Package: ripser
Ripser.pyRipser.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 forcomputing persistence cohomology of sparse and dense data sets,visualizing persistence diagrams,computing lowerstar filtrations on images, andcomputing representative cochains.Additionally, through extensive testing and continuous integration, Ripser.py is easy to install on Mac, Linux, and Windows platforms.To aid your use of the package, we've put together a large set of notebooks that demonstrate many of the features available. Complete documentation about the package can be found atripser.scikit-tda.org.Related ProjectsIf you're looking for the original C++ library, you can find it atRipser/ripser.If you're looking for a GPU-accelerated version of Ripser, you can find it atRipser++SetupRipser.py is available on all major platforms. All that is required is that you install the standard Python numerical computing libraries and Cython.Dependencies:Cythonnumpyscipyscikit-learnpersimWindows users:If you are using a Windows machine, you will also need to installMinGWon your system.Mac users:Updating your Xcode and Xcode command line tools will probably fix any issues you have with installation.Cython should be the only library required before installation. To install, type the following commands into your environment:pip install cython
pip install ripserIf you are having trouble installing, please let us know!Optional dependencyRipser.py when compiled from source can have asteroid1shot by replacing the standardunordered_mapfrom the STL by one of the fastest implementation available:robin_hood. Benchmarking of Ripser.py using therobin_hoodimplementation showed speed-ups up to30%.To be able to userobin_hoodinstead of STL, you only need to clone the repository containing the implementation:# Run this command at the root of the project
git clone https://github.com/martinus/robin-hood-hashing ripser/robinhood1The Python package is already compiled withrobin_hoodby default.UsageThe interface is as simple as can be:import numpy as np
from ripser import ripser
from persim import plot_diagrams

data = np.random.random((100,2))
diagrams = ripser(data)['dgms']
plot_diagrams(diagrams, show=True)We also supply a Scikit-learn transformer style object if you would prefer to use that:import numpy as np
from ripser import Rips

rips = Rips()
data = np.random.random((100,2))
diagrams = rips.fit_transform(data)
rips.plot(diagrams)ContributionsWe welcome all kinds of contributions! Please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don't feel you can't contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.If you found a bug, have questions, or are just having trouble with the library, please open an issue in ourissue trackerand we'll try to help resolve the concern.LicenseRipser.py is available under an MIT license! The core C++ code is derived from Ripser, which is also available under an MIT license and copyright to Ulrich Bauer. The modifications, Python code, and documentation is copyright to Christopher Tralie and Nathaniel Saul.CitingIf you use this package, please site the JoSS paper found hereand the JACT paper about Ripser found here.You can use the following bibtex entries:@article{ctralie2018ripser,
  doi = {10.21105/joss.00925},
  url = {https://doi.org/10.21105/joss.00925},
  year  = {2018},
  month = {Sep},
  publisher = {The Open Journal},
  volume = {3},
  number = {29},
  pages = {925},
  author = {Christopher Tralie and Nathaniel Saul and Rann Bar-On},
  title = {{Ripser.py}: A Lean Persistent Homology Library for Python},
  journal = {The Journal of Open Source Software}
}

@article{Bauer2021Ripser,
    AUTHOR = {Bauer, Ulrich},
     TITLE = {Ripser: efficient computation of {V}ietoris-{R}ips persistence
              barcodes},
   JOURNAL = {J. Appl. Comput. Topol.},
  FJOURNAL = {Journal of Applied and Computational Topology},
    VOLUME = {5},
      YEAR = {2021},
    NUMBER = {3},
     PAGES = {391--423},
      ISSN = {2367-1726},
   MRCLASS = {55N31 (55-04)},
  MRNUMBER = {4298669},
       DOI = {10.1007/s41468-021-00071-5},
       URL = {https://doi.org/10.1007/s41468-021-00071-5},
}