Acknowledgments

I would like to thank:

• @ubauer for creating the original Ripser on which this project is based.
• @ctralie and @sauln for creating ripser.py which has been a source of inspiration.
• Žiga Virk, for giving ideas and helping with the theoretical side of things.

References

Bauer, U. (2019). Ripser: efficient computation of Vietoris-Rips persistence barcodes. arXiv preprint arXiv:1908.02518.

Kaji, S., Sudo, T., & Ahara, K. (2020). Cubical Ripser: Software for computing persistent homology of image and volume data. arXiv preprint arXiv:2005.12692.

Wagner, H., Chen, C., & Vuçini, E. (2012). Efficient computation of persistent homology for cubical data. In Topological methods in data analysis and visualization II (pp. 91-106). Springer, Berlin, Heidelberg.

Chen, C., & Kerber, M. (2011, March). Persistent homology computation with a twist. In Proceedings 27th European Workshop on Computational Geometry (Vol. 11, pp. 197-200).

De Silva, V., Morozov, D., & Vejdemo-Johansson, M. (2011). Persistent cohomology and circular coordinates. Discrete & Computational Geometry, 45(4), 737-759.

Zomorodian, A., & Carlsson, G. (2005). Computing persistent homology. Discrete & Computational Geometry, 33(2), 249-274.

Edelsbrunner, H. (1993, July). The union of balls and its dual shape. In Proceedings of the ninth annual symposium on Computational geometry (pp. 218-231).

Čufar, M. & Virk, Ž. (2021). Fast computation of persistent homology representatives with involuted persistent homology. arXiv preprint arxiv:2105.03629