'''
    PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.

This program 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 Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.

Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
import pandas as pd
from enum import Enum

from pm4py.util import exec_utils, constants, xes_constants, pandas_utils
from pm4py.util.business_hours import soj_time_business_hours_diff
from typing import Optional, Dict, Any, Union


class Parameters(Enum):
    ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
    START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
    TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
    AGGREGATION_MEASURE = "aggregationMeasure"
    BUSINESS_HOURS = "business_hours"
    BUSINESS_HOUR_SLOTS = "business_hour_slots"
    WORKCALENDAR = "workcalendar"


DIFF_KEY = "@@diff"


def apply(
    dataframe: pd.DataFrame,
    parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> Dict[str, float]:
    """
    Gets the service time per activity on a Pandas dataframe.

    Parameters
    ----------
    dataframe
        Pandas dataframe
    parameters
        Parameters of the algorithm, including:

        - Parameters.ACTIVITY_KEY => activity key
        - Parameters.START_TIMESTAMP_KEY => start timestamp key
        - Parameters.TIMESTAMP_KEY => timestamp key
        - Parameters.AGGREGATION_MEASURE => performance aggregation measure (sum, min, max, mean, median)
        - Parameters.BUSINESS_HOURS => calculates the difference of time based on the business hours, not the total time. Default: False
        - Parameters.BUSINESS_HOURS_SLOTS => work schedule of the company.
          The following list defines that business hours are Mondays 07:00 - 17:00
          and Tuesdays 07:00 - 12:00 and 13:00 - 17:00::

            [
                (7 * 60 * 60, 17 * 60 * 60),
                ((24 + 7) * 60 * 60, (24 + 12) * 60 * 60),
                ((24 + 13) * 60 * 60, (24 + 17) * 60 * 60),
            ]

    Returns
    -----------------
    soj_time_dict
        Service time dictionary

    """
    if parameters is None:
        parameters = {}

    business_hours = exec_utils.get_param_value(
        Parameters.BUSINESS_HOURS, parameters, False
    )
    business_hours_slots = exec_utils.get_param_value(
        Parameters.BUSINESS_HOUR_SLOTS,
        parameters,
        constants.DEFAULT_BUSINESS_HOUR_SLOTS,
    )

    workcalendar = exec_utils.get_param_value(
        Parameters.WORKCALENDAR,
        parameters,
        constants.DEFAULT_BUSINESS_HOURS_WORKCALENDAR,
    )

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
    )
    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,
    )
    aggregation_measure = exec_utils.get_param_value(
        Parameters.AGGREGATION_MEASURE, parameters, "mean"
    )

    if business_hours:
        dataframe[DIFF_KEY] = dataframe.apply(
            lambda x: soj_time_business_hours_diff(
                x[start_timestamp_key],
                x[timestamp_key],
                business_hours_slots,
                workcalendar,
            ),
            axis=1,
        )
    else:
        dataframe[DIFF_KEY] = pandas_utils.get_total_seconds(
            dataframe[timestamp_key] - dataframe[start_timestamp_key]
        )

    dataframe = dataframe.reset_index()

    column = dataframe.groupby(activity_key)[DIFF_KEY]
    if aggregation_measure == "median":
        ret_dict = column.median().to_dict()
    elif aggregation_measure == "min":
        ret_dict = column.min().to_dict()
    elif aggregation_measure == "max":
        ret_dict = column.max().to_dict()
    elif aggregation_measure == "sum":
        ret_dict = column.sum().to_dict()
    else:
        ret_dict = column.mean().to_dict()

    # assure to avoid problems with np.float64, by using the Python float type
    for el in ret_dict:
        ret_dict[el] = float(ret_dict[el])

    return ret_dict
