Mailman 3 June 2021 - SciPy-Dev

New feature: fitting distributions to censored data.
by Warren Weckesser 29 Aug '22

29 Aug '22

Hey folks, A new enhancement for fitting probability distributions to censored data by maximum likelihood estimation is in progress in https://github.com/scipy/scipy/pull/13699 This is a feature in the statistics roadmap, and is part of the work for the CZI EOSS cycle 1 grant. Censored data is data where the true value of the measurement is unknown, but there is a known bound. The common terminology is: * left-censored: The true value is greater than some known bound. * right-censored: The true value is less than some known bound. * interval-censored: The true value is between known bounds. (By allowing the bounds to be +inf and -inf, we can think of all these cases as interval-censored.) In the PR, a new class, `CensoredData`, is defined to represent censored data. The `fit` method of the univariate continuous distributions is updated to accept an instance of `CensoredData`. The `CensoredData` class constructor accepts two arrays, `lower` and `upper`, to hold the known bounds of the data. (If `lower[i] == upper[i]`, it means that data value is not censored.) Because it is quite common for data to be encountered where the only censored values in the data set are either all right-censored or all left-censored, two convenience methods, `CensoredData.right_censored(x, censored)` and `CensoredData.left_censored(x, censored)`, are provide to create a `CensoredData` instance from an array of values and a corresponding boolean array that indicates if the value is censored. Here's a quick example, with data from https://www.itl.nist.gov/div898/handbook/apr/section4/apr413.htm. The data table shows 10 regular values and 10 right-censored values. The results reported there for fitting the two-parameter Weibull distribution (location fixed at 0) to that data are shape = 1.7208 scale = 606.5280 Here's the calculation using the proposed API in SciPy: In [55]: from scipy.stats import weibull_min, CensoredData Create the `CensoredData` instance: In [56]: x = np.array([54, 187, 216, 240, 244, 335, 361, 373, 375, 386] + [500]*10) In [57]: data = CensoredData.right_censored(x, x == 500) In [58]: print(data) CensoredData(20 values: 10 not censored, 10 right-censored) Fit `weibull_min` (with the location fixed at 0) to the censored data: In [59]: shape, loc, scale = weibull_min.fit(data, floc=0) In [60]: shape Out[60]: 1.720797180719942 In [61]: scale Out[61]: 606.527565269458 Matt Haberland has already suggested quite a few improvements to the PR. Additional comments would be appreciated. Warren

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Global Optimization Benchmarks
by Andrea Gavana 11 Jul '21

