Source code for pm4py.statistics.concurrent_activities.pandas.get

from enum import Enum

from pm4py.algo.discovery.dfg.adapters.pandas.df_statistics import (
    get_concurrent_events_dataframe,
)
from pm4py.util import exec_utils, constants, xes_constants
from typing import Optional, Dict, Any, Union, Tuple
import pandas as pd




class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    CASE_ID_KEY = constants.PARAMETER_CONSTANT_CASEID_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
    STRICT = "strict"





def apply(
    dataframe: pd.DataFrame,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[Tuple[str, str], int]:
    """
    Gets the number of times for which two activities have been concurrent in the log

    Parameters
    --------------
    dataframe
        Pandas dataframe
    parameters
        Parameters of the algorithm, including:
        - Parameters.ACTIVITY_KEY => activity key
        - Parameters.CASE_ID_KEY => case id
        - Parameters.START_TIMESTAMP_KEY => start timestamp
        - Parameters.TIMESTAMP_KEY => complete timestamp
        - Parameters.STRICT => Determine if only entries that are strictly concurrent
            (i.e. the length of the intersection as real interval is > 0) should be obtained. Default: False

    Returns
    --------------
    ret_dict
        Dictionaries associating to a couple of activities (tuple) the number of times for which they have been
        executed in parallel in the log
    """
    if parameters is None:
        parameters = {}

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
    )
    case_id_glue = exec_utils.get_param_value(
        Parameters.CASE_ID_KEY, parameters, constants.CASE_CONCEPT_NAME
    )
    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, None
    )
    strict = exec_utils.get_param_value(Parameters.STRICT, parameters, False)

    concurrent_dataframe = get_concurrent_events_dataframe(
        dataframe,
        start_timestamp_key=start_timestamp_key,
        timestamp_key=timestamp_key,
        case_id_glue=case_id_glue,
        activity_key=activity_key,
        strict=strict,
    )

    ret_dict0 = (
        concurrent_dataframe.groupby([activity_key, activity_key + "_2"])
        .size()
        .to_dict()
    )
    ret_dict = {}

    # assure to avoid problems with np.float64, by using the Python float type
    for el in ret_dict0:
        # avoid getting two entries for the same set of concurrent activities
        el2 = tuple(sorted(el))
        ret_dict[el2] = int(ret_dict0[el])

    return ret_dict