def test_is_chordal(self):
assert_false(nx.is_chordal(self.non_chordal_G))
assert_true(nx.is_chordal(self.chordal_G))
assert_true(nx.is_chordal(self.connected_chordal_G))
assert_true(nx.is_chordal(nx.complete_graph(3)))
assert_true(nx.is_chordal(nx.cycle_graph(3)))
assert_false(nx.is_chordal(nx.cycle_graph(5)))
def setUp(self):
G=nx.Graph()
G.add_edge(0,1,weight=3)
G.add_edge(0,2,weight=2)
G.add_edge(0,3,weight=6)
G.add_edge(0,4,weight=4)
G.add_edge(1,3,weight=5)
G.add_edge(1,5,weight=5)
G.add_edge(2,4,weight=1)
G.add_edge(3,4,weight=2)
G.add_edge(3,5,weight=1)
G.add_edge(4,5,weight=4)
self.G=G
self.exact_weighted={0: 4.0, 1: 0.0, 2: 8.0, 3: 6.0, 4: 8.0, 5: 0.0}
self.K = nx.krackhardt_kite_graph()
self.P3 = nx.path_graph(3)
self.P4 = nx.path_graph(4)
self.K5 = nx.complete_graph(5)
self.C4=nx.cycle_graph(4)
self.T=nx.balanced_tree(r=2, h=2)
self.Gb = nx.Graph()
self.Gb.add_edges_from([(0, 1), (0, 2), (1, 3), (2, 3),
(2, 4), (4, 5), (3, 5)])
self.F = nx.florentine_families_graph()
self.D = nx.cycle_graph(3, create_using=nx.DiGraph())
self.D.add_edges_from([(3, 0), (4, 3)])
def test_directed_node_connectivity():
G = nx.cycle_graph(10, create_using=nx.DiGraph()) # only one direction
D = nx.cycle_graph(10).to_directed() # 2 reciprocal edges
assert_equal(1, approx.node_connectivity(G))
assert_equal(1, approx.node_connectivity(G, 1, 4))
assert_equal(2, approx.node_connectivity(D))
assert_equal(2, approx.node_connectivity(D, 1, 4))
def test_global_parameters(self):
b,c=nx.intersection_array(nx.cycle_graph(5))
g=nx.global_parameters(b,c)
assert_equal(list(g),[(0, 0, 2), (1, 0, 1), (1, 1, 0)])
b,c=nx.intersection_array(nx.cycle_graph(3))
g=nx.global_parameters(b,c)
assert_equal(list(g),[(0, 0, 2), (1, 1, 0)])
def test_bellman_ford(self):
# single node graph
G = nx.DiGraph()
G.add_node(0)
assert_equal(nx.bellman_ford(G, 0), ({0: None}, {0: 0}))
assert_raises(KeyError, nx.bellman_ford, G, 1)
# negative weight cycle
G = nx.cycle_graph(5, create_using=nx.DiGraph())
G.add_edge(1, 2, weight=-7)
for i in range(5):
assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, i)
G = nx.cycle_graph(5) # undirected Graph
G.add_edge(1, 2, weight=-3)
for i in range(5):
assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, i)
# no negative cycle but negative weight
G = nx.cycle_graph(5, create_using=nx.DiGraph())
G.add_edge(1, 2, weight=-3)
assert_equal(nx.bellman_ford(G, 0), ({0: None, 1: 0, 2: 1, 3: 2, 4: 3}, {0: 0, 1: 1, 2: -2, 3: -1, 4: 0}))
# not connected
G = nx.complete_graph(6)
G.add_edge(10, 11)
G.add_edge(10, 12)
assert_equal(
nx.bellman_ford(G, 0), ({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1})
)