11 Jul '21

Dear SciPy Developers & Users, long time no see :-) . I thought to start 2021 with a bit of a bang, to try and forget how bad 2020 has been... So I am happy to present you with a revamped version of the Global Optimization Benchmarks from my previous exercise in 2013. This new set of benchmarks pretty much superseeds - and greatly expands - the previous analysis that you can find at this location: http://infinity77.net/global_optimization/ . The approach I have taken this time is to select as many benchmark test suites as possible: most of them are characterized by test function *generators*, from which we can actually create almost an unlimited number of unique test problems. Biggest news are: 1. This whole exercise is made up of *6,825* test problems divided across *16* different test suites: most of these problems are of low dimensionality (2 to 6 variables) with a few benchmarks extending to 9+ variables. With all the sensitivities performed during this exercise on those benchmarks, the overall grand total number of functions evaluations stands at *3,859,786,025* - close to *4 billion*. Not bad. 2. A couple of "new" optimization algorithms I have ported to Python: - MCS: Multilevel Coordinate Search <https://www.mat.univie.ac.at/~neum/software/mcs/>, it’s my translation to Python of the original Matlab code from A. Neumaier and W. Huyer (giving then for free also GLS and MINQ) I have added a few, minor improvements compared to the original implementation. - BiteOpt: BITmask Evolution OPTimization <https://github.com/avaneev/biteopt> , I have converted the C++ code into Python and added a few, minor modifications. Enough chatting for now. The 13 tested algorithms are described here: http://infinity77.net/go_2021/ High level description & results of the 16 benchmarks: http://infinity77.net/go_2021/thebenchmarks.html Each benchmark test suite has its own dedicated page, with more detailed results and sensitivities. List of tested algorithms: 1. *AMPGO*: Adaptive Memory Programming for Global Optimization: this is my Python implementation of the algorithm described here: http://leeds-faculty.colorado.edu/glover/fred%20pubs/416%20-%20AMP%20(TS)%2… I have added a few improvements here and there based on my Master Thesis work on the standard Tunnelling Algorithm of Levy, Montalvo and Gomez. After AMPGO was integrated in lmfit <https://github.com/lmfit/lmfit-py>, I have improved it even more - in my opinion. 2. *BasinHopping*: Basin hopping is a random algorithm which attempts to find the global minimum of a smooth scalar function of one or more variables. The algorithm was originally described by David Wales: http://www-wales.ch.cam.ac.uk/ BasinHopping is now part of the standard SciPy distribution. 3. *BiteOpt*: BITmask Evolution OPTimization, based on the algorithm presented in this GitHub link: https://github.com/avaneev/biteopt I have converted the C++ code into Python and added a few, minor modifications. 4. *CMA-ES*: Covariance Matrix Adaptation Evolution Strategy, based on the following algorithm: http://www.lri.fr/~hansen/cmaesintro.html http://www.lri.fr/~hansen/cmaes_inmatlab.html#python (Python code for the algorithm) 5. *CRS2*: Controlled Random Search with Local Mutation, as implemented in the NLOpt <http://ab-initio.mit.edu/wiki/index.php/NLopt> package: http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms#Controlled_Random_… 6. *DE*: Differential Evolution, described in the following page: http://www1.icsi.berkeley.edu/~storn/code.html DE is now part of the standard SciPy distribution, and I have taken the implementation as it stands in SciPy: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.differe… 7. *DIRECT*: the DIviding RECTangles procedure, described in: https://www.tol-project.org/export/2776/tolp/OfficialTolArchiveNetwork/NonL… http://ab-initio.mit.edu/wiki/index.php/NLopt_Algorithms#DIRECT_and_DIRECT-L (Python code for the algorithm) 8. *DualAnnealing*: the Dual Annealing algorithm, taken directly from the SciPy implementation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.dual_an… 9. *LeapFrog*: the Leap Frog procedure, which I have been recommended for use, taken from: https://github.com/flythereddflagg/lpfgopt 10. *MCS*: Multilevel Coordinate Search, it’s my translation to Python of the original Matlab code from A. Neumaier and W. Huyer (giving then for free also GLS <https://www.mat.univie.ac.at/~neum/software/ls/> and MINQ <https://www.mat.univie.ac.at/~neum/software/minq/>): https://www.mat.univie.ac.at/~neum/software/mcs/ I have added a few, minor improvements compared to the original implementation. See the MCS <http://infinity77.net/go_2021/mcs.html#mcs> section for a quick and dirty comparison between the Matlab code and my Python conversion. 