately, the current function is messy to generalize, because axis=0 means “spread the mask along axis 1”, and vice versa. Additionally, the name is not particularly good for an ND function. My proposal in PR 14998 <https://github.com/numpy/numpy/pull/14998> is to introduce a new function, mask_extend_axis, which fixes this shortcoming. Given an 3D array:: >>> a3d = np.zeros((2, 2, 2), dtype=int) >>> a3d[0, 0, 0] = 1 >>> a3d = np.ma.masked_equal(a3d, 1) >>> print(a3d) [[[-- 0] [0 0]] [[0 0] [0 0]]] This, in my opinion, has clearer axis semantics: >>> print(np.ma.mask_extend_axis(a2d, axis=0)) [[[-- 0] [0 0]] [[-- 0] [0 0]]] >>> print(np.ma.mask_extend_axis(a2d, axis=1)) [[[-- 0] [-- 0]] [[0 0] [0 0]]] >>> print(np.ma.mask_extend_axis(a2d, axis=2)) [[[-- --] [0 0]] [[0 0] [0 0]]] Stretching over multiple axes remains possible: >>> print(np.ma.mask_extend_axis(a2d, axis=(1, 2))) [[[-- --] [-- 0]] [[0 0] [0 0]]] # extending sequentially is not the same as extending in parallel >>> print(np.ma.mask_extend_axis(np.ma.mask_extend_axis(a2d, axis=1), axis=2)) [[[-- --] [-- --]] [[0 0] [0 0]]] Questions for the mailing list then: - Can you think of a better name than mask_extend_axis? - Does my proposed meaning of axis make more sense to you than the one used by mask_rowcols?

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Accuracy improvement in np.mean
by mpro 13 Jan '20

13 Jan '20

Hi, 18 days ago I submitted pull request that improves accuracy of mean function by calculating mean sequentially along certain axis. Happy to get some feedback. Magdalena -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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Numpy support for Microblaze
by pnp 12 Jan '20

12 Jan '20

Are there some steps to add support to a new cpu such as microblaze? Regards. -- Sent from: http://numpy-discussion.10968.n7.nabble.com/

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