Python networkx.cubical_graph函数代码示例

    def test_periodic(self):
        G = nx.grid_2d_graph(0, 0, periodic=True)
        assert_equal(dict(G.degree()), {})

        for m, n, H in [(2, 2, nx.cycle_graph(4)), (1, 7, nx.cycle_graph(7)),
                        (7, 1, nx.cycle_graph(7)),
                        (2, 5, nx.circular_ladder_graph(5)),
                        (5, 2, nx.circular_ladder_graph(5)),
                        (2, 4, nx.cubical_graph()),
                        (4, 2, nx.cubical_graph())]:
            G = nx.grid_2d_graph(m, n, periodic=True)
            assert_true(nx.could_be_isomorphic(G, H))

 def test_labels_and_colors(self):
     G = nx.cubical_graph()
     pos = nx.spring_layout(G)  # positions for all nodes
     # nodes
     nx.draw_networkx_nodes(G, pos,
                            nodelist=[0, 1, 2, 3],
                            node_color='r',
                            node_size=500,
                            alpha=0.8)
     nx.draw_networkx_nodes(G, pos,
                            nodelist=[4, 5, 6, 7],
                            node_color='b',
                            node_size=500,
                            alpha=0.8)
     # edges
     nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5)
     nx.draw_networkx_edges(G, pos,
                            edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)],
                            width=8, alpha=0.5, edge_color='r')
     nx.draw_networkx_edges(G, pos,
                            edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)],
                            width=8, alpha=0.5, edge_color='b')
     # some math labels
     labels = {}
     labels[0] = r'$a$'
     labels[1] = r'$b$'
     labels[2] = r'$c$'
     labels[3] = r'$d$'
     labels[4] = r'$\alpha$'
     labels[5] = r'$\beta$'
     labels[6] = r'$\gamma$'
     labels[7] = r'$\delta$'
     nx.draw_networkx_labels(G, pos, labels, font_size=16)
     plt.show()

 def test_cubical(self):
     G = nx.cubical_graph()
     assert_equal(list(nx.square_clustering(G).values()),
                  [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])
     assert_equal(list(nx.square_clustering(G,[1,2]).values()),[0.5, 0.5])
     assert_equal(nx.square_clustering(G,[1])[1],0.5)
     assert_equal(nx.square_clustering(G,[1,2]),{1: 0.5, 2: 0.5})

    def CutOffLessThan5D(self, pairs, dataTbl, cmc):

        # build matrix from pairs
        sna.loadGraphFromCsv(foMF + ".pairs", " ")
        sna.runMeasure("totaldegreeCentrality")

        print("\n display results .\n")
        sna.displayResults("totaldegreeCentrality")
        print("\n")
        try:
            print("Loading network from file\n(%s)" % (foMF + ".pairs"))
            self.graph = networkx.read_edgelist(foMF + ".pairs", delimiter=" ")
            self.graph.name = "Social Network"

            print("Attempt to draw graph.\n")
            G = networkx.cubical_graph()

            print("created G")
            networkx.draw(G)

            plt.show()  # display

            print("Degree centrality")
            d = degree_centrality(G)
            for v in G.nodes():
                print("%0.2d %5.3f" % (v, d[v]))

        except:
            print("Unable to open file:", filename)

        print("cutoff < 5 degrees end method.\n")

 def test_cubical(self):
     G = nx.cubical_graph()
     assert_equal(nx.triangles(G).values(),
                  [0, 0, 0, 0, 0, 0, 0, 0])
     assert_equal(nx.triangles(G,1),0)
     assert_equal(nx.triangles(G,[1,2]).values(),[0, 0])
     assert_equal(nx.triangles(G,1),0)
     assert_equal(nx.triangles(G,[1,2]),{1: 0, 2: 0})

def test_cartesian_product_classic():
    # test some classic product graphs
    P2 = nx.path_graph(2)
    P3 = nx.path_graph(3)
    # cube = 2-path X 2-path
    G=cartesian_product(P2,P2)
    G=cartesian_product(P2,G)
    assert_true(nx.is_isomorphic(G,nx.cubical_graph()))

