'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.algo.simulation.playout.petri_net.variants import extensive
from pm4py.algo.simulation.playout.petri_net.variants import stochastic_playout, basic_playout
from pm4py.util import exec_utils
from enum import Enum
from pm4py.objects.petri_net.obj import PetriNet, Marking
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
[docs]class Variants(Enum):
BASIC_PLAYOUT = basic_playout
STOCHASTIC_PLAYOUT = stochastic_playout
EXTENSIVE = extensive
DEFAULT_VARIANT = Variants.BASIC_PLAYOUT
VERSIONS = {Variants.BASIC_PLAYOUT, Variants.EXTENSIVE, Variants.STOCHASTIC_PLAYOUT}
[docs]def apply(net: PetriNet, initial_marking: Marking, final_marking: Marking = None, parameters: Optional[Dict[Any, Any]] = None, variant=DEFAULT_VARIANT) -> EventLog:
"""
Do the playout of a Petrinet generating a log
Parameters
-----------
net
Petri net to play-out
initial_marking
Initial marking of the Petri net
final_marking
(if provided) Final marking of the Petri net
parameters
Parameters of the algorithm
variant
Variant of the algorithm to use:
- Variants.BASIC_PLAYOUT: selects random traces from the model, without looking at the
frequency of the transitions
- Variants.STOCHASTIC_PLAYOUT: selects random traces from the model, looking at the
stochastic frequency of the transitions. Requires the provision of the stochastic map
or the log.
- Variants.EXTENSIVE: gets all the traces from the model. can be expensive
"""
return exec_utils.get_variant(variant).apply(net, initial_marking, final_marking=final_marking,
parameters=parameters)