Package: python-pywt
Version: 0.1.7~svn97-1
Severity: normal
Yet another obstacle I found while analysing some of my data.
If pruning has occured, wp.reconstruct() might fail when I guess it should have
not.
,---
| /usr/lib/python2.4/site-packages/pywt/wavelet_packets.pyc in
reconstruct(self, update)
| 85 raise ValueError, "Can not reconstruct. Tree is
missing data"
| 86 else:
| ---> 87 rec = idwt(data_a, data_d, self.wavelet, self.mode,
correct_size=True)
| 88 if update:
| 89 self.data = rec
|
| /usr/lib/python2.4/site-packages/pywt/_pywt.so in pywt._pywt.idwt()
|
| ValueError: Coefficients arrays must satisfy (0 <= len(cA) - len(cD) <= 1).
`---
Length at the place of failure are
ipdb> len(data_a)
260
ipdb> len(data_d)
252
behavior stays the same when I change padding mode to 'sym'.
Also problem doesn't occur if none of the nodes got pruned
I am attaching again that example file with embedded data snippet and adjusted
threshold to reproduce the problem.
-- System Information:
Debian Release: lenny/sid
APT prefers testing
APT policy: (991, 'testing'), (990, 'unstable'), (300, 'experimental')
Architecture: i386 (i686)
Kernel: Linux 2.6.26-1-686 (SMP w/2 CPU cores)
Locale: LANG=C, LC_CTYPE=C (charmap=ANSI_X3.4-1968)
Shell: /bin/sh linked to /bin/bash
Versions of packages python-pywt depends on:
ii libc6 2.7-15 GNU C Library: Shared libraries
ii python 2.5.2-2 An interactive high-level object-o
ii python-central 0.6.8 register and build utility for Pyt
ii python-numpy 1:1.1.0-3 Numerical Python adds a fast array
python-pywt recommends no packages.
python-pywt suggests no packages.
-- debconf-show failed
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from pywt import WaveletPacket
import pylab
import math
import numpy
#x = pylab.arrayrange(612-80, 20, -0.5)/150.
#data = pylab.sin(20*pylab.log(x)) * pylab.sign((pylab.log(x)))
data = numpy.array(
[-0.34573659, -0.38810292, -0.45543289, -0.30603009, -0.1243628 ,
-0.12558162, 0.0298495 , 0.51478487, 0.8938365 , 0.73296654,
0.25564462, 0.18708593, 0.33945757, 0.11521387, 0.01981713,
0.17223004, 0.27229831, 0.34833428, 0.32712582, 0.57411432,
0.84752387, 0.63009703, 0.25494909, 0.17051926, 0.47685269,
0.51836729, 0.30156392, 0.57711917, 0.87500638, 0.74624652,
0.29505485, -0.12490962, -0.05453217, 0.22704136, 0.4834733 ,
0.53429389, 0.32919055, 0.37594539, 0.81264687, 0.70471901,
-0.21471409, -0.494104 , -0.12448001, 0.20966017, 0.69090933,
0.74746501, 0.35667574, 0.17011094, 0.28702021, 0.42441538,
-0.06605265, -0.98859781, -1.03666663, -0.25471625, 0.12900937,
0.05600312, 0.08820164, 0.25174344, 0.38751057, 0.2893925 ,
-0.06552365, -0.37944648, -0.41763771, -0.26762265, -0.06390376,
0.1418543 , -0.06881711, -0.59654725, -0.44554675, 0.03705724,
-0.10672796, -0.48229194, -0.47481367, 0.0671913 , 0.7202397 ,
0.55356973, -0.20201714, -0.24423063, 0.2410042 , 0.1758634 ,
-0.38244367, -0.85359514, -0.89553839, -0.66121352, -0.4836694 ,
-0.26402989, -0.02437426, 0.11115174, -0.01598396, -0.33173251,
-0.2154655 , -0.03343722, -0.23002645, -0.37420931, -0.40847069,
-0.38042581, -0.37264973, -0.34534425, -0.34621239, -0.45104039,
