def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = colifilt(mandrill, g0b, g0a)
assert_almost_equal_to_summary(A,
verif['mandrill_colifilt'],
tolerance=TOLERANCE)
def test_different_size_h():
colifilt(lena, (-1,2,1), (-0.5,-1,2,-1,0.5))
def test_odd_filter():
colifilt(lena, (-1,2,-1), (-1,2,1))
def inverse_hu(self, pyramid, gain_mask=None):
"""Perform an *n*-level dual-tree complex wavelet (DTCWT) 1D
reconstruction.
:param pyramid: A :py:class:`DTCWT.Pyramid`-like object containing the transformed signal.
:param gain_mask: Gain to be applied to each subband.
:returns: Reconstructed real array.
The *l*-th element of *gain_mask* is gain for wavelet subband at level l.
If gain_mask[l] == 0, no computation is performed for band *l*. Default
*gain_mask* is all ones. Note that *l* is 0-indexed.
.. codeauthor:: Rich Wareham <*****@*****.**>, Aug 2013
.. codeauthor:: Nick Kingsbury, Cambridge University, May 2002
.. codeauthor:: Cian Shaffrey, Cambridge University, May 2002
"""
# Which wavelets are to be used?
biort = self.biort
qshift = self.qshift
Yl = pyramid.lowpass
Yh = pyramid.highpasses
a = len(Yh) # No of levels.
if gain_mask is None:
gain_mask = np.ones(a) # Default gain_mask.
# Try to load coefficients if biort is a string parameter
try:
h0o, g0o, h1o, g1o = _biort(biort)
except TypeError:
h0o, g0o, h1o, g1o = biort
# Try to load coefficients if qshift is a string parameter
try:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
except TypeError:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift
level = a - 1 # No of levels = no of rows in L.
if level < 0:
# if there are no levels in the input, just return the Yl value
return Yl
""" 此处可以将 hi Lo 拆开 逐层恢复，考虑到卷积是可拆分的 (x1 + x2) * k = x1 * k + x2 * k """
low_band_signal = np.copy(Yl)
high_band_signals = [c2q1d(np.copy(item)) for item in Yh[::-1]]
for i in range(level):
low_band_signal = colifilt(low_band_signal, g0b, g0a)
high_band_signals[i] = colifilt(high_band_signals[i], g1b, g1a)
if low_band_signal.shape[0] != high_band_signals[i + 1].shape[0]:
low_band_signal = low_band_signal[1:-1, ...]
if high_band_signals[i].shape[0] != high_band_signals[i +
1].shape[0]:
high_band_signals[i] = high_band_signals[i][1:-1, ...]
for j in range(i):
high_band_signals[j] = colifilt(high_band_signals[j], g0b, g0a)
if high_band_signals[j].shape[0] != high_band_signals[
i + 1].shape[0]:
high_band_signals[j] = high_band_signals[j][1:-1, ...]
low_band_signal = colfilter(low_band_signal, g0o)
high_band_signals[-1] = colfilter(high_band_signals[-1], g1o)
for i in range(level):
high_band_signals[i] = colfilter(high_band_signals[i], g0o)
return low_band_signal, high_band_signals
def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = colifilt(mandrill, g0b, g0a)
assert_almost_equal_to_summary(A, verif['mandrill_colifilt'], tolerance=TOLERANCE)
def test_different_size_h():
with raises(ValueError):
colifilt(mandrill, (-1,2,1), (-0.5,-1,2,-1,0.5))
def test_output_size_non_mult_4():
Y = colifilt(lena, (-1,0,0,1), (1,0,0,-1))
assert Y.shape == (lena.shape[0]*2, lena.shape[1])
def test_good_input_size():
colifilt(lena[:,:511], (-1,1), (1,-1))
def test_non_orthogonal_input_non_mult_4():
Y = colifilt(mandrill, (1,0,0,1), (1,0,0,1))
assert Y.shape == (mandrill.shape[0]*2, mandrill.shape[1])
def test_output_size_non_mult_4():
Y = colifilt(mandrill, (-1,0,0,1), (1,0,0,-1))
assert Y.shape == (mandrill.shape[0]*2, mandrill.shape[1])
def test_output_size():
Y = colifilt(mandrill, (-1,1), (1,-1))
assert Y.shape == (mandrill.shape[0]*2, mandrill.shape[1])
def test_good_input_size():
colifilt(mandrill[:,:511], (-1,1), (1,-1))
def test_bad_input_size():
with raises(ValueError):
colifilt(mandrill[:511,:], (-1,1), (1,-1))
def test_zero_input():
Y = colifilt(np.zeros_like(mandrill), (-1,1), (1,-1))
assert np.all(Y[:0] == 0)
def test_zero_input():
Y = colifilt(np.zeros_like(lena), (-1,1), (1,-1))
assert np.all(Y[:0] == 0)
def test_bad_input_size():
colifilt(lena[:511,:], (-1,1), (1,-1))
def inverse(self, pyramid, gain_mask=None):
"""Perform an *n*-level dual-tree complex wavelet (DTCWT) 1D
reconstruction.
:param pyramid: A :py:class:`dtcwt.Pyramid`-like object containing the transformed signal.
:param gain_mask: Gain to be applied to each subband.
