Source code for pm4py.statistics.overlap.cases.log.get

from enum import Enum
from typing import Dict, Optional, Any, List, Union

from pm4py.objects.log.obj import EventLog
from pm4py.statistics.overlap.utils import compute
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.objects.conversion.log import converter




class Parameters(Enum):
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY





def apply(
    log: EventLog,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> List[int]:
    """
    Computes the case overlap statistic from an interval event log

    Parameters
    -----------------
    log
        Interval event log
    parameters
        Parameters of the algorithm, including:
        - Parameters.TIMESTAMP_KEY => attribute representing the completion timestamp
        - Parameters.START_TIMESTAMP_KEY => attribute representing the start timestamp

    Returns
    ----------------
    case overlap
        List associating to each case the number of open cases during the life of a case
    """
    if parameters is None:
        parameters = {}

    log = converter.apply(
        log, variant=converter.Variants.TO_EVENT_LOG, parameters=parameters
    )

    timestamp_key = exec_utils.get_param_value(
        Parameters.TIMESTAMP_KEY,
        parameters,
        xes_constants.DEFAULT_TIMESTAMP_KEY,
    )
    start_timestamp_key = exec_utils.get_param_value(
        Parameters.START_TIMESTAMP_KEY,
        parameters,
        xes_constants.DEFAULT_TIMESTAMP_KEY,
    )

    points = []
    for trace in log:
        case_points = []
        for event in trace:
            case_points.append(
                (
                    event[start_timestamp_key].timestamp(),
                    event[timestamp_key].timestamp(),
                )
            )
        points.append(
            (min(x[0] for x in case_points), max(x[1] for x in case_points))
        )

    return compute.apply(points, parameters=parameters)