#!/usr/bin/env python import random from nose.tools import * import networkx import networkx as nx class TestFunction(object): def setUp(self): self.G=networkx.Graph({0:[1,2,3], 1:[1,2,0], 4:[]}, name='Test') self.Gdegree={0:3, 1:2, 2:2, 3:1, 4:0} self.Gnodes=list(range(5)) self.Gedges=[(0,1),(0,2),(0,3),(1,0),(1,1),(1,2)] self.DG=networkx.DiGraph({0:[1,2,3], 1:[1,2,0], 4:[]}) self.DGin_degree={0:1, 1:2, 2:2, 3:1, 4:0} self.DGout_degree={0:3, 1:3, 2:0, 3:0, 4:0} self.DGnodes=list(range(5)) self.DGedges=[(0,1),(0,2),(0,3),(1,0),(1,1),(1,2)] def test_nodes(self): assert_equal(self.G.nodes(),networkx.nodes(self.G)) assert_equal(self.DG.nodes(),networkx.nodes(self.DG)) def test_edges(self): assert_equal(self.G.edges(),networkx.edges(self.G)) assert_equal(self.DG.edges(),networkx.edges(self.DG)) assert_equal(self.G.edges(nbunch=[0,1,3]),networkx.edges(self.G,nbunch=[0,1,3])) assert_equal(self.DG.edges(nbunch=[0,1,3]),networkx.edges(self.DG,nbunch=[0,1,3])) def test_nodes_iter(self): assert_equal(list(self.G.nodes_iter()),list(networkx.nodes_iter(self.G))) assert_equal(list(self.DG.nodes_iter()),list(networkx.nodes_iter(self.DG))) def test_edges_iter(self): assert_equal(list(self.G.edges_iter()),list(networkx.edges_iter(self.G))) assert_equal(list(self.DG.edges_iter()),list(networkx.edges_iter(self.DG))) assert_equal(list(self.G.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.G,nbunch=[0,1,3]))) assert_equal(list(self.DG.edges_iter(nbunch=[0,1,3])),list(networkx.edges_iter(self.DG,nbunch=[0,1,3]))) def test_degree(self): assert_equal(self.G.degree(),networkx.degree(self.G)) assert_equal(self.DG.degree(),networkx.degree(self.DG)) assert_equal(self.G.degree(nbunch=[0,1]),networkx.degree(self.G,nbunch=[0,1])) assert_equal(self.DG.degree(nbunch=[0,1]),networkx.degree(self.DG,nbunch=[0,1])) assert_equal(self.G.degree(weight='weight'),networkx.degree(self.G,weight='weight')) assert_equal(self.DG.degree(weight='weight'),networkx.degree(self.DG,weight='weight')) def test_neighbors(self): assert_equal(self.G.neighbors(1),networkx.neighbors(self.G,1)) assert_equal(self.DG.neighbors(1),networkx.neighbors(self.DG,1)) def test_number_of_nodes(self): assert_equal(self.G.number_of_nodes(),networkx.number_of_nodes(self.G)) assert_equal(self.DG.number_of_nodes(),networkx.number_of_nodes(self.DG)) def test_number_of_edges(self): assert_equal(self.G.number_of_edges(),networkx.number_of_edges(self.G)) assert_equal(self.DG.number_of_edges(),networkx.number_of_edges(self.DG)) def test_is_directed(self): assert_equal(self.G.is_directed(),networkx.is_directed(self.G)) assert_equal(self.DG.is_directed(),networkx.is_directed(self.DG)) def test_subgraph(self): assert_equal(self.G.subgraph([0,1,2,4]).adj,networkx.subgraph(self.G,[0,1,2,4]).adj) assert_equal(self.DG.subgraph([0,1,2,4]).adj,networkx.subgraph(self.DG,[0,1,2,4]).adj) def test_create_empty_copy(self): G=networkx.create_empty_copy(self.G, with_nodes=False) assert_equal(G.nodes(),[]) assert_equal(G.graph,{}) assert_equal(G.node,{}) assert_equal(G.edge,{}) G=networkx.create_empty_copy(self.G) assert_equal(G.nodes(),self.G.nodes()) assert_equal(G.graph,{}) assert_equal(G.node,{}.fromkeys(self.G.nodes(),{})) assert_equal(G.edge,{}.fromkeys(self.G.nodes(),{})) def test_degree_histogram(self): assert_equal(networkx.degree_histogram(self.G), [1,1,1,1,1]) def