def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = coldfilt(mandrill, h1b, h1a)
assert_almost_equal_to_summary(A,
verif['mandrill_coldfilt'],
tolerance=TOLERANCE)
def test_different_size():
coldfilt(lena, (-0.5,-1,2,1,0.5), (-1,2,-1))
def test_odd_filter():
coldfilt(lena, (-1,2,-1), (-1,2,1))
def test_different_size():
with raises(ValueError):
coldfilt(mandrill, (-0.5,-1,2,1,0.5), (-1,2,-1))
def forward(self, X, nlevels=3, include_scale=False):
"""Perform a *n*-level DTCWT decompostion on a 1D column vector *X* (or on
the columns of a matrix *X*).
:param X: 1D real array or 2D real array whose columns are to be transformed
:param nlevels: Number of levels of wavelet decomposition
:returns: A :py:class:`dtcwt.Pyramid`-like object representing the transform result.
If *biort* or *qshift* are strings, they are used as an argument to the
:py:func:`biort` or :py:func:`qshift` functions. Otherwise, they are
interpreted as tuples of vectors giving filter coefficients. In the *biort*
case, this should be (h0o, g0o, h1o, g1o). In the *qshift* case, this should
be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
.. 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
# Need this because colfilter and friends assumes input is 2d
X = asfarray(X)
if len(X.shape) == 1:
X = np.atleast_2d(X).T
# 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
L = np.asanyarray(X.shape)
# ensure that X is an even length, thus enabling it to be extended if needs be.
if X.shape[0] % 2 != 0:
raise ValueError("Size of input X must be a multiple of 2")
if nlevels == 0:
if include_scale:
return Pyramid(X, (), ())
else:
return Pyramid(X, ())
# initialise
Yh = [None] * nlevels
if include_scale:
# This is only required if the user specifies scales are to be outputted
Yscale = [None] * nlevels
# Level 1.
Hi = colfilter(X, h1o)
Lo = colfilter(X, h0o)
Yh[0] = Hi[::2, :] + 1j * Hi[1::2, :] # Convert Hi to complex form.
if include_scale:
Yscale[0] = Lo
# Levels 2 and above.
for level in xrange(1, nlevels):
# Check to see if height of Lo is divisable by 4, if not extend.
if Lo.shape[0] % 4 != 0:
Lo = np.vstack((Lo[0, :], Lo, Lo[-1, :]))
Hi = coldfilt(Lo, h1b, h1a)
Lo = coldfilt(Lo, h0b, h0a)
Yh[level] = Hi[::2, :] + 1j * Hi[1::2, :] # Convert Hi to complex form.
if include_scale:
Yscale[level] = Lo
Yl = Lo
if include_scale:
return Pyramid(Yl, Yh, Yscale)
else:
return Pyramid(Yl, Yh)
def test_good_input_size_non_orthogonal():
coldfilt(lena[:,:511], (1,1), (1,1))
def test_coldfilt():
h0o, g0o, h1o, g1o = biort('near_sym_b')
h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b = qshift('qshift_d')
A = coldfilt(mandrill, h1b, h1a)
assert_almost_equal_to_summary(A, verif['mandrill_coldfilt'], tolerance=TOLERANCE)
def test_different_size():
with raises(ValueError):
coldfilt(mandrill, (-0.5, -1, 2, 1, 0.5), (-1, 2, -1))
def test_bad_input_size():
with raises(ValueError):
coldfilt(mandrill[:511, :], (-1, 1), (1, -1))
def test_output_size():
Y = coldfilt(mandrill, (-1,1), (1,-1))
assert Y.shape == (mandrill.shape[0]/2, mandrill.shape[1])
def test_odd_filter():
with raises(ValueError):
coldfilt(mandrill, (-1, 2, -1), (-1, 2, 1))
def test_good_input_size_non_orthogonal():
coldfilt(mandrill[:,:511], (1,1), (1,1))
def test_good_input_size():
coldfilt(mandrill[:,:511], (-1,1), (1,-1))
def test_bad_input_size():
with raises(ValueError):
coldfilt(mandrill[:511,:], (-1,1), (1,-1))
def test_bad_input_size():
coldfilt(lena[:511,:], (-1,1), (1,-1))
def test_good_input_size():
coldfilt(mandrill[:, :511], (-1, 1), (1, -1))
def test_good_input_size():
coldfilt(lena[:,:511], (-1,1), (1,-1))
def test_good_input_size_non_orthogonal():
coldfilt(mandrill[:, :511], (1, 1), (1, 1))
def test_output_size():
Y = coldfilt(lena, (-1,1), (1,-1))
assert Y.shape == (lena.shape[0]/2, lena.shape[1])
def test_output_size():
Y = coldfilt(mandrill, (-1, 1), (1, -1))
assert Y.shape == (mandrill.shape[0] / 2, mandrill.shape[1])
def forward(self, X, nlevels=3, include_scale=False):
"""Perform a *n*-level DTCWT decompostion on a 1D column vector *X* (or on
the columns of a matrix *X*).
:param X: 1D real array or 2D real array whose columns are to be transformed
:param nlevels: Number of levels of wavelet decomposition
:returns: A :py:class:`DTCWT.Pyramid`-like object representing the transform result.
If *biort* or *qshift* are strings, they are used as an argument to the
:py:func:`biort` or :py:func:`qshift` functions. Otherwise, they are
interpreted as tuples of vectors giving filter coefficients. In the *biort*
case, this should be (h0o, g0o, h1o, g1o). In the *qshift* case, this should
be (h0a, h0b, g0a, g0b, h1a, h1b, g1a, g1b).
.. 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
# Need this because colfilter and friends assumes input is 2d
X = asfarray(X)
if len(X.shape) == 1:
X = np.atleast_2d(X).T
# 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
L = np.asanyarray(X.shape)
# ensure that X is an even length, thus enabling it to be extended if needs be.
if X.shape[0] % 2 != 0:
raise ValueError('Size of input X must be a multiple of 2')
if nlevels == 0:
if include_scale:
return Pyramid(X, (), ())
else:
return Pyramid(X, ())
# initialise
Yh = [
None,
] * nlevels
if include_scale:
# This is only required if the user specifies scales are to be outputted
Yscale = [
None,
] * nlevels
# Level 1.
Hi = colfilter(X, h1o)
Lo = colfilter(X, h0o)
Yh[0] = Hi[::2, :] + 1j * Hi[1::2, :] # Convert Hi to complex form.
if include_scale:
Yscale[0] = Lo
# Levels 2 and above.
for level in xrange(1, nlevels):
# Check to see if height of Lo is divisable by 4, if not extend.
if Lo.shape[0] % 4 != 0:
Lo = np.vstack((Lo[0, :], Lo, Lo[-1, :]))
Hi = coldfilt(Lo, h1b, h1a)
Lo = coldfilt(Lo, h0b, h0a)
Yh[level] = Hi[::2, :] + 1j * Hi[
1::2, :] # Convert Hi to complex form.
if include_scale:
Yscale[level] = Lo
Yl = Lo
if include_scale:
return Pyramid(Yl, Yh, Yscale)
else:
return Pyramid(Yl, Yh)
def test_odd_filter():
with raises(ValueError):
coldfilt(mandrill, (-1,2,-1), (-1,2,1))
