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arxiv: 1907.10121 · v1 · submitted 2019-07-23 · 💻 cs.MS · cs.DS· cs.SE· physics.comp-ph

Recognition: 2 theorem links

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

Pauli Virtanen , Ralf Gommers , Travis E. Oliphant , Matt Haberland , Tyler Reddy , David Cournapeau , Evgeni Burovski , Pearu Peterson
show 27 more authors
Warren Weckesser Jonathan Bright St\'efan J. van der Walt Matthew Brett Joshua Wilson K. Jarrod Millman Nikolay Mayorov Andrew R. J. Nelson Eric Jones Robert Kern Eric Larson CJ Carey \.Ilhan Polat Yu Feng Eric W. Moore Jake VanderPlas Denis Laxalde Josef Perktold Robert Cimrman Ian Henriksen E.A. Quintero Charles R Harris Anne M. Archibald Ant\^onio H. Ribeiro Fabian Pedregosa Paul van Mulbregt SciPy 1.0 Contributors
Authors on Pith no claims yet

Pith reviewed 2026-05-17 05:20 UTC · model grok-4.3

classification 💻 cs.MS cs.DScs.SEphysics.comp-ph
keywords SciPyPythonscientific computingopen sourcealgorithmslibrarynumerical methods
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The pith

SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SciPy 1.0 as an open source library that supplies core algorithms for scientific computing after 16 years of development. It covers areas such as integration, optimization, signal processing, statistics, linear algebra, and image processing, and reports broad adoption through contributor numbers, dependent packages, and downloads. A sympathetic reader would care because the library underpins reproducible work in research and industry, including high-profile uses like gravitational wave analysis. The overview also notes development practices that support ongoing maintenance and extension.

Core claim

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. It has become a de facto standard with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year, including usage in almost half of all machine

What carries the argument

The SciPy library itself, a collection of Python modules that implement and organize fundamental scientific algorithms for direct use in research and analysis workflows.

If this is right

  • Users gain access to tested implementations of core algorithms instead of writing them anew for each project.
  • Dependent packages and repositories can focus development effort on specialized extensions rather than basic numerical routines.
  • High-profile applications such as gravitational wave detection and astronomical imaging can rely on the library's maintained performance and correctness.
  • The library's structure supports incremental additions of new methods while preserving backward compatibility for existing codebases.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The reported scale of dependent projects implies that changes to SciPy can propagate effects across a large portion of the scientific Python ecosystem.
  • Continued community contributions could naturally extend the library into adjacent areas such as machine learning utilities without central coordination.
  • The documented usage in machine learning projects points to opportunities for tighter integration with training and inference pipelines in future releases.

Load-bearing premise

The usage statistics, contributor counts, and described capabilities accurately reflect the library state at the time of writing and that the overview covers the most relevant recent technical developments without significant omissions.

What would settle it

Fresh download and dependency data from package indexes showing adoption well below the reported millions per year or code review finding major algorithmic areas absent from the listed capabilities.

read the original abstract

SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 2 minor

Summary. The manuscript describes SciPy 1.0, an open-source scientific computing library for Python released in late 2017 after 16 years of development from version 0.1. It outlines the library's broad functionality spanning clustering, Fourier transforms, integration, interpolation, linear algebra, image processing, optimization, signal processing, sparse matrix handling, computational geometry, and statistics. The central claim is that SciPy has become a de facto standard, evidenced by more than 600 unique contributors, thousands of dependent packages, over 100,000 dependent repositories, millions of downloads per year, usage in nearly half of GitHub machine learning projects, and applications in high-profile efforts such as LIGO gravitational wave analysis and the first image of a black hole (M87). The paper also covers development practices and recent technical developments.

Significance. If the described capabilities and publicly verifiable adoption metrics hold, the paper is significant as a reference document for the scientific Python ecosystem. It explicitly credits the collaborative open-source model through contributor counts and dependent-project statistics, and the public code availability allows direct verification of the library scope and usage examples. This strengthens the manuscript's value for guiding users, contributors, and educators without relying on untestable derivations.

minor comments (2)
  1. Abstract: the phrase 'de facto standard' would benefit from a brief supporting clause referencing the specific metrics or a dedicated usage-statistics table to strengthen the claim without lengthening the paragraph.
  2. Development practices section: clarify how continuous integration and testing procedures ensure compatibility across the listed scientific domains, as this directly supports reproducibility claims.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript, accurate summary of SciPy 1.0's scope and impact, and recommendation to accept. The report correctly highlights the library's adoption metrics, collaborative development, and role in high-profile scientific projects.

