pm4py.vis.save_vis_ocdfg#

pm4py.vis.save_vis_ocdfg(ocdfg: Dict[str, Any], file_path: str, annotation: str = 'frequency', act_metric: str = 'events', edge_metric='event_couples', act_threshold: int = 0, edge_threshold: int = 0, performance_aggregation: str = 'mean', bgcolor: str = 'white', rankdir: str = 'LR', graph_title: str | None = None, variant_str: str = 'classic', **kwargs)[source]#

Saves the visualization of an OC-DFG.

Parameters:

  • ocdfg – Object-centric directly-follows graph

  • file_path (str) – Destination path

  • annotation (str) – “frequency” or “performance”

  • act_metric (str) – Metric for activities (“events”, “unique_objects”, “total_objects”)

  • edge_metric (str) – Metric for edges (“event_couples”, “unique_objects”, “total_objects”)

  • act_threshold (int) – Threshold on activities frequency

  • edge_threshold (int) – Threshold on edges frequency

  • performance_aggregation (str) – Aggregation measure for performance: mean, median, min, max, sum

  • bgcolor (str) – Background color (default: white)

  • rankdir (str) – Graph direction (“LR” or “TB”)

  • graph_title – Title of the visualization (if provided)

  • variant_str (str) – Variant (“classic” or “elkjs”)

import pm4py

ocdfg = pm4py.discover_ocdfg(ocel)
pm4py.save_vis_ocdfg(ocdfg, 'ocdfg.png', annotation='frequency')
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