SymPy: symbolic computing in Python

Autor: Aaron Meurer, Christopher P. Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B. Kirpichev, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K. Moore, Sartaj Singh, Thilina Rathnayake, Sean Vig, Brian E. Granger, Richard P. Muller, Francesco Bonazzi, Harsh Gupta, Shivam Vats, Fredrik Johansson, Fabian Pedregosa, Matthew J. Curry, Andy R. Terrel, Štěpán Roučka, Ashutosh Saboo, Isuru Fernando, Sumith Kulal, Robert Cimrman, Anthony Scopatz

Publikováno v: PeerJ Computer Science, Vol 3, p e103 (2017)

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 a popular symbol

Externí odkaz: https://doaj.org/article/8edd19d7644a4c829de99840adfe1f52

SymPy: symbolic computing in Python

Autor: Matthew Rocklin, Sumith Kulal, Richard P. Muller, Brian E. Granger, Amit Kumar, Andy R. Terrel, Ashutosh Saboo, Aaron Meurer, Thilina Rathnayake, Jason K. Moore, Mateusz Paprocki, Štěpán Roučka, Isuru Dilanka Fernando, Shivam Vats, Ondřej Čertík, Robert Cimrman, Anthony Scopatz, Sartaj Singh, Christopher Smith, Sergey B Kirpichev, Sergiu Ivanov, Matthew Curry, Fredrik Johansson, Francesco Bonazzi, Fabian Pedregosa, Harsh Gupta, Sean Vig

Publikováno v: PeerJ Computer Science, Vol 3, p e103 (2017)
PeerJ Computer Science
PeerJ Computer Science, 2017, 3, pp.e103. ⟨10.7287/peerj.preprints.2083v3⟩
PeerJ Comput.Sci.
PeerJ Comput.Sci., 2017, 3, pp.e103. ⟨10.7717/peerj-cs.103⟩
PeerJ Computer Science, PeerJ, 2017, pp.e103. ⟨10.7287/peerj.preprints.2083v3⟩

Článek pojednává o architektuře SymPy, popisuje vlastnosti tohoto softwaru a vybrané pod-moduly. 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 int

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dae17756f2a651547bbc0856f20d83cd
https://peerj.com/articles/cs-103/

A Computational Framework for Uncertainty Quantification and Stochastic Optimization in Unit Commitment With Wind Power Generation

Autor: Sangmin Lee, Victor M. Zavala, Emil M. Constantinescu, Mihai Anitescu, Matthew Rocklin

Publikováno v: IEEE Transactions on Power Systems. 26:431-441

We present a computational framework for integrating a state-of-the-art numerical weather prediction (NWP) model in stochastic unit commitment/economic dispatch formulations that account for wind power uncertainty. We first enhance the NWP model with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::99cb3e5c70e1141bf506692f8fb34ab0
https://doi.org/10.1109/tpwrs.2010.2048133

Uncertainty Modeling with SymPy Stats

Autor: Matthew Rocklin

Publikováno v: Proceedings of the Python in Science Conference.

We add a random variable type to a mathematical modeling language. We demonstrate through examples how this is a highly separable way to introduce uncertainty and produce and query stochastic models. We motivate the use of symbolics and thin compiler

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4626534ced2dfc310604181125f09eda
https://doi.org/10.25080/majora-54c7f2c8-009

Latent Clustering on Graphs with Multiple Edge Types

Autor: Ali Pinar, Matthew Rocklin

Publikováno v: Lecture Notes in Computer Science ISBN: 9783642212857
WAW

We study clustering on graphs with multiple edge types. Our main motivation is that similarities between objects can be measured in many different metrics, and so allowing graphs with multivariate edges significantly increases modeling power. In this

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d71522a0a0484dd13bdca1115131bd3b
https://doi.org/10.1007/978-3-642-21286-4_4