def HouseXGraph():
"""
Returns a house X graph with 5 nodes.
A house X graph is a house graph with two additional edges. The
upper-right corner is connected to the lower-left. And the
upper-left corner is connected to the lower-right.
This constructor depends on NetworkX numeric labeling.
PLOTTING: Upon construction, the position dictionary is filled to
override the spring-layout algorithm. By convention, the house X
graph is drawn with the first node in the lower-left corner of the
house, the second in the lower-right corner of the house. The third
node is in the upper-left corner connecting the roof to the wall,
and the fourth is in the upper-right corner connecting the roof to
the wall. The fifth node is the top of the roof, connected only to
the third and fourth.
EXAMPLES: Construct and show a house X graph
::
sage: g = graphs.HouseXGraph()
sage: g.show() # long time
"""
pos_dict = {0: (-1, 0), 1: (1, 0), 2: (-1, 1), 3: (1, 1), 4: (0, 2)}
import networkx
G = networkx.house_x_graph()
return graph.Graph(G, pos=pos_dict, name="House Graph")
def HouseXGraph():
"""
Returns a house X graph with 5 nodes.
A house X graph is a house graph with two additional edges. The
upper-right corner is connected to the lower-left. And the
upper-left corner is connected to the lower-right.
This constructor depends on NetworkX numeric labeling.
PLOTTING: Upon construction, the position dictionary is filled to
override the spring-layout algorithm. By convention, the house X
graph is drawn with the first node in the lower-left corner of the
house, the second in the lower-right corner of the house. The third
node is in the upper-left corner connecting the roof to the wall,
and the fourth is in the upper-right corner connecting the roof to
the wall. The fifth node is the top of the roof, connected only to
the third and fourth.
EXAMPLES: Construct and show a house X graph
::
sage: g = graphs.HouseXGraph()
sage: g.show() # long time
"""
pos_dict = {0:(-1,0),1:(1,0),2:(-1,1),3:(1,1),4:(0,2)}
import networkx
G = networkx.house_x_graph()
return graph.Graph(G, pos=pos_dict, name="House Graph")
def GenerateGame(num_players=2, num_mines=2, graph_type='small'):
if graph_type == 'small':
graph = nx.house_x_graph()
elif graph_type == 'star':
graph = nx.generators.classic.star_graph(num_players * num_mines * 25)
elif graph_type == 'newman':
graph = nx.generators.random_graphs.newman_watts_strogatz_graph(num_players * num_mines * 5, num_players * 2, 0.15)
for node in graph:
graph.node[node]['mine'] = 0
print('Graph:', len(graph.nodes()), len(graph.edges()))
# num_mines = 2
# num_players = 2
mine_nodes = np.random.choice(graph.nodes(), num_mines, replace=False)
for mine in mine_nodes:
graph.node[mine]['mine'] = 1
for source, target in graph.edges_iter():
graph[source][target]['claimed'] = -1
game = dict()
game['num_players'] = num_players
game['current_player'] = 0
game['player_mines'] = [[] for _ in range(num_players)]
game['player_score'] = [0] * num_players
game['player_reward'] = [0] * num_players
game['player_paths'] = [dict() for _ in range(num_players)]
game['latest_path_id'] = [0] * num_players
game['graph'] = graph
game['mines'] = mine_nodes
CalcPoints(game)
return game
def test_line_graph_batch():
"""
Tests that `line_graph` works with multiple graphs in a batch.
"""
# Arrange.
# Create test graphs.
graph1 = nx.house_graph()
incidence1 = nx.incidence_matrix(graph1).toarray()
# We will have to pad the first incidence matrix with two columns,
# because the second graph has two extra edges.
incidence1 = np.pad(incidence1, ((0, 0), (0, 2)))
graph2 = nx.house_x_graph()
incidence2 = nx.incidence_matrix(graph2).toarray()
# Act.
# Get the line graph for both graphs individually, and both together
# as a batch.
graph1_line = convolution.line_graph(incidence1)
graph2_line = convolution.line_graph(incidence2)
batch = np.stack([incidence1, incidence2], axis=0)
line_graph_batch = convolution.line_graph(batch)
# Assert.
# Both ways of doing it should produce the same results.
np.testing.assert_array_equal(graph1_line, line_graph_batch[0])
np.testing.assert_array_equal(graph2_line, line_graph_batch[1])
def test_incidence_matrix_batch():
"""
Tests that `incidence_matrix` works with multiple
graphs in a batch.
"""
# Arrange.
# Create test graphs.
graph1 = nx.to_numpy_array(nx.house_graph())
graph2 = nx.to_numpy_array(nx.house_x_graph())
# Act.
# Get the incidence matrix for both graphs individually, and both together
# as a batch.
graph1_incidence = convolution.incidence_matrix(graph1)
# The first graph will be padded with two extra rows columns so that it
# can be batched with the other one, which has two more edges.
graph1_incidence = np.pad(graph1_incidence, ((0, 0), (0, 2)))
graph2_incidence = convolution.incidence_matrix(graph2)
batch = np.stack([graph1, graph2], axis=0)
batch_incidence = convolution.incidence_matrix(batch)
# Assert.
# Both ways of doing it should produce the same results.
np.testing.assert_array_equal(graph1_incidence, batch_incidence[0])
np.testing.assert_array_equal(graph2_incidence, batch_incidence[1])
def get_graph_list(num_factors):
graph_list = []
# 2, 3 bipartite graph
g = nx.turan_graph(n=5, r=2)
graph_list.append(nx.to_numpy_array(g))
g = nx.house_x_graph()
graph_list.append(nx.to_numpy_array(g))
g = nx.balanced_tree(r=3, h=2)
graph_list.append(nx.to_numpy_array(g))
g = nx.grid_2d_graph(m=3, n=3)
graph_list.append(nx.to_numpy_array(g))
g = nx.hypercube_graph(n=3)
graph_list.append(nx.to_numpy_array(g))
g = nx.octahedral_graph()
graph_list.append(nx.to_numpy_array(g))
return graph_list[:num_factors]
# -*- coding: utf-8 -*-
"""
Created on Thu Jan 18 16:39:14 2018
@author: Mustafa Hajij and @isMunim
"""
import networkx as nx
from unionfind import *
from kruskalsalgorithm import *
# Loading a graph example from networkx
graph=nx.house_x_graph()
print("The nodes of the graph are given by the list : ")
print(graph.nodes())
print("The edges of the graph are given by the list : ")
print(graph.edges())
# here we are giving weights to the edges
initweight=1
for edge in graph.edges():
graph.add_edge(edge[0], edge[1], weight=initweight)
initweight=initweight+1
# check the weights of the edges:
for edge in graph.edges():
a=edge[0]
b=edge[1]
print("edge "+str(edge)+" has weight " + str(graph[a][b]['weight']) )
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())
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')
if 'visited' in g[u][v]:
del g[u][v]['visited']
if __name__ == '__main__':
targets = {
'bull': nx.bull_graph(), # 1-connected planar
'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)
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_house_x(self):
expected = True
actual = is_planar(nx.house_x_graph())
self.assertEqual(expected, actual)
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)
