Source code for pm4py.evaluation.soundness.woflan.place_invariants.place_invariants

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import numpy as np
import sympy

[docs]def compute_place_invariants(net): """ We compute the NUllspace of the incidence matrix and obtain the place-invariants. :param net: Petri Net of which we want to know the place invariants. :return: Set of place invariants of the given Petri Net. """ def compute_incidence_matrix(net): """ Given a Petri Net, the incidence matrix is computed. An incidence matrix has n rows (places) and m columns (transitions). :param net: Petri Net object :return: Incidence matrix """ n = len(net.transitions) m = len(net.places) C = np.zeros((m, n)) i = 0 transition_list = list(net.transitions) place_list = list(net.places) while i < n: t = transition_list[i] for in_arc in t.in_arcs: # arcs that go to transition C[place_list.index(in_arc.source), i] -= 1 for out_arc in t.out_arcs: # arcs that lead away from transition C[place_list.index(, i] += 1 i += 1 return C def extract_basis_vectors(incidence_matrix): """ The name of the method describes what we want t achieve. We calculate the nullspace of the transposed identity matrix. :param incidence_matrix: Numpy Array :return: a collection of numpy arrays that form a base of transposed A """ # To have the same dimension as described as in and to get the correct nullspace, we have to transpose A = np.transpose(incidence_matrix) # exp from book x = sympy.Matrix(A).nullspace() # TODO: Question here: Will x be always rational? Depends on sympy implementation. Normaly, yes, we we will have rational results x = np.array(x).astype(np.float64) return x A = compute_incidence_matrix(net) return extract_basis_vectors(A)