def test_triangles():
paths = [
(11, 12, 13, 11), # first 3-clique
(21, 22, 23, 21), # second 3-clique
(11, 21), # connected by an edge
]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
# subgraph and ccs are the same in all cases here
assert_equal(
fset(nx.k_edge_components(G, k=1)),
fset(nx.k_edge_subgraphs(G, k=1))
)
assert_equal(
fset(nx.k_edge_components(G, k=2)),
fset(nx.k_edge_subgraphs(G, k=2))
)
assert_equal(
fset(nx.k_edge_components(G, k=3)),
fset(nx.k_edge_subgraphs(G, k=3))
)
_check_edge_connectivity(G)
def test_five_clique():
# Make a graph that can be disconnected less than 4 edges, but no node has
# degree less than 4.
G = nx.disjoint_union(nx.complete_graph(5), nx.complete_graph(5))
paths = [
# add aux-connections
(1, 100, 6),
(2, 100, 7),
(3, 200, 8),
(4, 200, 100),
]
G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
assert min(dict(nx.degree(G)).values()) == 4
# For k=3 they are the same
assert fset(nx.k_edge_components(G,
k=3)) == fset(nx.k_edge_subgraphs(G, k=3))
# For k=4 they are the different
# the aux nodes are in the same CC as clique 1 but no the same subgraph
assert fset(nx.k_edge_components(G, k=4)) != fset(
nx.k_edge_subgraphs(G, k=4))
# For k=5 they are not the same
assert fset(nx.k_edge_components(G, k=5)) != fset(
nx.k_edge_subgraphs(G, k=5))
# For k=6 they are the same
assert fset(nx.k_edge_components(G,
k=6)) == fset(nx.k_edge_subgraphs(G, k=6))
_check_edge_connectivity(G)
def test_local_subgraph_difference_directed():
dipaths = [
(1, 2, 3, 4, 1),
(1, 3, 1),
]
G = nx.DiGraph(it.chain(*[pairwise(path) for path in dipaths]))
assert_equal(
fset(nx.k_edge_components(G, k=1)),
fset(nx.k_edge_subgraphs(G, k=1))
)
# Unlike undirected graphs, when k=2, for directed graphs there is a case
# where the k-edge-ccs are not the same as the k-edge-subgraphs.
# (in directed graphs ccs and subgraphs are the same when k=2)
assert_not_equal(
fset(nx.k_edge_components(G, k=2)),
fset(nx.k_edge_subgraphs(G, k=2))
)
assert_equal(
fset(nx.k_edge_components(G, k=3)),
fset(nx.k_edge_subgraphs(G, k=3))
)
_check_edge_connectivity(G)
def test_triangles():
paths = [
(11, 12, 13, 11), # first 3-clique
(21, 22, 23, 21), # second 3-clique
(11, 21), # connected by an edge
]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
# subgraph and ccs are the same in all cases here
assert_equal(
fset(nx.k_edge_components(G, k=1)),
fset(nx.k_edge_subgraphs(G, k=1))
)
assert_equal(
fset(nx.k_edge_components(G, k=2)),
fset(nx.k_edge_subgraphs(G, k=2))
)
assert_equal(
fset(nx.k_edge_components(G, k=3)),
fset(nx.k_edge_subgraphs(G, k=3))
)
_check_edge_connectivity(G)
def test_undirected_aux_graph():
# Graph similar to the one in
# http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
a, b, c, d, e, f, g, h, i = 'abcdefghi'
paths = [(a, d, b, f, c), (a, e, b), (a, e, b, c, g, b, a), (c, b),
(f, g, f), (h, i)]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
aux_graph = EdgeComponentAuxGraph.construct(G)
components_1 = fset(aux_graph.k_edge_subgraphs(k=1))
target_1 = fset([{a, b, c, d, e, f, g}, {h, i}])
assert_equal(target_1, components_1)
# Check that the undirected case for k=1 agrees with CCs
alt_1 = fset(nx.k_edge_subgraphs(G, k=1))
assert_equal(alt_1, components_1)
components_2 = fset(aux_graph.k_edge_subgraphs(k=2))
target_2 = fset([{a, b, c, d, e, f, g}, {h}, {i}])
assert_equal(target_2, components_2)
# Check that the undirected case for k=2 agrees with bridge components
alt_2 = fset(nx.k_edge_subgraphs(G, k=2))
assert_equal(alt_2, components_2)
components_3 = fset(aux_graph.k_edge_subgraphs(k=3))
target_3 = fset([{a}, {b, c, f, g}, {d}, {e}, {h}, {i}])
assert_equal(target_3, components_3)
components_4 = fset(aux_graph.k_edge_subgraphs(k=4))
target_4 = fset([{a}, {b}, {c}, {d}, {e}, {f}, {g}, {h}, {i}])
assert_equal(target_4, components_4)
_check_edge_connectivity(G)
def test_local_subgraph_difference_directed():
dipaths = [
(1, 2, 3, 4, 1),
(1, 3, 1),
]
G = nx.DiGraph(it.chain(*[pairwise(path) for path in dipaths]))
assert_equal(
fset(nx.k_edge_components(G, k=1)),
fset(nx.k_edge_subgraphs(G, k=1))
)
