def detect_sequential_cut(self, conn_components, this_nx_graph,
strongly_connected_components):
"""
Detect sequential cut in DFG graph
Parameters
--------------
conn_components
Connected components of the graph
this_nx_graph
NX graph calculated on the DFG
strongly_connected_components
Strongly connected components
"""
if len(strongly_connected_components) > 1:
conn_matrix = detection_utils.get_connection_matrix(
strongly_connected_components, self.dfg)
comps = []
closed = set()
for i in range(conn_matrix.shape[0]):
if max(conn_matrix[i, :]) == 0:
if len(comps) == 0:
comps.append([])
comps[-1].append(i)
closed.add(i)
cyc_continue = len(comps) >= 1
while cyc_continue:
cyc_continue = False
curr_comp = []
for i in range(conn_matrix.shape[0]):
if i not in closed:
i_j = set()
for j in range(conn_matrix.shape[1]):
if conn_matrix[i][j] == 1.0:
i_j.add(j)
i_j_minus = i_j.difference(closed)
if len(i_j_minus) == 0:
curr_comp.append(i)
closed.add(i)
if curr_comp:
cyc_continue = True
comps.append(curr_comp)
last_cond = False
for i in range(conn_matrix.shape[0]):
if i not in closed:
if not last_cond:
last_cond = True
comps.append([])
comps[-1].append(i)
if len(comps) > 1:
comps = [
detection_utils.perform_list_union(
list(
set(strongly_connected_components[i])
for i in comp)) for comp in comps
]
return [True, comps]
return [False, [], []]
def detect_sequential_cut(subtree, dfg, strongly_connected_components):
"""
Detect sequential cut in DFG graph
Parameters
--------------
dfg
DFG
strongly_connected_components
Strongly connected components
"""
if subtree.contains_empty_trace():
return [False, [], []]
if len(strongly_connected_components) > 1:
conn_matrix = detection_utils.get_connection_matrix(
strongly_connected_components, dfg)
comps = []
closed = set()
for i in range(conn_matrix.shape[0]):
if max(conn_matrix[i, :]) == 0:
if len(comps) == 0:
comps.append([])
comps[-1].append(i)
closed.add(i)
cyc_continue = len(comps) >= 1
while cyc_continue:
cyc_continue = False
curr_comp = []
for i in range(conn_matrix.shape[0]):
if i not in closed:
i_j = set()
for j in range(conn_matrix.shape[1]):
if conn_matrix[i][j] == 1.0:
i_j.add(j)
i_j_minus = i_j.difference(closed)
if len(i_j_minus) == 0:
curr_comp.append(i)
closed.add(i)
if curr_comp:
cyc_continue = True
comps.append(curr_comp)
last_cond = False
for i in range(conn_matrix.shape[0]):
if i not in closed:
if not last_cond:
last_cond = True
comps.append([])
comps[-1].append(i)
if len(comps) > 1:
comps = [
detection_utils.perform_list_union(
list(set(strongly_connected_components[i]) for i in comp))
for comp in comps
]
# this part assures that the sequential cut follows completely the definition, i.e. there are no
# subsequent components with no edges between them (except when the detected cut is binary).
#
# Remind: the tree returned by IM is folded at the end, this ensures that the cut is still maximal
dfg = [x[0] for x in dfg]
i = 0
while i < len(comps) - 1:
outer_edges = [
d for d in dfg if d[0] in comps[i] and d[1] not in comps[i]
and d[1] not in comps[i + 1]
]
# a situation that is not managed by considering only the outer edges is the one
# that sees a chain of invisibles at the end. In this case, it can be detected that the end activity
# has no edge toward any of the following components.
end_activities_wo_exedge = set(
a for a in subtree.end_activities
if a in comps[i] and a not in [x[0] for x in dfg])
# before merging the connected components, make sure that at the end there are two (or more)
# connected components
if (outer_edges
or end_activities_wo_exedge) and len(comps) > 2:
comps[i] = comps[i].union(comps[i + 1])
del comps[i + 1]
continue
i = i + 1
return [True, comps]
return [False, [], []]