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'''
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.montecarlo.variants import petri_semaph_fifo
from pm4py.util import exec_utils
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
import pandas as pd
from pm4py.objects.conversion.log import converter as log_converter
class Variants(Enum):
PETRI_SEMAPH_FIFO = petri_semaph_fifo
DEFAULT_VARIANT = Variants.PETRI_SEMAPH_FIFO
VERSIONS = {Variants.PETRI_SEMAPH_FIFO}
def apply(log: Union[EventLog, pd.DataFrame], net: PetriNet, im: Marking, fm: Marking, variant=DEFAULT_VARIANT, parameters: Optional[Dict[Any, Any]] = None) -> Tuple[EventLog, Dict[str, Any]]:
"""
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
"""
log = log_converter.apply(log, variant=log_converter.Variants.TO_EVENT_LOG, parameters=parameters)
return exec_utils.get_variant(variant).apply(log, net, im, fm, parameters=parameters)
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