Libraries¶
Scikit-TDA provides a complete suite of TDA tools designed for academic or industry uses.
To install the entire suite
>>> pip install scikit-tda
Below, you’ll find information on each individual package, along with resources to explore more. Each package is well tested, well documented, easy to install, and open for contributions. If you find any bugs in the code or documentation, please let us know on github.
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.
Installation is as easy as
>>> pip install ripser
Check out complete documentation for Ripser.py at ripser.scikit-tda.org and the source code at github.com/scikit-tda/ripser.py.
Kepler Mapper¶
Kepler Mapper is a library implementing the Mapper algorithm in Python. Mapper can be used for visualization of the topological structures in a high-dimensional data point cloud data. Kepler Mapper leverages Scikit-Learn API compatible cluster and scaling algorithms to streamline the construction of the algorithm. The library also provides multiple visualization tools built on D3.js or Plotly.
Installation is as easy as
>>> pip install kmapper
Check out complete documentation for Kepler Mapper at kepler-mapper.scikit-tda.org and the source code at github.com/scikit-tda/kepler-mapper.
Persim¶
Once diagrams are constructed, the Persim package comes into play. This package houses many methods for comparison and analysis of persistence diagrams. It currently houses implementations of
Persistence Images
Diagram distances (Bottleneck distance, Sliced Wasserstein Kernel, Heat Kernel)
Diagram visualization
Installation is as easy as
>>> pip install persim
Check out complete documentation for Persim at persim.scikit-tda.org and the source code at github.com/scikit-tda/persim.
DREiMac¶
DREiMac is a library for topological data coordinatization, visualization, and dimensionality reduction. Currently, DREiMac is able to find topology-preserving representations of point clouds taking values in the circle, in higher dimensional tori, in the real and complex projective space, and in lens spaces.
Installation is as easy as
>>> pip install dreimac
Check out complete documentation for DREiMac at dreimac.scikit-tda.org and the source code at github.com/scikit-tda/dreimac.
CechMate¶
This library provides easy to use constructors for custom filtrations that are suitable for use with Phat. Phat currently provides a clean interface for persistence reduction algorithms for boundary matrices. This tool helps bridge the gap between data and boundary matrices. Currently, we support construction of
Alpha filtrations,
Rips filtrations, and
Cech filtrations, and
provide an easy interface for Phat.
Installation is as easy as
>>> pip install cechmate
Check out complete documentation for CechMate at cechmate.scikit-tda.org and the source code at github.com/scikit-tda/cechmate.
TaDAsets¶
This package provides some nice utilities for creating and loading data sets that are useful for Topological Data Analysis. Currently, we provide various synthetic data sets with particular topological features and various levels of noise and dimension. Currently includes
n-spheres,
torus,
swiss rolls, and
figure 8s.
Installation is as easy as
>>> pip install tadasets
Check out complete documentation for TaDAsets at tadasets.scikit-tda.org and the source code at github.com/scikit-tda/tadasets.
scikit-tda