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
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, ENS Paris - École normale supérieure - Paris, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
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 :
Pré-publication, Document de travail
2016
Liste complète des métadonnées

https://hal.inria.fr/hal-01404156
Contributeur : Fabian Pedregosa <>
Soumis le : lundi 28 novembre 2016 - 14:15:20
Dernière modification le : samedi 10 décembre 2016 - 01:05:11

Identifiants

Collections

Citation

Aaron Meurer​, Christopher P Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B Kirpichev, et al.. SymPy: Symbolic computing in Python. 2016. <hal-01404156>

Partager

Métriques

Consultations de la notice

349