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 41 articles )

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  1. Beltramo, Gabriele; Skraba, Primoz: Persistent homology in (\ell_\infty) metric (2022)
  2. Espinoza, Jesús F.; Frías-Armenta, Martín-Eduardo; Hernández-Hernández, Héctor A.: Collapsibility and homological properties of (\mathfrakI)-contractible transformations (2022)
  3. Lesnick, Michael; Wright, Matthew: Computing minimal presentations and bigraded Betti numbers of 2-parameter persistent homology (2022)
  4. Zhao, Yan; Wang, Yanying; Ding, Yanhong; Han, Huiyun: Topological data analysis for the energy and stability of endohedral metallofullerenes (2022)
  5. Bauer, Ulrich: Ripser: efficient computation of Vietoris-Rips persistence barcodes (2021)
  6. Curto, Carina; Paik, Joshua; Rivin, Igor: Betti curves of rank one symmetric matrices (2021)
  7. Luo, Hengrui; Patania, Alice; Kim, Jisu; Vejdemo-Johansson, Mikael: Generalized penalty for circular coordinate representation (2021)
  8. Tauzin, Guillaume; Lupo, Umberto; Tunstall, Lewis; Pérez, Julian Burella; Caorsi, Matteo; Medina-Mardones, Anibal M.; Dassatti, Alberto; Hess, Kathryn: \textitgiotto-tda: a topological data analysis toolkit for machine learning and data exploration (2021)
  9. Wang, Rui; Zhao, Rundong; Ribando-Gros, Emily; Chen, Jiahui; Tong, Yiying; Wei, Guo-Wei: HERMES: persistent spectral graph software (2021)
  10. 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)
  11. Bendich, Paul; Bubenik, Peter; Wagner, Alexander: Stabilizing the unstable output of persistent homology computations (2020)
  12. Breiding, Paul; Marigliano, Orlando: Random points on an algebraic manifold (2020)
  13. Cang, Zixuan; Munch, Elizabeth; Wei, Guo-Wei: Evolutionary homology on coupled dynamical systems with applications to protein flexibility analysis (2020)
  14. Cang, Zixuan; Wei, Guo-Wei: Persistent cohomology for data with multicomponent heterogeneous information (2020)
  15. Chachólski, Wojciech; Riihimäki, Henri: Metrics and stabilization in one parameter persistence (2020)
  16. Charó, Gisela D.; Artana, Guillermo; Sciamarella, Denisse: Topology of dynamical reconstructions from Lagrangian data (2020)
  17. Chowdhury, Samir; Clause, Nathaniel; Mémoli, Facundo; Sánchez, Jose Ángel; Wellner, Zoe: New families of stable simplicial filtration functors (2020)
  18. Goldfarb, Boris: Singular persistent homology with geometrically parallelizable computation (2020)
  19. Gonzalez, Georgina; Ushakova, Arina; Sazdanovic, Radmila; Arsuaga, Javier: Prediction in cancer genomics using topological signatures and machine learning (2020)
  20. Hess, Kathryn: Topological adventures in neuroscience (2020)

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