Mailman 3 October 2020 - SciPy-Dev

proposal to drop Python 3.6 and NumPy 1.14 / 1.15
by Ralf Gommers 15 Nov '20

15 Nov '20

Hi all, Python 3.9 is coming out before the next SciPy release, and NEP 29 recommends to support only Python >=3.7 and NumPy >= 1.16 from last month onwards [1]. I think supporting 3 Python versions and 5 NumPy versions in SciPy 1.6.0 is easily enough. That would also bring us back in line with SciPy on conda-forge, which built against NumPy 1.16 for 1.5.2. Any objections? Cheers, Ralf [1] https://numpy.org/neps/nep-0029-deprecation_policy.html#support-table

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Reviewing/dev/merge traction on some optimize PRs
by Andrew Nelson 30 Oct '20

30 Oct '20

Hi all, there are a few optimize PRs that I could do with help on the reviewing/dev/merge cycle on. They're mainly to solve various issues which a few people have been reporting related to the numerical differentiation changes for minimize methods in 1.5. In order of importance: https://github.com/scipy/scipy/pull/12889 https://github.com/scipy/scipy/pull/13009 https://github.com/scipy/scipy/pull/12998 (https://github.com/scipy/scipy/pull/11263 this one needs me to do a rebase and fix a merge conflict, and I don't think there are reviewer objections to this.) I'm asking for help because they're non-trivial changes and I'd like to see them merged (or the issues addressed) well before 1.6 to give a chance for further issues to be resolved. Also, they're all touching the same area of code and when one gets merged they might create further merge conflicts. -- _____________________________________ Dr. Andrew Nelson _____________________________________

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buglet in signal.bilinear_zpk example
by Neal Becker 29 Oct '20

29 Oct '20

The labels on the 2 plots in the example here are swapped: https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.bilinear_… The 1st plot is the z domain and the 2nd plot is the s domain. -- *Those who don't understand recursion are doomed to repeat it*

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NumPy 1.19.3 release
by Charles R Harris 29 Oct '20

29 Oct '20

Hi All, On behalf of the NumPy team I am pleased to announce that NumPy 1.19.3 has been released. NumPy 1.19.3 is a small maintenance release with two major improvements: - Python 3.9 binary wheels on all supported platforms, - OpenBLAS fixes for Windows 10 version 2004 fmod bug. This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.12 to avoid some of the fmod problems on Windows 10 version 2004. Microsoft is aware of the problem and users should upgrade when the fix becomes available, the fix here is limited in scope. NumPy Wheels for this release can be downloaded from the PyPI <https://pypi.org/project/numpy/1.19.3/>, source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.19.3>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 8 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - Charles Harris - Chris Brown + - Daniel Vanzo + - E. Madison Bray + - Hugo van Kemenade + - Ralf Gommers - Sebastian Berg - @danbeibei + *Pull requests merged* A total of 10 pull requests were merged for this release. - #17298: BLD: set upper versions for build dependencies - #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs - #17446: ENH: Warn on unsupported Python 3.10+ - #17450: MAINT: Update test_requirements.txt. - #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler - #17568: BUG: Cygwin Workaround for #14787 on affected platforms - #17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides - #17652: MAINT: Backport openblas_support from master. - #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac. - #17660: TST: Simplify source path names in test_extending. Cheers, Charles Harris

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Adding non-parametric methods to scipy.stats
by Romain Jacob 27 Oct '20

27 Oct '20

Hello everyone, I have been working for some time on the implementation of non-parametric methods to compute confidence intervals for percentiles. There are some very interesting results in the literature (see e.g. a nice pitch in [1]) which I think it would be great to add to SciPy to make them more readily available. It also seems to be rather in line with "recent" discussions of the roadmap for scipy.stats [2]. I would be interested in contributing this. What do you think? Cheers, -- Romain [1] https://ieeexplore.ieee.org/document/6841797 [2] https://github.com/scipy/scipy/issues/10577 -- Romain Jacob Postdoctoral Researcher ETH Zurich - Computer Engineering and Networks Laboratory www.romainjacob.net <https://www.romainjacob.net/> @RJacobPartner <https://twitter.com/RJacobPartner> Gloriastrasse 35, ETZ G75 8092 Zurich +41 7 68 16 88 22

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Determining License for fortran inclusion in SciPy
by Sam Wallan 27 Oct '20

