pm4py.visualization.ocel.ocdfg.variants.classic module#

PM4Py – A Process Mining Library for Python

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class pm4py.visualization.ocel.ocdfg.variants.classic.Parameters(*values)[source]#

Bases: Enum

FORMAT = 'format'#
BGCOLOR = 'bgcolor'#
RANKDIR = 'rankdir'#
ACT_METRIC = 'act_metric'#
EDGE_METRIC = 'edge_metric'#
ACT_THRESHOLD = 'act_threshold'#
EDGE_THRESHOLD = 'edge_threshold'#
ANNOTATION = 'annotation'#
PERFORMANCE_AGGREGATION_MEASURE = 'aggregationMeasure'#
ENABLE_GRAPH_TITLE = 'enable_graph_title'#
GRAPH_TITLE = 'graph_title'#
pm4py.visualization.ocel.ocdfg.variants.classic.ot_to_color(ot: str) → str[source]#
pm4py.visualization.ocel.ocdfg.variants.classic.add_activity(G: Digraph, act, freq, act_prefix, nodes, annotation, min_freq, max_freq)[source]#

Adds an activity node to the graph

pm4py.visualization.ocel.ocdfg.variants.classic.add_frequency_edge(G: Digraph, ot, act1, act2, freq, edge_prefix, nodes, min_freq, max_freq)[source]#

Adds a edge (frequency annotation)

pm4py.visualization.ocel.ocdfg.variants.classic.add_performance_edge(G: Digraph, ot, act1, act2, perf, edge_prefix, nodes, aggregation_measure)[source]#

Adds an edge (performance annotation)

pm4py.visualization.ocel.ocdfg.variants.classic.add_start_node(G: Digraph, ot, act, freq, edge_prefix, nodes, annotation, min_freq, max_freq)[source]#

Adds a start node to the graph

pm4py.visualization.ocel.ocdfg.variants.classic.add_end_node(G: Digraph, ot, act, freq, edge_prefix, nodes, annotation, min_freq, max_freq)[source]#

Adds an end node to the graph

pm4py.visualization.ocel.ocdfg.variants.classic.apply(ocdfg: Dict[str, Any], parameters: Dict[Any, Any] | None = None) → Digraph[source]#

Visualizes an OC-DFG as a Graphviz di-graph.

Parameters:

  • ocdfg – OC-DFG

  • parameters – Parameters of the algorithm:

    • Parameters.FORMAT => the format of the output visualization (default: “png”)

    • Parameters.BGCOLOR => the default background color (default: “bgcolor”)

    • Parameters.RANKDIR => direction of the graph (“LR” for left-to-right; “TB” for top-to-bottom)

    • Parameters.ACT_METRIC => the metric to use for the activities. Available values:

      • “events” => number of events (default)

      • “unique_objects” => number of unique objects

      • “total_objects” => number of total objects

    • Parameters.EDGE_METRIC => the metric to use for the edges. Available values:

      • “event_couples” => number of event couples (default)

      • “unique_objects” => number of unique objects

      • “total_objects” => number of total objects

    • Parameters.ACT_THRESHOLD => the threshold to apply on the activities frequency (default: 0). Only activities having a frequency >= than this are kept in the graph.

    • Parameters.EDGE_THRESHOLD => the threshold to apply on the edges frequency (default 0). Only edges having a frequency >= than this are kept in the graph.

    • Parameters.ANNOTATION => the annotation to use for the visualization. Values:

      • “frequency”: frequency annotation

      • “performance”: performance annotation

    • Parameters.PERFORMANCE_AGGREGATION_MEASURE => the aggregation measure to use for the performance:

      • mean

      • median

      • min

      • max

      • sum

Returns:

Graphviz DiGraph

Return type:

viz

pm4py.visualization.ocel.ocdfg.variants.classic.save(gviz: Digraph, output_file_path: str, parameters=None)[source]#

Save the diagram

Parameters:

  • gviz – GraphViz diagram

  • output_file_path – Path where the GraphViz output should be saved

pm4py.visualization.ocel.ocdfg.variants.classic.view(gviz: Digraph, parameters=None)[source]#

View the diagram

Parameters:

gviz – GraphViz diagram

pm4py.visualization.ocel.ocdfg.variants.classic.matplotlib_view(gviz: Digraph, parameters=None)[source]#

Views the diagram using Matplotlib

Parameters:

gviz – Graphviz