def toFun(self, n):
idx = simplify(n).as_long()
# In case the solver randomly select a solution to be a fun
# without fmap existing in the model
arity = self.arity[idx]
if self.fmap is None or self.fmap[idx] is None:
return self.defaultFun(arity)
defn = self.getArrayDecl(self.fmap[idx], self.erl.List).as_list()
# Also prune keys with the wrong arity (mainly to genericFun specs adding an Exists axiom)
kvs = [self.encodeKVPair(args, val) for [args, val] in defn[0:-1] if self.getActualArity(args) == arity]
if len(kvs) == 0:
default = self.encodeDefault(defn[-1])
return cc.mk_const_fun(arity, default)
else:
default = cc.get_value_from_fun_entry(kvs[0])
# Do not add the arity.
# It will be deducted from the number of the arguments of the
# first input.
# return {"t": cc.JSON_TYPE_FUN, "v": kvs, "x": arity}
return cc.mk_fun(arity, kvs, default)
def toFun(self, n):
idx = simplify(n).as_long()
# In case the solver randomly select a solution to be a fun
# without fmap existing in the model
arity = self.arity[idx]
if self.fmap is None or self.fmap[idx] is None:
return self.defaultFun(arity)
defn = self.getArrayDecl(self.fmap[idx], self.erl.List).as_list()
# Also prune keys with the wrong arity (mainly to genericFun specs adding an Exists axiom)
kvs = [self.encodeKVPair(args, val) for [args, val] in defn[0:-1] if self.getActualArity(args) == arity]
if len(kvs) == 0:
default = self.encodeDefault(defn[-1])
return cc.mk_const_fun(arity, default)
else:
default = cc.get_value_from_fun_entry(kvs[0])
# Do not add the arity.
# It will be deducted from the number of the arguments of the
# first input.
# return {"t": cc.JSON_TYPE_FUN, "v": kvs, "x": arity}
return cc.mk_fun(arity, kvs, default)
def defaultFun(self, arity):
default = self.encodeDefault(self.erl.Term.int(0))
return cc.mk_const_fun(arity, default)
def defaultFun(self, arity):
default = self.encodeDefault(self.erl.Term.int(0))
return cc.mk_const_fun(arity, default)
