API Reference

Distances

persim.wasserstein	Perform the Wasserstein distance matching between persistence diagrams.
persim.bottleneck	Perform the Bottleneck distance matching between persistence diagrams.
persim.sliced_wasserstein	Implementation of Sliced Wasserstein distance as described in Sliced Wasserstein Kernel for Persistence Diagrams by Mathieu Carriere, Marco Cuturi, Steve Oudot (https://arxiv.org/abs/1706.03358)
persim.heat	Return the pseudo-metric between two diagrams based on the continuous heat kernel as described in "A Stable Multi-Scale Kernel for Topological Machine Learning" by Jan Reininghaus, Stefan Huber, Ulrich Bauer, and Roland Kwitt (CVPR 2015)
persim.gromov_hausdorff	Estimate the mGH distance between simple unweighted graphs, represented as compact metric spaces based on their shortest path lengths.

Persistence Images

persim.PersistenceImager	Transformer which converts persistence diagrams into persistence images.
persim.PersImage	Initialize a persistence image generator.

Persistence Landscapes

persim.PersLandscapeExact	Persistence Landscape Exact class.
persim.PersLandscapeApprox	Persistence Landscape Approximate class.
persim.PersistenceLandscaper	A scikit-learn transformer for converting persistence diagrams into persistence landscapes.

Diagram Visualization

persim.plot_diagrams	A helper function to plot persistence diagrams.
persim.bottleneck_matching	Visualize bottleneck matching between two diagrams
persim.wasserstein_matching	Visualize bottleneck matching between two diagrams

Persistence barcode measure

persim.persistent_entropy

The persistent entropy has been defined in [1].