def test_sedgewick(self):
expected = True
g = nx.sedgewick_maze_graph()
for o in enumerate_dfs_ordering_naively(g):
@spec_order(o)
def func(g):
return is_planar(g)
actual = func(g)
self.assertEqual(expected, actual)
def practice():
Peterson = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()
G = nx.random_tree(20)
nx.draw(G, pos=nx.spring_layout(G), with_labels=True)
#nx.draw_shell(G, nlist=[range(5, 10), range(5)], with_labels=True, font_weight='bold')
plt.show()
def classic_small_graphs():
petersen = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()
g_list = [petersen, tutte, maze, tet]
for n, g in enumerate(g_list):
plt.subplot(221+n)
nx.draw(g, with_labels=True, font_weight='bold')
plt.show()
def generate_random_graph():
d = 1
class_map = {}
G = nx.sedgewick_maze_graph()
strG = nx.Graph()
for s, t in G.edges:
strG.add_edge(str(s), str(t))
G = strG
for node in G.nodes(data=True):
r = random.randint(0, d)
node[1]['group'] = r
class_map[node[0]] = r
return G, class_map
gnx = nx.Graph()
gnx.add_edge(0, 1)
gnx.add_edge(1, 2)
gnx.add_edge(1, 3)
gnx.add_edge(4, 1)
assert g.degree(0) == gnx.degree(0)
assert g.degree(1) == gnx.degree(1)
assert g.degree(4) == gnx.degree(4)
diameter, center = g.diameter_center()
assert (center in nx.center(gnx)) == True
# Use network x to generate a random graph and replicate it as a custom graph.
# Then check it returns the same value for some functions
def verify_graph(nx_graph):
g_gen = Graph()
for edge in nx.edges(nx_graph):
g_gen.add_edge(*edge)
diameter, center = g_gen.diameter_center()
assert (center in nx.center(nx_graph)) == True
assert diameter == nx.diameter(nx_graph)
verify_graph(nx.sedgewick_maze_graph())
verify_graph(nx.karate_club_graph())
import networkx as nx
import pygraphviz as pgv
from PIL import Image
maze=nx.sedgewick_maze_graph()
G=pgv.AGraph(strict=False,directed=True)
G.add_nodes_from(maze.nodes())
G.add_edges_from(maze.edges())
G.layout(prog='dot')
G.draw('file.png')
Image.open('file.png').show()
def test_properties_named_small_graphs(self):
G = nx.bull_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 5
assert sorted(d for n, d in G.degree()) == [1, 1, 2, 3, 3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 2
G = nx.chvatal_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 12 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.cubical_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 8 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.desargues_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
G = nx.diamond_graph()
assert G.number_of_nodes() == 4
assert sorted(d for n, d in G.degree()) == [2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.dodecahedral_graph()
assert G.number_of_nodes() == 20
assert G.number_of_edges() == 30
assert list(d for n, d in G.degree()) == 20 * [3]
assert nx.diameter(G) == 5
assert nx.radius(G) == 5
G = nx.frucht_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
assert nx.diameter(G) == 4
assert nx.radius(G) == 3
G = nx.heawood_graph()
assert G.number_of_nodes() == 14
assert G.number_of_edges() == 21
assert list(d for n, d in G.degree()) == 14 * [3]
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.hoffman_singleton_graph()
assert G.number_of_nodes() == 50
assert G.number_of_edges() == 175
assert list(d for n, d in G.degree()) == 50 * [7]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 6
