pyphi.graph

Bridge between PyPhi value types and graph representations.

Exports static structure (directed graphs over the TPM-inferred causal connectivity, FactoredTPM.infer_cm; pass connectivity="declared" to use the substrate’s declared connectivity matrix verbatim) and dynamics (the two-timeslice dynamic Bayesian network export, which carries each unit’s conditional probability distribution over its inferred parents).

Functions

in_degree(substrate[, connectivity])	In-degree per node index.
is_dag(substrate[, connectivity])	Whether the substrate graph is acyclic.
is_strongly_connected(substrate[, connectivity])	Whether the substrate graph is strongly connected (a complex must be).
out_degree(substrate[, connectivity])	Out-degree per node index.
simple_cycles(substrate[, connectivity])	All simple cycles as lists of node indices.
strongly_connected_components(substrate[, ...])	Strongly connected components as tuples of node indices.
substrate_from_networkx(graph, tpm, *[, ...])	Build a Substrate from a DiGraph topology plus a TPM.
substrate_to_dbn(substrate)	Return the substrate's 2-timeslice DBN as a networkx.DiGraph.
substrate_to_dbn_dict(substrate)	Return the substrate's 2-timeslice DBN as a plain dict.
substrate_to_networkx(substrate[, connectivity])	Return a node-labeled directed graph over the substrate's connectivity.
system_to_networkx(system[, connectivity])	Return the substrate graph with per-node state and membership attributes.
to_adjacency(substrate[, connectivity])	Return the chosen connectivity matrix as a node-labeled DataFrame.
to_graphml(substrate, path[, connectivity])	Write the substrate graph to a GraphML file.