def apply_embedding_greedy(bpmn_graph,
dfg,
activities_count,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Embed decoration information inside the BPMN graph
Parameters
-----------
bpmn_graph
BPMN graph
dfg
Directly-Follows graph
activities_count
Count of occurrences of the activities
log
Log object
aggregated_statistics
Aggregated statistics object
parameters
Possible parameters of the algorithm
Returns
-----------
bpmn_graph
Annotated BPMN graph
"""
del parameters
del log
del aggregated_statistics
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
spaths = vis_trans_shortest_paths.get_shortest_paths(net,
enable_extension=True)
aggregated_statistics = vis_trans_shortest_paths.get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
bpmn_graph = bpmn_embedding.embed_info_into_bpmn(bpmn_graph,
bpmn_aggreg_statistics,
"performance")
return bpmn_graph
def apply_embedding(bpmn_graph,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Embed decoration information inside the BPMN graph
Parameters
-----------
bpmn_graph
BPMN graph object
log
(Optional) log where the replay technique should be applied
aggregated_statistics
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters, of the algorithm
Returns
-----------
bpmn_graph
Annotated BPMN graph
"""
if parameters is None:
parameters = {}
net, initial_marking, final_marking, elements_correspondence, inv_elements_correspondence, el_corr_keys_map = \
bpmn_to_petri.apply(bpmn_graph)
if aggregated_statistics is None and log is not None:
aggregated_statistics = token_decoration.get_decorations(
log,
net,
initial_marking,
final_marking,
parameters=parameters,
measure="performance")
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
bpmn_graph = bpmn_embedding.embed_info_into_bpmn(
bpmn_graph, bpmn_aggreg_statistics, "performance")
return bpmn_graph