Grouper.visualization.visualize_space module

Grouper.visualization.visualize_space.calculate_centrality(space_network)

Compute various centrality measures.

Return type:: Dict[str, Dict]

Grouper.visualization.visualize_space.calculate_diversity_index(kernel_matrix)

Compute molecular diversity index based on pairwise similarity.

Return type:: float

Grouper.visualization.visualize_space.calculate_edge_density(space_set, kernel, cutoffs=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])

Grouper.visualization.visualize_space.calculate_modularity(space_set, kernel, community=None, cutoffs=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], options=None)

Return type:: Dict[str, ArrayLike]

Grouper.visualization.visualize_space.calculate_network_properties(space_network, options=None)

Return type:: Dict[str, ArrayLike]

Grouper.visualization.visualize_space.calculate_substructure_frequencies(space_set)

Identify frequently occurring substructures in the chemical space.

Return type:: Dict[str, int]

Grouper.visualization.visualize_space.chemical_space_network(space_set, kernel, cutoff, known_values=None, options=None)

Return type:: Graph

Grouper.visualization.visualize_space.find_shortest_paths(space_network, source, target)

Find shortest paths between two molecules in the chemical space.

Return type:: List[List[str]]

Grouper.visualization.visualize_space.plot_feature_similarity(feature_set, kernel, cutoff, options)

Return type:: Figure

Grouper.visualization.visualize_space.visualize_chemical_space(kernel_matrix, method='PCA')

Project the chemical space into a 2D representation using PCA or t-SNE.

Return type:: Figure

Grouper.visualization.visualize_space.visualize_substructure_correlation(space_set, graph_property, options=None)

Visualize the correlation between substructures in the chemical space.

Return type:: Figure