| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/tests/ |
| test_hierarchy.py | 3 import networkx as nx namespace
6 G = nx.cycle_graph(5)
7 assert_raises(nx.NetworkXError,nx.flow_hierarchy,G)
10 G = nx.cycle_graph(5,create_using=nx.DiGraph())
11 assert_equal(nx.flow_hierarchy(G),0.0)
14 G = nx.full_rary_tree(2,16,create_using=nx.DiGraph())
15 assert_equal(nx.flow_hierarchy(G),1.0
[all...] |
| test_vitality.py | 3 import networkx as nx namespace
8 G=nx.cycle_graph(3)
9 v=nx.closeness_vitality(G)
14 G=nx.Graph()
16 v=nx.closeness_vitality(G,weight='weight')
20 G=nx.DiGraph()
22 v=nx.closeness_vitality(G)
26 G=nx.DiGraph()
28 v=nx.closeness_vitality(G,weight='weight')
32 G=nx.MultiDiGraph(
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/ |
| intersection.py | 12 import networkx as nx namespace
47 G=nx.bipartite_random_graph(n, m, p, seed=seed)
48 return nx.projected_graph(G, range(n))
75 G = nx.empty_graph(n + m)
80 return nx.projected_graph(G, range(n))
111 G = nx.empty_graph(n + m)
117 return nx.projected_graph(G, range(n))
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/generators/tests/ |
| test_atlas.py | 2 import networkx as nx namespace
7 self.GAG=nx.graph_atlas_g()
|
| test_ego.py | 8 import networkx as nx namespace
12 G=nx.star_graph(3)
13 H=nx.ego_graph(G,0)
14 assert_true(nx.is_isomorphic(G,H))
18 H=nx.ego_graph(G,0)
19 assert_true(nx.is_isomorphic(nx.star_graph(3),H))
20 G=nx.path_graph(3)
21 H=nx.ego_graph(G,0)
23 H=nx.ego_graph(G,0,undirected=True
[all...] |
| test_geometric.py | 3 import networkx as nx namespace
7 G=nx.random_geometric_graph(50,0.25)
11 G=nx.geographical_threshold_graph(50,100)
15 G=nx.waxman_graph(50,0.5,0.1)
17 G=nx.waxman_graph(50,0.5,0.1,L=1)
21 G = nx.navigable_small_world_graph(5,p=1,q=0)
22 gg = nx.grid_2d_graph(5,5).to_directed()
23 assert_true(nx.is_isomorphic(G,gg))
25 G = nx.navigable_small_world_graph(5,p=1,q=0,dim=3)
26 gg = nx.grid_graph([5,5,5]).to_directed(
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/json_graph/ |
| node_link.py | 9 import networkx as nx namespace
29 >>> G = nx.Graph([(1,2)])
85 >>> G = nx.Graph([(1,2)])
96 graph = nx.MultiGraph()
98 graph = nx.Graph()
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/ |
| leda.py | 23 import networkx as nx namespace
43 G=nx.read_leda('file.leda')
68 G=nx.parse_leda(string)
82 G = nx.DiGraph()
84 G = nx.Graph()
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/readwrite/tests/ |
| test_gpickle.py | 3 import networkx as nx namespace
8 G=nx.Graph(name="test")
18 nx.write_gpickle(G,fname);
19 Gin=nx.read_gpickle(fname);
|
| test_leda.py | 3 import networkx as nx namespace
10 G=nx.parse_leda(data)
11 G=nx.parse_leda(data.split('\n'))
26 G=nx.parse_leda(data)
31 Gin=nx.read_leda(fname)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/ |
| relabel.py | 7 import networkx as nx namespace
30 >>> G=nx.path_graph(3) # nodes 0-1-2
32 >>> H=nx.relabel_nodes(G,mapping)
36 >>> G=nx.path_graph(26) # nodes 0..25
38 >>> H=nx.relabel_nodes(G,mapping) # nodes a..z
40 >>> G1=nx.relabel_nodes(G,mapping) # nodes 1..26
44 >>> G=nx.path_graph(3) # nodes 0-1-2
46 >>> G=nx.relabel_nodes(G,mapping, copy=False)
53 >>> G=nx.path_graph(3)
56 >>> H=nx.relabel_nodes(G,mapping
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/utils/ |
| rcm.py | 9 import networkx as nx namespace
37 >>> G = nx.path_graph(4)
39 >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
58 for c in nx.connected_components(G):
86 >>> G = nx.path_graph(4)
88 >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
142 spl = nx.shortest_path_length(G, v)
|
| union_find.py | 10 import networkx as nx namespace
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/ |
| pairs.py | 3 import networkx as nx namespace
30 >>> G = nx.DiGraph()
34 >>> list(nx.node_attribute_xy(G,'color'))
93 >>> G = nx.DiGraph()
95 >>> list(nx.node_degree_xy(G,x='out',y='in'))
97 >>> list(nx.node_degree_xy(G,x='in',y='out'))
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/assortativity/tests/ |
| test_connectivity.py | 3 import networkx as nx namespace