assert sorted(d for n, d in G.degree()) == [2, 2, 2, 3, 3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.house_x_graph()
assert G.number_of_nodes() == 5
assert G.number_of_edges() == 8
assert sorted(d for n, d in G.degree()) == [2, 3, 3, 4, 4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 1
G = nx.icosahedral_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 30
assert (list(
d for n, d in G.degree()) == [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5])
assert nx.diameter(G) == 3
assert nx.radius(G) == 3
G = nx.krackhardt_kite_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 18
assert (sorted(
d for n, d in G.degree()) == [1, 2, 3, 3, 3, 4, 4, 5, 5, 6])
G = nx.moebius_kantor_graph()
assert G.number_of_nodes() == 16
assert G.number_of_edges() == 24
assert list(d for n, d in G.degree()) == 16 * [3]
assert nx.diameter(G) == 4
G = nx.octahedral_graph()
assert G.number_of_nodes() == 6
assert G.number_of_edges() == 12
assert list(d for n, d in G.degree()) == 6 * [4]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.pappus_graph()
assert G.number_of_nodes() == 18
assert G.number_of_edges() == 27
assert list(d for n, d in G.degree()) == 18 * [3]
assert nx.diameter(G) == 4
G = nx.petersen_graph()
assert G.number_of_nodes() == 10
assert G.number_of_edges() == 15
assert list(d for n, d in G.degree()) == 10 * [3]
assert nx.diameter(G) == 2
assert nx.radius(G) == 2
G = nx.sedgewick_maze_graph()
assert G.number_of_nodes() == 8
assert G.number_of_edges() == 10
assert sorted(d for n, d in G.degree()) == [1, 2, 2, 2, 3, 3, 3, 4]
G = nx.tetrahedral_graph()
assert G.number_of_nodes() == 4
assert G.number_of_edges() == 6
assert list(d for n, d in G.degree()) == [3, 3, 3, 3]
assert nx.diameter(G) == 1
assert nx.radius(G) == 1
G = nx.truncated_cube_graph()
assert G.number_of_nodes() == 24
assert G.number_of_edges() == 36
assert list(d for n, d in G.degree()) == 24 * [3]
G = nx.truncated_tetrahedron_graph()
assert G.number_of_nodes() == 12
assert G.number_of_edges() == 18
assert list(d for n, d in G.degree()) == 12 * [3]
G = nx.tutte_graph()
assert G.number_of_nodes() == 46
assert G.number_of_edges() == 69
assert list(d for n, d in G.degree()) == 46 * [3]
# Test create_using with directed or multigraphs on small graphs
pytest.raises(nx.NetworkXError,
nx.tutte_graph,
create_using=nx.DiGraph)
MG = nx.tutte_graph(create_using=nx.MultiGraph)
assert sorted(MG.edges()) == sorted(G.edges())
def basic_operation_tutorial():
# Create a graph.
G = nx.Graph()
# Nodes.
G.add_node(1)
G.add_nodes_from([2, 3])
H = nx.path_graph(10) # Creates a graph.
G.add_nodes_from(H)
G.add_node(H)
#print('G.nodes = {}.'.format(G.nodes))
print('G.nodes = {}.'.format(list(G.nodes)))
# Edges.
G.add_edge(1, 2)
e = (2, 3)
G.add_edge(*e) # Unpack edge tuple.
G.add_edges_from([(1, 2), (1, 3)])
G.add_edges_from(H.edges)
#print('G.edges = {}.'.format(G.edges))
print('G.edges = {}.'.format(list(G.edges)))
# Remove all nodes and edges.
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'.
G.add_edge(3, 'm')
print('G.number_of_nodes() = {}.'.format(G.number_of_nodes()))
print('G.number_of_edges() = {}.'.format(G.number_of_edges()))
# Set-like views of the nodes, edges, neighbors (adjacencies), and degrees of nodes in a graph.
print('G.adj[1] = {}.'.format(list(G.adj[1]))) # or G.neighbors(1).
print('G.degree[1] = {}.'.format(
G.degree[1])) # The number of edges incident to 1.
