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BUG: threshold_firm returns shrunk value when the only large value is at index 0 #883

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BUG: threshold_firm returns shrunk value when the only large value is at index 0 #883

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5 changes: 2 additions & 3 deletions pywt/_thresholding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,7 +250,6 @@ def threshold_firm(data, value_low, value_high):
thresholded[magnitude == 0] = 0

# restore hard-thresholding behavior for values > value_high
large_vals = np.where(magnitude > value_high)
if np.any(large_vals[0]):
thresholded[large_vals] = data[large_vals]
large_vals = magnitude > value_high
thresholded[large_vals] = data[large_vals]
return thresholded
12 changes: 12 additions & 0 deletions pywt/tests/test_thresholding.py
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Expand Up @@ -200,3 +200,15 @@ def test_threshold_zero_value_with_zeros():
assert_(not np.isnan(out_soft).any())
assert_(not np.isnan(out_garrote).any())
assert_(not np.isnan(out_firm).any())


def test_threshold_firm_large_value_first():
# Values above value_high must be returned unchanged even when the only
# such value sits at index 0 (of the first axis).
assert_allclose(pywt.threshold_firm(np.array([5.0, 0.5]), 1.0, 2.0),
[5.0, 0.0], rtol=1e-12)
assert_allclose(pywt.threshold_firm(np.array([[-5.0, 0.5], [0.5, 0.5]]),
1.0, 2.0),
[[-5.0, 0.0], [0.0, 0.0]], rtol=1e-12)
assert_allclose(pywt.threshold_firm(np.array([3.0 + 4.0j, 0.5]), 1.0, 2.0),
[3.0 + 4.0j, 0.0], rtol=1e-12)