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BUG: return 0 not NaN when thresholding zeros at value 0 #878

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Merged
rgommers merged 1 commit into from
Sep 17, 2026
+45 −3
Merged

BUG: return 0 not NaN when thresholding zeros at value 0 #878

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13 changes: 10 additions & 3 deletions pywt/_thresholding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,11 +17,14 @@ def soft(data, value, substitute=0):
data = np.asarray(data)
magnitude = np.absolute(data)

with np.errstate(divide='ignore'):
with np.errstate(divide='ignore', invalid='ignore'):
# divide by zero okay as np.inf values get clipped, so ignore warning.
# 0/0 (value==0 and data==0) is invalid and yields NaN; zero those below.
thresholded = (1 - value/magnitude)
thresholded.clip(min=0, max=None, out=thresholded)
thresholded = data * thresholded
# Keep dtype; sign(0) is undefined, but soft-threshold(0) is 0 for any λ≥0.
thresholded[magnitude == 0] = 0

if substitute == 0:
return thresholded
Expand All @@ -35,11 +38,13 @@ def nn_garrote(data, value, substitute=0):
data = np.asarray(data)
magnitude = np.absolute(data)

with np.errstate(divide='ignore'):
with np.errstate(divide='ignore', invalid='ignore'):
# divide by zero okay as np.inf values get clipped, so ignore warning.
# 0/0 (value==0 and data==0) is invalid and yields NaN; zero those below.
thresholded = (1 - value**2/magnitude**2)
thresholded.clip(min=0, max=None, out=thresholded)
thresholded = data * thresholded
thresholded[magnitude == 0] = 0

if substitute == 0:
return thresholded
Expand Down Expand Up @@ -235,12 +240,14 @@ def threshold_firm(data, value_low, value_high):

data = np.asarray(data)
magnitude = np.absolute(data)
with np.errstate(divide='ignore'):
with np.errstate(divide='ignore', invalid='ignore'):
# divide by zero okay as np.inf values get clipped, so ignore warning.
# 0/0 (value_low==0 and data==0) is invalid and yields NaN; zero those below.
vdiff = value_high - value_low
thresholded = value_high * (1 - value_low/magnitude) / vdiff
thresholded.clip(min=0, max=None, out=thresholded)
thresholded = data * thresholded
thresholded[magnitude == 0] = 0

# restore hard-thresholding behavior for values > value_high
large_vals = np.where(magnitude > value_high)
Expand Down
35 changes: 35 additions & 0 deletions pywt/tests/test_thresholding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -165,3 +165,38 @@ def test_threshold_firm():
mt_abs_firm = np.abs(d_firm[mt])
assert_(np.all(mt_abs_firm < np.abs(d_hard[mt])))
assert_(np.all(mt_abs_firm > np.abs(d_soft[mt])))


def test_threshold_zero_value_with_zeros():
# Issue 866: value==0 and exact-zero data used to yield NaN (0/0).
data = np.array([0.0, 1.0, -2.0])
expected = np.array([0.0, 1.0, -2.0])

assert_allclose(pywt.threshold(data, 0.0, 'soft'), expected, rtol=1e-12)
assert_allclose(pywt.threshold(data, 0.0, 'garrote'), expected, rtol=1e-12)
assert_allclose(pywt.threshold_firm(data, 0.0, 0.0), expected, rtol=1e-12)

# all zeros remain zeros (and must not warn: pytest treats warnings as errors)
zeros = np.zeros(8)
assert_allclose(pywt.threshold(zeros, 0.0, 'soft'), zeros, rtol=1e-12)
assert_allclose(pywt.threshold(zeros, 0.0, 'garrote'), zeros, rtol=1e-12)
assert_allclose(pywt.threshold_firm(zeros, 0.0, 0.0), zeros, rtol=1e-12)

# complex: same 0/0 path, result must be 0+0j not nan+nanj
cdata = np.array([0.0, 1.0 + 1.0j])
cexpected = np.array([0.0 + 0.0j, 1.0 + 1.0j])
assert_allclose(pywt.threshold(cdata, 0.0, 'soft'), cexpected, rtol=1e-12)
assert_allclose(pywt.threshold(cdata, 0.0, 'garrote'), cexpected, rtol=1e-12)
assert_allclose(pywt.threshold_firm(cdata, 0.0, 0.0), cexpected, rtol=1e-12)

for dtype in float_dtypes:
typed = np.asarray(data if dtype in real_dtypes else cdata, dtype=dtype)
out_soft = pywt.threshold(typed, 0.0, 'soft')
out_garrote = pywt.threshold(typed, 0.0, 'garrote')
out_firm = pywt.threshold_firm(typed, 0.0, 0.0)
assert_equal(out_soft.dtype, typed.dtype)
assert_equal(out_garrote.dtype, typed.dtype)
assert_equal(out_firm.dtype, typed.dtype)
assert_(not np.isnan(out_soft).any())
assert_(not np.isnan(out_garrote).any())
assert_(not np.isnan(out_firm).any())