'''
    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 pm4py.util.constants import STOCHASTIC_DISTRIBUTION


def apply(smap, parameters=None):
    """
    Embed the stochastic map into the Petri net

    Parameters
    ---------------
    smap
        Stochastic map
    parameters
        Possible parameters of the algorithm

    Returns
    ---------------
    void
    """
    if parameters is None:
        parameters = {}

    for t in smap:
        t.properties[STOCHASTIC_DISTRIBUTION] = smap[t]


def extract(net, parameters=None):
    """
    Extract the stochastic map from the Petri net

    Parameters
    --------------
    net
        Petri net
    parameters
        Possible parameters of the algorithm

    Returns
    --------------
    void
    """
    if parameters is None:
        parameters = {}

    smap = {}

    for t in net.transitions:
        smap[t] = t.properties[STOCHASTIC_DISTRIBUTION]

    return smap
