Source code for pm4py.objects.stochastic_petri.tangible_reachability

'''
    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 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) 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