Work

Ripser.py: A Lean Persistent Homology Library for Python

Year: 2018

Type: article

Abstract: Carlsson, 2009) is a field focused on understanding the shape and structure of data by computing topological descriptors that summarize features as connected components, loops, and voids.TDA has found wide applications across nonlinear time series analysis (Perea & Harer, 2015), computer vision (Perea & Carlsson, 2014), computational neuroscience (Giusti, Pastalkova, Curto, & Itskov, 2015), (Bendich, Marron, Miller, Pieloch, & Skwerer, 2016), computational biology (Iyer-Pascuzzi et al., 2010), (Wu et al., 2017), and materials science (Kramar, Goullet, Kondic, & Mischaikow, 2013), to name a few of the many areas of impact in recent years.

Source: The Journal of Open Source Software

DOI: 10.21105/joss.00925

Authors: Christopher Tralie, Nathaniel Saul, Rann Bar-On

Institutions: Duke University, Washington State University

Language: English

Topic: Topological and Geometric Data Analysis

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: National Science Foundation, National Aeronautics and Space Administration, Washington Space Grant Consortium, Division of Biological Infrastructure

Awards: G2611221299, Collaborative Research: ABI Innovation: A Scalable Framework for Visual Exploration and Hypotheses Extraction of Phenomics Data using Topological Analytics, BIGDATA: F: DKA: CSD: Topological Data Analysis and Machine-Learning with Community-Accepted Features, ESTABLISHED IN 1989, WASHINGTON NASA SPACE GRANT CONSORTIUM WAS ONE OF THE FIRST SPACE GRANT CONSORTIUMS IN THE COUNTRY AND IS FUNDED BY NASA AS A DE, G7738416871