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

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    PM4Py 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 General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.algo.conformance.tokenreplay import algorithm as token_replay
from pm4py.statistics.variants.log import get as variants_get
from pm4py.visualization.petri_net.common import visualize
from pm4py.visualization.petri_net.util import performance_map
from pm4py.util import exec_utils, xes_constants
from enum import Enum
from pm4py.util.constants import PARAMETER_CONSTANT_ACTIVITY_KEY, PARAMETER_CONSTANT_TIMESTAMP_KEY
from pm4py.objects.petri_net.obj import PetriNet, Marking
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
import graphviz


[docs]class Parameters(Enum): FORMAT = "format" DEBUG = "debug" RANKDIR = "set_rankdir" ACTIVITY_KEY = PARAMETER_CONSTANT_ACTIVITY_KEY TIMESTAMP_KEY = PARAMETER_CONSTANT_TIMESTAMP_KEY AGGREGATION_MEASURE = "aggregationMeasure" FONT_SIZE = "font_size" STAT_LOCALE = "stat_locale"
[docs]def get_decorations(log, net, initial_marking, final_marking, parameters=None, measure="frequency", ht_perf_method="last"): """ Calculate decorations in order to annotate the Petri net Parameters ----------- log Trace log net Petri net initial_marking Initial marking final_marking Final marking parameters Parameters associated to the algorithm measure Measure to represent on the process model (frequency/performance) ht_perf_method Method to use in order to annotate hidden transitions (performance value could be put on the last possible point (last) or in the first possible point (first) Returns ------------ decorations Decorations to put on the process model """ if parameters is None: parameters = {} aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters, None) 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) stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {}) variants_idx = variants_get.get_variants_from_log_trace_idx(log, parameters=parameters) variants = variants_get.convert_variants_trace_idx_to_trace_obj(log, variants_idx) parameters_tr = {token_replay.Variants.TOKEN_REPLAY.value.Parameters.ACTIVITY_KEY: activity_key, token_replay.Variants.TOKEN_REPLAY.value.Parameters.VARIANTS: variants} # do the replay aligned_traces = token_replay.apply(log, net, initial_marking, final_marking, parameters=parameters_tr) # apply petri_reduction technique in order to simplify the Petri net # net = reduction.apply(net, parameters={"aligned_traces": aligned_traces}) element_statistics = performance_map.single_element_statistics(log, net, initial_marking, aligned_traces, variants_idx, activity_key=activity_key, timestamp_key=timestamp_key, ht_perf_method=ht_perf_method) aggregated_statistics = performance_map.aggregate_statistics(element_statistics, measure=measure, aggregation_measure=aggregation_measure, stat_locale=stat_locale) return aggregated_statistics
[docs]def apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> graphviz.Digraph: """ Apply method for Petri net visualization (it calls the graphviz_visualization method) adding performance representation obtained by token replay 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 (including the activity key used during the replay, and the timestamp key) Returns ----------- viz Graph object """ if aggregated_statistics is None: if log is not None: aggregated_statistics = get_decorations(log, net, initial_marking, final_marking, parameters=parameters, measure="performance") return visualize.apply(net, initial_marking, final_marking, parameters=parameters, decorations=aggregated_statistics)