Source code for pm4py.algo.conformance.tokenreplay.algorithm

from pm4py.algo.conformance.tokenreplay.variants import token_replay, backwards
from enum import Enum
from pm4py.util import exec_utils
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.util import typing




class Variants(Enum):
    TOKEN_REPLAY = token_replay
    BACKWARDS = backwards



VERSIONS = {Variants.TOKEN_REPLAY, Variants.BACKWARDS}
DEFAULT_VARIANT = Variants.TOKEN_REPLAY




def apply(
    log: Union[EventLog, EventStream, pd.DataFrame],
    net: PetriNet,
    initial_marking: Marking,
    final_marking: Marking,
    parameters: Optional[Dict[Any, Any]] = None,
    variant=DEFAULT_VARIANT,
) -> typing.ListAlignments:
    """
    Method to apply token-based replay

    Parameters
    -----------
    log
        Log
    net
        Petri net
    initial_marking
        Initial marking
    final_marking
        Final marking
    parameters
        Parameters of the algorithm, including:
            Parameters.ACTIVITY_KEY -> Activity key
    variant
        Variant of the algorithm to use:
            - Variants.TOKEN_REPLAY
            - Variants.BACKWARDS
    """
    if parameters is None:
        parameters = {}
    return exec_utils.get_variant(variant).apply(
        log, net, initial_marking, final_marking, parameters=parameters
    )





def get_diagnostics_dataframe(
    log: Union[EventLog, EventStream, pd.DataFrame],
    tbr_output: typing.ListAlignments,
    variant=DEFAULT_VARIANT,
    parameters: Optional[Dict[Any, Any]] = None,
) -> pd.DataFrame:
    """
    Gets the results of token-based replay in a dataframe

    Parameters
    --------------
    log
        Event log
    tbr_output
        Output of the token-based replay technique
    variant
        Variant of the algorithm to use:
            - Variants.TOKEN_REPLAY
            - Variants.BACKWARDS

    Returns
    --------------
    dataframe
        Diagnostics dataframe
    """
    if parameters is None:
        parameters = {}

    return exec_utils.get_variant(variant).get_diagnostics_dataframe(
        log, tbr_output, parameters=parameters
    )