11. *PSWARM*: Particle Swarm optimization algorithm, it has been described in many online papers. I have used a compiled version of the C source code from: http://www.norg.uminho.pt/aivaz/pswarm/ 12. *SCE*: Shuffled Complex Evolution, described in: Duan, Q., S. Sorooshian, and V. Gupta, Effective and efficient global optimization for conceptual rainfall-runoff models, Water Resour. Res., 28, 1015-1031, 1992. The version I used was graciously made available by Matthias Cuntz via a personal e-mail. 13. *SHGO*: Simplicial Homology Global Optimization, taken directly from the SciPy implementation: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.shgo.ht… List of benchmark test suites: 1. SciPy Extended <http://infinity77.net/go_2021/scipy_extended.html#scipy-extended>: 235 multivariate problems (where the number of independent variables ranges from 2 to 17), again with multiple local/global minima. I have added about 40 new functions to the standard SciPy benchmarks <https://github.com/scipy/scipy/tree/master/benchmarks/benchmarks/go_benchma…> and fixed a few bugs in the existing benchmark models in the SciPy repository. 2. GKLS <http://infinity77.net/go_2021/gkls.html#gkls>: 1,500 test functions, with dimensionality varying from 2 to 6, generated with the super famous GKLS Test Functions Generator <https://arxiv.org/abs/1103.2695>. I have taken the original C code (available at http://netlib.org/toms/) and converted it to Python. 3. GlobOpt <http://infinity77.net/go_2021/globopt.html#globopt>: 288 tough problems, with dimensionality varying from 2 to 5, created with another test function generator which I arbitrarily named “GlobOpt”: https://www.researchgate.net/publication/225566516_A_new_class_of_test_func… . The original code is in C++ and I have bridged it to Python using Cython. *Many thanks* go to Professor Marco Locatelli for providing an updated copy of the C++ source code. 4. MMTFG <http://infinity77.net/go_2021/mmtfg.html#mmtfg>: sort-of an acronym for “Multi-Modal Test Function with multiple Global minima”, this test suite implements the work of Jani Ronkkonen: https://www.researchgate.net/publication/220265526_A_Generator_for_Multimod… . It contains 981 test problems with dimensionality varying from 2 to 4. The original code is in C and I have bridge it to Python using Cython. 5. GOTPY <http://infinity77.net/go_2021/gotpy.html#gotpy>: a generator of benchmark functions using the Bocharov-Feldbaum “Method-Min”, containing 400 test problems with dimensionality varying from 2 to 5. I have taken the Python implementation from https://github.com/redb0/gotpy and improved it in terms of runtime. Original paper from http://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=at&paperid=11985&… . 6. Huygens <http://infinity77.net/go_2021/huygens.html#huygens>: this benchmark suite is very different from the rest, as it uses a “fractal” approach to generate test functions. It is based on the work of Cara MacNish on Fractal Functions <https://www.cs.bham.ac.uk/research/projects/ecb/displayDataset.php?id=150>. The original code is in Java, and at the beginning I just converted it to Python: given it was slow as a turtle, I have re-implemented it in Fortran and wrapped it using f2py <http://www.scipy.org/F2py>, then generating 600 2-dimensional test problems out of it. 7. LGMVG <http://infinity77.net/go_2021/lgmvg.html#lgmvg>: not sure about the meaning of the acronym, but the implementation follows the “Max-Set of Gaussians Landscape Generator” described in http://boyuan.global-optimization.com/LGMVG/index.htm . Source code is given in Matlab, but it’s fairly easy to convert it to Python. This test suite contains 304 problems with dimensionality varying from 2 to 5. 8. NgLi <http://infinity77.net/go_2021/ngli.html#ngli>: Stemming from the work of Chi-Kong Ng and Duan Li, this is a test problem generator for unconstrained optimization, but it’s fairly easy to assign bound constraints to it. The methodology is described in https://www.sciencedirect.com/science/article/pii/S0305054814001774 , while the Matlab source code can be found in http://www1.se.cuhk.edu.hk/~ckng/generator/ . I have used the Matlab script to generate 240 problems with dimensionality varying from 2 to 5 by outputting the generator parameters in text files, then used Python to create the objective functions based on those parameters and the benchmark methodology. 