27 Oct '20

Hello All, We are working to add support for the Tukey-Kramer statistical test, and it relies upon an involved computation of the critical values of the Studentized Range, for which a Fortran routine has been published. Using this Fortran would be much quicker in terms of development and likely quicker in computation than our best Python implementation. The code is published in a journal article, but I'm not sure if that means that the code is publicly available, or what license or copyright might be retained. I have contacted one of the original authors to ask him about it and am awaiting a response. The paper the code is in is available from JSTOR, and the raw code is available in various places <http://ftp.uni-bayreuth.de/math/statlib/apstat/190> on the internet both in original form and sometimes with improvements. Algorithm AS 190: Probabilities and Upper Quantiles for the Studentized Range <https://www.jstor.org/stable/2347300?seq=1> R. E. Lund and J. R. Lund Journal of the Royal Statistical Society. Series C (Applied Statistics) Vol. 32, No. 2 (1983), pp. 204-210 (7 pages) Published By: Wiley DOI: 10.2307/2347300 In SciPy there is already use of several algorithms also published by Journal of the Royal Statistical Society (these can also be found on JSTOR). In git history I see they were added some 18 years ago: PRHO: Algorithm AS 89 Appl. Statist. (1975) Vol.24, No. 3, P377 in scipy/stats/statlib/spearman.f POLY: ALGORITHM AS 181.2 APPL. STATIST. (1982) VOL. 31, NO. 2 in scipy/stats/statlib/swilk.f I’ve investigated online for other licensed use of these algorithms, but I have not seen anything concrete. I closest use I was able to find is a direct translation of the algorithm <https://foundry.sandia.gov/releases/latest/javadoc-api/gov/sandia/cognition…> we are interested in, AS 190, to Java by Sandia National Laboratories, which "is released under the open source BSD License.” <https://foundry.sandia.gov/> Does anyone have experience with this source or insight into whether the inclusion of this Fortran in SciPy is fair game? Cheers, Sam Wallan

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help needed
by Amardeep Singh 24 Oct '20

24 Oct '20

Hi All I am a new joiner.Using macbook. Can someone please guide me how to debug scipy using clion? I am able to build but not sure about debugging the c code. ProductName: Mac OS X ProductVersion: 10.15.7 BuildVersion: 19H2 thx

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Re ENH 12755: adding a fast ill-condition sensing capability to arls()
by rondall jones 21 Oct '20

21 Oct '20

Early comments regarding this package mentioned the preference for integration in some manner with existing scipy solvers. It took me a while to see a way to do that. The problem was the lack of a quick method for detecting the likelihood of ill-conditioning (including singularity) which would make a regularized solver preferable to a standard solver like lstsq(). But I was able to develop such a quick method, called "strange()" -- because it detects strange behavior that is usually indicative of an ill-conditioned system. This method takes only about 4n^2 flops, rather than O(n^3) which the regularized solvers seem to take. So: lsmr and arls take a few times as long as lstsq. And lstsq takes a few times as long as "strange()". So inside arls() it is low-cost to call strange() first to see if it is safe to call the faster lstsq(). The special solvers -- arlseq, arlsgt, and arlsnn -- all get the benefit of this change as they depend on arls(). But that this capability applies only to the case of single right-hand-sides, as the benefit of this approach dissolves rapidly when multiple right-hand-sides are used, as all their solutions in arls() share a single call for the SVD. Also updating the in-code user documentation. Ron Jones

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ANN: SciPy 1.5.3
by Tyler Reddy 20 Oct '20

