Drawing graphs in Python with networkx
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import networkx as nx import matplotlib.pyplot as plt G=nx.Graph() ###########################Adding nodes########################### G.add_node(1) G.add_nodes_from([2,3,4]) #Add an nbunch: iterable container of nodes (e.g. a list, set, graph, file, etc..) H=nx.path_graph(1) #G now contains the nodes of H as nodes of G. G.add_nodes_from(H) #Graph of Graph #G.add_node(H) G.add_node("b") # adds node "behnam" G.add_nodes_from("behnam") # adds 6 nodes: 'b','e' ,'h', 'n', 'a','m' ###########################Adding edges########################### G.add_edge(1, 4) edge_2_3=(2,3) G.add_edge(*edge_2_3) # unpack edge tuple with * #An edge can be associated with any object x using G.add_edge(n1,n2,object=x). edge_2_4=(2,4,{'weight':3.1415}) G.add_edge(*edge_2_4) G.add_edge(1, 2, weight=4.7 ) G.add_weighted_edges_from([(3,4,0.125)]) ###########################Accessing nodes,edges and neighbors########################### print 'Nodes' print G.nodes() print 'List of edges' print G.edges() print 'Neighbors' print G.neighbors(1) #Accessing edges print 'Accessing edges' print G[3] print G[4] print G[4][2]['weight'] for (u,v,d) in G.edges(data='weight'): if d>0.5: print('(%d, %d, %.3f)'%(u,v,d)) ###########################Adding attributes to nodes########################### G.add_node(1, time='5pm') print G.node[1] G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})]) ###########################Multigraphs########################### #allow you to add the same edge twice, possibly with different edge data. MG=nx.MultiGraph() MG.add_weighted_edges_from([(1,2,.5), (1,2,.75), (2,3,.5)]) MG.degree(weight='weight') GG=nx.Graph() for n,nbrs in MG.adjacency_iter(): for nbr,edict in nbrs.items(): minvalue=min([d['weight'] for d in edict.values()]) GG.add_edge(n,nbr, weight = minvalue) print 'shortest path from 1 to 3' print nx.shortest_path(GG,1,3) ###########################Graph operations########################### #subgraph(G, nbunch) - induce subgraph of G on nodes in nbunh #union(G1,G2) - graph union #disjoint_union(G1,G2) - graph union assuming all nodes are different #cartesian_product(G1,G2) - return Cartesian product graph #compose(G1,G2) - combine graphs identifying nodes common to both #complement(G) - graph complement #create_empty_copy(G) - return an empty copy of the same graph class #convert_to_undirected(G) - return an undirected representation of G #convert_to_directed(G) - return a directed representation of G ###########################Ploting Graphs ########################### #nx.draw_spectral(G) #nx.draw_circular(G) #nx.draw_random(G) pos=nx.spring_layout(G) # positions for all nodes nx.draw_networkx_labels(G,pos,font_size=20,font_family='sans-serif') nx.draw(G,pos) plt.show() |
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