Circularity Check

0 steps flagged

No significant circularity: purely descriptive overview

full rationale

The paper is a descriptive overview of the SciPy library's history, features, development practices, and publicly reported usage metrics (contributors, downloads, dependents, GitHub usage). No derivations, equations, predictions, fitted parameters, or first-principles results are present. Central claims rest on external verifiable counts and cited high-profile applications rather than any internal construction or self-referential logic that could reduce to the paper's own inputs. The document is therefore self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper contains no mathematical derivations, fitted parameters, or postulated entities; it is a descriptive overview of existing software.

pith-pipeline@v0.9.0 · 5653 in / 1071 out tokens · 42865 ms · 2026-05-17T05:20:09.072861+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

160 extracted references · 160 canonical work pages · cited by 17 Pith papers · 2 internal anchors

  1. [1]

    Oliphant, T. E. Guide to NumPy (Trelgol Publishing USA, 2006), 1st edn. URL https://oez.es/Guide%20to% 20NumPy.pdf

    work page 2006

  2. [2]

    van der Walt, S., Colbert, S. C. & Varoquaux, G. The NumPy array: a structure for efficient numerical computation. Comput. Sci. & Eng. 13, 22–30 (2011). URL https://doi.org/10.1109/MCSE.2011.37

    work page doi:10.1109/mcse.2011.37 2011

  3. [3]

    Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). URL http://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf

    work page 2011

  4. [4]

    van der Walt, S. et al. scikit-image: image processing in Python. PeerJ 2, e453 (2014). URL https://doi.org/10. 7717/peerj.453

    work page 2014

  5. [5]

    Nitz, A. et al. gwastro/pycbc: PyCBC v1.13.2 release (2018). URL https://doi.org/10.5281/zenodo. 1596771

    work page doi:10.5281/zenodo 2018

  6. [6]

    & Stephens, B

    Vallisneri, M., Kanner, J., Williams, R., Weinstein, A. & Stephens, B. The LIGO open science center. J. Physics: Conf. Ser. 610, 012021 (2015). URL https://doi.org//10.1088/1742-6596/610/1/012021

    work page doi:10.1088/1742-6596/610/1/012021 2015

  7. [7]

    Abbott, B. P. et al. GW150914: First results from the search for binary black hole coalescence with Advanced LIGO. Phys. Rev. D 93, 122003 (2016). URL https://doi.org/10.1103/PhysRevD.93.122003

    work page doi:10.1103/physrevd.93.122003 2016

  8. [8]

    Abbott, B. P. et al. GW170817: Observation of gravitational waves from a binary neutron star inspiral. Phys. Rev. Lett. 119, 161101 (2017). URL https://doi.org/10.1103/PhysRevLett.119.161101

    work page doi:10.1103/physrevlett.119.161101 2017

  9. [9]

    First M87 event horizon telescope results

    The Event Horizon Telescope Collaboration et al. First M87 event horizon telescope results. III. data processing and calibration. The Astrophys. J. Lett. 875, L3 (2019). URL https://doi.org/10.3847/2041-8213/ab0c57

    work page doi:10.3847/2041-8213/ab0c57 2019

  10. [10]

    At Mathworks, support + fun = success: CEO Jack Little believes in power of his workers – and their ideas (20 Apr

    Blanton, K. At Mathworks, support + fun = success: CEO Jack Little believes in power of his workers – and their ideas (20 Apr. 1997). The Boston Globe J5. URL https://www.newspapers.com/clip/26952040/the_ boston_globe/

    work page arXiv 1997

  11. [11]

    Jack Dangermond’s digital mapping lays it all out (14 Aug

    Howell, D. Jack Dangermond’s digital mapping lays it all out (14 Aug. 2009). Investor’s Bus. Dly. URL https://web.archive.org/web/20100510064955/http://www.investors.com/ NewsAndAnalysis/Article.aspx?id=503454

    work page arXiv 2009

  12. [12]

    Simple solutions (3 Oct

    Port, O. Simple solutions (3 Oct. 2005). BusinessWeek 24–24. URL https://www.scribd.com/document/ 134233902/Business-Week-03-Oct-2005

    work page 2005

  13. [13]

    Numeric and scientific (2017)

    Open Source Community. Numeric and scientific (2017). URL https://wiki.python.org/moin/ NumericAndScientific

    work page 2017

  14. [14]

    Python conferences (2018)