# not connected, with a component not containing the source that
# contains a negative cost cycle.
G = nx.complete_graph(6)
G.add_edges_from([("A", "B", {"load": 3}), ("B", "C", {"load": -10}), ("C", "A", {"load": 2})])
assert_equal(
nx.bellman_ford(G, 0, weight="load"),
({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}, {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}),
)
# multigraph
P, D = nx.bellman_ford(self.MXG, "s")
assert_equal(P["v"], "u")
assert_equal(D["v"], 9)
P, D = nx.bellman_ford(self.MXG4, 0)
assert_equal(P[2], 1)
assert_equal(D[2], 4)
# other tests
(P, D) = nx.bellman_ford(self.XG, "s")
assert_equal(P["v"], "u")
assert_equal(D["v"], 9)
G = nx.path_graph(4)
assert_equal(nx.bellman_ford(G, 0), ({0: None, 1: 0, 2: 1, 3: 2}, {0: 0, 1: 1, 2: 2, 3: 3}))
assert_equal(nx.bellman_ford(G, 3), ({0: 1, 1: 2, 2: 3, 3: None}, {0: 3, 1: 2, 2: 1, 3: 0}))
G = nx.grid_2d_graph(2, 2)
pred, dist = nx.bellman_ford(G, (0, 0))
assert_equal(sorted(pred.items()), [((0, 0), None), ((0, 1), (0, 0)), ((1, 0), (0, 0)), ((1, 1), (0, 1))])
assert_equal(sorted(dist.items()), [((0, 0), 0), ((0, 1), 1), ((1, 0), 1), ((1, 1), 2)])
def test_bellman_ford(self):
# single node graph
G = nx.DiGraph()
G.add_node(0)
assert_equal(nx.bellman_ford(G, 0), ({0: None}, {0: 0}))
# negative weight cycle
G = nx.cycle_graph(5, create_using = nx.DiGraph())
G.add_edge(1, 2, weight = -7)
assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, 0)
G = nx.cycle_graph(5)
G.add_edge(1, 2, weight = -7)
assert_raises(nx.NetworkXUnbounded, nx.bellman_ford, G, 0)
# not connected
G = nx.complete_graph(6)
G.add_edge(10, 11)
G.add_edge(10, 12)
assert_equal(nx.bellman_ford(G, 0),
({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
{0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}))
# not connected, with a component not containing the source that
# contains a negative cost cycle.
G = nx.complete_graph(6)
G.add_edges_from([('A', 'B', {'load': 3}),
('B', 'C', {'load': -10}),
('C', 'A', {'load': 2})])
assert_equal(nx.bellman_ford(G, 0, weight = 'load'),
({0: None, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0},
{0: 0, 1: 1, 2: 1, 3: 1, 4: 1, 5: 1}))
# multigraph
P, D = nx.bellman_ford(self.MXG,'s')
assert_equal(P['v'], 'u')
assert_equal(D['v'], 9)
P, D = nx.bellman_ford(self.MXG4, 0)
assert_equal(P[2], 1)
assert_equal(D[2], 4)
# other tests
(P,D)= nx.bellman_ford(self.XG,'s')
assert_equal(P['v'], 'u')
assert_equal(D['v'], 9)
G=nx.path_graph(4)
assert_equal(nx.bellman_ford(G,0),
({0: None, 1: 0, 2: 1, 3: 2}, {0: 0, 1: 1, 2: 2, 3: 3}))
G=nx.grid_2d_graph(2,2)
pred,dist=nx.bellman_ford(G,(0,0))
assert_equal(sorted(pred.items()),
[((0, 0), None), ((0, 1), (0, 0)),
((1, 0), (0, 0)), ((1, 1), (0, 1))])
assert_equal(sorted(dist.items()),
[((0, 0), 0), ((0, 1), 1), ((1, 0), 1), ((1, 1), 2)])
def setUp(self):
from networkx import convert_node_labels_to_integers as cnlti
self.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1,
ordering="sorted")
self.cycle = nx.cycle_graph(7)
self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
self.neg_weights = nx.DiGraph()
self.neg_weights.add_edge(0, 1, weight=1)
self.neg_weights.add_edge(0, 2, weight=3)
self.neg_weights.add_edge(1, 3, weight=1)
self.neg_weights.add_edge(2, 3, weight=-2)
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 setUp(self):
from networkx import convert_node_labels_to_integers as cnlti
self.grid = cnlti(nx.grid_2d_graph(4, 4), first_label=1, ordering="sorted")
self.cycle = nx.cycle_graph(7)
self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
self.XG = nx.DiGraph()
self.XG.add_weighted_edges_from(
[
("s", "u", 10),
("s", "x", 5),
("u", "v", 1),
("u", "x", 2),
("v", "y", 1),
("x", "u", 3),
("x", "v", 5),
("x", "y", 2),
("y", "s", 7),
("y", "v", 6),
]
)
self.MXG = nx.MultiDiGraph(self.XG)
self.MXG.add_edge("s", "u", weight=15)
self.XG2 = nx.DiGraph()
self.XG2.add_weighted_edges_from(
[[1, 4, 1], [4, 5, 1], [5, 6, 1], [6, 3, 1], [1, 3, 50], [1, 2, 100], [2, 3, 100]]
)
self.XG3 = nx.Graph()
self.XG3.add_weighted_edges_from([[0, 1, 2], [1, 2, 12], [2, 3, 1], [3, 4, 5], [4, 5, 1], [5, 0, 10]])
