/usr/share/pyshared/pywt/multilevel.py is in python-pywt 0.2.0-3.
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# Copyright (c) 2006-2010 Filip Wasilewski <http://filipwasilewski.pl/>
# See COPYING for license details.
# $Id: multilevel.py 154 2010-03-13 13:18:59Z filipw $
"""
Multilevel 1D and 2D Discrete Wavelet Transform
and Inverse Discrete Wavelet Transform.
"""
__all__ = ['wavedec', 'waverec', 'wavedec2', 'waverec2']
from _pywt import Wavelet, MODES
from _pywt import dwt, idwt, dwt_max_level
from multidim import dwt2, idwt2
from numerix import as_float_array
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 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 wavedec2(data, wavelet, mode='sym', level=None):
"""
Multilevel 2D Discrete Wavelet Transform.
data - 2D 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 .
Returns coefficients list - [cAn, (cHn, cVn, cDn), ... (cH1, cV1, cD1)]
"""
data = as_float_array(data)
if len(data.shape) != 2:
raise ValueError("Expected 2D input data.")
if not isinstance(wavelet, Wavelet):
wavelet = Wavelet(wavelet)
if level is None:
size = min(data.shape)
level = dwt_max_level(size, 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, ds = dwt2(a, wavelet, mode)
coeffs_list.append(ds)
coeffs_list.append(a)
coeffs_list.reverse()
return coeffs_list
def waverec2(coeffs, wavelet, mode='sym'):
"""
Multilevel 2D Inverse Discrete Wavelet Transform.
coeffs - coefficients list [cAn, (cHn, cVn, cDn), ... (cH1, cV1, cD1)]
wavelet - wavelet to use (Wavelet object or name string)
mode - signal extension mode, see MODES
Returns 2D array of reconstructed data.
"""
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 = idwt2((a, d), wavelet, mode)
return a
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