def __init__(self, petri_net: PetriNet, mark: np.array):
"""
Constructor
"""
assert (isinstance(petri_net, PetriNet)), "Has to be a Petri Net"
self._petriNet = petri_net
self._dim = petri_net.get_dim()
self._accelerations = np.array([])
self._root = CovNode(mark, self._dim, 0, None)
self._vertices = [self._root] # a list of all the vertices
self._front = [
self._root
] # a list of the vertices which still weren't processed
self._verSet = {
tuple(self._root.marking)
} # A set of all the marks, used to increase speed(hash)
# Options for the run of generate_cov_tree():
self.timeout = float("inf") # The time out for generate_cov_tree()
self.keep_accelerations = True # Remember and reuse previous accelerations
self.use_z3_to_guess_accelerations = False # Use z3 to try to guess some accelerations before starting for
self.z3_timeout = 10 # z3_timeout time
self.check_for_correctness = True # in the end of generate_cov_tree() preform a few checks for correctness
# setting the graph traversal:
self._type_of_graph_traversal = 1
self.pushFront = self.push_into_front_function()
self.popFront = self.pop_next_vertex_func()
self.deleteFromFront = self.remove_from_front_function()
# Variables for performance:
self.verbose = False
self.max_size = 1
self.max_size_of_acc = 0
self.use_of_acc = 0
self.count = 0
self.max_depth = 0
self.number_of_deleted_nodes = 0
self.number_of_deleted_decedents = 0
self.max_depth_of_acc = 0
self.average_num_of_successors = 0
self.num_of_comparisons = 0
self.average_vertics_size = 1
self.num_of_accelaration_tried = 0
self.num_of_rechecks = 0
def export_petri_to_valmari(petri_net: PetriNet, filename):
file = open(filename, "a")
# file.write("samples = []")
# file.write("omega = float('inf')")
# file.write("def Init():")
file.write("%d %d \n" % (petri_net.get_dim(), len(petri_net.get_transitions())))
trun_num = 1
for tran in petri_net.get_transitions():
place_num = 1
for p in (tran.get_incidence() + tran.get_pre()):
if p <= 0:
place_num += 1
continue
file.write("-%d \n" % trun_num)
file.write("0 %d \n" % p)
file.write("%d \n" % place_num)
place_num += 1
place_num = 1
for p in tran.get_pre():
if p <= 0:
place_num += 1
continue
file.write("%d \n" % place_num)
file.write("0 %d \n" % p)
file.write("-%d \n" % trun_num)
place_num += 1
trun_num += 1
file.write("0 \n")
place_num = 1
for p in petri_net.get_mark():
if p <= 0:
place_num += 1
continue
if p == float("inf"):
file.write("-32767 \n")
file.write("%d \n" % place_num)
else:
file.write("-%d \n" % p)
file.write("%d \n" % place_num)
place_num += 1
file.write("0 \n")
file.close()
def run_benchmark(name,
petri: petri_net.PetriNet,
time_out,
init_marking=None,
with_acc=True):
benchmark = Benchmark(name)
print("starting %s" % name + " numb of trans: " +
str(len(petri.get_transitions())) + " num of places: " +
str(petri.get_dim()))
start_time = time.time()
if init_marking is None:
cov = CovTree(petri, petri.get_mark())
else:
cov = CovTree(petri, init_marking)
cov.keep_accelerations = with_acc
cov.type_of_graph_traversal = "DFS"
cov.use_z3_to_guess_accelerations = False
cov.check_for_correctness = False
cov.verbose = True
cov.timeout = time_out
benchmark.clover = cov.generate_cov_tree()
benchmark.timeout = benchmark.clover is None
elapsed_time = time.time() - start_time
benchmark.max_vertices = cov.max_size
benchmark.max_accelerations = cov.max_size_of_acc
benchmark.final_accelerations = len(cov._accelerations)
benchmark.final_vertices = len(cov._verSet)
benchmark.used_accelerations = cov.use_of_acc
benchmark.time = elapsed_time
benchmark.places = len(petri.get_places())
benchmark.transitions = len(petri.get_transitions())
benchmark.num_of_comparisons = cov.num_of_comparisons
benchmark.num_of_recheck = cov.num_of_rechecks
return benchmark, cov