def test_preflow_push_makes_enough_space():
#From ticket #1542
G = nx.DiGraph()
nx.add_path(G, [0, 1, 3], capacity=1)
nx.add_path(G, [1, 2, 3], capacity=1)
R = preflow_push(G, 0, 3, value_only=False)
assert_equal(R.graph['flow_value'], 1)
def test_notarborescence2():
# Not an arborescence due to in-degree violation.
G = nx.MultiDiGraph()
nx.add_path(G, range(5))
G.add_edge(6, 4)
assert_false(nx.is_branching(G))
assert_false(nx.is_arborescence(G))
def test_all_simple_paths_multigraph():
G = nx.MultiGraph([(1, 2), (1, 2)])
paths = nx.all_simple_paths(G, 1, 1)
assert_equal(paths, [])
nx.add_path(G, [3, 1, 10, 2])
paths = nx.all_simple_paths(G, 1, 2)
assert_equal(set(tuple(p) for p in paths), {(1, 2), (1, 2), (1, 10, 2)})
def test_bidirectional_dijkstra_ignore():
G = nx.Graph()
nx.add_path(G, [1, 2, 10])
nx.add_path(G, [1, 3, 10])
assert_raises(
nx.NetworkXNoPath,
_bidirectional_dijkstra,
G,
1, 2,
ignore_nodes=[1],
)
assert_raises(
nx.NetworkXNoPath,
_bidirectional_dijkstra,
G,
1, 2,
ignore_nodes=[2],
)
assert_raises(
nx.NetworkXNoPath,
_bidirectional_dijkstra,
G,
1, 2,
ignore_nodes=[1, 2],
)
def setUp(self):
#
# -- s1 --
# / | \
# c1-----c2----c3
# / \ / | \ \
# r1 r2 r3 r4 r5 r6
#
topo = fnss.Topology()
icr_candidates = ["c1", "c2", "c3"]
nx.add_path(topo, icr_candidates)
topo.add_edge("c1", "s1")
topo.add_edge("c2", "s1")
topo.add_edge("c3", "s1")
topo.add_edge("c1", "r1")
topo.add_edge("c1", "r2")
topo.add_edge("c2", "r3")
topo.add_edge("c2", "r4")
topo.add_edge("c2", "r5")
topo.add_edge("c3", "r6")
topo.graph['icr_candidates'] = set(icr_candidates)
for router in icr_candidates:
fnss.add_stack(topo, router, 'router')
for src in ['s1']:
fnss.add_stack(topo, src, 'source')
for rcv in ['r1', 'r2', 'r3', 'r4', 'r5', 'r6']:
fnss.add_stack(topo, rcv, 'receiver')
self.topo = cacheplacement.IcnTopology(topo)
def test_edges_with_data_not_equal(self):
G = nx.MultiGraph()
nx.add_path(G, [0, 1, 2], weight=1)
H = nx.MultiGraph()
nx.add_path(H, [0, 1, 2], weight=2)
self._test_not_equal(G.edges(data=True, keys=True),
H.edges(data=True, keys=True))
def nrr_topology(cls):
"""Return topology for testing NRR caching strategies
"""
# Topology sketch
#
# 0 ---- 2----- 4
# | \
# | s
# | /
# 1 ---- 3 ---- 5
#
topology = IcnTopology(fnss.Topology())
nx.add_path(topology, [0, 2, 4, "s", 5, 3, 1])
topology.add_edge(2, 3)
receivers = (0, 1)
source = "s"
caches = (2, 3, 4, 5)
contents = (1, 2, 3, 4)
fnss.add_stack(topology, source, 'source', {'contents': contents})
for v in caches:
fnss.add_stack(topology, v, 'router', {'cache_size': 1})
for v in receivers:
fnss.add_stack(topology, v, 'receiver', {})
fnss.set_delays_constant(topology, 1, 'ms')
return topology
def test_not_connected():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
nx.add_path(G, [4, 5])
for interface_func in [nx.minimum_edge_cut, nx.minimum_node_cut]:
for flow_func in flow_funcs:
assert_raises(nx.NetworkXError, interface_func, G, flow_func=flow_func)
def compare_graph_paths_names(g, paths, names):
expected = nx.DiGraph()
for p in paths:
nx.add_path(expected, p)
assert_equal(sorted(expected.nodes), sorted(g.nodes))
assert_equal(sorted(expected.edges()), sorted(g.edges()))
g_names = [g.get_edge_data(s, e)['Name'] for s, e in g.edges()]
assert_equal(names, sorted(g_names))
def test_path(self):
path = list(range(10))
shuffle(path)
G = nx.Graph()
nx.add_path(G, path)
for method in self._methods:
order = nx.spectral_ordering(G, method=method)
ok_(order in [path, list(reversed(path))])
def test_weighted(self):
G = nx.Graph()
nx.add_cycle(G, range(7), weight=2)
ans = nx.average_shortest_path_length(G, weight='weight')
assert_almost_equal(ans, 4)
G = nx.Graph()
nx.add_path(G, range(5), weight=2)
ans = nx.average_shortest_path_length(G, weight='weight')
assert_almost_equal(ans, 4)
def setUp(self):
self.P4=nx.path_graph(4)
self.D=nx.DiGraph()
self.D.add_edges_from([(0, 2), (0, 3), (1, 3), (2, 3)])
self.M=nx.MultiGraph()
nx.add_path(self.M, range(4))
self.M.add_edge(0,1)
self.S=nx.Graph()
self.S.add_edges_from([(0,0),(1,1)])
def test_P5_multiple_target(self):
"""Betweenness centrality: P5 multiple target"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 1, 2: 1, 3: 0.5, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3, 4],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def setUp(self):
deg=[3,2,2,1,0]
self.G=havel_hakimi_graph(deg)
self.P=nx.path_graph(3)
self.WG=nx.Graph( (u,v,{'weight':0.5,'other':0.3})
for (u,v) in self.G.edges() )
self.WG.add_node(4)
self.DG=nx.DiGraph()
nx.add_path(self.DG, [0,1,2])
def test_not_connected():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
nx.add_path(G, [4, 5])
