def idwt2(coeffs, wavelet, mode='sym'):
"""
2D Inverse Discrete Wavelet Transform. Reconstruct data from coefficients
arrays.
coeffs - four 2D coefficients arrays arranged as follows:
(approximation,
(horizontal details,
vertical details,
diagonal details)
)
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
"""
if len(coeffs) != 2 or len(coeffs[1]) != 3:
raise ValueError("Invalid coeffs param")
# L -low-pass data, H - high-pass data
LL, (LH, HL, HH) = coeffs
(LL, LH, HL, HH) = (transpose(LL), transpose(LH), transpose(HL), transpose(HH))
for arr in (LL, LH, HL, HH):
if len(arr.shape) != 2:
raise TypeError("All input coefficients arrays must be 2D")
del arr
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
mode = MODES.from_object(mode)
# idwt columns
L = []
append_L = L.append
for rowL, rowH in izip(LL, LH):
append_L(idwt(rowL, rowH, wavelet, mode, 1))
del LL, LH
H = []
append_H = H.append
for rowL, rowH in izip(HL, HH):
append_H(idwt(rowL, rowH, wavelet, mode, 1))
del HL, HH
L = transpose(L)
H = transpose(H)
# idwt rows
data = []
append_data = data.append
for rowL, rowH in izip(L, H):
append_data(idwt(rowL, rowH, wavelet, mode, 1))
return array(data, default_dtype)
def reconstruct(self, update=False):
"""
Reconstruct node's data value using coefficients from subnodes.
If update param is True, then reconstructed data replaces node's data.
Returns None if node is marked as ZeroTree.
Returns original node data if all subnodes are None or are marked as ZeroTrees.
Returns IDWT reconstructed data returned by reconstruct() method of two nodes otherwise.
"""
if self.isZeroTree:
return None
elif (self.a is None or self.a.isZeroTree) and (self.d is None or self.d.isZeroTree):
return self.data
else:
data_a = None
data_d = None
if self.a is not None:
data_a = self.a.reconstruct()
if self.d is not None:
data_d = self.d.reconstruct()
if data_a is None and data_d is None:
raise ValueError, "Can not reconstruct. Tree is missing data"
else:
rec = idwt(data_a, data_d, self.wavelet, self.mode, correct_size=True)
if update:
self.data = rec
return rec
def _reconstruct(self, update):
data_a, data_d = None, None
node_a, node_d = self._get_node(self.A), self._get_node(self.D)
if node_a is not None:
data_a = node_a.reconstruct() # TODO: (update) ???
if node_d is not None:
data_d = node_d.reconstruct() # TODO: (update) ???
if data_a is None and data_d is None:
raise ValueError("Node is a leaf node and cannot be reconstructed"
" from subnodes.")
else:
rec = idwt(data_a, data_d, self.wavelet, self.mode,
correct_size=True)
if update:
self.data = rec
return rec
def _reconstruct(self, update):
data_a, data_d = None, None
node_a, node_d = self._get_node(self.A), self._get_node(self.D)
if node_a is not None:
data_a = node_a.reconstruct() # TODO: (update) ???
if node_d is not None:
data_d = node_d.reconstruct() # TODO: (update) ???
if data_a is None and data_d is None:
raise ValueError("Node is a leaf node and cannot be reconstructed"
" from subnodes.")
else:
rec = idwt(data_a,
data_d,
self.wavelet,
self.mode,
correct_size=True)
if update:
self.data = rec
return rec
def waverec(coeffs, wavelet, mode='sym'):
"""
Multilevel 1D Inverse Discrete Wavelet Transform.
coeffs - coefficients list [cAn, cDn, cDn-1, ..., cD2, cD1]
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
"""
if not isinstance(coeffs, (list, tuple)):
raise ValueError("Expected sequence of coefficient arrays.")
if len(coeffs) < 2:
raise ValueError("Coefficient list too short (minimum 2 arrays required).")
a, ds = coeffs[0], coeffs[1:]
for d in ds:
a = idwt(a, d, wavelet, mode, 1)
return a
def waverec(coeffs, wavelet, mode='sym'):
"""
Multilevel 1D Inverse Discrete Wavelet Transform.
coeffs - coefficients list [cAn, cDn, cDn-1, ..., cD2, cD1]
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
"""
if not isinstance(coeffs, (list, tuple)):
raise ValueError("Expected sequence of coefficient arrays.")
if len(coeffs) < 2:
raise ValueError(
"Coefficient list too short (minimum 2 arrays required).")
a, ds = coeffs[0], coeffs[1:]
for d in ds:
a = idwt(a, d, wavelet, mode, 1)
return a
def idwt2(coeffs, wavelet, mode='sym'):
"""
2D Inverse Discrete Wavelet Transform. Reconstruct data from coefficients
arrays.
