def test_autofiltering_dataframe(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
input_log = os.path.join(INPUT_DATA_DIR, "running-example.csv")
dataframe = csv_import_adapter.import_dataframe_from_path_wo_timeconversion(input_log, sep=',')
dataframe = auto_filter.apply_auto_filter(dataframe)
del dataframe
def test_autofiltering_dataframe(self):
# to avoid static method warnings in tests,
# that by construction of the unittest package have to be expressed in such way
self.dummy_variable = "dummy_value"
input_log = os.path.join(INPUT_DATA_DIR, "running-example.csv")
dataframe = pd.read_csv(input_log)
dataframe = dataframe_utils.convert_timestamp_columns_in_df(dataframe)
dataframe = auto_filter.apply_auto_filter(dataframe)
del dataframe
def apply(dataframe, parameters=None):
"""
Gets the performance DFG
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
[dfg, dfg_perf
] = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False,
measure="both")
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
dfg_perf = {x: y for x, y in dfg_perf.items() if x in dfg}
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
gviz = dfg_vis_factory.apply(dfg_perf,
activities_count=activities_count,
variant="performance",
parameters={
"format": "svg",
"start_activities": start_activities,
"end_activities": end_activities
})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_dfg(dfg, start_activities,
end_activities)
net, im, fm = dfg_conv_factory.apply(dfg,
parameters={
"start_activities":
start_activities,
"end_activities": end_activities
})
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "dfg", "perf", None, "", activity_key
def apply(dataframe, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by performance metric
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
[dfg, dfg_perf
] = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False,
measure="both")
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
dfg_perf = {x: y for x, y in dfg_perf.items() if x in dfg}
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
spaths = get_shortest_paths(net)
bpmn_graph, el_corr, inv_el_corr, el_corr_keys_map = petri_to_bpmn.apply(
net, im, fm)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg_perf, spaths, activities_count, variant="performance")
bpmn_aggreg_statistics = convert_performance_map.convert_performance_map_to_bpmn(
aggregated_statistics, inv_el_corr)
#bpmn_graph = bpmn_embedding.embed_info_into_bpmn(bpmn_graph, bpmn_aggreg_statistics, "performance")
bpmn_graph = bpmn_diagram_layouter.apply(bpmn_graph)
bpmn_string = bpmn_exporter.get_string_from_bpmn(bpmn_graph)
gviz = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "svg"})
gviz2 = bpmn_vis_factory.apply_petri(
net,
im,
fm,
aggregated_statistics=aggregated_statistics,
variant="performance",
parameters={"format": "dot"})
gviz_base64 = get_base64_from_file(gviz2.name)
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return get_base64_from_file(gviz.name), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "indbpmn", "perf", bpmn_string, ".bpmn", activity_key
def apply(dataframe, parameters=None):
"""
Gets the Petri net through Inductive Miner, decorated by frequency metric
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
dfg = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
net, im, fm = inductive_miner.apply_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
spaths = get_shortest_paths(net)
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg, spaths, activities_count, variant="frequency")
gviz = pn_vis_factory.apply(net,
im,
fm,
parameters={"format": "svg"},
variant="frequency",
aggregated_statistics=aggregated_statistics)
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
ret_graph = get_graph.get_graph_from_petri(net, im, fm)
