Source code for pm4py.statistics.overlap.cases.log.get
'''
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 enum import Enum
from typing import Dict, Optional, Any, List, Union
from pm4py.objects.log.obj import EventLog
from pm4py.statistics.overlap.utils import compute
from pm4py.util import exec_utils, constants, xes_constants
from pm4py.objects.conversion.log import converter
[docs]
class Parameters(Enum):
TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_TIMESTAMP_KEY
START_TIMESTAMP_KEY = constants.PARAMETER_CONSTANT_START_TIMESTAMP_KEY
[docs]
def apply(
log: EventLog,
parameters: Optional[Dict[Union[str, Parameters], Any]] = None,
) -> List[int]:
"""
Computes the case overlap statistic from an interval event log
Parameters
-----------------
log
Interval event log
parameters
Parameters of the algorithm, including:
- Parameters.TIMESTAMP_KEY => attribute representing the completion timestamp
- Parameters.START_TIMESTAMP_KEY => attribute representing the start timestamp
Returns
----------------
case overlap
List associating to each case the number of open cases during the life of a case
"""
if parameters is None:
parameters = {}
log = converter.apply(
log, variant=converter.Variants.TO_EVENT_LOG, parameters=parameters
)
timestamp_key = exec_utils.get_param_value(
Parameters.TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
start_timestamp_key = exec_utils.get_param_value(
Parameters.START_TIMESTAMP_KEY,
parameters,
xes_constants.DEFAULT_TIMESTAMP_KEY,
)
points = []
for trace in log:
case_points = []
for event in trace:
case_points.append(
(
event[start_timestamp_key].timestamp(),
event[timestamp_key].timestamp(),
)
)
points.append(
(min(x[0] for x in case_points), max(x[1] for x in case_points))
)
return compute.apply(points, parameters=parameters)