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, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat & 14 others Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E.A. Quintero, Charles R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, SciPy 1.0 Contributors

Research output: Other contributionResearch

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.
Original languageEnglish
PublisherarXiv.org
Number of pages22
Volume1907.10121
Publication statusPublished - 23 Jul 2019

Cite this

Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., ... SciPy 1.0 Contributors (2019, Jul 23). SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python. arXiv.org.
Virtanen, Pauli ; Gommers, Ralf ; Oliphant, Travis E. ; Haberland, Matt ; Reddy, Tyler ; Cournapeau, David ; Burovski, Evgeni ; Peterson, Pearu ; Weckesser, Warren ; Bright, Jonathan ; van der Walt, Stéfan J. ; Brett, Matthew ; Millman, K. Jarrod ; Mayorov, Nikolay ; Nelson, Andrew R. J. ; Jones, Eric ; Kern, Robert ; Larson, Eric ; Carey, CJ ; Polat, İlhan ; Feng, Yu ; Moore, Eric W. ; VanderPlas, Jake ; Laxalde, Denis ; Perktold, Josef ; Cimrman, Robert ; Henriksen, Ian ; Quintero, E.A. ; Harris, Charles R. ; Archibald, Anne M. ; Ribeiro, Antônio H. ; Pedregosa, Fabian ; van Mulbregt, Paul ; SciPy 1.0 Contributors. / SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python. 2019. arXiv.org. 22 p.
@misc{565da6642a1243de898b28bf7274ffab,
title = "SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python",
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.",
author = "Pauli Virtanen and Ralf Gommers and Oliphant, {Travis E.} and Matt Haberland and Tyler Reddy and David Cournapeau and Evgeni Burovski and Pearu Peterson and Warren Weckesser and Jonathan Bright and {van der Walt}, {St{\'e}fan J.} and Matthew Brett and Millman, {K. Jarrod} and Nikolay Mayorov and Nelson, {Andrew R. J.} and Eric Jones and Robert Kern and Eric Larson and CJ Carey and İlhan Polat and Yu Feng and Moore, {Eric W.} and Jake VanderPlas and Denis Laxalde and Josef Perktold and Robert Cimrman and Ian Henriksen and E.A. Quintero and Harris, {Charles R.} and Archibald, {Anne M.} and Ribeiro, {Ant{\^o}nio H.} and Fabian Pedregosa and {van Mulbregt}, Paul and {SciPy 1.0 Contributors} and Juan Nunez-Iglesias",
year = "2019",
month = "7",
day = "23",
language = "English",
volume = "1907.10121",
publisher = "arXiv.org",
type = "Other",

}

Virtanen, P, Gommers, R, Oliphant, TE, Haberland, M, Reddy, T, Cournapeau, D, Burovski, E, Peterson, P, Weckesser, W, Bright, J, van der Walt, SJ, Brett, M, Millman, KJ, Mayorov, N, Nelson, ARJ, Jones, E, Kern, R, Larson, E, Carey, CJ, Polat, İ, Feng, Y, Moore, EW, VanderPlas, J, Laxalde, D, Perktold, J, Cimrman, R, Henriksen, I, Quintero, EA, Harris, CR, Archibald, AM, Ribeiro, AH, Pedregosa, F, van Mulbregt, P & SciPy 1.0 Contributors 2019, SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python. arXiv.org.

SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python. / Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt; Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; Brett, Matthew; Millman, K. Jarrod; Mayorov, Nikolay; Nelson, Andrew R. J.; Jones, Eric; Kern, Robert; Larson, Eric; Carey, CJ; Polat, İlhan; Feng, Yu; Moore, Eric W.; VanderPlas, Jake; Laxalde, Denis; Perktold, Josef; Cimrman, Robert; Henriksen, Ian; Quintero, E.A.; Harris, Charles R.; Archibald, Anne M.; Ribeiro, Antônio H.; Pedregosa, Fabian; van Mulbregt, Paul; SciPy 1.0 Contributors.

22 p. arXiv.org. 2019, .

Research output: Other contributionResearch

TY - GEN

T1 - SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python

AU - Virtanen, Pauli

AU - Gommers, Ralf

AU - Oliphant, Travis E.

AU - Haberland, Matt

AU - Reddy, Tyler

AU - Cournapeau, David

AU - Burovski, Evgeni

AU - Peterson, Pearu

AU - Weckesser, Warren

AU - Bright, Jonathan

AU - van der Walt, Stéfan J.

AU - Brett, Matthew

AU - Millman, K. Jarrod

AU - Mayorov, Nikolay

AU - Nelson, Andrew R. J.

AU - Jones, Eric

AU - Kern, Robert

AU - Larson, Eric

AU - Carey, CJ

AU - Polat, İlhan

AU - Feng, Yu

AU - Moore, Eric W.

AU - VanderPlas, Jake

AU - Laxalde, Denis

AU - Perktold, Josef

AU - Cimrman, Robert

AU - Henriksen, Ian

AU - Quintero, E.A.

AU - Harris, Charles R.

AU - Archibald, Anne M.

AU - Ribeiro, Antônio H.

AU - Pedregosa, Fabian

AU - van Mulbregt, Paul

AU - SciPy 1.0 Contributors

AU - Nunez-Iglesias, Juan

PY - 2019/7/23

Y1 - 2019/7/23

N2 - 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.

AB - 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.

M3 - Other contribution

VL - 1907.10121

PB - arXiv.org

ER -

Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D et al. SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python. 2019. 22 p.