Produção Nacional
Revisado por pares
SciPy 1.0: fundamental algorithms for scientific computing in Python
2020; Nature Portfolio; Volume: 17; Issue: 3 Linguagem: Inglês
10.1038/s41592-019-0686-2
ISSN1548-7105
AutoresPauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew Nelson, Eric D. Jones, Robert Kern, Eric R. Larson, CJ Carey, İlhan Polat, Yu Feng, Eric Moore, Jake Vanderplas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, C. R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, A. Vijaykumar, Alessandro Pietro Bardelli, Alex Rothberg, Andreas Hilboll, Andreas Kloeckner, Anthony Scopatz, Antony Lee, Ariel Rokem, Charles Woods, Chad Fulton, Charles Masson, Christian Häggström, C Fitzgerald, David Nicholson, David Hagen, Dmitrii V. Ṗasechnik, Emanuele Olivetti, Éric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, George S. Young, Gavin A Price, Gert‐Ludwig Ingold, Gregory E. Allen, Gregory R. Lee, Hervé Audren, Irvin Probst, J. P. Dietrich, Jacob Silterra, James T. Webber, Janko Slavič, Joel Nothman, Johannes Büchner, Johannes Kulick, Johannes L. Schönberger, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano, Juan Nunez-Iglesias, Justin Kuczynski, K. Tritz, Martin Thoma, M. Newville, Matthias Kümmerer, Maximilian Bolingbroke, Michael Tartre, M. Pak, Nathaniel J. Smith, Nikolai Nowaczyk, Nikolay Shebanov, Oleksandr Pavlyk, Per A. Brodtkorb, Perry Lee, Robert T. McGibbon, Roman Feldbauer, Sam Lewis, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik, 拓也 大嶋, Thomas J. Pingel, Thomas Robitaille, Thomas Spura, Thouis R. Jones, Tim Cera, Tim Leslie, Tiziano Zito, Tom Krauss, Utkarsh Upadhyay, Yaroslav O. Halchenko, Yoshiki Vázquez‐Baeza,
Tópico(s)Particle physics theoretical and experimental studies
ResumoAbstract 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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