coeffs - four 2D coefficients arrays arranged as follows (in the same way
as dwt2 output -- see dwt2 description for details):
(approximation,
(horizontal details,
vertical details,
diagonal details)
)
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
"""
if len(coeffs) != 2 or len(coeffs[1]) != 3:
raise ValueError("Invalid coeffs param")
# L -low-pass data, H - high-pass data
LL, (LH, HL, HH) = coeffs
if not LL is None: LL = transpose(LL)
if not LH is None: LH = transpose(LH)
if not HL is None: HL = transpose(HL)
if not HH is None: HH = transpose(HH)
all_none = True
for arr in (LL, LH, HL, HH):
if arr is not None:
all_none = False
if len(arr.shape) != 2:
raise TypeError("All input coefficients arrays must be 2D.")
del arr
if all_none:
raise ValueError(
"At least one input coefficients array must not be None.")
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
mode = MODES.from_object(mode)
# idwt columns
L = []
append_L = L.append
if LL is None and LH is None:
L = None
else:
if LL is None:
LL = cycle([
None
]) # IDWT can handle None input values - equals to zero-array
if LH is None:
LH = cycle([
None
]) # IDWT can handle None input values - equals to zero-array
for rowL, rowH in izip(LL, LH):
append_L(idwt(rowL, rowH, wavelet, mode, 1))
del LL, LH
H = []
append_H = H.append
if HL is None and HH is None:
H = None
else:
if HL is None:
HL = cycle([
None
]) # IDWT can handle None input values - equals to zero-array
if HH is None:
HH = cycle([
None
]) # IDWT can handle None input values - equals to zero-array
for rowL, rowH in izip(HL, HH):
append_H(idwt(rowL, rowH, wavelet, mode, 1))
del HL, HH
if L is not None:
L = transpose(L)
if H is not None:
H = transpose(H)
# idwt rows
data = []
append_data = data.append
if L is None:
L = cycle(
[None]) # IDWT can handle None input values - equals to zero-array
if H is None:
H = cycle(
[None]) # IDWT can handle None input values - equals to zero-array
for rowL, rowH in izip(L, H):
append_data(idwt(rowL, rowH, wavelet, mode, 1))
return array(data, default_dtype)
def idwt2(coeffs, wavelet, mode='sym'):
"""
2D Inverse Discrete Wavelet Transform. Reconstruct data from coefficients
arrays.
coeffs - four 2D coefficients arrays arranged as follows (in the same way
as dwt2 output -- see dwt2 description for details):
(approximation,
(horizontal details,
vertical details,
diagonal details)
)
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
"""
if len(coeffs) != 2 or len(coeffs[1]) != 3:
raise ValueError("Invalid coeffs param")
# L -low-pass data, H - high-pass data
LL, (LH, HL, HH) = coeffs
if LL is not None:
LL = transpose(LL)
if LH is not None:
LH = transpose(LH)
if HL is not None:
HL = transpose(HL)
if HH is not None:
HH = transpose(HH)
all_none = True
for arr in (LL, LH, HL, HH):
if arr is not None:
all_none = False
if len(arr.shape) != 2:
raise TypeError("All input coefficients arrays must be 2D.")
del arr
if all_none:
raise ValueError(
"At least one input coefficients array must not be None.")
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
mode = MODES.from_object(mode)
# idwt columns
L = []
if LL is None and LH is None:
L = None
else:
if LL is None:
# IDWT can handle None input values - equals to zero-array
LL = cycle([None])
if LH is None:
# IDWT can handle None input values - equals to zero-array
LH = cycle([None])
for rowL, rowH in izip(LL, LH):
L.append(idwt(rowL, rowH, wavelet, mode, 1))
del LL, LH
H = []
if HL is None and HH is None:
H = None
else:
if HL is None:
# IDWT can handle None input values - equals to zero-array
HL = cycle([None])
if HH is None:
# IDWT can handle None input values - equals to zero-array
HH = cycle([None])
for rowL, rowH in izip(HL, HH):
H.append(idwt(rowL, rowH, wavelet, mode, 1))
del HL, HH
if L is not None:
L = transpose(L)
if H is not None:
H = transpose(H)
# idwt rows
data = []
if L is None:
# IDWT can handle None input values - equals to zero-array
L = cycle([None])
if H is None:
# IDWT can handle None input values - equals to zero-array
H = cycle([None])
for rowL, rowH in izip(L, H):
data.append(idwt(rowL, rowH, wavelet, mode, 1))
return array(data, default_dtype)