9. MPM2 <http://infinity77.net/go_2021/mpm2.html#mpm2>: Implementing the “Multiple Peaks Model 2”, there is a Python implementation at https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py . This is a test problem generator also used in the smoof <https://github.com/jakobbossek/smoof> library, I have taken the code almost as is and generated 480 benchmark functions with dimensionality varying from 2 to 5. 10. RandomFields <http://infinity77.net/go_2021/randomfields.html#randomfields>: as described in https://www.researchgate.net/publication/301940420_Global_optimization_test… , it generates benchmark functions by “smoothing” one or more multidimensional discrete random fields and composing them. No source code is given, but the implementation is fairly straightforward from the article itself. 11. NIST <http://infinity77.net/go_2021/nist.html#nist>: not exactly the realm of Global Optimization solvers, but the NIST StRD <https://www.itl.nist.gov/div898/strd/nls/nls_main.shtml> dataset can be used to generate a single objective function as “sum of squares”. I have used the NIST dataset as implemented in lmfit <https://github.com/lmfit/lmfit-py/tree/master/NIST_STRD>, thus creating 27 test problems with dimensionality ranging from 2 to 9. 12. GlobalLib <http://infinity77.net/go_2021/globallib.html#globallib>: Arnold Neumaier maintains <https://www.mat.univie.ac.at/~neum/glopt/coconut/Benchmark/Benchmark.html> a suite of test problems termed “COCONUT Benchmark” and Sahinidis has converted the GlobalLib and PricentonLib AMPL/GAMS dataset into C/Fortran code (http://archimedes.cheme.cmu.edu/?q=dfocomp ). I have used a simple C parser to convert the benchmarks from C to Python. The global minima are taken from Sahinidis <http://thales.cheme.cmu.edu/dfo/comparison/comp.html> or from Neumaier or refined using the NEOS server <https://neos-server.org/neos/> when the accuracy of the reported minima is too low. The suite contains 181 test functions with dimensionality varying between 2 and 9. 13. CVMG <http://infinity77.net/go_2021/cvmg.html#cvmg>: another “landscape generator”, I had to dig it out using the Wayback Machine at http://web.archive.org/web/20100612044104/https://www.cs.uwyo.edu/~wspears/… , the acronym stands for “Continuous Valued Multimodality Generator”. Source code is in C++ but it’s fairly easy to port it to Python. In addition to the original implementation (that uses the Sigmoid <https://en.wikipedia.org/wiki/Sigmoid_function> as a softmax/transformation function) I have added a few others to create varied landscapes. 360 test problems have been generated, with dimensionality ranging from 2 to 5. 14. NLSE <http://infinity77.net/go_2021/nlse.html#nlse>: again, not really the realm of Global optimization solvers, but Nonlinear Systems of Equations can be transformed to single objective functions to optimize. I have drawn from many different sources (Publications <https://www.sciencedirect.com/science/article/pii/S0898122111004299>, ALIAS/COPRIN <http://www-sop.inria.fr/coprin/logiciels/ALIAS/Benches/node1.html#SECTION00…> and many others) to create 44 systems of nonlinear equations with dimensionality ranging from 2 to 8. 15. Schoen <http://infinity77.net/go_2021/schoen.html#schoen>: based on the early work of Fabio Schoen and his short note <https://link.springer.com/article/10.1007%2FBF01096734> on a simple but interesting idea on a test function generator, I have taken the C code in the note and converted it into Python, thus creating 285 benchmark functions with dimensionality ranging from 2 to 6. *Many thanks* go to Professor Fabio Schoen for providing an updated copy of the source code and for the email communications. 16. Robust <http://infinity77.net/go_2021/robust.html#robust>: the last benchmark test suite for this exercise, it is actually composed of 5 different kind-of analytical test function generators, containing deceptive, multimodal, flat functions depending on the settings. Matlab source code is available at http://www.alimirjalili.com/RO.html , I simply converted it to Python and created 420 benchmark functions with dimensionality ranging from 2 to 6. Enjoy, and Happy 2021 :-) . Andrea.

