Source code for pm4py.visualization.petri_net.variants.alignments

'''
    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.visualization.petri_net.common import visualize
from pm4py.visualization.petri_net.util import alignments_decoration
from pm4py.objects.petri_net.obj import PetriNet, Marking
from typing import Optional, Dict, Any




def apply(
    net: PetriNet,
    initial_marking: Marking,
    final_marking: Marking,
    log=None,
    aggregated_statistics=None,
    parameters: Optional[Dict[Any, Any]] = None,
) -> str:
    """
    Apply method for Petri net visualization (it calls the
    graphviz_visualization method)

    Parameters
    -----------
    net
        Petri net
    initial_marking
        Initial marking
    final_marking
        Final marking
    log
        (Optional) log
    aggregated_statistics
        Dictionary containing the frequency statistics
    parameters
        Algorithm parameters

    Returns
    -----------
    viz
        Graph object
    """
    if aggregated_statistics is None and log is not None:
        aggregated_statistics = (
            alignments_decoration.get_alignments_decoration(
                net, initial_marking, final_marking, log=log
            )
        )

    return visualize.apply(
        net,
        initial_marking,
        final_marking,
        parameters=parameters,
        decorations=aggregated_statistics,
    )