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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.

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.

If 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++

Ripser.py is available onpypi with wheels for all major platforms. To install, type the following command into your environment:

pip install ripser

If the above command fails or if you want to develop and contribute toripser.py, you can buildripser.py locally. To do so, clone thisrepository. From within the cloned repository, executepip install . to buildlocally, orpip install -e . for a local,editablebuild. Either of the above two commands will install all required dependencies.Explicitly, the dependencies ofripser.py are

• Cython,
• numpy,
• scipy,
• scikit-learn,
• persim,

and their required dependencies.

Windows users: If you are using a Windows machine, youmay also need to installMinGW on your system.

Mac users: Updating your Xcode and Xcode command line tools will probably fix any issues you have with installation.

Ripser.py when compiled from source can have asteroid¹ shot by replacing the standardunordered_map from the STL by one of the fastest implementation available:robin_hood. Benchmarking of Ripser.py using therobin_hood implementation showed speed-ups up to30%.

To be able to userobin_hood instead of STL, you only need to clone the repository containing the implementation:

# Run this command at the root of the projectgit clone https://github.com/martinus/robin-hood-hashing robinhood

After cloning robinhood with the above command, installripser.py with

pip install -v .

This will install a local version ofripser.py with verbose output. In the verbose output,you will see confirmation that robinhood was found or not.

¹ The Python package is already compiled withrobin_hood by default.

If you are having trouble installing, please let us know!

The interface is as simple as can be:

import numpy as npfrom ripser import ripserfrom persim import plot_diagramsdata = 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 npfrom ripser import Ripsrips = Rips()data = np.random.random((100,2))diagrams = rips.fit_transform(data)rips.plot(diagrams)

We 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 tracker and we'll try to help resolve the concern.

Ripser.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.

If you use this package, please site the JoSS paper found here and 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},}