return get_base64_from_gviz(gviz), export_petri_as_string(
net, im, fm
), ".pnml", "parquet", activities, start_activities, end_activities, gviz_base64, ret_graph, "inductive", "freq", None, "", activity_key
def apply(df, parameters=None, classic_output=False):
"""
Gets a simple model out of a Pandas dataframe
Parameters
-------------
df
Pandas dataframe
parameters
Parameters of the algorithm, including:
maximum_number_activities -> Maximum number of activities to keep
discovery_algorithm -> Discovery algorithm to use (alpha, inductive)
desidered_output -> Desidered output of the algorithm (default: Petri)
include_filtered_df -> Include the filtered dataframe in the output
include_dfg_frequency -> Include the DFG of frequencies in the output
include_dfg_performance -> Include the DFG of performance in the output
include_filtered_dfg_frequency -> Include the filtered DFG of frequencies in the output
include_filtered_dfg_performance -> Include the filtered DFG of performance in the output
classic_output
Determine if the output shall contains directly the objects (e.g. net, initial_marking, final_marking)
or can return a more detailed dictionary
"""
if parameters is None:
parameters = {}
if PARAMETER_CONSTANT_CASEID_KEY not in parameters:
parameters[PARAMETER_CONSTANT_CASEID_KEY] = CASE_CONCEPT_NAME
if PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:
parameters[PARAMETER_CONSTANT_ACTIVITY_KEY] = DEFAULT_NAME_KEY
if PARAMETER_CONSTANT_TIMESTAMP_KEY not in parameters:
parameters[PARAMETER_CONSTANT_TIMESTAMP_KEY] = DEFAULT_TIMESTAMP_KEY
if PARAMETER_CONSTANT_ATTRIBUTE_KEY not in parameters:
parameters[PARAMETER_CONSTANT_ATTRIBUTE_KEY] = parameters[
PARAMETER_CONSTANT_ACTIVITY_KEY]
returned_dictionary = {}
caseid_glue = parameters[PARAMETER_CONSTANT_CASEID_KEY]
activity_key = parameters[PARAMETER_CONSTANT_ACTIVITY_KEY]
timest_key = parameters[PARAMETER_CONSTANT_TIMESTAMP_KEY]
net = None
initial_marking = None
final_marking = None
bpmn_graph = None
maximum_number_activities = parameters[
"maximum_number_activities"] if "maximum_number_activities" in parameters else 20
discovery_algorithm = parameters[
"discovery_algorithm"] if "discovery_algorithm" in parameters else "alphaclassic"
desidered_output = parameters[
"desidered_output"] if "desidered_output" in parameters else "petri"
include_filtered_df = parameters[
"include_filtered_df"] if "include_filtered_df" in parameters else True
include_dfg_frequency = parameters[
"include_dfg_frequency"] if "include_dfg_frequency" in parameters else True
include_dfg_performance = parameters[
"include_dfg_performance"] if "include_dfg_performance" in parameters else True
include_filtered_dfg_frequency = parameters[
"include_filtered_dfg_frequency"] if "include_filtered_dfg_frequency" in parameters else True
include_filtered_dfg_performance = parameters[
"include_filtered_dfg_performance"] if "include_filtered_dfg_performance" in parameters else True
df = attributes_filter.filter_df_keeping_spno_activities(
df,
activity_key=activity_key,
max_no_activities=maximum_number_activities)
filtered_df = None
if "alpha" in discovery_algorithm:
filtered_df = start_activities_filter.apply_auto_filter(
df, parameters=parameters)
filtered_df = end_activities_filter.apply_auto_filter(
filtered_df, parameters=parameters)
filtered_df = filter_topvariants_soundmodel.apply(
filtered_df, parameters=parameters)
elif "inductive" in discovery_algorithm:
filtered_df = auto_filter.apply_auto_filter(df, parameters=parameters)
[dfg_frequency,
dfg_performance] = dfg_util.get_dfg_graph(df,
measure="both",
perf_aggregation_key="mean",
case_id_glue=caseid_glue,
activity_key=activity_key,
timestamp_key=timest_key)
[filtered_dfg_frequency, filtered_dfg_performance
