SymPy: Symbolic computing in Python

Aaron Meurer 1 Christopher P Smith 2 Mateusz Paprocki 3 Ondřej Čertík 4 Sergey B Kirpichev 5 Matthew Rocklin 3 Amit Kumar 6 Sergiu Ivanov 7 Jason K Moore 8 Sartaj Singh 9 Thilina Rathnayake 10 Sean Vig 11 Brian E Granger 12 Richard P Muller 13 Francesco Bonazzi 14 Harsh Gupta 15 Shivam Vats 15 Fredrik Johansson 16 Fabian Pedregosa 17 Matthew J Curry 18, 19, 13 Andy R Terrel 20, 21 Štěpán Roučka 22 Ashutosh Saboo 23 Isuru Fernando 10 Sumith Kulal 24 Robert Cimrman 25 Anthony Scopatz 1
1 Department of Mechanical Engineering [Columbia]
2 Polar Semiconductor, LLC [Bloomington]
3 Continuum Analytics [Austin]
4 LANL - Los Alamos National Laboratory
5 Faculty of Physics [MSU, Moscow]
6 Department of Applied Mathematics [New Delhi]
7 LACL - Laboratoire d'Algorithmique Complexité et Logique
8 Department of Mechanical and Aerospace Engineering [Davis]
9 Department of Mathematical Sciences [Varanasi]
10 Department of Computer Science and Engineering [Moratuwa]
11 University of Illinois at Urbana-Champaign [Urbana]
12 CAL POLY - California Polytechnic State University [San Luis Obispo]
13 SNL - Sandia National Laboratories [Albuquerque]
14 Department of Theory and Bio-Systems [Potsdam]
15 IIT Kharagpur - Indian Institute of Technology Kharagpur
16 LFANT - Lithe and fast algorithmic number theory
IMB - Institut de Mathématiques de Bordeaux, Inria Bordeaux - Sud-Ouest
17 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
18 Department of Physics and Astronomy [Albuquerque]
19 CQuIC - Center for Quantum Information and Control [Albuquerque]
20 Fashion Metric [Austin]
21 NUMFOCUS [Austin]
22 Department of Surface and Plasma Science [Prague]
23 BITS Pilani - Birla Institute of Technology and Science
24 IIT Bombay - Indian Institute of Technology Bombay
25 NTC - New Technologies Research Centre [Plzeň]
Abstract : SymPy is an open source computer algebra system written in pure Python. It is built with a focus on extensibility and ease of use, through both interactive and programmatic applications. These characteristics have led SymPy to become the standard symbolic library for the scientific Python ecosystem. This paper presents the architecture of SymPy, a description of its features, and a discussion of select domain specific submodules. The supplementary materials provide additional examples and further outline details of the architecture and features of SymPy.
Type de document :
Article dans une revue
PeerJ Computer Science, PeerJ, 2017, pp.e103. 〈10.7287/peerj.preprints.2083v3〉
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https://hal.inria.fr/hal-01404156
Contributeur : Fabian Pedregosa <>
Soumis le : lundi 28 novembre 2016 - 14:15:20
Dernière modification le : jeudi 7 février 2019 - 16:38:48

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Aaron Meurer, Christopher P Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B Kirpichev, et al.. SymPy: Symbolic computing in Python. PeerJ Computer Science, PeerJ, 2017, pp.e103. 〈10.7287/peerj.preprints.2083v3〉. 〈hal-01404156〉

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