# Source code for pm4py.algo.discovery.footprints.log.variants.trace_by_trace

```
'''
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/>.
'''
from enum import Enum
from pm4py.util import constants
from pm4py.algo.discovery.dfg import algorithm as dfg_discovery
from pm4py.objects.conversion.log import converter
from pm4py.objects.log.obj import EventLog
from pm4py.util import xes_constants
from pm4py.util import exec_utils
from enum import Enum
from typing import Optional, Dict, Any, Union, Tuple
from pm4py.objects.log.obj import EventLog, EventStream
[docs]class Outputs(Enum):
DFG = "dfg"
SEQUENCE = "sequence"
PARALLEL = "parallel"
START_ACTIVITIES = "start_activities"
END_ACTIVITIES = "end_activities"
ACTIVITIES = "activities"
SKIPPABLE = "skippable"
ACTIVITIES_ALWAYS_HAPPENING = "activities_always_happening"
MIN_TRACE_LENGTH = "min_trace_length"
TRACE = "trace"
[docs]def apply(log, parameters: Optional[Dict[Union[str, Parameters], Any]] = None) -> Dict[str, Any]:
"""
Discovers a footprint object from an event log
(the footprints are returned case-by-case)
Parameters
--------------
log
Log
parameters
Parameters of the algorithm:
- Parameters.ACTIVITY_KEY
Returns
--------------
footprints_obj
List of footprints for the cases of the log
"""
if parameters is None:
parameters = {}
activity_key = exec_utils.get_param_value(Parameters.ACTIVITY_KEY, parameters, xes_constants.DEFAULT_NAME_KEY)
log = converter.apply(log, variant=converter.TO_EVENT_LOG, parameters=parameters)
ret = []
for trace in log:
dfg = dfg_discovery.apply(EventLog([trace]), parameters=parameters)
parallel = {(x, y) for (x, y) in dfg if (y, x) in dfg}
sequence = {(x, y) for (x, y) in dfg if not (y, x) in dfg}
trace = tuple(x[activity_key] for x in trace)
activities = set(trace)
if len(trace) > 0:
start_activities = {trace[0]}
end_activities = {trace[-1]}
else:
start_activities = set()
end_activities = set()
ret.append(
{Outputs.DFG.value: dfg, Outputs.SEQUENCE.value: sequence, Outputs.PARALLEL.value: parallel, Outputs.ACTIVITIES.value: activities,
Outputs.START_ACTIVITIES.value: start_activities, Outputs.END_ACTIVITIES.value: end_activities,
Outputs.MIN_TRACE_LENGTH.value: len(trace), Outputs.TRACE.value: trace})
return ret
```