self.XG4 = nx.Graph()
self.XG4.add_weighted_edges_from(
[[0, 1, 2], [1, 2, 2], [2, 3, 1], [3, 4, 1], [4, 5, 1], [5, 6, 1], [6, 7, 1], [7, 0, 1]]
)
self.MXG4 = nx.MultiGraph(self.XG4)
self.MXG4.add_edge(0, 1, weight=3)
self.G = nx.DiGraph() # no weights
self.G.add_edges_from(
[
("s", "u"),
("s", "x"),
("u", "v"),
("u", "x"),
("v", "y"),
("x", "u"),
("x", "v"),
("x", "y"),
("y", "s"),
("y", "v"),
]
)
def setUp(self):
self.P3 = nx.path_graph(3)
self.P4 = nx.path_graph(4)
self.K5 = nx.complete_graph(5)
self.C4 = nx.cycle_graph(4)
self.C5 = nx.cycle_graph(5)
self.T = nx.balanced_tree(r=2, h=2)
self.Gb = nx.DiGraph()
self.Gb.add_edges_from([(0, 1), (0, 2), (0, 4), (2, 1),
(2, 3), (4, 3)])
def setUp(self):
self.path = nx.path_graph(7)
self.directed_path = nx.path_graph(7, create_using=nx.DiGraph())
self.cycle = nx.cycle_graph(7)
self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
self.gnp = nx.gnp_random_graph(30, 0.1)
self.directed_gnp = nx.gnp_random_graph(30, 0.1, directed=True)
self.K20 = nx.complete_graph(20)
self.K10 = nx.complete_graph(10)
self.K5 = nx.complete_graph(5)
self.G_list = [self.path, self.directed_path, self.cycle,
self.directed_cycle, self.gnp, self.directed_gnp, self.K10,
self.K5, self.K20]
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_from_numpy_matrix_type(self):
A = np.matrix([[1]])
G = nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']), int)
A = np.matrix([[1]]).astype(np.float)
G = nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']), float)
A = np.matrix([[1]]).astype(np.str)
G = nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']), str)
A = np.matrix([[1]]).astype(np.bool)
G = nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']), bool)
A = np.matrix([[1]]).astype(np.complex)
G = nx.from_numpy_matrix(A)
assert_equal(type(G[0][0]['weight']), complex)
A = np.matrix([[1]]).astype(np.object)
assert_raises(TypeError, nx.from_numpy_matrix, A)
G = nx.cycle_graph(3)
A = nx.adj_matrix(G).todense()
H = nx.from_numpy_matrix(A)
assert_true(all(type(m) == int and type(n) == int for m, n in H.edges()))
H = nx.from_numpy_array(A)
assert_true(all(type(m) == int and type(n) == int for m, n in H.edges()))
def test_multigraph(self):
"""Tests the node boundary of a multigraph."""
G = nx.MultiGraph(list(nx.cycle_graph(5).edges()) * 2)
S = {0, 1}
boundary = nx.node_boundary(G, S)
expected = {2, 4}
assert_equal(boundary, expected)
def test_multigraph(self):
"""Tests the edge boundary of a multigraph."""
G = nx.MultiGraph(list(nx.cycle_graph(5).edges()) * 2)
S = {0, 1}
boundary = list(nx.edge_boundary(G, S))
expected = [(0, 4), (0, 4), (1, 2), (1, 2)]
assert_equal(boundary, expected)
def test_graph(self):
g = nx.cycle_graph(10)
G = nx.Graph()
G.add_nodes_from(g)
G.add_weighted_edges_from((u, v, u) for u, v in g.edges())
# Dict of dicts
dod = to_dict_of_dicts(G)
GG = from_dict_of_dicts(dod, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GG.edges()))
GW = to_networkx_graph(dod, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(sorted(G.edges()), sorted(GW.edges()))
GI = nx.Graph(dod)
assert_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_equal(sorted(G.edges()), sorted(GI.edges()))
# Dict of lists
dol = to_dict_of_lists(G)
GG = from_dict_of_lists(dol, create_using=nx.Graph())
# dict of lists throws away edge data so set it to none
enone = [(u, v, {}) for (u, v, d) in G.edges(data=True)]
assert_nodes_equal(sorted(G.nodes()), sorted(GG.nodes()))
assert_edges_equal(enone, sorted(GG.edges(data=True)))
GW = to_networkx_graph(dol, create_using=nx.Graph())
assert_nodes_equal(sorted(G.nodes()), sorted(GW.nodes()))
assert_edges_equal(enone, sorted(GW.edges(data=True)))
GI = nx.Graph(dol)
assert_nodes_equal(sorted(G.nodes()), sorted(GI.nodes()))
assert_edges_equal(enone, sorted(GI.edges(data=True)))
def test_multigraph():
G = nx.cycle_graph(4)
M = nx.MultiGraph(G.edges())
M.add_edges_from(G.edges())
M.remove_edge(1, 2)
A, B = kernighan_lin_bisection(M)
assert_partition_equal([A, B], [{0, 1}, {2, 3}])
def test_undirected_edge_contraction(self):
"""Tests for edge contraction in an undirected graph."""
