Mailman 3 April 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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SciPy Community Meetups are back!
by Pamphile Roy 29 May '21

29 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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Regarding contribution in Scipy organization for GSoC 21
by Amulya Gupta 20 May '21

20 May '21

Dear Sir/Ma'am, I am Amulya Gupta, a sophomore from BITS,Pilani. I have been coding for the past 3 years. I am profound in Python(Modules: nose test, poetry, numpy, matplotlib, cmake, pipenv, tkinter, turtle and currently learning mypy etc), C++, Mysql, Java script. I have experience in open source with an internship with an organisation called NumFocus. I want to openly contribute to the project "*Add type annotations to a submodule*" from SciPy. Please can I talk to the mentor about his expectations from the student applying. How many more students have applied for the same project and how does the mentor has pre-planned about the project and his inputs for the proposal buildup. Regards Amulya Gupta

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numpy/npy_math.h vs C/C++ stdlib math.h/<cmath>
by Evgeni Burovski 09 May '21

09 May '21

Hi, Prompted by Sturla's comment, https://github.com/scipy/scipy/pull/13931#discussion_r619728364, suggesting to change a call of `npy_log` into an `std::log`, here's a question: what is the recommendation going forward for the math functions (log, exp, trig functions etc) in scipy C/C++ code: do we import them from `numpy/npy_math.h` or do we rely on the standard library? My main concern is more exotic platforms/toolchains and related edge cases--- do we rely on numpy or the standard library vendors? `npy_math.h` still often needs to be included for e.g. numpy data types, so it's not a question of avoiding an extra #include. Cheers, Evgeni

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Can a PR be configured to run only the build of the docs?
by Warren Weckesser 29 Apr '21

29 Apr '21

I am working on an addition to the docs that only touches one existing .rst file and adds one new .rst file. For the initial pull request, I want to run just CircleCI and generate the CircleCI docs artifact. I haven't followed all the changes to how CI is currently configured and run. Is there text that I can add to the commit message (or some other method) so that only the CircleCI jobs are run? Warren

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ENH: Add Distance Correlation to scipy.stats
by Sambit Panda 29 Apr '21

29 Apr '21

Hi all, I hope that everyone was is doing well. I just wanted feedback on my issue supporting Distance Correlation within SciPy: https://github.com/scipy/scipy/issues/13728https://github.com/scipy/scipy/i…. The core motivation for inclusion is mentioned in the issue: > Multivariate independence testing is a desirable problem to solve in many fields such as connectomics. Looking through the scipy.stats module, there are very few multivariate tests. In fact, there is 1, multiscale_graphcorr<https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.multiscale…>, which I had added a few months ago. Distance correlation (Dcorr) is another highly cited independence test that operates very well on multivariate data [1]. In fact, due to the fact the distance and kernel methods are equivalent [2], and that independence and two-sample testing is equivalent [3], we would be able to condense the implementations of Dcorr, Hilbert-Schmidt Independence Criterion (Hsic), Maximal Mean Discrepancy (MMD), and Energy into a single test. Our implementation also includes a number of optimizations, including a test statistic implementation that operates in n log n time instead of n^2 time when the data is univariate and the metric used is euclidean [4], and a chi-square approximation to the p-value that skips the computationally-intense permutation test [5]. Adding this provides another accurate multivariate test to SciPy, and makes the scipy.stats module as a whole far better. > 1. https://projecteuclid.org/journals/annals-of-statistics/volume-35/issue-6/M… > 2. https://link.springer.com/article/10.1007/s10182-020-00378-1 > 3. https://arxiv.org/abs/1910.08883 > 4. https://www.sciencedirect.com/science/article/abs/pii/S0167947319300313 > 5. https://arxiv.org/abs/1912.12150 As for modifications to the SciPy source code, they should be fairly minimal. I had received a comment that my idea was similar to this issue: https://github.com/scipy/scipy/issues/10680https://github.com/scipy/scipy/i…, but the key difference is that I don’t propose adding the original package of the source code: https://github.com/neurodata/hyppohttps://github.com/neurodata/hyppo as a dependency. When I merged scipy.stats.multiscale_graphcorr, I added a number of helper functions to help me compute the test statistic. The majority of the modifications I propose will likely be to those helper functions, and a new function that computes the test statistic and p-value in a similar structure to those tests in scipy.stats. I mention what needs to be ported and locations where I need to make changes in the issue. It is also worth mentioning that similar Cython files exist in an old repository, in a less refined state, i.e. https://github.com/neurodata/mgcpy-old/blob/master/mgcpy/independence_tests…. Licensing was also mentioned as a potential problem, but I don’t think that it should be. I created the hyppo package and give permission for inclusion within SciPy. I also believe that Apache License v2.0 is a fairly permissive and common open source license, right? Sorry for the long email, Sambit Panda BME Ph.D. Student NeuroData<https://neurodata.io/> @ Johns Hopkins Website<https://sampan.me/>・GitHub<https://github.com/sampan501>・Scholar<https://scholar.google.com/citations?user=-V3CmPoAAAAJ&hl=en>

