def __init__(self, var: FieldScalarModel):
self.var = var
self.domain: RangelistModel = RangelistModel()
self.propagators: List[VariableBoundPropagator] = []
self.domain_sz = -1
self.domain_offsets = []
# Whether this field is referenced by a constraint
self.constrained = False
def test_partition_mixed_nonconsecutive_values(self):
rangelist = RangelistModel(
[1, 3, 5, [7, 10], 12, [13, 15], 17, [19, 22]])
bins = CoverpointBinCollectionModel.mk_collection("bin", rangelist, 4)
bins.finalize(0)
self.assertEqual(4, len(bins.bin_l))
self.assertEqual(4, bins.get_n_bins())
def test_partition_mixed_consecutive_values(self):
rangelist = RangelistModel(
[1, 2, 3, [4, 7], 8, 11, 13, [9, 13], 14, 15])
bins = CoverpointBinCollectionModel.mk_collection("bin", rangelist, 4)
bins.finalize(0)
self.assertEqual(4, len(bins.bin_l))
self.assertEqual(4, bins.get_n_bins())
def test_partition_individual_nonconsecutive_values(self):
rangelist = RangelistModel(
[1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25])
bins = CoverpointBinCollectionModel.mk_collection("bin", rangelist, 4)
bins.finalize(0)
self.assertEqual(4, len(bins.bin_l))
self.assertEqual(4, bins.get_n_bins())
for b in bins.bin_l:
self.assertTrue(isinstance(b, CoverpointBinSingleBagModel))
def test_partition_single_range_odd(self):
rangelist = RangelistModel([[1, 18]])
bins = CoverpointBinCollectionModel.mk_collection("bin", rangelist, 4)
bins.finalize(0)
self.assertEqual(4, len(bins.bin_l))
self.assertEqual(4, bins.get_n_bins())
self.assertTrue(isinstance(bins.bin_l[3], CoverpointBinSingleBagModel))
self.assertEqual(18, bins.bin_l[3].binspec.range_l[-1][1])
def test_partition_single_range_even(self):
rangelist = RangelistModel([[1, 16]])
bins = CoverpointBinCollectionModel.mk_collection("bin", rangelist, 4)
bins.finalize(0)
self.assertEqual(4, len(bins.bin_l))
self.assertEqual(4, bins.get_n_bins())
self.assertTrue(
isinstance(bins.bin_l[3], CoverpointBinSingleRangeModel))
self.assertEqual(16, bins.bin_l[3].target_val_high)
def test_coverpoint_bins(self):
stim = FieldCompositeModel("stim", True)
f = FieldScalarModel("a", 16, False, True)
stim.add_field(f)
f2 = FieldScalarModel("b", 16, False, True)
stim.add_field(f2)
cg = CovergroupModel("cg")
cp = CoverpointModel(ExprFieldRefModel(f), "cp1",
CoverageOptionsModel())
cg.add_coverpoint(cp)
cp.add_bin_model(CoverpointBinArrayModel("bn1", 0, 1, 16))
cp.add_bin_model(CoverpointBinCollectionModel.mk_collection("bn2", RangelistModel([
[17,65535-16-1]
]), 16))
cp.add_bin_model(CoverpointBinArrayModel("bn3", 0, 65535-16, 65535))
cp2 = CoverpointModel(ExprFieldRefModel(f2), "cp2",
CoverageOptionsModel())
cg.add_coverpoint(cp2)
bn = CoverpointBinArrayModel("cp", 0, 1, 16)
cp2.add_bin_model(bn)
gen = GeneratorModel("top")
gen.add_field(stim)
gen.add_covergroup(cg)
gen.finalize()
# Need a special randomizer to deal with generators
r = Randomizer()
count = 0
for i in range(1000):
r.do_randomize([gen])
cg.sample()
count += 1
cov = cg.get_coverage()
if cov == 100:
break
self.assertEqual(cg.get_coverage(), 100)
# Ensure that we converge relatively quickly
self.assertLessEqual(count, 64)
def get_bin_range(self, idx):
print("get_bin_range: " + str(idx))
return RangelistModel([self.target_val])
def __init__(self, name, binspec: RangelistModel = None):
super().__init__(name)
self.binspec = binspec if binspec is not None else RangelistModel()
self.n_bins = 1