G = nx.cycle_graph(4)
actual = nx.contracted_edge(G, (0, 1))
expected = nx.complete_graph(3)
expected.add_edge(0, 0)
assert_true(nx.is_isomorphic(actual, expected))
def test_C4(self):
"""Edge betweenness centrality: C4"""
G=nx.cycle_graph(4)
b=nx.edge_betweenness_centrality(G, weight='weight', normalized=False)
b_answer={(0, 1):2,(0, 3):2, (1, 2):2, (2, 3): 2}
for n in sorted(G.edges()):
assert_almost_equal(b[n],b_answer[n])
def test_directed_edge_connectivity():
G = nx.cycle_graph(10, create_using=nx.DiGraph()) # only one direction
D = nx.cycle_graph(10).to_directed() # 2 reciprocal edges
for flow_func in flow_funcs:
assert_equal(1, nx.edge_connectivity(G, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
assert_equal(1, local_edge_connectivity(G, 1, 4, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
assert_equal(1, nx.edge_connectivity(G, 1, 4, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
assert_equal(2, nx.edge_connectivity(D, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
assert_equal(2, local_edge_connectivity(D, 1, 4, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
assert_equal(2, nx.edge_connectivity(D, 1, 4, flow_func=flow_func),
msg=msg.format(flow_func.__name__))
def test_nonexistent_edge(self):
"""Tests that attempting to contract a non-existent edge raises an
exception.
"""
G = nx.cycle_graph(4)
nx.contracted_edge(G, (0, 2))
def test_cycle_graph():
G = nx.cycle_graph(5)
solution = [{0, 2}, {0, 3}, {1, 3}, {1, 4}, {2, 4}]
cuts = list(nx.all_node_cuts(G))
assert_true(len(solution) == len(cuts))
for cut in cuts:
assert_true(cut in solution)
def test_valid_not_path(self):
G = nx.cycle_graph(4)
G.add_edge(0, 4)
G.add_edge(1, 4)
G.add_edge(5, 2)
assert_true(nx.is_perfect_matching(G, {(1, 4), (0, 3), (5, 2)}))
def test_biconnected_component_subgraphs_cycle():
G=nx.cycle_graph(3)
G.add_cycle([1,3,4,5])
G.add_edge(1,3,eattr='red') # test copying of edge data
G.node[1]['nattr']='blue'
G.graph['gattr']='green'
Gc = set(biconnected.biconnected_component_subgraphs(G))
assert_equal(len(Gc),2)
g1,g2=Gc
if 0 in g1:
assert_true(nx.is_isomorphic(g1,nx.Graph([(0,1),(0,2),(1,2)])))
assert_true(nx.is_isomorphic(g2,nx.Graph([(1,3),(1,5),(3,4),(4,5)])))
assert_equal(g2[1][3]['eattr'],'red')
assert_equal(g2.node[1]['nattr'],'blue')
assert_equal(g2.graph['gattr'],'green')
g2[1][3]['eattr']='blue'
assert_equal(g2[1][3]['eattr'],'blue')
assert_equal(G[1][3]['eattr'],'red')
else:
assert_true(nx.is_isomorphic(g1,nx.Graph([(1,3),(1,5),(3,4),(4,5)])))
assert_true(nx.is_isomorphic(g2,nx.Graph([(0,1),(0,2),(1,2)])))
assert_equal(g1[1][3]['eattr'],'red')
assert_equal(g1.node[1]['nattr'],'blue')
assert_equal(g1.graph['gattr'],'green')
g1[1][3]['eattr']='blue'
assert_equal(g1[1][3]['eattr'],'blue')
assert_equal(G[1][3]['eattr'],'red')
def test_annotator():
# making graph
graph = nx.cycle_graph(5)
graph.add_edge(3, 6)
for n, d in graph.nodes(data=True):
d['label'] = 'X'
# annotate
a = Annotator()
graph = a.transform([graph], part_name='name', part_id='id').next()
# test annotation
cyc = 0
other = 0
ids = set()
for n, d in graph.nodes(data=True):
ids.add(d['id'][0]) # unpack because list unhashable
if d['name'] == ['XXXXX']:
cyc += 1
if d['name'] == ['X']:
other += 1
assert cyc == 5
assert other == 1
assert len(ids) == 2
def draw_network(request):
G = nx.cycle_graph(24)
pos = nx.spring_layout(G, iterations=200)
nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues)
response = HttpResponse(mimetype='image/png')
plt.savefig(response, format='png')
return response
def test_cycle_graph(self):
"""Tests that the cycle graph on five vertices is strongly
regular.