# Unlike undirected graphs, when k=2, for directed graphs there is a case
# where the k-edge-ccs are not the same as the k-edge-subgraphs.
# (in directed graphs ccs and subgraphs are the same when k=2)
assert_not_equal(
fset(nx.k_edge_components(G, k=2)),
fset(nx.k_edge_subgraphs(G, k=2))
)
assert_equal(
fset(nx.k_edge_components(G, k=3)),
fset(nx.k_edge_subgraphs(G, k=3))
)
_check_edge_connectivity(G)
def test_empty_input():
G = nx.Graph()
assert [] == list(nx.k_edge_components(G, k=5))
assert [] == list(nx.k_edge_subgraphs(G, k=5))
G = nx.DiGraph()
assert [] == list(nx.k_edge_components(G, k=5))
assert [] == list(nx.k_edge_subgraphs(G, k=5))
def test_empty_input():
G = nx.Graph()
assert_equal([], list(nx.k_edge_components(G, k=5)))
assert_equal([], list(nx.k_edge_subgraphs(G, k=5)))
G = nx.DiGraph()
assert_equal([], list(nx.k_edge_components(G, k=5)))
assert_equal([], list(nx.k_edge_subgraphs(G, k=5)))
def combine_connected_subclusters(mcl_subclusters, mcl_groups_to_combine):
""" Creates a graph to identify all connected subclusters with each other and returns the nested array of clusters with their respective subclusters. """
mcl_cluster_connection_graph = nx.Graph()
mcl_cluster_connection_graph.add_edges_from(mcl_groups_to_combine)
connected_subclusters = []
for new_cluster in nx.k_edge_subgraphs(mcl_cluster_connection_graph, k=1):
connected_subclusters.append(
[mcl_subclusters[a]["mcl_subcluster"] for a in new_cluster])
assert len(list(nx.k_edge_subgraphs(
mcl_cluster_connection_graph,
k=1))) == len(connected_subclusters), "Combining went wrong"
return connected_subclusters
def test_four_clique():
paths = [
(11, 12, 13, 14, 11, 13, 14, 12), # first 4-clique
(21, 22, 23, 24, 21, 23, 24, 22), # second 4-clique
# paths connecting the 4 cliques such that they are
# 3-connected in G, but not in the subgraph.
# Case where the nodes bridging them do not have degree less than 3.
(100, 13),
(12, 100, 22),
(13, 200, 23),
(14, 300, 24),
]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
# The subgraphs and ccs are different for k=3
local_ccs = fset(nx.k_edge_components(G, k=3))
subgraphs = fset(nx.k_edge_subgraphs(G, k=3))
assert local_ccs != subgraphs
# The cliques ares in the same cc
clique1 = frozenset(paths[0])
clique2 = frozenset(paths[1])
assert clique1.union(clique2).union({100}) in local_ccs
# but different subgraphs
assert clique1 in subgraphs
assert clique2 in subgraphs
assert G.degree(100) == 3
_check_edge_connectivity(G)
def test_four_clique():
