Source code for pm4py.objects.petri_net.inhibitor_reset.semantics

'''
    PM4Py – A Process Mining Library for Python
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'''
import copy
from pm4py.objects.petri_net.sem_interface import Semantics
from pm4py.objects.petri_net.obj import ResetNet
from pm4py.objects.petri_net.obj import InhibitorNet


[docs] class InhibitorResetSemantics(Semantics):
[docs] def is_enabled(self, t, pn, m, **kwargs): """ Verifies whether a given transition is enabled in a given Petri net and marking Parameters ---------- :param t: transition to check :param pn: Petri net :param m: marking to check Returns ------- :return: true if enabled, false otherwise """ return is_enabled(t, pn, m)
[docs] def execute(self, t, pn, m, **kwargs): """ Executes a given transition in a given Petri net and Marking Parameters ---------- :param t: transition to execute :param pn: Petri net :param m: marking to use Returns ------- :return: newly reached marking if :param t: is enabled, None otherwise """ return execute(t, pn, m)
[docs] def weak_execute(self, t, pn, m, **kwargs): """ Execute a transition even if it is not fully enabled Parameters ---------- :param t: transition to execute :param pn: Petri net :param m: marking to use Returns ------- :return: newly reached marking if :param t: is enabled, None otherwise """ return weak_execute(t, m)
[docs] def enabled_transitions(self, pn, m, **kwargs): """ Returns a set of enabled transitions in a Petri net and given marking Parameters ---------- :param pn: Petri net :param m: marking of the pn Returns ------- :return: set of enabled transitions """ return enabled_transitions(pn, m)
# 29/08/2021: the following methods have been incapsulated in the InhibitorResetSemantics class. # the long term idea is to remove them. However, first we need to adapt the existing code to the new # structure. Moreover, for performance reason, it is better to leave the code here, without having # to instantiate a InhibitorResetSemantics object.
[docs] def is_enabled(t, pn, m): if t not in pn.transitions: return False else: for a in t.in_arcs: if isinstance(a, InhibitorNet.InhibitorArc): if m[a.source] > 0: return False elif isinstance(a, ResetNet.ResetArc): pass elif m[a.source] < a.weight: return False return True
[docs] def execute(t, pn, m): if not is_enabled(t, pn, m): return None m_out = copy.copy(m) for a in t.in_arcs: if isinstance(a, ResetNet.ResetArc): m_out[a.source] = 0 del m_out[a.source] elif isinstance(a, InhibitorNet.InhibitorArc): pass else: m_out[a.source] -= a.weight if m_out[a.source] == 0: del m_out[a.source] for a in t.out_arcs: m_out[a.target] += a.weight return m_out
[docs] def weak_execute(t, m): m_out = copy.copy(m) for a in t.in_arcs: if isinstance(a, ResetNet.ResetArc): m_out[a.source] = 0 del m_out[a.source] elif isinstance(a, InhibitorNet.InhibitorArc): pass else: m_out[a.source] -= a.weight if m_out[a.source] <= 0: del m_out[a.source] for a in t.out_arcs: m_out[a.target] += a.weight return m_out
[docs] def enabled_transitions(pn, m): enabled = set() for t in pn.transitions: if is_enabled(t, pn, m): enabled.add(t) return enabled