"""
G = nx.cycle_graph(5)
assert_true(is_strongly_regular(G))
def main():
args = parse_arguments()
# Generate graph for given parameters
if args.cycle:
graph = networkx.cycle_graph(args.vertices)
graph = networkx.path_graph(args.vertices)
elif args.star:
graph = networkx.star_graph(args.vertices)
elif args.wheel:
graph = networkx.wheel_graph(args.vertices)
elif args.ladder:
graph = networkx.ladder_graph(args.vertices)
elif args.fill_rate:
if args.fill_rate > 50.0:
graph = networkx.gnp_random_graph(args.vertices, args.fill_rate / 100.0, None, args.directed)
else:
graph = networkx.fast_gnp_random_graph(args.vertices, args.fill_rate / 100.0, None, args.directed)
else:
graph = networkx.gnm_random_graph(args.vertices, args.edges, None, args.directed)
# Print generated graph
print("{} {}".format(graph.number_of_nodes(), graph.number_of_edges()))
for edge in graph.edges():
print("{} {}".format(edge[0] + 1, edge[1] + 1))
# Export generated graph to .png
if args.image_path:
export_to_png(graph, args.image_path)
def test_C4(self):
"""Edge betweenness centrality: C4"""
G=networkx.cycle_graph(4)
b=edge_current_flow_subset(G,G.nodes(),G.nodes(),normalized=True)
b_answer=edge_current_flow(G,normalized=True)
for n in sorted(G.edges()):
assert_almost_equal(b[n],b_answer[n])
def test_cycle(self):
C = nx.cycle_graph(7)
assert nx.astar_path(C, 0, 3) == [0, 1, 2, 3]
assert nx.dijkstra_path(C, 0, 4) == [0, 6, 5, 4]
def test_cycle(self):
n = 5
G = nx.cycle_graph(n, create_using=nx.DiGraph())
assert nx.dominance_frontiers(G, 0) == {i: set() for i in range(n)}
def test_articulation_points_cycle():
G = nx.cycle_graph(3)
G.add_cycle([1, 3, 4])
pts = set(biconnected.articulation_points(G))
assert_equal(pts, set([1]))
def test_articulation_points_cycle():
G = nx.cycle_graph(3)
G.add_cycle([1, 3, 4])
pts = set(nx.articulation_points(G))
assert_equal(pts, {1})
def test_unweighted_digraph(self):
G = nx.DiGraph(nx.cycle_graph(3))
vitality = nx.closeness_vitality(G)
assert vitality == {0: 4, 1: 4, 2: 4}
def setup_class(cls):
cls.BB = nx.barbell_graph(4, 0)
cls.square = nx.cycle_graph(4)
cls.tri = nx.cycle_graph(3)
import numpy as np
import networkx
import scipy
import BankNetwork
import RiskyAssets
import importlib
import matplotlib.pyplot as plt
import time
importlib.reload(BankNetwork)
testgraph = networkx.powerlaw_cluster_graph(10000, 100, 0.1)
adjacency = networkx.to_numpy_matrix(testgraph)
# networkx.draw(testgraph)
cycle = networkx.cycle_graph(10)
networkx.draw(cycle)
star = networkx.star_graph(10)
networkx.draw(star)
complete = networkx.complete_graph(10)
networkx.draw(complete)
a = networkx.to_numpy_matrix(complete)
def vec_gene(v):
"""
Generate submatrix from vector used in the construction of the expanded adjacency matrix
Args:
v (numpy.ndarray) : the generating vector
Returns:
#! python """ Draw a graph with matplotlib, color by degree. You must have matplotlib for this to work. """ __author__ = """Aric Hagberg ([email protected])""" try: import matplotlib.pyplot as plt except: raise import networkx as nx G=nx.cycle_graph(24) pos=nx.spring_layout(G,iterations=200) nx.draw(G,pos,node_color=range(24),node_size=800,cmap=plt.cm.Blues) plt.savefig("node_colormap.png") # save as png plt.show() # display
def test_biconnected_components_cycle():
G = nx.cycle_graph(3)
G.add_cycle([1, 3, 4])
answer = [{0, 1, 2}, {1, 3, 4}]
assert_components_equal(list(nx.biconnected_components(G)), answer)
def test_is_biconnected():
G = nx.cycle_graph(3)
assert_true(nx.is_biconnected(G))
G.add_cycle([1, 3, 4])
assert_false(nx.is_biconnected(G))
def test_not_connected(self):
G = nx.cycle_graph(4)
G.add_cycle([5, 6, 7])
assert_false(nx.is_distance_regular(G))
def test_unweighted(self):
G = nx.cycle_graph(3)
vitality = nx.closeness_vitality(G)
assert vitality == {0: 2, 1: 2, 2: 2}
def test_biconnected_components_cycle():
G = nx.cycle_graph(3)
G.add_cycle([1, 3, 4])
pts = set(map(frozenset, biconnected.biconnected_components(G)))
assert_equal(pts, set([frozenset([0, 1, 2]), frozenset([1, 3, 4])]))
def generate_cycle():
return nx.cycle_graph(random.randint(5, 15))
