diff env/lib/python3.9/site-packages/networkx/algorithms/isomorphism/isomorph.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/isomorphism/isomorph.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,233 @@
+"""
+Graph isomorphism functions.
+"""
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = [
+    "could_be_isomorphic",
+    "fast_could_be_isomorphic",
+    "faster_could_be_isomorphic",
+    "is_isomorphic",
+]
+
+
+def could_be_isomorphic(G1, G2):
+    """Returns False if graphs are definitely not isomorphic.
+    True does NOT guarantee isomorphism.
+
+    Parameters
+    ----------
+    G1, G2 : graphs
+       The two graphs G1 and G2 must be the same type.
+
+    Notes
+    -----
+    Checks for matching degree, triangle, and number of cliques sequences.
+    """
+
+    # Check global properties
+    if G1.order() != G2.order():
+        return False
+
+    # Check local properties
+    d1 = G1.degree()
+    t1 = nx.triangles(G1)
+    c1 = nx.number_of_cliques(G1)
+    props1 = [[d, t1[v], c1[v]] for v, d in d1]
+    props1.sort()
+
+    d2 = G2.degree()
+    t2 = nx.triangles(G2)
+    c2 = nx.number_of_cliques(G2)
+    props2 = [[d, t2[v], c2[v]] for v, d in d2]
+    props2.sort()
+
+    if props1 != props2:
+        return False
+
+    # OK...
+    return True
+
+
+graph_could_be_isomorphic = could_be_isomorphic
+
+
+def fast_could_be_isomorphic(G1, G2):
+    """Returns False if graphs are definitely not isomorphic.
+
+    True does NOT guarantee isomorphism.
+
+    Parameters
+    ----------
+    G1, G2 : graphs
+       The two graphs G1 and G2 must be the same type.
+
+    Notes
+    -----
+    Checks for matching degree and triangle sequences.
+    """
+    # Check global properties
+    if G1.order() != G2.order():
+        return False
+
+    # Check local properties
+    d1 = G1.degree()
+    t1 = nx.triangles(G1)
+    props1 = [[d, t1[v]] for v, d in d1]
+    props1.sort()
+
+    d2 = G2.degree()
+    t2 = nx.triangles(G2)
+    props2 = [[d, t2[v]] for v, d in d2]
+    props2.sort()
+
+    if props1 != props2:
+        return False
+
+    # OK...
+    return True
+
+
+fast_graph_could_be_isomorphic = fast_could_be_isomorphic
+
+
+def faster_could_be_isomorphic(G1, G2):
+    """Returns False if graphs are definitely not isomorphic.
+
+    True does NOT guarantee isomorphism.
+
+    Parameters
+    ----------
+    G1, G2 : graphs
+       The two graphs G1 and G2 must be the same type.
+
+    Notes
+    -----
+    Checks for matching degree sequences.
+    """
+    # Check global properties
+    if G1.order() != G2.order():
+        return False
+
+    # Check local properties
+    d1 = sorted(d for n, d in G1.degree())
+    d2 = sorted(d for n, d in G2.degree())
+
+    if d1 != d2:
+        return False
+
+    # OK...
+    return True
+
+
+faster_graph_could_be_isomorphic = faster_could_be_isomorphic
+
+
+def is_isomorphic(G1, G2, node_match=None, edge_match=None):
+    """Returns True if the graphs G1 and G2 are isomorphic and False otherwise.
+
+    Parameters
+    ----------
+    G1, G2: graphs
+        The two graphs G1 and G2 must be the same type.
+
+    node_match : callable
+        A function that returns True if node n1 in G1 and n2 in G2 should
+        be considered equal during the isomorphism test.
+        If node_match is not specified then node attributes are not considered.
+
+        The function will be called like
+
+           node_match(G1.nodes[n1], G2.nodes[n2]).
+
+        That is, the function will receive the node attribute dictionaries
+        for n1 and n2 as inputs.