8 G=nx.path_graph(4)
10 nd = nx.average_degree_connectivity(G)
15 nd = nx.average_degree_connectivity(D)
20 nd = nx.average_degree_connectivity(D, source='in', target='in')
24 nd = nx.average_degree_connectivity(D, source='in', target='in')
28 G=nx.path_graph(4)
31 nd = nx.average_degree_connectivity(G,weight='weight')
34 nd = nx.average_degree_connectivity(G)
39 nd = nx.average_degree_connectivity(D,weight='weight'
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/ |
| projection.py | 4 import networkx as nx namespace
48 >>> B = nx.path_graph(4)
59 >>> B = nx.Graph()
89 raise nx.NetworkXError("not defined for multigraphs")
93 G=nx.MultiDiGraph()
95 G=nx.DiGraph()
99 G=nx.MultiGraph()
101 G=nx.Graph()
150 >>> B = nx.path_graph(4)
182 raise nx.NetworkXError("not defined for multigraphs"
[all...] |
| spectral.py | 5 import networkx as nx namespace
38 >>> G = nx.path_graph(4)
62 A = nx.to_numpy_matrix(G, nodelist, weight=weight)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/bipartite/tests/ |
| test_centrality.py | 2 import networkx as nx namespace
8 self.P4 = nx.path_graph(4)
9 self.K3 = nx.complete_bipartite_graph(3,3)
10 self.C4 = nx.cycle_graph(4)
11 self.davis = nx.davis_southern_women_graph()
47 G = nx.Graph()
|
| test_spectral_bipartivity.py | 4 import networkx as nx namespace
26 G=nx.star_graph(2)
30 G=nx.star_graph(3)
34 G=nx.star_graph(4)
41 G=nx.complete_bipartite_graph(2,3)
45 G=nx.complete_bipartite_graph(2,3)
49 G=nx.complete_bipartite_graph(2,3)
55 G=nx.complete_bipartite_graph(2,3)
60 G=nx.complete_bipartite_graph(2,3)
66 G=nx.complete_bipartite_graph(2,3
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/ |
| current_flow_closeness.py | 15 import networkx as nx namespace
85 raise nx.NetworkXError('current_flow_closeness_centrality ',
88 raise nx.NetworkXError(\
90 if not nx.is_connected(G):
91 raise nx.NetworkXError("Graph not connected.")
99 H = nx.relabel_nodes(G,dict(zip(ordering,range(n))))
|
| load.py | 18 import networkx as nx namespace
109 (pred,length)=nx.predecessor(G,source,cutoff=cutoff,return_seen=True)
111 (pred,length)=nx.dijkstra_predecessor_and_distance(G,source,weight=weight)
170 (pred,length)=nx.predecessor(G,source,cutoff=cutoff,return_seen=True)
|
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/centrality/tests/ |
| test_closeness_centrality.py | 5 import networkx as nx namespace
10 self.K = nx.krackhardt_kite_graph()
11 self.P3 = nx.path_graph(3)
12 self.P4 = nx.path_graph(4)
13 self.K5 = nx.complete_graph(5)
15 self.C4=nx.cycle_graph(4)
16 self.T=nx.balanced_tree(r=2, h=2)
17 self.Gb = nx.Graph()
22 F = nx.florentine_families_graph()
27 c=nx.closeness_centrality(self.K5
[all...] |
| test_degree_centrality.py | 7 import networkx as nx namespace
13 self.K = nx.krackhardt_kite_graph()
14 self.P3 = nx.path_graph(3)
15 self.K5 = nx.complete_graph(5)
17 F = nx.Graph() # Florentine families
40 G = nx.DiGraph()
52 d = nx.degree_centrality(self.K5)
58 d = nx.degree_centrality(self.P3)
64 d = nx.degree_centrality(self.K)
71 d = nx.degree_centrality(self.F
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/chordal/tests/ |
| test_chordal.py | 3 import networkx as nx namespace
9 connected_chordal_G=nx.Graph()
14 chordal_G = nx.Graph()
20 non_chordal_G = nx.Graph()
25 assert_false(nx.is_chordal(self.non_chordal_G))
26 assert_true(nx.is_chordal(self.chordal_G))
27 assert_true(nx.is_chordal(self.connected_chordal_G))
28 assert_true(nx.is_chordal(nx.complete_graph(3)))
29 assert_true(nx.is_chordal(nx.cycle_graph(3))
[all...] |
| /prebuilts/python/linux-x86/2.7.5/lib/python2.7/site-packages/setoolsgui/networkx/algorithms/components/ |
| connected.py | 22 import networkx as nx namespace
47 raise nx.NetworkXError("""Not allowed for directed graph G.
53 c=nx.single_source_shortest_path_length(G,v)
99 >>> G=nx.path_graph(4)
100 >>> print(nx.is_connected(G))
112 raise nx.NetworkXError(\
117 raise nx.NetworkXPointlessConcept(
120 return len(nx.single_source_shortest_path_length(G,
141 >>> G=nx.path_graph(4)
143 >>> H=nx.connected_component_subgraphs(G)[0
[all...] |
