class QaEnergyDetector(gr_unittest.TestCase):
"""
QA related to EnergyDetector class.
"""
def setUp(self):
"""
Set globals for all tests. Called before a test is started.
"""
self.tb = OpERAFlow(name='top')
def tear_down(self):
"""
Destroy globals for all tests. Called right after a test if finished.
"""
self.tb = None
def test_001(self):
"""
Test the energy of a simple sequence (1, 2, -1, -2).
"""
# input and expected results
src_data = (1, 1, 1, 1)
expected_result = 1
# blocks
fft_size = len(src_data)
mavg_size = 1
src = blocks.vector_source_c(data=src_data)
dst = blocks.probe_signal_f()
ed = EnergySSArch(fft_size, mavg_size, EnergyDecision(1))
#radio_device = RadioDevice(the_source = src, the_sink = dst)
radio_device = RadioDevice()
radio_device.add_arch(source=src, arch=ed, sink=dst, uhd_device=None, name='ed')
################ FIM NOVO RADIO DEVICE
## flowgraph
##self.tb.add_arch(ed, radio_device, 'ed')
self.tb.add_radio(radio_device)
self.tb.run()
result_data = dst.level()
self.assertEqual(expected_result, result_data)
def test_002(self):
"""
Test a sequence with float number (0.1, 0.1, 0.1, 0.1).
"""
# input and expected results
src_data = (0.1, 0.1, 0.1, 0.1)
expected_result = 0
# blocks
fft_size = len(src_data)
mavg_size = 1
src = blocks.vector_source_c(data=src_data)
ed = EnergyDetectorC(fft_size, mavg_size, EnergyDecision(1))
dst = blocks.probe_signal_f()
# flowgraph
self.tb.connect(src, ed, dst)
self.tb.run()
result_data = dst.level()
self.assertEqual(expected_result, result_data)
def test_003(self):
"""
Test EDTopBlock with the input (1, 1, 1, 1, 1, 1, 1, 1).
"""
arr = (1, 1, 1, 1, 1, 1, 1, 1)
expected_out = 8
ed = EnergySSArch(fft_size=len(arr),
mavg_size=8,
algorithm=EnergyDecision(expected_out - 1)
)
src = blocks.vector_source_c(data=arr, vlen=1)
sink = blocks.probe_signal_f()
device = RadioDevice()
device.add_arch(source=src, arch=ed, sink=sink, uhd_device=None, name='ed')
self.tb.add_radio(device, 'ed')
self.tb.run()
##self.assertEqual(1 , device.sink.level()) # didn't work
self.assertEqual(1, device.output()[0])
def test_004(self):
"""
Test EDTopBlock with a simple input (1, 2, 3, 4).
"""
arr = (1.0, 2.0, 3.0, 4.0)
expected_result = 30 # before expected result was 2536
ed = EnergySSArch(fft_size=len(arr),
mavg_size=1,
algorithm=EnergyDecision(expected_result + 1) # (expected_out + 1)
)
src = blocks.vector_source_c(data=arr, vlen=1)
sink = blocks.probe_signal_f()
device = RadioDevice()
device.add_arch(source=src, arch=ed, sink=sink, uhd_device=None, name='ed')
self.tb.add_radio(device, 'ed')
self.tb.start()
self.tb.wait()
###self.assertEqual(expected_result , device.sink.output()[1])
self.assertEqual(expected_result, device.ed.output()[1]) # uses 'name' parameter of the add_arch method
class QaFeedback(gr_unittest.TestCase):
"""
QA tests related to feeback.
"""
def setUp(self):
"""
Set globals for all tests. Called before a test is started.
"""
self.tb = OpERAFlow('QaFeedback')
def tear_down(self):
"""
Destroy globals for all tests. Called right after a test if finished.
"""
self.tb = None
def test_001(self):
"""
Test Feedback Algorithm architecture.
This test validates the feedback architecture when the 'manager' says the channel is idle and the 'learner'
says is occupied.
"""
return
"""
::TODO::
Update this test.
"""
print 't1'
data_l = [1]
data_m = [0]
# Bayes learning parameters
in_th = 10
min_th = 0.001
max_th = 20
delta_th = 0.001
k = 1
# Feeback architecture
bl_algo = BayesLearningThreshold(in_th=in_th,
min_th=min_th,
max_th=max_th,
delta_th=delta_th,
k=k)
fb_algo = FeedbackAlgorithm(bl_algo, AlwaysTimeFeedback())
fb = FeedbackF(fb_algo)
# Data blocks
src_l = blocks.vector_source_f(data_l)
src_m = blocks.vector_source_f(data_m)
# Flow graph
tb = gr.top_block()
tb.connect(src_l, (fb, 0))
tb.connect(src_m, (fb, 1))
tb.run()
# bayes feedback has to be 0
self.assertEqual(bl_algo.feedback, 0)
def test_002(self):
"""
Test Feedback Algorithm architecture
This test validates the feedback architecture when the 'manager' says the channel is occupied and the 'learner'
says is idle.
"""
return
"""
::TODO::
Update this test.
"""
data_l = [0]
data_m = [1]
# Bayes learning parameters
in_th = 10
min_th = 0.001
max_th = 20
delta_th = 0.001
k = 1
# Feeback architecture
bl_algo = BayesLearningThreshold(in_th=in_th,
min_th=min_th,
max_th=max_th,
delta_th=delta_th,
k=k)
fb_algo = FeedbackSSArch2(bl_algo, AlwaysTimeFeedback()) ### learner, manager, a_feedback_strategy
fb = FeedbackF(fb_algo)
# Data blocks
src_l = blocks.vector_source_f(data_l)
src_m = blocks.vector_source_f(data_m)
# Flow graph
tb = gr.top_block()
tb.connect(src_l, (fb, 0))
tb.connect(src_m, (fb, 1))
tb.run()
# bayes feedback has to be 0
self.assertEqual(bl_algo.feedback, 1)
def test_003(self):
"""
Test a more elaborate scenario with feedback.
In this test the FeedbackTopBlock is utilized with n waveform algorithm as manager, an energy and a feedback
algorithm.
"""
return
"""
::TODO::
Update this test.
"""
# Random 'signal' utilized in the test
arr = [random.random() for i in xrange(1024)]
fft_size = 1024
# Bayes learning parameters
in_th = 1
min_th = 0.001
max_th = 20
delta_th = 0.001
k = 1
# Feeback architecture
bl_algo = BayesLearningThreshold(in_th=in_th,
min_th=min_th,
max_th=max_th,
delta_th=delta_th,
k=k)
# detectors utilized
bl = EnergyDetectorC(fft_size, 1, bl_algo)
ev = WaveformSSArch(fft_size, WaveformDecision(0.7))
# top block
t = FeedbackSSArch(block_manager=ev,
block_learner=bl,
feedback_algorithm=FeedbackAlgorithm(bl_algo, AlwaysTimeFeedback())
### learner, manager, a_feedback_strategy
)
source = blocks.vector_source_c(data=arr, vlen=1)
sink = blocks.probe_signal_f()
device = RadioDevice()
device.add_arch(source=source, arch=t, sink=sink, uhd_device=None, name='ss_arch')
self.tb.add_path(t, device, 'ss')
self.tb.run()
# As the waveform will (probably) not detected the channel as occupied, the feedback system should decrease the threshold by 1
self.assertEqual(0, bl_algo.feedback)
