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Use soft thresholding for an infinite firm upper threshold #888

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Use soft thresholding for an infinite firm upper threshold #888

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3 changes: 3 additions & 0 deletions pywt/_thresholding.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,6 +238,9 @@ def threshold_firm(data, value_low, value_high):
raise ValueError(
"value_high must be greater than or equal to value_low.")

if np.isinf(value_high):
return soft(data, value_low)

data = np.asarray(data)
magnitude = np.absolute(data)
with np.errstate(divide='ignore', invalid='ignore'):
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17 changes: 17 additions & 0 deletions pywt/tests/test_thresholding.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,5 @@
import numpy as np
import pytest
from numpy.testing import assert_, assert_allclose, assert_equal, assert_raises

import pywt
Expand Down Expand Up @@ -200,3 +201,19 @@ 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())


@pytest.mark.parametrize("dtype", [np.float32, np.float64, np.complex64, np.complex128])
@pytest.mark.parametrize("value_low", [0.0, 2.0])
def test_threshold_firm_infinite_upper_threshold(dtype, value_low):
data = np.array([-4, -2, -1, 0, 1, 2, 4], dtype=dtype)
if np.issubdtype(dtype, np.complexfloating):
data = data * (1 + 1j)
original = data.copy()

actual = pywt.threshold_firm(data, value_low, np.inf)
expected = pywt.threshold(data, value_low, mode='soft')

assert_allclose(actual, expected)
assert actual.dtype == expected.dtype
assert_allclose(data, original)