Source code for pm4py.visualization.petrinet.variants.token_decoration_frequency

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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)