    # 3x3 grid
    G=cartesian_product(P3,P3)
    assert_true(nx.is_isomorphic(G,nx.grid_2d_graph(3,3)))

 def test_is_distance_regular(self):
     assert_true(nx.is_distance_regular(nx.icosahedral_graph()))
     assert_true(nx.is_distance_regular(nx.petersen_graph()))
     assert_true(nx.is_distance_regular(nx.cubical_graph()))
     assert_true(nx.is_distance_regular(nx.complete_bipartite_graph(3,3)))
     assert_true(nx.is_distance_regular(nx.tetrahedral_graph()))
     assert_true(nx.is_distance_regular(nx.dodecahedral_graph()))
     assert_true(nx.is_distance_regular(nx.pappus_graph()))
     assert_true(nx.is_distance_regular(nx.heawood_graph()))
     assert_true(nx.is_distance_regular(nx.cycle_graph(3)))
     # no distance regular
     assert_false(nx.is_distance_regular(nx.path_graph(4)))

def test_tensor_product_classic_result():
    K2 = nx.complete_graph(2)
    G = nx.petersen_graph()
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.desargues_graph()))

    G = nx.cycle_graph(5)
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.cycle_graph(10)))

    G = nx.tetrahedral_graph()
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.cubical_graph()))

def test_tensor_product():
    null=nx.null_graph()
    empty1=nx.empty_graph(1)
    empty10=nx.empty_graph(10)
    K2=nx.complete_graph(2)
    K3=nx.complete_graph(3)
    K5=nx.complete_graph(5)
    K10=nx.complete_graph(10)
    P2=nx.path_graph(2)
    P3=nx.path_graph(3)
    P5=nx.path_graph(5)
    P10=nx.path_graph(10)
    # null graph
    G=tensor_product(null,null)
    assert_true(nx.is_isomorphic(G,null))
    # null_graph X anything = null_graph and v.v.
    G=tensor_product(null,empty10)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(null,K3)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(null,K10)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(null,P3)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(null,P10)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(empty10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(K3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(K10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(P3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=tensor_product(P10,null)
    assert_true(nx.is_isomorphic(G,null))

    G=tensor_product(P5,K3)
    assert_equal(nx.number_of_nodes(G),5*3)
    G=tensor_product(K3,K5)
    assert_equal(nx.number_of_nodes(G),3*5)

    G = nx.petersen_graph()
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.desargues_graph()))

    G = nx.cycle_graph(5)
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.cycle_graph(10)))

    G = nx.tetrahedral_graph()
    G = tensor_product(G,K2)
    assert_true(nx.is_isomorphic(G,nx.cubical_graph()))

    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = tensor_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if H.has_edge(u_H,v_H) and G.has_edge(u_G,v_G):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

import matplotlib.pyplot as plt

import networkx as nx

G=nx.cubical_graph()
pos=nx.spring_layout(G) # positions for all nodes
print(G)
# nodes
nx.draw_networkx_nodes(G,pos,
                       nodelist=[0,1,2,3],
                       node_color='r',
                       node_size=500,
                   alpha=0.8)
nx.draw_networkx_nodes(G,pos,
                       nodelist=[4,5,6,7],
                       node_color='b',
                       node_size=500,
                   alpha=0.8)

# edges
nx.draw_networkx_edges(G,pos,width=1.0,alpha=0.5)
nx.draw_networkx_edges(G,pos,
                       edgelist=[(0,1),(1,2),(2,3),(3,0)],
                       width=8,alpha=0.5,edge_color='r')
nx.draw_networkx_edges(G,pos,
                       edgelist=[(4,5),(5,6),(6,7),(7,4)],
                       width=8,alpha=0.5,edge_color='b')


# some math labels
labels={}

def test_cartesian_product():
    null=nx.null_graph()
    empty1=nx.empty_graph(1)
    empty10=nx.empty_graph(10)
    K3=nx.complete_graph(3)
    K5=nx.complete_graph(5)
    K10=nx.complete_graph(10)
    P2=nx.path_graph(2)
    P3=nx.path_graph(3)
    P5=nx.path_graph(5)
    P10=nx.path_graph(10)
    # null graph
    G=cartesian_product(null,null)
    assert_true(nx.is_isomorphic(G,null))
    # null_graph X anything = null_graph and v.v.
    G=cartesian_product(null,empty10)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(null,K3)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(null,K10)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(null,P3)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(null,P10)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(empty10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(K3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(K10,null)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(P3,null)
    assert_true(nx.is_isomorphic(G,null))
    G=cartesian_product(P10,null)
    assert_true(nx.is_isomorphic(G,null))