test_density(self): assert_equal(networkx.density(self.G), 0.5) assert_equal(networkx.density(self.DG), 0.3) G=networkx.Graph() G.add_node(1) assert_equal(networkx.density(G), 0.0) def test_freeze(self): G=networkx.freeze(self.G) assert_equal(G.frozen,True) assert_raises(networkx.NetworkXError, G.add_node, 1) assert_raises(networkx.NetworkXError, G.add_nodes_from, [1]) assert_raises(networkx.NetworkXError, G.remove_node, 1) assert_raises(networkx.NetworkXError, G.remove_nodes_from, [1]) assert_raises(networkx.NetworkXError, G.add_edge, 1,2) assert_raises(networkx.NetworkXError, G.add_edges_from, [(1,2)]) assert_raises(networkx.NetworkXError, G.remove_edge, 1,2) assert_raises(networkx.NetworkXError, G.remove_edges_from, [(1,2)]) assert_raises(networkx.NetworkXError, G.clear) def test_is_frozen(self): assert_equal(networkx.is_frozen(self.G), False) G=networkx.freeze(self.G) assert_equal(G.frozen, networkx.is_frozen(self.G)) assert_equal(G.frozen,True) def test_info(self): G=networkx.path_graph(5) info=networkx.info(G) expected_graph_info='\n'.join(['Name: path_graph(5)', 'Type: Graph', 'Number of nodes: 5', 'Number of edges: 4', 'Average degree: 1.6000']) assert_equal(info,expected_graph_info) info=networkx.info(G,n=1) expected_node_info='\n'.join( ['Node 1 has the following properties:', 'Degree: 2', 'Neighbors: 0 2']) assert_equal(info,expected_node_info) def test_info_digraph(self): G=networkx.DiGraph(name='path_graph(5)') G.add_path([0,1,2,3,4]) info=networkx.info(G) expected_graph_info='\n'.join(['Name: path_graph(5)', 'Type: DiGraph', 'Number of nodes: 5', 'Number of edges: 4', 'Average in degree: 0.8000', 'Average out degree: 0.8000']) assert_equal(info,expected_graph_info) info=networkx.info(G,n=1) expected_node_info='\n'.join( ['Node 1 has the following properties:', 'Degree: 2', 'Neighbors: 2']) assert_equal(info,expected_node_info) assert_raises(networkx.NetworkXError,networkx.info,G,n=-1) def test_neighbors(self): graph = nx.complete_graph(100) pop = random.sample(graph.nodes(), 1) nbors = list(nx.neighbors(graph, pop[0])) # should be all the other vertices in the graph assert_equal(len(nbors), len(graph) - 1) graph = nx.path_graph(100) node = random.sample(graph.nodes(), 1)[0] nbors = list(nx.neighbors(graph, node)) # should be all the other vertices in the graph if node != 0 and node != 99: assert_equal(len(nbors), 2) else: assert_equal(len(nbors), 1) # create a star graph with 99 outer nodes graph = nx.star_graph(99) nbors = list(nx.neighbors(graph, 0)) assert_equal(len(nbors), 99) def test_non_neighbors(self): graph = nx.complete_graph(100) pop = random.sample(graph.nodes(), 1) nbors = list(nx.non_neighbors(graph, pop[0])) # should be all the other vertices in the graph assert_equal(len(nbors), 0) graph = nx.path_graph(100) node = random.sample(graph.nodes(), 1)[0] nbors = list(nx.non_neighbors(graph, node)) # should be all the other vertices in the graph if node != 0 and node != 99: assert_equal(len(nbors), 97) else: assert_equal(len(nbors), 98) # create a star graph with 99 outer nodes graph = nx.star_graph(99) nbors = list(nx.non_neighbors(graph, 0)) assert_equal(len(nbors), 0) # disconnected graph graph = nx.Graph() graph.add_nodes_from(range(10)) nbors = list(nx.non_neighbors(graph, 0)) assert_equal(len(nbors), 9)