def test_001(self):
""" Run test:
- No overlap
- Hamming window from Python
- Constant input signal of amplitude 1
The given input signal has a known power of 1. Therefore, the integral of the
PSD estimation result should be pretty close to 1."""
fft_len = 256
overlap = 0
alpha = 0.1
window = hamming(fft_len)
src_data = (1,) * (100 * fft_len)
src = blocks.vector_source_c(src_data, False)
welch = specest.welchsp(fft_len, overlap, alpha, False, window)
sink = blocks.vector_sink_f(fft_len)
self.tb.connect(src, welch, sink)
self.tb.run()
dst_data = sink.data()
dst_data = array(dst_data[-fft_len:])
power_est = sum(dst_data) * 2 * pi / fft_len
print power_est
self.assertAlmostEqual(power_est, 1, 3)
def test_001(self):
""" Run test:
- No overlap
- Hamming window from Python
- Constant input signal of amplitude 1
The given input signal has a known power of 1. Therefore, the integral of the
PSD estimation result should be pretty close to 1."""
fft_len = 256
overlap = 0
alpha = 0.1
window = hamming(fft_len)
src_data = (1, ) * (100 * fft_len)
src = blocks.vector_source_c(src_data, False)
welch = specest.welchsp(fft_len, overlap, alpha, False, window)
sink = blocks.vector_sink_f(fft_len)
self.tb.connect(src, welch, sink)
self.tb.run()
dst_data = sink.data()
dst_data = array(dst_data[-fft_len:])
power_est = sum(dst_data) * 2 * pi / fft_len
print(power_est)
self.assertAlmostEqual(power_est, 1, 3)
def test_default_002(self):
""" Same test as before, but different parameters (FFT length, moving average and
the window comes from C++). """
fft_len = 1024
src_data = (1,) * (100* fft_len)
src = blocks.vector_source_c(src_data, False)
welch = specest.welchsp(fft_len)
sink = blocks.vector_sink_f(fft_len)
self.tb.connect(src, welch, sink)
self.tb.run()
dst_data = sink.data()
dst_data = array(dst_data[-fft_len:])
power_est = sum(dst_data) * 2 * pi / fft_len
self.assertAlmostEqual(power_est, 1, 5)
def test_default_002(self):
""" Same test as before, but different parameters (FFT length, moving average and
the window comes from C++). """
fft_len = 1024
src_data = (1, ) * (100 * fft_len)
src = blocks.vector_source_c(src_data, False)
welch = specest.welchsp(fft_len)
sink = blocks.vector_sink_f(fft_len)
self.tb.connect(src, welch, sink)
self.tb.run()
dst_data = sink.data()
dst_data = array(dst_data[-fft_len:])
power_est = sum(dst_data) * 2 * pi / fft_len
self.assertAlmostEqual(power_est, 1, 5)