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Static Typing
by Pamphile Roy 04 Jul '21

04 Jul '21

Hi everyone, In a recent PR (https://github.com/scipy/scipy/pull/14284 <https://github.com/scipy/scipy/pull/14284>), we had a discussion about static typing. My understanding from previous PRs, the inclusion of MyPy in the CI and discussions during our last community meetups, was that we wanted to gradually start adding type annotations in our code base. Thanks to the great help of Bas van Beek and others, we have a few common bricks we can use to ease the process. What is your opinion about adding annotations? I expressed my position in the issue. TL;DR I am in favour of adding type annotations. This greatly helps anyone using an IDE and downstream libraries like SciKit-Learn are basically waiting for us. Lastly, I am putting here again the link to the last Python Community survey https://www.jetbrains.com/lp/python-developers-survey-2020/ <https://www.jetbrains.com/lp/python-developers-survey-2020/>, static typing is the most wanted feature. Cheers, Pamphile

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GSoC: Integrating library UNU.RAN in SciPy
by Tirth Patel 04 Jul '21

04 Jul '21

Hi all, I have been working on integrating UNU.RAN in scipy.stats for a few weeks on my fork (https://github.com/tirthasheshpatel/scipy/pull/6) with my mentors, Christoph and Nicholas. The design is still fairly basic but is almost ready for initial reviews. Also, it builds on Windows, macOS, and Linux. Tests pass with an exception of unrelated failures on Linux. I am planning to propose a PR on SciPy this week but before I do that, I thought it would be a good idea to summarize the progress here. The methods I propose to add and details regarding the parameters they take can be found in this excel sheet: https://docs.google.com/spreadsheets/d/1r36HypXwpit7sHt9YAe3K7yPL7HNNuQjTkb…. Feel free to comment on the sheet if you have any suggestions. The API looks something like this: import numpy as np from scipy.stats import TransformedDensityRejection pdf = lambda x: 1 - x*x dpdf = lambda x: -2*x urng = np.random.default_rng(123) rng = TransformedDensityRejection(pdf, dpdf, domain=(-1,1), seed=urng) # sample a 100,000 samples from the distribution rvs = rng.rvs(100_000) # evaluate the upper bound of the PPF at some points in the domain u = np.linspace(0, 1, num=1000) ppf_hat = rng.ppf_hat(u) Internal API ------------ For Python callbacks, I have used the `ccallback` API and used setjmp/longjmp for error handling. Using `ctypes` gives similar performance but I wasn't able to figure out a way to handle errors occurring in UNU.RAN and in the callbacks. Here is the C code I wrote to acquire and release the callbacks: https://github.com/tirthasheshpatel/scipy/blob/unuran_c_thunk/scipy/stats/u… For more details on the internal design of the API, I have attempted to create a few flowcharts on draw.io but they are very much a WIP: https://drive.google.com/file/d/1TH70SSvvc5eF6-YmDO8kFNvLqKaGROW-/view?usp=… Other Notes ----------- I think NumPy 1.19 introduced a stable interface for its Generator API. Before that the RandomState API was stable. But RandomState doesn't have a Cython Interface and its Python interface is very slow to be practically useful. For instance, it takes around 7ms to sample 100,000 samples from the normal distribution using the TDR method on NumPy >= 1.19 while it takes around 911ms on NumPy == 1.18 which is around 130 times slower! Is there a plan to drop 1.16 soon and can we use the unstable Generator API from 1.17 onwards or would it too unsafe? Maybe this discussion isn't suited here but I just thought to put it out as a note. Anyways, please feel free to comment on the design, naming convention, etc and suggest changes/ideas that would be good to incorporate before proposing a PR on SciPy. Thanks for reading this! -- Kind Regards, Tirth Patel

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SciPy Community Meetups
by Pamphile Roy 28 Jun '21

28 Jun '21

Hi everyone, The next SciPy community meetup (EU/AFR/USA) will be on June 25 (Friday) at 16a.m. CEST. Everyone is invited to join in and edit the work-in-progress meeting topics and notes https://hackmd.io/@tupui/scipy-meetup-3/edit <https://hackmd.io/@tupui/scipy-meetup-3/edit> You can find the previous notes https://hackmd.io/@tupui/scipy-meetup-2 <https://hackmd.io/@tupui/scipy-meetup-2> Cheers, Pamphile

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Query about scipy.signal development
by Namami Shanker 25 Jun '21

