'''
    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
'''
from enum import Enum
from typing import Optional, Dict, Any

from pm4py.objects.conversion.log import converter as log_converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.util import typing
from pm4py.util.business_hours import BusinessHours


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
    BUSINESS_HOURS = "business_hours"
    BUSINESS_HOUR_SLOTS = "business_hour_slots"
    WORKCALENDAR = "workcalendar"


def apply(
    log: EventLog, parameters: Optional[Dict[Any, Any]] = None
) -> typing.TemporalProfile:
    """
    Gets the temporal profile from the log.

    Implements the approach described in:
    Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. "Temporal Conformance Checking at Runtime based on Time-infused Process Models." arXiv preprint arXiv:2008.07262 (2020).

    Parameters
    ----------
    log
        Event log
    parameters
        Parameters, including:

        - Parameters.ACTIVITY_KEY => the attribute to use as activity
        - Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp
        - Parameters.TIMESTAMP_KEY => the attribute to use as timestamp
        - 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
    -------
    temporal_profile
        Temporal profile of the log
    """
    if parameters is None:
        parameters = {}

    from statistics import mean, stdev

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

    activity_key = exec_utils.get_param_value(
        Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY
    )
    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,
    )

    diff_time_recordings = {}

    for trace in log:
        for i in range(len(trace) - 1):
            act_i = trace[i][activity_key]
            time_i = trace[i][timestamp_key].timestamp()
            for j in range(i + 1, len(trace)):
                time_j = trace[j][start_timestamp_key].timestamp()
                if time_j >= time_i:
                    act_j = trace[j][activity_key]
                    if not (act_i, act_j) in diff_time_recordings:
                        diff_time_recordings[(act_i, act_j)] = []
                    if business_hours:
                        bh = BusinessHours(
                            trace[i][timestamp_key],
                            trace[j][start_timestamp_key],
                            business_hour_slots=business_hours_slots,
                        )
                        diff_time_recordings[(act_i, act_j)].append(
                            bh.get_seconds()
                        )
                    else:
                        diff_time_recordings[(act_i, act_j)].append(
                            time_j - time_i
                        )

    temporal_profile = {}
    for ac in diff_time_recordings:
        if len(diff_time_recordings[ac]) > 1:
            temporal_profile[ac] = (
                mean(diff_time_recordings[ac]),
                stdev(diff_time_recordings[ac]),
            )
        else:
            temporal_profile[ac] = (diff_time_recordings[ac][0], 0)

    return temporal_profile
