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Giotto-TDA is a well-tested suite of computational topology tools, compatible with the scikit-learn API and framework. From the docs:

giotto-tda is a high performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the GNU AGPLv3 license. It is part of the Giotto family of open-source projects.

Supported data types include:

Supported filtrations include:

Persistence diagrams can also be converted into other representations including persistence landscapes, persistence images and Betti curves. In line with the scikit-learn framework, preprocessing, persistent homology and diagram representations can be combined into a single pipeline.

Under the hood, many demanding workloads are implemented in C++, vectorised and parallelised.

complex/alpha complex/cech complex/cover-mapper complex/cubical complex/flag complex/rips complex/rips-weighted dist/bottleneck dist/wasserstein feat/amplitude feat/entropy feat/polynomial lang/python repr/image repr/kernel repr/landscape repr/silhouette type/images type/persistence vis/betti-surface vis/diagram