# Source code for pm4py.analysis

```
'''
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 typing import List, Optional, Tuple, Dict, Union
from pm4py.objects.log.obj import Trace, EventLog
from pm4py.objects.petri_net.obj import PetriNet, Marking
from pm4py.utils import get_properties
from pm4py.util.pandas_utils import check_is_pandas_dataframe, check_pandas_dataframe_columns
import pandas as pd
[docs]def construct_synchronous_product_net(trace: Trace, petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking) -> Tuple[PetriNet, Marking, Marking]:
"""
constructs the synchronous product net between a trace and a Petri net process model.
Parameters
----------------
trace
Trace of an event log
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
Returns
----------------
sync_net
Synchronous product net
sync_im
Initial marking of the sync net
sync_fm
Final marking of the sync net
"""
from pm4py.objects.petri_net.utils.petri_utils import construct_trace_net
from pm4py.objects.petri_net.utils.synchronous_product import construct
from pm4py.objects.petri_net.utils.align_utils import SKIP
trace_net, trace_im, trace_fm = construct_trace_net(trace)
sync_net, sync_im, sync_fm = construct(trace_net, trace_im, trace_fm, petri_net, initial_marking, final_marking,
SKIP)
return sync_net, sync_im, sync_fm
[docs]def solve_marking_equation(petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking, cost_function: Dict[PetriNet.Transition, float] = None) -> float:
"""
Solves the marking equation of a Petri net.
The marking equation is solved as an ILP problem.
An optional transition-based cost function to minimize can be provided as well.
Parameters
---------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
cost_function
optional cost function to use when solving the marking equation.
Returns
----------------
h_value
Heuristics value calculated resolving the marking equation
"""
from pm4py.algo.analysis.marking_equation import algorithm as marking_equation
if cost_function is None:
cost_function = dict()
for t in petri_net.transitions:
cost_function[t] = 1
me = marking_equation.build(
petri_net, initial_marking, final_marking, parameters={'costs': cost_function})
return marking_equation.get_h_value(me)
[docs]def solve_extended_marking_equation(trace: Trace, sync_net: PetriNet, sync_im: Marking,
sync_fm: Marking, split_points: Optional[List[int]] = None) -> float:
"""
Gets an heuristics value (underestimation of the cost of an alignment) between a trace
and a synchronous product net using the extended marking equation with the standard cost function
(e.g. sync moves get cost equal to 0, invisible moves get cost equal to 1,
other move on model / move on log get cost equal to 10000), with an optimal provisioning of the split
points
Parameters
----------------
trace
Trace
sync_net
Synchronous product net
sync_im
Initial marking (of the sync net)
sync_fm
Final marking (of the sync net)
split_points
If specified, the indexes of the events of the trace to be used as split points.
If not specified, the split points are identified automatically
Returns
----------------
h_value
Heuristics value calculated resolving the marking equation
"""
from pm4py.algo.analysis.extended_marking_equation import algorithm as extended_marking_equation
parameters = {}
if split_points is not None:
parameters[extended_marking_equation.Variants.CLASSIC.value.Parameters.SPLIT_IDX] = split_points
me = extended_marking_equation.build(
trace, sync_net, sync_im, sync_fm, parameters=parameters)
return extended_marking_equation.get_h_value(me)
[docs]def check_soundness(petri_net: PetriNet, initial_marking: Marking,
final_marking: Marking) -> bool:
"""
Check if a given Petri net is a sound WF-net.
A Petri net is a WF-net iff:
- it has a unique source place
- it has a unique end place
- every element in the WF-net is on a path from the source to the sink place
A WF-net is sound iff:
- it contains no live-locks
- it contains no deadlocks
- we are able to always reach the final marking
For a formal definition of sound WF-net, consider: http://www.padsweb.rwth-aachen.de/wvdaalst/publications/p628.pdf
Parameters
---------------
petri_net
Petri net
initial_marking
Initial marking
final_marking
Final marking
Returns
--------------
boolean
Soundness
"""
from pm4py.algo.analysis.woflan import algorithm as woflan
return woflan.apply(petri_net, initial_marking, final_marking)
[docs]def insert_artificial_start_end(log: Union[EventLog, pd.DataFrame]) -> Union[EventLog, pd.DataFrame]:
"""
Inserts the artificial start/end activities in an event log / Pandas dataframe
Parameters
------------------
log
Event log / Pandas dataframe
Returns
------------------
log
Event log / Pandas dataframe with artificial start / end activities
"""
properties = get_properties(log)
if check_is_pandas_dataframe(log):
check_pandas_dataframe_columns(log)
from pm4py.objects.log.util import dataframe_utils
return dataframe_utils.insert_artificial_start_end(log, parameters=properties)
else:
from pm4py.objects.log.util import artificial
return artificial.insert_artificial_start_end(log, parameters=properties)
[docs]def check_is_workflow_net(net: PetriNet) -> bool:
"""
Checks if the input Petri net satisfies the WF-net conditions:
1. unique source place
2. unique sink place
3. every node is on a path from the source to the sink
Parameters
------------------
net
PetriNet
Returns
------------------
True iff the input net is a WF-net.
"""
from pm4py.algo.analysis.workflow_net import algorithm
return algorithm.apply(net)
```