25 Jun '21

Hello all, I recently came across Object-oriented design of digital filtering in scipy.signal (https://link.getmailspring.com/link/231BC7A9-2408-4CD5-887C-E6156C368A0F@ge…) article on Scipy wiki. This was proposed as one of the project ideas during GSOC21 for Scipy. However, I searched through the mailing list, and it seems that no one was selected for this project. I am an Electrical Engineering undergraduate and have knowledge in signal processing. I am interested in working on this project. I have also contributed to Scipy before and I am familiar with the development environment and workflow. I have a few questions. I’ll be thankful if any maintainer can address them. Is any scipy developer currently working on this project? Does Scipy take part in any other Open Source programs for working with students other than GSoC? Thank you, Namami Shanker Github (https://link.getmailspring.com/link/231BC7A9-2408-4CD5-887C-E6156C368A0F@ge…) | LinkedIn (https://link.getmailspring.com/link/231BC7A9-2408-4CD5-887C-E6156C368A0F@ge…)

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Proposal to translate hyp2f1 for complex numbers into Cython
by Steppi, Albert 24 Jun '21

24 Jun '21

Hi everyone, I've submitted the PR gh-14256, which is a beginning towards moving the implementation of Gauss's hypergeometric function hyp2f1 for complex numbers from Fortran into Cython. Josh Wilson mentioned at https://github.com/scipy/scipy/pull/8151#issuecomment-845227864 that this would be appreciated, and I'd like to work on this little by little in my spare time. The current Fortran implementation has several outstanding issues gh-4894, gh-7340, gh-8054, and gh-8083. Pull requests gh-8110 and gh-8151 were submitted by GitHub user FormerPhysicist to fix some of the outstanding issues but these have languished for almost four years now due to a lack of reviewers conversant in both numerical computation of special functions and Fortran 77. This PR has a fix for gh-7430. I intend to address other defects in the implementation of hyp2f1 in future PRs. Please see my opening comment in gh-14256 for more information on the contents of this PR and plans for the future. Feel free to join the discussion of this update in gh-14256! Thanks, Albert Albert Steppi III, PhD Scientific Software Developer Laboratory of Systems Pharmacology Harvard Medical School

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NumPy 1.21.0 release
by Charles R Harris 24 Jun '21

