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BUG: match wavedecn_shapes levels to per-axis wavelets #893

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BUG: match wavedecn_shapes levels to per-axis wavelets #893

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2 changes: 1 addition & 1 deletion pywt/_multilevel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -926,7 +926,7 @@ def wavedecn_shapes(shape, wavelet, mode='symmetric', level=None, axes=None):
modes = _modes_per_axis(mode, axes)
dec_lengths = [w.dec_len for w in wavelets]

level = _check_level(min(axes_shapes), max(dec_lengths), level)
level = _check_level(axes_shapes, dec_lengths, level)

shapes = []
for i in range(level):
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30 changes: 30 additions & 0 deletions pywt/tests/test_multilevel.py
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Expand Up @@ -697,6 +697,36 @@ def test_wavedecn_shapes_and_size():
assert_equal(size, expected_size)


@pytest.mark.parametrize('shape, axes, wavelets', [
((128, 32), None, ('db16', 'db1')),
((32, 128), (1, 0), ('db16', 'db1')),
((2, 128, 32), (1, 2), ('db16', 'db1')),
((128, 2, 32), (-1, 0), ('db1', 'db16')),
])
@pytest.mark.parametrize('mode', [
'symmetric', 'periodization', ('zero', 'periodization'),
])
@pytest.mark.parametrize('level', [None, 0, 2])
def test_wavedecn_shapes_per_axis_wavelets(shape, axes, wavelets, mode, level):
# The longest filter is applied to the longest axis. Metadata must use
# the same decomposition level as the transform, also for strided input.
data = np.random.RandomState(0).standard_normal(shape)[::-1]
with warnings.catch_warnings():
warnings.simplefilter('error', UserWarning)
shapes = pywt.wavedecn_shapes(shape, wavelets, mode=mode, level=level,
axes=axes)
coeffs = pywt.wavedecn(data, wavelets, mode=mode, level=level,
axes=axes)
arr, slices, actual_shapes = pywt.ravel_coeffs(coeffs, axes=axes)
assert_equal(shapes, actual_shapes)
assert_equal(pywt.wavedecn_size(shapes), arr.size)

restored_coeffs = pywt.unravel_coeffs(arr, slices, shapes)
reconstructed = pywt.waverecn(restored_coeffs, wavelets, mode=mode,
axes=axes)
assert_allclose(reconstructed, data, rtol=1e-12, atol=1e-12)


def test_dwtn_max_level():
# predicted and empirical dwtn_max_level match
for wav in [pywt.Wavelet('db2'), 'sym8']:
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