Mailman 3 May 2021 - SciPy-Dev

New feature: fitting distributions to censored data.
by Warren Weckesser 28 Aug '22

28 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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Enhancement Request: SuperLU_dist/SuperLU_mt replace current scipy.sparse.superLU
by Neil Godber 09 Jun '21

09 Jun '21

Hi dev-list, I've logged an enhancement request on github to bring the parallel versions of SuperLU into scipy. As requested I'm circulating the enhancement here to solicit comment/feedback/engagement. Content of request is as follows (with light editing and links): "Is your feature request related to a problem? Please describe. The direct solution of sparse matrices is a common problem that arises across many domains. Current scipy.sparse.linalg provides spsolve and splu to solve such classes of problems. Neither solution appears to utilise more than one or two threads leaving a lot of the available compute on modern machines unused. Other solutions (MUMPS, Pardiso) exploit multicore processors but are either minimally supported and/or locked behind proprietary libraries. Describe the solution you'd like Fortunately SuperLU has two multithreaded implementations superLU_mt and superLU_dt, both actively maintained (SuperLU: Home Page (nersc.gov) <https://portal.nersc.gov/project/sparse/superlu/>). Ideally SciPy would replace the current sequential implementation with one of the parallel implementations to yield large performance improvements. Describe alternatives you've considered pyMKL (Pardiso dwfmarchant/pyMKL: Python wrappers to Intel MKL routines (github.com) <https://github.com/dwfmarchant/pyMKL>) - MKL with issues associated with that for non Intel processors, unmaintained relatively to scipy, PyTrilinos (implements all three superLU types but only available on osx and linux PyTrilinos | Trilinos <https://trilinos.github.io/pytrilinos.html>), petsc (petsc4py PETSc / petsc · GitLab <https://gitlab.com/petsc/petsc>) (implements all three superLU types but only available on osx and linux). All are relatively inaccessible compared to ubiquity and accessibility of scipy. Additional context (e.g. screenshots) Anecdotally large number of projects use various non scipy sparse solvers to achieve high level of performance. This comes with considerable time and maintenance investment. It would be fantastic for scipy to provide a 'performance competitive' sparse direct solver out of the hood as it does for dense matrices." Cheers, Neil

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Re: [SciPy-Dev] SciPy Community Meetups are back!
by Pamphile Roy 30 May '21

30 May '21

Hi everyone, The next meeting (PAC/ASIA/EU) will be on June 3 (Thursday) at 9am CEST. Feel free to add some inputs/comments to the meeting notes. https://hackmd.io/@tupui/scipy-meetup-2/edit Cheers, Pamphile ps. I will try to be there but I have some last minute change... > On 28 May 2021, at 22:09, Pamphile Roy <roy.pamphile(a)gmail.com> wrote:

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SciPy Community Meetups are back!
by Pamphile Roy 28 May '21

28 May '21

Hi everyone, I would like to propose regular SciPy Community meetings again! https://doodle.com/poll/fd62d6755texie5s <https://doodle.com/poll/fd62d6755texie5s> For this first meeting, it would be good if as many people as possible could join as we would decide things like frequency of such meeting. Everyone is invited and encouraged to join in and edit the work-in-progress meeting topics and notes at: https://hackmd.io/@tupui/scipy-meetup/edit <https://hackmd.io/@tupui/scipy-meetup/edit> Cheers, Pamphile PS. share the news: https://twitter.com/PamphileRoy/status/1388067651721830401?s=20 <https://twitter.com/PamphileRoy/status/1388067651721830401?s=20>

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Proposed 1.7.0 Release Schedule
by Tyler Reddy 27 May '21

27 May '21

Hi, SciPy 1.6.0 was released December 31/2020 (a bit late), a little more than 4 months ago, and I think we'd like to keep a roughly biannual release cadence. I'd like to proposed the following schedule for 1.7.0: - May 26/2021: branch 1.7.x - May 29/2021: rc1 - June 11/2021: rc2 (if needed) - June 20/2021: final release As always, it is a good idea to start tagging things that should be in the 1.7.0 release & please do help with reviewing PRs/issues that are tagged--current counts are: - PRs: 24 open with 1.7.0 milestone - issues: 8 open with 1.7.0 milestone Also great to update the release notes wiki as appropriate: https://github.com/scipy/scipy/wiki/Release-note-entries-for-SciPy-1.7.0 Thoughts/objections for the proposed schedule? The new build time dependency/transpiler, Pythran, would be a notable addition to this release, assuming that moves forward. Best wishes, Tyler

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master branch open for development of SciPy 1.8.0
by Tyler Reddy 26 May '21

26 May '21

I've branched maintenance/1.7.x and master branch is now open for the development of SciPy 1.8.0. (There's a strange Windows-only CI failure after bumping to 1.8.0, but it is getting late) Best wishes, Tyler

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NumPy 1.21.0rc1 has been released.
by Charles R Harris 24 May '21

24 May '21

Hi All, On behalf of the NumPy team I am pleased to announce the release of NumPy 1.21.0rc1. The highlights are - continued SIMD work covering more functions and platforms, - initial work on the new dtype infrastructure and casting, - improved documentation, - improved annotations, - the new ``PCG64DXSM`` bitgenerator for random numbers. This NumPy release is the result of 561 merged pull requests contributed by 171 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/1.21.0rc1/>; source archives, release notes, and wheel hashes are available on Github <https://github.com/numpy/numpy/releases/tag/v1.21.0rc1>. Linux users will need pip >= 0.19.3 in order to install manylinux2010 and manylinux2014 wheels. *Contributors* A total of 171 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 + - Inessa Pawson - Isabela Presedo-Floyd - Ismael Jimenez + - Isuru Fernando - Jakob Jakobson - James Gerity + - Jamie Macey + - Jasmin Classen + - Jody Klymak + - Joseph Fox-Rabinovitz - Jérome Eertmans + - Kamil Choudhury + - Kasia Leszek + - Keller Meier + - 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 - 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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Unpacking the mailing list: Discourse, Slack = Mailing list
by Pamphile Roy 22 May '21

22 May '21

Hi everyone, We had a chat Ralf, Stéfan and I about moving away from the mailing list. The idea would be to move over Discourse under the umbrella of https://discuss.scientific-python.org <https://discuss.scientific-python.org/> Stéfan proposes to do the migration work and I can also provide some help if needed (the complete history would be imported). I also propose to help administrate/moderate this space. As Serge expressed during the meetup, we should be careful when moving to Discourse as some people might be reluctant to come. Which would fragment the community. Hence, we should force the move by sunsetting the mail list. On the same topic, I would discuss the possibility to use Slack (or another platform for real-time chat). This has proven to be helpful for NumPy and other projects and we should also see the benefits. Let us know what you think. Cheers, Pamphile

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Introduction!
by Kevin Topolski 21 May '21

21 May '21

Hi all, I introduced myself at the last Scipy community meeting and recently, made my first pull request to Scipy so I thought this would be another great opportunity to introduce myself to a larger audience! I got my Ph'd in Chemical Engineering and have used Numpy and Scipy for my own projects and studies for about 2 years now. After using optimization and other computational techniques in my work, I started an interest to develop these tools in open source and saw Scipy as the place to do so! I would love to get your feedback all as I am new to developing and open source. I look forward to learning from you all! Link to my first PR: https://github.com/scipy/scipy/pull/14103 Best and cheers! Kevin Topolski

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