paths = [
(11, 12, 13, 14, 11, 13, 14, 12), # first 4-clique
(21, 22, 23, 24, 21, 23, 24, 22), # second 4-clique
# paths connecting the 4 cliques such that they are
# 3-connected in G, but not in the subgraph.
# Case where the nodes bridging them do not have degree less than 3.
(100, 13),
(12, 100, 22),
(13, 200, 23),
(14, 300, 24),
]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
# The subgraphs and ccs are different for k=3
local_ccs = fset(nx.k_edge_components(G, k=3))
subgraphs = fset(nx.k_edge_subgraphs(G, k=3))
assert_not_equal(local_ccs, subgraphs)
# The cliques ares in the same cc
clique1 = frozenset(paths[0])
clique2 = frozenset(paths[1])
assert_in(clique1.union(clique2).union({100}), local_ccs)
# but different subgraphs
assert_in(clique1, subgraphs)
assert_in(clique2, subgraphs)
assert_equal(G.degree(100), 3)
_check_edge_connectivity(G)
def test_undirected_aux_graph():
# Graph similar to the one in
# http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0136264
a, b, c, d, e, f, g, h, i = 'abcdefghi'
paths = [
(a, d, b, f, c),
(a, e, b),
(a, e, b, c, g, b, a),
(c, b),
(f, g, f),
(h, i)
]
G = nx.Graph(it.chain(*[pairwise(path) for path in paths]))
aux_graph = EdgeComponentAuxGraph.construct(G)
components_1 = fset(aux_graph.k_edge_subgraphs(k=1))
target_1 = fset([{a, b, c, d, e, f, g}, {h, i}])
assert_equal(target_1, components_1)
# Check that the undirected case for k=1 agrees with CCs
alt_1 = fset(nx.k_edge_subgraphs(G, k=1))
assert_equal(alt_1, components_1)
components_2 = fset(aux_graph.k_edge_subgraphs(k=2))
target_2 = fset([{a, b, c, d, e, f, g}, {h}, {i}])
assert_equal(target_2, components_2)
# Check that the undirected case for k=2 agrees with bridge components
alt_2 = fset(nx.k_edge_subgraphs(G, k=2))
assert_equal(alt_2, components_2)
components_3 = fset(aux_graph.k_edge_subgraphs(k=3))
target_3 = fset([{a}, {b, c, f, g}, {d}, {e}, {h}, {i}])
assert_equal(target_3, components_3)
components_4 = fset(aux_graph.k_edge_subgraphs(k=4))
target_4 = fset([{a}, {b}, {c}, {d}, {e}, {f}, {g}, {h}, {i}])
assert_equal(target_4, components_4)
_check_edge_connectivity(G)
def test_five_clique():
# Make a graph that can be disconnected less than 4 edges, but no node has
# degree less than 4.
G = nx.disjoint_union(nx.complete_graph(5), nx.complete_graph(5))
paths = [
# add aux-connections
(1, 100, 6), (2, 100, 7), (3, 200, 8), (4, 200, 100),
]
G.add_edges_from(it.chain(*[pairwise(path) for path in paths]))
assert_equal(min(dict(nx.degree(G)).values()), 4)
# For k=3 they are the same
assert_equal(
fset(nx.k_edge_components(G, k=3)),
fset(nx.k_edge_subgraphs(G, k=3))
)
# For k=4 they are the different
# the aux nodes are in the same CC as clique 1 but no the same subgraph
assert_not_equal(
fset(nx.k_edge_components(G, k=4)),
fset(nx.k_edge_subgraphs(G, k=4))
)
# For k=5 they are not the same
assert_not_equal(
fset(nx.k_edge_components(G, k=5)),
fset(nx.k_edge_subgraphs(G, k=5))
)
# For k=6 they are the same
assert_equal(
fset(nx.k_edge_components(G, k=6)),
fset(nx.k_edge_subgraphs(G, k=6))
)
_check_edge_connectivity(G)
def clusterDetection(self, customParams):
"""
Group the variants based on a given threshold; to do so, edges with a weight above the threshold are deleted from the given graph respresenting the optimal mappings
:param customParams: custom parameter object containing the threshold the algorithm should use
:return: list of subgraphs where each subgraph represents a cluster of variants
"""
horizontalTreshold = customParams.getHorizontalThreshold()
#filteredEdges = [(u, v) for (u, v, d) in self.__G.edges(data=True) if (d['weight'] > horizontalTreshold and d['weight'] != -1)]
filteredEdges = [
(u, v) for (u, v, d) in self.__G.edges(data=True)
if (d['weight'] > horizontalTreshold and d['weight'] != -1
or d['weight'] == -42)
]
self.__G.remove_edges_from(filteredEdges)
return [
nx.Graph(self.__G.subgraph(c))
for c in nx.k_edge_subgraphs(self.__G, k=1)
