ai-content-maker/.venv/Lib/site-packages/networkx/algorithms/components/semiconnected.py

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2024-05-03 04:18:51 +03:00
"""Semiconnectedness."""
import networkx as nx
from networkx.utils import not_implemented_for, pairwise
__all__ = ["is_semiconnected"]
@not_implemented_for("undirected")
def is_semiconnected(G, topo_order=None):
"""Returns True if the graph is semiconnected, False otherwise.
A graph is semiconnected if, and only if, for any pair of nodes, either one
is reachable from the other, or they are mutually reachable.
Parameters
----------
G : NetworkX graph
A directed graph.
topo_order: list or tuple, optional
A topological order for G (if None, the function will compute one)
Returns
-------
semiconnected : bool
True if the graph is semiconnected, False otherwise.
Raises
------
NetworkXNotImplemented
If the input graph is undirected.
NetworkXPointlessConcept
If the graph is empty.
Examples
--------
>>> G = nx.path_graph(4, create_using=nx.DiGraph())
>>> print(nx.is_semiconnected(G))
True
>>> G = nx.DiGraph([(1, 2), (3, 2)])
>>> print(nx.is_semiconnected(G))
False
See Also
--------
is_strongly_connected
is_weakly_connected
is_connected
is_biconnected
"""
if len(G) == 0:
raise nx.NetworkXPointlessConcept(
"Connectivity is undefined for the null graph."
)
if not nx.is_weakly_connected(G):
return False
G = nx.condensation(G)
if topo_order is None:
topo_order = nx.topological_sort(G)
return all(G.has_edge(u, v) for u, v in pairwise(topo_order))