pm4py.objects.conversion.wf_net.variants.to_process_tree module#

class pm4py.objects.conversion.wf_net.variants.to_process_tree.Parameters(*values)[source]#

Bases: Enum

DEBUG = 'debug'#
FOLD = 'fold'#
pm4py.objects.conversion.wf_net.variants.to_process_tree.generate_label_for_transition(t)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.generate_new_binary_transition(t1, t2, operator, net)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.loop_requirement(t1, t2)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.binary_loop_detection(net)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.concurrent_requirement(t1, t2)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.binary_concurrency_detection(net)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.choice_requirement(t1, t2)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.binary_choice_detection(net)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.sequence_requirement(t1, t2)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.binary_sequence_detection(net)[source]#
pm4py.objects.conversion.wf_net.variants.to_process_tree.group_blocks_in_net(net, parameters=None)[source]#

Groups the blocks in the Petri net

Parameters:

  • net – Petri net

  • parameters – Parameters of the algorithm

Returns:

Petri net (blocks are grouped according to the algorithm)

Return type:

grouped_net

pm4py.objects.conversion.wf_net.variants.to_process_tree.apply(net, im, fm, parameters=None)[source]#

Transforms a WF-net to a process tree

Parameters:

  • net – Petri net

  • im – Initial marking

  • fm – Final marking

Returns:

Process tree

Return type:

tree