] = dfg_util.get_dfg_graph(filtered_df,
measure="both",
perf_aggregation_key="mean",
case_id_glue=caseid_glue,
activity_key=activity_key,
timestamp_key=timest_key)
if "alpha" in discovery_algorithm:
net, initial_marking, final_marking = alpha_miner.apply_dfg(
filtered_dfg_frequency, parameters=parameters)
if filtered_df is not None and include_filtered_df:
returned_dictionary["filtered_df"] = filtered_df
if net is not None and desidered_output == "petri":
returned_dictionary["net"] = net
if initial_marking is not None and desidered_output == "petri":
returned_dictionary["initial_marking"] = initial_marking
if final_marking is not None and desidered_output == "petri":
returned_dictionary["final_marking"] = final_marking
if bpmn_graph is not None and desidered_output == "bpmn":
returned_dictionary["bpmn_graph"] = bpmn_graph
if dfg_frequency is not None and include_dfg_frequency:
returned_dictionary["dfg_frequency"] = dfg_frequency
if dfg_performance is not None and include_dfg_performance:
returned_dictionary["dfg_performance"] = dfg_performance
if filtered_dfg_frequency is not None and include_filtered_dfg_frequency:
returned_dictionary["filtered_dfg_frequency"] = filtered_dfg_frequency
if filtered_dfg_performance is not None and include_filtered_dfg_performance:
returned_dictionary[
"filtered_dfg_performance"] = filtered_dfg_performance
if classic_output:
if net is not None and desidered_output == "petri":
return net, initial_marking, final_marking
return returned_dictionary
def apply(dataframe, parameters=None):
"""
Gets the performance HNet
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else ws_constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=ws_constants.MAX_NO_ACTIVITIES)
dataframe, end_activities_count = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
start_activities_count = start_activities_filter.get_start_activities(
dataframe, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(start_activities_count.keys())
end_activities = list(end_activities_count.keys())
dfg_frequency, dfg_performance = df_statistics.get_dfg_graph(
dataframe,
case_id_glue=case_id_glue,
activity_key=activity_key,
timestamp_key=timestamp_key,
measure="both",
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
heu_net = HeuristicsNet(dfg_frequency,
performance_dfg=dfg_performance,
activities=activities,
start_activities=start_activities,
end_activities=end_activities,
activities_occurrences=activities_count)
heu_net.calculate(dfg_pre_cleaning_noise_thresh=ws_constants.
DEFAULT_DFG_CLEAN_MULTIPLIER * decreasingFactor)
vis = heu_vis_factory.apply(heu_net, parameters={"format": "svg"})
vis2 = heu_vis_factory.apply(heu_net, parameters={"format": "dot"})
gviz_base64 = get_base64_from_file(vis2.name)
return get_base64_from_file(vis.name), None, "", "parquet", activities, start_activities, end_activities, gviz_base64, [], "heuristics", "perf", None, "", activity_key
def apply(dataframe, parameters=None):
"""
Gets the process tree using Inductive Miner Directly-Follows
Parameters
------------
dataframe
Dataframe
parameters
Parameters of the algorithm
Returns
------------
base64
Base64 of an SVG representing the model
model
Text representation of the model
format
Format of the model
"""
if parameters is None:
parameters = {}
decreasingFactor = parameters[
"decreasingFactor"] if "decreasingFactor" in parameters else constants.DEFAULT_DEC_FACTOR
activity_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_ACTIVITY_KEY] if pm4_constants.PARAMETER_CONSTANT_ACTIVITY_KEY in parameters else xes.DEFAULT_NAME_KEY
timestamp_key = parameters[
pm4_constants.
PARAMETER_CONSTANT_TIMESTAMP_KEY] if pm4_constants.PARAMETER_CONSTANT_TIMESTAMP_KEY in parameters else xes.DEFAULT_TIMESTAMP_KEY
case_id_glue = parameters[
pm4_constants.
