'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py 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 General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
import pkgutil
from typing import Optional, Dict, Any, Union
import pandas as pd
from pm4py.algo.discovery.temporal_profile.variants import log
from pm4py.objects.log.obj import EventLog
from pm4py.util import typing
[docs]def apply(elog: Union[EventLog, pd.DataFrame], parameters: Optional[Dict[Any, Any]] = None) -> typing.TemporalProfile:
"""
Discovers the temporal profile out of the provided log object.
Implements the approach described in:
Stertz, Florian, Jürgen Mangler, and Stefanie Rinderle-Ma. "Temporal Conformance Checking at Runtime based on Time-infused Process Models." arXiv preprint arXiv:2008.07262 (2020).
Parameters
----------
elog
Event log
parameters
Parameters, including:
- Parameters.ACTIVITY_KEY => the attribute to use as activity
- Parameters.START_TIMESTAMP_KEY => the attribute to use as start timestamp
- Parameters.TIMESTAMP_KEY => the attribute to use as timestamp
Returns
-------
temporal_profile
Temporal profile of the log
"""
if pkgutil.find_loader("pandas"):
import pandas as pd
from pm4py.algo.discovery.temporal_profile.variants import dataframe
if type(elog) is pd.DataFrame:
return dataframe.apply(elog, parameters=parameters)
return log.apply(elog, parameters=parameters)