Ripser is a lean C++ code for the computation of Vietoris–Rips persistence barcodes. It can do just this one thing, but does it extremely well. To see a live demo of Ripser’s capabilities, go to The computation happens inside the browser (using PNaCl on Chrome and JavaScript via Emscripten on other browsers). The main features of Ripser: time- and memory-efficient; less than 1000 lines of code in a single C++ file; support for coefficients in prime finite fields; no external dependencies (optional support for Google’s sparsehash).

References in zbMATH (referenced in 28 articles )

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  1. Adams, Henry; Aminian, Manuchehr; Farnell, Elin; Kirby, Michael; Mirth, Joshua; Neville, Rachel; Peterson, Chris; Shonkwiler, Clayton: A fractal dimension for measures via persistent homology (2020)
  2. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  3. Cang, Zixuan; Munch, Elizabeth; Wei, Guo-Wei: Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis (2020)
  4. Chachólski, Wojciech; Riihimäki, Henri: Metrics and stabilization in one parameter persistence (2020)
  5. Chowdhury, Samir; Clause, Nathaniel; Mémoli, Facundo; Sánchez, Jose Ángel; Wellner, Zoe: New families of stable simplicial filtration functors (2020)
  6. Goldfarb, Boris: Singular persistent homology with geometrically parallelizable computation (2020)
  7. Gonzalez, Georgina; Ushakova, Arina; Sazdanovic, Radmila; Arsuaga, Javier: Prediction in cancer genomics using topological signatures and machine learning (2020)
  8. Hess, Kathryn: Topological adventures in neuroscience (2020)
  9. Perea, Jose A.: Sparse circular coordinates via principal (\mathbbZ)-bundles (2020)
  10. Ruehle, Fabian: Data science applications to string theory (2020)
  11. Shen, Chen; Patrangenaru, Vic: Topological object data analysis methods with an application to medical imaging (2020)
  12. Shizuo Kaji, Takeki Sudo, Kazushi Ahara: Cubical Ripser: Software for computing persistent homology of image and volume data (2020) arXiv
  13. Som, Anirudh; Ramamurthy, Karthikeyan Natesan; Turaga, Pavan: Geometric metrics for topological representations (2020)
  14. Turner, Katharine; Spreemann, Gard: Same but different: distance correlations between topological summaries (2020)
  15. Wang, Yuan; Wang, Bei: Topological inference of manifolds with boundary (2020)
  16. Yalnız, Gökhan; Budanur, Nazmi Burak: Inferring symbolic dynamics of chaotic flows from persistence (2020)
  17. Daniel Luetgehetmann, Dejan Govc, Jason Smith, Ran Levi: Computing persistent homology of directed flag complexes (2019) arXiv
  18. Maroulas, Vasileios; Mike, Joshua L.; Oballe, Christopher: Nonparametric estimation of probability density functions of random persistence diagrams (2019)
  19. Mémoli, Facundo; Singhal, Kritika: A primer on persistent homology of finite metric spaces (2019)
  20. Michael Lesnick, Matthew Wright: Computing Minimal Presentations and Betti Numbers of 2-Parameter Persistent Homology (2019) arXiv

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