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gh-149879: Fix test_math on Cygwin #150432

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gh-149879: Fix test_math on Cygwin #150432

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cd77dae
gh-149879: Fix test_math on Cygwin
vstinner May 25, 2026
7e743a3
Fix test_statistics
vstinner May 26, 2026
a144a7a
Move @skip_on_newlib to test.support
vstinner May 26, 2026
412dfa9
Rename test_random() to test_fma_random()
vstinner May 26, 2026
9cb8cf5
unskip testLog2()
vstinner May 26, 2026
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4 changes: 4 additions & 0 deletions Lib/test/support/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2806,6 +2806,10 @@ def exceeds_recursion_limit():
is_s390x = hasattr(os, 'uname') and os.uname().machine == 's390x'
skip_on_s390x = unittest.skipIf(is_s390x, 'skipped on s390x')

# Cygwin uses the newlib C library
skip_on_newlib = unittest.skipIf(sys.platform == 'cygwin',
'the test fails on newlib C library')

Py_TRACE_REFS = hasattr(sys, 'getobjects')

_JIT_ENABLED = sys._jit.is_enabled()
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22 changes: 14 additions & 8 deletions Lib/test/test_math.py
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Expand Up @@ -922,6 +922,7 @@ def testHypot(self):
@requires_IEEE_754
@unittest.skipIf(HAVE_DOUBLE_ROUNDING,
"hypot() loses accuracy on machines with double rounding")
@support.skip_on_newlib
def testHypotAccuracy(self):
# Verify improved accuracy in cases that were known to be inaccurate.
#
Expand Down Expand Up @@ -1253,12 +1254,6 @@ def testLog2(self):
self.assertEqual(math.log2(4), 2.0)
self.assertEqual(math.log2(MyIndexable(4)), 2.0)

# Large integer values
self.assertEqual(math.log2(2**1023), 1023.0)
self.assertEqual(math.log2(2**1024), 1024.0)
self.assertEqual(math.log2(2**2000), 2000.0)
self.assertEqual(math.log2(MyIndexable(2**2000)), 2000.0)

self.assertRaises(ValueError, math.log2, 0.0)
self.assertRaises(ValueError, math.log2, 0)
self.assertRaises(ValueError, math.log2, MyIndexable(0))
Expand All @@ -1276,12 +1271,19 @@ def testLog2(self):
@requires_IEEE_754
# log2() is not accurate enough on Mac OS X Tiger (10.4)
@support.requires_mac_ver(10, 5)
@support.skip_on_newlib
def testLog2Exact(self):
# Check that we get exact equality for log2 of powers of 2.
actual = [math.log2(math.ldexp(1.0, n)) for n in range(-1074, 1024)]
expected = [float(n) for n in range(-1074, 1024)]
self.assertEqual(actual, expected)

# Large integer values
self.assertEqual(math.log2(2**1023), 1023.0)
self.assertEqual(math.log2(2**1024), 1024.0)
self.assertEqual(math.log2(2**2000), 2000.0)
self.assertEqual(math.log2(MyIndexable(2**2000)), 2000.0)

def testLog10(self):
self.assertRaises(TypeError, math.log10)
self.ftest('log10(0.1)', math.log10(0.1), -1)
Expand Down Expand Up @@ -2615,6 +2617,7 @@ def test_fma_nan_results(self):
self.assertIsNaN(math.fma(a, math.nan, b))
self.assertIsNaN(math.fma(a, b, math.nan))

@support.skip_on_newlib
def test_fma_infinities(self):
# Cases involving infinite inputs or results.
positives = [1e-300, 2.3, 1e300, math.inf]
Expand Down Expand Up @@ -2685,7 +2688,7 @@ def test_fma_infinities(self):
# gh-73468: On some platforms, libc fma() doesn't implement IEE 754-2008
# properly: it doesn't use the right sign when the result is zero.
@unittest.skipIf(
sys.platform.startswith(("freebsd", "wasi", "netbsd", "emscripten"))
sys.platform.startswith(("freebsd", "wasi", "netbsd", "emscripten", "cygwin"))
or (sys.platform == "android" and platform.machine() == "x86_64")
or support.linked_to_musl(), # gh-131032
f"this platform doesn't implement IEE 754-2008 properly")
Expand Down Expand Up @@ -2743,6 +2746,7 @@ def test_fma_zero_result(self):
self.assertIsNegativeZero(math.fma(y-x, -(x+y), -z))
self.assertIsPositiveZero(math.fma(x-y, -(x+y), z))

@support.skip_on_newlib
def test_fma_overflow(self):
a = b = float.fromhex('0x1p512')
c = float.fromhex('0x1p1023')
Expand Down Expand Up @@ -2776,11 +2780,13 @@ def test_fma_overflow(self):
c = float.fromhex('0x1.fffffffffffffp+1023')
self.assertEqual(math.fma(a, b, -c), c)

@support.skip_on_newlib
def test_fma_single_round(self):
a = float.fromhex('0x1p-50')
self.assertEqual(math.fma(a - 1.0, a + 1.0, 1.0), a*a)

def test_random(self):
@support.skip_on_newlib
def test_fma_random(self):
# A collection of randomly generated inputs for which the naive FMA
# (with two rounds) gives a different result from a singly-rounded FMA.

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3 changes: 2 additions & 1 deletion Lib/test/test_statistics.py
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Expand Up @@ -16,7 +16,7 @@
import sys
import unittest
from test import support
from test.support import import_helper, requires_IEEE_754
from test.support import import_helper, requires_IEEE_754, skip_on_newlib

from decimal import Decimal
from fractions import Fraction
Expand Down Expand Up @@ -2799,6 +2799,7 @@ def test_sqrtprod_helper_function_fundamentals(self):
@unittest.skipIf(HAVE_DOUBLE_ROUNDING,
"accuracy not guaranteed on machines with double rounding")
@support.cpython_only # Allow for a weaker sumprod() implementation
@skip_on_newlib
def test_sqrtprod_helper_function_improved_accuracy(self):
# Test a known example where accuracy is improved
x, y, target = 0.8035720646477457, 0.7957468097636939, 0.7996498651651661
Expand Down
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