def verify_max_constraint(self, colname, constraint, detect=False):
"""
Verify whether a given column satisfies the maximum value
constraint specified.
"""
if not self.column_exists(colname):
return False
value = constraint.value
precision = getattr(constraint, 'precision', 'fuzzy') or 'fuzzy'
assert precision in PRECISIONS
if self.is_null(value): # a null maximum is not considered to be an
return True # active constraint, so is always satisfied
M = self.get_max(colname)
if self.is_null(M): # If there are no values, no value can
return True # the maximum constraint
if isinstance(value, datetime.datetime):
M = self.to_datetime(M)
if not self.types_compatible(M, value, colname):
result = False
elif precision == 'closed':
result = M <= value
elif precision == 'open':
result = M < value
else:
result = fuzzy_less_than(M, value, self.epsilon)
if detect and not bool(result):
self.detect_max_constraint(colname, value, precision, self.epsilon)
return result
def test_fuzzy_less_than(self):
goods = (
(1.0, 1.0),
(1.00999, 1.0),
(MILLION, MILLION),
(MILLION + 9999, MILLION),
(MILLION + 10000, MILLION),
(REAL_MILLION, REAL_MILLION),
(MILLION + 9999.0, REAL_MILLION),
(MILLION + 10000.0, MILLION),
(1e-100, 1e-100),
(1.00999e-100, 1e-100),
(-1.0, -1.0),
(-.990001, -1.0),
(-MILLION, -MILLION),
(-MILLION + 9999, -MILLION),
(-MILLION + 10000, -MILLION),
(-REAL_MILLION, -REAL_MILLION),
(-REAL_MILLION + 9999.0, -REAL_MILLION),
(-REAL_MILLION + 10000.0, -MILLION),
(-1e-100, -1e-100),
(-0.999999e-100, -1e-100),
)
bad_goods = (
(MILLION + 10000 + SMALL, MILLION), # Should fail mathematically.
# But rounding
)
bads = (
(1.01000001, 1.0),
(MILLION + 10001, MILLION),
(MILLION + 10000.0000000001, MILLION),
(1.010001e-100, 1.0e-100),
(-0.989999, -1.0),
(-MILLION + 100001, -MILLION),
(-MILLION + 10000.0000000001, -MILLION),
(-0.98999e-100, -1.0e-100),
)
cvt = ConstraintVerificationTester(self, df=None)
self.assertEqual(MILLION + 10000 + SMALL, MILLION + 10000)
epsilon = 0.01
for (x, y) in goods + bad_goods:
self.assertTrue(fuzzy_less_than(x, y, epsilon))
for (x, y) in bads:
self.assertFalse(fuzzy_less_than(x, y, epsilon))
def test_fuzzy_less_than_zero(self):
verifier = pdc.PandasConstraintVerifier(df=None)
epsilon = 0.01
for x in NEG_REALS:
self.assertTrue(fuzzy_less_than(x, 0.0, epsilon))
self.assertFalse(fuzzy_less_than(0.0, x, epsilon))
for x in POS_REALS:
self.assertFalse(fuzzy_less_than(x, 0.0, epsilon))
self.assertTrue(fuzzy_less_than(0.0, x, epsilon))
self.assertTrue(fuzzy_less_than(0.0, 0.0, epsilon))
self.assertTrue(fuzzy_less_than(0.0, SMALL / 2, epsilon)) # == 0.0
self.assertEqual(SMALL / 2, 0.0)
def verify_max_constraint(self, colname, constraint, detect=False):
"""
Verify whether a given column satisfies the maximum value
constraint specified.
"""
if not self.column_exists(colname):
return False
value = constraint.value
precision = getattr(constraint, 'precision', 'fuzzy') or 'fuzzy'
assert precision in PRECISIONS
if self.is_null(value): # a null maximum is not considered to be an
return True # active constraint, so is always satisfied
M = self.get_max(colname)
if self.is_null(M): # If there are no values, no value can
return True # the maximum constraint
if (isinstance(value, datetime.datetime)
or isinstance(value, datetime.date)):
M = self.to_datetime(M)
if not self.types_compatible(M, value):
result = False
elif (precision == 'closed' or isinstance(value, datetime.datetime)
or isinstance(value, datetime.date)):
result = M <= value
elif precision == 'open':
result = M < value
else:
result = fuzzy_less_than(M, value, self.epsilon)
if detect and not bool(result):
self.detect_max_constraint(colname, value, precision, self.epsilon)
return result