Source code for pm4py.sim

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__doc__ = """
The ``pm4py.sim`` module contains the simulation algorithms offered in ``pm4py``

from collections import Counter
from typing import Union, Tuple

from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.objects.process_tree.obj import ProcessTree

[docs]def play_out(*args: Union[Tuple[PetriNet, Marking, Marking], dict, Counter, ProcessTree], **kwargs) -> EventLog: """ Performs the playout of the provided model, i.e., gets a set of traces from the model. The function either takes a petri net, initial and final marking, or, a process tree as an input. :param args: model (Petri net with initial and final marking, or process tree) :param kwargs: optional parameters of the method, including: - parameters: dictionary containing the parameters of the playout, including: - smap: (if provided) stochastic map to be used to stochastically choose the transition - log: (if provided) EventLog to be used to compute the stochastic map, if smap not provided :rtype: ``EventLog`` .. code-block:: python3 import pm4py net, im, fm = pm4py.read_pnml('model.pnml') log = pm4py.play_out(net, im, fm) """ if len(args) == 3: from pm4py.objects.petri_net.obj import PetriNet if isinstance(args[0], PetriNet): from pm4py.objects.petri_net.obj import ResetNet, InhibitorNet from pm4py.algo.simulation.playout.petri_net import algorithm from pm4py.objects.petri_net.semantics import ClassicSemantics from pm4py.objects.petri_net.inhibitor_reset.semantics import InhibitorResetSemantics net = args[0] im = args[1] fm = args[2] parameters = kwargs["parameters"] if "parameters" in kwargs else None if parameters is None: parameters = {} variant = algorithm.Variants.BASIC_PLAYOUT # if the log, or the stochastic map of the transitions, is provided # use the stochastic playout in place of the basic playout # (that means, the relative weight of the transitions in a marking # will be considered during transition's picking) if "log" in parameters or "smap" in parameters: variant = algorithm.Variants.STOCHASTIC_PLAYOUT semantics = ClassicSemantics() if isinstance(net, ResetNet) or isinstance(net, InhibitorNet): semantics = InhibitorResetSemantics() parameters["petri_semantics"] = semantics return algorithm.apply(net, im, final_marking=fm, variant=variant, parameters=parameters) elif isinstance(args[0], dict): from pm4py.algo.simulation.playout.dfg import algorithm as dfg_playout return dfg_playout.apply(args[0], args[1], args[2], **kwargs) elif len(args) == 1: from pm4py.objects.process_tree.obj import ProcessTree if type(args[0]) is ProcessTree: from pm4py.algo.simulation.playout.process_tree import algorithm return algorithm.apply(args[0], **kwargs) raise Exception("unsupported model for playout")
[docs]def generate_process_tree(**kwargs) -> ProcessTree: """ Generates a process tree Reference paper: PTandLogGenerator: A Generator for Artificial Event Data :param kwargs: dictionary containing the parameters of the process tree generator algorithm :rtype: ``ProcessTree`` .. code-block:: python3 import pm4py process_tree = pm4py.generate_process_tree() """ from pm4py.algo.simulation.tree_generator import algorithm return algorithm.apply(**kwargs)