def filter_dfg_contain_activity(dfg0,
start_activities0,
end_activities0,
activities_count0,
activity,
parameters=None):
"""
Filters the DFG keeping only nodes that can reach / are reachable from activity
Parameters
---------------
dfg0
Directly-follows graph
start_activities0
Start activities
end_activities0
End activities
activities_count0
Activities count
activity
Activity that should be reachable / should reach all the nodes of the filtered graph
parameters
Parameters
Returns
---------------
dfg
Filtered DFG
start_activities
Filtered start activities
end_activities
Filtered end activities
activities_count
Filtered activities count
"""
if parameters is None:
parameters = {}
# since the dictionaries/sets are modified, a deepcopy is the best option to ensure data integrity
dfg = deepcopy(dfg0)
start_activities = deepcopy(start_activities0)
end_activities = deepcopy(end_activities0)
activities_count = deepcopy(activities_count0)
changed = True
while changed:
changed = False
predecessors = dfg_utils.get_predecessors(dfg, activities_count)
successors = dfg_utils.get_successors(dfg, activities_count)
predecessors_act = predecessors[activity].union({activity})
successors_act = successors[activity].union({activity})
start_activities1 = {
x: y
for x, y in start_activities.items() if x in predecessors_act
}
end_activities1 = {
x: y
for x, y in end_activities.items() if x in successors_act
}
if start_activities != start_activities1 or end_activities != end_activities1:
changed = True
start_activities = start_activities1
end_activities = end_activities1
reachable_nodes = predecessors_act.union(successors_act)
if reachable_nodes != set(activities_count.keys()):
changed = True
activities_count = {
x: y
for x, y in activities_count.items() if x in reachable_nodes
}
dfg = {
x: y
for x, y in dfg.items()
if x[0] in reachable_nodes and x[1] in reachable_nodes
}
return dfg, start_activities, end_activities, activities_count
def filter_dfg_from_activity(dfg0,
start_activities0,
end_activities0,
activities_count0,
source_activity,
parameters=None):
"""
Filters the DFG, making "source_activity" the only possible source activity of the graph
Parameters
---------------
dfg0
Directly-follows graph
start_activities0
Start activities
end_activities0
End activities
activities_count0
Activities count
source_activity
Source activity (only possible start activity after the filtering)
parameters
Parameters
Returns
---------------
dfg
Filtered DFG
start_activities
Filtered start activities
end_activities
Filtered end activities
activities_count
Filtered activities count
"""
if parameters is None:
parameters = {}
# since the dictionaries/sets are modified, a deepcopy is the best option to ensure data integrity
dfg = deepcopy(dfg0)
end_activities = deepcopy(end_activities0)
activities_count = deepcopy(activities_count0)
dfg = {x: y for x, y in dfg.items() if x[1] != source_activity}
start_activities = {source_activity: activities_count[source_activity]}
changed = True
while changed:
changed = False
predecessors = dfg_utils.get_predecessors(dfg, activities_count)
successors = dfg_utils.get_successors(dfg, activities_count)
predecessors_from_ea = set()
for ea in end_activities:
predecessors_from_ea = predecessors_from_ea.union(predecessors[ea])
predecessors_from_ea.add(ea)
reachable_nodes = predecessors_from_ea.intersection(
successors[source_activity]).union({source_activity})
if reachable_nodes != set(activities_count.keys()):
changed = True
activities_count = {
x: y
for x, y in activities_count.items() if x in reachable_nodes
}
end_activities = {
x: y
for x, y in end_activities.items() if x in reachable_nodes
}
dfg = {
x: y
for x, y in dfg.items()
if x[0] in reachable_nodes and x[1] in reachable_nodes
}
return dfg, start_activities, end_activities, activities_count