# Report the edges and degree from a subset of all nodes using an nbunch.
# An nbunch is any of: None (meaning all nodes), a node, or an iterable container of nodes that is not itself a node in the graph.
print("G.edges([2, 'm']) = {}.".format(G.edges([2, 'm'])))
print('G.degree([2, 3]) = {}.'.format(G.degree([2, 3])))
# Remove nodes and edges from the graph in a similar fashion to adding.
G.remove_node(2)
G.remove_nodes_from('spam')
print('G.nodes = {}.'.format(list(G.nodes)))
G.remove_edge(1, 3)
# When creating a graph structure by instantiating one of the graph classes you can specify data in several formats.
G.add_edge(1, 2)
H = nx.DiGraph(G) # Creates a DiGraph using the connections from G.
print('H.edges() = {}.'.format(list(H.edges())))
edgelist = [(0, 1), (1, 2), (2, 3)]
H = nx.Graph(edgelist)
#--------------------
# Access edges and neighbors.
print('G[1] = {}.'.format(G[1])) # Same as G.adj[1].
print('G[1][2] = {}.'.format(G[1][2])) # Edge 1-2.
print('G.edges[1, 2] = {}.'.format(G.edges[1, 2]))
# Get/set the attributes of an edge using subscript notation if the edge already exists.
G.add_edge(1, 3)
G[1][3]['color'] = 'blue'
G.edges[1, 2]['color'] = 'red'
# Fast examination of all (node, adjacency) pairs is achieved using G.adjacency(), or G.adj.items().
# Note that for undirected graphs, adjacency iteration sees each edge twice.
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.adj.items():
for nbr, eattr in nbrs.items():
wt = eattr['weight']
if wt < 0.5: print(f'({n}, {nbr}, {wt:.3})')
# Convenient access to all edges is achieved with the edges property.
for (u, v, wt) in FG.edges.data('weight'):
if wt < 0.5: print(f'({u}, {v}, {wt:.3})')
#--------------------
# Attributes.
# Graph attributes.
G = nx.Graph(day='Friday')
print('G.graph = {}.'.format(G.graph))
G.graph['day'] = 'Monday'
# Node attributes: add_node(), add_nodes_from(), or G.nodes.
G.add_node(1, time='5pm')
G.add_nodes_from([3], time='2pm')
print('G.nodes[1] = {}.'.format(G.nodes[1]))
G.nodes[1]['room'] = 714
print('G.nodes.data() = {}.'.format(G.nodes.data()))
print('G.nodes[1] = {}.'.format(
G.nodes[1])) # List the attributes of a node.
print('G.nodes[1].keys() = {}.'.format(G.nodes[1].keys()))
#print('G[1] = {}.'.format(G[1])) # G[1] = G.adj[1].
# Edge attributes: add_edge(), add_edges_from(), or subscript notation.
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.edges[3, 4]['weight'] = 4.2
print('G.edges.data() = {}.'.format(G.edges.data()))
print('G.edges[3, 4] = {}.'.format(
G.edges[3, 4])) # List the attributes of an edge.
print('G.edges[3, 4].keys() = {}.'.format(G.edges[3, 4].keys()))
#--------------------
# Directed graphs.
DG = nx.DiGraph()
DG.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 0.75)])
print("DG.out_degree(1, weight='weight') = {}.".format(
DG.out_degree(1, weight='weight')))
print("DG.degree(1, weight='weight') = {}.".format(
DG.degree(
1, weight='weight'))) # The sum of in_degree() and out_degree().
print('DG.successors(1) = {}.'.format(list(DG.successors(1))))
print('DG.neighbors(1) = {}.'.format(list(DG.neighbors(1))))
# Convert G to undirected graph.
#H = DG.to_undirected()
H = nx.Graph(DG)
