def test_interlacing_edges(self):
"""Test interlacing edge function for correctness."""
a = [i for i in "ABCDEFZ"]
b = [i for i in "AGCBIZ"]
a2 = [1, 2, 3]
a3 = [1, 2]
tuples = [(a, b, [("C", "B")]), (a2, a2, [(1, 2), (2, 3)]), (a2, a3, [(1, 2)])]
for x, y, result in tuples:
self.assertEqual(interlacing_edges(x, y), result)
def test_interlacing_edges(self):
'''Test interlacing edge function for correctness.'''
a = [i for i in 'ABCDEFZ']
b = [i for i in 'AGCBIZ']
a2 = [1, 2, 3]
a3 = [1, 2]
tuples = [(a, b, [('C', 'B')]), (a2, a2, [(1, 2), (2, 3)]),
(a2, a3, [(1, 2)])]
for x, y, result in tuples:
self.assertEqual(interlacing_edges(x, y), result)
def grouped_shortest_pair(g, shortest_path, shortest_path_2):
'''Find shortest pair of paths of two interlacing original paths.
Last step of the optimal edge-disjoint and vertex-disjoint shortest-pair
algorithms.
@param g: original NetworkX Graph or DiGraph
@param shortest_path: list of path nodes
@param shortest_path_2: list of path nodes
@return path1: first non-interlacing shortest path
@return path2: second non-interlacing shortest path
'''
#shortest_path = int(shortest_path)
#shortest_path2 = int(shortest_path2)
src = shortest_path[0]
dst = shortest_path[-1]
assert src == shortest_path_2[0]
assert dst == shortest_path_2[-1]
g3 = nx.Graph()
g3.add_path(shortest_path)
g3.add_path(shortest_path_2)
# copy edges on path:
for a, b in edges_on_path(shortest_path):
g3[a][b]['weight'] = g[a][b]['weight']
g3.add_path(shortest_path_2)
for a, b in edges_on_path(shortest_path_2):
g3[a][b]['weight'] = g[a][b]['weight']
for a, b in interlacing_edges(shortest_path, shortest_path_2):
g3.remove_edge(a, b)
# Find a path through graph and remove edges used.
path1 = BFS(g3, src, dst)
for a, b in edges_on_path(path1):
g3.remove_edge(a, b)
path2 = BFS(g3, src, dst)
for a, b in edges_on_path(path2):
g3.remove_edge(a, b)
assert g3.number_of_edges() == 0
return path1, path2
def grouped_shortest_pair(g, shortest_path, shortest_path_2):
'''Find shortest pair of paths of two interlacing original paths.
Last step of the optimal edge-disjoint and vertex-disjoint shortest-pair
algorithms.
@param g: original NetworkX Graph or DiGraph
@param shortest_path: list of path nodes
@param shortest_path_2: list of path nodes
@return path1: first non-interlacing shortest path
@return path2: second non-interlacing shortest path
'''
#shortest_path = int(shortest_path)
#shortest_path2 = int(shortest_path2)
src = shortest_path[0]
dst = shortest_path[-1]
assert src == shortest_path_2[0]
assert dst == shortest_path_2[-1]
g3 = nx.Graph()
g3.add_path(shortest_path)
g3.add_path(shortest_path_2)
# copy edges on path:
for a, b in edges_on_path(shortest_path):
g3[a][b]['weight'] = g[a][b]['weight']
g3.add_path(shortest_path_2)
for a, b in edges_on_path(shortest_path_2):
g3[a][b]['weight'] = g[a][b]['weight']
for a, b in interlacing_edges(shortest_path, shortest_path_2):
g3.remove_edge(a, b)
# Find a path through graph and remove edges used.
path1 = BFS(g3, src, dst)
for a, b in edges_on_path(path1):
g3.remove_edge(a, b)
path2 = BFS(g3, src, dst)
for a, b in edges_on_path(path2):
g3.remove_edge(a, b)
assert g3.number_of_edges() == 0
return path1, path2