API Reference

In this section, you will find detailed documentation of all the functions, classes, and methods available in our API.

Whether you are a developer who is new to our API or an experienced user looking for more advanced features, we hope you find this documentation helpful. If you have any questions or feedback, please don’t hesitate to reach out to us.

Algorithms

Numpy backend

AA(n_archetypes, *[, max_iter, tol, init, ...])	Archetype Analysis.
BiAA(n_archetypes[, max_iter, tol, init, ...])	Biarchetype Analysis with Numpy backend.

JAX backend

AA(n_archetypes[, max_iter, tol, init, ...])	Archetype Analysis with JAX backend.
BiAA(n_archetypes[, max_iter, tol, init, ...])	Biarchetype Analysis with JAX backend.

Torch backend

AA(n_archetypes[, max_iter, tol, init, ...])	Archetype Analysis with Torch backend.
BiAA(n_archetypes[, max_iter, tol, init, ...])	Biarchetype Analysis with Torch backend.

Visualization

simplex(points[, origin, show_points, ...])	A simplex plot of points with multiple optional parameters to obtain a customized visualization.
stacked_bar(points[, ax, labels, ...])	A stacked bar plot of points with multiple optional parameters to obtain a customized visualization.
heatmap(data[, labels, n_archetypes, scores, ax])	Plot a heatmap of the data.

Datasets

make_archetypal_dataset(archetypes, shape[, ...])	Generate a dataset from archetypes.
permute_dataset(data[, perms])	Permute a dataset along each dimension.
shuffle_dataset(data[, ndim, generator])	Shuffle a dataset along each dimension.
sort_by_archetype_similarity(data, alphas, ...)	Sort a dataset using the archetypal spaces previously computed.
sort_by_labels(data, labels)	Sort a dataset using the labels.