def execute_script():
ocel = pm4py.read_ocel(os.path.join("..", "tests", "input_data", "ocel", "example_log.jsonocel"))
# we wants to use the traditional algorithms for streaming also on object-centric event logs.
# for this purpose, first we create two different event streams, one for the "order" object type
# and one for the "element" object type.
order_stream = live_event_stream.LiveEventStream()
element_stream = live_event_stream.LiveEventStream()
# Then, we register an algorithm for every one of them, which is a simple printer of the received events.
order_stream_printer = event_stream_printer.EventStreamPrinter()
element_stream_printer = event_stream_printer.EventStreamPrinter()
order_stream.register(order_stream_printer)
element_stream.register(element_stream_printer)
# Then, we create the distributor object.
# This registers different event streams for different object types.
flatts_distributor = ocel_flatts_distributor.OcelFlattsDistributor()
flatts_distributor.register("order", order_stream)
flatts_distributor.register("element", element_stream)
order_stream.start()
element_stream.start()
# in this way, we iterate over the events of an OCEL
for ev in ocel_iterator.apply(ocel):
# ... and the OCEL event is sent to all the "flattened" event streams.
flatts_distributor.append(ev)
# since the "flattened" event streams register a printer each, what we get is a print
# of all the events that reach these instances.
order_stream.stop()
element_stream.stop()
def execute_script():
ocel = pm4py.read_ocel(
os.path.join("..", "tests", "input_data", "ocel",
"example_log.jsonocel"))
model = pm4py.discover_oc_petri_net(ocel)
print(model.keys())
pm4py.view_ocpn(model, format="svg")
def execute_script():
ocel = pm4py.read_ocel(os.path.join("..", "tests", "input_data", "ocel", "example_log.jsonocel"))
# extracts some features on the objects and embed them in a Pandas dataframe
data_objects, feature_names_objects = objects_feature_extraction.apply(ocel)
objects_features_df = pd.DataFrame(data_objects, columns=feature_names_objects)
print(objects_features_df)
# extracts some features on the events and embed them in a Pandas dataframe
data_events, feature_names_events = events_feature_extraction.apply(ocel)
events_features_df = pd.DataFrame(data_events, columns=feature_names_events)
print(events_features_df)
def execute_script():
ocel = pm4py.read_ocel(
os.path.join("..", "tests", "input_data", "ocel",
"example_log.jsonocel"))
ocdfg = pm4py.discover_ocdfg(ocel)
# views the model with the frequency annotation
pm4py.view_ocdfg(ocdfg, format="svg")
# views the model with the performance annotation
pm4py.view_ocdfg(ocdfg,
format="svg",
annotation="performance",
performance_aggregation="median")
def test_discovery_ocfg_p2(self):
target_path = os.path.join("test_output_data", "model.svg")
ocel = pm4py.read_ocel(
os.path.join("input_data", "ocel", "example_log.jsonocel"))
ocdfg = pm4py.discover_ocdfg(ocel)
pm4py.save_vis_ocdfg(ocdfg,
target_path,
annotation="performance",
act_metric="unique_objects",
edge_metric="ev_couples",
act_threshold=2,
edge_threshold=1)
os.remove(target_path)
def test_discovery_ocfg_p6(self):
target_path = os.path.join("test_output_data", "model.svg")
ocel = pm4py.read_ocel(
os.path.join("input_data", "ocel", "example_log.jsonocel"))
ocdfg = pm4py.discover_ocdfg(ocel, business_hours=True)
pm4py.save_vis_ocdfg(ocdfg,
target_path,
annotation="performance",
act_metric="unique_objects",
edge_metric="total_objects",
act_threshold=2,
edge_threshold=1,
performance_aggregation="median")
os.remove(target_path)
def execute_script():
ocel = pm4py.read_ocel("../tests/input_data/ocel/example_log.jsonocel")
print(ocel)
print("attribute names: ", pm4py.ocel_get_attribute_names(ocel))
print("object types: ", pm4py.ocel_get_object_types(ocel))
print("flattening to order: ")
print(pm4py.ocel_flattening(ocel, "order"))
pm4py.write_ocel(ocel, "prova.jsonocel")
os.remove("prova.jsonocel")
print("ocel_object_type_activities")
print(pm4py.ocel_object_type_activities(ocel))
print("ocel_objects_ot_count")
print(pm4py.ocel_objects_ot_count(ocel))
print("filter_ocel_event_attribute")
ocel1 = pm4py.filter_ocel_event_attribute(ocel, "ocel:activity", ["Create Order"])
print(ocel1.get_extended_table())
print("filter_ocel_object_attribute")
ocel2 = pm4py.filter_ocel_object_attribute(ocel, "ocel:type", ["order", "delivery"])
print(ocel2.get_extended_table())
print("filter_ocel_object_types_allowed_activities")
ocel3 = pm4py.filter_ocel_object_types_allowed_activities(ocel, {"order": {"Create Order"}, "element": {"Create Order"}})
print(ocel3.get_extended_table())
print("filter_ocel_start_events_per_object_type")
ocel4 = pm4py.filter_ocel_start_events_per_object_type(ocel, "order")
print(ocel4.get_extended_table())
print("filter_ocel_end_events_per_object_type")
ocel5 = pm4py.filter_ocel_end_events_per_object_type(ocel, "order")
print(ocel5.get_extended_table())
print("filter_ocel_object_per_type_count")
ocel6 = pm4py.filter_ocel_object_per_type_count(ocel, {"order": 1, "element": 2})
print(ocel6.get_extended_table())
print("filter_ocel_events_timestamp")
ocel7 = pm4py.filter_ocel_events_timestamp(ocel, "1981-01-01 00:00:00", "1982-01-01 00:00:00")
print(ocel7.get_extended_table())
