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| author | Jarrod Millman <jarrod.millman@gmail.com> | 2019-10-17 20:51:46 -0700 |
|---|---|---|
| committer | Jarrod Millman <jarrod.millman@gmail.com> | 2019-10-18 00:47:00 -0700 |
| commit | dc73628a6291e470d76d42e9efaa6e09599d8ce8 (patch) | |
| tree | aefc2262ea2fca44cc36d9537278da83f8a8ff68 | |
| parent | d50eef197f1e7f452dee8b85c2cec3039124f43c (diff) | |
| download | networkx-dc73628a6291e470d76d42e9efaa6e09599d8ce8.tar.gz | |
PEP8 fixes to tests
116 files changed, 716 insertions, 693 deletions
diff --git a/examples/applications/plot_circuits.py b/examples/applications/plot_circuits.py index e6285fab..9438da69 100644 --- a/examples/applications/plot_circuits.py +++ b/examples/applications/plot_circuits.py @@ -96,5 +96,6 @@ def main(): formula = circuit_to_formula(circuit) print(formula_to_string(formula)) + if __name__ == '__main__': main() diff --git a/examples/drawing/plot_lanl_routes.py b/examples/drawing/plot_lanl_routes.py index 6aa616ab..13778591 100644 --- a/examples/drawing/plot_lanl_routes.py +++ b/examples/drawing/plot_lanl_routes.py @@ -32,6 +32,7 @@ except ImportError: raise ImportError("This example needs Graphviz and either " "PyGraphviz or pydot") + def lanl_graph(): """ Return the lanl internet view graph from lanl.edges """ diff --git a/examples/drawing/plot_unix_email.py b/examples/drawing/plot_unix_email.py index 792bd17c..bc8f4990 100644 --- a/examples/drawing/plot_unix_email.py +++ b/examples/drawing/plot_unix_email.py @@ -36,6 +36,7 @@ import networkx as nx # unix mailbox recipe # see https://docs.python.org/3/library/mailbox.html + def mbox_graph(): mbox = mailbox.mbox("unix_email.mbox") # parse unix mailbox diff --git a/examples/graph/plot_roget.py b/examples/graph/plot_roget.py index bf359511..07c664d3 100644 --- a/examples/graph/plot_roget.py +++ b/examples/graph/plot_roget.py @@ -40,6 +40,7 @@ import sys import matplotlib.pyplot as plt from networkx import nx + def roget_graph(): """ Return the thesaurus graph from the roget.dat example in the Stanford Graph Base. diff --git a/examples/javascript/force.py b/examples/javascript/force.py index 6fee8c17..0d97e0c9 100644 --- a/examples/javascript/force.py +++ b/examples/javascript/force.py @@ -33,9 +33,11 @@ print('Wrote node-link JSON data to force/force.json') # Serve the file over http to allow for cross origin requests app = flask.Flask(__name__, static_folder="force") + @app.route('/') def static_proxy(): return app.send_static_file('force.html') + print('\nGo to http://localhost:8000 to see the example\n') app.run(port=8000) diff --git a/examples/subclass/plot_printgraph.py b/examples/subclass/plot_printgraph.py index a9ad473c..9d8e1974 100644 --- a/examples/subclass/plot_printgraph.py +++ b/examples/subclass/plot_printgraph.py @@ -22,6 +22,7 @@ import matplotlib.pyplot as plt import networkx as nx from networkx import Graph + class PrintGraph(Graph): """ Example subclass of the Graph class. diff --git a/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py b/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py index 8bb538dc..c1642c18 100644 --- a/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py +++ b/networkx/algorithms/approximation/tests/test_approx_clust_coeff.py @@ -9,28 +9,28 @@ def test_petersen(): # Actual coefficient is 0 G = nx.petersen_graph() assert (average_clustering(G, trials=int(len(G) / 2)) == - nx.average_clustering(G)) + nx.average_clustering(G)) def test_petersen_seed(): # Actual coefficient is 0 G = nx.petersen_graph() assert (average_clustering(G, trials=int(len(G) / 2), seed=1) == - nx.average_clustering(G)) + nx.average_clustering(G)) def test_tetrahedral(): # Actual coefficient is 1 G = nx.tetrahedral_graph() assert (average_clustering(G, trials=int(len(G) / 2)) == - nx.average_clustering(G)) + nx.average_clustering(G)) def test_dodecahedral(): # Actual coefficient is 0 G = nx.dodecahedral_graph() assert (average_clustering(G, trials=int(len(G) / 2)) == - nx.average_clustering(G)) + nx.average_clustering(G)) def test_empty(): diff --git a/networkx/algorithms/approximation/tests/test_connectivity.py b/networkx/algorithms/approximation/tests/test_connectivity.py index 42cd9a15..d9053541 100644 --- a/networkx/algorithms/approximation/tests/test_connectivity.py +++ b/networkx/algorithms/approximation/tests/test_connectivity.py @@ -104,8 +104,8 @@ def test_directed_node_connectivity(): D = nx.cycle_graph(10).to_directed() # 2 reciprocal edges assert 1 == approx.node_connectivity(G) assert 1 == approx.node_connectivity(G, 1, 4) - assert 2 == approx.node_connectivity(D) - assert 2 == approx.node_connectivity(D, 1, 4) + assert 2 == approx.node_connectivity(D) + assert 2 == approx.node_connectivity(D, 1, 4) class TestAllPairsNodeConnectivityApprox: @@ -122,8 +122,8 @@ class TestAllPairsNodeConnectivityApprox: cls.K10 = nx.complete_graph(10) cls.K5 = nx.complete_graph(5) cls.G_list = [cls.path, cls.directed_path, cls.cycle, - cls.directed_cycle, cls.gnp, cls.directed_gnp, cls.K10, - cls.K5, cls.K20] + cls.directed_cycle, cls.gnp, cls.directed_gnp, cls.K10, + cls.K5, cls.K20] def test_cycles(self): K_undir = approx.all_pairs_node_connectivity(self.cycle) diff --git a/networkx/algorithms/approximation/tests/test_kcomponents.py b/networkx/algorithms/approximation/tests/test_kcomponents.py index 9a0c5161..c1c65416 100644 --- a/networkx/algorithms/approximation/tests/test_kcomponents.py +++ b/networkx/algorithms/approximation/tests/test_kcomponents.py @@ -259,6 +259,6 @@ class TestAntiGraph: assert sum(d for n, d in G.degree()) == sum(d for n, d in A.degree()) # AntiGraph is a ThinGraph, so all the weights are 1 assert (sum(d for n, d in A.degree()) == - sum(d for n, d in A.degree(weight='weight'))) + sum(d for n, d in A.degree(weight='weight'))) assert (sum(d for n, d in G.degree(nodes)) == - sum(d for n, d in A.degree(nodes))) + sum(d for n, d in A.degree(nodes))) diff --git a/networkx/algorithms/assortativity/tests/test_mixing.py b/networkx/algorithms/assortativity/tests/test_mixing.py index 620236f6..fbaf73b7 100644 --- a/networkx/algorithms/assortativity/tests/test_mixing.py +++ b/networkx/algorithms/assortativity/tests/test_mixing.py @@ -43,7 +43,6 @@ class TestDegreeMixingDict(BaseTestDegreeMixing): class TestDegreeMixingMatrix(BaseTestDegreeMixing): - def test_degree_mixing_matrix_undirected(self): a_result = np.array([[0, 0, 0], [0, 0, 2], diff --git a/networkx/algorithms/bipartite/tests/test_centrality.py b/networkx/algorithms/bipartite/tests/test_centrality.py index 143d5364..3a8d6ac5 100644 --- a/networkx/algorithms/bipartite/tests/test_centrality.py +++ b/networkx/algorithms/bipartite/tests/test_centrality.py @@ -2,6 +2,7 @@ import networkx as nx from networkx.algorithms import bipartite from networkx.testing import almost_equal + class TestBipartiteCentrality(object): @classmethod @@ -11,7 +12,7 @@ class TestBipartiteCentrality(object): cls.C4 = nx.cycle_graph(4) cls.davis = nx.davis_southern_women_graph() cls.top_nodes = [n for n, d in cls.davis.nodes(data=True) - if d['bipartite'] == 0] + if d['bipartite'] == 0] def test_degree_centrality(self): d = bipartite.degree_centrality(self.P4, [1, 3]) diff --git a/networkx/algorithms/bipartite/tests/test_covering.py b/networkx/algorithms/bipartite/tests/test_covering.py index 2fd7173b..61b63b86 100644 --- a/networkx/algorithms/bipartite/tests/test_covering.py +++ b/networkx/algorithms/bipartite/tests/test_covering.py @@ -19,7 +19,7 @@ class TestMinEdgeCover: G = nx.Graph() G.add_edge(0, 1) assert (bipartite.min_edge_cover(G) == - {(0, 1), (1, 0)}) + {(0, 1), (1, 0)}) def test_bipartite_default(self): G = nx.Graph() diff --git a/networkx/algorithms/bipartite/tests/test_generators.py b/networkx/algorithms/bipartite/tests/test_generators.py index 33a39769..22c77658 100644 --- a/networkx/algorithms/bipartite/tests/test_generators.py +++ b/networkx/algorithms/bipartite/tests/test_generators.py @@ -84,13 +84,13 @@ class TestGeneratorsBipartite(): bseq = [2, 2, 2, 2, 2, 2] G = configuration_model(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 2, 2, 2] bseq = [3, 3, 3, 3] G = configuration_model(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 1, 1, 1] bseq = [3, 3, 3] @@ -98,7 +98,7 @@ class TestGeneratorsBipartite(): assert G.is_multigraph() assert not G.is_directed() assert (sorted(d for n, d in G.degree()) == - [1, 1, 1, 2, 2, 2, 3, 3, 3]) + [1, 1, 1, 2, 2, 2, 3, 3, 3]) GU = nx.project(nx.Graph(G), range(len(aseq))) assert GU.number_of_nodes() == 6 @@ -140,7 +140,7 @@ class TestGeneratorsBipartite(): bseq = [2, 2, 2, 2, 2, 2] G = havel_hakimi_graph(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 2, 2, 2] bseq = [3, 3, 3, 3] @@ -148,7 +148,7 @@ class TestGeneratorsBipartite(): assert G.is_multigraph() assert not G.is_directed() assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) GU = nx.project(nx.Graph(G), range(len(aseq))) assert GU.number_of_nodes() == 6 @@ -190,13 +190,13 @@ class TestGeneratorsBipartite(): bseq = [2, 2, 2, 2, 2, 2] G = reverse_havel_hakimi_graph(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 2, 2, 2] bseq = [3, 3, 3, 3] G = reverse_havel_hakimi_graph(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 1, 1, 1] bseq = [3, 3, 3] @@ -204,7 +204,7 @@ class TestGeneratorsBipartite(): assert G.is_multigraph() assert not G.is_directed() assert (sorted(d for n, d in G.degree()) == - [1, 1, 1, 2, 2, 2, 3, 3, 3]) + [1, 1, 1, 2, 2, 2, 3, 3, 3]) GU = nx.project(nx.Graph(G), range(len(aseq))) assert GU.number_of_nodes() == 6 @@ -246,13 +246,13 @@ class TestGeneratorsBipartite(): bseq = [2, 2, 2, 2, 2, 2] G = alternating_havel_hakimi_graph(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 2, 2, 2] bseq = [3, 3, 3, 3] G = alternating_havel_hakimi_graph(aseq, bseq) assert (sorted(d for n, d in G.degree()) == - [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) + [2, 2, 2, 2, 2, 2, 3, 3, 3, 3]) aseq = [2, 2, 2, 1, 1, 1] bseq = [3, 3, 3] @@ -260,7 +260,7 @@ class TestGeneratorsBipartite(): assert G.is_multigraph() assert not G.is_directed() assert (sorted(d for n, d in G.degree()) == - [1, 1, 1, 2, 2, 2, 3, 3, 3]) + [1, 1, 1, 2, 2, 2, 3, 3, 3]) GU = nx.project(nx.Graph(G), range(len(aseq))) assert GU.number_of_nodes() == 6 @@ -332,7 +332,7 @@ class TestGeneratorsBipartite(): assert len(G) == n + m assert nx.is_bipartite(G) X, Y = nx.algorithms.bipartite.sets(G) - #print(X) + # print(X) assert set(range(n)) == X assert set(range(n, n + m)) == Y assert edges == len(list(G.edges())) @@ -345,7 +345,7 @@ class TestGeneratorsBipartite(): assert len(G) == n + m assert nx.is_bipartite(G) X, Y = nx.algorithms.bipartite.sets(G) - #print(X) + # print(X) assert set(range(n)) == X assert set(range(n, n + m)) == Y assert edges == len(list(G.edges())) diff --git a/networkx/algorithms/centrality/tests/test_betweenness_centrality.py b/networkx/algorithms/centrality/tests/test_betweenness_centrality.py index 59396a57..5862d1ba 100644 --- a/networkx/algorithms/centrality/tests/test_betweenness_centrality.py +++ b/networkx/algorithms/centrality/tests/test_betweenness_centrality.py @@ -2,6 +2,7 @@ import networkx as nx from networkx.testing import almost_equal + def weighted_G(): G = nx.Graph() G.add_edge(0, 1, weight=3) @@ -68,7 +69,7 @@ class TestBetweennessCentrality(object): assert almost_equal(b[n], b_answer[n]) def test_sample_from_P3(self): - G= nx.path_graph(3) + G = nx.path_graph(3) b_answer = {0: 0.0, 1: 1.0, 2: 0.0} b = nx.betweenness_centrality(G, k=3, diff --git a/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py b/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py index c67130b1..3dc0f18c 100644 --- a/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py +++ b/networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py @@ -2,6 +2,7 @@ import networkx as nx from networkx.testing import almost_equal + class TestSubsetBetweennessCentrality: def test_K5(self): @@ -77,21 +78,21 @@ class TestSubsetBetweennessCentrality: """Betweenness Centrality Subset: Diamond Multi Path""" G = nx.Graph() G.add_edges_from([ - (1,2), - (1,3), - (1,4), - (1,5), - (1,10), - (10,11), - (11,12), - (12,9), - (2,6), - (3,6), - (4,6), - (5,7), - (7,8), - (6,8), - (8,9) + (1, 2), + (1, 3), + (1, 4), + (1, 5), + (1, 10), + (10, 11), + (11, 12), + (12, 9), + (2, 6), + (3, 6), + (4, 6), + (5, 7), + (7, 8), + (6, 8), + (8, 9) ]) b = nx.betweenness_centrality_subset( G, @@ -101,18 +102,18 @@ class TestSubsetBetweennessCentrality: ) expected_b = { - 1: 0, - 2: 1./10, - 3: 1./10, - 4: 1./10, - 5: 1./10, - 6: 3./10, - 7:1./10, - 8:4./10, - 9:0, - 10:1./10, - 11:1./10, - 12:1./10, + 1: 0, + 2: 1./10, + 3: 1./10, + 4: 1./10, + 5: 1./10, + 6: 3./10, + 7: 1./10, + 8: 4./10, + 9: 0, + 10: 1./10, + 11: 1./10, + 12: 1./10, } for n in sorted(G): diff --git a/networkx/algorithms/centrality/tests/test_closeness_centrality.py b/networkx/algorithms/centrality/tests/test_closeness_centrality.py index 6ab4e15d..479520c8 100644 --- a/networkx/algorithms/centrality/tests/test_closeness_centrality.py +++ b/networkx/algorithms/centrality/tests/test_closeness_centrality.py @@ -5,6 +5,7 @@ import pytest import networkx as nx from networkx.testing import almost_equal + class TestClosenessCentrality: @classmethod def setup_class(cls): @@ -17,7 +18,7 @@ class TestClosenessCentrality: cls.T = nx.balanced_tree(r=2, h=2) cls.Gb = nx.Graph() cls.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), - (2, 4), (4, 5), (3, 5)]) + (2, 4), (4, 5), (3, 5)]) F = nx.florentine_families_graph() cls.F = F @@ -200,7 +201,6 @@ class TestClosenessCentrality: for n in sorted(XG): assert almost_equal(c[n], d[n], places=3) - # # Tests for incremental closeness centrality. # diff --git a/networkx/algorithms/centrality/tests/test_current_flow_closeness.py b/networkx/algorithms/centrality/tests/test_current_flow_closeness.py index e3ab72e7..a18909cc 100644 --- a/networkx/algorithms/centrality/tests/test_current_flow_closeness.py +++ b/networkx/algorithms/centrality/tests/test_current_flow_closeness.py @@ -6,6 +6,7 @@ scipy = pytest.importorskip('scipy') import networkx as nx from networkx.testing import almost_equal + class TestFlowClosenessCentrality(object): def test_K4(self): diff --git a/networkx/algorithms/centrality/tests/test_degree_centrality.py b/networkx/algorithms/centrality/tests/test_degree_centrality.py index c5aef07f..687d81d7 100644 --- a/networkx/algorithms/centrality/tests/test_degree_centrality.py +++ b/networkx/algorithms/centrality/tests/test_degree_centrality.py @@ -6,6 +6,7 @@ import networkx as nx from networkx.testing import almost_equal + class TestDegreeCentrality: def setup_method(self): diff --git a/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py b/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py index 33e68146..a2efac94 100644 --- a/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py +++ b/networkx/algorithms/centrality/tests/test_eigenvector_centrality.py @@ -8,6 +8,7 @@ scipy = pytest.importorskip('scipy') import networkx as nx from networkx.testing import almost_equal + class TestEigenvectorCentrality(object): def test_K5(self): @@ -65,7 +66,7 @@ class TestEigenvectorCentralityDirected(object): G.add_edges_from(edges, weight=2.0) cls.G = G.reverse() cls.G.evc = [0.25368793, 0.19576478, 0.32817092, 0.40430835, - 0.48199885, 0.15724483, 0.51346196, 0.32475403] + 0.48199885, 0.15724483, 0.51346196, 0.32475403] H = nx.DiGraph() @@ -76,7 +77,7 @@ class TestEigenvectorCentralityDirected(object): G.add_edges_from(edges) cls.H = G.reverse() cls.H.evc = [0.25368793, 0.19576478, 0.32817092, 0.40430835, - 0.48199885, 0.15724483, 0.51346196, 0.32475403] + 0.48199885, 0.15724483, 0.51346196, 0.32475403] def test_eigenvector_centrality_weighted(self): G = self.G diff --git a/networkx/algorithms/centrality/tests/test_harmonic_centrality.py b/networkx/algorithms/centrality/tests/test_harmonic_centrality.py index 3adcfc5c..6b9ee9de 100644 --- a/networkx/algorithms/centrality/tests/test_harmonic_centrality.py +++ b/networkx/algorithms/centrality/tests/test_harmonic_centrality.py @@ -5,6 +5,7 @@ import networkx as nx from networkx.algorithms.centrality import harmonic_centrality from networkx.testing import almost_equal + class TestClosenessCentrality: @classmethod def setup_class(cls): @@ -19,7 +20,7 @@ class TestClosenessCentrality: cls.Gb = nx.DiGraph() cls.Gb.add_edges_from([(0, 1), (0, 2), (0, 4), (2, 1), - (2, 3), (4, 3)]) + (2, 3), (4, 3)]) def test_p3_harmonic(self): c = harmonic_centrality(self.P3) diff --git a/networkx/algorithms/centrality/tests/test_katz_centrality.py b/networkx/algorithms/centrality/tests/test_katz_centrality.py index 102a0515..0fdc2938 100644 --- a/networkx/algorithms/centrality/tests/test_katz_centrality.py +++ b/networkx/algorithms/centrality/tests/test_katz_centrality.py @@ -5,6 +5,7 @@ import networkx as nx from networkx.testing import almost_equal import pytest + class TestKatzCentrality(object): def test_K5(self): diff --git a/networkx/algorithms/centrality/tests/test_load_centrality.py b/networkx/algorithms/centrality/tests/test_load_centrality.py index 5d296999..c7f76b9a 100644 --- a/networkx/algorithms/centrality/tests/test_load_centrality.py +++ b/networkx/algorithms/centrality/tests/test_load_centrality.py @@ -2,6 +2,7 @@ import networkx as nx from networkx.testing import almost_equal + class TestLoadCentrality: @classmethod @@ -29,7 +30,7 @@ class TestLoadCentrality: cls.T = nx.balanced_tree(r=2, h=2) cls.Gb = nx.Graph() cls.