def test_cycle(self):
G = nx.cycle_graph(100, create_using=nx.DiGraph())
ok_(nx.is_semiconnected(G))
G = nx.path_graph(100, create_using=nx.DiGraph())
G.add_edge(0, 99)
ok_(nx.is_semiconnected(G))
def test_cycle(self):
G = nx.cycle_graph(3)
assert_false(nx.is_simple_path(G, [0, 1, 2, 0]))
def test_cycle(self):
n = 5
G = nx.cycle_graph(n, create_using=nx.DiGraph())
assert nx.immediate_dominators(
G, 0) == {i: max(i - 1, 0)
for i in range(n)}
def test_degree_seq_c4(self):
G = networkx.cycle_graph(4)
degree_start = sorted(G.degree().values())
G = double_edge_swap(G, 1, 100)
degseq = sorted(G.degree().values())
assert_true(degree_start == degseq)
def simulate_series(simulation_data):
disease_params = simulation_data
#disease_params['order'] = 1
simulation_data['G'] = simulation_data['network_n'] # genes
simulation_data['S'] = simulation_data['P']
simulation_data['patients_number'] = simulation_data['S']
# make network
if(simulation_data['network_type'] == 'BA'):
g = nx.barabasi_albert_graph(simulation_data['network_n'], simulation_data['network_m'])
elif(simulation_data['network_type'] == 'ER'):
g = nx.erdos_renyi_graph(simulation_data['network_n'], simulation_data['network_p'])
elif(simulation_data['model'] == '2D'):
g = nx.grid_2d_graph(simulation_data['network_x'], simulation_data['network_y'], periodic = True)
elif(simulation_data['model'] == 'CYC'):
g = nx.cycle_graph(simulation_data['network_n'])
elif(simulation_data['model'] == 'REG'):
g = nx.random_regular_graph(simulation_data['network_d'], simulation_data['network_n'])
#neighbors_dict = all_neighbors_order(g, simulation_params['order'])
colored_graph = color_nodes_order(g, disease_params['D'], disease_params['p'], disease_params['order'])
#colored_graph = color_nodes_order(g, disease_params['D'], disease_params['p'], disease_params['order'])
g_strip = g.copy()
solitary = [ n for n,d in g_strip.degree_iter() if d == 0 ]
g_strip.remove_nodes_from(solitary)
layout = nx.spring_layout(g_strip)
result = {}
#result['layout'] = layout
#result['g'] = g
#result['g_strip'] = g_strip
for disease_params['rho_0'] in disease_params['rho_0_list']:
result[str(disease_params['rho_0'])] = {}
result_disease = simulate_artificial_disease(disease_params, simulation_data, colored_graph, colored_graph)
collective_genes = get_collective_gene_expression(simulation_data, result_disease['expressed_genes_under'], result_disease['expressed_genes_over'], result_disease['phenotype_table'], mode = 'normal')
filtered = collective_genes['flt']
for flt in simulation_data['f_list'] :
#result[str(disease_params['rho_0'])][str(flt)] = {}
tmp_result = {}
tmp_result['extracted_genes'] = list(set(filtered['dis_over_flt_' + str(flt)]))
tmp_result['disease_genes'] = list(set(filtered['dis_under_flt_' + str(flt)]))
tmp_result['true_poositive_genes'] = list(set(filtered['dis_under_flt_' + str(flt)]) & set(filtered['dis_over_flt_' + str(flt)]))
tmp_result['disease_params'] = disease_params
tmp_result['layout'] = layout
tmp_result['g'] = g
tmp_result['g_strip'] = g_strip
tmp_result['rho_0'] = disease_params['rho_0']
tmp_result['flt'] = flt
result[str(disease_params['rho_0'])][str(flt)] = tmp_result
return result
def test_shortest_simple_paths_directed():
G = nx.cycle_graph(7, create_using=nx.DiGraph())
paths = nx.shortest_simple_paths(G, 0, 3)
assert_equal([path for path in paths], [[0, 1, 2, 3]])
def test_cycle_numpy(self):
dist = nx.floyd_warshall_numpy(nx.cycle_graph(7))
assert_equal(dist[0, 3], 3)
assert_equal(dist[0, 4], 3)
def test_negative_weight_cycle(self):
G = nx.cycle_graph(5, create_using=nx.DiGraph())
G.add_edge(1, 2, weight=-7)
for i in range(5):
pytest.raises(nx.NetworkXUnbounded,
nx.single_source_bellman_ford_path, G, i)
pytest.raises(nx.NetworkXUnbounded,
nx.single_source_bellman_ford_path_length, G, i)
pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford,
G, i)
pytest.raises(nx.NetworkXUnbounded,
nx.bellman_ford_predecessor_and_distance, G, i)
pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, i)
G = nx.cycle_graph(5) # undirected Graph
G.add_edge(1, 2, weight=-3)
for i in range(5):
