Source code for pm4py.objects.conversion.wf_net.variants.to_bpmn

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[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.Flow(entering_node, task)) bpmn_graph.add_flow(BPMN.Flow(task, exiting_node)) entering_dictio[trans] = entering_node exiting_dictio[trans] = exiting_node for arc in net.arcs: bpmn_graph.add_flow(BPMN.Flow(exiting_dictio[arc.source], entering_dictio[])) 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.Flow(start_node, entering_dictio[place])) for place in fm: bpmn_graph.add_flow(BPMN.Flow(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