Source code for pm4py.objects.conversion.wf_net.variants.to_bpmn
[docs]
def apply(net, im, fm, parameters=None):
"""
Converts an accepting Petri net into a BPMN diagram
Parameters
--------------
accepting_petri_net
Accepting Petri net (list containing net + im + fm)
parameters
Parameters of the algorithm
Returns
--------------
bpmn_graph
BPMN diagram
"""
if parameters is None:
parameters = {}
from pm4py.objects.bpmn.obj import BPMN
from pm4py.objects.bpmn.util import reduction
bpmn_graph = BPMN()
entering_dictio = {}
exiting_dictio = {}
for place in net.places:
node = BPMN.ExclusiveGateway()
bpmn_graph.add_node(node)
entering_dictio[place] = node
exiting_dictio[place] = node
for trans in net.transitions:
if trans.label is None:
if len(trans.in_arcs) > 1:
node = BPMN.ParallelGateway(
gateway_direction=BPMN.Gateway.Direction.CONVERGING
)
elif len(trans.out_arcs) > 1:
node = BPMN.ParallelGateway(
gateway_direction=BPMN.Gateway.Direction.DIVERGING
)
else:
node = BPMN.ExclusiveGateway(
gateway_direction=BPMN.Gateway.Direction.UNSPECIFIED
)
bpmn_graph.add_node(node)
entering_dictio[trans] = node
exiting_dictio[trans] = node
else:
if len(trans.in_arcs) > 1:
entering_node = BPMN.ParallelGateway(
gateway_direction=BPMN.Gateway.Direction.CONVERGING
)
else:
entering_node = BPMN.ExclusiveGateway(
gateway_direction=BPMN.Gateway.Direction.UNSPECIFIED
)
if len(trans.out_arcs) > 1:
exiting_node = BPMN.ParallelGateway(
gateway_direction=BPMN.Gateway.Direction.DIVERGING
)
else:
exiting_node = BPMN.ExclusiveGateway(
gateway_direction=BPMN.Gateway.Direction.UNSPECIFIED
)
task = BPMN.Task(name=trans.label)
bpmn_graph.add_node(task)
bpmn_graph.add_flow(BPMN.SequenceFlow(entering_node, task))
bpmn_graph.add_flow(BPMN.SequenceFlow(task, exiting_node))
entering_dictio[trans] = entering_node
exiting_dictio[trans] = exiting_node
for arc in net.arcs:
bpmn_graph.add_flow(
BPMN.SequenceFlow(
exiting_dictio[arc.source], entering_dictio[arc.target]
)
)
start_node = BPMN.StartEvent(name="start", isInterrupting=True)
end_node = BPMN.NormalEndEvent(name="end")
bpmn_graph.add_node(start_node)
bpmn_graph.add_node(end_node)
for place in im:
bpmn_graph.add_flow(
BPMN.SequenceFlow(start_node, entering_dictio[place])
)
for place in fm:
bpmn_graph.add_flow(BPMN.SequenceFlow(exiting_dictio[place], end_node))
bpmn_graph = reduction.apply(bpmn_graph)
for node in bpmn_graph.get_nodes():
node.set_process(bpmn_graph.get_process_id())
for edge in bpmn_graph.get_flows():
edge.set_process(bpmn_graph.get_process_id())
return bpmn_graph