pytest.raises(nx.NetworkXUnbounded,
nx.single_source_bellman_ford_path, G, i)
pytest.raises(nx.NetworkXUnbounded,
nx.single_source_bellman_ford_path_length, G, i)
pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford,
G, i)
pytest.raises(nx.NetworkXUnbounded,
nx.bellman_ford_predecessor_and_distance, G, i)
pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, i)
G = nx.DiGraph([(1, 1, {"weight": -1})])
pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford_path,
G, 1)
pytest.raises(nx.NetworkXUnbounded,
nx.single_source_bellman_ford_path_length, G, 1)
pytest.raises(nx.NetworkXUnbounded, nx.single_source_bellman_ford, G,
1)
pytest.raises(nx.NetworkXUnbounded,
nx.bellman_ford_predecessor_and_distance, G, 1)
pytest.raises(nx.NetworkXUnbounded, nx.goldberg_radzik, G, 1)
# no negative cycle but negative weight
G = nx.cycle_graph(5, create_using=nx.DiGraph())
G.add_edge(1, 2, weight=-3)
assert nx.single_source_bellman_ford_path(G, 0) == {
0: [0],
1: [0, 1],
2: [0, 1, 2],
3: [0, 1, 2, 3],
4: [0, 1, 2, 3, 4],
}
assert nx.single_source_bellman_ford_path_length(G, 0) == {
0: 0,
1: 1,
2: -2,
3: -1,
4: 0,
}
assert nx.single_source_bellman_ford(G, 0) == (
{
0: 0,
1: 1,
2: -2,
3: -1,
4: 0
},
{
0: [0],
1: [0, 1],
2: [0, 1, 2],
3: [0, 1, 2, 3],
4: [0, 1, 2, 3, 4]
},
)
assert nx.bellman_ford_predecessor_and_distance(G, 0) == (
{
0: [],
1: [0],
2: [1],
3: [2],
4: [3]
},
{
0: 0,
1: 1,
2: -2,
3: -1,
4: 0
},
)
assert nx.goldberg_radzik(G, 0) == (
{
0: None,
1: 0,
2: 1,
3: 2,
4: 3
},
{
0: 0,
1: 1,
2: -2,
3: -1,
4: 0
},
)
def test_without_self_loops(self):
"""Tests for node contraction without preserving self-loops."""
G = nx.cycle_graph(4)
actual = nx.contracted_nodes(G, 0, 1, self_loops=False)
expected = nx.complete_graph(3)
assert_true(nx.is_isomorphic(actual, expected))
def setup(self):
"""Creates some graphs for use in the unit tests."""
cnlti = nx.convert_node_labels_to_integers
self.grid = cnlti(nx.grid_2d_graph(4, 4),
first_label=1,
ordering="sorted")
self.cycle = nx.cycle_graph(7)
self.directed_cycle = nx.cycle_graph(7, create_using=nx.DiGraph())
self.XG = nx.DiGraph()
self.XG.add_weighted_edges_from([
("s", "u", 10),
("s", "x", 5),
("u", "v", 1),
("u", "x", 2),
("v", "y", 1),
("x", "u", 3),
("x", "v", 5),
("x", "y", 2),
("y", "s", 7),
("y", "v", 6),
])
self.MXG = nx.MultiDiGraph(self.XG)
self.MXG.add_edge("s", "u", weight=15)
self.XG2 = nx.DiGraph()
self.XG2.add_weighted_edges_from([
[1, 4, 1],
[4, 5, 1],
[5, 6, 1],
[6, 3, 1],
[1, 3, 50],
[1, 2, 100],
[2, 3, 100],
])
self.XG3 = nx.Graph()
self.XG3.add_weighted_edges_from([[0, 1, 2], [1, 2, 12], [2, 3, 1],
[3, 4, 5], [4, 5, 1], [5, 0, 10]])
self.XG4 = nx.Graph()
self.XG4.add_weighted_edges_from([
[0, 1, 2],
[1, 2, 2],
[2, 3, 1],
[3, 4, 1],
[4, 5, 1],
[5, 6, 1],
[6, 7, 1],
[7, 0, 1],
])
self.MXG4 = nx.MultiGraph(self.XG4)
self.MXG4.add_edge(0, 1, weight=3)
self.G = nx.DiGraph() # no weights
self.G.add_edges_from([
("s", "u"),
("s", "x"),
("u", "v"),
("u", "x"),
("v", "y"),
("x", "u"),
("x", "v"),
("x", "y"),
("y", "s"),
("y", "v"),
])
import seaborn
import itertools
######################################################################
# The Heisenberg Hamiltonian is defined as
#
# .. math:: \hat{H} \ = \ \displaystyle\sum_{(i, j) \in E} X_i X_j \ + \ Z_i Z_j \ + \ Y_i Y_j,
#
# where :math:`X_i`, :math:`Y_i` and :math:`Z_i` are the Pauli gates
# acting on the :math:`i`-th qubit. In addition, :math:`E` is the set of
# edges in the graph :math:`G \ = \ (V, \ E)` describing the interactions
# between the qubits. In this demonstration, we define the interaction graph to
# be the cycle graph:
#
interaction_graph = nx.cycle_graph(4)
nx.draw(interaction_graph)
######################################################################
# With this, we can calculate the matrix representation of the Heisenberg
# Hamiltonian in the computational basis:
#
def create_hamiltonian_matrix(n, graph):
matrix = np.zeros((2**n, 2**n))
for i in graph.edges:
x = y = z = 1
for j in range(0, n):
row += str(ising_model[1][(i, j)]) + '\t'
print(row)
input()
print("\nEmbedding logical problem into physical layout ...")
