Analysis

PCA

get_explained_variance_ratio(explained_variance)
get_cum_explained_variance_ratio(...)
get_optimal_n_pcs(cum_explained_variance_ratio)
pca_transform_contours(contours, ...)	Transform contour coordinates to principal directions in the PC space.

Hierarchical Clustering

cluster_contours(pc[, n_clusters, n_pcs, ...])	K-means clustering of contour principal components.
assign_clusters_id(pc, contours, centroids)	Assign the contours with id of the closest centroid.
get_labeled_contours_df(contours, cluster_id_df)	Return contour coordinates, cluster id, and distance to centroid.
get_mean_cluster_contours(labeled_contours_df)	Return mean contour of each cluster.
hierarchical_cluster_contour(labeled_contours_df)	Compute data structure for rendering dendrogram.
get_cluster_order(branches)	Get the cluster id of contours in order of dendrogram.
get_distribution(properties_df)	Return proportion of each cluster.
get_shannon_entropy(distribution)	Calculate Shannon entropy from distribution (probability) of each shape mode.
reorder_clusters(cluster_id, object_index)	Reorder cluster id according to dendrogram order.
reorder_centroids(centroids, object_index)	Reorder centroids according to dendrogram order.