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3), - (2, 4), (4, 5), (3, 5)]) + (2, 4), (4, 5), (3, 5)]) cls.F = nx.florentine_families_graph() cls.LM = nx.les_miserables_graph() cls.D = nx.cycle_graph(3, create_using=nx.DiGraph()) diff --git a/networkx/algorithms/centrality/tests/test_percolation_centrality.py b/networkx/algorithms/centrality/tests/test_percolation_centrality.py index ea59b66f..34e398e1 100644 --- a/networkx/algorithms/centrality/tests/test_percolation_centrality.py +++ b/networkx/algorithms/centrality/tests/test_percolation_centrality.py @@ -2,6 +2,7 @@ import networkx as nx from networkx.testing import almost_equal + def example1a_G(): G = nx.Graph() G.add_node(1, percolation=0.1) diff --git a/networkx/algorithms/centrality/tests/test_reaching.py b/networkx/algorithms/centrality/tests/test_reaching.py index 98ef5743..99005164 100644 --- a/networkx/algorithms/centrality/tests/test_reaching.py +++ b/networkx/algorithms/centrality/tests/test_reaching.py @@ -10,6 +10,7 @@ import pytest from networkx import nx from networkx.testing import almost_equal + class TestGlobalReachingCentrality: """Unit tests for the global reaching centrality function.""" diff --git a/networkx/algorithms/centrality/tests/test_subgraph.py b/networkx/algorithms/centrality/tests/test_subgraph.py index 8ba68e08..8d76d95b 100644 --- a/networkx/algorithms/centrality/tests/test_subgraph.py +++ b/networkx/algorithms/centrality/tests/test_subgraph.py @@ -8,6 +8,7 @@ import networkx as nx from networkx.algorithms.centrality.subgraph_alg import * from networkx.testing import almost_equal + class TestSubgraph: def test_subgraph_centrality(self): diff --git a/networkx/algorithms/coloring/tests/test_coloring.py b/networkx/algorithms/coloring/tests/test_coloring.py index 77574b9e..f7567916 100644 --- a/networkx/algorithms/coloring/tests/test_coloring.py +++ b/networkx/algorithms/coloring/tests/test_coloring.py @@ -62,7 +62,7 @@ class TestColoring: if not hasattr(colors, '__len__'): colors = [colors] assert any(verify_length(coloring, n_colors) - for n_colors in colors) + for n_colors in colors) assert verify_coloring(graph, coloring) for strategy, arglist in SPECIAL_TEST_CASES.items(): diff --git a/networkx/algorithms/community/label_propagation.py b/networkx/algorithms/community/label_propagation.py index ec750fac..c40bc31f 100644 --- a/networkx/algorithms/community/label_propagation.py +++ b/networkx/algorithms/community/label_propagation.py @@ -91,7 +91,7 @@ def asyn_lpa_communities(G, weight=None, seed=None): max_freq = max(label_freq.values()) best_labels = [label for label, freq in label_freq.items() if freq == max_freq] - + # Continue until all nodes have a majority label if labels[node] not in best_labels: labels[node] = seed.choice(best_labels) diff --git a/networkx/algorithms/community/tests/test_label_propagation.py b/networkx/algorithms/community/tests/test_label_propagation.py index 32586884..1f0b8bfe 100644 --- a/networkx/algorithms/community/tests/test_label_propagation.py +++ b/networkx/algorithms/community/tests/test_label_propagation.py @@ -98,7 +98,7 @@ def test_connected_communities(): def test_termination(): - # ensure termination of asyn_lpa_communities in two cases + # ensure termination of asyn_lpa_communities in two cases # that led to an endless loop in a previous version test1 = nx.karate_club_graph() test2 = nx.caveman_graph(2, 10) diff --git a/networkx/algorithms/community/tests/test_quality.py b/networkx/algorithms/community/tests/test_quality.py index eda7bb35..f5c19122 100644 --- a/networkx/algorithms/community/tests/test_quality.py +++ b/networkx/algorithms/community/tests/test_quality.py @@ -19,6 +19,7 @@ from networkx.algorithms.community import performance from networkx.algorithms.community.quality import inter_community_edges from networkx.testing import almost_equal + class TestPerformance(object): """Unit tests for the :func:`performance` function.""" diff --git a/networkx/algorithms/components/tests/test_attracting.py b/networkx/algorithms/components/tests/test_attracting.py index e6befd35..98cfd4ab 100644 --- a/networkx/algorithms/components/tests/test_attracting.py +++ b/networkx/algorithms/components/tests/test_attracting.py @@ -9,7 +9,7 @@ class TestAttractingComponents(object): def setup_class(cls): cls.G1 = nx.DiGraph() cls.G1.add_edges_from([(5, 11), (11, 2), (11, 9), (11, 10), - (7, 11), (7, 8), (8, 9), (3, 8), (3, 10)]) + (7, 11), (7, 8), (8, 9), (3, 8), (3, 10)]) cls.G2 = nx.DiGraph() cls.G2.add_edges_from([(0, 1), (0, 2), (1, 1), (1, 2), (2, 1)]) diff --git a/networkx/algorithms/components/tests/test_semiconnected.py b/networkx/algorithms/components/tests/test_semiconnected.py index 35a7c090..3449d3aa 100644 --- a/networkx/algorithms/components/tests/test_semiconnected.py +++ b/networkx/algorithms/components/tests/test_semiconnected.py @@ -2,6 +2,7 @@ from itertools import chain import networkx as nx import pytest + class TestIsSemiconnected(object): def test_undirected(self): diff --git a/networkx/algorithms/connectivity/tests/test_connectivity.py b/networkx/algorithms/connectivity/tests/test_connectivity.py index ed2a3ab9..cdfe3b7f 100644 --- a/networkx/algorithms/connectivity/tests/test_connectivity.py +++ b/networkx/algorithms/connectivity/tests/test_connectivity.py @@ -119,10 +119,10 @@ def test_complete_graphs(): G = nx.complete_graph(n) assert n - 1 == nx.node_connectivity(G, flow_func=flow_func), msg.format(flow_func.__name__) assert n - 1 == nx.node_connectivity(G.to_directed(), - flow_func=flow_func), msg.format(flow_func.__name__) + flow_func=flow_func), msg.format(flow_func.__name__) assert n - 1 == nx.edge_connectivity(G, flow_func=flow_func), msg.format(flow_func.__name__) assert n - 1 == nx.edge_connectivity(G.to_directed(), - flow_func=flow_func), msg.format(flow_func.__name__) + flow_func=flow_func), msg.format(flow_func.__name__) def test_empty_graphs(): @@ -281,8 +281,8 @@ class TestAllPairsNodeConnectivity: cls.K10 = nx.complete_graph(10) cls.K5 = nx.complete_graph(5) cls.G_list = [cls.path, cls.directed_path, cls.cycle, - cls.directed_cycle, cls.gnp, cls.directed_gnp, - cls.K10, cls.K5, cls.K20] + cls.directed_cycle, cls.gnp, cls.directed_gnp, + cls.K10, cls.K5, cls.K20] def test_cycles(self): K_undir = nx.all_pairs_node_connectivity(self.cycle) @@ -334,7 +334,7 @@ class TestAllPairsNodeConnectivity: A[u][v] = A[v][u] = nx.node_connectivity(G, u, v) C = nx.all_pairs_node_connectivity(G) assert (sorted((k, sorted(v)) for k, v in A.items()) == - sorted((k, sorted(v)) for k, v in C.items())) + sorted((k, sorted(v)) for k, v in C.items())) def test_all_pairs_connectivity_directed(self): G = nx.DiGraph() @@ -345,7 +345,7 @@ class TestAllPairsNodeConnectivity: A[u][v] = nx.node_connectivity(G, u, v) C = nx.all_pairs_node_connectivity(G) assert (sorted((k, sorted(v)) for k, v in A.items()) == - sorted((k, sorted(v)) for k, v in C.items())) + sorted((k, sorted(v)) for k, v in C.items())) def test_all_pairs_connectivity_nbunch_combinations(self): G = nx.complete_graph(5) @@ -355,7 +355,7 @@ class TestAllPairsNodeConnectivity: A[u][v] = A[v][u] = nx.node_connectivity(G, u, v) C = nx.all_pairs_node_connectivity(G, nbunch=nbunch) assert (sorted((k, sorted(v)) for k, v in A.items()) == - sorted((k, sorted(v)) for k, v in C.items())) + sorted((k, sorted(v)) for k, v in C.items())) def test_all_pairs_connectivity_nbunch_iter(self): G = nx.complete_graph(5) @@ -365,4 +365,4 @@ class TestAllPairsNodeConnectivity: A[u][v] = A[v][u] = nx.node_connectivity(G, u, v) C = nx.all_pairs_node_connectivity(G, nbunch=iter(nbunch)) assert (sorted((k, sorted(v)) for k, v in A.items()) == - sorted((k, sorted(v)) for k, v in C.items())) + sorted((k, sorted(v)) for k, v in C.items())) diff --git a/networkx/algorithms/connectivity/tests/test_edge_augmentation.py b/networkx/algorithms/connectivity/tests/test_edge_augmentation.py index f87d837c..657c4ad1 100644 --- a/networkx/algorithms/connectivity/tests/test_edge_augmentation.py +++ b/networkx/algorithms/connectivity/tests/test_edge_augmentation.py @@ -449,11 +449,11 @@ def _check_augmentations(G, avail=None, max_k=None, weight=None, if orig_k == 0: # the approximation ratio is 3 if G is not connected assert (info2['total_weight'] <= - info1['total_weight'] * 3) + info1['total_weight'] * 3) else: # the approximation ratio is 2 if G is was connected assert (info2['total_weight'] <= - info1['total_weight'] * 2) + info1['total_weight'] * 2) _check_unconstrained_bridge_property(G, info1) diff --git a/networkx/algorithms/connectivity/tests/test_stoer_wagner.py b/networkx/algorithms/connectivity/tests/test_stoer_wagner.py index 10fa53c8..3ae74499 100644 --- a/networkx/algorithms/connectivity/tests/test_stoer_wagner.py +++ b/networkx/algorithms/connectivity/tests/test_stoer_wagner.py @@ -2,6 +2,7 @@ from itertools import chain import networkx as nx import pytest + def _check_partition(G, cut_value, partition, weight): assert isinstance(partition, tuple) assert len(partition) == 2 diff --git a/networkx/algorithms/flow/tests/test_gomory_hu.py b/networkx/algorithms/flow/tests/test_gomory_hu.py index aadd38da..b82a32f7 100644 --- a/networkx/algorithms/flow/tests/test_gomory_hu.py +++ b/networkx/algorithms/flow/tests/test_gomory_hu.py @@ -40,7 +40,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v) == - cut_value) + cut_value) def test_karate_club_graph(self): G = nx.karate_club_graph() @@ -51,7 +51,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v) == - cut_value) + cut_value) def test_davis_southern_women_graph(self): G = nx.davis_southern_women_graph() @@ -62,7 +62,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v) == - cut_value) + cut_value) def test_florentine_families_graph(self): G = nx.florentine_families_graph() @@ -73,7 +73,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v) == - cut_value) + cut_value) def test_les_miserables_graph_cutset(self): G = nx.les_miserables_graph() @@ -84,7 +84,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v) == - cut_value) + cut_value) def test_karate_club_graph_cutset(self): G = nx.karate_club_graph() @@ -110,7 +110,7 @@ class TestGomoryHuTree: for u, v in combinations(G, 2): cut_value, edge = self.minimum_edge_weight(T, u, v) assert (nx.minimum_cut_value(G, u, v, capacity='weight') == - cut_value) + cut_value) def test_directed_raises(self): with pytest.raises(nx.NetworkXNotImplemented): diff --git a/networkx/algorithms/flow/tests/test_maxflow.py b/networkx/algorithms/flow/tests/test_maxflow.py index b0d8695d..9961047d 100644 --- a/networkx/algorithms/flow/tests/test_maxflow.py +++ b/networkx/algorithms/flow/tests/test_maxflow.py @@ -395,7 +395,7 @@ class TestMaxFlowMinCutInterface: if interface_func in max_min_funcs: result = result[0] assert fv == result, msgi.format(flow_func.__name__, - interface_func.__name__) + interface_func.__name__) def test_minimum_cut_no_cutoff(self): G = self.G @@ -418,7 +418,7 @@ class TestMaxFlowMinCutInterface: if interface_func in max_min_funcs: result = result[0] assert fv == result, msgi.format(flow_func.__name__, - interface_func.__name__) + interface_func.__name__) def test_kwargs_default_flow_func(self): G = self.H @@ -439,7 +439,7 @@ class TestMaxFlowMinCutInterface: if interface_func in max_min_funcs: result = result[0] assert fv == result, msgi.format(flow_func.__name__, - interface_func.__name__) + interface_func.__name__) # Tests specific to one algorithm diff --git a/networkx/algorithms/flow/tests/test_maxflow_large_graph.py b/networkx/algorithms/flow/tests/test_maxflow_large_graph.py index 2dd82f20..cfe056d5 100644 --- a/networkx/algorithms/flow/tests/test_maxflow_large_graph.py +++ b/networkx/algorithms/flow/tests/test_maxflow_large_graph.py @@ -74,7 +74,7 @@ def validate_flows(G, s, t, soln_value, R, flow_func): if u == s: assert exc == -soln_value, msg.format(flow_func.__name__) elif u == t: - assert exc ==soln_value, msg.format(flow_func.__name__) + assert exc == soln_value, msg.format(flow_func.__name__) else: assert exc == 0, msg.format(flow_func.__name__) diff --git a/networkx/algorithms/flow/tests/test_mincost.py b/networkx/algorithms/flow/tests/test_mincost.py index e4728100..9fe315be 100644 --- a/networkx/algorithms/flow/tests/test_mincost.py +++ b/networkx/algorithms/flow/tests/test_mincost.py @@ -330,7 +330,7 @@ class TestMinCostFlow: G.nodes[4]['demand'] = -13 G.nodes[3]['demand'] = 13 - G.add_edges_from([(0,2), (0, 3), (2, 1)], capacity=20, weight=0.1) + G.add_edges_from([(0, 2), (0, 3), (2, 1)], capacity=20, weight=0.1) pytest.raises(nx.NetworkXUnfeasible, nx.min_cost_flow, G) def test_infinite_capacity_neg_digon(self): diff --git a/networkx/algorithms/isomorphism/tests/test_ismags.py b/networkx/algorithms/isomorphism/tests/test_ismags.py index 5999210b..85f1a97e 100644 --- a/networkx/algorithms/isomorphism/tests/test_ismags.py +++ b/networkx/algorithms/isomorphism/tests/test_ismags.py @@ -57,7 +57,7 @@ class TestSelfIsomorphism(object): assert ismags.is_isomorphic() assert ismags.subgraph_is_isomorphic() assert (list(ismags.subgraph_isomorphisms_iter(symmetry=True)) == - [{n: n for n in graph.nodes}]) + [{n: n for n in graph.nodes}]) def test_edgecase_self_isomorphism(self): """ @@ -93,7 +93,7 @@ class TestSelfIsomorphism(object): assert ismags.is_isomorphic() assert ismags.subgraph_is_isomorphic() assert (list(ismags.subgraph_isomorphisms_iter(symmetry=True)) == - [{n: n for n in graph.nodes}]) + [{n: n for n in graph.nodes}]) class TestSubgraphIsomorphism(object): @@ -106,7 +106,7 @@ class TestSubgraphIsomorphism(object): g2.add_edges_from([(n, m) for n, m in zip(g2, range(4, 8))]) ismags = iso.ISMAGS(g2, g1) assert (list(ismags.subgraph_isomorphisms_iter(symmetry=True)) == - [{n: n for n in g1.nodes}]) + [{n: n for n in g1.nodes}]) def test_isomorphism2(self): g1 = nx.Graph() @@ -121,14 +121,14 @@ class TestSubgraphIsomorphism(object): {0: 0, 1: 1, 3: 2}, {2: 0, 1: 1, 3: 2}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric)) + _matches_to_sets(expected_symmetric)) matches = ismags.subgraph_isomorphisms_iter(symmetry=False) expected_asymmetric = [{0: 2, 1: 1, 2: 0}, {0: 2, 1: 1, 3: 0}, {2: 2, 1: 1, 3: 0}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric + expected_asymmetric)) + _matches_to_sets(expected_symmetric + expected_asymmetric)) def test_labeled_nodes(self): g1 = nx.Graph() @@ -141,12 +141,12 @@ class TestSubgraphIsomorphism(object): matches = ismags.subgraph_isomorphisms_iter(symmetry=True) expected_symmetric = [{0: 0, 1: 1, 2: 2}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric)) + _matches_to_sets(expected_symmetric)) matches = ismags.subgraph_isomorphisms_iter(symmetry=False) expected_asymmetric = [{0: 2, 1: 1, 2: 0}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric + expected_asymmetric)) + _matches_to_sets(expected_symmetric + expected_asymmetric)) def test_labeled_edges(self): g1 = nx.Graph() @@ -159,12 +159,12 @@ class TestSubgraphIsomorphism(object): matches = ismags.subgraph_isomorphisms_iter(symmetry=True) expected_symmetric = [{0: 0, 1: 1, 2: 2}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric)) + _matches_to_sets(expected_symmetric)) matches = ismags.subgraph_isomorphisms_iter(symmetry=False) expected_asymmetric = [{1: 2, 0: 0, 2: 1}] assert (_matches_to_sets(matches) == - _matches_to_sets(expected_symmetric + expected_asymmetric)) + _matches_to_sets(expected_symmetric + expected_asymmetric)) class TestWikipediaExample(object): diff --git a/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py b/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py index 788cc913..dc564088 100644 --- a/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py +++ b/networkx/algorithms/isomorphism/tests/test_isomorphvf2.py @@ -34,7 +34,7 @@ class TestWikipediaExample(object): g2.add_edges_from(self.g2edges) gm = iso.GraphMatcher(g1, g2) assert gm.is_isomorphic() - #Just testing some cases + # Just testing some cases assert gm.subgraph_is_monomorphic() mapping = sorted(gm.mapping.items()) @@ -53,7 +53,6 @@ class TestWikipediaExample(object): gm = iso.GraphMatcher(g1, g3) assert gm.subgraph_is_isomorphic() - def test_subgraph_mono(self): g1 = nx.Graph() g2 = nx.Graph() @@ -112,7 +111,7 @@ class TestVF2GraphDB(object): graph = self.create_graph(os.path.join(head, 'si2_b06_m200.B99')) gm = iso.GraphMatcher(graph, subgraph) assert gm.subgraph_is_isomorphic() - #Just testing some cases + # Just testing some cases assert gm.subgraph_is_monomorphic() # There isn't a similar test implemented for subgraph monomorphism, @@ -169,7 +168,7 @@ def test_multiedge(): else: gm = iso.DiGraphMatcher(g1, g2) assert gm.is_isomorphic() - #Testing if monomorphism works in multigraphs + # Testing if monomorphism works in multigraphs assert gm.subgraph_is_monomorphic() @@ -191,12 +190,12 @@ def test_selfloop(): else: gm = iso.DiGraphMatcher(g1, g2) assert gm.is_isomorphic() - + def test_selfloop_mono(): # Simple test for graphs with selfloops edges0 = [(0, 1), (0, 2), (1, 2), (1, 3), - (2, 4), (3, 1), (3, 2), (4, 2), (4, 5), (5, 4)] + (2, 4), (3, 1), (3, 2), (4, 2), (4, 5), (5, 4)] edges = edges0 + [(2, 2)] nodes = list(range(6)) @@ -252,7 +251,7 @@ def test_monomorphism_iter1(): assert {'A': 'Z', 'B': 'X', 'C': 'Y'} in x assert len(x) == 3 gm21 = iso.DiGraphMatcher(g2, g1) - #Check if StopIteration exception returns False + # Check if StopIteration exception returns False assert not gm21.subgraph_is_monomorphic() @@ -299,6 +298,7 @@ def test_multiple(): # assert_true(m['B'] == 'B') # assert_true('C' not in m) + def test_noncomparable_nodes(): node1 = object() node2 = object() @@ -308,7 +308,7 @@ def test_noncomparable_nodes(): G = nx.path_graph([node1, node2, node3]) gm = iso.GraphMatcher(G, G) assert gm.is_isomorphic() - #Just testing some cases + # Just testing some cases assert gm.subgraph_is_monomorphic() # DiGraph @@ -316,5 +316,5 @@ def test_noncomparable_nodes(): H = nx.path_graph([node3, node2, node1], create_using=nx.DiGraph) dgm = iso.DiGraphMatcher(G, H) assert dgm.is_isomorphic() - #Just testing some cases + # Just testing some cases assert gm.subgraph_is_monomorphic() diff --git a/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py b/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py index a86dcbbb..51004c0e 100644 --- a/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py +++ b/networkx/algorithms/isomorphism/tests/test_vf2userfunc.py @@ -124,7 +124,7 @@ class TestNodeMatch_Graph(object): # make the weights disagree self.g1.add_edge('A', 'B', weight=2) assert not nx.is_isomorphic(self.g1, self.g2, - node_match=self.nm, edge_match=self.em) + node_match=self.nm, edge_match=self.em) class TestEdgeMatch_MultiGraph(object): diff --git a/networkx/algorithms/link_analysis/tests/test_hits.py b/networkx/algorithms/link_analysis/tests/test_hits.py index 8634cadb..1d84253c 100644 --- a/networkx/algorithms/link_analysis/tests/test_hits.py +++ b/networkx/algorithms/link_analysis/tests/test_hits.py @@ -26,9 +26,9 @@ class TestHITS: G.add_edges_from(edges, weight=1) cls.G = G cls.G.a = dict(zip(sorted(G), [0.000000, 0.000000, 0.366025, - 0.133975, 0.500000, 0.000000])) + 0.133975, 0.500000, 0.000000])) cls.G.h = dict(zip(sorted(G), [0.366025, 0.000000, 0.211325, - 0.000000, 0.211325, 0.211325])) + 0.000000, 0.211325, 0.211325])) def test_hits(self): G = self.G diff --git a/networkx/algorithms/link_analysis/tests/test_pagerank.py b/networkx/algorithms/link_analysis/tests/test_pagerank.py index 57c5a839..87a37872 100644 --- a/networkx/algorithms/link_analysis/tests/test_pagerank.py +++ b/networkx/algorithms/link_analysis/tests/test_pagerank.py @@ -28,14 +28,14 @@ class TestPageRank(object): G.add_edges_from(edges) cls.G = G cls.G.pagerank = dict(zip(sorted(G), - [0.03721197, 0.05395735, 0.04150565, - 0.37508082, 0.20599833, 0.28624589])) + [0.03721197, 0.05395735, 0.04150565, + 0.37508082, 0.20599833, 0.28624589])) cls.dangling_node_index = 1 cls.dangling_edges = {1: 2, 2: 3, - 3: 0, 4: 0, 5: 0, 6: 0} + 3: 0, 4: 0, 5: 0, 6: 0} cls.G.dangling_pagerank = dict(zip(sorted(G), - [0.10844518, 0.18618601, 0.0710892, - 0.2683668, 0.15919783, 0.20671497])) + [0.10844518, 0.18618601, 0.0710892, + 0.2683668, 0.15919783, 0.20671497])) def test_pagerank(self): G = self.G diff --git a/networkx/algorithms/operators/tests/test_all.py b/networkx/algorithms/operators/tests/test_all.py index 094a99aa..7d44ef22 100644 --- a/networkx/algorithms/operators/tests/test_all.py +++ b/networkx/algorithms/operators/tests/test_all.py @@ -104,12 +104,12 @@ def test_union_all_and_compose_all(): pytest.raises(nx.NetworkXError, nx.union, K3, P3) H1 = nx.union_all([H, G1], rename=('H', 'G1')) assert (sorted(H1.nodes()) == - ['G1A', 'G1B', 'G1C', 'G1D', - 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) + ['G1A', 'G1B', 'G1C', 'G1D', + 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) H2 = nx.union_all([H, G2], rename=("H", "")) assert (sorted(H2.nodes()) == - ['1', '2', '3', '4', + ['1', '2', '3', '4', 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) assert not H1.has_edge('NB', 