'''
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.petrinet.common import visualize
from pm4py.visualization.petrinet.util import performance_map
from pm4py.visualization.petrinet.parameters import Parameters
from pm4py.util import exec_utils, xes_constants
[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)
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)
return aggregated_statistics
[docs]def apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None):
"""
Apply method for Petri net visualization (it calls the graphviz_visualization
method) adding frequency 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="frequency")
return visualize.apply(net, initial_marking, final_marking, parameters=parameters,
decorations=aggregated_statistics)