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GitHub actions restrictions
by Andrew Nelson 29 Apr '21

29 Apr '21

https://docs.github.com/en/actions/managing-workflow-runs/approving-workflo… >From the GH website: When a first-time contributor submits a pull request to a public repository, a maintainer with write access must approve any workflow runs. The purpose behind this is so that things like mining for Bitcoin is prevented. A.

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Can a PR be configured to run
by Warren Weckesser 29 Apr '21

29 Apr '21

1 0

ANN: SciPy 1.6.3
by Tyler Reddy 26 Apr '21

26 Apr '21

Hi all, On behalf of the SciPy development team I'm pleased to announce the release of SciPy 1.6.3, which is a bug fix release. Sources and binary wheels can be found at: https://pypi.org/project/scipy/ and at: https://github.com/scipy/scipy/releases/tag/v1.6.3 <https://github.com/scipy/scipy/releases/tag/v1.6.1> One of a few ways to install this release with pip: pip install scipy==1.6.3 ===================== SciPy 1.6.3 Release Notes ===================== SciPy 1.6.3 is a bug-fix release with no new features compared to 1.6.2. Authors ====== * Peter Bell * Ralf Gommers * Matt Haberland * Peter Mahler Larsen * Tirth Patel * Tyler Reddy * Pamphile ROY + * Xingyu Liu + A total of 8 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 1.6.3 ------------------------------- * `#13772 <https://github.com/scipy/scipy/issues/13772>`__: Divide by zero in distance.yule * `#13796 <https://github.com/scipy/scipy/issues/13796>`__: CI: prerelease_deps failures * `#13890 <https://github.com/scipy/scipy/issues/13890>`__: TST: spatial rotation failure in (1.6.3) wheels repo (ARM64) Pull requests for 1.6.3 ------------------------------ * `#13755 <https://github.com/scipy/scipy/pull/13755>`__: CI: fix the matplotlib warning emitted during builing docs * `#13773 <https://github.com/scipy/scipy/pull/13773>`__: BUG: Divide by zero in yule dissimilarity of constant vectors * `#13799 <https://github.com/scipy/scipy/pull/13799>`__: CI/MAINT: deprecated np.typeDict * `#13819 <https://github.com/scipy/scipy/pull/13819>`__: substitute np.math.factorial with math.factorial * `#13895 <https://github.com/scipy/scipy/pull/13895>`__: TST: add random seeds in Rotation module Checksums ========= MD5 ~~~ 3b75d493f6c93b662f927d6c2ac60053 scipy-1.6.3-cp37-cp37m-macosx_10_9_x86_64.whl ada6fa32f066dc58033ab47a4fbcd208 scipy-1.6.3-cp37-cp37m-manylinux1_i686.whl 29c6d6edbe9c2ba17dc4edd89ed31abb scipy-1.6.3-cp37-cp37m-manylinux1_x86_64.whl cde2f4824337fda5b2522795fff2135d scipy-1.6.3-cp37-cp37m-manylinux2014_aarch64.whl 27fa4101babcfa7928e4c23e33212e71 scipy-1.6.3-cp37-cp37m-win32.whl b0ef1b6ba4d3b54ad8f17dd10156c3f8 scipy-1.6.3-cp37-cp37m-win_amd64.whl 66b3ea5bb7869af010ae9d6a5015360b scipy-1.6.3-cp38-cp38-macosx_10_9_x86_64.whl 0ef8582f203fdd4afe1ca56fc5b309f5 scipy-1.6.3-cp38-cp38-manylinux1_i686.whl 1194e9d88ef98595619acd657439fae4 scipy-1.6.3-cp38-cp38-manylinux1_x86_64.whl 