def dummy():
"""
Dummy method
"""
bpmn_graph = None
image_format = None
bpmn_figure = None
path = None
bpmn_diagram_to_figure(bpmn_graph, image_format)
view(bpmn_figure)
save(bpmn_figure, path)
def apply(bpmn_graph, parameters=None, bpmn_aggreg_statistics=None):
"""
Visualize a BPMN graph from a BPMN graph using the given parameters
Parameters
-----------
bpmn_graph
BPMN graph object
bpmn_aggreg_statistics
Element-wise statistics that should be represented on the BPMN graph
parameters
Possible parameters, of the algorithm, including:
format -> Format of the image to render (pdf, png, svg)
Returns
----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
del bpmn_aggreg_statistics
image_format = parameters["format"] if "format" in parameters else "png"
file_name = bpmn_diagram_to_figure(bpmn_graph,
image_format,
bpmn_aggreg_statistics=None)
return file_name
def apply_petri(net,
initial_marking,
final_marking,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Visualize a BPMN graph from a Petri net, decorated with frequency, using the given parameters
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
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, including:
format -> Format of the image to render (pdf, png, svg)
aggregationMeasure -> Measure to use to aggregate statistics
pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Specification of the activity key
(if not concept:name)
pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY -> Specification of the timestamp key
(if not time:timestamp)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
bpmn_graph, elements_correspondence, inv_el_corr, el_corr_keys_map = bpmn_converter.apply(
net, initial_marking, final_marking)
if aggregated_statistics is None and log is not None:
aggregated_statistics = alignments_decoration.get_alignments_decoration(
net, initial_marking, final_marking, log=log)
bpmn_aggreg_statistics = None
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_el_corr)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_through_conv_greedy(bpmn_graph,
dfg,
activities_count,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Decorate BPMN graph through conversion to Petri net, using shortest paths in the Petri net
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
-------------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
del log
del aggregated_statistics
image_format = parameters["format"] if "format" in parameters else "png"
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)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_through_conv(bpmn_graph,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Visualize a BPMN graph decorating it through conversion to a Petri net
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, including:
format -> Format of the image to render (pdf, png, svg)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
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",
ht_perf_method="first")
bpmn_aggreg_statistics = None
if aggregated_statistics is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_elements_correspondence)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
def apply_petri(net,
initial_marking,
final_marking,
log=None,
aggregated_statistics=None,
parameters=None):
"""
Visualize a BPMN graph from a Petri net using the given parameters
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
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, including:
format -> Format of the image to render (pdf, png, svg)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
del log
del aggregated_statistics
image_format = parameters["format"] if "format" in parameters else "png"
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = bpmn_converter.apply(
net, initial_marking, final_marking)
file_name = bpmn_diagram_to_figure(bpmn_graph,
image_format,
bpmn_aggreg_statistics=None)
return file_name
def apply_petri_greedy(net,
initial_marking,
final_marking,
log=None,
aggr_stat=None,
parameters=None):
"""
Visualize a BPMN graph from a Petri net, decorated with performance, using the given parameters (greedy algorithm)
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log where the replay technique should be applied
aggr_stat
(Optional) element-wise statistics calculated on the Petri net
parameters
Possible parameters of the algorithm, including:
format -> Format of the image to render (pdf, png, svg)
aggregationMeasure -> Measure to use to aggregate statistics
pm4py.util.constants.PARAMETER_CONSTANT_ACTIVITY_KEY -> Specification of the activity key
(if not concept:name)
pm4py.util.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY -> Specification of the timestamp key
(if not time:timestamp)
Returns
-----------
file_name
Path of the figure in which the rendered BPMN has been saved
"""
if parameters is None:
parameters = {}
image_format = parameters["format"] if "format" in parameters else "png"
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = bpmn_converter.apply(
net, initial_marking, final_marking)
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else DEFAULT_NAME_KEY
if aggr_stat is None and log is not None:
dfg = dfg_factory.apply(log, variant="performance")
activities_count = attributes_filter.get_attribute_values(
log, activity_key)
spaths = vis_trans_shortest_paths.get_shortest_paths(net)
aggr_stat = vis_trans_shortest_paths.get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = None
if aggr_stat is not None:
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggr_stat, inv_el_corr)
file_name = bpmn_diagram_to_figure(
bpmn_graph,
image_format,
bpmn_aggreg_statistics=bpmn_aggreg_statistics)
return file_name
