diff env/lib/python3.9/site-packages/networkx/algorithms/components/weakly_connected.py @ 0:4f3585e2f14b draft default tip

"planemo upload commit 60cee0fc7c0cda8592644e1aad72851dec82c959"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/algorithms/components/weakly_connected.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,168 @@
+"""Weakly connected components."""
+import networkx as nx
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = [
+    "number_weakly_connected_components",
+    "weakly_connected_components",
+    "is_weakly_connected",
+]
+
+
+@not_implemented_for("undirected")
+def weakly_connected_components(G):
+    """Generate weakly connected components of G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph
+
+    Returns
+    -------
+    comp : generator of sets
+        A generator of sets of nodes, one for each weakly connected
+        component of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    Examples
+    --------
+    Generate a sorted list of weakly connected components, largest first.
+
+    >>> G = nx.path_graph(4, create_using=nx.DiGraph())
+    >>> nx.add_path(G, [10, 11, 12])
+    >>> [
+    ...     len(c)
+    ...     for c in sorted(nx.weakly_connected_components(G), key=len, reverse=True)
+    ... ]
+    [4, 3]
+
+    If you only want the largest component, it's more efficient to
+    use max instead of sort:
+
+    >>> largest_cc = max(nx.weakly_connected_components(G), key=len)
+
+    See Also
+    --------
+    connected_components
+    strongly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    seen = set()
+    for v in G:
+        if v not in seen:
+            c = set(_plain_bfs(G, v))
+            yield c
+            seen.update(c)
+
+
+@not_implemented_for("undirected")
+def number_weakly_connected_components(G):
+    """Returns the number of weakly connected components in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        A directed graph.
+
+    Returns
+    -------
+    n : integer
+        Number of weakly connected components
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    See Also
+    --------
+    weakly_connected_components
+    number_connected_components
+    number_strongly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    return sum(1 for wcc in weakly_connected_components(G))
+
+
+@not_implemented_for("undirected")
+def is_weakly_connected(G):
+    """Test directed graph for weak connectivity.
+
+    A directed graph is weakly connected if and only if the graph
+    is connected when the direction of the edge between nodes is ignored.
+
+    Note that if a graph is strongly connected (i.e. the graph is connected
+    even when we account for directionality), it is by definition weakly
+    connected as well.
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+        A directed graph.
+
+    Returns
+    -------
+    connected : bool
+        True if the graph is weakly connected, False otherwise.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is undirected.
+
+    See Also
+    --------
+    is_strongly_connected
+    is_semiconnected
+    is_connected
+    is_biconnected
+    weakly_connected_components
+
+    Notes
+    -----
+    For directed graphs only.
+
+    """
+    if len(G) == 0:
+        raise nx.NetworkXPointlessConcept(
+            """Connectivity is undefined for the null graph."""
+        )
+
+    return len(list(weakly_connected_components(G))[0]) == len(G)
+
+
+def _plain_bfs(G, source):
+    """A fast BFS node generator
+
+    The direction of the edge between nodes is ignored.
+
+    For directed graphs only.
+
+    """
+    Gsucc = G.succ
+    Gpred = G.pred
+
+    seen = set()
+    nextlevel = {source}
+    while nextlevel:
+        thislevel = nextlevel
+        nextlevel = set()
+        for v in thislevel:
+            if v not in seen:
+                yield v
+                seen.add(v)
+                nextlevel.update(Gsucc[v])
+                nextlevel.update(Gpred[v])