24 Jun '21

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.0. The highlights are - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - universal2 wheels for Python 3.8 and Python 3.9 on Mac, - improved documentation, - improved annotations, - new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 581 merged pull requests contributed by 175 people. The Python versions supported for this release are 3.7-3.9, support for Python 3.10 will be added after Python 3.10 is released. Wheels can be downloaded from PyPI <https://pypi.org/project/numpy/1.21.0/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.0>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 175 people contributed to this release. People with a "+" by their names contributed a patch for the first time. - @8bitmp3 + - @DWesl + - @Endolith - @Illviljan + - @Lbogula + - @Lisa + - @Patrick + - @Scian + - @h-vetinari + - @h6197627 + - @jbCodeHub + - @legoffant + - @sfolje0 + - @tautaus + - @yetanothercheer + - Abhay Raghuvanshi + - Adrian Price-Whelan + - Aerik Pawson + - Agbonze Osazuwa + - Aitik Gupta + - Al-Baraa El-Hag - Alex Henrie - Alexander Hunt + - Alizé Papp + - Allan Haldane - Amarnath1904 + - Amrit Krishnan + - Andras Deak - AngelGris + - Anne Archibald - Anthony Vo + - Antony Lee - Atharva-Vidwans + - Ayush Verma + - Bas van Beek - Bharat Raghunathan - Bhargav V + - Brian Soto - Carl Michal + - Charles Harris - Charles Stern + - Chiara Marmo + - Chris Barnes + - Chris Vavaliaris - Christina Hedges + - Christoph Gohlke - Christopher Dahlin + - Christos Efstathiou + - Chunlin Fang - Constanza Fierro + - Daniel Evans + - Daniel Montes + - Dario Mory + - David Carlier + - David Stansby - Deepyaman Datta + - Derek Homeier - Dong Keun Oh + - Dylan Cutler + - Eric Larson - Eric Wieser - Eva Jau + - Evgeni Burovski - FX Coudert + - Faris A Chugthai + - Filip Ter + - Filip Trojan + - François Le Lay + - Ganesh Kathiresan - Giannis Zapantis + - Giulio Procopio + - Greg Lucas + - Hollow Man + - Holly Corbett + - I-Shen Leong + - Inessa Pawson - Isabela Presedo-Floyd - Ismael Jimenez + - Isuru Fernando - Jakob Jakobson - James Gerity + - Jamie Macey + - Jasmin Classen + - Jody Klymak + - Joseph Fox-Rabinovitz - Jérome Eertmans + - Jérôme Kieffer + - Kamil Choudhury + - Kasia Leszek + - Keller Meier + - Kenichi Maehashi - Kevin Sheppard - Kulin Seth + - Kumud Lakara + - Laura Kopf + - Laura Martens + - Leo Singer + - Leonardus Chen + - Lima Tango + - Lumir Balhar + - Maia Kaplan + - Mainak Debnath + - Marco Aurélio da Costa + - Marta Lemanczyk + - Marten van Kerkwijk - Mary Conley + - Marysia Winkels + - Mateusz Sokół + - Matt Haberland - Matt Hall + - Matt Ord + - Matthew Badin + - Matthias Bussonnier - Matthias Geier - Matti Picus - Matías Ríos + - Maxim Belkin + - Melissa Weber Mendonça - Meltem Eren Copur + - Michael Dubravski + - Michael Lamparski - Michal W. Tarnowski + - Michał Górny + - Mike Boyle + - Mike Toews - Misal Raj + - Mitchell Faas + - Mukulikaa Parhari + - Neil Girdhar + - Nicholas McKibben + - Nico Schlömer - Nicolas Hug + - Nilo Kruchelski + - Nirjas Jakilim + - Ohad Ravid + - Olivier Grisel - Pamphile ROY + - Panos Mavrogiorgos + - Patrick T. Komiske III + - Pearu Peterson - Peter Hawkins + - Raghuveer Devulapalli - Ralf Gommers - Raúl Montón Pinillos + - Rin Arakaki + - Robert Kern - Rohit Sanjay - Roman Yurchak - Ronan Lamy - Ross Barnowski - Ryan C Cooper - Ryan Polley + - Ryan Soklaski - Sabrina Simao + - Sayed Adel - Sebastian Berg - Shen Zhou + - Stefan van der Walt - Sylwester Arabas + - Takanori Hirano - Tania Allard + - Thomas J. Fan + - Thomas Orgis + - Tim Hoffmann - Tomoki, Karatsu + - Tong Zou + - Touqir Sajed + - Tyler Reddy - Wansoo Kim - Warren Weckesser - Weh Andreas + - Yang Hau - Yashasvi Misra + - Zolboo Erdenebaatar + - Zolisa Bleki Cheers, Charles Harris

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PR: Implement higher-order SVD in scipy.linalg
by Jan Naumann 24 Jun '21

24 Jun '21

Dear all, I just opened a pull request to add the higher-order SVD (HOSVD) in scipy.linalg: https://github.com/scipy/scipy/pull/14284 The HOSVD is a technique used for example in the field of tensor networks for higher dimensional structures. It generalizes the idea of the SVD to higher dimensions. The implementation is based on Ref. [1]. Best, Jan Naumann [1] https://doi.org/10.1137/S0895479896305696

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Request for Information: How to Contribute?
by Alex Sherstinsky 23 Jun '21

23 Jun '21

Dear SciPy-Dev: I am a new subscriber to this mailing list (I just joined) -- thank you for accepting me! I joined this mailing list, because I was interested in making a small contribution to the project. Specifically, I implemented a bootstrap based estimator with an algorithmically computed optimal number of samples. The benefit is that the developer does not have to guess the number of samples (the algorithm computes it automatically). Since I could not find an existing widely available implementation, I wanted to inquire if I could please submit my implementation. Could you please let me know what the process is for contributing? Thank you very much, and Regards. Sincerely, --Alex Sherstinsky (https://www.linkedin.com/in/alexsherstinsky/)

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