def apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None):
"""
Apply method for Petri net visualization (it calls the graphviz_visualization
method) adding performance representation obtained by token replay
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log
aggregated_statistics
Dictionary containing the frequency statistics
parameters
Algorithm parameters (including the activity key used during the replay, and the timestamp key)
Returns
-----------
viz
Graph object
"""
if aggregated_statistics is None:
if log is not None:
aggregated_statistics = get_decorations(log, net, initial_marking, final_marking, parameters=parameters,
measure="performance")
return visualize.apply(net, initial_marking, final_marking, parameters=parameters,
decorations=aggregated_statistics)
def apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None):
"""
Apply method for Petri net visualization (useful for recall from factory; it calls the
graphviz_visualization method)
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log
aggregated_statistics
Dictionary containing the frequency statistics
parameters
Algorithm parameters
Returns
-----------
viz
Graph object
"""
if aggregated_statistics is None and log is not None:
aggregated_statistics = alignments_decoration.get_alignments_decoration(net, initial_marking, final_marking,
log=log)
return visualize.apply(net, initial_marking, final_marking, parameters=parameters,
decorations=aggregated_statistics)
def apply(net, initial_marking, final_marking, log=None, aggregated_statistics=None, parameters=None):
"""
Apply method for Petri net visualization (useful for recall from factory; it calls the
graphviz_visualization method)
Parameters
-----------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
(Optional) log
aggregated_statistics
Dictionary containing the frequency statistics
parameters
Algorithm parameters
Returns
-----------
viz
Graph object
"""
# remove unused variables
del log
del aggregated_statistics
return visualize.apply(net, initial_marking, final_marking, parameters=parameters)
def get_decorated_net(net, initial_marking, final_marking, log, parameters=None, variant="frequency"):
"""
Get a decorated net according to the specified variant (decorate Petri net based on DFG)
Parameters
------------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
Log to use to decorate the Petri net
parameters
Algorithm parameters
variant
Specify if the decoration should take into account the frequency or the performance
Returns
------------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters,
"sum" if "frequency" in variant else "mean")
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {})
# we find the DFG
if variant == "performance":
dfg = performance.performance(log, parameters=parameters)
else:
dfg = native.native(log, parameters=parameters)
# we find shortest paths
spaths = get_shortest_paths(net)
# we find the number of activities occurrences in the log
activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(net, dfg, spaths,
activities_count,
variant=variant,
aggregation_measure=aggregation_measure,
stat_locale=stat_locale)
return visualize.apply(net, initial_marking, final_marking, parameters=parameters,
decorations=aggregated_statistics)
def get_decorated_net(net,
initial_marking,
final_marking,
log,
parameters=None,
variant="frequency"):
"""
Get a decorated net according to the specified variant (decorate Petri net based on DFG)
Parameters
------------
net
Petri net
initial_marking
Initial marking
final_marking
Final marking
log
Log to use to decorate the Petri net
parameters
Algorithm parameters
variant
Specify if the decoration should take into account the frequency or the performance
Returns
------------
gviz
GraphViz object
"""
if parameters is None:
parameters = {}
aggregation_measure = "mean"
if "frequency" in variant:
aggregation_measure = "sum"
elif "performance" in variant:
aggregation_measure = "mean"
if "aggregationMeasure" in parameters:
aggregation_measure = parameters["aggregationMeasure"]
activity_key = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY] if PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
# we find the DFG
dfg = dfg_factory.apply(log, variant=variant, parameters=parameters)
# we find shortest paths
spaths = get_shortest_paths(net)
# we find the number of activities occurrences in the trace log
activities_count = attributes_filter.get_attribute_values(
log, activity_key, parameters=parameters)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net,
dfg,
spaths,
activities_count,
variant=variant,
aggregation_measure=aggregation_measure)
return visualize.apply(net,
initial_marking,
final_marking,
parameters=parameters,
decorations=aggregated_statistics)
