Source code for pm4py.simulation.montecarlo.simulator

'''
    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.simulation.montecarlo.variants import petri_semaph_fifo
from pm4py.simulation.montecarlo.outputs import Outputs
from pm4py.util import exec_utils
from enum import Enum
import deprecation
from pm4py.meta import VERSION
import warnings


[docs]class Variants(Enum): PETRI_SEMAPH_FIFO = petri_semaph_fifo
DEFAULT_VARIANT = Variants.PETRI_SEMAPH_FIFO VERSIONS = {Variants.PETRI_SEMAPH_FIFO}
[docs]@deprecation.deprecated(deprecated_in="2.2.5", removed_in="3.0", current_version=VERSION, details="Use the pm4py.algo.simulation.montecarlo package") def apply(log, net, im, fm, variant=DEFAULT_VARIANT, parameters=None): """ Performs a Monte Carlo simulation of an accepting Petri net without duplicate transitions and where the preset is always distinct from the postset Parameters ------------- log Event log net Accepting Petri net without duplicate transitions and where the preset is always distinct from the postset im Initial marking fm Final marking variant Variant of the algorithm to use: - Variants.PETRI_SEMAPH_FIFO parameters Parameters of the algorithm: Parameters.PARAM_NUM_SIMULATIONS => (default: 100) Parameters.PARAM_FORCE_DISTRIBUTION => Force a particular stochastic distribution (e.g. normal) when the stochastic map is discovered from the log (default: None; no distribution is forced) Parameters.PARAM_ENABLE_DIAGNOSTICS => Enable the printing of diagnostics (default: True) Parameters.PARAM_DIAGN_INTERVAL => Interval of time in which diagnostics of the simulation are printed (default: 32) Parameters.PARAM_CASE_ARRIVAL_RATIO => Case arrival of new cases (default: None; inferred from the log) Parameters.PARAM_PROVIDED_SMAP => Stochastic map that is used in the simulation (default: None; inferred from the log) Parameters.PARAM_MAP_RESOURCES_PER_PLACE => Specification of the number of resources available per place (default: None; each place gets the default number of resources) Parameters.PARAM_DEFAULT_NUM_RESOURCES_PER_PLACE => Default number of resources per place when not specified (default: 1; each place gets 1 resource and has to wait for the resource to finish) Parameters.PARAM_SMALL_SCALE_FACTOR => Scale factor for the sleeping time of the actual simulation (default: 864000.0, 10gg) Parameters.PARAM_MAX_THREAD_EXECUTION_TIME => Maximum execution time per thread (default: 60.0, 1 minute) Returns ------------ simulated_log Simulated event log simulation_result Result of the simulation: Outputs.OUTPUT_PLACES_INTERVAL_TREES => inteval trees that associate to each place the times in which it was occupied. Outputs.OUTPUT_TRANSITIONS_INTERVAL_TREES => interval trees that associate to each transition the intervals of time in which it could not fire because some token was in the output. Outputs.OUTPUT_CASES_EX_TIME => Throughput time of the cases included in the simulated log Outputs.OUTPUT_MEDIAN_CASES_EX_TIME => Median of the throughput times Outputs.OUTPUT_CASE_ARRIVAL_RATIO => Case arrival ratio that was specified in the simulation Outputs.OUTPUT_TOTAL_CASES_TIME => Total time occupied by cases of the simulated log """ warnings.warn("Use the pm4py.algo.simulation.montecarlo package") return exec_utils.get_variant(variant).apply(log, net, im, fm, parameters=parameters)