    Open Source Community. Python conferences (2018). URL https://wiki.python.org/moin/ PythonConferences

    work page 2018

  15. [15]

    Python/C API reference manual (2001)

    van Rossum, G. Python/C API reference manual (2001). URL http://citeseerx.ist.psu.edu/viewdoc/ download?doi=10.1.1.211.6702&rep=rep1&type=pdf

    work page 2001

  16. [16]

    The Python programming language (2019)

    Developers, C. The Python programming language (2019). URL https://github.com/python/cpython

    work page 2019

  17. [17]

    The matrix object proposal (very long) (1995)

    Hugunin, J. The matrix object proposal (very long) (1995). URL https://mail.python.org/pipermail/ matrix-sig/1995-August/000002.html

    work page 1995

  18. [18]

    Extending Python for numerical computation (1995)

    Hugunin, J. Extending Python for numerical computation (1995). URL http://hugunin.net/papers/ hugunin95numpy.html

    work page 1995

  19. [19]

    Oliphant, T. E. Moving forward from the last decade of SciPy (2010). URL https://conference.scipy.org/ scipy2010/slides/travis_oliphant_keynote.pdf

    work page 2010

  20. [20]

    Oliphant, T. E. Some Python modules (1999). URL https://web.archive.org/web/19990125091242/ http://oliphant.netpedia.net:80/. 14/22

    work page arXiv 1999

  21. [21]

    Oliphant, T. E. Modules to enhance numerical Python (2000). URL https://web.archive.org/web/ 20001206213500/http://oliphant.netpedia.net:80/

    work page 2000

  22. [22]

    Oliphant, T. E. Multipack (a collection of FORTRAN routines interfaced with NumPy). URL http://pylab. sourceforge.net/multipack.html

    work page

  23. [23]

    Hindmarsh, A. C. ODEPACK, a systematized collection of ODE solvers. Sci. Comput. 55–64 (1983). URL https: //www3.nd.edu/˜powers/ame.60636/hindmarsh1983.pdf

    work page 1983

  24. [24]

    Piessens, R., de Doncker-Kapenga, E., ¨Uberhuber, C. W. & Kahaner, D. K. QUADPACK: a subroutine package for automatic integration (Springer-Verlag, New York, NY , USA, 1983). URL https://doi.org/10.1007/ 978-3-642-61786-7

    work page 1983

  25. [25]

    J., Garbow, B

    Mor´e, J. J., Garbow, B. S. & Hillstrom, K. E. User guide for MINPACK-1. Tech. Rep. ANL-80-74, Argonne National Lab- oratory, Argonne, IL, USA (1980). URL http://cds.cern.ch/record/126569/files/CM-P00068642. pdf

    work page 1980

  26. [26]

    F2PY: a tool for connecting Fortran and Python programs

    Peterson, P. F2PY: a tool for connecting Fortran and Python programs. Int. J. Comput. Sci. Eng. 4, 296–305 (2009). URL https://doi.org/10.1504/IJCSE.2009.029165

    work page doi:10.1504/ijcse.2009.029165 2009

  27. [27]

    Python modules (2000)

    Strangman, G. Python modules (2000). URL https://web.archive.org/web/20001022231108/http: //www.nmr.mgh.harvard.edu/Neural_Systems_Group/gary/python.html

    work page arXiv 2000

  28. [28]

    SciPy.org (2001)

    SciPy Developers. SciPy.org (2001). URL https://web.archive.org/web/20010309040805/http:// scipy.org:80/

    work page arXiv 2001

  29. [29]

    Vaught, T. N. SciPy developer mailing list now online (2001). URL https://mail.python.org/pipermail/ scipy-dev/2001-June/000000.html

    work page 2001

  30. [30]

    ANN: SciPy 0.10 – scientific computing with Python (2001)

    Jones, E. ANN: SciPy 0.10 – scientific computing with Python (2001). URL https://mail.python.org/ pipermail/python-list/2001-August/106419.html

    work page 2001

  31. [31]

    Vaught, T. N. Reference documentation and tutorial documentation are now available for download as tar- balls (2002). URL https://web.archive.org/web/20021013204556/http://www.scipy.org:80/ scipy/site_content/site_news/docs_released1

    work page arXiv 2002

  32. [32]

    Vaught, T. N. [ANN] SciPy ’02 - Python for scientific computing workshop (2002). URLhttps://mail.python. org/pipermail/numpy-discussion/2002-June/001511.html

    work page 2002

  33. [33]

