pm4py.visualization.petri_net.variants package

Submodules

pm4py.visualization.petri_net.variants.alignments module

pm4py.visualization.petri_net.variants.alignments.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[source]

Apply method for Petri net visualization (it calls the graphviz_visualization method)

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

Returns

Graph object

Return type

viz

pm4py.visualization.petri_net.variants.greedy_decoration_frequency module

class pm4py.visualization.petri_net.variants.greedy_decoration_frequency.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
DEBUG = 'debug'
FONT_SIZE = 'font_size'
FORMAT = 'format'
RANKDIR = 'set_rankdir'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.visualization.petri_net.variants.greedy_decoration_frequency.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[source]

Apply frequency decoration through greedy algorithm (decorate Petri net based on DFG)

Parameters
  • net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • log – Log to use to decorate the Petri net

  • aggregated_statistics – Dictionary containing the frequency statistics

  • parameters – Algorithm parameters

Returns

GraphViz object

Return type

gviz

pm4py.visualization.petri_net.variants.greedy_decoration_frequency.get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant='frequency')[source]

Get a decorated net according to the specified variant (decorate Petri net based on DFG)

Parameters
  • net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • log – Log to use to decorate the Petri net

  • parameters – Algorithm parameters

  • variant – Specify if the decoration should take into account the frequency or the performance

Returns

GraphViz object

Return type

gviz

pm4py.visualization.petri_net.variants.greedy_decoration_performance module

class pm4py.visualization.petri_net.variants.greedy_decoration_performance.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
DEBUG = 'debug'
FONT_SIZE = 'font_size'
FORMAT = 'format'
RANKDIR = 'set_rankdir'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.visualization.petri_net.variants.greedy_decoration_performance.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[source]

Apply performance decoration through greedy algorithm (decorate Petri net based on DFG)

Parameters
  • net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • log – Log to use to decorate the Petri net

  • aggregated_statistics – Dictionary containing the frequency statistics

  • parameters – Algorithm parameters

Returns

GraphViz object

Return type

gviz

pm4py.visualization.petri_net.variants.greedy_decoration_performance.get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant='frequency')[source]

Get a decorated net according to the specified variant (decorate Petri net based on DFG)

Parameters
  • net – Petri net

  • initial_marking – Initial marking

  • final_marking – Final marking

  • log – Log to use to decorate the Petri net

  • parameters – Algorithm parameters

  • variant – Specify if the decoration should take into account the frequency or the performance

Returns

GraphViz object

Return type

gviz

pm4py.visualization.petri_net.variants.token_decoration_frequency module

class pm4py.visualization.petri_net.variants.token_decoration_frequency.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
DEBUG = 'debug'
FONT_SIZE = 'font_size'
FORMAT = 'format'
RANKDIR = 'set_rankdir'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.visualization.petri_net.variants.token_decoration_frequency.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[source]

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

Graph object

Return type

viz

pm4py.visualization.petri_net.variants.token_decoration_frequency.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 module

class pm4py.visualization.petri_net.variants.token_decoration_performance.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
DEBUG = 'debug'
FONT_SIZE = 'font_size'
FORMAT = 'format'
RANKDIR = 'set_rankdir'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.visualization.petri_net.variants.token_decoration_performance.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[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

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.wo_decoration module

class pm4py.visualization.petri_net.variants.wo_decoration.Parameters(value)[source]

Bases: enum.Enum

An enumeration.

ACTIVITY_KEY = 'pm4py:param:activity_key'
AGGREGATION_MEASURE = 'aggregationMeasure'
DEBUG = 'debug'
FONT_SIZE = 'font_size'
FORMAT = 'format'
RANKDIR = 'set_rankdir'
TIMESTAMP_KEY = 'pm4py:param:timestamp_key'
pm4py.visualization.petri_net.variants.wo_decoration.apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None)[source]

Apply method for Petri net visualization (it calls the graphviz_visualization method)

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

Returns

Graph object

Return type

viz

Module contents