for flow_func in flow_funcs:
assert_equal(nx.node_connectivity(G), 0,
msg=msg.format(flow_func.__name__))
assert_equal(nx.edge_connectivity(G), 0,
msg=msg.format(flow_func.__name__))
def test_P5(self):
"""Betweenness Centrality Subset: P5"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = {0: 0, 1: 0.5, 2: 0.5, 3: 0, 4: 0, 5: 0}
b = nx.betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n], b_answer[n])
def test_P5(self):
"""Edge betweenness centrality: P5"""
G = nx.Graph()
nx.add_path(G, range(5))
b_answer = dict.fromkeys(G.edges(), 0)
b_answer[(0, 1)] = b_answer[(1, 2)] = b_answer[(2, 3)] = 0.5
b = nx.edge_betweenness_centrality_subset(G, sources=[0], targets=[3],
weight=None)
for n in sorted(G.edges()):
assert_almost_equal(b[n], b_answer[n])
def test_all_pairs_connectivity_directed(self):
G = nx.DiGraph()
nodes = [0, 1, 2, 3]
nx.add_path(G, nodes)
A = {n: {} for n in G}
for u, v in itertools.permutations(nodes, 2):
A[u][v] = nx.node_connectivity(G, u, v)
C = nx.all_pairs_node_connectivity(G)
assert_equal(sorted((k, sorted(v)) for k, v in A.items()),
sorted((k, sorted(v)) for k, v in C.items()))
def test_directed_path_normalized(self):
"""Betweenness centrality: directed path normalized"""
G=nx.DiGraph()
nx.add_path(G, [0, 1, 2])
b=nx.betweenness_centrality(G,
weight=None,
normalized=True)
b_answer={0: 0.0, 1: 0.5, 2: 0.0}
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_path(self):
# based on setupClass numpy is installed if we get here
from numpy.random import shuffle
path = list(range(10))
shuffle(path)
G = nx.Graph()
nx.add_path(G, path)
for method in self._methods:
order = nx.spectral_ordering(G, method=method)
ok_(order in [path, list(reversed(path))])
def test_disconnected(self):
G = nx.Graph()
nx.add_path(G, range(0, 10, 2))
nx.add_path(G, range(1, 10, 2))
for method in self._methods:
order = nx.spectral_ordering(G, method=method)
assert_equal(set(order), set(G))
seqs = [list(range(0, 10, 2)), list(range(8, -1, -2)),
list(range(1, 10, 2)), list(range(9, -1, -2))]
ok_(order[:5] in seqs)
ok_(order[5:] in seqs)
def test_disconnected_path(self):
"""Betweenness centrality: disconnected path"""
G=nx.Graph()
nx.add_path(G, [0, 1, 2])
nx.add_path(G, [3, 4, 5, 6])
b_answer={0:0,1:1,2:0,3:0,4:2,5:2,6:0}
b=nx.betweenness_centrality(G,
weight=None,
normalized=False)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_path_weighted_projected_directed_graph(self):
G = nx.DiGraph()
nx.add_path(G, range(4))
P = bipartite.weighted_projected_graph(G, [1, 3])
assert_nodes_equal(list(P), [1, 3])
assert_edges_equal(list(P.edges()), [(1, 3)])
P[1][3]['weight'] = 1
P = bipartite.weighted_projected_graph(G, [0, 2])
assert_nodes_equal(list(P), [0, 2])
assert_edges_equal(list(P.edges()), [(0, 2)])
P[0][2]['weight'] = 1
def test_P5_directed(self):
"""Betweenness centrality: P5 directed"""
G=nx.DiGraph()
nx.add_path(G, range(5))
b_answer={0:0,1:1,2:1,3:0,4:0,5:0}
b=betweenness_centrality_subset(G,
sources=[0],
targets=[3],
weight=None)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_add_path(self):
G = self.G.copy()
nlist = [12, 13, 14, 15]
nx.add_path(G, nlist)
assert_edges_equal(G.edges(nlist), [(12, 13), (13, 14), (14, 15)])
G = self.G.copy()
nx.add_path(G, nlist, weight=2.0)
assert_edges_equal(G.edges(nlist, data=True),
[(12, 13, {'weight': 2.}),
(13, 14, {'weight': 2.}),
(14, 15, {'weight': 2.})])
def test_directed_multigraph_path(self):
G = nx.MultiDiGraph()
nx.add_path(G, range(6))
partition = [{0, 1}, {2, 3}, {4, 5}]
M = nx.quotient_graph(G, partition, relabel=True)
assert_equal(sorted(M), [0, 1, 2])
assert_equal(sorted(M.edges()), [(0, 1), (1, 2)])
for n in M:
assert_equal(M.node[n]['nedges'], 1)
assert_equal(M.node[n]['nnodes'], 2)
assert_equal(M.node[n]['density'], 0.5)
def test_shortest_augmenting_path_two_phase():
k = 5
p = 1000
G = nx.DiGraph()
for i in range(k):
G.add_edge('s', (i, 0), capacity=1)
nx.add_path(G, ((i, j) for j in range(p)), capacity=1)
G.add_edge((i, p - 1), 't', capacity=1)
R = shortest_augmenting_path(G, 's', 't', two_phase=True)
assert_equal(R.graph['flow_value'], k)
R = shortest_augmenting_path(G, 's', 't', two_phase=False)
assert_equal(R.graph['flow_value'], k)
def test_disconnected_path_endpoints(self):
"""Betweenness centrality: disconnected path endpoints"""
G=nx.Graph()
nx.add_path(G, [0, 1, 2])
nx.add_path(G, [3, 4, 5, 6])
b_answer={0:2,1:3,2:2,3:3,4:5,5:5,6:3}
b=nx.betweenness_centrality(G,
weight=None,
normalized=False,
endpoints=True)
for n in sorted(G):
assert_almost_equal(b[n],b_answer[n])
def test_default_attribute(self):
G = nx.Graph(name="Fred")
G.add_node(1, label=1, color='green')
nx.add_path(G, [0, 1, 2, 3])