'NA') @@ -119,7 +119,7 @@ def test_union_all_and_compose_all(): G2 = nx.union_all([G2, G2], rename=('', 'copy')) assert (sorted(G2.nodes()) == - ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4']) + ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4']) assert sorted(G2.neighbors('copy4')) == [] assert sorted(G2.neighbors('copy1')) == ['copy2', 'copy3', 'copy4'] @@ -141,8 +141,8 @@ def test_union_all_and_compose_all(): G3.add_edge(11, 22) G4 = nx.union_all([G1, G2, G3], rename=("G1", "G2", "G3")) assert (sorted(G4.nodes()) == - ['G1A', 'G1B', 'G21', 'G22', - 'G311', 'G322']) + ['G1A', 'G1B', 'G21', 'G22', + 'G311', 'G322']) def test_union_all_multigraph(): @@ -155,7 +155,7 @@ def test_union_all_multigraph(): GH = nx.union_all([G, H]) assert set(GH) == set(G) | set(H) assert (set(GH.edges(keys=True)) == - set(G.edges(keys=True)) | set(H.edges(keys=True))) + set(G.edges(keys=True)) | set(H.edges(keys=True))) def test_input_output(): diff --git a/networkx/algorithms/operators/tests/test_binary.py b/networkx/algorithms/operators/tests/test_binary.py index b27f26c0..030fe62d 100644 --- a/networkx/algorithms/operators/tests/test_binary.py +++ b/networkx/algorithms/operators/tests/test_binary.py @@ -171,7 +171,7 @@ def test_symmetric_difference_multigraph(): assert set(gh.nodes()) == set(h.nodes()) assert sorted(gh.edges()) == 3 * [(0, 1)] assert (sorted(sorted(e) for e in gh.edges(keys=True)) == - [[0, 1, 1], [0, 1, 2], [0, 1, 3]]) + [[0, 1, 1], [0, 1, 2], [0, 1, 3]]) def test_union_and_compose(): @@ -194,12 +194,12 @@ def test_union_and_compose(): pytest.raises(nx.NetworkXError, nx.union, K3, P3) H1 = nx.union(H, G1, rename=('H', 'G1')) assert (sorted(H1.nodes()) == - ['G1A', 'G1B', 'G1C', 'G1D', - 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) + ['G1A', 'G1B', 'G1C', 'G1D', + 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) H2 = nx.union(H, G2, rename=("H", "")) assert (sorted(H2.nodes()) == - ['1', '2', '3', '4', + ['1', '2', '3', '4', 'H1', 'H2', 'H3', 'H4', 'HA', 'HB', 'HC', 'HD']) assert not H1.has_edge('NB', 'NA') @@ -209,7 +209,7 @@ def test_union_and_compose(): G2 = nx.union(G2, G2, rename=('', 'copy')) assert (sorted(G2.nodes()) == - ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4']) + ['1', '2', '3', '4', 'copy1', 'copy2', 'copy3', 'copy4']) assert sorted(G2.neighbors('copy4')) == [] assert sorted(G2.neighbors('copy1')) == ['copy2', 'copy3', 'copy4'] @@ -241,7 +241,7 @@ def test_union_multigraph(): GH = nx.union(G, H) assert set(GH) == set(G) | set(H) assert (set(GH.edges(keys=True)) == - set(G.edges(keys=True)) | set(H.edges(keys=True))) + set(G.edges(keys=True)) | set(H.edges(keys=True))) def test_disjoint_union_multigraph(): @@ -254,7 +254,7 @@ def test_disjoint_union_multigraph(): GH = nx.disjoint_union(G, H) assert set(GH) == set(G) | set(H) assert (set(GH.edges(keys=True)) == - set(G.edges(keys=True)) | set(H.edges(keys=True))) + set(G.edges(keys=True)) | set(H.edges(keys=True))) def test_compose_multigraph(): @@ -267,12 +267,12 @@ def test_compose_multigraph(): GH = nx.compose(G, H) assert set(GH) == set(G) | set(H) assert (set(GH.edges(keys=True)) == - set(G.edges(keys=True)) | set(H.edges(keys=True))) + set(G.edges(keys=True)) | set(H.edges(keys=True))) H.add_edge(1, 2, key=2) GH = nx.compose(G, H) assert set(GH) == set(G) | set(H) assert (set(GH.edges(keys=True)) == - set(G.edges(keys=True)) | set(H.edges(keys=True))) + set(G.edges(keys=True)) | set(H.edges(keys=True))) def test_full_join_graph(): @@ -287,14 +287,14 @@ def test_full_join_graph(): assert set(U) == set(G) | set(H) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H)) + len(G.edges()) + len(H.edges()) + len(G) * len(H)) # Rename U = nx.full_join(G, H, rename=('g', 'h')) assert set(U) == set(['g0', 'g1', 'g2', 'h3', 'h4']) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H)) + len(G.edges()) + len(H.edges()) + len(G) * len(H)) # Rename graphs with string-like nodes G = nx.Graph() @@ -307,7 +307,7 @@ def test_full_join_graph(): assert set(U) == set(['ga', 'gb', 'gc', 'hd', 'he']) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H)) + len(G.edges()) + len(H.edges()) + len(G) * len(H)) # DiGraphs G = nx.DiGraph() @@ -320,14 +320,14 @@ def test_full_join_graph(): assert set(U) == set(G) | set(H) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G)*len(H) * 2) + len(G.edges()) + len(H.edges()) + len(G)*len(H) * 2) # DiGraphs Rename U = nx.full_join(G, H, rename=('g', 'h')) assert set(U) == set(['g0', 'g1', 'g2', 'h3', 'h4']) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) + len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) def test_full_join_multigraph(): @@ -342,14 +342,14 @@ def test_full_join_multigraph(): assert set(U) == set(G) | set(H) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H)) + len(G.edges()) + len(H.edges()) + len(G) * len(H)) # MultiGraphs rename U = nx.full_join(G, H, rename=('g', 'h')) assert set(U) == set(['g0', 'g1', 'g2', 'h3', 'h4']) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H)) + len(G.edges()) + len(H.edges()) + len(G) * len(H)) # MultiDiGraphs G = nx.MultiDiGraph() @@ -362,14 +362,14 @@ def test_full_join_multigraph(): assert set(U) == set(G) | set(H) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) + len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) # MultiDiGraphs rename U = nx.full_join(G, H, rename=('g', 'h')) assert set(U) == set(['g0', 'g1', 'g2', 'h3', 'h4']) assert len(U) == len(G) + len(H) assert (len(U.edges()) == - len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) + len(G.edges()) + len(H.edges()) + len(G) * len(H) * 2) def test_mixed_type_union(): diff --git a/networkx/algorithms/operators/tests/test_product.py b/networkx/algorithms/operators/tests/test_product.py index b6649aa8..bdb4c6cc 100644 --- a/networkx/algorithms/operators/tests/test_product.py +++ b/networkx/algorithms/operators/tests/test_product.py @@ -107,11 +107,11 @@ def test_cartesian_product_multigraph(): GH = nx.cartesian_product(G, H) assert set(GH) == {(1, 3), (2, 3), (2, 4), (1, 4)} assert ({(frozenset([u, v]), k) for u, v, k in GH.edges(keys=True)} == - {(frozenset([u, v]), k) for u, v, k in - [((1, 3), (2, 3), 0), ((1, 3), (2, 3), 1), - ((1, 3), (1, 4), 0), ((1, 3), (1, 4), 1), - ((2, 3), (2, 4), 0), ((2, 3), (2, 4), 1), - ((2, 4), (1, 4), 0), ((2, 4), (1, 4), 1)]}) + {(frozenset([u, v]), k) for u, v, k in + [((1, 3), (2, 3), 0), ((1, 3), (2, 3), 1), + ((1, 3), (1, 4), 0), ((1, 3), (1, 4), 1), + ((2, 3), (2, 4), 0), ((2, 3), (2, 4), 1), + ((2, 4), (1, 4), 0), ((2, 4), (1, 4), 1)]}) def test_cartesian_product_raises(): @@ -160,13 +160,13 @@ def test_cartesian_product_size(): G = nx.cartesian_product(P5, K3) assert nx.number_of_nodes(G) == 5 * 3 assert (nx.number_of_edges(G) == - nx.number_of_edges(P5) * nx.number_of_nodes(K3) + - nx.number_of_edges(K3) * nx.number_of_nodes(P5)) + nx.number_of_edges(P5) * nx.number_of_nodes(K3) + + nx.number_of_edges(K3) * nx.number_of_nodes(P5)) G = nx.cartesian_product(K3, K5) assert nx.number_of_nodes(G) == 3 * 5 assert (nx.number_of_edges(G) == - nx.number_of_edges(K5) * nx.number_of_nodes(K3) + - nx.number_of_edges(K3) * nx.number_of_nodes(K5)) + nx.number_of_edges(K5) * nx.number_of_nodes(K3) + + nx.number_of_edges(K3) * nx.number_of_nodes(K5)) def test_cartesian_product_classic(): diff --git a/networkx/algorithms/operators/tests/test_unary.py b/networkx/algorithms/operators/tests/test_unary.py index f954a081..75c74f71 100644 --- a/networkx/algorithms/operators/tests/test_unary.py +++ b/networkx/algorithms/operators/tests/test_unary.py @@ -36,9 +36,9 @@ def test_complement_2(): G1.add_edge('A', 'D') G1C = nx.complement(G1) assert (sorted(G1C.edges()) == - [('B', 'A'), ('B', 'C'), - ('B', 'D'), ('C', 'A'), ('C', 'B'), - ('C', 'D'), ('D', 'A'), ('D', 'B'), ('D', 'C')]) + [('B', 'A'), ('B', 'C'), + ('B', 'D'), ('C', 'A'), ('C', 'B'), + ('C', 'D'), ('D', 'A'), ('D', 'B'), ('D', 'C')]) def test_reverse1(): diff --git a/networkx/algorithms/shortest_paths/tests/test_dense.py b/networkx/algorithms/shortest_paths/tests/test_dense.py index b2446d01..6c9ab466 100644 --- a/networkx/algorithms/shortest_paths/tests/test_dense.py +++ b/networkx/algorithms/shortest_paths/tests/test_dense.py @@ -19,11 +19,11 @@ class TestFloyd: assert dist['s']['v'] == 9 assert path['s']['v'] == 'u' assert (dist == - {'y': {'y': 0, 'x': 12, 's': 7, 'u': 15, 'v': 6}, - 'x': {'y': 2, 'x': 0, 's': 9, 'u': 3, 'v': 4}, - 's': {'y': 7, 'x': 5, 's': 0, 'u': 8, 'v': 9}, - 'u': {'y': 2, 'x': 2, 's': 9, 'u': 0, 'v': 1}, - 'v': {'y': 1, 'x': 13, 's': 8, 'u': 16, 'v': 0}}) + {'y': {'y': 0, 'x': 12, 's': 7, 'u': 15, 'v': 6}, + 'x': {'y': 2, 'x': 0, 's': 9, 'u': 3, 'v': 4}, + 's': {'y': 7, 'x': 5, 's': 0, 'u': 8, 'v': 9}, + 'u': {'y': 2, 'x': 2, 's': 9, 'u': 0, 'v': 1}, + 'v': {'y': 1, 'x': 13, 's': 8, 'u': 16, 'v': 0}}) GG = XG.to_undirected() # make sure we get lower weight @@ -58,14 +58,14 @@ class TestFloyd: path, dist = nx.floyd_warshall_predecessor_and_distance(XG) inf = float("inf") assert (dist == - {'v': {'v': 0, 'x': 5.0, 'y': 6.0, 'u': 7.0}, - 'x': {'x': 0, 'u': 2.0, 'v': inf, 'y': inf}, - 'y': {'y': 0, 'x': 5.0, 'v': inf, 'u': 7.0}, - 'u': {'u': 0, 'v': inf, 'x': inf, 'y': inf}}) + {'v': {'v': 0, 'x': 5.0, 'y': 6.0, 'u': 7.0}, + 'x': {'x': 0, 'u': 2.0, 'v': inf, 'y': inf}, + 'y': {'y': 0, 'x': 5.0, 'v': inf, 'u': 7.0}, + 'u': {'u': 0, 'v': inf, 'x': inf, 'y': inf}}) assert (path == - {'v': {'x': 'v', 'y': 'v', 'u': 'x'}, - 'x': {'u': 'x'}, - 'y': {'x': 'y', 'u': 'x'}}) + {'v': {'x': 'v', 'y': 'v', 'u': 'x'}, + 'x': {'u': 'x'}, + 'y': {'x': 'y', 'u': 'x'}}) def test_reconstruct_path(self): with pytest.raises(KeyError): diff --git a/networkx/algorithms/shortest_paths/tests/test_generic.py b/networkx/algorithms/shortest_paths/tests/test_generic.py index d4953263..7f558fc7 100644 --- a/networkx/algorithms/shortest_paths/tests/test_generic.py +++ b/networkx/algorithms/shortest_paths/tests/test_generic.py @@ -4,6 +4,7 @@ import pytest import networkx as nx from networkx.testing import almost_equal + def validate_grid_path(r, c, s, t, p): assert isinstance(p, list) assert p[0] == s @@ -25,7 +26,7 @@ class TestGenericPath: def setup_class(cls): from networkx import convert_node_labels_to_integers as cnlti cls.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1, - ordering="sorted") + ordering="sorted") cls.cycle = nx.cycle_graph(7) cls.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph()) cls.neg_weights = nx.DiGraph() @@ -41,26 +42,26 @@ class TestGenericPath: assert nx.shortest_path(self.directed_cycle, 0, 3) == [0, 1, 2, 3] # now with weights assert (nx.shortest_path(self.cycle, 0, 3, weight='weight') == - [0, 1, 2, 3]) + [0, 1, 2, 3]) assert (nx.shortest_path(self.cycle, 0, 4, weight='weight') == - [0, 6, 5, 4]) + [0, 6, 5, 4]) validate_grid_path(4, 4, 1, 12, nx.shortest_path(self.grid, 1, 12, weight='weight')) assert (nx.shortest_path(self.directed_cycle, 0, 3, - weight='weight') == - [0, 1, 2, 3]) + weight='weight') == + [0, 1, 2, 3]) # weights and method specified assert (nx.shortest_path(self.directed_cycle, 0, 3, - weight='weight', method='dijkstra') == - [0, 1, 2, 3]) + weight='weight', method='dijkstra') == + [0, 1, 2, 3]) assert (nx.shortest_path(self.directed_cycle, 0, 3, - weight='weight', method='bellman-ford') == - [0, 1, 2, 3]) + weight='weight', method='bellman-ford') == + [0, 1, 2, 3]) # when Dijkstra's will probably (depending on precise implementation) # incorrectly return [0, 1, 3] instead assert (nx.shortest_path(self.neg_weights, 0, 3, weight='weight', - method='bellman-ford') == - [0, 2, 3]) + method='bellman-ford') == + [0, 2, 3]) # confirm bad method rejection pytest.raises(ValueError, nx.shortest_path, self.cycle, method='SPAM') # confirm absent source rejection @@ -87,21 +88,21 @@ class TestGenericPath: assert nx.shortest_path_length(self.directed_cycle, 0, 4) == 4 # now with weights assert (nx.shortest_path_length(self.cycle, 0, 3, - weight='weight') == - 3) + weight='weight') == + 3) assert (nx.shortest_path_length(self.grid, 1, 12, - weight='weight') == - 5) + weight='weight') == + 5) assert (nx.shortest_path_length(self.directed_cycle, 0, 4, - weight='weight') == - 4) + weight='weight') == + 4) # weights and method specified assert (nx.shortest_path_length(self.cycle, 0, 3, weight='weight', - method='dijkstra') == - 3) + method='dijkstra') == + 3) assert (nx.shortest_path_length(self.cycle, 0, 3, weight='weight', - method='bellman-ford') == - 3) + method='bellman-ford') == + 3) # confirm bad method rejection pytest.raises(ValueError, nx.shortest_path_length, @@ -151,8 +152,8 @@ class TestGenericPath: ans = dict(nx.shortest_path_length(self.cycle, 0)) assert ans == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1} assert (ans == - dict(nx.single_source_shortest_path_length(self.cycle, - 0))) + dict(nx.single_source_shortest_path_length(self.cycle, + 0))) ans = dict(nx.shortest_path_length(self.grid, 1)) assert ans[16] == 6 # now with weights @@ -216,7 +217,7 @@ class TestGenericPath: method='bellman-ford')) assert ans[0] == {0: 0, 1: 1, 2: 2, 3: 3, 4: 3, 5: 2, 6: 1} assert (ans == - dict(nx.all_pairs_bellman_ford_path_length(self.cycle))) + dict(nx.all_pairs_bellman_ford_path_length(self.cycle))) def test_has_path(self): G = nx.Graph() @@ -230,26 +231,26 @@ class TestGenericPath: nx.add_path(G, [0, 1, 2, 3]) nx.add_path(G, [0, 10, 20, 3]) assert ([[0, 1, 2, 3], [0, 10, 20, 3]] == - sorted(nx.all_shortest_paths(G, 0, 3))) + sorted(nx.all_shortest_paths(G, 0, 3))) # with weights G = nx.Graph() nx.add_path(G, [0, 1, 2, 3]) nx.add_path(G, [0, 10, 20, 3]) assert ([[0, 1, 2, 3], [0, 10, 20, 3]] == - sorted(nx.all_shortest_paths(G, 0, 3, weight='weight'))) + sorted(nx.all_shortest_paths(G, 0, 3, weight='weight'))) # weights and method specified G = nx.Graph() nx.add_path(G, [0, 1, 2, 3]) nx.add_path(G, [0, 10, 20, 3]) assert ([[0, 1, 2, 3], [0, 10, 20, 3]] == - sorted(nx.all_shortest_paths(G, 0, 3, weight='weight', - method='dijkstra'))) + sorted(nx.all_shortest_paths(G, 0, 3, weight='weight', + method='dijkstra'))) G = nx.Graph() nx.add_path(G, [0, 1, 2, 3]) nx.add_path(G, [0, 10, 20, 3]) assert ([[0, 1, 2, 3], [0, 10, 20, 3]] == - sorted(nx.all_shortest_paths(G, 0, 3, weight='weight', - method='bellman-ford'))) + sorted(nx.all_shortest_paths(G, 0, 3, weight='weight', + method='bellman-ford'))) def test_all_shortest_paths_raise(self): with pytest.raises(nx.NetworkXNoPath): @@ -353,7 +354,6 @@ class TestAverageShortestPathLengthNumpy(object): numpy = pytest.importorskip('numpy') npt = pytest.importorskip('numpy.testing') - def test_specified_methods_numpy(self): G = nx.Graph() nx.add_cycle(G, range(7), weight=2) diff --git a/networkx/algorithms/shortest_paths/tests/test_unweighted.py b/networkx/algorithms/shortest_paths/tests/test_unweighted.py index 9cf63fbd..b2c41e5d 100644 --- a/networkx/algorithms/shortest_paths/tests/test_unweighted.py +++ b/networkx/algorithms/shortest_paths/tests/test_unweighted.py @@ -28,12 +28,12 @@ class TestUnweightedPath: def test_bidirectional_shortest_path(self): assert (nx.bidirectional_shortest_path(self.cycle, 0, 3) == - [0, 1, 2, 3]) + [0, 1, 2, 3]) assert (nx.bidirectional_shortest_path(self.cycle, 0, 4) == - [0, 6, 5, 4]) + [0, 6, 5, 4]) validate_grid_path(4, 4, 1, 12, nx.bidirectional_shortest_path(self.grid, 1, 12)) assert (nx.bidirectional_shortest_path(self.directed_cycle, 0, 3) == - [0, 1, 2, 3]) + [0, 1, 2, 3]) def test_shortest_path_length(self): assert nx.shortest_path_length(self.cycle, 0, 3) == 3 diff --git a/networkx/algorithms/shortest_paths/tests/test_weighted.py b/networkx/algorithms/shortest_paths/tests/test_weighted.py index 6d97787a..4de07d21 100644 --- a/networkx/algorithms/shortest_paths/tests/test_weighted.py +++ b/networkx/algorithms/shortest_paths/tests/test_weighted.py @@ -166,7 +166,7 @@ class TestWeightedPath(WeightedTestBase): def test_dijkstra_predecessor1(self): G = nx.path_graph(4) assert (nx.dijkstra_predecessor_and_distance(G, 0) == - ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})) + ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})) def test_dijkstra_predecessor2(self): # 4-cycle @@ -412,36 +412,36 @@ class TestBellmanFordAndGoldbergRadzik(WeightedTestBase): G = nx.cycle_graph(5, create_using=nx.DiGraph()) G.add_edge(1, 2, weight=-3) assert (nx.single_source_bellman_ford_path(G, 0) == - {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]}) + {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]}) assert (nx.single_source_bellman_ford_path_length(G, 0) == - {0: 0, 1: 1, 2: -2, 3: -1, 4: 0}) + {0: 0, 1: 1, 2: -2, 3: -1, 4: 0}) assert (nx.single_source_bellman_ford(G, 0) == - ({0: 0, 1: 1, 2: -2, 3: -1, 4: 0}, - {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]})) + ({0: 0, 1: 1, 2: -2, 3: -1, 4: 0}, + {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3], 4: [0, 1, 2, 3, 4]})) assert (nx.bellman_ford_predecessor_and_distance(G, 0) == - ({0: [], 1: [0], 2: [1], 3: [2], 4: [3]}, - {0: 0, 1: 1, 2: -2, 3: -1, 4: 0})) + ({0: [], 1: [0], 2: [1], 3: [2], 4: [3]}, + {0: 0, 1: 1, 2: -2, 3: -1, 4: 0})) assert (nx.goldberg_radzik(G, 0) == - ({0: None, 1: 0, 2: 1, 3: 2, 4: 3}, - {0: 0, 1: 1, 2: -2, 3: -1, 4: 0})) + ({0: None, 1: 0, 2: 1, 3: 2, 4: 3}, + {0: 0, 1: 1, 2: -2, 3: -1, 4: 0})) def test_not_connected(self): G = nx.complete_graph(6) G.add_edge(10, 11) G.add_edge(10, 12) assert (nx.single_source_bellman_ford_path(G, 0) == - {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]}) + {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]}) assert (nx.single_source_bellman_ford_path_length(G, 0) == - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}) + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}) assert (nx.single_source_bellman_ford(G, 0) == - ({0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, - {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]})) + ({0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, + {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]})) assert (nx.bellman_ford_predecessor_and_distance(G, 0) == - ({0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]}, - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) + ({0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]}, + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) assert (nx.goldberg_radzik(G, 0) == - ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) + ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) # not connected, with a component not containing the source that # contains a negative cost cycle. @@ -450,18 +450,18 @@ class TestBellmanFordAndGoldbergRadzik(WeightedTestBase): ('B', 'C', {'load': -10}), ('C', 'A', {'load': 2})]) assert (nx.single_source_bellman_ford_path(G, 0, weight='load') == - {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]}) + {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]}) assert (nx.single_source_bellman_ford_path_length(G, 0, weight='load') == - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}) + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}) assert (nx.single_source_bellman_ford(G, 0, weight='load') == - ({0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, - {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]})) + ({0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}, + {0: [0], 1: [0, 1], 2: [0, 2], 3: [0, 3], 4: [0, 4], 5: [0, 5]})) assert (nx.bellman_ford_predecessor_and_distance(G, 0, weight='load') == - ({0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]}, - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) + ({0: [], 1: [0], 2: [0], 3: [0], 4: [0], 5: [0]}, + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) assert (nx.goldberg_radzik(G, 0, weight='load') == - ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, - {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) + ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, + {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})) def test_multigraph(self): assert nx.bellman_ford_path(self.MXG, 's', 'v') == ['s', 'x', 'u', 'v'] @@ -509,25 +509,25 @@ class TestBellmanFordAndGoldbergRadzik(WeightedTestBase): def test_path_graph(self): G = nx.path_graph(4) assert (nx.single_source_bellman_ford_path(G, 0) == - {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3]}) + {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3]}) assert (nx.single_source_bellman_ford_path_length(G, 0) == - {0: 0, 1: 1, 2: 2, 3: 3}) + {0: 0, 1: 1, 2: 2, 3: 3}) assert (nx.single_source_bellman_ford(G, 0) == - ({0: 0, 1: 1, 2: 2, 3: 3}, {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3]})) + ({0: 0, 1: 1, 2: 2, 3: 3}, {0: [0], 1: [0, 1], 2: [0, 1, 2], 3: [0, 1, 2, 3]})) assert (nx.bellman_ford_predecessor_and_distance(G, 0) == - ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})) + ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})) assert (nx.goldberg_radzik(G, 0) == - ({0: None, 1: 0, 2: 1, 3: 2}, {0: 0, 1: 1, 2: 2, 3: 3})) + ({0: None, 1: 0, 2: 1, 3: 2}, {0: 0, 1: 1, 2: 2, 3: 3})) assert (nx.single_source_bellman_ford_path(G, 3) == - {0: [3, 2, 1, 0], 1: [3, 2, 1], 2: [3, 2], 3: [3]}) + {0: [3, 2, 1, 0], 1: [3, 2, 1], 2: [3, 2], 3: [3]}) assert (nx.single_source_bellman_ford_path_length(G, 3) == - {0: 3, 1: 2, 2: 1, 3: 0}) + {0: 3, 1: 2, 2: 1, 3: 0}) assert (nx.single_source_bellman_ford(G, 3) == - ({0: 3, 1: 2, 2: 1, 3: 0}, {0: [3, 2, 1, 0], 1: [3, 2, 1], 2: [3, 2], 3: [3]})) + ({0: 3, 1: 2, 2: 1, 3: 0}, {0: [3, 2, 1, 0], 1: [3, 2, 1], 2: [3, 2], 3: [3]})) assert (nx.bellman_ford_predecessor_and_distance(G, 3) == - ({0: [1], 1: [2], 2: [3], 3: []}, {0: 3, 1: 2, 2: 1, 3: 0})) + ({0: [1], 1: [2], 2: [3], 3: []}, {0: 3, 1: 2, 2: 1, 3: 0})) assert (nx.goldberg_radzik(G, 3) == - ({0: 1, 1: 2, 2: 3, 3: None}, {0: 3, 1: 2, 2: 1, 3: 0})) + ({0: 1, 1: 2, 2: 3, 3: None}, {0: 3, 1: 2, 2: 1, 3: 0})) def test_4_cycle(self): # 4-cycle @@ -556,10 +556,10 @@ class TestBellmanFordAndGoldbergRadzik(WeightedTestBase): def test_negative_weight(self): G = nx.DiGraph() G.add_nodes_from('abcd') - G.add_edge('a','d', weight = 0) - G.add_edge('a','b', weight = 1) - G.add_edge('b','c', weight = -3) - G.add_edge('c','d', weight = 1) + G.add_edge('a', 'd', weight=0) + G.add_edge('a', 'b', weight=1) + G.add_edge('b', 'c', weight=-3) + G.add_edge('c', 'd', weight=1) assert nx.bellman_ford_path(G, 'a', 'd') == ['a', 'b', 'c', 'd'] assert nx.bellman_ford_path_length(G, 'a', 'd') == -1 @@ -592,10 +592,10 @@ class TestJohnsonAlgorithm(WeightedTestBase): ('2', '3', 1)]) paths = nx.johnson(G) assert paths == {'1': {'1': ['1'], '3': ['1', '2', '3'], - '2': ['1', '2']}, '0': {'1': ['0', '1'], - '0': ['0'], '3': ['0', '1', '2', '3'], - '2': ['0', '1', '2']}, '3': {'3': ['3']}, - '2': {'3': ['2', '3'], '2': ['2']}} + '2': ['1', '2']}, '0': {'1': ['0', '1'], + '0': ['0'], '3': ['0', '1', '2', '3'], + '2': ['0', '1', '2']}, '3': {'3': ['3']}, + '2': {'3': ['2', '3'], '2': ['2']}} def test_unweighted_graph(self): with pytest.raises(nx.NetworkXError): diff --git a/networkx/algorithms/similarity.py b/networkx/algorithms/similarity.py index f558ba64..f0306c68 100644 --- a/networkx/algorithms/similarity.py +++ b/networkx/algorithms/similarity.py @@ -793,7 +793,6 @@ def optimize_edit_paths(G1, G2, node_match=None, edge_match=None, yield from sorted(other, key=lambda t: t[4] + t[1].ls + t[3].ls) - def get_edit_paths(matched_uv, pending_u, pending_v, Cv, matched_gh, pending_g, pending_h, Ce, matched_cost): """ @@ -826,15 +825,15 @@ def optimize_edit_paths(G1, G2, node_match=None, edge_match=None, #debug_print('matched-cost:', matched_cost) #debug_print('pending-u:', pending_u) #debug_print('pending-v:', pending_v) - #debug_print(Cv.C) + # debug_print(Cv.C) #assert list(sorted(G1.nodes)) == list(sorted(list(u for u, v in matched_uv if u is not None) + pending_u)) #assert list(sorted(G2.nodes)) == list(sorted(list(v for u, v in matched_uv if v is not None) + pending_v)) #debug_print('pending-g:', pending_g) #debug_print('pending-h:', pending_h) - #debug_print(Ce.C) + # debug_print(Ce.C) #assert list(sorted(G1.edges)) == list(sorted(list(g for g, h in matched_gh if g is not None) + pending_g)) #assert list(sorted(G2.edges)) == list(sorted(list(h for g, h in matched_gh if h is not None) + pending_h)) - #debug_print() + # debug_print() if prune(matched_cost + Cv.ls + Ce.ls): return @@ -933,7 +932,7 @@ def optimize_edit_paths(G1, G2, node_match=None, edge_match=None, ).reshape(n, n) Cv = make_CostMatrix(C, m, n) #debug_print('Cv: {} x {}'.format(m, n)) - #debug_print(Cv.C) + # debug_print(Cv.C) pending_g = list(G1.edges) pending_h = list(G2.edges) @@ -973,8 +972,8 @@ def optimize_edit_paths(G1, G2, node_match=None, edge_match=None, ).reshape(n, n) Ce = make_CostMatrix(C, m, n) #debug_print('Ce: {} x {}'.format(m, n)) - #debug_print(Ce.C) - #debug_print() + # debug_print(Ce.C) + # debug_print() class MaxCost: def __init__(self): diff --git a/networkx/algorithms/tests/test_boundary.py b/networkx/algorithms/tests/test_boundary.py index ce64ae0b..8dbe3d69 100644 --- a/networkx/algorithms/tests/test_boundary.py +++ b/networkx/algorithms/tests/test_boundary.py @@ -109,13 +109,13 @@ class TestEdgeBoundary(object): assert list(nx.edge_boundary(P10, [], [])) == [] assert list(nx.edge_boundary(P10, [1, 2, 3])) == [(3, 4)] assert (sorted(nx.edge_boundary(P10, [4, 5, 6])) == - [(4, 3), (6, 7)]) + [(4, 3), (6, 7)]) assert (sorted(nx.edge_boundary(P10, [3, 4, 5, 6, 7])) == - [(3, 2), (7, 8)]) + [(3, 2), (7, 8)]) assert list(nx.edge_boundary(P10, [8, 9, 10])) == [(8, 7)] assert sorted(nx.edge_boundary(P10, [4, 5, 6], [9, 10])) == [] assert (list(nx.edge_boundary(P10, [1, 2, 3], [3, 4, 5])) == - [(2, 3), (3, 4)]) + [(2, 3), (3, 4)]) def test_complete_graph(self): K10 = cnlti(nx.complete_graph(10), first_label=1) diff --git a/networkx/algorithms/tests/test_chordal.py b/networkx/algorithms/tests/test_chordal.py index e7719275..cb1bb82f 100644 --- a/networkx/algorithms/tests/test_chordal.py +++ b/networkx/algorithms/tests/test_chordal.py @@ -55,7 +55,7 @@ class TestMCS: cliqueset = nx.chordal_graph_cliques(G) for (u, v) in G.edges(): assert (frozenset([u, v]) in cliqueset - or frozenset([v, u]) in cliqueset) + or frozenset([v, u]) in cliqueset) def test_chordal_find_cliquesCC(self): cliques = set([frozenset([1, 2, 3]), frozenset([2, 3, 4]), diff --git a/networkx/algorithms/tests/test_clique.py b/networkx/algorithms/tests/test_clique.py index 595015a9..e097c9bd 100644 --- a/networkx/algorithms/tests/test_clique.py +++ b/networkx/algorithms/tests/test_clique.py @@ -34,7 +34,7 @@ class TestCliques: def test_find_cliques2(self): hcl = list(nx.find_cliques(self.H)) assert (sorted(map(sorted, hcl)) == - [[1, 2], [1, 4, 5, 6], [2, 3], [3, 4, 6]]) + [[1, 2], [1, 4, 5, 6], [2, 3], [3, 4, 6]]) def test_clique_number(self): G = self.G @@ -60,19 +60,19 @@ class TestCliques: assert nx.number_of_cliques(G, [1, 2]) == {1: 1, 2: 2} assert nx.number_of_cliques(G, 2) == 2 assert (nx.number_of_cliques(G) == - {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, - 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) + {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, + 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) assert (nx.number_of_cliques(G, nodes=list(G)) == - {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, - 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) + {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, + 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) assert (nx.number_of_cliques(G, nodes=[2, 3, 4]) == - {2: 2, 3: 1, 4: 2}) + {2: 2, 3: 1, 4: 2}) assert (nx.number_of_cliques(G, cliques=self.cl) == - {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, - 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) + {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, + 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) assert (nx.number_of_cliques(G, list(G), cliques=self.cl) == - {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, - 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) + {1: 1, 2: 2, 3: 1, 4: 2, 5: 1, + 6: 2, 7: 1, 8: 1, 9: 1, 10: 1, 11: 1}) def test_node_clique_number(self): G = self.G @@ -82,27 +82,27 @@ class TestCliques: assert nx.node_clique_number(G, [1, 2]) == {1: 4, 2: 4} assert nx.node_clique_number(G, 1) == 4 assert (nx.node_clique_number(G) == - {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4, - 7: 3, 8: 2, 9: 2, 10: 2, 11: 2}) + {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4, + 7: 3, 8: 2, 9: 2, 10: 2, 11: 2}) assert (nx.node_clique_number(G, cliques=self.cl) == - {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4, - 7: 3, 8: 2, 9: 2, 10: 2, 11: 2}) + {1: 4, 2: 4, 3: 4, 4: 3, 5: 3, 6: 4, + 7: 3, 8: 2, 9: 2, 10: 2, 11: 2}) def test_cliques_containing_node(self): G = self.G assert (nx.cliques_containing_node(G, 1) == - [[2, 6, 1, 3]]) + [[2, 6, 1, 3]]) assert (list(nx.cliques_containing_node(G, [1]).values()) == - [[[2, 6, 1, 3]]]) + [[[2, 6, 1, 3]]]) assert ([sorted(c) for c in list(nx.cliques_containing_node(G, [1, 2]).values())] == - [[[2, 6, 1, 3]], [[2, 6, 1, 3], [2, 6, 4]]]) + [[[2, 6, 1, 3]], [[2, 6, 1, 3], [2, 6, 4]]]) result = nx.cliques_containing_node(G, [1, 2]) for k, v in result.items(): result[k] = sorted(v) assert (result == - {1: [[2, 6, 1, 3]], 2: [[2, 6, 1, 3], [2, 6, 4]]}) + {1: [[2, 6, 1, 3]], 2: [[2, 6, 1, 3], [2, 6, 4]]}) assert (nx.cliques_containing_node(G, 1) == - [[2, 6, 1, 3]]) + [[2, 6, 1, 3]]) expected = [{2, 6, 1, 3}, {2, 6, 4}] answer = [set(c) for c in nx.cliques_containing_node(G, 2)] assert answer in (expected, list(reversed(expected))) @@ -115,7 +115,7 @@ class TestCliques: G = self.G B = nx.make_clique_bipartite(G) assert (sorted(B) == - [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) + [-5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) # Project onto the nodes of the original graph. H = nx.project(B, range(1, 12)) assert H.adj == G.adj @@ -212,4 +212,4 @@ class TestEnumerateAllCliques: ['a', 'b', 'c', 'e']] assert (sorted(map(sorted, cliques)) == - sorted(map(sorted, expected_cliques))) + sorted(map(sorted, expected_cliques))) diff --git a/networkx/algorithms/tests/test_cluster.py b/networkx/algorithms/tests/test_cluster.py index 2e76114c..f68957b8 100644 --- a/networkx/algorithms/tests/test_cluster.py +++ b/networkx/algorithms/tests/test_cluster.py @@ -11,15 +11,15 @@ class TestTriangles: def test_path(self): G = nx.path_graph(10) assert (list(nx.triangles(G).values()) == - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]) assert (nx.triangles(G) == - {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, - 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}) + {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, + 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}) def test_cubical(self): G = nx.cubical_graph() assert (list(nx.triangles(G).values()) == - [0, 0, 0, 0, 0, 0, 0, 0]) + [0, 0, 0, 0, 0, 0, 0, 0]) assert nx.triangles(G, 1) == 0 assert list(nx.triangles(G, [1, 2]).values()) == [0, 0] assert nx.triangles(G, 1) == 0 @@ -45,10 +45,10 @@ class TestDirectedClustering: def test_path(self): G = nx.path_graph(10, create_using=nx.DiGraph()) assert (list(nx.clustering(G).values()) == - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert (nx.clustering(G) == - {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, - 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) + {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, + 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) def test_k5(self): G = nx.complete_graph(5, create_using=nx.DiGraph()) @@ -56,11 +56,11 @@ class TestDirectedClustering: assert nx.average_clustering(G) == 1 G.remove_edge(1, 2) assert (list(nx.clustering(G).values()) == - [11. / 12., 1.0, 1.0, 11. / 12., 11. / 12.]) - assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 11. /12.} + [11. / 12., 1.0, 1.0, 11. / 12., 11. / 12.]) + assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 11. / 12.} G.remove_edge(2, 1) assert (list(nx.clustering(G).values()) == - [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) + [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} def test_triangle_and_edge(self): @@ -79,10 +79,10 @@ class TestDirectedWeightedClustering: def test_path(self): G = nx.path_graph(10, create_using=nx.DiGraph()) assert (list(nx.clustering(G, weight='weight').values()) == - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert (nx.clustering(G, weight='weight') == - {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, - 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) + {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, + 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) def test_k5(self): G = nx.complete_graph(5, create_using=nx.DiGraph()) @@ -90,11 +90,11 @@ class TestDirectedWeightedClustering: assert nx.average_clustering(G, weight='weight') == 1 G.remove_edge(1, 2) assert (list(nx.clustering(G, weight='weight').values()) == - [11. / 12., 1.0, 1.0, 11. / 12., 11. / 12.]) - assert nx.clustering(G, [1, 4], weight='weight') == {1: 1.0, 4: 11. /12.} + [11. / 12., 1.0, 1.0, 11. / 12., 11. / 12.]) + assert nx.clustering(G, [1, 4], weight='weight') == {1: 1.0, 4: 11. / 12.} G.remove_edge(2, 1) assert (list(nx.clustering(G, weight='weight').values()) == - [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) + [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) assert nx.clustering(G, [1, 4], weight='weight') == {1: 1.0, 4: 0.83333333333333337} def test_triangle_and_edge(self): @@ -114,15 +114,15 @@ class TestWeightedClustering: def test_path(self): G = nx.path_graph(10) assert (list(nx.clustering(G, weight='weight').values()) == - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert (nx.clustering(G, weight='weight') == - {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, - 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) + {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, + 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) def test_cubical(self): G = nx.cubical_graph() assert (list(nx.clustering(G, weight='weight').values()) == - [0, 0, 0, 0, 0, 0, 0, 0]) + [0, 0, 0, 0, 0, 0, 0, 0]) assert nx.clustering(G, 1) == 0 assert list(nx.clustering(G, [1, 2], weight='weight').values()) == [0, 0] assert nx.clustering(G, 1, weight='weight') == 0 @@ -134,7 +134,7 @@ class TestWeightedClustering: assert nx.average_clustering(G, weight='weight') == 1 G.remove_edge(1, 2) assert (list(nx.clustering(G, weight='weight').values()) == - [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) + [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) assert nx.clustering(G, [1, 4], weight='weight') == {1: 1.0, 4: 0.83333333333333337} def test_triangle_and_edge(self): @@ -154,15 +154,15 @@ class TestClustering: def test_path(self): G = nx.path_graph(10) assert (list(nx.clustering(G).values()) == - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert (nx.clustering(G) == - {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, - 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) + {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, + 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) def test_cubical(self): G = nx.cubical_graph() assert (list(nx.clustering(G).values()) == - [0, 0, 0, 0, 0, 0, 0, 0]) + [0, 0, 0, 0, 0, 0, 0, 0]) assert nx.clustering(G, 1) == 0 assert list(nx.clustering(G, [1, 2]).values()) == [0, 0] assert nx.clustering(G, 1) == 0 @@ -174,7 +174,7 @@ class TestClustering: assert nx.average_clustering(G) == 1 G.remove_edge(1, 2) assert (list(nx.clustering(G).values()) == - [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) + [5. / 6., 1.0, 1.0, 5. / 6., 5. / 6.]) assert nx.clustering(G, [1, 4]) == {1: 1.0, 4: 0.83333333333333337} @@ -209,15 +209,15 @@ class TestSquareClustering: def test_path(self): G = nx.path_graph(10) assert (list(nx.square_clustering(G).values()) == - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) + [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) assert (nx.square_clustering(G) == - {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, - 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) + {0: 0.0, 1: 0.0, 2: 0.0, 3: 0.0, 4: 0.0, + 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.0, 9: 0.0}) def test_cubical(self): G = nx.cubical_graph() assert (list(nx.square_clustering(G).values()) == - [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]) + [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5]) assert list(nx.square_clustering(G, [1, 2]).values()) == [0.5, 0.5] assert nx.square_clustering(G, [1])[1] == 0.5 assert nx.square_clustering(G, [1, 2]) == {1: 0.5, 2: 0.5} @@ -229,7 +229,7 @@ class TestSquareClustering: def test_bipartite_k5(self): G = nx.complete_bipartite_graph(5, 5) assert (list(nx.square_clustering(G).values()) == - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]) def test_lind_square_clustering(self): """Test C4 for figure 1 Lind et al (2005)""" diff --git a/networkx/algorithms/tests/test_core.py b/networkx/algorithms/tests/test_core.py index 585ca6b2..52d1444b 100644 --- a/networkx/algorithms/tests/test_core.py +++ b/networkx/algorithms/tests/test_core.py @@ -128,16 +128,16 @@ class TestCore: def test_k_truss(self): # k=-1 k_truss_subgraph = nx.k_truss(self.G, -1) - assert sorted(k_truss_subgraph.nodes()) == list(range(1,21)) + assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21)) # k=0 k_truss_subgraph = nx.k_truss(self.G, 0) - assert sorted(k_truss_subgraph.nodes()) == list(range(1,21)) + assert sorted(k_truss_subgraph.nodes()) == list(range(1, 21)) # k=1 k_truss_subgraph = nx.k_truss(self.G, 1) - assert sorted(k_truss_subgraph.nodes()) == list(range(1,13)) + assert sorted(k_truss_subgraph.nodes()) == list(range(1, 13)) # k=2 k_truss_subgraph = nx.k_truss(self.G, 2) - assert sorted(k_truss_subgraph.nodes()) == list(range(1,9)) + assert sorted(k_truss_subgraph.nodes()) == list(range(1, 9)) # k=3 k_truss_subgraph = nx.k_truss(self.G, 3) assert sorted(k_truss_subgraph.nodes()) == [] diff --git a/networkx/algorithms/tests/test_cycles.py b/networkx/algorithms/tests/test_cycles.py index 003f660a..c8344715 100644 --- a/networkx/algorithms/tests/test_cycles.py +++ b/networkx/algorithms/tests/test_cycles.py @@ -43,7 +43,7 @@ class TestCycles: cy = networkx.cycle_basis(G, 9) sort_cy = sorted(sorted(c) for c in cy[:-1]) + [sorted(cy[-1])] assert sort_cy == [[0, 1, 2, 3], [0, 1, 6, 7, 8], [0, 3, 4, 5], - ['A', 'B', 'C']] + ['A', 'B', 'C']] def test_cycle_basis(self): with pytest.raises(nx.NetworkXNotImplemented): diff --git a/networkx/algorithms/tests/test_dag.py b/networkx/algorithms/tests/test_dag.py index bb841c93..93231c46 100644 --- a/networkx/algorithms/tests/test_dag.py +++ b/networkx/algorithms/tests/test_dag.py @@ -203,11 +203,11 @@ class TestDAG: def test_all_topological_sorts_2(self): DG = nx.DiGraph([(1, 3), (2, 1), (2, 4), (4, 3), (4, 5)]) assert (sorted(nx.all_topological_sorts(DG)) == - [[2, 1, 4, 3, 5], - [2, 1, 4, 5, 3], - [2, 4, 1, 3, 5], - [2, 4, 1, 5, 3], - [2, 4, 5, 1, 3]]) + [[2, 1, 4, 3, 5], + [2, 1, 4, 5, 3], + [2, 4, 1, 3, 5], + [2, 4, 1, 5, 3], + [2, 4, 5, 1, 3]]) def test_all_topological_sorts_3(self): def unfeasible(): @@ -232,14 +232,14 @@ class TestDAG: for i in range(7): DG.add_node(i) assert (sorted(map(list, permutations(DG.nodes))) == - sorted(nx.all_topological_sorts(DG))) + sorted(nx.all_topological_sorts(DG))) def test_all_topological_sorts_multigraph_1(self): DG = nx.MultiDiGraph([(1, 2), (1, 2), (2, 3), (3, 4), (3, 5), (3, 5), (3, 5)]) assert (sorted(nx.all_topological_sorts(DG)) == - sorted([[1, 2, 3, 4, 5], - [1, 2, 3, 5, 4]])) + sorted([[1, 2, 3, 4, 5], + [1, 2, 3, 5, 4]])) def test_all_topological_sorts_multigraph_2(self): N = 9 @@ -248,7 +248,7 @@ class TestDAG: edges.extend([(i, i+1)] * i) DG = nx.MultiDiGraph(edges) assert (list(nx.all_topological_sorts(DG)) == - [list(range(1, N+1))]) + [list(range(1, N+1))]) def test_ancestors(self): G = nx.DiGraph() @@ -393,7 +393,7 @@ class TestDAG: def test_lexicographical_topological_sort(self): G = nx.DiGraph([(1, 2), (2, 3), (1, 4), (1, 5), (2, 6)]) assert (list(nx.lexicographical_topological_sort(G)) == - [1, 2, 3, 4, 5, 6]) + [1, 2, 3, 4, 5, 6]) assert (list(nx.lexicographical_topological_sort( G, key=lambda x: x)) == [1, 2, 3, 4, 5, 6]) diff --git a/networkx/algorithms/tests/test_distance_measures.py b/networkx/algorithms/tests/test_distance_measures.py index 49e4569b..0a402070 100644 --- a/networkx/algorithms/tests/test_distance_measures.py +++ b/networkx/algorithms/tests/test_distance_measures.py @@ -112,10 +112,10 @@ class TestResistanceDistance: def test_laplacian_submatrix(self): from networkx.algorithms.distance_measures import _laplacian_submatrix M = sp_sparse.csr_matrix([[1, 2, 3], - [4, 5, 6], - [7, 8, 9]], dtype=np.float32) + [4, 5, 6], + [7, 8, 9]], dtype=np.float32) N = sp_sparse.csr_matrix([[5, 6], - [8, 9]], dtype=np.float32) + [8, 9]], dtype=np.float32) Mn, Mn_nodelist = _laplacian_submatrix(1, M, [1, 2, 3]) assert Mn_nodelist == [2, 3] assert np.allclose(Mn.toarray(), N.toarray()) @@ -124,16 +124,16 @@ class TestResistanceDistance: with pytest.raises(nx.NetworkXError): from networkx.algorithms.distance_measures import _laplacian_submatrix M = sp_sparse.csr_matrix([[1, 2], - [4, 5], - [7, 8]], dtype=np.float32) + [4, 5], + [7, 8]], dtype=np.float32) _laplacian_submatrix(1, M, [1, 2, 3]) def test_laplacian_submatrix_matrix_node_dim(self): with pytest.raises(nx.NetworkXError): from networkx.algorithms.distance_measures import _laplacian_submatrix M = sp_sparse.csr_matrix([[1, 2, 3], - [4, 5, 6], - [7, 8, 9]], dtype=np.float32) + [4, 5, 6], + [7, 8, 9]], dtype=np.float32) _laplacian_submatrix(1, M, [1, 2, 3, 4]) def test_resistance_distance(self): diff --git a/networkx/algorithms/tests/test_dominance.py b/networkx/algorithms/tests/test_dominance.py index 5f1f36fb..218d8081 100644 --- a/networkx/algorithms/tests/test_dominance.py +++ b/networkx/algorithms/tests/test_dominance.py @@ -24,20 +24,20 @@ class TestImmediateDominators(object): n = 5 G = nx.path_graph(n, create_using=nx.DiGraph()) assert (nx.immediate_dominators(G, 0) == - {i: max(i - 1, 0) for i in range(n)}) + {i: max(i - 1, 0) for i in range(n)}) def test_cycle(self): n = 5 G = nx.cycle_graph(n, create_using=nx.DiGraph()) assert (nx.immediate_dominators(G, 0) == - {i: max(i - 1, 0) for i in range(n)}) + {i: max(i - 1, 0) for i in range(n)}) def test_unreachable(self): n = 5 assert n > 1 G = nx.path_graph(n, create_using=nx.DiGraph()) assert (nx.immediate_dominators(G, n // 2) == - {i: max(i - 1, n // 2) for i in range(n // 2, n)}) + {i: max(i - 1, n // 2) for i in range(n // 2, n)}) def test_irreducible1(self): # Graph taken from Figure 2 of @@ -47,7 +47,7 @@ class TestImmediateDominators(object): edges = [(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)] G = nx.DiGraph(edges) assert (nx.immediate_dominators(G, 5) == - {i: 5 for i in range(1, 6)}) + {i: 5 for i in range(1, 6)}) def test_irreducible2(self): # Graph taken from Figure 4 of @@ -58,18 +58,18 @@ class TestImmediateDominators(object): (6, 4), (6, 5)] G = nx.DiGraph(edges) assert (nx.immediate_dominators(G, 6) == - {i: 6 for i in range(1, 7)}) + {i: 6 for i in range(1, 7)}) def test_domrel_png(self): # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)] G = nx.DiGraph(edges) assert (nx.immediate_dominators(G, 1) == - {1: 1, 2: 1, 3: 2, 4: 2, 5: 2, 6: 2}) + {1: 1, 2: 1, 3: 2, 4: 2, 5: 2, 6: 2}) # Test postdominance. with nx.utils.reversed(G): assert (nx.immediate_dominators(G, 6) == - {1: 2, 2: 6, 3: 5, 4: 5, 5: 2, 6: 6}) + {1: 2, 2: 6, 3: 5, 4: 5, 5: 2, 6: 6}) def test_boost_example(self): # Graph taken from Figure 1 of @@ -78,11 +78,11 @@ class TestImmediateDominators(object): (5, 7), (6, 4)] G = nx.DiGraph(edges) assert (nx.immediate_dominators(G, 0) == - {0: 0, 1: 0, 2: 1, 3: 1, 4: 3, 5: 4, 6: 4, 7: 1}) + {0: 0, 1: 0, 2: 1, 3: 1, 4: 3, 5: 4, 6: 4, 7: 1}) # Test postdominance. with nx.utils.reversed(G): assert (nx.immediate_dominators(G, 7) == - {0: 1, 1: 7, 2: 7, 3: 4, 4: 5, 5: 7, 6: 4, 7: 7}) + {0: 1, 1: 7, 2: 7, 3: 4, 4: 5, 5: 7, 6: 4, 7: 7}) class TestDominanceFrontiers(object): @@ -107,20 +107,20 @@ class TestDominanceFrontiers(object): n = 5 G = nx.path_graph(n, create_using=nx.DiGraph()) assert (nx.dominance_frontiers(G, 0) == - {i: set() for i in range(n)}) + {i: set() for i in range(n)}) def test_cycle(self): n = 5 G = nx.cycle_graph(n, create_using=nx.DiGraph()) assert (nx.dominance_frontiers(G, 0) == - {i: set() for i in range(n)}) + {i: set() for i in range(n)}) def test_unreachable(self): n = 5 assert n > 1 G = nx.path_graph(n, create_using=nx.DiGraph()) assert (nx.dominance_frontiers(G, n // 2) == - {i: set() for i in range(n // 2, n)}) + {i: set() for i in range(n // 2, n)}) def test_irreducible1(self): # Graph taken from Figure 2 of @@ -130,9 +130,9 @@ class TestDominanceFrontiers(object): edges = [(1, 2), (2, 1), (3, 2), (4, 1), (5, 3), (5, 4)] G = nx.DiGraph(edges) assert ({u: df - for u, df in nx.dominance_frontiers(G, 5).items()} == - {1: set([2]), 2: set([1]), 3: set([2]), - 4: set([1]), 5: set()}) + for u, df in nx.dominance_frontiers(G, 5).items()} == + {1: set([2]), 2: set([1]), 3: set([2]), + 4: set([1]), 5: set()}) def test_irreducible2(self): # Graph taken from Figure 4 of @@ -143,20 +143,20 @@ class TestDominanceFrontiers(object): (6, 4), (6, 5)] G = nx.DiGraph(edges) assert (nx.dominance_frontiers(G, 6) == - {1: set([2]), 2: set([1, 3]), 3: set([2]), 4: set([2, 3]), 5: set([1]), 6: set([])}) + {1: set([2]), 2: set([1, 3]), 3: set([2]), 4: set([2, 3]), 5: set([1]), 6: set([])}) def test_domrel_png(self): # Graph taken from https://commons.wikipedia.org/wiki/File:Domrel.png edges = [(1, 2), (2, 3), (2, 4), (2, 6), (3, 5), (4, 5), (5, 2)] G = nx.DiGraph(edges) assert (nx.dominance_frontiers(G, 1) == - {1: set([]), 2: set([2]), 3: set([5]), 4: set([5]), - 5: set([2]), 6: set()}) + {1: set([]), 2: set([2]), 3: set([5]), 4: set([5]), + 5: set([2]), 6: set()}) # Test postdominance. with nx.utils.reversed(G): assert (nx.dominance_frontiers(G, 6) == - {1: set(), 2: set([2]), 3: set([2]), 4: set([2]), - 5: set([2]), 6: set()}) + {1: set(), 2: set([2]), 3: set([2]), 4: set([2]), + 5: set([2]), 6: set()}) def test_boost_example(self): # Graph taken from Figure 1 of @@ -165,13 +165,13 @@ class TestDominanceFrontiers(object): (5, 7), (6, 4)] G = nx.DiGraph(edges) assert (nx.dominance_frontiers(G, 0) == - {0: set(), 1: set(), 2: set([7]), 3: set([7]), - 4: set([4, 7]), 5: set([7]), 6: set([4]), 7: set()}) + {0: set(), 1: set(), 2: set([7]), 3: set([7]), + 4: set([4, 7]), 5: set([7]), 6: set([4]), 7: set()}) # Test postdominance. with nx.utils.reversed(G): assert (nx.dominance_frontiers(G, 7) == - {0: set(), 1: set(), 2: set([1]), 3: set([1]), - 4: set([1, 4]), 5: set([1]), 6: set([4]), 7: set()}) + {0: set(), 1: set(), 2: set([1]), 3: set([1]), + 4: set([1, 4]), 5: set([1]), 6: set([4]), 7: set()}) def test_discard_issue(self): # https://github.com/networkx/networkx/issues/2071 @@ -192,9 +192,9 @@ class TestDominanceFrontiers(object): ) df = nx.dominance_frontiers(g, 'b0') assert df == {'b4': set(), 'b5': set(['b3']), 'b6': set(['b7']), - 'b7': set(['b3']), - 'b0': set(), 'b1': set(['b1']), 'b2': set(['b3']), - 'b3': set(['b1']), 'b8': set(['b7'])} + 'b7': set(['b3']), + 'b0': set(), 'b1': set(['b1']), 'b2': set(['b3']), + 'b3': set(['b1']), 'b8': set(['b7'])} def test_loop(self): g = nx.DiGraph() diff --git a/networkx/algorithms/tests/test_link_prediction.py b/networkx/algorithms/tests/test_link_prediction.py index 3cb9b602..5c8fa4d7 100644 --- a/networkx/algorithms/tests/test_link_prediction.py +++ b/networkx/algorithms/tests/test_link_prediction.py @@ -36,11 +36,11 @@ class TestResourceAllocationIndex(): def test_notimplemented(self): assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) def test_no_common_neighbor(self): G = nx.Graph() @@ -73,11 +73,11 @@ class TestJaccardCoefficient(): def test_notimplemented(self): assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) def test_no_common_neighbor(self): G = nx.Graph() @@ -115,11 +115,11 @@ class TestAdamicAdarIndex(): def test_notimplemented(self): assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) def test_no_common_neighbor(self): G = nx.Graph() @@ -157,11 +157,11 @@ class TestPreferentialAttachment(): def test_notimplemented(self): assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.DiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiGraph([(0, 1), (1, 2)]), [(0, 2)]) assert pytest.raises(nx.NetworkXNotImplemented, self.func, - nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) + nx.MultiDiGraph([(0, 1), (1, 2)]), [(0, 2)]) def test_zero_degrees(self): G = nx.Graph() @@ -179,7 +179,7 @@ class TestCNSoundarajanHopcroft(): def setup_class(cls): cls.func = staticmethod(nx.cn_soundarajan_hopcroft) cls.test = partial(_test_func, predict_func=cls.func, - community='community') + community='community') def test_K5(self): G = nx.complete_graph(5) @@ -392,7 +392,7 @@ class TestWithinInterCluster(): cls.delta = 0.001 cls.func = staticmethod(nx.within_inter_cluster) cls.test = partial(_test_func, predict_func=cls.func, - delta=cls.delta, community='community') + delta=cls.delta, community='community') def test_K5(self): G = nx.complete_graph(5) diff --git a/networkx/algorithms/tests/test_lowest_common_ancestors.py b/networkx/algorithms/tests/test_lowest_common_ancestors.py index c5b4ef03..b8ca9549 100644 --- a/networkx/algorithms/tests/test_lowest_common_ancestors.py +++ b/networkx/algorithms/tests/test_lowest_common_ancestors.py @@ -167,41 +167,41 @@ class TestDAGLCA: cls.root_distance = nx.shortest_path_length(cls.DG, source=0) cls.gold = {(1, 1): 1, - (1, 2): 1, - (1, 3): 1, - (1, 4): 0, - (1, 5): 0, - (1, 6): 0, - (1, 7): 0, - (1, 8): 0, - (2, 2): 2, - (2, 3): 2, - (2, 4): 0, - (2, 5): 5, - (2, 6): 6, - (2, 7): 7, - (2, 8): 7, - (3, 3): 8, - (3, 4): 4, - (3, 5): 5, - (3, 6): 6, - (3, 7): 7, - (3, 8): 8, - (4, 4): 4, - (4, 5): 0, - (4, 6): 0, - (4, 7): 0, - (4, 8): 0, - (5, 5): 5, - (5, 6): 5, - (5, 7): 5, - (5, 8): 5, - (6, 6): 6, - (6, 7): 5, - (6, 8): 6, - (7, 7): 7, - (7, 8): 7, - (8, 8): 8} + (1, 2): 1, + (1, 3): 1, + (1, 4): 0, + (1, 5): 0, + (1, 6): 0, + (1, 7): 0, + (1, 8): 0, + (2, 2): 2, + (2, 3): 2, + (2, 4): 0, + (2, 5): 5, + (2, 6): 6, + (2, 7): 7, + (2, 8): 7, + (3, 3): 8, + (3, 4): 4, + (3, 5): 5, + (3, 6): 6, + (3, 7): 7, + (3, 8): 8, + (4, 4): 4, + (4, 5): 0, + (4, 6): 0, + (4, 7): 0, + (4, 8): 0, + (5, 5): 5, + (5, 6): 5, + (5, 7): 5, + (5, 8): 5, + (6, 6): 6, + (6, 7): 5, + (6, 8): 6, + (7, 7): 7, + (7, 8): 7, + (8, 8): 8} cls.gold.update(((0, n), 0) for n in cls.DG) def assert_lca_dicts_same(self, d1, d2, G=None): @@ -218,7 +218,7 @@ class TestDAGLCA: for a, b in ((min(pair), max(pair)) for pair in chain(d1, d2)): assert (root_distance[get_pair(d1, a, b)] == - root_distance[get_pair(d2, a, b)]) + root_distance[get_pair(d2, a, b)]) def test_all_pairs_lowest_common_ancestor1(self): """Produces the correct results.""" @@ -297,7 +297,7 @@ class TestDAGLCA: G = nx.DiGraph([(0, 1), (2, 1)]) sentinel = object() assert (nx.lowest_common_ancestor(G, 0, 2, default=sentinel) is - sentinel) + sentinel) def test_lowest_common_ancestor2(self): """Test that the one-pair function works on identity.""" diff --git a/networkx/algorithms/tests/test_mis.py b/networkx/algorithms/tests/test_mis.py index eb36fd3b..8f27f7e0 100644 --- a/networkx/algorithms/tests/test_mis.py +++ b/networkx/algorithms/tests/test_mis.py @@ -77,8 +77,8 @@ class TestMaximalIndependantSet(object): G = self.florentine indep = nx.maximal_independent_set(G, ["Medici", "Bischeri"]) assert (sorted(indep) == - sorted(["Medici", "Bischeri", "Castellani", "Pazzi", - "Ginori", "Lamberteschi"])) + sorted(["Medici", "Bischeri", "Castellani", "Pazzi", + "Ginori", "Lamberteschi"])) def test_bipartite(self): G = nx.complete_bipartite_graph(12, 34) diff --git a/networkx/algorithms/tests/test_moral.py b/networkx/algorithms/tests/test_moral.py index c9f4de94..dee4949b 100644 --- a/networkx/algorithms/tests/test_moral.py +++ b/networkx/algorithms/tests/test_moral.py @@ -3,7 +3,6 @@ import networkx as nx from networkx.algorithms.moral import moral_graph - def test_get_moral_graph(): graph = nx.DiGraph() graph.add_nodes_from([1, 2, 3, 4, 5, 6, 7]) diff --git a/networkx/algorithms/tests/test_planar_drawing.py b/networkx/algorithms/tests/test_planar_drawing.py index d9100d19..ee9fe5cf 100644 --- a/networkx/algorithms/tests/test_planar_drawing.py +++ b/networkx/algorithms/tests/test_planar_drawing.py @@ -107,7 +107,7 @@ def check_embedding_data(embedding_data): msg = "Planar drawing does not conform to the embedding (internal " \ "triangulation)" assert planar_drawing_conforms_to_embedding(embedding, - pos_internally), msg + pos_internally), msg check_edge_intersections(embedding, pos_internally) diff --git a/networkx/algorithms/tests/test_richclub.py b/networkx/algorithms/tests/test_richclub.py index a55ffd27..e2eb02e4 100644 --- a/networkx/algorithms/tests/test_richclub.py +++ b/networkx/algorithms/tests/test_richclub.py @@ -28,8 +28,8 @@ def test_richclub2(): T = nx.balanced_tree(2, 10) rc = nx.richclub.rich_club_coefficient(T, normalized=False) assert rc == {0: 4092 / (2047 * 2046.0), - 1: (2044.0 / (1023 * 1022)), - 2: (2040.0 / (1022 * 1021))} + 1: (2044.0 / (1023 * 1022)), + 2: (2040.0 / (1022 * 1021))} def test_richclub3(): @@ -37,21 +37,21 @@ def test_richclub3(): G = nx.karate_club_graph() rc = nx.rich_club_coefficient(G, normalized=False) assert rc == {0: 156.0 / 1122, - 1: 154.0 / 1056, - 2: 110.0 / 462, - 3: 78.0 / 240, - 4: 44.0 / 90, - 5: 22.0 / 42, - 6: 10.0 / 20, - 7: 10.0 / 20, - 8: 10.0 / 20, - 9: 6.0 / 12, - 10: 2.0 / 6, - 11: 2.0 / 6, - 12: 0.0, - 13: 0.0, - 14: 0.0, - 15: 0.0, } + 1: 154.0 / 1056, + 2: 110.0 / 462, + 3: 78.0 / 240, + 4: 44.0 / 90, + 5: 22.0 / 42, + 6: 10.0 / 20, + 7: 10.0 / 20, + 8: 10.0 / 20, + 9: 6.0 / 12, + 10: 2.0 / 6, + 11: 2.0 / 6, + 12: 0.0, + 13: 0.0, + 14: 0.0, + 15: 0.0, } def test_richclub4(): @@ -59,9 +59,9 @@ def test_richclub4(): G.add_edges_from([(0, 1), (0, 2), (0, 3), (0, 4), (4, 5), (5, 9), (6, 9), (7, 9), (8, 9)]) rc = nx.rich_club_coefficient(G, normalized=False) assert rc == {0: 18 / 90.0, - 1: 6 / 12.0, - 2: 0.0, - 3: 0.0} + 1: 6 / 12.0, + 2: 0.0, + 3: 0.0} def test_richclub_exception(): diff --git a/networkx/algorithms/tests/test_similarity.py b/networkx/algorithms/tests/test_similarity.py index b43bff86..a2c5d5ac 100644 --- a/networkx/algorithms/tests/test_similarity.py +++ b/networkx/algorithms/tests/test_similarity.py @@ -9,9 +9,11 @@ from networkx.generators.classic import * def nmatch(n1, n2): return n1 == n2 + def ematch(e1, e2): return e1 == e2 + def getCanonical(): G = nx.Graph() G.add_node('A', label='A') @@ -106,9 +108,9 @@ class TestSimilarity: return 100 assert graph_edit_distance(G1, G2, - node_subst_cost=node_subst_cost, - node_del_cost=node_del_cost, - node_ins_cost=node_ins_cost) == 6 + node_subst_cost=node_subst_cost, + node_del_cost=node_del_cost, + node_ins_cost=node_ins_cost) == 6 def test_graph_edit_distance_edge_cost(self): G1 = path_graph(6) @@ -137,9 +139,9 @@ class TestSimilarity: return 1.0 assert graph_edit_distance(G1, G2, - edge_subst_cost=edge_subst_cost, - edge_del_cost=edge_del_cost, - edge_ins_cost=edge_ins_cost) == 0.23 + edge_subst_cost=edge_subst_cost, + edge_del_cost=edge_del_cost, + edge_ins_cost=edge_ins_cost) == 0.23 def test_graph_edit_distance_upper_bound(self): G1 = circular_ladder_graph(2) @@ -165,7 +167,7 @@ class TestSimilarity: ([(0, 2), (1, 0), (2, 1)], [((0, 1), (0, 2)), ((1, 2), (0, 1)), (None, (1, 2))]), ([(0, 2), (1, 1), (2, 0)], [((0, 1), (1, 2)), ((1, 2), (0, 1)), (None, (0, 2))])] assert (set(canonical(*p) for p in paths) == - set(canonical(*p) for p in expected_paths)) + set(canonical(*p) for p in expected_paths)) def test_optimize_graph_edit_distance(self): G1 = circular_ladder_graph(2) diff --git a/networkx/algorithms/tests/test_simple_paths.py b/networkx/algorithms/tests/test_simple_paths.py index 52993c9c..5ef3248c 100644 --- a/networkx/algorithms/tests/test_simple_paths.py +++ b/networkx/algorithms/tests/test_simple_paths.py @@ -245,7 +245,7 @@ def test_shortest_simple_paths(): assert next(paths) == [1, 2, 3, 4, 8, 12] assert next(paths) == [1, 5, 6, 7, 8, 12] assert ([len(path) for path in nx.shortest_simple_paths(G, 1, 12)] == - sorted([len(path) for path in nx.all_simple_paths(G, 1, 12)])) + sorted([len(path) for path in nx.all_simple_paths(G, 1, 12)])) def test_shortest_simple_paths_directed(): @@ -304,14 +304,14 @@ def test_weighted_shortest_simple_path_issue2427(): G.add_edge('IN', 'B', weight=2) G.add_edge('B', 'OUT', weight=2) assert (list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")) == - [['IN', 'OUT'], ['IN', 'B', 'OUT']]) + [['IN', 'OUT'], ['IN', 'B', 'OUT']]) G = nx.Graph() G.add_edge('IN', 'OUT', weight=10) G.add_edge('IN', 'A', weight=1) G.add_edge('IN', 'B', weight=1) G.add_edge('B', 'OUT', weight=1) assert (list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")) == - [['IN', 'B', 'OUT'], ['IN', 'OUT']]) + [['IN', 'B', 'OUT'], ['IN', 'OUT']]) def test_directed_weighted_shortest_simple_path_issue2427(): @@ -321,14 +321,14 @@ def test_directed_weighted_shortest_simple_path_issue2427(): G.add_edge('IN', 'B', weight=2) G.add_edge('B', 'OUT', weight=2) assert (list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")) == - [['IN', 'OUT'], ['IN', 'B', 'OUT']]) + [['IN', 'OUT'], ['IN', 'B', 'OUT']]) G = nx.DiGraph() G.add_edge('IN', 'OUT', weight=10) G.add_edge('IN', 'A', weight=1) G.add_edge('IN', 'B', weight=1) G.add_edge('B', 'OUT', weight=1) assert (list(nx.shortest_simple_paths(G, 'IN', 'OUT', weight="weight")) == - [['IN', 'B', 'OUT'], ['IN', 'OUT']]) + [['IN', 'B', 'OUT'], ['IN', 'OUT']]) def