def and_fml(path):
if len(path) == 1:
return PLTLFormula([path[0], None, None])
left = PLTLFormula([path[0], None, None])
return PLTLFormula(['&', left, and_fml(path[1:])])
def constants(self, varName):
const = str(varName[0])
if (const == "S_TRUE"):
const = "TRUE"
elif (const == "S_FALSE"):
const = "FALSE"
return PLTLFormula([const, None, None])
def dict_var(self, f, vars_dict):
if f is None:
return None
if (f._isLeaf()):
if f.label == 'TRUE' or f.label == 'FALSE':
var_name = f.label
else:
var_name = vars_dict[f.label]
f = PLTLFormula([var_name, None, None])
return f
left = self.dict_var(f.left, vars_dict)
right = self.dict_var(f.right, vars_dict)
return PLTLFormula([f.label, left, right])
def truthTable(self, formula):
nodes = list(set(formula.getAllNodes()))
self.truthAssignmentTable = {node: [None for _ in range(self.traceLength)] for node in nodes}
for i in range(self.vars):
fml = PLTLFormula([self.literals[i], None, None])
self.truthAssignmentTable[fml] = [bool(measurement[i]) for measurement in self.traceVector]
def or_fml(path):
if len(path) == 1:
return and_fml(path[0])
left = and_fml(path[0])
right = or_fml(path[1:])
return PLTLFormula(['|', left, right])
def or_fml(path):
# print('PATH:', path)
if len(path) == 1:
return and_fml(path[0])
left = and_fml(path[0])
right = or_fml(path[1:])
return PLTLFormula(['|', left, right])
def m2f(self, size , model):
def truth_value(depth, variables):
for v in variables:
vflag = model[variables[v]]
if(v[0] == depth and vflag.is_true()):
# logging.debug('%s := %s'%(str(v[0]), str(vflag)))
return v[1]
#Can filter SAT variables?
label = truth_value(size, self.x)
# print('label', label)
# print('Depth', depth)
if(label in self.Constants):
if label == 'Top':
label = 'TRUE'
else:
label = 'FALSE'
f = PLTLFormula([label, None, None])
# print('Formula', f)
return f
if(label in self.AP):
f = PLTLFormula([str(label), None, None])
# print('Formula', f)
return f
elif(label in self.unaryOp):
left = truth_value(size, self.l)
f = PLTLFormula([label, self.m2f(left, model)])
# print('Formula',f)
return f
elif(label in self.binaryOp):
left = truth_value(size, self.l)
right = truth_value(size, self.r)
f = PLTLFormula([label, self.m2f(left, model), self.m2f(right, model)])
# print('Formula',f)
return f
def variable(self, cname):
var = cname[0]
return PLTLFormula([var, None, None])
def formula(self, formulaArgs):
return PLTLFormula(formulaArgs)
def variable(self, number):
var = 'p' + number[0]
return PLTLFormula([var, None, None])
def variable(self, varName):
return PLTLFormula([str(varName[0]), None, None])
def variable(self, cname):
var = cname[0]
if var in self.var_dict:
return self.var_dict[var]
return PLTLFormula([var, None, None])