G.add_edge(1, 2, weight=3)
G.graph['node_default'] = {'color': 'yellow'}
G.graph['edge_default'] = {'weight': 7}
fh = io.BytesIO()
self.writer(G, fh)
fh.seek(0)
H = nx.read_graphml(fh, node_type=int)
assert_nodes_equal(G.nodes(), H.nodes())
assert_edges_equal(G.edges(), H.edges())
assert_equal(G.graph, H.graph)
def test_bidirectional_shortest_path_ignore():
G = nx.Graph()
nx.add_path(G, [1, 2])
nx.add_path(G, [1, 3])
nx.add_path(G, [1, 4])
pytest.raises(nx.NetworkXNoPath,
_bidirectional_shortest_path,
G,
1,
2,
ignore_nodes=[1])
pytest.raises(nx.NetworkXNoPath,
_bidirectional_shortest_path,
G,
1,
2,
ignore_nodes=[2])
G = nx.Graph()
nx.add_path(G, [1, 3])
nx.add_path(G, [1, 4])
nx.add_path(G, [3, 2])
pytest.raises(nx.NetworkXNoPath,
_bidirectional_shortest_path,
G,
1,
2,
ignore_nodes=[1, 2])
def test_multicast_tree(self):
topo = fnss.Topology()
nx.add_path(topo, [2, 1, 3, 4])
sp = dict(nx.all_pairs_shortest_path(topo))
tree = util.multicast_tree(sp, 1, [2, 3])
self.assertSetEqual(set(tree), set([(1, 2), (1, 3)]))
def test_connected_double_edge_swap_not_connected():
with pytest.raises(nx.NetworkXError):
G = nx.path_graph(3)
nx.add_path(G, [10, 11, 12])
G = nx.connected_double_edge_swap(G)
def test_ssp_multigraph():
with pytest.raises(nx.NetworkXNotImplemented):
G = nx.MultiGraph()
nx.add_path(G, [1, 2, 3])
list(nx.shortest_simple_paths(G, 1, 4))
def add_transition_path(self, transition_list, w=None):
if w == None:
w = self.std_tran_weight
nx.add_path(self, transition_list, type="transition", weight=w)
nx.add_path(self, transition_list[::-1], type="transition", weight=w)
self.add_self_loops()
def test_ssp_source_missing2():
with pytest.raises(nx.NetworkXNoPath):
G = nx.Graph()
nx.add_path(G, [0, 1, 2])
nx.add_path(G, [3, 4, 5])
list(nx.shortest_simple_paths(G, 0, 3))
def main():
# Create a graph
graph = Graph()
# Create graph connections (Actual distance)
#Subdomain A
#Client -> Switch
graph.connect('ClientA', 'SwitchA', client_switch)
graph.connect('ClientB', 'SwitchA', client_switch)
graph.connect('ClientC', 'SwitchA', client_switch)
graph.connect('ClientD', 'SwitchA', client_switch)
graph.connect('ClientE', 'SwitchA', client_switch)
#Switch -> client
graph.connect('SwitchA', 'ClientA', switch_client)
graph.connect('SwitchA', 'ClientB', switch_client)
graph.connect('SwitchA', 'ClientC', switch_client)
graph.connect('SwitchA', 'ClientD', switch_client)
graph.connect('SwitchA', 'ClientE', switch_client)
#Branch
graph.connect('SwitchA', 'SwitchB', switch_client)
#Subdomain B
#Client -> Switch
graph.connect('ClientF', 'SwitchB', client_switch)
graph.connect('ClientG', 'SwitchB', client_switch)
graph.connect('ClientH', 'SwitchB', client_switch)
graph.connect('ClientI', 'SwitchB', client_switch)
graph.connect('ClientJ', 'SwitchB', client_switch)
graph.connect('ClientK', 'SwitchB', client_switch)
#Switch -> client
graph.connect('SwitchB', 'ClientF', switch_client)
graph.connect('SwitchB', 'ClientG', switch_client)
graph.connect('SwitchB', 'ClientH', switch_client)
graph.connect('SwitchB', 'ClientI', switch_client)
graph.connect('SwitchB', 'ClientJ', switch_client)
graph.connect('SwitchB', 'ClientK', switch_client)
#Port opennings between non branched
graph.connect('ClientA', 'ClientB', client_client)
graph.connect('ClientJ', 'ClientB', client_client)
graph.connect('ClientH', 'ClientB', client_client)
graph.connect('ClientB', 'ClientK', client_client)
# Create heuristics (inverse vulnerabilities)
heuristics = {}
heuristics['ClientA'] = no_vulns
heuristics['ClientB'] = no_vulns
heuristics['ClientC'] = no_vulns
heuristics['ClientD'] = no_vulns
heuristics['ClientE'] = no_vulns
heuristics['ClientF'] = no_vulns
heuristics['ClientG'] = no_vulns
heuristics['ClientH'] = minor
heuristics['ClientI'] = no_vulns
heuristics['ClientJ'] = no_vulns
heuristics['ClientK'] = no_vulns
heuristics['SwitchA'] = minor
heuristics['SwitchB'] = medium
# Run the search algorithm
print(
"-------------------------------------------------------------------------------------"
)
print(
"-------------------------------------------------------------------------------------"
)
print("Current Graph Nodes are: \n")
print(
"-------------------------------------------------------------------------------------"
)
for i in graph.nodes():
print(i)
print(
"-------------------------------------------------------------------------------------"
)
print(
"\nInput the nodes you are determining the likely threat path for: \n")
#Get traversal input
beginning_node = None
target_node = None
while (beginning_node not in graph.nodes()):
beginning_node = input("Please input starting node: ")