test_weight_name(): @@ -450,7 +450,7 @@ def validate_path(G, s, t, soln_len, path): assert path[0] == s assert path[-1] == t assert soln_len == sum(G[u][v].get('weight', 1) - for u, v in zip(path[:-1], path[1:])) + for u, v in zip(path[:-1], path[1:])) def validate_length_path(G, s, t, soln_len, length, path): diff --git a/networkx/algorithms/tests/test_swap.py b/networkx/algorithms/tests/test_swap.py index cde16c22..8a1afaa9 100644 --- a/networkx/algorithms/tests/test_swap.py +++ b/networkx/algorithms/tests/test_swap.py @@ -3,7 +3,7 @@ import pytest import networkx as nx #import random -#random.seed(0) +# random.seed(0) def test_double_edge_swap(): diff --git a/networkx/algorithms/tests/test_threshold.py b/networkx/algorithms/tests/test_threshold.py index dbf0a726..f28d9371 100644 --- a/networkx/algorithms/tests/test_threshold.py +++ b/networkx/algorithms/tests/test_threshold.py @@ -65,7 +65,7 @@ class TestGeneratorThreshold(): assert nxt.uncompact([3, 1, 2]) == ['d', 'd', 'd', 'i', 'd', 'd'] assert nxt.uncompact(['d', 'd', 'i', 'd']) == ['d', 'd', 'i', 'd'] assert (nxt.uncompact(nxt.uncompact([(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])) == - nxt.uncompact([(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])) + nxt.uncompact([(1, 'd'), (2, 'd'), (3, 'i'), (0, 'd')])) assert pytest.raises(TypeError, nxt.uncompact, [3., 1., 2.]) def test_creation_sequence_to_weights(self): @@ -77,7 +77,7 @@ class TestGeneratorThreshold(): with pytest.raises(ValueError): nxt.weights_to_creation_sequence(deg, with_labels=True, compact=True) assert (nxt.weights_to_creation_sequence(deg, with_labels=True) == - [(3, 'd'), (1, 'd'), (2, 'd'), (0, 'd')]) + [(3, 'd'), (1, 'd'), (2, 'd'), (0, 'd')]) assert nxt.weights_to_creation_sequence(deg, compact=True) == [4] def test_find_alternating_4_cycle(self): @@ -92,7 +92,7 @@ class TestGeneratorThreshold(): for n, m in [(3, 0), (0, 3), (0, 2), (0, 1), (1, 3), (3, 1), (1, 2), (2, 3)]: assert (nxt.shortest_path(cs1, n, m) == - nx.shortest_path(G, n, m)) + nx.shortest_path(G, n, m)) spl = nxt.shortest_path_length(cs1, 3) spl2 = nxt.shortest_path_length([t for v, t in cs1], 2) @@ -114,15 +114,15 @@ class TestGeneratorThreshold(): def test_shortest_path_length(self): assert nxt.shortest_path_length([3, 1, 2], 1) == [1, 0, 1, 2, 1, 1] assert (nxt.shortest_path_length(['d', 'd', 'd', 'i', 'd', 'd'], 1) == - [1, 0, 1, 2, 1, 1]) + [1, 0, 1, 2, 1, 1]) assert (nxt.shortest_path_length(('d', 'd', 'd', 'i', 'd', 'd'), 1) == - [1, 0, 1, 2, 1, 1]) + [1, 0, 1, 2, 1, 1]) assert pytest.raises(TypeError, nxt.shortest_path, [3., 1., 2.], 1) def random_threshold_sequence(self): assert len(nxt.random_threshold_sequence(10, 0.5)) == 10 assert (nxt.random_threshold_sequence(10, 0.5, seed=42) == - ['d', 'i', 'd', 'd', 'd', 'i', 'i', 'i', 'd', 'd']) + ['d', 'i', 'd', 'd', 'd', 'i', 'i', 'i', 'd', 'd']) assert pytest.raises(ValueError, nxt.random_threshold_sequence, 10, 1.5) def test_right_d_threshold_sequence(self): @@ -142,19 +142,19 @@ class TestGeneratorThreshold(): wseq = nxt.creation_sequence_to_weights(nxt.uncompact([3, 1, 2, 3, 3, 2, 3])) assert (wseq == - [s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]]) + [s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]]) wseq = nxt.creation_sequence_to_weights([3, 1, 2, 3, 3, 2, 3]) assert (wseq == - [s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]]) + [s * 0.125 for s in [4, 4, 4, 3, 5, 5, 2, 2, 2, 6, 6, 6, 1, 1, 7, 7, 7]]) wseq = nxt.creation_sequence_to_weights(list(enumerate('ddidiiidididi'))) assert (wseq == - [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]) + [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]) wseq = nxt.creation_sequence_to_weights('ddidiiidididi') assert (wseq == - [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]) + [s * 0.1 for s in [5, 5, 4, 6, 3, 3, 3, 7, 2, 8, 1, 9, 0]]) wseq = nxt.creation_sequence_to_weights('ddidiiidididid') ws = [s / float(12) for s in [6, 6, 5, 7, 4, 4, 4, 8, 3, 9, 2, 10, 1, 11]] @@ -184,7 +184,7 @@ class TestGeneratorThreshold(): G = nxt.threshold_graph(cs) assert nxt.density('ddiiddid') == nx.density(G) assert (sorted(nxt.degree_sequence(cs)) == - sorted(d for n, d in G.degree())) + sorted(d for n, d in G.degree())) ts = nxt.triangle_sequence(cs) assert ts == list(nx.triangles(G).values()) @@ -236,6 +236,6 @@ class TestGeneratorThreshold(): cs = 'ddiiddid' G = nxt.threshold_graph(cs) assert pytest.raises(nx.exception.NetworkXError, - nxt.threshold_graph, cs, create_using=nx.DiGraph()) + nxt.threshold_graph, cs, create_using=nx.DiGraph()) MG = nxt.threshold_graph(cs, create_using=nx.MultiGraph()) assert sorted(MG.edges()) == sorted(G.edges()) diff --git a/networkx/algorithms/traversal/tests/test_bfs.py b/networkx/algorithms/traversal/tests/test_bfs.py index 966ab4d2..de0e080a 100644 --- a/networkx/algorithms/traversal/tests/test_bfs.py +++ b/networkx/algorithms/traversal/tests/test_bfs.py @@ -12,11 +12,11 @@ class TestBFS: def test_successor(self): assert (dict(nx.bfs_successors(self.G, source=0)) == - {0: [1], 1: [2, 3], 2: [4]}) + {0: [1], 1: [2, 3], 2: [4]}) def test_predecessor(self): assert (dict(nx.bfs_predecessors(self.G, source=0)) == - {1: 0, 2: 1, 3: 1, 4: 2}) + {1: 0, 2: 1, 3: 1, 4: 2}) def test_bfs_tree(self): T = nx.bfs_tree(self.G, source=0) @@ -59,18 +59,18 @@ class TestBreadthLimitedSearch: def bfs_test_successor(self): assert (dict(nx.bfs_successors(self.G, source=1, depth_limit=3)) == - {1: [0, 2], 2: [3, 7], 3: [4], 7: [8]}) + {1: [0, 2], 2: [3, 7], 3: [4], 7: [8]}) result = {n: sorted(s) for n, s in nx.bfs_successors(self.D, source=7, depth_limit=2)} assert result == {8: [9], 2: [3], 7: [2, 8]} def bfs_test_predecessor(self): assert (dict(nx.bfs_predecessors(self.G, source=1, - depth_limit=3)) == - {0: 1, 2: 1, 3: 2, 4: 3, 7: 2, 8: 7}) + depth_limit=3)) == + {0: 1, 2: 1, 3: 2, 4: 3, 7: 2, 8: 7}) assert (dict(nx.bfs_predecessors(self.D, source=7, - depth_limit=2)) == - {2: 7, 3: 2, 8: 7, 9: 8}) + depth_limit=2)) == + {2: 7, 3: 2, 8: 7, 9: 8}) def bfs_test_tree(self): T = nx.bfs_tree(self.G, source=3, depth_limit=1) @@ -79,4 +79,4 @@ class TestBreadthLimitedSearch: def bfs_test_edges(self): edges = nx.bfs_edges(self.G, source=9, depth_limit=4) assert list(edges) == [(9, 8), (9, 10), (8, 7), - (7, 2), (2, 1), (2, 3)] + (7, 2), (2, 1), (2, 3)] diff --git a/networkx/algorithms/traversal/tests/test_dfs.py b/networkx/algorithms/traversal/tests/test_dfs.py index 75e25693..327a53f0 100644 --- a/networkx/algorithms/traversal/tests/test_dfs.py +++ b/networkx/algorithms/traversal/tests/test_dfs.py @@ -17,22 +17,22 @@ class TestDFS: def test_preorder_nodes(self): assert (list(nx.dfs_preorder_nodes(self.G, source=0)) == - [0, 1, 2, 4, 3]) + [0, 1, 2, 4, 3]) assert list(nx.dfs_preorder_nodes(self.D)) == [0, 1, 2, 3] def test_postorder_nodes(self): assert (list(nx.dfs_postorder_nodes(self.G, source=0)) == - [3, 4, 2, 1, 0]) + [3, 4, 2, 1, 0]) assert list(nx.dfs_postorder_nodes(self.D)) == [1, 0, 3, 2] def test_successor(self): assert (nx.dfs_successors(self.G, source=0) == - {0: [1], 1: [2], 2: [4], 4: [3]}) + {0: [1], 1: [2], 2: [4], 4: [3]}) assert nx.dfs_successors(self.D) == {0: [1], 2: [3]} def test_predecessor(self): assert (nx.dfs_predecessors(self.G, source=0) == - {1: 0, 2: 1, 3: 4, 4: 2}) + {1: 0, 2: 1, 3: 4, 4: 2}) assert nx.dfs_predecessors(self.D) == {1: 0, 3: 2} def test_dfs_tree(self): @@ -96,29 +96,29 @@ class TestDepthLimitedSearch: def dls_test_preorder_nodes(self): assert list(nx.dfs_preorder_nodes(self.G, source=0, - depth_limit=2)) == [0, 1, 2] + depth_limit=2)) == [0, 1, 2] assert list(nx.dfs_preorder_nodes(self.D, source=1, - depth_limit=2)) == ([1, 0]) + depth_limit=2)) == ([1, 0]) def dls_test_postorder_nodes(self): assert list(nx.dfs_postorder_nodes(self.G, - source=3, depth_limit=3)) == [1, 7, 2, 5, 4, 3] + source=3, depth_limit=3)) == [1, 7, 2, 5, 4, 3] assert list(nx.dfs_postorder_nodes(self.D, - source=2, depth_limit=2)) == ([3, 7, 2]) + source=2, depth_limit=2)) == ([3, 7, 2]) def dls_test_successor(self): result = nx.dfs_successors(self.G, source=4, depth_limit=3) assert ({n: set(v) for n, v in result.items()} == - {2: {1, 7}, 3: {2}, 4: {3, 5}, 5: {6}}) + {2: {1, 7}, 3: {2}, 4: {3, 5}, 5: {6}}) result = nx.dfs_successors(self.D, source=7, depth_limit=2) assert ({n: set(v) for n, v in result.items()} == - {8: {9}, 2: {3}, 7: {8, 2}}) + {8: {9}, 2: {3}, 7: {8, 2}}) def dls_test_predecessor(self): assert (nx.dfs_predecessors(self.G, source=0, depth_limit=3) == - {1: 0, 2: 1, 3: 2, 7: 2}) + {1: 0, 2: 1, 3: 2, 7: 2}) assert (nx.dfs_predecessors(self.D, source=2, depth_limit=3) == - {8: 7, 9: 8, 3: 2, 7: 2}) + {8: 7, 9: 8, 3: 2, 7: 2}) def test_dls_tree(self): T = nx.dfs_tree(self.G, source=3, depth_limit=1) @@ -127,7 +127,7 @@ class TestDepthLimitedSearch: def test_dls_edges(self): edges = nx.dfs_edges(self.G, source=9, depth_limit=4) assert list(edges) == [(9, 8), (8, 7), - (7, 2), (2, 1), (2, 3), (9, 10)] + (7, 2), (2, 1), (2, 3), (9, 10)] def test_dls_labeled_edges(self): edges = list(nx.dfs_labeled_edges(self.G, source=5, depth_limit=1)) diff --git a/networkx/algorithms/tree/tests/test_branchings.py b/networkx/algorithms/tree/tests/test_branchings.py index ba83a47d..1765cfe5 100644 --- a/networkx/algorithms/tree/tests/test_branchings.py +++ b/networkx/algorithms/tree/tests/test_branchings.py @@ -129,25 +129,25 @@ def assert_equal_branchings(G1, G2, attr='weight', default=1): def test_optimal_branching1(): G = build_branching(optimal_arborescence_1) assert recognition.is_arborescence(G), True - assert branchings.branching_weight(G) == 131 + assert branchings.branching_weight(G) == 131 def test_optimal_branching2a(): G = build_branching(optimal_branching_2a) assert recognition.is_arborescence(G), True - assert branchings.branching_weight(G) == 53 + assert branchings.branching_weight(G) == 53 def test_optimal_branching2b(): G = build_branching(optimal_branching_2b) assert recognition.is_arborescence(G), True - assert branchings.branching_weight(G) == 53 + assert branchings.branching_weight(G) == 53 def test_optimal_arborescence2(): G = build_branching(optimal_arborescence_2) assert recognition.is_arborescence(G), True - assert branchings.branching_weight(G) == 51 + assert branchings.branching_weight(G) == 51 def test_greedy_suboptimal_branching1a(): diff --git a/networkx/classes/tests/test_digraph_historical.py b/networkx/classes/tests/test_digraph_historical.py index b39ae3cc..340b1350 100644 --- a/networkx/classes/tests/test_digraph_historical.py +++ b/networkx/classes/tests/test_digraph_historical.py @@ -23,7 +23,7 @@ class TestDiGraphHistorical(HistoricalTests): assert sorted(d for n, d in G.in_degree()) == [0, 0, 0, 0, 1, 2, 2] assert (dict(G.in_degree()) == - {'A': 0, 'C': 2, 'B': 1, 'D': 2, 'G': 0, 'K': 0, 'J': 0}) + {'A': 0, 'C': 2, 'B': 1, 'D': 2, 'G': 0, 'K': 0, 'J': 0}) def test_out_degree(self): G = self.G() @@ -31,9 +31,9 @@ class TestDiGraphHistorical(HistoricalTests): G.add_edges_from([('A', 'B'), ('A', 'C'), ('B', 'D'), ('B', 'C'), ('C', 'D')]) assert (sorted([v for k, v in G.in_degree()]) == - [0, 0, 0, 0, 1, 2, 2]) + [0, 0, 0, 0, 1, 2, 2]) assert (dict(G.out_degree()) == - {'A': 2, 'C': 1, 'B': 2, 'D': 0, 'G': 0, 'K': 0, 'J': 0}) + {'A': 2, 'C': 1, 'B': 2, 'D': 0, 'G': 0, 'K': 0, 'J': 0}) def test_degree_digraph(self): H = nx.DiGraph() diff --git a/networkx/classes/tests/test_function.py b/networkx/classes/tests/test_function.py index 79e76f75..8a0b7d65 100644 --- a/networkx/classes/tests/test_function.py +++ b/networkx/classes/tests/test_function.py @@ -27,7 +27,7 @@ class TestFunction(object): assert_edges_equal(self.G.edges(nbunch=[0, 1, 3]), list(nx.edges(self.G, nbunch=[0, 1, 3]))) assert (sorted(self.DG.edges(nbunch=[0, 1, 3])) == - sorted(nx.edges(self.DG, nbunch=[0, 1, 3]))) + sorted(nx.edges(self.DG, nbunch=[0, 1, 3]))) def test_degree(self): assert_edges_equal(self.G.degree(), list(nx.degree(self.G))) @@ -35,11 +35,11 @@ class TestFunction(object): assert_edges_equal(self.G.degree(nbunch=[0, 1]), list(nx.degree(self.G, nbunch=[0, 1]))) assert (sorted(self.DG.degree(nbunch=[0, 1])) == - sorted(nx.degree(self.DG, nbunch=[0, 1]))) + sorted(nx.degree(self.DG, nbunch=[0, 1]))) assert_edges_equal(self.G.degree(weight='weight'), list(nx.degree(self.G, weight='weight'))) assert (sorted(self.DG.degree(weight='weight')) == - sorted(nx.degree(self.DG, weight='weight'))) + sorted(nx.degree(self.DG, weight='weight'))) def test_neighbors(self): assert list(self.G.neighbors(1)) == list(nx.neighbors(self.G, 1)) @@ -161,13 +161,13 @@ class TestFunction(object): def test_subgraph(self): assert (self.G.subgraph([0, 1, 2, 4]).adj == - nx.subgraph(self.G, [0, 1, 2, 4]).adj) + nx.subgraph(self.G, [0, 1, 2, 4]).adj) assert (self.DG.subgraph([0, 1, 2, 4]).adj == - nx.subgraph(self.DG, [0, 1, 2, 4]).adj) + nx.subgraph(self.DG, [0, 1, 2, 4]).adj) assert (self.G.subgraph([0, 1, 2, 4]).adj == - nx.induced_subgraph(self.G, [0, 1, 2, 4]).adj) + nx.induced_subgraph(self.G, [0, 1, 2, 4]).adj) assert (self.DG.subgraph([0, 1, 2, 4]).adj == - nx.induced_subgraph(self.DG, [0, 1, 2, 4]).adj) + nx.induced_subgraph(self.DG, [0, 1, 2, 4]).adj) # subgraph-subgraph chain is allowed in function interface H = nx.induced_subgraph(self.G.subgraph([0, 1, 2, 4]), [0, 1, 4]) assert H._graph is not self.G @@ -175,9 +175,9 @@ class TestFunction(object): def test_edge_subgraph(self): assert (self.G.edge_subgraph([(1, 2), (0, 3)]).adj == - nx.edge_subgraph(self.G, [(1, 2), (0, 3)]).adj) + nx.edge_subgraph(self.G, [(1, 2), (0, 3)]).adj) assert (self.DG.edge_subgraph([(1, 2), (0, 3)]).adj == - nx.edge_subgraph(self.DG, [(1, 2), (0, 3)]).adj) + nx.edge_subgraph(self.DG, [(1, 2), (0, 3)]).adj) def test_restricted_view(self): H = nx.restricted_view(self.G, [0, 2, 5], [(1, 2), (3, 4)]) diff --git a/networkx/classes/tests/test_graph.py b/networkx/classes/tests/test_graph.py index 93758c75..d1204f43 100644 --- a/networkx/classes/tests/test_graph.py +++ b/networkx/classes/tests/test_graph.py @@ -83,7 +83,7 @@ class BaseGraphTester(object): G.add_edge(1, 2, weight=2) G.add_edge(2, 3, weight=3) assert (sorted(d for n, d in G.degree(weight='weight')) == - [2, 3, 5]) + [2, 3, 5]) assert dict(G.degree(weight='weight')) == {1: 2, 2: 5, 3: 3} assert G.degree(1, weight='weight') == 2 assert G.degree([1], weight='weight') == [(1, 2)] @@ -165,7 +165,7 @@ class BaseAttrGraphTester(BaseGraphTester): G.add_edge(1, 2, weight=2, other=3) G.add_edge(2, 3, weight=3, other=4) assert (sorted(d for n, d in G.degree(weight='weight')) == - [2, 3, 5]) + [2, 3, 5]) assert dict(G.degree(weight='weight')) == {1: 2, 2: 5, 3: 3} assert G.degree(1, weight='weight') == 2 assert_nodes_equal((G.degree([1], weight='weight')), [(1, 2)]) @@ -495,7 +495,7 @@ class TestGraph(BaseAttrGraphTester): def test_adjacency(self): G = self.K3 assert (dict(G.adjacency()) == - {0: {1: {}, 2: {}}, 1: {0: {}, 2: {}}, 2: {0: {}, 1: {}}}) + {0: {1: {}, 2: {}}, 1: {0: {}, 2: {}}, 2: {0: {}, 1: {}}}) def test_getitem(self): G = self.K3 @@ -578,7 +578,7 @@ class TestGraph(BaseAttrGraphTester): G = self.Graph() G.add_edges_from([(0, 1), (0, 2, {'weight': 3})]) assert G.adj == {0: {1: {}, 2: {'weight': 3}}, 1: {0: {}}, - 2: {0: {'weight': 3}}} + 2: {0: {'weight': 3}}} G = self.Graph() G.add_edges_from([(0, 1), (0, 2, {'weight': 3}), (1, 2, {'data': 4})], data=2) @@ -707,7 +707,7 @@ class TestEdgeSubgraph(object): def test_correct_edges(self): """Tests that the subgraph has the correct edges.""" assert ([(0, 1, 'edge01'), (3, 4, 'edge34')] == - sorted(self.H.edges(data='name'))) + sorted(self.H.edges(data='name'))) def test_add_node(self): """Tests that adding a node to the original graph does not @@ -748,10 +748,10 @@ class TestEdgeSubgraph(object): # Making a change to G should make a change in H and vice versa. self.G.edges[0, 1]['name'] = 'foo' assert (self.G.edges[0, 1]['name'] == - self.H.edges[0, 1]['name']) + self.H.edges[0, 1]['name']) self.H.edges[3, 4]['name'] = 'bar' assert (self.G.edges[3, 4]['name'] == - self.H.edges[3, 4]['name']) + self.H.edges[3, 4]['name']) def test_graph_attr_dict(self): """Tests that the graph attribute dictionary of the two graphs diff --git a/networkx/classes/tests/test_graphviews.py b/networkx/classes/tests/test_graphviews.py index f6c00368..20422518 100644 --- a/networkx/classes/tests/test_graphviews.py +++ b/networkx/classes/tests/test_graphviews.py @@ -43,9 +43,9 @@ class TestReverseView(object): M = MyGraph() M.add_edge(1, 2) RM = nx.reverse_view(M) - print("RM class",RM.__class__) + print("RM class", RM.__class__) RMC = RM.copy() - print("RMC class",RMC.__class__) + print("RMC class", RMC.