    F., Hinsen, K., Hugunin, J

    Ascher, D., Dubois, P. F., Hinsen, K., Hugunin, J. & Oliphant, T. E. An open source project: Numerical Python (2001). URL http://www.numpy.org/_downloads/numeric-manual.pdf

    work page 2001

  34. [34]

    How Python slithered into astronomy (2011)

    Greenfield, P. How Python slithered into astronomy (2011). URL https://conference.scipy.org/ scipy2011/slides/greenfield_keynote_astronomy.pdf

    work page 2011

  35. [35]

    & Lorensen, B

    Schroeder, W., Martin, K. & Lorensen, B. The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics (Kitware, 2004), 3rd edn. URL http://cds.cern.ch/record/798217/files/1930934122_TOC.pdf

    work page arXiv 2004

  36. [36]

    MayaVi uses Python for scientific data visualization (2003)

    Ramachandran, P. MayaVi uses Python for scientific data visualization (2003). URL https://web.archive.org/ web/20030731122452/http://pythonology.org/success&story=mayavi

    work page arXiv 2003

  37. [37]

    Jupyter Notebooks – a publishing format for reproducible computational workflows

    Kluyver, T.et al. Jupyter Notebooks – a publishing format for reproducible computational workflows. In Loizides, F. & Schmidt, B. (eds.) Positioning and Power in Academic Publishing: Players, Agents and Agendas, 87 – 90 (IOS Press, 2016). URL http://doi.org/10.3233/978-1-61499-649-1-87

    work page doi:10.3233/978-1-61499-649-1-87 2016

  38. [38]

    URL https://www.mathworks.com/products/ matlab.html

    MATLAB (The MathWorks, Inc., Natick, MA, United States). URL https://www.mathworks.com/products/ matlab.html

    work page

  39. [39]

    ANN: matplotlib 0.1 (2003)

    Hunter, J. ANN: matplotlib 0.1 (2003). URL https://mail.python.org/pipermail/python-list/ 2003-April/193167.html

    work page 2003

  40. [40]

    T., Hsu, J

    Greenfield, P., Miller, J. T., Hsu, J. & White, R. L. numarray: A new scientific array package for Python.PyCon DC (2003). URL http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.112.9899&rep= rep1&type=pdf

    work page 2003

  41. [41]

    v1.0 (2006)

    NumPy Developers. v1.0 (2006). URL https://github.com/numpy/numpy/releases/tag/v1.0

    work page 2006

  42. [42]

    Dubois, P. F. Python: Batteries included. Comput. Sci. & Eng. 9, 7–9 (2007). URL https://doi.org/10.1109/ MCSE.2007.51. 15/22

    work page 2007

  43. [43]

    Oliphant, T. E. Python for scientific computing. Comput. Sci. & Eng. 9, 10–20 (2007). URL https://doi.org/10. 1109/MCSE.2007.58

    work page 2007

  44. [44]

    & Granger, B

    P´erez, F. & Granger, B. E. IPython: a system for interactive scientific computing. Comput. Sci. & Eng. 9 (2007). URL https://doi.org/10.1109/MCSE.2007.53

    work page doi:10.1109/mcse.2007.53 2007

  45. [45]

    Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. & Eng. 9, 90–95 (2007). URL https://doi. org/10.1109/MCSE.2007.55

    work page doi:10.1109/mcse.2007.55 2007

  46. [46]

    R., Gutenkunst, R

    Myers, C. R., Gutenkunst, R. N. & Sethna, J. P. Python unleashed on systems biology. Comput. Sci. & Eng. 9 (2007). URL https://doi.org/10.1109/MCSE.2007.60

    work page doi:10.1109/mcse.2007.60 2007

  47. [47]

    Reaching for the stars with Python

    Greenfield, P. Reaching for the stars with Python. Comput. Sci. & Eng. 9, 38–40 (2007). URL https://doi.org/ 10.1109/MCSE.2007.62

    work page doi:10.1109/mcse.2007.62 2007

  48. [48]

    Krauss, R. W. & Book, W. J. A Python module for modeling and control design of flexible robots. Comput. Sci. & Eng. 9, 41–45 (2007). URL https://doi.org/10.1109/MCSE.2007.44

    work page doi:10.1109/mcse.2007.44 2007

  49. [49]

    & Vandersteegen, P

    Bienstman, P., Vanholme, L., Bogaerts, W., Dumon, P. & Vandersteegen, P. Python in nanophotonics research.Comput. Sci. & Eng. 9, 46–47 (2007). URL https://doi.org/10.1109/MCSE.2007.59

    work page doi:10.1109/mcse.2007.59 2007

  50. [50]

