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

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

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

[docs]def apply(log: Union[EventLog, EventStream], parameters: Optional[Dict[str, Any]] = None) -> List[int]: """ Counts the intersections of each interval event with the other interval events of the log (all the events are considered, not looking at the activity) Parameters ---------------- log Event log parameters Parameters of the algorithm, including: - Parameters.START_TIMESTAMP_KEY => the attribute to consider as start timestamp - Parameters.TIMESTAMP_KEY => the attribute to consider as timestamp Returns ----------------- overlap For each interval event, ordered by the order of appearance in the log, associates the number of intersecting events. """ if parameters is None: parameters = {} log = log_converter.apply(log, parameters=parameters) start_timestamp_key = exec_utils.get_param_value(Parameters.START_TIMESTAMP_KEY, parameters, xes_constants.DEFAULT_TIMESTAMP_KEY) timestamp_key = exec_utils.get_param_value(Parameters.TIMESTAMP_KEY, parameters, xes_constants.DEFAULT_TIMESTAMP_KEY) points = [] for trace in log: for event in trace: points.append((event[start_timestamp_key].timestamp(), event[timestamp_key].timestamp())) return compute.apply(points, parameters=parameters)