if (beginning_node not in graph.nodes()):
print("\nNode not present in graph, please try again\n")
while (target_node not in graph.nodes()):
target_node = input("Please input target node: ")
if (target_node not in graph.nodes()):
print("\nNode not present in graph, please try again\n")
print("\nSearching............\n")
#Traverse and retrieve results
path = astar_search(graph, heuristics, beginning_node, target_node)
node_titles = [item[0] for item in path[:-1]]
print(
"-------------------------------------------------------------------------------------"
)
print(
"-------------------------------------------------------------------------------------"
)
print("Search completed\n")
print("Displaying results.....")
print(
"-------------------------------------------------------------------------------------"
)
for i in path:
print(i)
print(
"-------------------------------------------------------------------------------------"
)
print("Based on the search algorithm, nodes of interest are: \n")
for i in node_titles:
print(i)
print(
"-------------------------------------------------------------------------------------"
)
print(
"-------------------------------------------------------------------------------------"
)
print("Generating graph visualisation..........\n")
# Build nxGraph
G = nx.MultiDiGraph()
G.add_nodes_from(graph.nodes())
G.add_edge('ClientA', 'SwitchA', label=2)
G.add_edge('ClientB', 'SwitchA', label=2)
G.add_edge('ClientC', 'SwitchA', label=2)
G.add_edge('ClientD', 'SwitchA', label=2)
G.add_edge('ClientE', 'SwitchA', label=2)
G.add_edge('SwitchA', 'ClientA', label=4)
G.add_edge('SwitchA', 'ClientB', label=4)
G.add_edge('SwitchA', 'ClientC', label=4)
G.add_edge('SwitchA', 'ClientD', label=4)
G.add_edge('SwitchA', 'ClientE', label=4)
G.add_edge('SwitchA', 'SwitchB', label=4)
G.add_edge('ClientF', 'SwitchB', label=2)
G.add_edge('ClientG', 'SwitchB', label=2)
G.add_edge('ClientH', 'SwitchB', label=2)
G.add_edge('ClientI', 'SwitchB', label=2)
G.add_edge('ClientJ', 'SwitchB', label=2)
G.add_edge('ClientK', 'SwitchB', label=2)
G.add_edge('SwitchB', 'ClientF', label=4)
G.add_edge('SwitchB', 'ClientG', label=4)
G.add_edge('SwitchB', 'ClientH', label=4)
G.add_edge('SwitchB', 'ClientI', label=4)
G.add_edge('SwitchB', 'ClientJ', label=4)
G.add_edge('SwitchB', 'ClientK', label=4)
G.add_edge('ClientA', 'ClientB', label=2)
G.add_edge('ClientJ', 'ClientB', label=2)
G.add_edge('ClientH', 'ClientB', label=2)
G.add_edge('ClientB', 'ClientK', label=2)
# Build SubGraph
sg = nx.MultiDiGraph()
nx.add_path(sg, node_titles)
def visualize(G, sg, name='attack-graph.html'):
N = pvnet.Network(height='100%',
width='100%',
bgcolor='#222222',
font_color='white',
directed=True)
opts = '''
var options = {
"physics": {
"forceAtlas2Based": {
"gravitationalConstant": -100,
"centralGravity": 0.01,
"springLength": 100,
"springConstant": 0.09,
"avoidOverlap": 1
},
"minVelocity": 0.75,
"solver": "forceAtlas2Based",
"timestep": 0.22
}
}
'''
N.set_options(opts)
for n in G:
if n in sg: # if the node is part of the sub-graph
color = 'green'
size = 40
else:
color = 'red'
size = 30
N.add_node(n, label=n, color=color, size=size)
for e in G.edges():
if e in sg.edges(): # if the edge is part of sub-graph
color = 'green'
width = 5
else:
color = 'red'
width = 1
N.add_edge((e[0]), (e[1]), color=color, width=width)
return N.show(name)
visualize(G, sg)
def test_target_missing():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
paths = list(nx.all_simple_paths(nx.MultiGraph(G), 1, 4))
def test_bidirectional_dijkstra_no_path(self):
with pytest.raises(nx.NetworkXNoPath):
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
nx.add_path(G, [4, 5, 6])
path = nx.bidirectional_dijkstra(G, 1, 6)
def test_source_missing():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
paths = list(nx.all_simple_paths(nx.MultiGraph(G), 0, 3))
def test_has_path(self):
G = nx.Graph()
nx.add_path(G, range(3))
nx.add_path(G, range(3, 5))
assert nx.has_path(G, 0, 2)
assert not nx.has_path(G, 0, 4)
})])
# エッジの追加(方法1)
edges = [(0, 1, {
"color": "lightblue"
}), (1, 2, {
"color": "lightblue"
}), (2, 3, {
"color": "lightblue"
}), (3, 0, {
"color": "lightblue"
})]
G.add_edges_from(edges)
# エッジの追加(方法2)
nx.add_path(G, [0, 4, 5, 6, 0], color="royalblue")
nx.add_path(G, [0, 7, 8, 9, 0], color="steelblue")
# ノードの色をセット
node_color = [node["color"] for node in G.nodes.values()]
# エッジの色をセット
edge_color = [edge["color"] for edge in G.edges.values()]
# グラフを出力
fig = plt.figure()
pos = nx.circular_layout(G)
nx.draw_networkx(G, pos, edge_color=edge_color, node_color=node_color)
plt.axis("off")
fig.savefig("test.png")
def test_two_cliques_num(self):