PARAMETER_CONSTANT_CASEID_KEY] if pm4_constants.PARAMETER_CONSTANT_CASEID_KEY in parameters else CASE_CONCEPT_NAME
parameters[pm4_constants.RETURN_EA_COUNT_DICT_AUTOFILTER] = True
dataframe = attributes_filter.filter_df_keeping_spno_activities(
dataframe,
activity_key=activity_key,
max_no_activities=constants.MAX_NO_ACTIVITIES)
dataframe, end_activities = auto_filter.apply_auto_filter(
dataframe, parameters=parameters)
end_activities = list(end_activities.keys())
activities_count = attributes_filter.get_attribute_values(
dataframe, activity_key, parameters=parameters)
activities = list(activities_count.keys())
start_activities = list(
start_activities_filter.get_start_activities(
dataframe, parameters=parameters).keys())
dfg = df_statistics.get_dfg_graph(dataframe,
activity_key=activity_key,
timestamp_key=timestamp_key,
case_id_glue=case_id_glue,
sort_caseid_required=False,
sort_timestamp_along_case_id=False)
dfg = clean_dfg_based_on_noise_thresh(
dfg,
activities,
decreasingFactor * constants.DEFAULT_DFG_CLEAN_MULTIPLIER,
parameters=parameters)
tree = inductive_miner.apply_tree_dfg(dfg,
parameters,
activities=activities,
start_activities=start_activities,
end_activities=end_activities)
gviz = pt_vis_factory.apply(tree, parameters={"format": "svg"})
gviz_base64 = base64.b64encode(str(gviz).encode('utf-8'))
return get_base64_from_gviz(gviz), None, "", "parquet", activities, start_activities, end_activities, gviz_base64, [], "tree", "freq", None, "", activity_key
def execute_script():
time1 = time.time()
dataframe = csv_import_adapter.import_dataframe_from_path_wo_timeconversion(
inputLog, sep=SEP, quotechar=QUOTECHAR)
time2 = time.time()
print("time2 - time1: " + str(time2 - time1))
parameters_filtering = {
constants.PARAMETER_CONSTANT_CASEID_KEY: CASEID_GLUE,
constants.PARAMETER_CONSTANT_ACTIVITY_KEY: ACTIVITY_KEY
}
if enable_auto_filter:
dataframe = auto_filter.apply_auto_filter(
dataframe, parameters=parameters_filtering)
else:
dataframe = attributes_filter.apply_auto_filter(
dataframe, parameters=parameters_filtering)
time3 = time.time()
print("time3 - time2: " + str(time3 - time2))
if enable_filtering_on_cases:
dataframe = case_filter.filter_on_ncases(dataframe,
case_id_glue=CASEID_GLUE,
max_no_cases=max_no_cases)
time4 = time.time()
dataframe = csv_import_adapter.convert_caseid_column_to_str(
dataframe, case_id_glue=CASEID_GLUE)
dataframe = csv_import_adapter.convert_timestamp_columns_in_df(
dataframe, timest_columns=TIMEST_COLUMNS, timest_format=TIMEST_FORMAT)
time6 = time.time()
print("time6 - time4: " + str(time6 - time4))
# dataframe = dataframe.sort_values('time:timestamp')
time7 = time.time()
print("time7 - time6: " + str(time7 - time6))
# show the filtered dataframe on the screen
activities_count = attributes_filter.get_attribute_values(
dataframe, attribute_key=ACTIVITY_KEY)
[dfg_frequency, dfg_performance
] = df_statistics.get_dfg_graph(dataframe,
measure="both",
perf_aggregation_key="median",
case_id_glue=CASEID_GLUE,
activity_key=ACTIVITY_KEY,
timestamp_key=TIMEST_KEY)
if enable_filtering_df:
print("len dfg_frequency 0=", len(dfg_frequency))
dfg_frequency = dfg_filtering.apply(
dfg_frequency, {"noiseThreshold": filtering_df_noise})
print("len dfg_frequency 1=", len(dfg_frequency))
time8 = time.time()
print("time8 - time7: " + str(time8 - time7))
gviz = dfg_vis_factory.apply(dfg_frequency,
activities_count=activities_count,
parameters={"format": "svg"})
dfg_vis_factory.view(gviz)
net, initial_marking, final_marking = inductive_factory.apply_dfg(
dfg_frequency)
# net, initial_marking, final_marking = alpha_factory.apply_dfg(dfg_frequency)
spaths = get_shortest_paths(net)
time9 = time.time()
print("time9 - time8: " + str(time9 - time8))
aggregated_statistics = get_decorations_from_dfg_spaths_acticount(
net, dfg_performance, spaths, activities_count, variant="performance")
gviz = pn_vis_factory.apply(net,
initial_marking,
final_marking,
variant="performance",
aggregated_statistics=aggregated_statistics,
parameters={"format": "svg"})
time10 = time.time()
print("time10 - time9: " + str(time10 - time9))
print("time10 - time1: " + str(time10 - time1))
pn_vis_factory.view(gviz)