# Construct logical problem graph
prob_graph = nx.Graph()
prob_graph.add_edges_from([(1, 2), (2, 3), (1, 3)])
# Construct an embedding
embedding = {1: [1], 2: [2], 3: [3, 4]}
# Map our Ising model onto the embedding
qubits = list(i for x in embedding.values() for i in x)
target = nx.cycle_graph(qubits)
th, tJ = dwave.embedding.embed_ising(ising_model[0], ising_model[1], embedding,
target)
print("\nQMI (unscaled):\n")
for i in range(1, 5):
row = ''
for j in range(1, 5):
if j == i:
row += str(th[i]) + '\t'
elif (i, j) in tJ:
row += str(tJ[(i, j)]) + '\t'
else:
row += str(0) + '\t'
print(row)
def test_dumbbell(self):
G = nx.cycle_graph(100, create_using=nx.DiGraph())
G.add_edges_from((i + 100, (i + 1) % 100 + 100) for i in range(100))
ok_(not nx.is_semiconnected(G)) # G is disconnected.
G.add_edge(100, 99)
ok_(nx.is_semiconnected(G))
import networkx as nx import matplotlib.pyplot as plt import numpy numpy.random.seed(11) G = nx.cycle_graph(8) E = nx.complete_bipartite_graph(4, 3) fig, myax = plt.subplots() nx.draw(G, ax=myax, with_labels=True) nx.draw(E, ax=myax, node_color="y", with_labels=True) plt.show()
rows = int(input("Number of rows: "))
cols = int(input("Number of cols: "))
G = nx.grid_2d_graph(rows, cols)
pos = nx.spectral_layout(G)
break
elif mode == 4:
nodes = int(input("Number of generations (<= 5): "))
if nodes > 5:
print("Invalid input! Please execute script again.")
sys.exit()
G = nx.dorogovtsev_goltsev_mendes_graph(nodes)
pos = nx.spring_layout(G, k=1, iterations=100)
break
elif mode == 5:
nodes = int(input("Number of nodes: "))
G = nx.cycle_graph(nodes)
pos = nx.circular_layout(G)
break
elif mode == 6:
nodes = int(input("Number of nodes: "))
G = nx.circular_ladder_graph(nodes)
pos = nx.spring_layout(G, k=1, iterations=100)
break
elif mode == 7:
nodesK = int(input("Number of nodes in candy: "))
nodesP = int(input("Number of nodes in stick: "))
G = nx.lollipop_graph(nodesK, nodesP)
pos = nx.spring_layout(G, k=1, iterations=100)
break
elif mode == 8:
nodes = int(input("Number of nodes: "))
# needs mayavi2
# run with ipython -wthread
import networkx as nx
import numpy as np
from mayavi import mlab
# some graphs to try
#H=nx.krackhardt_kite_graph()
#H=nx.Graph();H.add_edge('a','b');H.add_edge('a','c');H.add_edge('a','d')
#H=nx.grid_2d_graph(4,5)
H = nx.cycle_graph(20)
# reorder nodes from 0,len(G)-1
G = nx.convert_node_labels_to_integers(H)
# 3d spring layout
pos = nx.spring_layout(G, dim=3)
# numpy array of x,y,z positions in sorted node order
xyz = np.array([pos[v] for v in sorted(G)])
# scalar colors
scalars = np.array(G.nodes()) + 5
mlab.figure(1, bgcolor=(0, 0, 0))
mlab.clf()
pts = mlab.points3d(xyz[:, 0],
xyz[:, 1],
xyz[:, 2],
scalars,
scale_factor=0.1,
scale_mode='none',
colormap='Blues',
def setUp(self):
self.P4 = nx.path_graph(4)
self.K3 = nx.complete_bipartite_graph(3,3)
self.C4 = nx.cycle_graph(4)
self.davis = nx.davis_southern_women_graph()
self.top_nodes = [n for n,d in self.davis.nodes(data=True)
if d['bipartite']==0]