] #or also use nx.connected_components(G)
def part2(grid):
graph = nx.Graph()
# Build graph
for x in range(128):
for y in range(128):
if grid[x][y]:
graph.add_node((x, y))
# Iterate over graph and add edges between adjacent nodes
for x in range(128):
for y in range(128):
if not graph.has_node((x, y)):
continue
up = (x, y - 1)
down = (x, y + 1)
left = (x - 1, y)
right = (x + 1, y)
if graph.has_node(up):
graph.add_edge((x, y), up)
if graph.has_node(down):
graph.add_edge((x, y), down)
if graph.has_node(left):
graph.add_edge((x, y), left)
if graph.has_node(right):
graph.add_edge((x, y), right)
# Find the islands
subgraphs = list(nx.k_edge_subgraphs(graph, 1))
return len(subgraphs)
def partial_k_edge_augmentation(G, k, avail, weight=None):
"""Finds augmentation that k-edge-connects as much of the graph as possible.
When a k-edge-augmentation is not possible, we can still try to find a
small set of edges that partially k-edge-connects as much of the graph as
possible. All possible edges are generated between remaining parts.
This minimizes the number of k-edge-connected subgraphs in the resulting
graph and maxmizes the edge connectivity between those subgraphs.
Parameters
----------
G : NetworkX graph
An undirected graph.
k : integer
Desired edge connectivity
avail : dict or a set of 2 or 3 tuples
For more details, see :func:`k_edge_augmentation`.
weight : string
key to use to find weights if ``avail`` is a set of 3-tuples.
For more details, see :func:`k_edge_augmentation`.
Yields
------
edge : tuple
Edges in the partial augmentation of G. These edges k-edge-connect any
part of G where it is possible, and maximally connects the remaining
parts. In other words, all edges from avail are generated except for
those within subgraphs that have already become k-edge-connected.
Notes
-----
Construct H that augments G with all edges in avail.
Find the k-edge-subgraphs of H.
For each k-edge-subgraph, if the number of nodes is more than k, then find
the k-edge-augmentation of that graph and add it to the solution. Then add
all edges in avail between k-edge subgraphs to the solution.
See Also
--------
:func:`k_edge_augmentation`
Example
-------
>>> G = nx.path_graph((1, 2, 3, 4, 5, 6, 7))
>>> G.add_node(8)
>>> avail = [(1, 3), (1, 4), (1, 5), (2, 4), (2, 5), (3, 5), (1, 8)]
>>> sorted(partial_k_edge_augmentation(G, k=2, avail=avail))
[(1, 5), (1, 8)]
"""
def _edges_between_disjoint(H, only1, only2):
""" finds edges between disjoint nodes """
only1_adj = {u: set(H.adj[u]) for u in only1}
for u, neighbs in only1_adj.items():
# Find the neighbors of u in only1 that are also in only2
neighbs12 = neighbs.intersection(only2)
for v in neighbs12:
yield (u, v)
avail_uv, avail_w = _unpack_available_edges(avail, weight=weight, G=G)
# Find which parts of the graph can be k-edge-connected
H = G.copy()
H.add_edges_from(((u, v, {
'weight': w,
'generator': (u, v)
}) for (u, v), w in zip(avail, avail_w)))
k_edge_subgraphs = list(nx.k_edge_subgraphs(H, k=k))
# Generate edges to k-edge-connect internal subgraphs
for nodes in k_edge_subgraphs:
if len(nodes) > 1:
# Get the k-edge-connected subgraph
C = H.subgraph(nodes).copy()
# Find the internal edges that were available
sub_avail = {
d['generator']: d['weight']
for (u, v, d) in C.edges(data=True) if 'generator' in d
}
# Remove potential augmenting edges
C.remove_edges_from(sub_avail.keys())
# Find a subset of these edges that makes the compoment
# k-edge-connected and ignore the rest
for edge in nx.k_edge_augmentation(C, k=k, avail=sub_avail):
yield edge
# Generate all edges between CCs that could not be k-edge-connected
for cc1, cc2 in it.combinations(k_edge_subgraphs, 2):
for (u, v) in _edges_between_disjoint(H, cc1, cc2):
d = H.get_edge_data(u, v)
edge = d.get('generator', None)
if edge is not None:
yield edge
def partial_k_edge_augmentation(G, k, avail, weight=None):
"""Finds augmentation that k-edge-connects as much of the graph as possible.