+
+    edge_match : callable
+        A function that returns True if the edge attribute dictionary
+        for the pair of nodes (u1, v1) in G1 and (u2, v2) in G2 should
+        be considered equal during the isomorphism test.  If edge_match is
+        not specified then edge attributes are not considered.
+
+        The function will be called like
+
+           edge_match(G1[u1][v1], G2[u2][v2]).
+
+        That is, the function will receive the edge attribute dictionaries
+        of the edges under consideration.
+
+    Notes
+    -----
+    Uses the vf2 algorithm [1]_.
+
+    Examples
+    --------
+    >>> import networkx.algorithms.isomorphism as iso
+
+    For digraphs G1 and G2, using 'weight' edge attribute (default: 1)
+
+    >>> G1 = nx.DiGraph()
+    >>> G2 = nx.DiGraph()
+    >>> nx.add_path(G1, [1, 2, 3, 4], weight=1)
+    >>> nx.add_path(G2, [10, 20, 30, 40], weight=2)
+    >>> em = iso.numerical_edge_match("weight", 1)
+    >>> nx.is_isomorphic(G1, G2)  # no weights considered
+    True
+    >>> nx.is_isomorphic(G1, G2, edge_match=em)  # match weights
+    False
+
+    For multidigraphs G1 and G2, using 'fill' node attribute (default: '')
+
+    >>> G1 = nx.MultiDiGraph()
+    >>> G2 = nx.MultiDiGraph()
+    >>> G1.add_nodes_from([1, 2, 3], fill="red")
+    >>> G2.add_nodes_from([10, 20, 30, 40], fill="red")
+    >>> nx.add_path(G1, [1, 2, 3, 4], weight=3, linewidth=2.5)
+    >>> nx.add_path(G2, [10, 20, 30, 40], weight=3)
+    >>> nm = iso.categorical_node_match("fill", "red")
+    >>> nx.is_isomorphic(G1, G2, node_match=nm)
+    True
+
+    For multidigraphs G1 and G2, using 'weight' edge attribute (default: 7)
+
+    >>> G1.add_edge(1, 2, weight=7)
+    1
+    >>> G2.add_edge(10, 20)
+    1
+    >>> em = iso.numerical_multiedge_match("weight", 7, rtol=1e-6)
+    >>> nx.is_isomorphic(G1, G2, edge_match=em)
+    True
+
+    For multigraphs G1 and G2, using 'weight' and 'linewidth' edge attributes
+    with default values 7 and 2.5. Also using 'fill' node attribute with
+    default value 'red'.
+
+    >>> em = iso.numerical_multiedge_match(["weight", "linewidth"], [7, 2.5])
+    >>> nm = iso.categorical_node_match("fill", "red")
+    >>> nx.is_isomorphic(G1, G2, edge_match=em, node_match=nm)
+    True
+
+    See Also
+    --------
+    numerical_node_match, numerical_edge_match, numerical_multiedge_match
+    categorical_node_match, categorical_edge_match, categorical_multiedge_match
+
+    References
+    ----------
+    .. [1]  L. P. Cordella, P. Foggia, C. Sansone, M. Vento,
+       "An Improved Algorithm for Matching Large Graphs",
+       3rd IAPR-TC15 Workshop  on Graph-based Representations in
+       Pattern Recognition, Cuen, pp. 149-159, 2001.
+       http://amalfi.dis.unina.it/graph/db/papers/vf-algorithm.pdf
+    """
+    if G1.is_directed() and G2.is_directed():
+        GM = nx.algorithms.isomorphism.DiGraphMatcher
+    elif (not G1.is_directed()) and (not G2.is_directed()):
+        GM = nx.algorithms.isomorphism.GraphMatcher
+    else:
+        raise NetworkXError("Graphs G1 and G2 are not of the same type.")
+
+    gm = GM(G1, G2, node_match=node_match, edge_match=edge_match)
+
+    return gm.is_isomorphic()