# This graph has two major cliques [0,1,2,3,4] and [11,12,13,14]
# but the first one is bigger so that's the maximum_clique.
G = nx.complete_graph(5)
nx.add_path(G, [4, 5, 6, 7, 8])
nx.add_path(G, [2, 9, 10])
nx.add_path(G, [9, 11])
nx.add_path(G, [11, 12, 13, 14, 11])
nx.add_path(G, [12, 14])
nx.add_path(G, [13, 11])
clique_number = dnx.clique_number(G, dimod.ExactSolver())
self.assertEqual(clique_number, 5)
def test_edge_target_missing():
with pytest.raises(nx.NodeNotFound):
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
list(nx.all_simple_edge_paths(nx.MultiGraph(G), 1, 4))
def setUp(self):
self.Gi = nx.grid_2d_graph(5, 5)
self.Gs = nx.Graph()
nx.add_path(self.Gs, 'abcdef')
self.bigG = nx.grid_2d_graph(25,
25) # bigger than 500 nodes for sparse
def test_source_missing():
with pytest.raises(nx.NodeNotFound):
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
list(nx.all_simple_paths(nx.MultiGraph(G), 0, 3))
def processAlgorithm(self, parameters, context, feedback):
try:
import os, sys, math, string, random, tempfile
import processing as st
import networkx as nx
except Exception as e:
feedback.reportError(
QCoreApplication.translate('Error', '%s' % (e)))
feedback.reportError(QCoreApplication.translate('Error', ' '))
feedback.reportError(
QCoreApplication.translate(
'Error',
'Error loading modules - please install the necessary python module'
))
return {}
layer = self.parameterAsVectorLayer(parameters, self.Polygons, context)
if layer == None:
feedback.reportError(
QCoreApplication.translate(
'Error',
'Do not use the "Selected features only" option when applying the algorithm to selected features'
))
return {}
aMethod = parameters[self.Method]
Threshold = parameters[self.T]
tField = self.parameterAsString(parameters, self.tField, context)
sField = self.parameterAsString(parameters, self.sField, context)
dField = self.parameterAsString(parameters, self.dField, context)
mDict = {0: "Centerlines", 1: "All", 2: "Circles"}
Method = mDict[aMethod]
context.setInvalidGeometryCheck(QgsFeatureRequest.GeometryNoCheck)
field_check = layer.fields().indexFromName('ID')
if field_check == -1:
pr = layer.dataProvider()
pr.addAttributes([QgsField("ID", QVariant.Int)])
layer.updateFields()
layer.startEditing()
for feature in layer.getFeatures():
feature['ID'] = feature.id()
layer.updateFeature(feature)
layer.commitChanges()
fet = QgsFeature()
fields = QgsFields()
fields.append(QgsField("ID", QVariant.Int))
field_names = ['Distance', 'RDistance', 'SP_Dist', 'SP_RDist']
for name in field_names:
fields.append(QgsField(name, QVariant.Double))
fet2 = QgsFeature(fields)
(writer2, dest_id) = self.parameterAsSink(parameters, self.Output,
context, fields,
QgsWkbTypes.LineString,
layer.sourceCrs())
outDir = os.path.join(tempfile.gettempdir(), 'GA')
if not os.path.exists(outDir):
os.mkdir(outDir)
fname = ''.join(
random.choice(string.ascii_lowercase) for i in range(10))
infc = os.path.join(outDir, '%s.shp' % (fname))
Densify_Interval = parameters[self.Densify]
s = parameters[self.Simplify]
Precision, tol = 6, 1e-6
keepNodes = set([])
fields = QgsFields()
fields.append(QgsField("ID", QVariant.Int))
writer = QgsVectorFileWriter(
infc, "CP1250", fields, QgsWkbTypes.Point, layer.sourceCrs(),
"ESRI Shapefile") #.shp requirement of SAGA
features = layer.selectedFeatures()
total = layer.selectedFeatureCount()
if len(features) == 0:
features = layer.getFeatures()
total = layer.featureCount()
total = 100.0 / total
feedback.pushInfo(
QCoreApplication.translate('Update', 'Creating Vertices'))
thresholds = {}
for enum, feature in enumerate(features):
if total != -1:
feedback.setProgress(int(enum * total))
geomType = feature.geometry()
if sField:
s = feature[sField]
if dField:
Densify_Interval = feature[dField]
if s:
if s > 0:
geomType = geomType.simplify(s)
if Densify_Interval:
if Densify_Interval > 0:
geomType = geomType.densifyByDistance(Densify_Interval)
ID = feature['ID']
geom = []
if geomType.wkbType() == QgsWkbTypes.Polygon:
polygon = geomType.asPolygon()
geom = chain(*polygon)
else:
polygons = geomType.asMultiPolygon()
geom = chain(*chain(*polygons))
for points in geom:
if (round(points.x(),
Precision), round(points.y(),
Precision)) not in keepNodes:
pnt = QgsGeometry.fromPointXY(
QgsPointXY(points.x(), points.y()))
fet.setGeometry(pnt)
fet.setAttributes([ID])
writer.addFeature(fet)
keepNodes.update([(round(points.x(), Precision),
round(points.y(), Precision))])
if tField:
tValue = feature[tField]
try:
thresholds[ID] = int(tValue)
except Exception as e:
feedback.reportError(
QCoreApplication.translate(
'Error',
'Non-numeric value found in field for trim iteration function.'