:returns: Reconstructed real array.
The *l*-th element of *gain_mask* is gain for wavelet subband at level l.
If gain_mask[l] == 0, no computation is performed for band *l*. Default
*gain_mask* is all ones. Note that *l* is 0-indexed.
.. codeauthor:: Rich Wareham <*****@*****.**>, Aug 2013
.. codeauthor:: Nick Kingsbury, Cambridge University, May 2002
.. codeauthor:: Cian Shaffrey, Cambridge University, May 2002
"""
# Which wavelets are to be used?
biort = self.biort
qshift = self.qshift
Yl = pyramid.lowpass
Yh = pyramid.highpasses
a = len(Yh) # No of levels.
if gain_mask is None:
gain_mask = np.ones(a) # Default gain_mask.
# Try to load coefficients if biort is a string parameter
try:
h0o, g0o, h1o, g1o = _biort(biort)
except TypeError:
h0o, g0o, h1o, g1o = biort
# Try to load coefficients if qshift is a string parameter
try:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
except TypeError:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift
level = a - 1 # No of levels = no of rows in L.
if level < 0:
# if there are no levels in the input, just return the Yl value
return Yl
Lo = Yl
while level >= 1: # Reconstruct levels 2 and above in reverse order.
Hi = c2q1d(Yh[level] * gain_mask[level])
Lo = colifilt(Lo, g0b, g0a) + colifilt(Hi, g1b, g1a)
if Lo.shape[0] != 2 * Yh[level - 1].shape[
0]: # If Lo is not the same length as the next Yh => t1 was extended.
Lo = Lo[
1:-1,
...] # Therefore we have to clip Lo so it is the same height as the next Yh.
if np.any(
np.asanyarray(Lo.shape) != np.asanyarray(
Yh[level - 1].shape * np.array((2, 1)))):
raise ValueError('Yh sizes are not valid for DTWAVEIFM')
level -= 1
if level == 0: # Reconstruct level 1.
Hi = c2q1d(Yh[level] * gain_mask[level])
Z = colfilter(Lo, g0o) + colfilter(Hi, g1o)
# Return a 1d vector or a column vector
if Z.shape[1] == 1:
return Z.flatten()
else:
return Z
def test_output_size():
Y = colifilt(lena, (-1,1), (1,-1))
assert Y.shape == (lena.shape[0]*2, lena.shape[1])
def inverse(self, pyramid, gain_mask=None):
"""Perform an *n*-level dual-tree complex wavelet (DTCWT) 1D
reconstruction.
:param pyramid: A :py:class:`dtcwt.Pyramid`-like object containing the transformed signal.
:param gain_mask: Gain to be applied to each subband.
:returns: Reconstructed real array.
The *l*-th element of *gain_mask* is gain for wavelet subband at level l.
If gain_mask[l] == 0, no computation is performed for band *l*. Default
*gain_mask* is all ones. Note that *l* is 0-indexed.
.. codeauthor:: Rich Wareham <*****@*****.**>, Aug 2013
.. codeauthor:: Nick Kingsbury, Cambridge University, May 2002
.. codeauthor:: Cian Shaffrey, Cambridge University, May 2002
"""
# Which wavelets are to be used?
biort = self.biort
qshift = self.qshift
Yl = pyramid.lowpass
Yh = pyramid.highpasses
a = len(Yh) # No of levels.
if gain_mask is None:
gain_mask = np.ones(a) # Default gain_mask.
# Try to load coefficients if biort is a string parameter
try:
h0o, g0o, h1o, g1o = _biort(biort)
except TypeError:
h0o, g0o, h1o, g1o = biort
# Try to load coefficients if qshift is a string parameter
try:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = _qshift(qshift)
except TypeError:
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift
level = a - 1 # No of levels = no of rows in L.
if level < 0:
# if there are no levels in the input, just return the Yl value
return Yl
Lo = Yl
while level >= 1: # Reconstruct levels 2 and above in reverse order.
Hi = c2q1d(Yh[level] * gain_mask[level])
Lo = colifilt(Lo, g0b, g0a) + colifilt(Hi, g1b, g1a)
if (
Lo.shape[0] != 2 * Yh[level - 1].shape[0]
): # If Lo is not the same length as the next Yh => t1 was extended.
Lo = Lo[1:-1, ...] # Therefore we have to clip Lo so it is the same height as the next Yh.
if np.any(np.asanyarray(Lo.shape) != np.asanyarray(Yh[level - 1].shape * np.array((2, 1)))):
raise ValueError("Yh sizes are not valid for DTWAVEIFM")
level -= 1
if level == 0: # Reconstruct level 1.
Hi = c2q1d(Yh[level] * gain_mask[level])
Z = colfilter(Lo, g0o) + colfilter(Hi, g1o)
# Return a 1d vector or a column vector
if Z.shape[1] == 1:
return Z.flatten()
else:
return Z
def test_non_orthogonal_input_non_mult_4():
Y = colifilt(lena, (1,0,0,1), (1,0,0,1))
assert Y.shape == (lena.shape[0]*2, lena.shape[1])
def test_odd_filter():
with raises(ValueError):
colifilt(mandrill, (-1,2,-1), (-1,2,1))
