pylab.rcParams['figure.figsize'] = 6, 6
import random
random.seed(0)

import networkx as nx
G = nx.Graph()

G.add_node(1)
G.add_nodes_from([2,3])

H = nx.path_graph(10)

G.add_nodes_from(H)

G.add_node(H)

nx.draw(G)

G.add_edge(1,2)
nx.draw(G)

e = (2,3)
G.add_edge(*e)
nx.draw(G)

G.add_edges_from([(1,2),(1,3)])
G.add_edges_from(H.edges())
nx.draw(G)

G.remove_node(H)

nx.draw(G)

G.clear()

G.add_edges_from([(1,2),(1,3)])
G.add_node(1)
G.add_edge(1,2)
G.add_node("spam")       # adds node "spam"
G.add_nodes_from("spam") # adds 4 nodes: 's', 'p', 'a', 'm'

nx.draw(G)

G.number_of_nodes()

G.number_of_edges()

G.nodes()

G.edges()

G.neighbors(1)

G.remove_nodes_from("spam")
G.nodes()

G.remove_edge(1,3)

H = nx.DiGraph(G)
H.edges = [(1,2),(2,1)]

edgelist = [(0,1),(1,2),(2,3)]
H = nx.Graph(edgelist)
nx.draw(H)

G[1]

G[1][2]

G.add_edge(1,3)

G[1][3]['color'] = 'blue'

FG = nx.Graph()
FG.add_weighted_edges_from([(1,2,0.125),(1,3,0.75),(2,4,1.2),(3,4,0.375)])
for n,nbrs in FG.adjacency_iter():
   for nbr,eattr in nbrs.items():
       data=eattr['weight']
       if data<0.5: print('(%d, %d, %.3f)' % (n,nbr,data))

G = nx.Graph(day="Friday")
G.graph

G.graph['day'] = 'Monday'
G.graph

G.add_node(1, time = '5pm')
G.add_nodes_from([3], time = '2pm')
G.node[1]

G.node[1]['room'] = 714
G.nodes(data = True)

G.add_edge(1, 2, weight = 4.7 )
G.add_edges_from([(3,4),(4,5)], color = 'red')
G.add_edges_from([(1,2,{'color':'blue'}), (2,3,{'weight':8})])
G[1][2]['weight'] = 4.7
G.edge[1][2]['weight'] = 4

DG = nx.DiGraph()
DG.add_weighted_edges_from([(1,2,0.5), (3,1,0.75)])

DG.out_degree(1,weight = 'weight')

DG.degree(1,weight = 'weight')

DG.successors(1)

DG.neighbors(1)

DG

DG = nx.Graph(DG)

DG

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)

nx.shortest_path(GG,1,3)

G = nx.petersen_graph()
H = nx.subgraph(G, [1,2,3,4,5,6,7,8])
nx.draw(H)

H1 = nx.subgraph(G, [1,2,3,4])
H2 = nx.subgraph(G, [5,6,7,8])
HH = nx.union(H1, H2)
nx.draw_circular(HH)

H4 = nx.disjoint_union(H1, H1)
nx.draw_circular(H4)

H5 = nx.cartesian_product(H1, H1)
nx.draw_circular(H5)

H1 = nx.subgraph(G, [1,2,3,4])
nx.draw_circular(H1)

H2 = nx.subgraph(G, [3,4,5,6])
nx.draw_circular(H2)

HH = nx.compose(H1, H2)
nx.draw_circular(HH)

GP = nx.nx.complement(G)
nx.draw_circular(GP)

petersen = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()

nx.draw(petersen)

nx.draw(tutte)

nx.draw(maze)

nx.draw(tet)

K_5 = nx.complete_graph(5)
K_3_5 = nx.complete_bipartite_graph(3,5)
nx.draw(K_3_5)

barbell = nx.barbell_graph(10,10)
nx.draw(barbell)

lollipop = nx.lollipop_graph(10,20)
nx.draw(lollipop)

er = nx.erdos_renyi_graph(100,0.15)
nx.draw(er, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

ws = nx.watts_strogatz_graph(30,3,0.1)
nx.draw(ws, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

ba = nx.barabasi_albert_graph(100,5)
nx.draw(ba, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

red = nx.random_lobster(100,0.9,0.9)
nx.draw(red, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

nx.write_gml(red,"/home/matthew/tmp/red.gml")
mygraph = nx.read_gml("/home/matthew/tmp/red.gml")

nx.write_gml(er, "/home/matthew/tmp/er.gml")

nx.draw(mygraph, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

pylab.rcParams['figure.figsize'] = 10, 10

gephi_graph = nx.read_graphml("/home/matthew/tmp/red.graphml")

nx.draw(gephi_graph, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

pos = dict([(v,(gephi_graph.node[v]['x'], gephi_graph.node[v]['y'])) for v in gephi_graph.nodes()])
nx.draw(gephi_graph, pos = pos, with_labels = False, node_size = 20, linewidths = 0.5, width = 0.5, arrows = False)

G = nx.erdos_renyi_graph(20, 0.2)
print G.name + '\n' + len(G.name)*'-' + '\n'
print "Number of nodes:\t%i.\nNumber of edges:\t%i." % (G.order(), G.size())

G.nodes()

v = G.nodes()[0]
v

G.degree(v)

G.neighbors(v)

G.edges()

e = G.edges()[5]
e

nx.connected_components(G)

sorted(nx.degree(G).values())

nx.clustering(G)

nx.degree(G,4)

G.degree(4)

G.degree([2,3,4])

sorted(G.degree([2,3,4]).values())

sorted(G.degree().values())

pylab.rcParams['figure.figsize'] = 6, 6

nx.draw(G)

nx.draw_random(G)

nx.draw_circular(G)

nx.draw_spectral(G)

for node in G.nodes_iter():
    print "The degree of node %s is %i." % (node, G.degree(node))

for node, degree in G.degree_iter():
    print "The degree of node %i is %i." % (node, degree)

for node in G.nodes_iter():
    print "The neighbors of node %i in G are %s." % (node, G.neighbors(node))

for i in G.neighbors_iter(v):
    print i

for node in G.nodes_iter():
    for neighbor in G.neighbors_iter(node):
        print "Node %i is a neighbor of node %i." % (node, neighbor)

for i in G.edges_iter():
    print i


nx.node_boundary(G, [1,2,3])

nx.edge_boundary(G, [1,2,3])