))
return {}
feedback.pushInfo(
QCoreApplication.translate('Update', 'Creating Voronoi Polygons'))
del writer
tempVP = os.path.join(outDir, 'VL.shp') #.shp requirement of SAGA
param = {'POINTS': infc, 'POLYGONS': tempVP, 'FRAME': 10.0}
Voronoi = st.run("saga:thiessenpolygons",
param,
context=context,
feedback=None)
del keepNodes
edges = {}
feedback.pushInfo(
QCoreApplication.translate('Update', 'Calculating Edges'))
param = {'INPUT': Voronoi['POLYGONS'], 'OUTPUT': 'memory:'}
lines = st.run("qgis:polygonstolines",
param,
context=context,
feedback=feedback)
param = {'INPUT': lines['OUTPUT'], 'OUTPUT': 'memory:'}
exploded = st.run("native:explodelines",
param,
context=context,
feedback=feedback)
param = {
'INPUT': exploded['OUTPUT'],
'PREDICATE': 6,
'INTERSECT': layer,
'METHOD': 0
}
st.run("native:selectbylocation",
param,
context=context,
feedback=None)
total = 100.0 / exploded['OUTPUT'].selectedFeatureCount()
for enum, feature in enumerate(exploded['OUTPUT'].selectedFeatures()):
try:
if total != -1:
feedback.setProgress(int(enum * total))
part = feature.geometry().asPolyline()
startx = None
for point in part:
if startx == None:
startx, starty = (round(point.x(), Precision),
round(point.y(), Precision))
continue
endx, endy = (round(point.x(), Precision),
round(point.y(), Precision))
geom = QgsGeometry.fromPolylineXY(
[QgsPointXY(startx, starty),
QgsPointXY(endx, endy)])
ID = feature['ID']
Length = geom.length()
if Length > tol:
if ID in edges:
edges[ID].add_edge((startx, starty), (endx, endy),
weight=Length)
else:
Graph = nx.Graph()
Graph.add_edge((startx, starty), (endx, endy),
weight=Length)
edges[ID] = Graph
startx, starty = endx, endy
except Exception as e:
feedback.reportError(
QCoreApplication.translate('Error', '%s' % (e)))
feedback.pushInfo(
QCoreApplication.translate(
'Update', 'Calculating %s Centerlines' % (len(edges))))
if edges:
total = 100.0 / len(edges)
for enum, FID in enumerate(edges):
feedback.setProgress(int(enum * total))
G = edges[FID]
maxG = max(nx.connected_components(G),
key=len) #Largest Connected Graph
G = G.subgraph(maxG)
try:
if Threshold > 0 or tField:
if tField:
Threshold = thresholds[FID]
else:
Threshold = int(Threshold)
G2 = G.copy()
G3 = G.copy()
for n in range(int(Threshold)):
degree = G2.degree()
removeNodes = [k for k, v in degree if v == 1]
G2.remove_nodes_from(removeNodes)
degree = G2.degree()
if len(G2) < 2:
feedback.reportError(
QCoreApplication.translate(
'Update',
'No centerline found after trimming dangles for polygon ID %s - skipping'
% (FID)))
continue
endPoints = [k for k, v in degree if v == 1]
G3.remove_edges_from(G2.edges)
for source in endPoints:
length, path = nx.single_source_dijkstra(
G3, source, weight='weight')
Index = max(length, key=length.get)
nx.add_path(G2, path[Index])
del G3
source = list(
G2.nodes())[0] #Get length along all paths
for n in range(2):
length, path = nx.single_source_dijkstra(
G, source, weight='weight')
Index = max(length, key=length.get)
source = path[Index][-1]
length2, path2 = nx.single_source_dijkstra(
G, source, weight='weight')
for p in G2.edges:
points = []
points.append(QgsPointXY(p[0][0], p[0][1]))
points.append(QgsPointXY(p[1][0], p[1][1]))
D = max([
length[(p[0][0], p[0][1])],
length[(p[1][0], p[1][1])]
])
D2 = max([
length2[(p[0][0], p[0][1])],
length2[(p[1][0], p[1][1])]
])
dx = path[Index][0][0] - p[1][0]
dy = path[Index][0][1] - p[1][1]
dx2 = path[Index][0][0] - p[0][0]
dy2 = path[Index][0][1] - p[0][1]
SP = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
dx = path[Index][-1][0] - p[1][0]
dy = path[Index][-1][1] - p[1][1]
dx2 = path[Index][-1][0] - p[0][0]
dy2 = path[Index][-1][1] - p[0][1]
SP2 = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
fet2.setGeometry(
QgsGeometry.fromPolylineXY(points))
fet2.setAttributes([FID, D, D2, SP, SP2])
writer2.addFeature(fet2)
del G2
elif Method == 'All':
curLen = 0
G2 = G.copy()
while len(G2) != curLen:
