def test_fswavedecn_fswaverecn_variable_wavelets_and_modes():
# test with differing number of transform levels per axis
rstate = np.random.RandomState(0)
ndim = 3
data = rstate.standard_normal((16, ) * ndim)
wavelets = ('haar', 'db2', 'sym3')
modes = ('periodic', 'symmetric', 'periodization')
T = pywt.fswavedecn(data, wavelet=wavelets, mode=modes)
for ax in range(ndim):
# expect approx + dwt_max_level detail coeffs along each axis
assert_equal(len(T.coeff_slices[ax]),
pywt.dwt_max_level(data.shape[ax], wavelets[ax]) + 1)
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# number of wavelets doesn't match number of axes
assert_raises(ValueError, pywt.fswavedecn, data, wavelets[:2])
# number of modes doesn't match number of axes
assert_raises(ValueError,
pywt.fswavedecn,
data,
wavelets[0],
mode=modes[:2])
def test_fswavedecn_fswaverecn_zero_levels():
# zero level transform gives coefs matching the original data
rstate = np.random.RandomState(0)
ndim = 2
data = rstate.standard_normal((8, )*ndim)
T = pywt.fswavedecn(data, 'haar', levels=0)
assert_array_equal(T.coeffs, data)
rec = pywt.fswaverecn(T)
assert_array_equal(T.coeffs, rec)
def test_fswavedecn_fswaverecn_zero_levels():
# zero level transform gives coefs matching the original data
rstate = np.random.RandomState(0)
ndim = 2
data = rstate.standard_normal((8, ) * ndim)
T = pywt.fswavedecn(data, 'haar', levels=0)
assert_array_equal(T.coeffs, data)
rec = pywt.fswaverecn(T)
assert_array_equal(T.coeffs, rec)
def test_fswavedecn_fswaverecn_axes_subsets():
"""Fully separable DWT over only a subset of axes"""
rstate = np.random.RandomState(0)
# use anisotropic data to result in unique number of levels per axis
data = rstate.standard_normal((4, 8, 16, 32))
# test all combinations of 3 out of 4 axes transformed
for axes in combinations((0, 1, 2, 3), 3):
T = pywt.fswavedecn(data, 'haar', axes=axes)
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# some axes exceed data dimensions
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', axes=(1, 5))
def test_fswavedecn_fswaverecn_axes_subsets():
"""Fully separable DWT over only a subset of axes"""
rstate = np.random.RandomState(0)
# use anisotropic data to result in unique number of levels per axis
data = rstate.standard_normal((4, 8, 16, 32))
# test all combinations of 3 out of 4 axes transformed
for axes in combinations((0, 1, 2, 3), 3):
T = pywt.fswavedecn(data, 'haar', axes=axes)
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# some axes exceed data dimensions
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', axes=(1, 5))
def test_fswavedecn_fswaverecn_variable_levels():
# test with differing number of transform levels per axis
rstate = np.random.RandomState(0)
ndim = 3
data = rstate.standard_normal((16, )*ndim)
T = pywt.fswavedecn(data, 'haar', levels=(1, 2, 3))
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# levels doesn't match number of axes
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1))
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1, 1, 1))
# levels too large for array size
assert_warns(UserWarning, pywt.fswavedecn, data, 'haar',
levels=int(np.log2(np.min(data.shape)))+1)
def test_fswavedecn_fswaverecn_variable_levels():
# test with differing number of transform levels per axis
rstate = np.random.RandomState(0)
ndim = 3
data = rstate.standard_normal((16, )*ndim)
T = pywt.fswavedecn(data, 'haar', levels=(1, 2, 3))
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# levels doesn't match number of axes
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1))
assert_raises(ValueError, pywt.fswavedecn, data, 'haar', levels=(1, 1, 1, 1))
# levels too large for array size
assert_warns(UserWarning, pywt.fswavedecn, data, 'haar',
levels=int(np.log2(np.min(data.shape)))+1)
def test_fswavedecn_fswaverecn_roundtrip():
# verify proper round trip result for 1D through 4D data
# same DWT as wavedecn/waverecn so don't need to test all modes/wavelets
rstate = np.random.RandomState(0)
for ndim in range(1, 5):
for dt_in, dt_out in zip(dtypes_in, dtypes_out):
for levels in (1, None):
data = rstate.standard_normal((8, ) * ndim)
data = data.astype(dt_in)
T = pywt.fswavedecn(data, 'haar', levels=levels)
rec = pywt.fswaverecn(T)
if data.real.dtype in [np.float32, np.float16]:
assert_allclose(rec, data, rtol=1e-6, atol=1e-6)
else:
assert_allclose(rec, data, rtol=1e-14, atol=1e-14)
assert_(T.coeffs.dtype == dt_out)
assert_(rec.dtype == dt_out)
def test_fswavedecn_fswaverecn_roundtrip():
# verify proper round trip result for 1D through 4D data
# same DWT as wavedecn/waverecn so don't need to test all modes/wavelets
rstate = np.random.RandomState(0)
for ndim in range(1, 5):
for dt_in, dt_out in zip(dtypes_in, dtypes_out):
for levels in (1, None):
data = rstate.standard_normal((8, )*ndim)
data = data.astype(dt_in)
T = pywt.fswavedecn(data, 'haar', levels=levels)
rec = pywt.fswaverecn(T)
if data.real.dtype in [np.float32, np.float16]:
assert_allclose(rec, data, rtol=1e-6, atol=1e-6)
else:
assert_allclose(rec, data, rtol=1e-14, atol=1e-14)
assert_(T.coeffs.dtype == dt_out)
assert_(rec.dtype == dt_out)
def test_fswavedecn_fswaverecn_variable_wavelets_and_modes():
# test with differing number of transform levels per axis
rstate = np.random.RandomState(0)
ndim = 3
data = rstate.standard_normal((16, )*ndim)
wavelets = ('haar', 'db2', 'sym3')
modes = ('periodic', 'symmetric', 'periodization')
T = pywt.fswavedecn(data, wavelet=wavelets, mode=modes)
for ax in range(ndim):
# expect approx + dwt_max_level detail coeffs along each axis
assert_equal(len(T.coeff_slices[ax]),
pywt.dwt_max_level(data.shape[ax], wavelets[ax])+1)
rec = pywt.fswaverecn(T)
assert_allclose(rec, data, atol=1e-14)
# number of wavelets doesn't match number of axes
assert_raises(ValueError, pywt.fswavedecn, data, wavelets[:2])
# number of modes doesn't match number of axes
assert_raises(ValueError, pywt.fswavedecn, data, wavelets[0], mode=modes[:2])