__class__) print(RMC.edges) assert RMC.has_edge(2, 1) assert RMC.my_method() == "me" @@ -172,8 +172,8 @@ class TestChainsOfViews(object): cls.Rv = cls.DG.reverse() cls.MRv = cls.MDG.reverse() cls.graphs = [cls.G, cls.DG, cls.MG, cls.MDG, - cls.Gv, cls.DGv, cls.MGv, cls.MDGv, - cls.Rv, cls.MRv] + cls.Gv, cls.DGv, cls.MGv, cls.MDGv, + cls.Rv, cls.MRv] for G in cls.graphs: G.edges, G.nodes, G.degree diff --git a/networkx/classes/tests/test_multidigraph.py b/networkx/classes/tests/test_multidigraph.py index 11ceca77..45fd296e 100644 --- a/networkx/classes/tests/test_multidigraph.py +++ b/networkx/classes/tests/test_multidigraph.py @@ -25,16 +25,16 @@ class BaseMultiDiGraphTester(BaseMultiGraphTester): def test_edges_multi(self): G = self.K3 assert (sorted(G.edges()) == - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.edges(0)) == [(0, 1), (0, 2)] G.add_edge(0, 1) assert (sorted(G.edges()) == - [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) def test_out_edges(self): G = self.K3 assert (sorted(G.out_edges()) == - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)] pytest.raises((KeyError, nx.NetworkXError), G.out_edges, -1) assert sorted(G.out_edges(0, keys=True)) == [(0, 1, 0), (0, 2, 0)] @@ -42,11 +42,11 @@ class BaseMultiDiGraphTester(BaseMultiGraphTester): def test_out_edges_multi(self): G = self.K3 assert (sorted(G.out_edges()) == - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.out_edges(0)) == [(0, 1), (0, 2)] G.add_edge(0, 1, 2) assert (sorted(G.out_edges()) == - [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) def test_out_edges_data(self): G = self.K3 @@ -54,48 +54,48 @@ class BaseMultiDiGraphTester(BaseMultiGraphTester): G.remove_edge(0, 1) G.add_edge(0, 1, data=1) assert (sorted(G.edges(0, data=True)) == - [(0, 1, {'data': 1}), (0, 2, {})]) + [(0, 1, {'data': 1}), (0, 2, {})]) assert (sorted(G.edges(0, data='data')) == - [(0, 1, 1), (0, 2, None)]) + [(0, 1, 1), (0, 2, None)]) assert (sorted(G.edges(0, data='data', default=-1)) == - [(0, 1, 1), (0, 2, -1)]) + [(0, 1, 1), (0, 2, -1)]) def test_in_edges(self): G = self.K3 assert (sorted(G.in_edges()) == - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)] pytest.raises((KeyError, nx.NetworkXError), G.in_edges, -1) G.add_edge(0, 1, 2) assert (sorted(G.in_edges()) == - [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.in_edges(0, keys=True)) == [(1, 0, 0), (2, 0, 0)] def test_in_edges_no_keys(self): G = self.K3 assert (sorted(G.in_edges()) == - [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert sorted(G.in_edges(0)) == [(1, 0), (2, 0)] G.add_edge(0, 1, 2) assert (sorted(G.in_edges()) == - [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) + [(0, 1), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]) assert (sorted(G.in_edges(data=True, keys=False)) == - [(0, 1, {}), (0, 1, {}), (0, 2, {}), (1, 0, {}), - (1, 2, {}), (2, 0, {}), (2, 1, {})]) + [(0, 1, {}), (0, 1, {}), (0, 2, {}), (1, 0, {}), + (1, 2, {}), (2, 0, {}), (2, 1, {})]) def test_in_edges_data(self): G = self.K3 assert (sorted(G.in_edges(0, data=True)) == - [(1, 0, {}), (2, 0, {})]) + [(1, 0, {}), (2, 0, {})]) G.remove_edge(1, 0) G.add_edge(1, 0, data=1) assert (sorted(G.in_edges(0, data=True)) == - [(1, 0, {'data': 1}), (2, 0, {})]) + [(1, 0, {'data': 1}), (2, 0, {})]) assert (sorted(G.in_edges(0, data='data')) == - [(1, 0, 1), (2, 0, None)]) + [(1, 0, 1), (2, 0, None)]) assert (sorted(G.in_edges(0, data='data', default=-1)) == - [(1, 0, 1), (2, 0, -1)]) + [(1, 0, 1), (2, 0, -1)]) def is_shallow(self, H, G): # graph @@ -169,9 +169,9 @@ class BaseMultiDiGraphTester(BaseMultiGraphTester): assert list(G.degree(iter([0]))) == [(0, 4)] G.add_edge(0, 1, weight=0.3, other=1.2) assert (sorted(G.degree(weight='weight')) == - [(0, 4.3), (1, 4.3), (2, 4)]) + [(0, 4.3), (1, 4.3), (2, 4)]) assert (sorted(G.degree(weight='other')) == - [(0, 5.2), (1, 5.2), (2, 4)]) + [(0, 5.2), (1, 5.2), (2, 4)]) def test_in_degree(self): G = self.K3 @@ -263,13 +263,13 @@ class TestMultiDiGraph(BaseMultiDiGraphTester, TestMultiGraph): G.add_edges_from([(0, 1), (0, 1, {'weight': 3})], weight=2) assert G._succ == {0: {1: {0: {}, - 1: {'weight': 3}, - 2: {'weight': 2}, - 3: {'weight': 3}}}, - 1: {}} + 1: {'weight': 3}, + 2: {'weight': 2}, + 3: {'weight': 3}}}, + 1: {}} assert G._pred == {0: {}, 1: {0: {0: {}, 1: {'weight': 3}, - 2: {'weight': 2}, - 3: {'weight': 3}}}} + 2: {'weight': 2}, + 3: {'weight': 3}}}} G = self.Graph() edges = [(0, 1, {'weight': 3}), (0, 1, (('weight', 2),)), @@ -290,11 +290,11 @@ class TestMultiDiGraph(BaseMultiDiGraphTester, TestMultiGraph): G = self.K3 G.remove_edge(0, 1) assert G._succ == {0: {2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} assert G._pred == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0) pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, 0, 2, key=1) @@ -304,34 +304,34 @@ class TestMultiDiGraph(BaseMultiDiGraphTester, TestMultiGraph): G.add_edge(0, 1, key='parallel edge') G.remove_edge(0, 1, key='parallel edge') assert G._adj == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} assert G._succ == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} assert G._pred == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} G.remove_edge(0, 1) assert G._succ == {0: {2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} assert G._pred == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} pytest.raises((KeyError, nx.NetworkXError), G.remove_edge, -1, 0) def test_remove_edges_from(self): G = self.K3 G.remove_edges_from([(0, 1)]) assert G._succ == {0: {2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} assert G._pred == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} G.remove_edges_from([(0, 0)]) # silent fail diff --git a/networkx/classes/tests/test_multigraph.py b/networkx/classes/tests/test_multigraph.py index 13b4f2e0..c52251d7 100644 --- a/networkx/classes/tests/test_multigraph.py +++ b/networkx/classes/tests/test_multigraph.py @@ -27,9 +27,9 @@ class BaseMultiGraphTester(BaseAttrGraphTester): def test_adjacency(self): G = self.K3 assert (dict(G.adjacency()) == - {0: {1: {0: {}}, 2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}}) + {0: {1: {0: {}}, 2: {0: {}}}, + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}}) def deepcopy_edge_attr(self, H, G): assert G[1][2][0]['foo'] == H[1][2][0]['foo'] @@ -204,12 +204,12 @@ class TestMultiGraph(BaseMultiGraphTester, TestGraph): G = self.Graph() G.add_edges_from([(0, 1), (0, 1, {'weight': 3})]) assert G.adj == {0: {1: {0: {}, 1: {'weight': 3}}}, - 1: {0: {0: {}, 1: {'weight': 3}}}} + 1: {0: {0: {}, 1: {'weight': 3}}}} G.add_edges_from([(0, 1), (0, 1, {'weight': 3})], weight=2) assert G.adj == {0: {1: {0: {}, 1: {'weight': 3}, - 2: {'weight': 2}, 3: {'weight': 3}}}, - 1: {0: {0: {}, 1: {'weight': 3}, - 2: {'weight': 2}, 3: {'weight': 3}}}} + 2: {'weight': 2}, 3: {'weight': 3}}}, + 1: {0: {0: {}, 1: {'weight': 3}, + 2: {'weight': 2}, 3: {'weight': 3}}}} G = self.Graph() edges = [(0, 1, {'weight': 3}), (0, 1, (('weight', 2),)), (0, 1, 5), (0, 1, 's')] @@ -231,9 +231,9 @@ class TestMultiGraph(BaseMultiGraphTester, TestGraph): G = self.K3 G.remove_edge(0, 1) assert G.adj == {0: {2: {0: {}}}, - 1: {2: {0: {}}}, - 2: {0: {0: {}}, - 1: {0: {}}}} + 1: {2: {0: {}}}, + 2: {0: {0: {}}, + 1: {0: {}}}} with pytest.raises(nx.NetworkXError): G.remove_edge(-1, 0) @@ -265,8 +265,8 @@ class TestMultiGraph(BaseMultiGraphTester, TestGraph): G.add_edge(0, 1, key='parallel edge') G.remove_edge(0, 1, key='parallel edge') assert G.adj == {0: {1: {0: {}}, 2: {0: {}}}, - 1: {0: {0: {}}, 2: {0: {}}}, - 2: {0: {0: {}}, 1: {0: {}}}} + 1: {0: {0: {}}, 2: {0: {}}}, + 2: {0: {0: {}}, 1: {0: {}}}} G.remove_edge(0, 1) kd = {0: {}} assert G.adj == {0: {2: kd}, 1: {2: kd}, 2: {0: kd, 1: kd}} @@ -302,7 +302,7 @@ class TestEdgeSubgraph(object): def test_correct_edges(self): """Tests that the subgraph has the correct edges.""" assert ([(0, 1, 0, 'edge010'), (3, 4, 1, 'edge341')] == - sorted(self.H.edges(keys=True, data='name'))) + sorted(self.H.edges(keys=True, data='name'))) def test_add_node(self): """Tests that adding a node to the original graph does not @@ -343,10 +343,10 @@ class TestEdgeSubgraph(object): # Making a change to G should make a change in H and vice versa. self.G._adj[0][1][0]['name'] = 'foo' assert (self.G._adj[0][1][0]['name'] == - self.H._adj[0][1][0]['name']) + self.H._adj[0][1][0]['name']) self.H._adj[3][4][1]['name'] = 'bar' assert (self.G._adj[3][4][1]['name'] == - self.H._adj[3][4][1]['name']) + self.H._adj[3][4][1]['name']) def test_graph_attr_dict(self): """Tests that the graph attribute dictionary of the two graphs diff --git a/networkx/classes/tests/test_subgraphviews.py b/networkx/classes/tests/test_subgraphviews.py index dd93a494..f82823d3 100644 --- a/networkx/classes/tests/test_subgraphviews.py +++ b/networkx/classes/tests/test_subgraphviews.py @@ -291,7 +291,7 @@ class TestEdgeSubGraph(object): def test_correct_edges(self): """Tests that the subgraph has the correct edges.""" assert ([(0, 1, 'edge01'), (3, 4, 'edge34')] == - sorted(self.H.edges(data='name'))) + sorted(self.H.edges(data='name'))) def test_add_node(self): """Tests that adding a node to the original graph does not @@ -334,10 +334,10 @@ class TestEdgeSubGraph(object): # Making a change to G should make a change in H and vice versa. self.G.edges[0, 1]['name'] = 'foo' assert (self.G.edges[0, 1]['name'] == - self.H.edges[0, 1]['name']) + self.H.edges[0, 1]['name']) self.H.edges[3, 4]['name'] = 'bar' assert (self.G.edges[3, 4]['name'] == - self.H.edges[3, 4]['name']) + self.H.edges[3, 4]['name']) def test_graph_attr_dict(self): """Tests that the graph attribute dictionary of the two graphs diff --git a/networkx/convert_matrix.py b/networkx/convert_matrix.py index beb20325..474d53db 100644 --- a/networkx/convert_matrix.py +++ b/networkx/convert_matrix.py @@ -124,8 +124,8 @@ def to_pandas_adjacency(G, nodelist=None, dtype=None, order=None, """ import pandas as pd M = to_numpy_array(G, nodelist=nodelist, dtype=dtype, order=order, - multigraph_weight=multigraph_weight, weight=weight, - nonedge=nonedge) + multigraph_weight=multigraph_weight, weight=weight, + nonedge=nonedge) if nodelist is None: nodelist = list(G) return pd.DataFrame(data=M, index=nodelist, columns=nodelist) diff --git a/networkx/drawing/tests/test_layout.py b/networkx/drawing/tests/test_layout.py index d392d83f..be4a9738 100644 --- a/networkx/drawing/tests/test_layout.py +++ b/networkx/drawing/tests/test_layout.py @@ -8,6 +8,7 @@ import pytest import networkx as nx from networkx.testing import almost_equal + class TestLayout(object): @classmethod diff --git a/networkx/drawing/tests/test_pydot.py b/networkx/drawing/tests/test_pydot.py index cf912bf9..8ddfca97 100644 --- a/networkx/drawing/tests/test_pydot.py +++ b/networkx/drawing/tests/test_pydot.py @@ -14,6 +14,7 @@ from networkx.testing import assert_graphs_equal import pytest pydot = pytest.importorskip('pydot') + class TestPydot(object): def pydot_checks(self, G, prog): ''' diff --git a/networkx/drawing/tests/test_pylab.py b/networkx/drawing/tests/test_pylab.py index bff1c4a0..2ecd268d 100644 --- a/networkx/drawing/tests/test_pylab.py +++ b/networkx/drawing/tests/test_pylab.py @@ -96,11 +96,11 @@ class TestPylab(object): edge_color=[(0.4, 1.0, 0.0)]) # with rgba tuple and 4 edges - is interpretted with cmap nx.draw_networkx_edges(G, pos, edgelist=[(9, 10), (10, 11), - (10, 12), (10, 13)], + (10, 12), (10, 13)], edge_color=(0.0, 1.0, 1.0, 0.5)) # with rgba tuple in list nx.draw_networkx_edges(G, pos, edgelist=[(9, 10), (10, 11), - (10, 12), (10, 13)], + (10, 12), (10, 13)], edge_color=[(0.0, 1.0, 1.0, 0.5)]) # with color string and global alpha nx.draw_networkx_edges(G, pos, edgelist=[(11, 12), (11, 13)], diff --git a/networkx/generators/tests/test_classic.py b/networkx/generators/tests/test_classic.py index a605cb6a..284814fa 100644 --- a/networkx/generators/tests/test_classic.py +++ b/networkx/generators/tests/test_classic.py @@ -138,7 +138,7 @@ class TestGeneratorClassic(): assert_edges_equal(mb.edges(), b.edges()) def test_binomial_tree(self): - for n in range(0,4): + for n in range(0, 4): b = nx.binomial_tree(n) assert nx.number_of_nodes(b) == 2**n assert nx.number_of_edges(b) == (2**n - 1) @@ -350,7 +350,7 @@ class TestGeneratorClassic(): p = nx.path_graph(10) assert nx.is_connected(p) assert (sorted(d for n, d in p.degree()) == - [1, 1, 2, 2, 2, 2, 2, 2, 2, 2]) + [1, 1, 2, 2, 2, 2, 2, 2, 2, 2]) assert p.order() - 1 == p.size() dp = nx.path_graph(3, create_using=nx.DiGraph) @@ -377,7 +377,7 @@ class TestGeneratorClassic(): s = star_graph(10) assert (sorted(d for n, d in s.degree()) == - [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10]) + [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10]) pytest.raises(nx.NetworkXError, star_graph, 10, create_using=nx.DiGraph) @@ -395,7 +395,7 @@ class TestGeneratorClassic(): def test_turan_graph(self): assert nx.number_of_edges(nx.turan_graph(13, 4)) == 63 assert is_isomorphic(nx.turan_graph(13, 4), - nx.complete_multipartite_graph(3, 4, 3, 3)) + nx.complete_multipartite_graph(3, 4, 3, 3)) def test_wheel_graph(self): for n, G in [(0, nx.null_graph()), (1, nx.empty_graph(1)), @@ -406,7 +406,7 @@ class TestGeneratorClassic(): g = nx.wheel_graph(10) assert (sorted(d for n, d in g.degree()) == - [3, 3, 3, 3, 3, 3, 3, 3, 3, 9]) + [3, 3, 3, 3, 3, 3, 3, 3, 3, 9]) pytest.raises(nx.NetworkXError, nx.wheel_graph, 10, create_using=nx.DiGraph) diff --git a/networkx/generators/tests/test_cographs.py b/networkx/generators/tests/test_cographs.py index 6e0ce7ca..320aa56d 100644 --- a/networkx/generators/tests/test_cographs.py +++ b/networkx/generators/tests/test_cographs.py @@ -20,7 +20,7 @@ def test_random_cograph(): assert len(G) == 2 ** n - #Every connected subgraph of G has diameter <= 2 + # Every connected subgraph of G has diameter <= 2 if nx.is_connected(G): assert nx.diameter(G) <= 2 else: diff --git a/networkx/generators/tests/test_community.py b/networkx/generators/tests/test_community.py index b62bcc6d..0b66231f 100644 --- a/networkx/generators/tests/test_community.py +++ b/networkx/generators/tests/test_community.py @@ -30,7 +30,7 @@ def test_random_partition_graph(): G = nx.random_partition_graph([1, 2, 3, 4, 5], 0.5, 0.1) C = G.graph['partition'] assert C == [set([0]), set([1, 2]), set([3, 4, 5]), - set([6, 7, 8, 9]), set([10, 11, 12, 13, 14])] + set([6, 7, 8, 9]), set([10, 11, 12, 13, 14])] assert len(G) == 15 rpg = nx.random_partition_graph diff --git a/networkx/generators/tests/test_degree_seq.py b/networkx/generators/tests/test_degree_seq.py index 70062775..76e7f69a 100644 --- a/networkx/generators/tests/test_degree_seq.py +++ b/networkx/generators/tests/test_degree_seq.py @@ -31,10 +31,10 @@ class TestConfigurationModel(object): deg_seq = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] G = nx.configuration_model(deg_seq, seed=12345678) assert (sorted((d for n, d in G.degree()), reverse=True) == - [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]) + [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]) assert (sorted((d for n, d in G.degree(range(len(deg_seq)))), - reverse=True) == - [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]) + reverse=True) == + [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]) def test_random_seed(self): """Tests that each call with the same random seed generates the diff --git a/networkx/generators/tests/test_lattice.py b/networkx/generators/tests/test_lattice.py index dfa2e543..d164bccf 100644 --- a/networkx/generators/tests/test_lattice.py +++ b/networkx/generators/tests/test_lattice.py @@ -82,7 +82,7 @@ class TestGridGraph: g = nx.grid_graph(dim) assert len(g) == n * m assert nx.degree_histogram(g) == [0, 0, 4, 2 * (n + m) - 8, - (n - 2) * (m - 2)] + (n - 2) * (m - 2)] for n, m in [(1, 5), (5, 1)]: dim = [n, m] diff --git a/networkx/generators/tests/test_line.py b/networkx/generators/tests/test_line.py index 23fe0398..ea162bf4 100644 --- a/networkx/generators/tests/test_line.py +++ b/networkx/generators/tests/test_line.py @@ -134,7 +134,7 @@ class TestGeneratorInverseLine(): # there are two alternative inverse line graphs for this case # so long as we get one of them the test should pass assert (nx.is_isomorphic(H, G) or - nx.is_isomorphic(H, alternative_solution)) + nx.is_isomorphic(H, alternative_solution)) def test_cycle(self): G = nx.cycle_graph(5) @@ -180,7 +180,7 @@ class TestGeneratorInverseLine(): # K_5 minus an edge K5me = nx.complete_graph(5) - K5me.remove_edge(0,1) + K5me.remove_edge(0, 1) pytest.raises(nx.NetworkXError, nx.inverse_line_graph, K5me) def test_wrong_graph_type(self): diff --git a/networkx/generators/tests/test_random_graphs.py b/networkx/generators/tests/test_random_graphs.py index a81bd00d..57eedf14 100644 --- a/networkx/generators/tests/test_random_graphs.py +++ b/networkx/generators/tests/test_random_graphs.py @@ -248,16 +248,16 @@ class TestGeneratorsRandom(object): assert sum(1 for _ in G.edges()) == 0 def test_watts_strogatz_big_k(self): - #Test to make sure than n <= k + # Test to make sure than n <= k pytest.raises(NetworkXError, watts_strogatz_graph, 10, 11, 0.25) pytest.raises(NetworkXError, newman_watts_strogatz_graph, 10, 11, 0.25) - + # could create an infinite loop, now doesn't # infinite loop used to occur when a node has degree n-1 and needs to rewire watts_strogatz_graph(10, 9, 0.25, seed=0) newman_watts_strogatz_graph(10, 9, 0.5, seed=0) - #Test k==n scenario + # Test k==n scenario watts_strogatz_graph(10, 10, 0.25, seed=0) newman_watts_strogatz_graph(10, 10, 0.25, seed=0) diff --git a/networkx/generators/tests/test_small.py b/networkx/generators/tests/test_small.py index 43c22aa5..2cba086b 100644 --- a/networkx/generators/tests/test_small.py +++ b/networkx/generators/tests/test_small.py @@ -118,7 +118,7 @@ class TestGeneratorsSmall(): assert G.number_of_nodes() == 12 assert G.number_of_edges() == 30 assert (list(d for n, d in G.degree()) == - [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]) + [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]) assert nx.diameter(G) == 3 assert nx.radius(G) == 3 @@ -126,7 +126,7 @@ class TestGeneratorsSmall(): assert G.number_of_nodes() == 10 assert G.number_of_edges() == 18 assert (sorted(d for n, d in G.degree()) == - [1, 2, 3, 3, 3, 4, 4, 5, 5, 6]) + [1, 2, 3, 3, 3, 4, 4, 5, 5, 6]) G = nx.moebius_kantor_graph() assert G.number_of_nodes() == 16 diff --git a/networkx/generators/tests/test_stochastic.py b/networkx/generators/tests/test_stochastic.py index 1cedd0a5..bc217f31 100644 --- a/networkx/generators/tests/test_stochastic.py +++ b/networkx/generators/tests/test_stochastic.py @@ -23,7 +23,7 @@ class TestStochasticGraph(object): S = nx.stochastic_graph(G) assert nx.is_isomorphic(G, S) assert (sorted(S.edges(data=True)) == - [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) + [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) def test_in_place(self): """Tests for an in-place reweighting of the edges of the graph. @@ -34,7 +34,7 @@ class TestStochasticGraph(object): G.add_edge(0, 2, weight=1) nx.stochastic_graph(G, copy=False) assert (sorted(G.edges(data=True)) == - [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) + [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) def test_arbitrary_weights(self): G = nx.DiGraph() @@ -42,7 +42,7 @@ class TestStochasticGraph(object): G.add_edge(0, 2, weight=1) S = nx.stochastic_graph(G) assert (sorted(S.edges(data=True)) == - [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) + [(0, 1, {'weight': 0.5}), (0, 2, {'weight': 0.5})]) def test_multidigraph(self): G = nx.MultiDiGraph() @@ -50,7 +50,7 @@ class TestStochasticGraph(object): S = nx.stochastic_graph(G) d = dict(weight=0.25) assert (sorted(S.edges(data=True)) == - [(0, 1, d), (0, 1, d), (0, 2, d), (0, 2, d)]) + [(0, 1, d), (0, 1, d), (0, 2, d), (0, 2, d)]) def test_graph_disallowed(self): with pytest.raises(nx.NetworkXNotImplemented): diff --git a/networkx/linalg/tests/test_algebraic_connectivity.py b/networkx/linalg/tests/test_algebraic_connectivity.py index d406d658..f4a96a90 100644 --- a/networkx/linalg/tests/test_algebraic_connectivity.py +++ b/networkx/linalg/tests/test_algebraic_connectivity.py @@ -7,7 +7,6 @@ scipy = pytest.importorskip('scipy') scipy.sparse = pytest.importorskip('scipy.sparse') - import networkx as nx from networkx.testing import almost_equal diff --git a/networkx/linalg/tests/test_bethehessian.py b/networkx/linalg/tests/test_bethehessian.py index a0f17eb9..4b419aac 100644 --- a/networkx/linalg/tests/test_bethehessian.py +++ b/networkx/linalg/tests/test_bethehessian.py @@ -18,17 +18,17 @@ class TestBetheHessian(object): def test_bethe_hessian(self): "Bethe Hessian matrix" H = numpy.array([[ 4, -2, 0], - [-2, 5, -2], - [ 0, -2, 4]]) + [-2, 5, -2], + [ 0, -2, 4]]) permutation = [2, 0, 1] # Bethe Hessian gives expected form npt.assert_equal(nx.bethe_hessian_matrix(self.P, r=2).todense(), H) # nodelist is correctly implemented npt.assert_equal(nx.bethe_hessian_matrix(self.P, r=2, nodelist=permutation).todense(), - H[numpy.ix_(permutation, permutation)]) + H[numpy.ix_(permutation, permutation)]) # Equal to Laplacian matrix when r=1 npt.assert_equal(nx.bethe_hessian_matrix(self.G, r=1).todense(), - nx.laplacian_matrix(self.G).todense()) + nx.laplacian_matrix(self.G).todense()) # Correct default for the regularizer r npt.assert_equal(nx.bethe_hessian_matrix(self.G).todense(), - nx.bethe_hessian_matrix(self.G, r=1.25).todense()) + nx.bethe_hessian_matrix(self.G, r=1.25).todense()) diff --git a/networkx/linalg/tests/test_graphmatrix.py b/networkx/linalg/tests/test_graphmatrix.py index d1e06767..cafe1869 100644 --- a/networkx/linalg/tests/test_graphmatrix.py +++ b/networkx/linalg/tests/test_graphmatrix.py @@ -14,36 +14,36 @@ class TestGraphMatrix(object): deg = [3, 2, 2, 1, 0] cls.G = havel_hakimi_graph(deg) cls.OI = numpy.array([[-1, -1, -1, 0], - [1, 0, 