4b8fe1b85fd8d60bbc787a06c0d545ee scipy-1.6.3-cp38-cp38-manylinux2014_aarch64.whl 1d55aff23261c2fa8d7882bc7220c25d scipy-1.6.3-cp38-cp38-win32.whl e82a80c799f1e5190be0cbd25b554766 scipy-1.6.3-cp38-cp38-win_amd64.whl 8309d581e025539bafaa98ba9c3122a7 scipy-1.6.3-cp39-cp39-macosx_10_9_x86_64.whl 75a9ba6865ff2fefcb5df917a412d99d scipy-1.6.3-cp39-cp39-manylinux1_i686.whl fbfc53ba0ea23f56afb62c2a159e6303 scipy-1.6.3-cp39-cp39-manylinux1_x86_64.whl 4076d3cdb2b16f7d64229ac76c945b6f scipy-1.6.3-cp39-cp39-manylinux2014_aarch64.whl bd553432d6a55a6e139f76951766f31d scipy-1.6.3-cp39-cp39-win32.whl 6b9526aca2adf7b062ac92f184328147 scipy-1.6.3-cp39-cp39-win_amd64.whl 05b4ca400aa1157290abff69016f1cab scipy-1.6.3.tar.gz cef7aa950dfc7f5be79b8c630bfe0cc1 scipy-1.6.3.tar.xz f13ea8ab38fb03c17e38c3cda7877972 scipy-1.6.3.zip SHA256 ~~~~~~ 2a799714bf1f791fb2650d73222b248d18d53fd40d6af2df2c898db048189606 scipy-1.6.3-cp37-cp37m-macosx_10_9_x86_64.whl 9e3302149a369697c6aaea18b430b216e3c88f9a61b62869f6104881e5f9ef85 scipy-1.6.3-cp37-cp37m-manylinux1_i686.whl b79104878003487e2b4639a20b9092b02e1bad07fc4cf924b495cf413748a777 scipy-1.6.3-cp37-cp37m-manylinux1_x86_64.whl 44d452850f77e65e25b1eb1ac01e25770323a782bfe3a1a3e43847ad4266d93d scipy-1.6.3-cp37-cp37m-manylinux2014_aarch64.whl b30280fbc1fd8082ac822994a98632111810311a9ece71a0e48f739df3c555a2 scipy-1.6.3-cp37-cp37m-win32.whl 10dbcc7de03b8d635a1031cb18fd3eaa997969b64fdf78f99f19ac163a825445 scipy-1.6.3-cp37-cp37m-win_amd64.whl 1b21c6e0dc97b1762590b70dee0daddb291271be0580384d39f02c480b78290a scipy-1.6.3-cp38-cp38-macosx_10_9_x86_64.whl 1caade0ede6967cc675e235c41451f9fb89ae34319ddf4740194094ab736b88d scipy-1.6.3-cp38-cp38-manylinux1_i686.whl 19aeac1ad3e57338723f4657ac8520f41714804568f2e30bd547d684d72c392e scipy-1.6.3-cp38-cp38-manylinux1_x86_64.whl ad7269254de06743fb4768f658753de47d8b54e4672c5ebe8612a007a088bd48 scipy-1.6.3-cp38-cp38-manylinux2014_aarch64.whl d647757373985207af3343301d89fe738d5a294435a4f2aafb04c13b4388c896 scipy-1.6.3-cp38-cp38-win32.whl 33d1677d46111cfa1c84b87472a0274dde9ef4a7ef2e1f155f012f5f1e995d8f scipy-1.6.3-cp38-cp38-win_amd64.whl d449d40e830366b4c612692ad19fbebb722b6b847f78a7b701b1e0d6cda3cc13 scipy-1.6.3-cp39-cp39-macosx_10_9_x86_64.whl 23995dfcf269ec3735e5a8c80cfceaf384369a47699df111a6246b83a55da582 scipy-1.6.3-cp39-cp39-manylinux1_i686.whl fdf606341cd798530b05705c87779606fcdfaf768a8129c348ea94441da15b04 scipy-1.6.3-cp39-cp39-manylinux1_x86_64.whl f68eb46b86b2c246af99fcaa6f6e37c7a7a413e1084a794990b877f2ff71f7b6 scipy-1.6.3-cp39-cp39-manylinux2014_aarch64.whl 01b38dec7e9f897d4db04f8de4e20f0f5be3feac98468188a0f47a991b796055 scipy-1.6.3-cp39-cp39-win32.whl 3274ce145b5dc416c49c0cf8b6119f787f0965cd35e22058fe1932c09fe15d77 scipy-1.6.3-cp39-cp39-win_amd64.whl a75b014d3294fce26852a9d04ea27b5671d86736beb34acdfc05859246260707 scipy-1.6.3.tar.gz 3851fdcb1e6877241c3377aa971c85af0d44f90c57f4dd4e54e1b2bbd742635e scipy-1.6.3.tar.xz 0a723da627af61665c7793fdf869ad2606be9cf32e2c0abc0d230f62c4f4914f scipy-1.6.3.zip

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