pm4py.visualization.ocel.ocdfg.variants.elkjs module#
- class pm4py.visualization.ocel.ocdfg.variants.elkjs.Parameters(*values)[source]#
Bases:
Enum- ENCODING = 'encoding'#
- IFRAME_WIDTH = 'iframe_width'#
- IFRAME_HEIGHT = 'iframe_height'#
- LOCAL_JUPYTER_FILE_NAME = 'local_jupyter_file_name'#
- PERFORMANCE_AGGREGATION_MEASURE = 'aggregationMeasure'#
- ANNOTATION = 'annotation'#
- ACT_METRIC = 'act_metric'#
- EDGE_METRIC = 'edge_metric'#
- pm4py.visualization.ocel.ocdfg.variants.elkjs.wrap_text(text: str, max_length: int = 15) str[source]#
- pm4py.visualization.ocel.ocdfg.variants.elkjs.apply(ocdfg: Dict[str, Any], parameters: Dict[Any, Any] | None = None) str[source]#
Visualizes an OC-DFG using ELK.JS
- Parameters:
ocdfg – OC-DFG
parameters – Parameters of the algorithm: - 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.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:
Visualization file
- Return type:
viz
- pm4py.visualization.ocel.ocdfg.variants.elkjs.view(temp_file_name, parameters=None)[source]#
View the SNA visualization on the screen
- Parameters:
temp_file_name – Temporary file name
parameters – Possible parameters of the algorithm
- pm4py.visualization.ocel.ocdfg.variants.elkjs.save(temp_file_name, dest_file, parameters=None)[source]#
Save the SNA visualization from a temporary file to a well-defined destination file
- Parameters:
temp_file_name – Temporary file name
dest_file – Destination file
parameters – Possible parameters of the algorithm