Source code for pm4py.objects.stochastic_petri.tangible_reachability
'''
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.objects.petri_net.utils.reachability_graph import (
construct_reachability_graph,
)
from pm4py.objects.conversion.log import converter as log_converter
[docs]
def get_tangible_reachability_from_log_net_im_fm(
log, net, im, fm, parameters=None
):
"""
Gets the tangible reachability graph from a log and an accepting Petri net
Parameters
---------------
log
Event log
net
Petri net
im
Initial marking
fm
Final marking
Returns
------------
reachab_graph
Reachability graph
tangible_reach_graph
Tangible reachability graph
stochastic_info
Stochastic information
"""
if parameters is None:
parameters = {}
from pm4py.algo.simulation.montecarlo.utils import replay
stochastic_info = replay.get_map_from_log_and_net(
log_converter.apply(
log,
variant=log_converter.Variants.TO_EVENT_LOG,
parameters=parameters,
),
net,
im,
fm,
parameters=parameters,
)
reachability_graph, tangible_reachability_graph = (
get_tangible_reachability_from_net_im_sinfo(
net, im, stochastic_info, parameters=parameters
)
)
return reachability_graph, tangible_reachability_graph, stochastic_info
[docs]
def get_tangible_reachability_from_net_im_sinfo(
net, im, stochastic_info, parameters=None
):
"""
Gets the tangible reacahbility graph from a Petri net, an initial marking and a stochastic map
Parameters
-------------
net
Petri net
im
Initial marking
fm
Final marking
stochastic_info
Stochastic information
Returns
------------
reachab_graph
Reachability graph
tangible_reach_graph
Tangible reachability graph
"""
if parameters is None:
parameters = {}
reachab_graph = construct_reachability_graph(net, im, use_trans_name=True)
tang_reach_graph = get_tangible_reachability_from_reachability(
reachab_graph, stochastic_info
)
return reachab_graph, tang_reach_graph
[docs]
def get_tangible_reachability_from_reachability(reach_graph, stochastic_info):
"""
Gets the tangible reachability graph from the reachability graph and the stochastic transition map
Parameters
------------
reach_graph
Reachability graph
stochastic_info
Stochastic information
Returns
------------
tangible_reach_graph
Tangible reachability graph
"""
timed_transitions = []
for trans in stochastic_info.keys():
random_variable = stochastic_info[trans]
transition_type = random_variable.get_transition_type()
if transition_type == "TIMED":
timed_transitions.append(trans.name)
states_reach = list(reach_graph.states)
for s in states_reach:
state_outgoing_trans = list(s.outgoing)
state_ingoing_trans = list(s.incoming)
timed_trans_outgoing = [
x for x in state_outgoing_trans if x.name in timed_transitions
]
if not len(state_outgoing_trans) == len(timed_trans_outgoing):
for t in state_outgoing_trans:
reach_graph.transitions.remove(t)
t.from_state.outgoing.remove(t)
t.to_state.incoming.remove(t)
for t in state_ingoing_trans:
reach_graph.transitions.remove(t)
t.from_state.outgoing.remove(t)
t.to_state.incoming.remove(t)
reach_graph.states.remove(s)
states_reach = list(reach_graph.states)
for s in states_reach:
if len(s.incoming) == 0 and len(s.outgoing) == 0:
reach_graph.states.remove(s)
return reach_graph