    T., Staff, G

    Mardal, K.-A., Skavhaug, O., Lines, G. T., Staff, G. A. & Ødeg˚ard, ˚A. Using Python to solve partial differential equations. Comput. Sci. & Eng. 9, 48–51 (2007). URL https://doi.org/10.1109/MCSE.2007.64

    work page doi:10.1109/mcse.2007.64 2007

  51. [51]

    Millman, K. J. & Brett, M. Analysis of functional magnetic resonance imaging in Python. Comput. Sci. & Eng. 9, 52–55 (2007). URL https://doi.org/10.1109/MCSE.2007.46

    work page doi:10.1109/mcse.2007.46 2007

  52. [52]

    Python for internet GIS applications

    Shi, X. Python for internet GIS applications. Comput. Sci. & Eng. 9, 56–59 (2007). URL https://doi.org/10. 1109/MCSE.2007.57

    work page 2007

  53. [53]

    Computational physics education with Python

    B¨acker, A. Computational physics education with Python. Comput. Sci. & Eng. 9, 30–33 (2007). URL https: //doi.org/10.1109/MCSE.2007.48

    work page doi:10.1109/mcse.2007.48 2007

  54. [54]

    Myers, C. R. & Sethna, J. P. Python for education: Computational methods for nonlinear systems. Comput. Sci. & Eng. 9, 75–79 (2007). URL https://doi.org/10.1109/MCSE.2007.56

    work page doi:10.1109/mcse.2007.56 2007

  55. [55]

    Scikits (2019)

    SciPy Developers. Scikits (2019). URL https://www.scipy.org/scikits.html

    work page 2019

  56. [56]

    Millman, K. J. & P ´erez, F. Developing open-source scientific practice. Implement. Reproducible Res. CRC Press. Boca Raton, FL 149–183 (2014). URL http://www.jarrodmillman.com/publications/ millman2014developing.pdf

    work page 2014

  57. [57]

    & the Sphinx Team

    Brandl, G. & the Sphinx Team. Sphinx - Python documentation generator (2007). URL http://www.sphinx-doc. org/en/master/

    work page 2007

  58. [58]

    Virtanen, P. et al. pydocweb: A tool for collaboratively documenting Python modules via the web (2008). URL https://code.google.com/archive/p/pydocweb/

    work page 2008

  59. [59]

    The SciPy documentation project (technical overview)

    van der Walt, S. The SciPy documentation project (technical overview). In Varoquaux, G., Vaught, T. & Millman, K. J. (eds.) Proceedings of the 7th Python in Science Conference (SciPy 2008), 27–28 (2008). URL http://conference. scipy.org/proceedings/scipy2008/paper_5/

    work page 2008

  60. [60]

    The SciPy documentation project

    Harrington, J. The SciPy documentation project. In Varoquaux, G., Vaught, T. & Millman, K. J. (eds.) Proceedings of the 7th Python in Science Conference (SciPy 2008), 33–35 (2008). URL http://conference.scipy.org/ proceedings/scipy2008/paper_7/

    work page 2008

  61. [61]

    & Goldsmith, D

    Harrington, J. & Goldsmith, D. Progress report: NumPy and SciPy documentation in 2009. In Varoquaux, G., van der Walt, S. & Millman, K. J. (eds.) Proceedings of the 8th Python in Science Conference (SciPy 2009), 84–87 (2009). URL http://conference.scipy.org/proceedings/scipy2009/paper_14/

    work page 2009

  62. [62]

    P´erez, F., Langtangen, H. P. & LeVeque, R. CSE 2009: Python for scientific computing at CSE 2009. SIAM News (2009). URL https://archive.siam.org/news/news.php?id=1595

    work page 2009

  63. [63]

    Millman, K. J. & Aivazis, M. Python for scientists and engineers. Comput. Sci. & Eng. 13, 9–12 (2011). URL https://doi.org/10.1109/MCSE.2011.36

    work page doi:10.1109/mcse.2011.36 2011

  64. [64]

    P´erez, F., Granger, B. E. & Hunter, J. D. Python: an ecosystem for scientific computing. Comput. Sci. & Eng. 13, 13–21 (2011). URL https://doi.org/10.1109/MCSE.2010.119

    work page doi:10.1109/mcse.2010.119 2011

  65. [65]

