def verify_min_constraint(self, colname, constraint, detect=False):
"""
Verify whether a given column satisfies the minimum 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 minimum is not considered to be an
return True # active constraint, so is always satisfied
m = self.get_min(colname)
if self.is_null(m): # If there are no values, no value can
return True # the minimum 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_greater_than(m, value, self.epsilon)
if detect and not bool(result):
self.detect_min_constraint(colname, value, precision, self.epsilon)
return result
def test_fuzzy_greater_than(self):
goods = (
(1.0, 1.0),
(.9900001, 1.0),
(MILLION, MILLION),
(999900, MILLION),
(999899, MILLION),
(999899.999, REAL_MILLION),
(1e-100, 1e-100),
(0.99000001e-100, 1e-100),
(-1.0, -1.0),
(-1.009999, -1.0),
(-MILLION, -MILLION),
(-MILLION + 10000, -MILLION),
(-REAL_MILLION, -REAL_MILLION),
(-REAL_MILLION + 10000.0001, -REAL_MILLION),
(-REAL_MILLION + 10000.0, -MILLION),
(-1e-100, -1e-100),
(-0.99001e-100, -1e-100),
)
bad_goods = (
(999900 - SMALL, MILLION), # Should fail mathematically.
# But MILLION + SMALL == MILLION
)
bads = (
(0.9899999, 1.0),
(MILLION - 10001, MILLION),
(MILLION - 10000.0000000001, MILLION),
(0.989999 - 100, 1.0e-100),
(-1.01001, -1.0),
(-MILLION - 10001, -MILLION),
(-MILLION - 10000.0000000001, -MILLION),
(-1.0100001e-100, -1.0e-100),
)
cvt = ConstraintVerificationTester(self, df=None)
self.assertEqual(999900 - SMALL, 999900)
epsilon = 0.01
for (x, y) in goods + bad_goods:
self.assertTrue(fuzzy_greater_than(x, y, epsilon))
for (x, y) in bads:
self.assertFalse(fuzzy_greater_than(x, y, epsilon))
def test_fuzzy_greater_than_zero(self):
cvt = ConstraintVerificationTester(self, df=None)
epsilon = 0.01
for x in POS_REALS:
self.assertTrue(fuzzy_greater_than(x, 0.0, epsilon))
self.assertFalse(fuzzy_greater_than(0.0, x, epsilon))
for x in NEG_REALS:
self.assertFalse(fuzzy_greater_than(x, 0.0, epsilon))
self.assertTrue(fuzzy_greater_than(0.0, x, epsilon))
self.assertTrue(fuzzy_greater_than(0.0, 0.0, epsilon))
self.assertTrue(fuzzy_greater_than(0.0, SMALL / 2, epsilon)) # == 0.0
self.assertEqual(SMALL / 2, 0.0)
def verify_min_constraint(self, colname, constraint, detect=False):
"""
Verify whether a given column satisfies the minimum 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 minimum is not considered to be an
return True # active constraint, so is always satisfied
m = self.get_min(colname)
if self.is_null(m): # If there are no values, no value can
return True # the minimum 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_greater_than(m, value, self.epsilon)
if detect and not bool(result):
self.detect_min_constraint(colname, value, precision, self.epsilon)
return result