from pm4py.algo.discovery.dfg.variants import native, performance
from pm4py.statistics.attributes.log import get as attr_get
from pm4py.util import xes_constants as xes
from pm4py.visualization.petri_net.common import visualize
from pm4py.visualization.petri_net.util.vis_trans_shortest_paths import (
get_decorations_from_dfg_spaths_acticount,
)
from pm4py.visualization.petri_net.util.vis_trans_shortest_paths import (
get_shortest_paths,
)
from pm4py.util import exec_utils
from enum import Enum
from pm4py.util.constants import (
PARAMETER_CONSTANT_ACTIVITY_KEY,
PARAMETER_CONSTANT_TIMESTAMP_KEY,
)
from pm4py.objects.petri_net.obj import PetriNet, Marking
from typing import Optional, Dict, Any, Union
from pm4py.objects.log.obj import EventLog
import graphviz
[docs]
class Parameters(Enum):
FORMAT = "format"
DEBUG = "debug"
RANKDIR = "set_rankdir"
ACTIVITY_KEY = PARAMETER_CONSTANT_ACTIVITY_KEY
TIMESTAMP_KEY = PARAMETER_CONSTANT_TIMESTAMP_KEY
AGGREGATION_MEASURE = "aggregationMeasure"
FONT_SIZE = "font_size"
[docs]
def get_decorated_net(
net: PetriNet,
initial_marking: Marking,
final_marking: Marking,
log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
variant: str = "frequency",
) -> graphviz.Digraph:
"""
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
)
# 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,
)
return visualize.apply(
net,
initial_marking,
final_marking,
parameters=parameters,
decorations=aggregated_statistics,
)
[docs]
def apply(
net: PetriNet,
initial_marking: Marking,
final_marking: Marking,
log: EventLog = None,
aggregated_statistics=None,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> graphviz.Digraph:
"""
Apply frequency decoration through greedy algorithm (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
aggregated_statistics
Dictionary containing the frequency statistics
parameters
Algorithm parameters
Returns
------------
gviz
GraphViz object
"""
del aggregated_statistics
return get_decorated_net(
net,
initial_marking,
final_marking,
log,
parameters=parameters,
variant="frequency",
)
