Utils¶

Clusters¶

Functions to manipulate and sort clusters

aggregate_network_by_cluster(temporal_network, clusters, sort_clusters=None, output='averaged')[source]¶

Aggregates the temporal network over eacher cluster in a cluster set

Parameters

temporal_network (phasik.TemporalNetwork) – Temporal network to aggregate
clusters (array of int) – Cluster labels
sort_clusters (bool) – If True, sort cluster labels basde on ascending times
output ({‘weighted’, ‘averaged’, ‘binary’, ‘normalised’}, optional) – Determines the type of output edge weights

Returns

aggregates – Dict each key is a cluster label and each value is a tuple of the form (networkx.Graph, list of time indices of cluster).

Return type

dict

cluster_sort(clusters, final_labels=None)[source]¶

Return array of cluster labels sorted in order of appearance, with clusters unchanged

Example

>>> clusters = np.array([2, 2, 2, 3, 3, 1, 1, 1])
>>> cluster_sort(clusters)
[ 1 1 1 2 2 3 3 3 ]

convert_cluster_labels_to_dict(clusters)[source]¶

Returns dictionary where each key is a cluster label and each value is list of the time indices composing the cluster

Parameters: clusters (list of int) – List of cluster labels

rand_index_over_methods_and_sizes(valid_cluster_sets, reference_method='ward')[source]¶

Compute Rand Index to compare any method to a reference method, for all combinations of methods and number of clusters

Parameters

valid_cluster_sets (list)
reference_method (str)

Returns

rand_scores – Array of dimension (n_sizes, n_methods) with rand index scores

Return type

ndarray

Graphs¶

Utility functions for static graphs

create_graph_from_interactions(filename, sheet, source, target)[source]¶

Create a networkx.Graph from an excel sheet describing edges

Parameters

filename (str) – Path to the excel file
sheet (str) – Name of the sheet within the excel file
source (str) – Name of the column containing the source nodes
target (str) – Name of the column containing the target nodes

Returns

graph

Return type

networkx.Graph

graph_size_info(graph)[source]¶: Return basic size info on about graph

weighted_edges_as_df(network)[source]¶

Returns a pandas.Dataframe of weighted edges sorted by weight, from a networkx.Graph.

Columns are [‘i’, ‘j’, ‘weight’] and each row represents a different edge

Parameters: network (networkx.Graph) – A network from which to get weighted edges
Returns
Return type: pandas.DataFrame

Paths¶

Functions to deal with system paths

slugify(text, keep_characters=None)[source]¶

Turn any text into a string that can be used in a filename

Parameters

text (str) – text to slugify
keep_characters (list of str) – characters in this iterable will be kept in the final string. Defaults to [‘_’]. Any other non-alphanumeric characters will be removed.

Returns

slug

Return type

str