-0.41669124, -0.39197695, -0.40836936, -0.41242203, -0.50930172,
-0.66101074, -0.70279622, -0.54050344, -0.56988621, -0.60800582,
-0.43425202, -0.40210819, -0.26260108, 0.09008174, 0.34366187,
0.17083512, -0.26701128, -0.11252782, 0.54705787, 0.78186125,
0.42036369, 0.06707341, 0.38804147, 1.02385187, 0.78289628,
0.03741382, -0.05829134, 0.09545194, 0.19760814, 0.48429587,
0.65382117, 0.59562671, 0.15905438, -0.2841897 , -0.17725979,
-0.00769951, 0.13430153, 0.4074778 , 0.67588735, 0.73942482,
0.52059728, 0.50374937, 0.2956422 , -0.40126958, -0.46746603,
0.08772394, 0.42896676, 0.54153472, 0.60222602, 0.73305005,
0.94797075, 0.84156698, 0.14969313, -0.28262892, -0.01854044,
0.36948362, 0.75414896, 0.71608871, 0.12448686, 0.03415477,
0.71146995, 1.02768123, 0.13064073, -1.07957816, -1.18031585,
-0.4316408 , 0.20752712, 0.39356601, 0.11908912, -0.25279516,
-0.27945498, -0.23358169, -0.62040937, -0.91403097, -1.08253109,
-1.3643012 , -0.90408862, -0.13326721, -0.03532824, -0.30052918,
-0.30869764, 0.14190483, 0.16872986, -0.52904379, -0.82950765,
-0.57574642, -0.35260922, -0.40631461, -0.84412497, -1.18531418,
-0.94343036, -0.24132943, 0.0948167 , -0.54436576, -0.97514445,
-0.44504359, -0.06588518, -0.36086997, -1.01898432, -1.42372036,
-0.98361838, -0.49887896, -0.51098716, -0.49566799, -0.45534325,
-0.27971864, -0.01289146, -0.06996948, -0.22960928, -0.21719246,
-0.12586229, -0.26791307, -0.28670284, -0.08482055, -0.24696115,
-0.1251926 , 0.40114737, 0.4108803 , 0.23961042, 0.16801332,
-0.03215366, 0.03253647, 0.18988416, 0.0640558 , 0.11062526,
0.25257105, 0.04069111, -0.11823228, 0.04848703, 0.31178865,
0.54475868, 0.39140043, 0.10510263, 0.3624824 , 0.74902427,
0.74371761, 0.48868194, 0.2298651 , 0.13388744, 0.35414925,
0.45046723, 0.10623782, 0.08053232, 0.44768173, 0.58874184,
0.5156281 , 0.32252964, 0.19333769, 0.27537739, 0.41304928,
0.67002833, 0.59858018, 0.20810364, 0.66726983, 0.29272726,
-2.20618391, -3.60798836, -2.54817486, -0.59640121, 1.0147121 ,
1.74119568, 2.09057379, 2.30470371, 2.08168745, 1.40284204,
0.58587623, 0.18220828, 0.42282817, 0.75473732, 0.39250791,
-0.32354969, -0.55786163, -0.26725572, 0.03839639, -0.01886539,
-0.42838663, -0.74645865, -0.78762144, -0.97687823, -1.22139239,
-1.46928108, -1.8911221 , -1.86606503, -1.24852216, -0.90137899,
-0.8604095 , -0.61489344, -0.42976227, -0.3579028 , -0.2550112 ,
-0.3609733 , -0.74349201, -1.00684309, -1.03567755, -1.21463203,
-1.40274465, -1.18434763, -0.80010295, -0.7885856 , -0.91457433,
-0.72191888, -0.38094512, -0.16468807, -0.09077492, -0.10387847,
-0.33666795, -0.75029886, -0.69107533, -0.19301286, -0.16300914,
-0.54537416, -0.7384879 , -0.50050598, -0.08110276, 0.07275747,
0.03492078, -0.09517509, -0.21966211, -0.06074491, 0.38211671,
0.5190568 , -0.0422433 , -0.36352664, -0.10866001, 0.05437544,
0.20974973, 0.42473951, 0.51390868, 0.44973576, 0.43922538,
0.4202809 , 0.35035506, 0.34382021, 0.02264159, -0.19176336,
0.27686051, 0.61395586, 0.10884806, -0.52761126, -0.54345381,
-0.1845329 , 0.34844601, 0.85585701, 0.82573956, 0.29480994,