curLen = len(G2)
degree = G2.degree()
removeNodes = [k for k, v in degree if v == 1]
G2.remove_nodes_from(removeNodes)
source = list(G.nodes())[0]
for n in range(2):
length, path = nx.single_source_dijkstra(
G, source, weight='weight')
Index = max(length, key=length.get)
source = path[Index][-1]
nx.add_path(G2, path[Index])
source = list(
G2.nodes())[0] #Get length along all paths
for n in range(2):
length, path = nx.single_source_dijkstra(
G, source, weight='weight')
Index = max(length, key=length.get)
source = path[Index][-1]
length2, path2 = nx.single_source_dijkstra(
G, source, weight='weight')
for p in G2.edges:
points = []
points.append(QgsPointXY(p[0][0], p[0][1]))
points.append(QgsPointXY(p[1][0], p[1][1]))
D = max([
length[(p[0][0], p[0][1])],
length[(p[1][0], p[1][1])]
])
D2 = max([
length2[(p[0][0], p[0][1])],
length2[(p[1][0], p[1][1])]
])
dx = path[Index][0][0] - p[1][0]
dy = path[Index][0][1] - p[1][1]
dx2 = path[Index][0][0] - p[0][0]
dy2 = path[Index][0][1] - p[0][1]
SP = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
dx = path[Index][-1][0] - p[1][0]
dy = path[Index][-1][1] - p[1][1]
dx2 = path[Index][-1][0] - p[0][0]
dy2 = path[Index][-1][1] - p[0][1]
SP2 = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
fet2.setGeometry(
QgsGeometry.fromPolylineXY(points))
fet2.setAttributes([FID, D, D2, SP, SP2])
writer2.addFeature(fet2)
del G2
elif Method == 'Circles':
curLen = 0
G2 = G.copy()
while len(G2) != curLen:
curLen = len(G2)
degree = G2.degree()
removeNodes = [k for k, v in degree if v == 1]
G2.remove_nodes_from(removeNodes)
for p in G2.edges:
points = []
points.append(QgsPointXY(p[0][0], p[0][1]))
points.append(QgsPointXY(p[1][0], p[1][1]))
fet2.setGeometry(
QgsGeometry.fromPolylineXY(points))
fet2.setAttributes([FID])
writer2.addFeature(fet2)
del G2
else:
source = list(G.nodes())[0]
for n in range(2):
length, path = nx.single_source_dijkstra(
G, source, weight='weight')
Index = max(length, key=length.get)
source = path[Index][-1]
length2, path2 = nx.single_source_dijkstra(
G, source, weight='weight')
sx = None
for p in path[Index]:
if sx == None:
sx, sy = p[0], p[1]
continue
ex, ey = p[0], p[1]
D = max([length[(sx, sy)], length[(ex, ey)]])
D2 = max([length2[(sx, sy)], length2[(ex, ey)]])
dx = path[Index][0][0] - ex
dy = path[Index][0][1] - ey
dx2 = path[Index][0][0] - sx
dy2 = path[Index][0][1] - sy
SP = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
dx = path[Index][-1][0] - ex
dy = path[Index][-1][1] - ey
dx2 = path[Index][-1][0] - sx
dy2 = path[Index][-1][1] - sy
SP2 = max([
math.sqrt((dx**2) + (dy**2)),
math.sqrt((dx2**2) + (dy2**2))
])
points = [QgsPointXY(sx, sy), QgsPointXY(ex, ey)]
fet2.setGeometry(
QgsGeometry.fromPolylineXY(points))
fet2.setAttributes([FID, D, D2, SP, SP2])
writer2.addFeature(fet2)
sx, sy = ex, ey
except Exception as e:
feedback.reportError(
QCoreApplication.translate('Update', '%s' % (e)))
feedback.reportError(
QCoreApplication.translate(
'Update',
'No centerline found for polygon ID %s - skipping'
% (FID)))
continue
del writer2, edges
return {self.Output: dest_id}
def test_has_path(self):
G = nx.Graph()
nx.add_path(G, range(3))
nx.add_path(G, range(3, 5))
assert_true(nx.has_path(G, 0, 2))
assert_false(nx.has_path(G, 0, 4))
def test_all_shortest_paths(self):
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert_equal([[0, 1, 2, 3], [0, 10, 20, 3]],
sorted(nx.all_shortest_paths(G, 0, 3)))
# with weights
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert_equal([[0, 1, 2, 3], [0, 10, 20, 3]],
sorted(nx.all_shortest_paths(G, 0, 3, weight='weight')))
# weights and method specified
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert_equal([[0, 1, 2, 3], [0, 10, 20, 3]],
sorted(
nx.all_shortest_paths(G,
0,
3,
weight='weight',
method='dijkstra')))
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert_equal([[0, 1, 2, 3], [0, 10, 20, 3]],
sorted(
nx.all_shortest_paths(G,
0,
3,
