1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
|
#!/usr/bin/env python
#
# Copyright 2008,2010 Free Software Foundation, Inc.
#
# This file is part of GNU Radio
#
# GNU Radio is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3, or (at your option)
# any later version.
#
# GNU Radio is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with GNU Radio; see the file COPYING. If not, write to
# the Free Software Foundation, Inc., 51 Franklin Street,
# Boston, MA 02110-1301, USA.
#
import numpy
from gnuradio import gr, gr_unittest
import copy
#import pygsl.wavelet as wavelet # FIXME: pygsl not checked for in config
import math
import wavelet_swig
def sqr(x):
return x*x
def np2(k):
m = 0
n = k - 1
while n > 0:
m += 1
return m
class test_classify(gr_unittest.TestCase):
def setUp(self):
self.tb = gr.top_block()
def tearDown(self):
self.tb = None
# def test_000_(self):
# src_data = numpy.zeros(10)
# trg_data = numpy.zeros(10)
# src = gr.vector_source_f(src_data)
# dst = gr.vector_sink_f()
# self.tb.connect(src, dst)
# self.tb.run()
# rsl_data = dst.data()
# sum = 0
# for (u,v) in zip(trg_data, rsl_data):
# w = u - v
# sum += w * w
# sum /= float(len(trg_data))
# assert sum < 1e-6
def test_001_(self):
src_data = numpy.array([-1.0, 1.0, -1.0, 1.0])
trg_data = src_data * 0.5
src = gr.vector_source_f(src_data)
dst = gr.vector_sink_f()
rail = gr.rail_ff(-0.5, 0.5)
self.tb.connect(src, rail)
self.tb.connect(rail, dst)
self.tb.run()
rsl_data = dst.data()
sum = 0
for (u, v) in zip(trg_data, rsl_data):
w = u - v
sum += w * w
sum /= float(len(trg_data))
assert sum < 1e-6
def test_002_(self):
src_data = numpy.array([-1.0,
-1.0/2.0,
-1.0/3.0,
-1.0/4.0,
-1.0/5.0])
trg_data = copy.deepcopy(src_data)
src = gr.vector_source_f(src_data, False, len(src_data))
st = gr.stretch_ff(-1.0/5.0, len(src_data))
dst = gr.vector_sink_f(len(src_data))
self.tb.connect(src, st)
self.tb.connect(st, dst)
self.tb.run()
rsl_data = dst.data()
sum = 0
for (u, v) in zip(trg_data, rsl_data):
w = u - v
sum += w * w
sum /= float(len(trg_data))
assert sum < 1e-6
def test_003_(self):
src_grid = (0.0, 1.0, 2.0, 3.0, 4.0)
trg_grid = copy.deepcopy(src_grid)
src_data = (0.0, 1.0, 0.0, 1.0, 0.0)
src = gr.vector_source_f(src_data, False, len(src_grid))
sq = wavelet_swig.squash_ff(src_grid, trg_grid)
dst = gr.vector_sink_f(len(trg_grid))
self.tb.connect(src, sq)
self.tb.connect(sq, dst)
self.tb.run()
rsl_data = dst.data()
sum = 0
for (u, v) in zip(src_data, rsl_data):
w = u - v
sum += w * w
sum /= float(len(src_data))
assert sum < 1e-6
# def test_004_(self): # FIXME: requires pygsl
#
# n = 256
# o = 4
# ws = wavelet.workspace(n)
# w = wavelet.daubechies(o)
#
# a = numpy.arange(n)
# b = numpy.sin(a*numpy.pi/16.0)
# c = w.transform_forward(b, ws)
# d = w.transform_inverse(c, ws)
#
# src = gr.vector_source_f(b, False, n)
# wv = wavelet_swig.wavelet_ff(n, o, True)
#
# dst = gr.vector_sink_f(n)
# self.tb.connect(src, wv)
# self.tb.connect(wv, dst)
# self.tb.run()
# e = dst.data()
#
# sum = 0
# for (u, v) in zip(c, e):
# w = u - v
# sum += w * w
# sum /= float(len(c))
# assert sum < 1e-6
def test_005_(self):
src_data = (1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0)
dwav = numpy.array(src_data)
wvps = numpy.zeros(3)
# wavelet power spectrum
scl = 1.0/sqr(dwav[0])
k = 1
for e in range(len(wvps)):
wvps[e] = scl*sqr(dwav[k:k+(01<<e)]).sum()
k += 01<<e
src = gr.vector_source_f(src_data, False, len(src_data))
kon = wavelet_swig.wvps_ff(len(src_data))
dst = gr.vector_sink_f(int(math.ceil(math.log(len(src_data), 2))))
self.tb.connect(src, kon)
self.tb.connect(kon, dst)
self.tb.run()
snk_data = dst.data()
sum = 0
for (u,v) in zip(snk_data, wvps):
w = u - v
sum += w * w
sum /= float(len(snk_data))
assert sum < 1e-6
if __name__ == '__main__':
gr_unittest.run(test_classify, "test_classify.xml")
|