0.199698 , 0.58821958, 0.4097842 , -0.20117182, -0.68529505,
-0.59920287, 0.26265663, 0.71709168, 0.44523397, 0.42030042,
0.49547005, 0.40141755, 0.17532146, -0.37080652, -0.53917825,
-0.09434764, 0.14409591, 0.13886316, 0.16051713, 0.1942763 ,
0.50213385, 0.71408552, 0.05108832, -0.76438487, -0.72354162,
-0.19283007, 0.2826573 , 0.6727984 , 0.51177317, -0.12549853,
-0.06878713, 0.19995314, -0.35836998, -0.87287205, -0.77882993,
-0.40312099, 0.2622945 , 0.47721502, -0.07341472, -0.17401099,
0.36486512, 0.643592 , 0.37585711, -0.14986593, -0.53286755,
-0.32185614, 0.17663859, 0.0592976 , -0.3324503 , -0.40202484,
-0.19658361, 0.13577379, 0.10627493, -0.11760357, -0.05091718,
0.01814876, 0.0342852 , -0.06658879, -0.54144925, -0.78274566,
-0.4885661 , -0.33449933, -0.45626563, -0.54729432, -0.41983923,
0.03955875, 0.26681873, 0.14632468, -0.05237956, -0.19973294,
0.25775492, 0.52370071, 0.02199412, -0.19693415, -0.11562894,
0.13920403, 0.50373054, 0.14508082, -0.1909228 , 0.19551201,
0.57926255, 0.81666166, 0.63755333, 0.18111664, 0.10651836,
0.14200762, 0.28636914, 0.53723407, 0.35189441, 0.06825569,
0.21778929, 0.50156486, 0.68994433, 0.91903323, 0.88751686,
0.36196166, -0.23109406, -0.24008749, 0.4001154 , 0.85797894,
0.70444155, 0.25895256, 0.23918585, 0.82937747, 0.99962109,
0.73064375, 0.42680928, -0.01835891, 0.21443914, 0.82717419,
0.77688968, 0.55637294, 0.56584096, 0.56064045, 0.40366352,
0.19031163, 0.1587998 , 0.26224095, 0.51827961, 0.84558535,
0.82341897, 0.65705061, 1.01285124, 1.28659916, 0.53027087,
-0.4946177 , -0.57538098, 0.20680973, 0.9103815 , 1.01510572,
0.50717121, -0.01748215, 0.49862108, 1.36912179, 0.92974395,
-0.11509538, -0.30563056, -0.04620126, 0.21577227, 0.3976962 ,
0.14059401, -0.04423112, 0.15467754, 0.2684986 , 0.15839657,
-0.10493945, -0.01085244, 0.56424832, 0.75706172, 0.53177583,
0.42576602, 0.35808924, 0.35696232, 0.43182141, 0.49328208],
dtype=N.float32)
x = P.arange(-0.5,0.5,1.0/len(data))
#from sample_data import ecg as data
#data = numpy.array(data, numpy.float64) / 100.
## simple energy based criteria, not very efficient
def select(node, min_energy):
if node.energy() < math.log(min_energy):
print node.path, "marked as ZT", len(node.data)
node.markZeroTree()
return False # stop processing child nodes
return True
## create our tree
wp = WaveletPacket(data, wavelet='sym3', mode='sp1', maxlevel=6)
treshold = 0.21
base_energy = wp.energy()
wp.walk(select, (treshold*base_energy,))
print "Non-zero trees:"
for node in wp.get_nonzero():
print node.path, len(node.data)
new_data = wp.reconstruct()
print "Reconstructing %d samples using %d coefficients" % (wp.data_size, sum([len(node.data) for node in wp.get_nonzero()]))
print "Mean difference:", sum(abs(new_data - data))/len(data)
print "Retaining %.2f%% energy" % (100-(base_energy-wp.energy())*100./base_energy)
pylab.plot(x, data, label="orig")
pylab.plot(x, wp.data, label="rec")
pylab.xlim(min(x), max(x))
#pylab.plot(data, label="orig")
#pylab.plot(wp.data, label="rec")
#pylab.xlim(0, len(data)-1)
pylab.legend()
pylab.show()
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