pm4py.visualization.petri_net.variants.token_decoration_performance module#

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class pm4py.visualization.petri_net.variants.token_decoration_performance.Parameters(*values)[source]#

Bases: Enum

FORMAT = 'format'#
DEBUG = 'debug'#
RANKDIR = 'set_rankdir'#
ACTIVITY_KEY = 'pm4py:param:activity_key'#
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'#
AGGREGATION_MEASURE = 'aggregationMeasure'#
FONT_SIZE = 'font_size'#
STAT_LOCALE = 'stat_locale'#
pm4py.visualization.petri_net.variants.token_decoration_performance.get_decorations(log, net, initial_marking, final_marking, parameters=None, measure='frequency', ht_perf_method='last')[source]#

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 to put on the process model

Return type:

decorations

pm4py.visualization.petri_net.variants.token_decoration_performance.apply(net: PetriNet, initial_marking: Marking, final_marking: Marking, log: EventLog = None, aggregated_statistics=None, parameters: Dict[str | Parameters, Any] | None = None) → Digraph[source]#

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:

Graph object

Return type:

viz