20 Oct '20

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.5.3, which is a bug fix release that includes Linux ARM64 wheels for the first time. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.5.3 One of a few ways to install this release with pip: pip install scipy==1.5.3 ========================== SciPy 1.5.3 Release Notes ========================== SciPy 1.5.3 is a bug-fix release with no new features compared to 1.5.2. In particular, Linux ARM64 wheels are now available and a compatibility issue with XCode 12 has been fixed. Authors ====== * Peter Bell * CJ Carey * Thomas Duvernay + * Gregory Lee * Eric Moore * odidev * Dima Pasechnik * Tyler Reddy * Simon Segerblom Rex + * Daniel B. Smith * Will Tirone + * Warren Weckesser A total of 12 people contributed to this release. People with a "+" by their names contributed a patch for the first time. This list of names is automatically generated, and may not be fully complete. Issues closed for 1.5.3 ------------------------------ * `#9611 <https://github.com/scipy/scipy/issues/9611>`__: Overflow error with new way of p-value calculation in kendall... * `#10069 <https://github.com/scipy/scipy/issues/10069>`__: scipy.ndimage.watershed_ift regression in 1.0.0 * `#11260 <https://github.com/scipy/scipy/issues/11260>`__: BUG: DOP853 with complex data computes complex error norm, causing... * `#11479 <https://github.com/scipy/scipy/issues/11479>`__: RuntimeError: dictionary changed size during iteration on loading... * `#11972 <https://github.com/scipy/scipy/issues/11972>`__: BUG (solved): Error estimation in DOP853 ODE solver fails for... * `#12543 <https://github.com/scipy/scipy/issues/12543>`__: BUG: Picture rotated 180 degrees and rotated -180 degrees should... * `#12613 <https://github.com/scipy/scipy/issues/12613>`__: Travis X.4 and X.7 failures in master * `#12654 <https://github.com/scipy/scipy/issues/12654>`__: scipy.stats.combine_pvalues produces wrong results with method='mudholkar_george' * `#12819 <https://github.com/scipy/scipy/issues/12819>`__: BUG: Scipy Sparse slice indexing assignment Bug with zeros * `#12834 <https://github.com/scipy/scipy/issues/12834>`__: BUG: ValueError upon calling Scipy Interpolator objects * `#12836 <https://github.com/scipy/scipy/issues/12836>`__: ndimage.median can return incorrect values for integer inputs * `#12860 <https://github.com/scipy/scipy/issues/12860>`__: Build failure with Xcode 12 Pull requests for 1.5.3 ----------------------------- * `#12611 <https://github.com/scipy/scipy/pull/12611>`__: MAINT: prepare for SciPy 1.5.3 * `#12614 <https://github.com/scipy/scipy/pull/12614>`__: MAINT: prevent reverse broadcasting * `#12617 <https://github.com/scipy/scipy/pull/12617>`__: MAINT: optimize: Handle nonscalar size 1 arrays in fmin_slsqp... * `#12623 <https://github.com/scipy/scipy/pull/12623>`__: MAINT: stats: Loosen some test tolerances. * `#12638 <https://github.com/scipy/scipy/pull/12638>`__: CI, MAINT: pin pytest for Azure win * `#12668 <https://github.com/scipy/scipy/pull/12668>`__: BUG: Ensure factorial is not too large in mstats.kendalltau * `#12705 <https://github.com/scipy/scipy/pull/12705>`__: MAINT: \`openblas_support\` added sha256 hash * `#12706 <https://github.com/scipy/scipy/pull/12706>`__: BUG: fix incorrect 1d case of the fourier_ellipsoid filter * `#12721 <https://github.com/scipy/scipy/pull/12721>`__: BUG: use special.sindg in ndimage.rotate * `#12724 <https://github.com/scipy/scipy/pull/12724>`__: BUG: per #12654 adjusted mudholkar_george method to combine p... * `#12726 <https://github.com/scipy/scipy/pull/12726>`__: BUG: Fix DOP853 error norm for complex problems * `#12730 <https://github.com/scipy/scipy/pull/12730>`__: CI: pin xdist for Azure windows * `#12786 <https://github.com/scipy/scipy/pull/12786>`__: BUG: stats: Fix formula in the \`stats\` method of the ARGUS... * `#12795 <https://github.com/scipy/scipy/pull/12795>`__: CI: Pin setuptools on windows CI * `#12830 <https://github.com/scipy/scipy/pull/12830>`__: [BUG] sparse: Avoid using size attribute in LIL __setitem__ * `#12833 <https://github.com/scipy/scipy/pull/12833>`__: BUG: change list of globals items to list of a copy * `#12842 <https://github.com/scipy/scipy/pull/12842>`__: BUG: Use uint16 for cost in NI_WatershedElement * `#12845 <https://github.com/scipy/scipy/pull/12845>`__: BUG: avoid boolean or integer addition error in ndimage.measurements.median * `#12864 <https://github.com/scipy/scipy/pull/12864>`__: BLD: replace the #include of libqull_r.h with with this of qhull_ra.h... * `#12867 <https://github.com/scipy/scipy/pull/12867>`__: BUG: Fixes a ValueError yielded upon calling Scipy Interpolator... * `#12902 <https://github.com/scipy/scipy/pull/12902>`__: CI: Remove 'env' from pytest.ini * `#12913 <https://github.com/scipy/scipy/pull/12913>`__: MAINT: Ignore pytest's PytestConfigWarning 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changes to autosol.py / arls.py
by rondall jones 16 Oct '20

16 Oct '20

To Ilhan, Ralf, Stefan, others... Ilhan really wants arls() to give the user some feedback about the solution. I didn't understand his/your point at first but I eventually realized that I can and should return the artifacts I compute about the problem. And I really like the result. Ilhan was right. (There are no changes whatsoever to algorithms.) Also, Ilhan objected to the name, autosol, feeling that it implies too much. I had already changed all the routine names to arlsxxx, for "automatically regularized least squares". I guess I should have changed the file name, too, and am doing that too. I hope that doesn't foul up your review process. So I just uploaded arls() and its test_arls() with these changes" * each solver now returns: * the tradition numerical rank of A ( I checked other current solvers in scipy to be sure I was doing this in the same exact way) * the "usable rank" which involves both A and b, and is the crucial artifact needed to do autoregularization * the estimate of the right-hand-side RMS error, sigma. Some people will be surprised to see that this is possible. * the resulting Tikhonov regularization parameter, lambda * The in-code comments have been updated * The test_arls() code has been updated to use one function per test, as best as I understand how to do it at this point Unfortunately I don't know how to delete the old autosol.py and test_autosol.py. Maybe one of you code managers have to do that? Thanks for your interest and tolerance of my areas of ignorance. By the way, I am not seeking to REPLACE any current scipy solvers. Arls should only be an additional feature for a long time. But I think it will be a significant asset for scipy. I have mostly publicized these codes strictly via my web site in the past, as C++ has no central code repository like python. Rondall Jones

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