Source code for pm4py.visualization.petri_net.visualizer

'''
    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.objects.conversion.log import converter as log_conversion
from pm4py.visualization.common import gview
from pm4py.visualization.common import save as gsave
from pm4py.visualization.petri_net.variants import (
    wo_decoration,
    alignments,
    greedy_decoration_performance,
    greedy_decoration_frequency,
    token_decoration_performance,
    token_decoration_frequency,
)
from pm4py.util import exec_utils, pandas_utils
from enum import Enum
from pm4py.objects.petri_net.obj import PetriNet, Marking
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog, EventStream
import pandas as pd
from pm4py.objects.log.util import dataframe_utils
import graphviz




class Variants(Enum):
    WO_DECORATION = wo_decoration
    FREQUENCY = token_decoration_frequency
    PERFORMANCE = token_decoration_performance
    FREQUENCY_GREEDY = greedy_decoration_frequency
    PERFORMANCE_GREEDY = greedy_decoration_performance
    ALIGNMENTS = alignments



WO_DECORATION = Variants.WO_DECORATION
FREQUENCY_DECORATION = Variants.FREQUENCY
PERFORMANCE_DECORATION = Variants.PERFORMANCE
FREQUENCY_GREEDY = Variants.FREQUENCY_GREEDY
PERFORMANCE_GREEDY = Variants.PERFORMANCE_GREEDY
ALIGNMENTS = Variants.ALIGNMENTS




def apply(
    net: PetriNet,
    initial_marking: Marking = None,
    final_marking: Marking = None,
    log: Union[EventLog, EventStream, pd.DataFrame] = None,
    aggregated_statistics=None,
    parameters: Optional[Dict[Any, Any]] = None,
    variant=Variants.WO_DECORATION,
) -> graphviz.Digraph:
    if parameters is None:
        parameters = {}
    if log is not None:
        if pandas_utils.check_is_pandas_dataframe(log):
            log = dataframe_utils.convert_timestamp_columns_in_df(log)

        log = log_conversion.apply(
            log, parameters, log_conversion.TO_EVENT_LOG
        )
    return exec_utils.get_variant(variant).apply(
        net,
        initial_marking,
        final_marking,
        log=log,
        aggregated_statistics=aggregated_statistics,
        parameters=parameters,
    )





def save(gviz: graphviz.Digraph, output_file_path: str, parameters=None):
    """
    Save the diagram

    Parameters
    -----------
    gviz
        GraphViz diagram
    output_file_path
        Path where the GraphViz output should be saved
    """
    gsave.save(gviz, output_file_path, parameters=parameters)
    return ""





def view(gviz: graphviz.Digraph, parameters=None):
    """
    View the diagram

    Parameters
    -----------
    gviz
        GraphViz diagram
    """
    return gview.view(gviz, parameters=parameters)





def matplotlib_view(gviz: graphviz.Digraph, parameters=None):
    """
    Views the diagram using Matplotlib

    Parameters
    ---------------
    gviz
        Graphviz
    """

    return gview.matplotlib_view(gviz, parameters=parameters)