Ripser.py

Ripser.py is a lean persistent homology package for Python. Building on the blazing fast C++ Ripser package as the core computational engine, Ripser.py provides an intuitive interface for: computing persistence cohomology of sparse and dense data sets, visualizing persistence diagrams, computing lowerstar filtrations on images, and computing representative cochains. We supply a large set of interactive notebooks that demonstrate how to take advantage of all the features available. Ripser.py is an evolution of the original C++ Ripser project. We have put extensive work into making the package available to Python developers across all major platforms. If you are having trouble installing, please let us know by opening a github issue.


References in zbMATH (referenced in 9 articles , 1 standard article )

Showing results 1 to 9 of 9.
Sorted by year (citations)

  1. Beshkov, Kosio; Tiesinga, Paul: Geodesic-based distance reveals nonlinear topological features in neural activity from mouse visual cortex (2022)
  2. Chung, Yu-Min; Lawson, Austin: Persistence curves: a canonical framework for summarizing persistence diagrams (2022)
  3. Schweidtmann, Artur M.; Weber, Jana M.; Wende, Christian; Netze, Linus; Mitsos, Alexander: Obey validity limits of data-driven models through topological data analysis and one-class classification (2022)
  4. 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)
  5. Tymochko, Sarah; Singhal, Kritika; Heo, Giseon: Classifying sleep states using persistent homology and Markov chains: a pilot study (2021)
  6. Čufar, Matij: Ripserer.jl: flexible and efficient persistent homology computation in Julia (2020) not zbMATH
  7. Hart, Allen; Hook, James; Dawes, Jonathan: Embedding and approximation theorems for echo state networks (2020)
  8. Xu, Boyan; Tralie, Christopher J.; Antia, Alice; Lin, Michael; Perea, Jose A.: Twisty Takens: a geometric characterization of good observations on dense trajectories (2019)
  9. Christopher Tralie, Nathaniel Saul, Rann Bar-On: Ripser.py: A Lean Persistent Homology Library for Python (2018) not zbMATH