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
|
#!/usr/bin/env python
#
# Copyright 2011 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.
#
from gnuradio import gr, gr_unittest
import digital_swig, psk2
import random, cmath
class test_digital(gr_unittest.TestCase):
def setUp (self):
self.tb = gr.top_block ()
def tearDown (self):
self.tb = None
def test01 (self):
# Test complex/complex version
omega = 2
gain_omega = 0.001
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_cc(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 100*[complex(1, 1),]
self.src = gr.vector_source_c(data, False)
self.snk = gr.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100*[complex(0.99972, 0.99972)] # doesn't quite get to 1.0
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 30
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertComplexTuplesAlmostEqual (expected_result, dst_data, 5)
def test02 (self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.5
gain_mu = 0.01
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_ff(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 100*[1,]
self.src = gr.vector_source_f(data, False)
self.snk = gr.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 100*[0.99972, ] # doesn't quite get to 1.0
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 30
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertFloatTuplesAlmostEqual (expected_result, dst_data, 5)
def test03 (self):
# Test complex/complex version with varying input
omega = 2
gain_omega = 0.01
mu = 0.25
gain_mu = 0.1
omega_rel_lim = 0.0001
self.test = digital_swig.clock_recovery_mm_cc(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 1000*[complex(1, 1), complex(1, 1), complex(-1, -1), complex(-1, -1)]
self.src = gr.vector_source_c(data, False)
self.snk = gr.vector_sink_c()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000*[complex(-1.2, -1.2), complex(1.2, 1.2)]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 100
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertComplexTuplesAlmostEqual (expected_result, dst_data, 1)
def test04 (self):
# Test float/float version
omega = 2
gain_omega = 0.01
mu = 0.25
gain_mu = 0.1
omega_rel_lim = 0.001
self.test = digital_swig.clock_recovery_mm_ff(omega, gain_omega,
mu, gain_mu,
omega_rel_lim)
data = 1000*[1, 1, -1, -1]
self.src = gr.vector_source_f(data, False)
self.snk = gr.vector_sink_f()
self.tb.connect(self.src, self.test, self.snk)
self.tb.run()
expected_result = 1000*[-1.31, 1.31]
dst_data = self.snk.data()
# Only compare last Ncmp samples
Ncmp = 100
len_e = len(expected_result)
len_d = len(dst_data)
expected_result = expected_result[len_e - Ncmp:]
dst_data = dst_data[len_d - Ncmp:]
#print expected_result
#print dst_data
self.assertFloatTuplesAlmostEqual (expected_result, dst_data, 1)
if __name__ == '__main__':
gr_unittest.run(test_digital, "test_digital.xml")
|
