Fix not_implemented_for decorator for is_regular and related functions

networkx/algorithms/approximation/maxcut.py

@@ -4,7 +4,8 @@
 __all__ = ["randomized_partitioning", "one_exchange"]
 
 
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @py_random_state(1)
 @nx._dispatch(edge_attrs="weight")
 def randomized_partitioning(G, seed=None, p=0.5, weight=None):
@@ -49,7 +50,8 @@ def _swap_node_partition(cut, node):
     return cut - {node} if node in cut else cut.union({node})
 
 
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @py_random_state(2)
 @nx._dispatch(edge_attrs="weight")
 def one_exchange(G, initial_cut=None, seed=None, weight=None):

networkx/algorithms/approximation/tests/test_maxcut.py

@@ -1,9 +1,21 @@
 import random
 
+import pytest
+
 import networkx as nx
 from networkx.algorithms.approximation import maxcut
 
 
+@pytest.mark.parametrize(
+    "f", (nx.approximation.randomized_partitioning, nx.approximation.one_exchange)
+)
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_raises_on_directed_and_multigraphs(f, graph_constructor):
+    G = graph_constructor([(0, 1), (1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        f(G)
+
+
 def _is_valid_cut(G, set1, set2):
     union = set1.union(set2)
     assert union == set(G.nodes)

networkx/algorithms/community/asyn_fluid.py

@@ -10,7 +10,8 @@
 __all__ = ["asyn_fluidc"]
 
 
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @py_random_state(3)
 @nx._dispatch
 def asyn_fluidc(G, k, max_iter=100, seed=None):

networkx/algorithms/community/tests/test_asyn_fluid.py

@@ -5,6 +5,13 @@
 from networkx.algorithms.community import asyn_fluidc
 
 
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_raises_on_directed_and_multigraphs(graph_constructor):
+    G = graph_constructor([(0, 1), (1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.community.asyn_fluidc(G, 1)
+
+
 def test_exceptions():
     test = Graph()
     test.add_node("a")

networkx/algorithms/distance_regular.py

@@ -109,7 +109,8 @@ def global_parameters(b, c):
     return ((y, b[0] - x - y, x) for x, y in zip(b + [0], [0] + c))
 
 
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @nx._dispatch
 def intersection_array(G):
     """Returns the intersection array of a distance-regular graph.
@@ -181,7 +182,8 @@ def intersection_array(G):
 
 
 # TODO There is a definition for directed strongly regular graphs.
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @nx._dispatch
 def is_strongly_regular(G):
     """Returns True if and only if the given graph is strongly

networkx/algorithms/isomorphism/tests/test_tree_isomorphism.py

@@ -1,6 +1,8 @@
 import random
 import time
 
+import pytest
+
 import networkx as nx
 from networkx.algorithms.isomorphism.tree_isomorphism import (
     rooted_tree_isomorphism,
@@ -9,6 +11,14 @@
 from networkx.classes.function import is_directed
 
 
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_tree_isomorphism_raises_on_directed_and_multigraphs(graph_constructor):
+    t1 = graph_constructor([(0, 1)])
+    t2 = graph_constructor([(1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        nx.isomorphism.tree_isomorphism(t1, t2)
+
+
 # have this work for graph
 # given two trees (either the directed or undirected)
 # transform t2 according to the isomorphism

networkx/algorithms/isomorphism/tree_isomorphism.py

@@ -209,7 +209,8 @@ def rooted_tree_isomorphism(t1, root1, t2, root2):
     return isomorphism
 
 
-@not_implemented_for("directed", "multigraph")
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
 @nx._dispatch(graphs={"t1": 0, "t2": 1})
 def tree_isomorphism(t1, t2):
     """

networkx/algorithms/tests/test_distance_regular.py

@@ -4,6 +4,16 @@
 from networkx import is_strongly_regular
 
 
+@pytest.mark.parametrize(
+    "f", (nx.is_distance_regular, nx.intersection_array, nx.is_strongly_regular)
+)
+@pytest.mark.parametrize("graph_constructor", (nx.DiGraph, nx.MultiGraph))
+def test_raises_on_directed_and_multigraphs(f, graph_constructor):
+    G = graph_constructor([(0, 1), (1, 2)])
+    with pytest.raises(nx.NetworkXNotImplemented):
+        f(G)
+
+
 class TestDistanceRegular:
     def test_is_distance_regular(self):
         assert nx.is_distance_regular(nx.icosahedral_graph())