def wavedec(data, wavelet, mode='sym', level=None):
"""
Multilevel 1D Discrete Wavelet Transform of data.
Returns coefficients list - [cAn, cDn, cDn-1, ..., cD2, cD1]
data - input data
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
level - decomposition level. If level is None then it will be
calculated using `dwt_max_level` function.
"""
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
if level is None:
level = dwt_max_level(len(data), wavelet.dec_len)
elif level < 0:
raise ValueError("Level value of %d is too low . Minimum level is 0." %
level)
coeffs_list = []
a = data
for i in xrange(level):
a, d = dwt(a, wavelet, mode)
coeffs_list.append(d)
coeffs_list.append(a)
coeffs_list.reverse()
return coeffs_list
def wavedec(data, wavelet, mode='sym', level=None):
"""
Multilevel 1D Discrete Wavelet Transform of data.
Returns coefficients list - [cAn, cDn, cDn-1, ..., cD2, cD1]
data - input data
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
level - decomposition level. If level is None then it will be
calculated using `dwt_max_level` function.
"""
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
if level is None:
level = dwt_max_level(len(data), wavelet.dec_len)
elif level < 0:
raise ValueError("Level value of %d is too low . Minimum level is 0." % level)
coeffs_list = []
a = data
for i in xrange(level):
a, d = dwt(a, wavelet, mode)
coeffs_list.append(d)
coeffs_list.append(a)
coeffs_list.reverse()
return coeffs_list
def decompose(self):
"""
Decompose node data creating two subnodes with DWT coefficients"
"""
if self.level < self.maxlevel:
a, d = dwt(self.data, self.wavelet, self.mode)
self.createChild("a", a)
self.createChild("d", d)
return self.a, self.d
else:
raise ValueError("Maximum level value reached")
def _decompose(self):
if self.is_empty:
data_a, data_d = None, None
if self._get_node(self.A) is None:
self._create_subnode(self.A, data_a)
if self._get_node(self.B) is None:
self._create_subnode(self.B, data_b)
else:
data_a, data_d = dwt(self.data, self.wavelet, self.mode)
self._create_subnode(self.A, data_a)
self._create_subnode(self.D, data_d)
return self._get_node(self.A), self._get_node(self.D)
def _decompose(self):
if self.is_empty:
data_a, data_d = None, None
if self._get_node(self.A) is None:
self._create_subnode(self.A, data_a)
if self._get_node(self.D) is None:
self._create_subnode(self.D, data_d)
else:
data_a, data_d = dwt(self.data, self.wavelet, self.mode)
self._create_subnode(self.A, data_a)
self._create_subnode(self.D, data_d)
return self._get_node(self.A), self._get_node(self.D)
def dwt2(data, wavelet, mode='sym'):
"""
2D Discrete Wavelet Transform.
data - 2D array with input data
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
Returns approximaion and three details 2D coefficients arrays.
The result form four 2D coefficients arrays organized in tuples:
(approximation,
(horizontal details,
vertical details,
diagonal details)
)
which sometimes is also interpreted as layed out in one 2D array
of coefficients, where:
-----------------
| | |
| A(LL) | H(LH) |
| | |
(A, (H, V, D)) <---> -----------------
| | |
| V(HL) | D(HH) |
| | |
-----------------
"""
data = as_float_array(data)
if len(data.shape) != 2:
raise ValueError("Expected 2D data array")
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
mode = MODES.from_object(mode)
# filter rows
H, L = [], []
append_L = L.append
append_H = H.append
for row in data:
cA, cD = dwt(row, wavelet, mode)
append_L(cA)
append_H(cD)
del data
# filter columns
H = transpose(H)
L = transpose(L)
LL, LH = [], []
append_LL = LL.append
append_LH = LH.append
for row in L:
cA, cD = dwt(array(row, default_dtype), wavelet, mode)
append_LL(cA)
append_LH(cD)
del L
HL, HH = [], []
append_HL = HL.append
append_HH = HH.append
for row in H:
cA, cD = dwt(array(row, default_dtype), wavelet, mode)
append_HL(cA)
append_HH(cD)
del H
# build result structure
# (approx., (horizontal, vertical, diagonal))
ret = (transpose(LL), (transpose(LH), transpose(HL), transpose(HH)))
return ret
def dwt2(data, wavelet, mode='sym'):
"""
2D Discrete Wavelet Transform.
data - 2D array with input data
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
Returns approximation and three details 2D coefficients arrays.
The result form four 2D coefficients arrays organized in tuples:
(approximation,
(horizontal details,
vertical details,
diagonal details)
)
which sometimes is also interpreted as laid out in one 2D array
of coefficients, where:
-----------------
| | |
| A(LL) | H(LH) |
| | |
(A, (H, V, D)) <---> -----------------
| | |
| V(HL) | D(HH) |
| | |
-----------------
"""
data = as_float_array(data)
if len(data.shape) != 2:
raise ValueError("Expected 2D data array")
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
mode = MODES.from_object(mode)
# filter rows
H, L = [], []
for row in data:
cA, cD = dwt(row, wavelet, mode)
L.append(cA)
H.append(cD)
del data
# filter columns
H = transpose(H)
L = transpose(L)
LL, LH = [], []
for row in L:
cA, cD = dwt(array(row, default_dtype), wavelet, mode)
LL.append(cA)
LH.append(cD)
del L
HL, HH = [], []
for row in H:
cA, cD = dwt(array(row, default_dtype), wavelet, mode)
HL.append(cA)
HH.append(cD)
del H
# build result structure
# (approx., (horizontal, vertical, diagonal))
ret = (transpose(LL), (transpose(LH), transpose(HL), transpose(HH)))
return ret