#--------------------
# Multigraphs: Graphs which allow multiple edges between any pair of nodes.
MG = nx.MultiGraph()
#MDG = nx.MultiDiGraph()
MG.add_weighted_edges_from([(1, 2, 0.5), (1, 2, 0.75), (2, 3, 0.5)])
print("MG.degree(weight='weight') = {}.".format(
dict(MG.degree(weight='weight'))))
GG = nx.Graph()
for n, nbrs in MG.adjacency():
for nbr, edict in nbrs.items():
minvalue = min([d['weight'] for d in edict.values()])
GG.add_edge(n, nbr, weight=minvalue)
print('nx.shortest_path(GG, 1, 3) = {}.'.format(nx.shortest_path(GG, 1,
3)))
#--------------------
# Classic graph operations:
"""
subgraph(G, nbunch): induced subgraph view of G on nodes in nbunch
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
to_undirected(G): return an undirected representation of G
to_directed(G): return a directed representation of G
"""
#--------------------
# Graph generators.
# Use a call to one of the classic small graphs:
petersen = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()
# Use a (constructive) generator for a classic graph:
K_5 = nx.complete_graph(5)
K_3_5 = nx.complete_bipartite_graph(3, 5)
barbell = nx.barbell_graph(10, 10)
lollipop = nx.lollipop_graph(10, 20)
# Use a stochastic graph generator:
er = nx.erdos_renyi_graph(100, 0.15)
ws = nx.watts_strogatz_graph(30, 3, 0.1)
ba = nx.barabasi_albert_graph(100, 5)
red = nx.random_lobster(100, 0.9, 0.9)
#--------------------
# Read a graph stored in a file using common graph formats, such as edge lists, adjacency lists, GML, GraphML, pickle, LEDA and others.
nx.write_gml(red, './test.gml')
mygraph = nx.read_gml('./test.gml')
node_color='firebrick',
alpha=0.8,
)
#%%
#有向图
DG = nx.DiGraph()
DG.add_weighted_edges_from([(1, 2, 0.5), (3, 1, 0.75)])
print(DG.out_degree(1, weight='weight'))
print(DG.degree(1, weight='weight'))
print(list(DG.successors(1)))
print(list(DG.neighbors(1)))
#有向图和无向图的转换
#%%
#其他生成图的方法
petersen = nx.petersen_graph()
tutte = nx.tutte_graph()
maze = nx.sedgewick_maze_graph()
tet = nx.tetrahedral_graph()
K_5 = nx.complete_graph(5)
K_3_5 = nx.complete_bipartite_graph(3, 5)
barbell = nx.barbell_graph(10, 10)
lollipop = nx.lollipop_graph(10, 20)
#%%
plt.subplot(111)
nx.draw(K_3_5, with_labels=True, node_color='firebrick', alpha=0.8)
#%%
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')
def t3():
mz = nx.sedgewick_maze_graph()
showGraph(mz)
'chvatal': nx.chvatal_graph(), # 4-connected non-planar
'cubical': nx.cubical_graph(), # 3-connected planar
'desargues': nx.desargues_graph(), # 3-connected non-planar
'diamond': nx.diamond_graph(), # 2-connected planar
'dodecahedral': nx.dodecahedral_graph(), # 3-connected planar
'frucht': nx.frucht_graph(), # 3-connected planar
'heawood': nx.heawood_graph(), # 3-connected non-planar
'house': nx.house_graph(), # 2-connected planar
'house_x': nx.house_x_graph(), # 2-connected planar
'icosahedral': nx.icosahedral_graph(), # 5-connected planar
'krackhardt': nx.krackhardt_kite_graph(), # 1-connected planar
'moebius': nx.moebius_kantor_graph(), # non-planar
'octahedral': nx.octahedral_graph(), # 4-connected planar
'pappus': nx.pappus_graph(), # 3-connected non-planar
'petersen': nx.petersen_graph(), # 3-connected non-planar