When a k-edge-augmentation is not possible, we can still try to find a
small set of edges that partially k-edge-connects as much of the graph as
possible. All possible edges are generated between remaining parts.
This minimizes the number of k-edge-connected subgraphs in the resulting
graph and maxmizes the edge connectivity between those subgraphs.
Parameters
----------
G : NetworkX graph
An undirected graph.
k : integer
Desired edge connectivity
avail : dict or a set of 2 or 3 tuples
For more details, see :func:`k_edge_augmentation`.
weight : string
key to use to find weights if ``avail`` is a set of 3-tuples.
For more details, see :func:`k_edge_augmentation`.
Yields
------
edge : tuple
Edges in the partial augmentation of G. These edges k-edge-connect any
part of G where it is possible, and maximally connects the remaining
parts. In other words, all edges from avail are generated except for
those within subgraphs that have already become k-edge-connected.
Notes
-----
Construct H that augments G with all edges in avail.
Find the k-edge-subgraphs of H.
For each k-edge-subgraph, if the number of nodes is more than k, then find
the k-edge-augmentation of that graph and add it to the solution. Then add
all edges in avail between k-edge subgraphs to the solution.
See Also
--------
:func:`k_edge_augmentation`
Example
-------
>>> G = nx.path_graph((1, 2, 3, 4, 5, 6, 7))
>>> G.add_node(8)
>>> avail = [(1, 3), (1, 4), (1, 5), (2, 4), (2, 5), (3, 5), (1, 8)]
>>> sorted(partial_k_edge_augmentation(G, k=2, avail=avail))
[(1, 5), (1, 8)]
"""
def _edges_between_disjoint(H, only1, only2):
""" finds edges between disjoint nodes """
only1_adj = {u: set(H.adj[u]) for u in only1}
for u, neighbs in only1_adj.items():
# Find the neighbors of u in only1 that are also in only2
neighbs12 = neighbs.intersection(only2)
for v in neighbs12:
yield (u, v)
avail_uv, avail_w = _unpack_available_edges(avail, weight=weight, G=G)
# Find which parts of the graph can be k-edge-connected
H = G.copy()
H.add_edges_from(
((u, v, {'weight': w, 'generator': (u, v)})
for (u, v), w in zip(avail, avail_w)))
k_edge_subgraphs = list(nx.k_edge_subgraphs(H, k=k))
# Generate edges to k-edge-connect internal subgraphs
for nodes in k_edge_subgraphs:
if len(nodes) > 1:
# Get the k-edge-connected subgraph
C = H.subgraph(nodes).copy()
# Find the internal edges that were available
sub_avail = {
d['generator']: d['weight']
for (u, v, d) in C.edges(data=True)
if 'generator' in d
}
# Remove potential augmenting edges
C.remove_edges_from(sub_avail.keys())
# Find a subset of these edges that makes the compoment
# k-edge-connected and ignore the rest
for edge in nx.k_edge_augmentation(C, k=k, avail=sub_avail):
yield edge
# Generate all edges between CCs that could not be k-edge-connected
for cc1, cc2 in it.combinations(k_edge_subgraphs, 2):
for (u, v) in _edges_between_disjoint(H, cc1, cc2):
d = H.get_edge_data(u, v)
edge = d.get('generator', None)
if edge is not None:
yield edge
def part2(graph):
subgraphs = list(nx.k_edge_subgraphs(graph, 1))
return len(subgraphs)