Ripser.py: A Lean Persistent Homology Library for Python

Topological data analysis (TDA) (Edelsbrunner & Harer, 2010), (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.

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