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SymPy: Symbolic computing in Python

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1 Department of Mechanical Engineering, University of South Carolina, Columbia, South Carolina, United States
2 Other, Polar Semiconductor, Inc., Bloomington, Minnesota, United States
3 Continuum Analytics, Inc., Austin, Texas, United States
4 Los Alamos National Laboratory, Los Alamos, New Mexico, United States
5 Department of Applied Mathematics, Delhi Technological University, New Delhi, India
6 Université Paris Est Créteil, Créteil, France
7 Mechanical and Aerospace Engineering, University of California, Davis, Davis, California, United States
8 Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India
9 Department of Computer Science and Engineering, University of Moratuwa, Katubedda, Moratuwa, Sri Lanka
10 University of Illinois at Urbana-Champaign, Urbana, Illinois, United States
11 California Polytechnic State University, San Luis Obispo, California, United States
12 Center for Computing Research, Sandia National Laboratories, Albuquerque, New Mexico, United States
13 Department of Theory and Bio-Systems, Max Planck Institute of Colloids and Interfaces, Potsdam, Germany
14 Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India
15 INRIA Bordeaux-Sud-Ouest -- LFANT project-team, Talence, France
16 INRIA -- SIERRA project-team, Paris, France
17 Department of Physics and Astronomy, University of New Mexico, Albuquerque, New Mexico, United States
18 Center for Quantum Information and Control, University of New Mexico, Albuquerque, New Mexico, United States
19 Sandia National Laboratories, Albuquerque, New Mexico, United States
20 Birla Institute of Technology and Science, Pilani, K.K. Birla Goa Campus, Sancoale, Goa, India
21 Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
22 New Technologies -- Research Centre, University of West Bohemia, Plzeň, Czech Republic
DOI
10.7287/peerj.preprints.2083v2
Published
Accepted
Subject Areas
Scientific Computing and Simulation
Keywords
symbolic, python, computer algebra system
Copyright
© 2016 Meurer et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Meurer A, Smith CP, Paprocki M, Čertík O, Rocklin M, Kumar A, Ivanov S, Moore JK, Singh S, Rathnayake T, Vig S, Granger BE, Muller RP, Bonazzi F, Gupta H, Vats S, Johansson F, Pedregosa F, Curry MJ, Saboo A, Fernando I, Kulal S, Cimrman R, Scopatz A. 2016. SymPy: Symbolic computing in Python. PeerJ Preprints 4:e2083v2

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.

Author Comment

This paper has been submitted to SICOMP. The sources for this paper can be found at https://github.com/sympy/sympy-paper. Version 2 fixes the name of the author Sumith Kulal.

Supplemental Information

Quantum Examples Jupyter Notebook

DOI: 10.7287/peerj.preprints.2083v2/supp-2

Additional Information

Competing Interests

The authors declare that they have no competing interests.

Author Contributions

Aaron Meurer wrote the paper, performed the computation work, reviewed drafts of the paper.

Christopher P Smith wrote the paper, performed the computation work, reviewed drafts of the paper.

Mateusz Paprocki wrote the paper, performed the computation work, reviewed drafts of the paper.

Ondřej Čertík wrote the paper, performed the computation work, reviewed drafts of the paper.

Matthew Rocklin wrote the paper, performed the computation work, reviewed drafts of the paper.

AMiT Kumar wrote the paper, performed the computation work, reviewed drafts of the paper.

Sergiu Ivanov wrote the paper, performed the computation work, reviewed drafts of the paper.

Jason K Moore wrote the paper, performed the computation work, reviewed drafts of the paper.

Sartaj Singh wrote the paper, performed the computation work, reviewed drafts of the paper.

Thilina Rathnayake wrote the paper, performed the computation work, reviewed drafts of the paper.

Sean Vig wrote the paper, performed the computation work, reviewed drafts of the paper.

Brian E Granger wrote the paper, performed the computation work, reviewed drafts of the paper.

Richard P Muller wrote the paper, performed the computation work, reviewed drafts of the paper.

Francesco Bonazzi wrote the paper, performed the computation work, reviewed drafts of the paper.

Harsh Gupta wrote the paper, performed the computation work, reviewed drafts of the paper.

Shivam Vats wrote the paper, performed the computation work, reviewed drafts of the paper.

Fredrik Johansson wrote the paper, performed the computation work, reviewed drafts of the paper.

Fabian Pedregosa wrote the paper, performed the computation work, reviewed drafts of the paper.

Matthew J Curry wrote the paper, performed the computation work, reviewed drafts of the paper.

Ashutosh Saboo wrote the paper, performed the computation work, reviewed drafts of the paper.

Isuru Fernando wrote the paper, performed the computation work, reviewed drafts of the paper.

Sumith Kulal wrote the paper, performed the computation work, reviewed drafts of the paper.

Robert Cimrman wrote the paper, performed the computation work, reviewed drafts of the paper.

Anthony Scopatz wrote the paper, performed the computation work, reviewed drafts of the paper.

Data Deposition

The following information was supplied regarding data availability:

The source for the paper is at https://github.com/sympy/sympy-paper. The source code for SymPy is at https://github.com/sympy/sympy.

Funding

The authors received no funding for this work.

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