0, -1], - [0, 1, 0, 1], - [0, 0, 1, 0], - [0, 0, 0, 0]]) + [1, 0, 0, -1], + [0, 1, 0, 1], + [0, 0, 1, 0], + [0, 0, 0, 0]]) cls.A = numpy.array([[0, 1, 1, 1, 0], - [1, 0, 1, 0, 0], - [1, 1, 0, 0, 0], - [1, 0, 0, 0, 0], - [0, 0, 0, 0, 0]]) + [1, 0, 1, 0, 0], + [1, 1, 0, 0, 0], + [1, 0, 0, 0, 0], + [0, 0, 0, 0, 0]]) cls.WG = havel_hakimi_graph(deg) cls.WG.add_edges_from((u, v, {'weight': 0.5, 'other': 0.3}) - for (u, v) in cls.G.edges()) + for (u, v) in cls.G.edges()) cls.WA = numpy.array([[0, 0.5, 0.5, 0.5, 0], - [0.5, 0, 0.5, 0, 0], - [0.5, 0.5, 0, 0, 0], - [0.5, 0, 0, 0, 0], - [0, 0, 0, 0, 0]]) + [0.5, 0, 0.5, 0, 0], + [0.5, 0.5, 0, 0, 0], + [0.5, 0, 0, 0, 0], + [0, 0, 0, 0, 0]]) cls.MG = nx.MultiGraph(cls.G) cls.MG2 = cls.MG.copy() cls.MG2.add_edge(0, 1) cls.MG2A = numpy.array([[0, 2, 1, 1, 0], - [2, 0, 1, 0, 0], - [1, 1, 0, 0, 0], - [1, 0, 0, 0, 0], - [0, 0, 0, 0, 0]]) + [2, 0, 1, 0, 0], + [1, 1, 0, 0, 0], + [1, 0, 0, 0, 0], + [0, 0, 0, 0, 0]]) cls.MGOI = numpy.array([[-1, -1, -1, -1, 0], - [1, 1, 0, 0, -1], - [0, 0, 1, 0, 1], - [0, 0, 0, 1, 0], - [0, 0, 0, 0, 0]]) + [1, 1, 0, 0, -1], + [0, 0, 1, 0, 1], + [0, 0, 0, 1, 0], + [0, 0, 0, 0, 0]]) cls.no_edges_G = nx.Graph([(1, 2), (3, 2, {'weight': 8})]) cls.no_edges_A = numpy.array([[0, 0], [0, 0]]) diff --git a/networkx/linalg/tests/test_laplacian.py b/networkx/linalg/tests/test_laplacian.py index 003c3b36..0b631182 100644 --- a/networkx/linalg/tests/test_laplacian.py +++ b/networkx/linalg/tests/test_laplacian.py @@ -14,7 +14,7 @@ class TestLaplacian(object): deg = [3, 2, 2, 1, 0] cls.G = havel_hakimi_graph(deg) cls.WG = nx.Graph((u, v, {'weight': 0.5, 'other': 0.3}) - for (u, v) in cls.G.edges()) + for (u, v) in cls.G.edges()) cls.WG.add_node(4) cls.MG = nx.MultiGraph(cls.G) @@ -35,7 +35,7 @@ class TestLaplacian(object): npt.assert_equal(nx.laplacian_matrix(self.G).todense(), NL) npt.assert_equal(nx.laplacian_matrix(self.MG).todense(), NL) npt.assert_equal(nx.laplacian_matrix(self.G, nodelist=[0, 1]).todense(), - numpy.array([[1, -1], [-1, 1]])) + numpy.array([[1, -1], [-1, 1]])) npt.assert_equal(nx.laplacian_matrix(self.WG).todense(), WL) npt.assert_equal(nx.laplacian_matrix(self.WG, weight=None).todense(), NL) npt.assert_equal(nx.laplacian_matrix(self.WG, weight='other').todense(), OL) @@ -54,15 +54,15 @@ class TestLaplacian(object): [0., 0., 0., 0., 0.]]) npt.assert_almost_equal(nx.normalized_laplacian_matrix(self.G).todense(), - GL, decimal=3) + GL, decimal=3) npt.assert_almost_equal(nx.normalized_laplacian_matrix(self.MG).todense(), - GL, decimal=3) + GL, decimal=3) npt.assert_almost_equal(nx.normalized_laplacian_matrix(self.WG).todense(), - GL, decimal=3) + GL, decimal=3) npt.assert_almost_equal(nx.normalized_laplacian_matrix(self.WG, weight='other').todense(), - GL, decimal=3) + GL, decimal=3) npt.assert_almost_equal(nx.normalized_laplacian_matrix(self.Gsl).todense(), - Lsl, decimal=3) + Lsl, decimal=3) def test_directed_laplacian(self): "Directed Laplacian" diff --git a/networkx/linalg/tests/test_modularity.py b/networkx/linalg/tests/test_modularity.py index 9adbe5c2..edddff29 100644 --- a/networkx/linalg/tests/test_modularity.py +++ b/networkx/linalg/tests/test_modularity.py @@ -30,7 +30,7 @@ class TestModularity(object): permutation = [4, 0, 1, 2, 3] npt.assert_equal(nx.modularity_matrix(self.G), B) npt.assert_equal(nx.modularity_matrix(self.G, nodelist=permutation), - B[numpy.ix_(permutation, permutation)]) + B[numpy.ix_(permutation, permutation)]) def test_modularity_weight(self): "Modularity matrix with weights" @@ -61,5 +61,5 @@ class TestModularity(object): mm = nx.directed_modularity_matrix(self.DG, nodelist=sorted(self.DG)) npt.assert_equal(mm, B) npt.assert_equal(nx.directed_modularity_matrix(self.DG, - nodelist=node_permutation), - B[numpy.ix_(idx_permutation, idx_permutation)]) + nodelist=node_permutation), + B[numpy.ix_(idx_permutation, idx_permutation)]) diff --git a/networkx/linalg/tests/test_spectrum.py b/networkx/linalg/tests/test_spectrum.py index 63fdcf00..fb2b8bb2 100644 --- a/networkx/linalg/tests/test_spectrum.py +++ b/networkx/linalg/tests/test_spectrum.py @@ -15,7 +15,7 @@ class TestSpectrum(object): cls.G = havel_hakimi_graph(deg) cls.P = nx.path_graph(3) cls.WG = nx.Graph((u, v, {'weight': 0.5, 'other': 0.3}) - for (u, v) in cls.G.edges()) + for (u, v) in cls.G.edges()) cls.WG.add_node(4) cls.DG = nx.DiGraph() nx.add_path(cls.DG, [0, 1, 2]) @@ -44,7 +44,6 @@ class TestSpectrum(object): e = sorted(nx.normalized_laplacian_spectrum(self.WG, weight='other')) npt.assert_almost_equal(e, evals) - def test_adjacency_spectrum(self): "Adjacency eigenvalues" evals = numpy.array([-numpy.sqrt(2), 0, numpy.sqrt(2)]) diff --git a/networkx/readwrite/nx_shp.py b/networkx/readwrite/nx_shp.py index d6089264..aa05d8b5 100644 --- a/networkx/readwrite/nx_shp.py +++ b/networkx/readwrite/nx_shp.py @@ -275,7 +275,6 @@ def write_shp(G, outdir): newfield = ogr.FieldDefn(key, fields[key]) layer.CreateField(newfield) - drv = ogr.GetDriverByName("ESRI Shapefile") shpdir = drv.CreateDataSource(outdir) # delete pre-existing output first otherwise ogr chokes diff --git a/networkx/readwrite/tests/test_edgelist.py b/networkx/readwrite/tests/test_edgelist.py index a0d812c4..371216b0 100644 --- a/networkx/readwrite/tests/test_edgelist.py +++ b/networkx/readwrite/tests/test_edgelist.py @@ -81,9 +81,8 @@ class TestEdgelist: bytesIO = io.BytesIO(s) G = nx.read_edgelist(bytesIO, nodetype=int, data=True) assert_edges_equal(G.edges(data=True), - [(1, 2, {'weight': 2.0}), (2, 3, {'weight': 3.0})]) - - + [(1, 2, {'weight': 2.0}), (2, 3, {'weight': 3.0})]) + s = """\ # comment line 1 2 {'weight':2.0} @@ -97,7 +96,7 @@ class TestEdgelist: StringIO = io.StringIO(s) G = nx.read_edgelist(StringIO, nodetype=int, data=True) assert_edges_equal(G.edges(data=True), - [(1, 2, {'weight': 2.0}), (2, 3, {'weight': 3.0})]) + [(1, 2, {'weight': 2.0}), (2, 3, {'weight': 3.0})]) def test_write_edgelist_1(self): fh = io.BytesIO() diff --git a/networkx/readwrite/tests/test_gexf.py b/networkx/readwrite/tests/test_gexf.py index 0c5ee620..ea067a38 100644 --- a/networkx/readwrite/tests/test_gexf.py +++ b/networkx/readwrite/tests/test_gexf.py @@ -92,21 +92,21 @@ org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/\ cls.attribute_graph = nx.DiGraph() cls.attribute_graph.graph['node_default'] = {'frog': True} cls.attribute_graph.add_node('0', - label='Gephi', - url='https://gephi.org', - indegree=1, frog=False) + label='Gephi', + url='https://gephi.org', + indegree=1, frog=False) cls.attribute_graph.add_node('1', - label='Webatlas', - url='http://webatlas.fr', - indegree=2, frog=False) + label='Webatlas', + url='http://webatlas.fr', + indegree=2, frog=False) cls.attribute_graph.add_node('2', - label='RTGI', - url='http://rtgi.fr', - indegree=1, frog=True) + label='RTGI', + url='http://rtgi.fr', + indegree=1, frog=True) cls.attribute_graph.add_node('3', - label='BarabasiLab', - url='http://barabasilab.com', - indegree=1, frog=True) + label='BarabasiLab', + url='http://barabasilab.com', + indegree=1, frog=True) cls.attribute_graph.add_edge('0', '1', id='0') cls.attribute_graph.add_edge('0', '2', id='1') cls.attribute_graph.add_edge('1', '0', id='2') @@ -141,7 +141,7 @@ org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/\ assert sorted(G.nodes()) == sorted(H.nodes()) assert sorted(G.edges()) == sorted(H.edges()) assert (sorted(G.edges(data=True)) == - sorted(H.edges(data=True))) + sorted(H.edges(data=True))) self.simple_directed_fh.seek(0) def test_write_read_simple_directed_graphml(self): @@ -153,7 +153,7 @@ org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.gexf.net/\ assert sorted(G.nodes()) == sorted(H.nodes()) assert sorted(G.edges()) == sorted(H.edges()) assert (sorted(G.edges(data=True)) == - sorted(H.edges(data=True))) + sorted(H.edges(data=True))) self.simple_directed_fh.seek(0) def test_read_simple_undirected_graphml(self): diff --git a/networkx/readwrite/tests/test_gml.py b/networkx/readwrite/tests/test_gml.py index 121f375f..4f2090ea 100644 --- a/networkx/readwrite/tests/test_gml.py +++ b/networkx/readwrite/tests/test_gml.py @@ -145,20 +145,20 @@ graph [ def test_parse_gml(self): G = nx.parse_gml(self.simple_data, label='label') assert (sorted(G.nodes()) == - ['Node 1', 'Node 2', 'Node 3']) + ['Node 1', 'Node 2', 'Node 3']) assert ([e for e in sorted(G.edges())] == - [('Node 1', 'Node 2'), - ('Node 2', 'Node 3'), - ('Node 3', 'Node 1')]) + [('Node 1', 'Node 2'), + ('Node 2', 'Node 3'), + ('Node 3', 'Node 1')]) assert ([e for e in sorted(G.edges(data=True))] == - [('Node 1', 'Node 2', - {'color': {'line': 'blue', 'thickness': 3}, - 'label': 'Edge from node 1 to node 2'}), - ('Node 2', 'Node 3', - {'label': 'Edge from node 2 to node 3'}), - ('Node 3', 'Node 1', - {'label': 'Edge from node 3 to node 1'})]) + [('Node 1', 'Node 2', + {'color': {'line': 'blue', 'thickness': 3}, + 'label': 'Edge from node 1 to node 2'}), + ('Node 2', 'Node 3', + {'label': 'Edge from node 2 to node 3'}), + ('Node 3', 'Node 1', + {'label': 'Edge from node 3 to node 1'})]) def test_read_gml(self): (fd, fname) = tempfile.mkstemp() @@ -343,9 +343,9 @@ graph assert data == G.name assert {'name': data, str('data'): data} == G.graph assert (list(G.nodes(data=True)) == - [(0, dict(int=-1, data=dict(data=data)))]) + [(0, dict(int=-1, data=dict(data=data)))]) assert (list(G.edges(data=True)) == - [(0, 0, dict(float=-2.5, data=data))]) + [(0, 0, dict(float=-2.5, data=data))]) G = nx.Graph() G.graph['data'] = 'frozenset([1, 2, 3])' G = nx.parse_gml(nx.generate_gml(G), destringizer=literal_eval) diff --git a/networkx/readwrite/tests/test_graph6.py b/networkx/readwrite/tests/test_graph6.py index 48c6976f..d0d2bab7 100644 --- a/networkx/readwrite/tests/test_graph6.py +++ b/networkx/readwrite/tests/test_graph6.py @@ -17,7 +17,7 @@ class TestGraph6Utils(object): assert g6.data_to_n(g6.n_to_data(i))[0] == i assert g6.data_to_n(g6.n_to_data(i))[1] == [] assert (g6.data_to_n(g6.n_to_data(i) + [42, 43])[1] == - [42, 43]) + [42, 43]) class TestFromGraph6Bytes(TestCase): @@ -97,7 +97,7 @@ class TestWriteGraph6(TestCase): # Strip the trailing newline. gstr = gstr.getvalue().rstrip() assert (len(gstr) == - ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)) + ((i - 1) * i // 2 + 5) // 6 + (1 if i < 63 else 4)) def test_roundtrip(self): for i in list(range(13)) + [31, 47, 62, 63, 64, 72]: diff --git a/networkx/readwrite/tests/test_graphml.py b/networkx/readwrite/tests/test_graphml.py index 8c689888..6d1a4288 100644 --- a/networkx/readwrite/tests/test_graphml.py +++ b/networkx/readwrite/tests/test_graphml.py @@ -7,6 +7,7 @@ import tempfile import os from networkx.testing import almost_equal + class BaseGraphML(object): @classmethod def setup_class(cls): @@ -45,15 +46,15 @@ class BaseGraphML(object): cls.simple_directed_graph.add_node('n10') cls.simple_directed_graph.add_edge('n0', 'n2', id='foo') cls.simple_directed_graph.add_edges_from([('n1', 'n2'), - ('n2', 'n3'), - ('n3', 'n5'), - ('n3', 'n4'), - ('n4', 'n6'), - ('n6', 'n5'), - ('n5', 'n7'), - ('n6', 'n8'), - ('n8', 'n7'), - ('n8', 'n9'), + ('n2', 'n3'), + ('n3', 'n5'), + ('n3', 'n4'), + ('n4', 'n6'), + ('n6', 'n5'), + ('n5', 'n7'), + ('n6', 'n8'), + ('n8', 'n7'), + ('n8', 'n9'), ]) cls.simple_directed_fh = \ io.BytesIO(cls.simple_directed_data.encode('UTF-8')) @@ -170,7 +171,7 @@ class BaseGraphML(object): cls.simple_undirected_graph.add_node('n10') cls.simple_undirected_graph.add_edge('n0', 'n2', id='foo') cls.simple_undirected_graph.add_edges_from([('n1', 'n2'), - ('n2', 'n3'), + ('n2', 'n3'), ]) fh = io.BytesIO(cls.simple_undirected_data.encode('UTF-8')) cls.simple_undirected_fh = fh @@ -183,14 +184,14 @@ class TestReadGraphML(BaseGraphML): assert sorted(G.nodes()) == sorted(H.nodes()) assert sorted(G.edges()) == sorted(H.edges()) assert (sorted(G.edges(data=True)) == - sorted(H.edges(data=True))) + sorted(H.edges(data=True))) self.simple_directed_fh.seek(0) I = nx.parse_graphml(self.simple_directed_data) assert sorted(G.nodes()) == sorted(I.nodes()) assert sorted(G.edges()) == sorted(I.edges()) assert (sorted(G.edges(data=True)) == - sorted(I.edges(data=True))) + sorted(I.edges(data=True))) def test_read_simple_undirected_graphml(self): G = self.simple_undirected_graph @@ -931,8 +932,8 @@ class TestWriteGraphML(BaseGraphML): H = nx.read_graphml(fh) assert H.nodes['n0']['special'] is False assert H.nodes['n1']['special'] is 0 - assert H.edges['n0','n1',0]['special'] is False - assert H.edges['n0','n1',1]['special'] is 0 + assert H.edges['n0', 'n1', 0]['special'] is False + assert H.edges['n0', 'n1', 1]['special'] is 0 def test_multigraph_to_graph(self): # test converting multigraph to graph if no parallel edges found diff --git a/networkx/readwrite/tests/test_leda.py b/networkx/readwrite/tests/test_leda.py index edf9318d..7e6bf144 100644 --- a/networkx/readwrite/tests/test_leda.py +++ b/networkx/readwrite/tests/test_leda.py @@ -10,15 +10,15 @@ class TestLEDA(object): G = nx.parse_leda(data) G = nx.parse_leda(data.split('\n')) assert (sorted(G.nodes()) == - ['v1', 'v2', 'v3', 'v4', 'v5']) + ['v1', 'v2', 'v3', 'v4', 'v5']) assert (sorted(G.edges(data=True)) == - [('v1', 'v2', {'label': '4'}), - ('v1', 'v3', {'label': '3'}), - ('v2', 'v3', {'label': '2'}), - ('v3', 'v4', {'label': '3'}), - ('v3', 'v5', {'label': '7'}), - ('v4', 'v5', {'label': '6'}), - ('v5', 'v1', {'label': 'foo'})]) + [('v1', 'v2', {'label': '4'}), + ('v1', 'v3', {'label': '3'}), + ('v2', 'v3', {'label': '2'}), + ('v3', 'v4', {'label': '3'}), + ('v3', 'v5', {'label': '7'}), + ('v4', 'v5', {'label': '6'}), + ('v5', 'v1', {'label': 'foo'})]) def test_read_LEDA(self): fh = io.BytesIO() diff --git a/networkx/readwrite/tests/test_pajek.py b/networkx/readwrite/tests/test_pajek.py index b4a69e15..bcb1b11d 100644 --- a/networkx/readwrite/tests/test_pajek.py +++ b/networkx/readwrite/tests/test_pajek.py @@ -15,8 +15,8 @@ class TestPajek(object): cls.G = nx.MultiDiGraph() cls.G.add_nodes_from(['A1', 'Bb', 'C', 'D2']) cls.G.add_edges_from([('A1', 'A1'), ('A1', 'Bb'), ('A1', 'C'), - ('Bb', 'A1'), ('C', 'C'), ('C', 'D2'), - ('D2', 'Bb')]) + ('Bb', 'A1'), ('C', 'C'), ('C', 'D2'), + ('D2', 'Bb')]) cls.G.graph['name'] = 'Tralala' (fd, cls.fname) = tempfile.mkstemp() @@ -61,7 +61,7 @@ class TestPajek(object): import io G = nx.parse_pajek(self.data) fh = io.BytesIO() - nx.write_pajek(G,fh) + nx.write_pajek(G, fh) fh.seek(0) H = nx.read_pajek(fh) assert_nodes_equal(list(G), list(H)) @@ -80,7 +80,7 @@ class TestPajek(object): import warnings with warnings.catch_warnings(record=True) as w: - nx.write_pajek(G,fh) + nx.write_pajek(G, fh) assert len(w) == 4 def test_noname(self): diff --git a/networkx/tests/test_all_random_functions.py b/networkx/tests/test_all_random_functions.py index ec92d965..6d5146d2 100644 --- a/networkx/tests/test_all_random_functions.py +++ b/networkx/tests/test_all_random_functions.py @@ -19,7 +19,7 @@ py_rv = random.random() def t(f, *args, **kwds): - """call one function and check if global RNG changed""" + """call one function and check if global RNG changed""" global progress progress += 1 print(progress, ",", end="") diff --git a/networkx/tests/test_convert.py b/networkx/tests/test_convert.py index d79abfa6..bfa1ff27 100644 --- a/networkx/tests/test_convert.py +++ b/networkx/tests/test_convert.py @@ -236,9 +236,9 @@ class TestConvert(): assert self.edgelists_equal(nx.MultiGraph(nx.DiGraph(edges2)).edges(), edges1) assert self.edgelists_equal(nx.MultiGraph(nx.MultiDiGraph(edges1)).edges(), - edges1) + edges1) assert self.edgelists_equal(nx.MultiGraph(nx.MultiDiGraph(edges2)).edges(), - edges1) + edges1) assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges1)).edges(), edges1) assert self.edgelists_equal(nx.Graph(nx.MultiDiGraph(edges2)).edges(), edges1) diff --git a/networkx/tests/test_convert_numpy.py b/networkx/tests/test_convert_numpy.py index 49f92d69..c74b36ef 100644 --- a/networkx/tests/test_convert_numpy.py +++ b/networkx/tests/test_convert_numpy.py @@ -6,6 +6,7 @@ from networkx.testing.utils import assert_graphs_equal #numpy = pytest.importorskip("numpy") + class TestConvertNumpy(object): @classmethod def setup_class(cls): diff --git a/networkx/tests/test_relabel.py b/networkx/tests/test_relabel.py index f9c14380..78beefcb 100644 --- a/networkx/tests/test_relabel.py +++ b/networkx/tests/test_relabel.py @@ -52,7 +52,7 @@ class TestRelabel(): assert H.degree(3) == 1 H = nx.convert_node_labels_to_integers(G, ordering="increasing degree", - label_attribute='label') + label_attribute='label') degH = (d for n, d in H.degree()) degG = (d for n, d in G.degree()) assert sorted(degH) == sorted(degG) @@ -76,7 +76,7 @@ class TestRelabel(): assert sorted(degH) == sorted(degG) H = nx.convert_node_labels_to_integers(G, ordering="sorted", - label_attribute='label') + label_attribute='label') assert H.nodes[0]['label'] == 'A' assert H.nodes[1]['label'] == 'B' assert H.nodes[2]['label'] == 'C' diff --git a/networkx/utils/tests/test_decorators.py b/networkx/utils/tests/test_decorators.py index aec968d0..2486e2c8 100644 --- a/networkx/utils/tests/test_decorators.py +++ b/networkx/utils/tests/test_decorators.py @@ -10,6 +10,7 @@ from networkx.utils.decorators import nodes_or_number, preserve_random_state, \ py_random_state, np_random_state, random_state from networkx.utils.misc import PythonRandomInterface + def test_not_implemented_decorator(): @not_implemented_for('directed') def test1(G): @@ -169,7 +170,7 @@ class TestRandomState(object): @py_random_state(1) def instantiate_py_random_state(self, random_state): assert (isinstance(random_state, random.Random) or - isinstance(random_state, PythonRandomInterface)) + isinstance(random_state, PythonRandomInterface)) return random_state.random() def test_random_state_None(self): diff --git a/networkx/utils/tests/test_misc.py b/networkx/utils/tests/test_misc.py index 1de61473..17e059fc 100644 --- a/networkx/utils/tests/test_misc.py +++ b/networkx/utils/tests/test_misc.py @@ -202,6 +202,6 @@ def test_PythonRandomInterface(): assert rng.expovariate(1.5) == rs42.exponential(1/1.5) assert np.all(rng.shuffle([1, 2, 3]) == rs42.shuffle([1, 2, 3])) assert np.all(rng.sample([1, 2, 3], 2) == - rs42.choice([1, 2, 3], (2,), replace=False)) + rs42.choice([1, 2, 3], (2,), replace=False)) assert rng.randint(3, 5) == rs42.randint(3, 6) assert rng.random() == rs42.random_sample() diff --git a/networkx/utils/tests/test_rcm.py b/networkx/utils/tests/test_rcm.py index 16c1d87f..ce14812a 100644 --- a/networkx/utils/tests/test_rcm.py +++ b/networkx/utils/tests/test_rcm.py @@ -9,7 +9,7 @@ def test_reverse_cuthill_mckee(): (2, 4), (3, 5), (3, 8), (4, 6), (5, 6), (5, 7), (6, 7)]) rcm = list(reverse_cuthill_mckee_ordering(G)) assert rcm in [[0, 8, 5, 7, 3, 6, 2, 4, 1, 9], - [0, 8, 5, 7, 3, 6, 4, 2, 1, 9]] + [0, 8, 5, 7, 3, 6, 4, 2, 1, 9]] def test_rcm_alternate_heuristic(): diff --git a/networkx/utils/tests/test_unionfind.py b/networkx/utils/tests/test_unionfind.py index 8620f374..59720ff0 100644 --- a/networkx/utils/tests/test_unionfind.py +++ b/networkx/utils/tests/test_unionfind.py @@ -11,6 +11,7 @@ def test_unionfind(): x = nx.utils.UnionFind() x.union(0, 'a') + def test_subtree_union(): # See https://github.com/networkx/networkx/pull/3224 # (35db1b551ee65780794a357794f521d8768d5049). |