    Behnel, S. et al. Cython: The best of both worlds. Comput. Sci. & Eng. 13, 31–39 (2011). URL https://doi.org/ 10.1109/MCSE.2010.118. 16/22

    work page doi:10.1109/mcse.2010.118 2011

  66. [66]

    & Varoquaux, G

    Ramachandran, P. & Varoquaux, G. Mayavi: 3D visualization of scientific data.Comput. Sci. & Eng. 13, 40–51 (2011). URL https://doi.org/10.1109/MCSE.2011.35

    work page doi:10.1109/mcse.2011.35 2011

  67. [67]

    Muller, E. et al. Research topic: Python in neuroscience (2008). URL http://doi.org/10.3389/fninf.2015. 00011

    work page doi:10.3389/fninf.2015 2008

  68. [68]

    NumFocus: Open code = better science (2019)

    NumFocus Team. NumFocus: Open code = better science (2019). URL https://numfocus.org

    work page 2019

  69. [69]

    Network dependents - scipy/scipy (2019)

    GitHub. Network dependents - scipy/scipy (2019). URL https://github.com/scipy/scipy/network/ dependents

    work page 2019

  70. [70]

    F., Howe, S

    Boisvert, R. F., Howe, S. E. & Kahaner, D. K. The guide to available mathematical software problem classification system. Commun. Stat. Comput. 20, 811–842 (1991). URL https://doi.org/10.1080/03610919108812986

    work page doi:10.1080/03610919108812986 1991

  71. [71]

    & Perktold, J

    Seabold, S. & Perktold, J. Statsmodels: Econometric and statistical modeling with Python. In Proceedings of the 9th Python in Science Conference (SciPy 2010) , 57–61 (2010). URL http://conference.scipy.org/ proceedings/scipy2010/pdfs/seabold.pdf

    work page 2010

  72. [72]

    Salvatier, J., Wiecki, T. V . & Fonnesbeck, C. Probabilistic programming in Python using PyMC3.PeerJ Comput. Sci. 2, e55 (2016). URL https://doi.org/10.7717/peerj-cs.55

    work page doi:10.7717/peerj-cs.55 2016

  73. [73]

    emcee: The MCMC Hammer

    Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. emcee: The MCMC Hammer. Publ. Astron. Soc. Pac. 125, 306 (2013). URL https://doi.org/10.1086/670067. 1202.3665

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.1086/670067 2013

  74. [74]

    PyStan: the Python interface to Stan (2018)

    Stan Development Team. PyStan: the Python interface to Stan (2018). URL http://mc-stan.org

    work page 2018

  75. [75]

    Meurer, A. et al. SymPy: symbolic computing in Python. PeerJ Comput. Sci. 3, e103 (2017). URL https://doi. org/10.7717/peerj-cs.103

    work page doi:10.7717/peerj-cs.103 2017

  76. [76]

    & Swart, P

    Hagberg, A., Schult, D. & Swart, P. Exploring network structure, dynamics, and function using NetworkX. In Varoquaux, G., Vaught, T. & Millman, K. J. (eds.)Proceedings of the 7th Python in Science Conference (SciPy 2008), 11–15 (2008). URL http://conference.scipy.org/proceedings/scipy2008/paper_2/

    work page 2008

  77. [77]

    J., Newell, D

    Mohr, P. J., Newell, D. B. & Taylor, B. N. CODATA recommended values of the fundamental physical constants: 2014.J. Phys. Chem. Ref. Data 45, 043102 (2016). URL https://doi.org/10.1063/1.4954402

    work page doi:10.1063/1.4954402 2014

  78. [78]

    Curve and Surface Fitting with Splines (Oxford University Press, Inc., New York, NY , USA, 1993)

    Dierckx, P. Curve and Surface Fitting with Splines (Oxford University Press, Inc., New York, NY , USA, 1993)

    work page 1993

  79. [79]

    F., Pozo, R., Remington, K., Barrett, R

    Boisvert, R. F., Pozo, R., Remington, K., Barrett, R. F. & Dongarra, J. J. Matrix Market: a web resource for test matrix collections. In Quality of Numerical Software , 125–137 (Springer, 1997). URL https://doi.org/10.1007/ 978-1-5041-2940-4_9

    work page 1997

  80. [80]

    & Davis, G

    Rew, R. & Davis, G. NetCDF: an interface for scientific data access.IEEE Comput. Graph. Appl. 10, 76–82 (1990). URL https://doi.org/10.1109/38.56302

    work page doi:10.1109/38.56302 1990

Showing first 80 references.