"run_no_yield_async_fn", "run_yielding_async_fn", "async_yield",
"reset_code", "on_github_actions",
"requires_root_user", "requires_non_root_user",
+ "skip_if_double_rounding",
]
float.__getformat__("double").startswith("IEEE"),
"test requires IEEE 754 doubles")
+# detect evidence of double-rounding:
+x, y = 1e16, 2.9999 # use temporary values to defeat peephole optimizer
+HAVE_DOUBLE_ROUNDING = (x + y == 1e16 + 4)
+skip_if_double_rounding = unittest.skipIf(HAVE_DOUBLE_ROUNDING,
+ "accuracy not guaranteed on "
+ "machines with double rounding")
+del x, y, HAVE_DOUBLE_ROUNDING
+
+
def requires_zlib(reason='requires zlib'):
try:
import zlib
from test.support.script_helper import assert_python_ok
from test.support.testcase import ComplexesAreIdenticalMixin
from test.support.warnings_helper import check_warnings
-from test.support import requires_IEEE_754
+from test.support import requires_IEEE_754, skip_if_double_rounding
from unittest.mock import MagicMock, patch
try:
import pty, signal
pty = signal = None
-# Detect evidence of double-rounding: sum() does not always
-# get improved accuracy on machines that suffer from double rounding.
-x, y = 1e16, 2.9999 # use temporary values to defeat peephole optimizer
-HAVE_DOUBLE_ROUNDING = (x + y == 1e16 + 4)
-
# used as proof of globals being used
A_GLOBAL_VALUE = 123
A_SENTINEL = sentinel("A_SENTINEL")
complex(2, -0.0))
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "sum accuracy not guaranteed on machines with double rounding")
+ @skip_if_double_rounding
@support.cpython_only # Other implementations may choose a different algorithm
def test_sum_accuracy(self):
self.assertEqual(sum([0.1] * 10), 1.0)
# Python test set -- math module
# XXXX Should not do tests around zero only
-from test.support import verbose, requires_IEEE_754
+from test.support import (verbose, requires_IEEE_754,
+ skip_if_double_rounding)
from test import support
import unittest
import fractions
FLOAT_MAX = sys.float_info.max
FLOAT_MIN = sys.float_info.min
-# detect evidence of double-rounding: fsum is not always correctly
-# rounded on machines that suffer from double rounding.
-x, y = 1e16, 2.9999 # use temporary values to defeat peephole optimizer
-HAVE_DOUBLE_ROUNDING = (x + y == 1e16 + 4)
-
# locate file with test values
if __name__ == '__main__':
file = sys.argv[0]
self.assertTrue(math.isnan(math.frexp(NAN)[0]))
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "fsum is not exact on machines with double rounding")
+ @skip_if_double_rounding
def testFsum(self):
# math.fsum relies on exact rounding for correct operation.
# There's a known problem with IA32 floating-point that causes
self.assertRaises(TypeError, math.hypot, *([1.0]*18), 'spam')
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "hypot() loses accuracy on machines with double rounding")
+ @skip_if_double_rounding
@support.skip_on_newlib
def testHypotAccuracy(self):
# Verify improved accuracy in cases that were known to be inaccurate.
self.assertEqual(sumprod(*args), 0.0)
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "sumprod() accuracy not guaranteed on machines with double rounding")
+ @skip_if_double_rounding
@support.cpython_only # Other implementations may choose a different algorithm
def test_sumprod_accuracy(self):
sumprod = math.sumprod
)
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "sumprod() accuracy not guaranteed on machines with double rounding")
+ @skip_if_double_rounding
@support.cpython_only # Other implementations may choose a different algorithm
@support.requires_resource('cpu')
def test_sumprod_extended_precision_accuracy(self):
import sys
import unittest
from test import support
-from test.support import import_helper, requires_IEEE_754, skip_on_newlib
+from test.support import (import_helper, requires_IEEE_754,
+ skip_if_double_rounding, skip_on_newlib)
from decimal import Decimal
from fractions import Fraction
# === Helper functions and class ===
-# Test copied from Lib/test/test_math.py
-# detect evidence of double-rounding: fsum is not always correctly
-# rounded on machines that suffer from double rounding.
-x, y = 1e16, 2.9999 # use temporary values to defeat peephole optimizer
-HAVE_DOUBLE_ROUNDING = (x + y == 1e16 + 4)
-
def sign(x):
"""Return -1.0 for negatives, including -0.0, otherwise +1.0."""
return math.copysign(1, x)
self.assertEqual(sign(actual), sign(expected))
@requires_IEEE_754
- @unittest.skipIf(HAVE_DOUBLE_ROUNDING,
- "accuracy not guaranteed on machines with double rounding")
+ @skip_if_double_rounding
@support.cpython_only # Allow for a weaker sumprod() implementation
@skip_on_newlib
def test_sqrtprod_helper_function_improved_accuracy(self):