'sedgewick': nx.sedgewick_maze_graph(), # 1-connected planar
'tetrahedral': nx.tetrahedral_graph(), # 3-connected planar
'truncated_cube': nx.truncated_cube_graph(), # 3-conn. planar
'truncated_tetrahedron': nx.truncated_tetrahedron_graph(),
# 3-connected planar
'tutte': nx.tutte_graph()
} # 3-connected planar
for g_name, g in targets.items():
print g_name, is_planar(g)
# g = nx.petersen_graph()
# g = nx.frucht_graph()
# g = nx.krackhardt_kite_graph()
# g = nx.icosahedral_graph()
# g = nx.tutte_graph()
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')
def test_sedgewick(self):
expected = True
actual = is_planar(nx.sedgewick_maze_graph())
self.assertEqual(expected, actual)
import networkx as nx
import matplotlib.pyplot as plt
from pprint import pprint
from typing import List, Set, Dict, Tuple
G = nx.random_lobster(6, 0.9, 0.9)
G = nx.barabasi_albert_graph(8, 5)
G = nx.watts_strogatz_graph(8, 3, 0.1)
G = nx.erdos_renyi_graph(10, 0.15)
G = nx.complete_bipartite_graph(3, 5)
G = nx.dorogovtsev_goltsev_mendes_graph(2)
G = nx.petersen_graph()
G = nx.tutte_graph()
G = nx.sedgewick_maze_graph() # cool
# G = nx.tetrahedral_graph() # not cool
G = nx.ladder_graph(8)
G = nx.barbell_graph(5, 2)
G = nx.turan_graph(10, 3)
def g2d(g):
return {k: {k1 for k1 in v} for k, v in g.adj.items()}
#pprint(g2d(G))
#nx.draw_shell(G)
#plt.show()
# sorting nodes (with optional tails - additional strings for each node):
def small_graphs():
print("Make small graph")
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [1, 3], [2, 4], [1, 3]]])
draw_graph(G)
G = nx.make_small_graph(
["adjacencylist", "C_4", 4, [[2, 4], [3], [4], []]])
draw_graph(G)
G = nx.make_small_graph(
["edgelist", "C_4", 4, [[1, 2], [3, 4], [2, 3], [4, 1]]])
draw_graph(G)
print("LCF graph")
G = nx.LCF_graph(6, [3, -3], 3)
draw_graph(G)
G = nx.LCF_graph(14, [5, -5], 7)
draw_graph(G)
print("Bull graph")
G = nx.bull_graph()
draw_graph(G)
print("Chvátal graph")
G = nx.chvatal_graph()
draw_graph(G)
print("Cubical graph")
G = nx.cubical_graph()
draw_graph(G)
print("Desargues graph")
G = nx.desargues_graph()
draw_graph(G)
print("Diamond graph")
G = nx.diamond_graph()
draw_graph(G)
print("Dodechaedral graph")
G = nx.dodecahedral_graph()
draw_graph(G)
print("Frucht graph")
G = nx.frucht_graph()
draw_graph(G)
print("Heawood graph")
G = nx.heawood_graph()
draw_graph(G)
print("House graph")
G = nx.house_graph()
draw_graph(G)
print("House X graph")
G = nx.house_x_graph()
draw_graph(G)
print("Icosahedral graph")
G = nx.icosahedral_graph()
draw_graph(G)
print("Krackhardt kite graph")
G = nx.krackhardt_kite_graph()
draw_graph(G)
print("Moebius kantor graph")
G = nx.moebius_kantor_graph()
draw_graph(G)
print("Octahedral graph")
G = nx.octahedral_graph()
draw_graph(G)
print("Pappus graph")
G = nx.pappus_graph()
draw_graph(G)
print("Petersen graph")
G = nx.petersen_graph()
draw_graph(G)
print("Sedgewick maze graph")
G = nx.sedgewick_maze_graph()
draw_graph(G)
print("Tetrahedral graph")
G = nx.tetrahedral_graph()
draw_graph(G)
print("Truncated cube graph")
G = nx.truncated_cube_graph()
draw_graph(G)
print("Truncated tetrahedron graph")
G = nx.truncated_tetrahedron_graph()
draw_graph(G)
print("Tutte graph")
G = nx.tutte_graph()
draw_graph(G)