'''
PM4Py – A Process Mining Library for Python
Copyright (C) 2024 Process Intelligence Solutions UG (haftungsbeschränkt)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU Affero General Public License for more details.
You should have received a copy of the GNU Affero General Public License
along with this program. If not, see this software project's root or
visit <https://www.gnu.org/licenses/>.
Website: https://processintelligence.solutions
Contact: info@processintelligence.solutions
'''
from pm4py.statistics.attributes.log import get as attr_get
from pm4py.objects.dfg.utils import dfg_utils
from pm4py.util import xes_constants as xes
from pm4py.util import exec_utils
from pm4py.statistics.service_time.log import get as serv_time_get
from enum import Enum
from pm4py.util import constants
from enum import Enum
from collections import Counter
from typing import Optional, Dict, Any, Tuple
import graphviz
from pm4py.objects.log.obj import EventLog
from collections import Counter
from pm4py.visualization.dfg.util import dfg_gviz
[docs]
class Parameters(Enum):
ACTIVITY_KEY = constants.PARAMETER_CONSTANT_ACTIVITY_KEY
FORMAT = "format"
MAX_NO_EDGES_IN_DIAGRAM = "maxNoOfEdgesInDiagram"
START_ACTIVITIES = "start_activities"
END_ACTIVITIES = "end_activities"
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
FONT_SIZE = "font_size"
AGGREGATION_MEASURE = "aggregation_measure"
RANKDIR = "rankdir"
BGCOLOR = "bgcolor"
STAT_LOCALE = "stat_locale"
[docs]
def apply(dfg: Dict[Tuple[str, str], int], log: EventLog = None, parameters: Optional[Dict[Any, Any]] = None, activities_count : Dict[str, int] = None, serv_time: Dict[str, float] = None) -> graphviz.Digraph:
"""
Visualize a performance directly-follows graph
Parameters
-----------------
dfg
Performance Directly-follows graph
log
(if provided) Event log for the calculation of statistics
activities_count
(if provided) Dictionary associating to each activity the number of occurrences in the log.
serv_time
(if provided) Dictionary associating to each activity the average service time
parameters
Variant-specific parameters
Returns
-----------------
gviz
Graphviz digraph
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes.DEFAULT_NAME_KEY)
image_format = exec_utils.get_param_value(Parameters.FORMAT, parameters, "png")
max_no_of_edges_in_diagram = exec_utils.get_param_value(Parameters.MAX_NO_EDGES_IN_DIAGRAM, parameters, 100000)
start_activities = exec_utils.get_param_value(Parameters.START_ACTIVITIES, parameters, [])
end_activities = exec_utils.get_param_value(Parameters.END_ACTIVITIES, parameters, [])
font_size = exec_utils.get_param_value(Parameters.FONT_SIZE, parameters, 12)
font_size = str(font_size)
activities = dfg_utils.get_activities_from_dfg(dfg)
aggregation_measure = exec_utils.get_param_value(Parameters.AGGREGATION_MEASURE, parameters, "mean")
rankdir = exec_utils.get_param_value(Parameters.RANKDIR, parameters, constants.DEFAULT_RANKDIR_GVIZ)
bgcolor = exec_utils.get_param_value(Parameters.BGCOLOR, parameters, constants.DEFAULT_BGCOLOR)
stat_locale = exec_utils.get_param_value(Parameters.STAT_LOCALE, parameters, {})
# if all the aggregation measures are provided for a given key,
# then pick one of the values for the representation
dfg0 = dfg
dfg = {}
for key in dfg0:
try:
if aggregation_measure in dfg0[key]:
dfg[key] = dfg0[key][aggregation_measure]
else:
dfg[key] = dfg0[key]
except:
dfg[key] = dfg0[key]
if activities_count is None:
if log is not None:
activities_count = attr_get.get_attribute_values(log, activity_key, parameters=parameters)
else:
# the frequency of an activity in the log is at least the number of occurrences of
# incoming arcs in the DFG.
# if the frequency of the start activities nodes is also provided, use also that.
activities_count = Counter({key: 0 for key in activities})
for el in dfg:
activities_count[el[1]] += dfg[el]
if isinstance(start_activities, dict):
for act in start_activities:
activities_count[act] += start_activities[act]
if serv_time is None:
if log is not None:
serv_time = serv_time_get.apply(log, parameters=parameters)
else:
serv_time = {key: -1 for key in activities}
return dfg_gviz.graphviz_visualization(activities_count, dfg, image_format=image_format, measure="performance",
max_no_of_edges_in_diagram=max_no_of_edges_in_diagram,
start_activities=start_activities, end_activities=end_activities, serv_time=serv_time,
font_size=font_size, bgcolor=bgcolor, rankdir=rankdir)
