pm4py.objects.conversion.wf_net.variants package#

PM4Py – A Process Mining Library for Python

This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

Submodules#

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

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

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

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

PM4Py – A Process Mining Library for Python

You should have received a copy of the GNU Affero General Public License along with this program. If not, see this software project’s root or visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions Contact: info@processintelligence.solutions

class pm4py.objects.conversion.wf_net.variants.to_process_tree.Parameters(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)[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#

grouped_net: Petri net (blocks are grouped according to the algorithm)

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#

tree: Process tree