weight='weight',
method='bellman-ford')))
def setup_class(cls):
cls.Gi = nx.grid_2d_graph(5, 5)
cls.Gs = nx.Graph()
nx.add_path(cls.Gs, "abcdef")
cls.bigG = nx.grid_2d_graph(25, 25) # > 500 nodes for sparse
def test_ssp_target_missing():
with pytest.raises(nx.NodeNotFound):
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
list(nx.shortest_simple_paths(G, 1, 4))
def test_add_path(self):
G = self.G.copy()
nlist = [12, 13, 14, 15]
nx.add_path(G, nlist)
assert_edges_equal(G.edges(nlist), [(12, 13), (13, 14), (14, 15)])
G = self.G.copy()
nx.add_path(G, nlist, weight=2.0)
assert_edges_equal(G.edges(nlist, data=True),
[(12, 13, {'weight': 2.}),
(13, 14, {'weight': 2.}),
(14, 15, {'weight': 2.})])
G = self.G.copy()
nlist = [None]
nx.add_path(G, nlist)
assert_edges_equal(G.edges(nlist), [])
assert_nodes_equal(G, list(self.G) + [None])
G = self.G.copy()
nlist = iter([None])
nx.add_path(G, nlist)
assert_edges_equal(G.edges([None]), [])
assert_nodes_equal(G, list(self.G) + [None])
G = self.G.copy()
nlist = [12]
nx.add_path(G, nlist)
assert_edges_equal(G.edges(nlist), [])
assert_nodes_equal(G, list(self.G) + [12])
G = self.G.copy()
nlist = iter([12])
nx.add_path(G, nlist)
assert_edges_equal(G.edges([12]), [])
assert_nodes_equal(G, list(self.G) + [12])
G = self.G.copy()
nlist = []
nx.add_path(G, nlist)
assert_edges_equal(G.edges, self.G.edges)
assert_nodes_equal(G, list(self.G))
G = self.G.copy()
nlist = iter([])
nx.add_path(G, nlist)
assert_edges_equal(G.edges, self.G.edges)
assert_nodes_equal(G, list(self.G))
def test_all_simple_edge_paths_directed():
G = nx.DiGraph()
nx.add_path(G, [1, 2, 3])
nx.add_path(G, [3, 2, 1])
paths = nx.all_simple_edge_paths(G, 1, 3)
assert {tuple(p) for p in paths} == {((1, 2), (2, 3))}
def test_digraph_ignore2(self):
G = nx.DiGraph()
nx.add_path(G, range(4))
x = list(edge_dfs(G, [0], orientation="ignore"))
x_ = [(0, 1, FORWARD), (1, 2, FORWARD), (2, 3, FORWARD)]
assert x == x_
def test_ssp_source_missing():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
paths = list(nx.shortest_simple_paths(G, 0, 3))
def test_all_shortest_paths(self):
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert [[0, 1, 2, 3], [0, 10, 20,
3]] == sorted(nx.all_shortest_paths(G, 0, 3))
# with weights
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert [[0, 1, 2, 3],
[0, 10, 20,
3]] == sorted(nx.all_shortest_paths(G, 0, 3, weight="weight"))
# weights and method specified
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(
nx.all_shortest_paths(G, 0, 3, weight="weight", method="dijkstra"))
G = nx.Graph()
nx.add_path(G, [0, 1, 2, 3])
nx.add_path(G, [0, 10, 20, 3])
assert [[0, 1, 2, 3], [0, 10, 20, 3]] == sorted(
nx.all_shortest_paths(G,
0,
3,
weight="weight",
method="bellman-ford"))
def test_ssp_target_missing():
G = nx.Graph()
nx.add_path(G, [1, 2, 3])
paths = list(nx.shortest_simple_paths(G, 1, 4))
def test_digraph2(self):
G = nx.DiGraph()
nx.add_path(G, range(4))
x = list(edge_dfs(G, [0]))
x_ = [(0, 1), (1, 2), (2, 3)]
assert x == x_
def test_all_simple_paths_directed():
G = nx.DiGraph()
nx.add_path(G, [1, 2, 3])
nx.add_path(G, [3, 2, 1])
paths = nx.all_simple_paths(G, 1, 3)
assert_equal(set(tuple(p) for p in paths), {(1, 2, 3)})
def test_digraph_rev2(self):
G = nx.DiGraph()
nx.add_path(G, range(4))
x = list(edge_dfs(G, [3], orientation="reverse"))
x_ = [(2, 3, REVERSE), (1, 2, REVERSE), (0, 1, REVERSE)]
assert x == x_
def test_barbell():
G = nx.barbell_graph(8, 4)
nx.add_path(G, [7, 20, 21, 22])
nx.add_cycle(G, [22, 23, 24, 25])
pts = set(nx.articulation_points(G))
assert_equal(pts, {7, 8, 9, 10, 11, 12, 20, 21, 22})
answer = [
{12, 13, 14, 15, 16, 17, 18, 19},
{0, 1, 2, 3, 4, 5, 6, 7},
{22, 23, 24, 25},
{11, 12},
{10, 11},
{9, 10},
{8, 9},
{7, 8},
{21, 22},
{20, 21},
{7, 20},
]
assert_components_equal(list(nx.biconnected_components(G)), answer)
G.add_edge(2,17)
pts = set(nx.articulation_points(G))
assert_equal(pts, {7, 20, 21, 22})