    # order(GXH)=order(G)*order(H)
    G=cartesian_product(P5,K3)
    assert_equal(nx.number_of_nodes(G),5*3)
    assert_equal(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))
    G=cartesian_product(K3,K5)
    assert_equal(nx.number_of_nodes(G),3*5)
    assert_equal(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))

    # test some classic product graphs
    # cube = 2-path X 2-path
    G=cartesian_product(P2,P2)
    G=cartesian_product(P2,G)
    assert_true(nx.is_isomorphic(G,nx.cubical_graph()))

    # 3x3 grid
    G=cartesian_product(P3,P3)
    assert_true(nx.is_isomorphic(G,nx.grid_2d_graph(3,3)))

    G = nx.erdos_renyi_graph(10,2/10.)
    H = nx.erdos_renyi_graph(10,2/10.)
    GH = cartesian_product(G,H)

    for (u_G,u_H) in GH.nodes_iter():
        for (v_G,v_H) in GH.nodes_iter():
            if (u_G==v_G and H.has_edge(u_H,v_H)) or \
               (u_H==v_H and G.has_edge(u_G,v_G)):
                assert_true(GH.has_edge((u_G,u_H),(v_G,v_H)))
            else:
                assert_true(not GH.has_edge((u_G,u_H),(v_G,v_H)))

 def test_special_cases(self):
     for n, H in [(0, nx.null_graph()), (1, nx.path_graph(2)),
                  (2, nx.cycle_graph(4)), (3, nx.cubical_graph())]:
         G = nx.hypercube_graph(n)
         assert_true(nx.could_be_isomorphic(G, H))

import networkx as nx
import matplotlib.pylab as plt
from plot_multigraph import plot_multigraph

graphs = [
  ("bull", nx.bull_graph()),
  ("chvatal", nx.chvatal_graph()),
  ("cubical", nx.cubical_graph()),
  ("desargues", nx.desargues_graph()),
  ("diamond", nx.diamond_graph()),
  ("dodecahedral", nx.dodecahedral_graph()),
  ("frucht", nx.frucht_graph()),
  ("heawood", nx.heawood_graph()),
  ("house", nx.house_graph()),
  ("house_x", nx.house_x_graph()),
  ("icosahedral", nx.icosahedral_graph()),
  ("krackhardt_kite", nx.krackhardt_kite_graph()),
  ("moebius_kantor", nx.moebius_kantor_graph()),
  ("octahedral", nx.octahedral_graph()),
  ("pappus", nx.pappus_graph()),
  ("petersen", nx.petersen_graph()),
  ("sedgewick_maze", nx.sedgewick_maze_graph()),
  ("tetrahedral", nx.tetrahedral_graph()),
  ("truncated_cube", nx.truncated_cube_graph()),
  ("truncated_tetrahedron", nx.truncated_tetrahedron_graph()),
]

plot_multigraph(graphs, 4, 5, node_size=50)
plt.savefig('graphs/small.png')

# -*- coding: utf-8 -*-

import networkx as nx
import matplotlib.pylab as plt
from numpy.random import *
import colorsys

if __name__ == '__main__':
	sample = nx.cubical_graph()

	for i in xrange(0,50):
		sample.add_node(i)

	for i in xrange(0,50):
		sample.add_edge(i,poisson(lam=30))
	#print sample.edges()
	try: print nx.shortest_path(sample, 30, 0)
	except: print u"ぼっち"
    #graph layput
    #circular, random, shell, spring, spectral
    #ncolors = [colorsys.hsv_to_rgb(h / num_nodes, 1.0, 1.0)for h in range(num_nodes)]
	nx.draw(sample, pos=nx.spring_layout(sample), node_color='white',edge_color="g")

	plt.savefig("sample.png")
	nx.write_gexf(sample, "sample.gexf")

def generate_cubic():
    return nx.cubical_graph()