SciPy 1.0 : fundamental algorithms for scientific computing in Python

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Abstract

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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Autori: Pauli Virtanen iD (University of Jyväskylä); Ralf Gommers iD (Quansight (United States)); Travis E. Oliphant (Brigham Young University; Mayo Clinic; Quansight (United States); Anaconda (United States); Enthought (United States)); Matt Haberland iD (California Polytechnic State University; University of California, Los Angeles); Tyler Reddy iD (Los Alamos National Laboratory); David Cournapeau; Evgeni Burovski iD (National Research University Higher School of Economics); Pearu Peterson (Tallinn University of Technology); Warren Weckesser (University of California, Berkeley); Jonathan Bright; Stéfan J. van der Walt iD (University of California, Berkeley); Matthew Brett (University of Birmingham); Joshua Wilson; K. Jarrod Millman iD (University of California, Berkeley); Nikolay Mayorov (WayRay (Russia)); Andrew R. J. Nelson iD (Australian Nuclear Science and Technology Organisation); Eric Jones (Enthought (United States)); Robert Kern (Enthought (United States)); Eric Larson (University of Washington); C J Carey (University of Massachusetts Amherst); İlhan Polat iD; Yu Feng (University of California, Berkeley); Eric W. Moore (Bruker (United States)); Jake VanderPlas (University of Washington); Denis Laxalde iD; Josef Perktold; Robert Cimrman (University of West Bohemia in Pilsen); Ian Henriksen iD (Brigham Young University; Anaconda (United States); The University of Texas at Austin); E. A. Quintero iD; Charles R. Harris (Utah State University Space Dynamics Laboratory) e altri 82
Pubblicato in: Nature Methods, vol. 17, n. 3, pp. 261-272
Data di pubblicazione:03/02/2020
Editore:Nature Portfolio
Tipo di documento:Articolo
Lingua:inglese
DOI: 10.1038/s41592-019-0686-2
ISSN:1548-7091, 1548-7105
Accesso aperto: Accesso aperto Ibrido: articolo aperto in una rivista in abbonamento – Nature Methods (licenza: CC-BY, versione editoriale)
Citazioni ricevute:38.196
Soggetti:
Computational Physics and Python Applications (Physical Sciences > Computer Science > Artificial Intelligence)
Scientific Computing and Data Management (Social Sciences > Decision Sciences > Information Systems and Management)
Modeling and Simulation Systems (Physical Sciences > Computer Science > Computational Theory and Mathematics)
SciPy, scientific computing, Python, open source software, numerical algorithms, software development practices
Identificativi: OpenAlex W3003257820 · PMID 32015543

Dati dell'indice centrale: OpenAlex (licenza CC0).

Riassunto:SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
ISSN:1548-7091
1548-7105
DOI:10.1038/s41592-019-0686-2