def test_too_noisy():
r = random.Random(0) # generate random binary signal
signal = np.array([r.choice([-1, 1]) for i in range(int(config.Fs))])
src = BytesIO(common.dumps(signal * 0.5))
for r in calib.detector(config, src=src):
assert not r['success']
assert r['msg'] == 'too noisy signal'
def test_resample():
x = np.sin(2 * np.pi * 10 * np.linspace(0, 1, 1001))
src = BytesIO(common.dumps(x))
dst = BytesIO()
sampling.resample(src=src, dst=dst, df=0.0)
y = common.loads(dst.getvalue())
err = x[:len(y)] - y
assert np.max(np.abs(err)) < 1e-4
dst = BytesIO()
sampling.resample(src=BytesIO(b'\x00\x00'), dst=dst, df=0.0)
assert dst.tell() == 0
def test_drift(freq_err):
freq = config.Fc * (1 + freq_err / 1e6)
t = np.arange(int(1.0 * config.Fs)) * config.Ts
frame_length = 100
rms = 0.5
signal = rms * np.cos(2 * np.pi * freq * t)
src = BytesIO(common.dumps(signal))
iters = 0
for r in calib.detector(config, src, frame_length=frame_length):
assert r['success'] is True
assert abs(r['rms'] - rms) < 1e-3
assert abs(r['total'] - rms) < 1e-3
iters += 1
assert iters > 0
assert iters == config.baud / frame_length
def run(size, chan=None, df=0, success=True, cfg=None):
if cfg is None:
cfg = config.fastest()
tx_data = os.urandom(size)
tx_audio = BytesIO()
main.send(config=cfg, src=BytesIO(tx_data), dst=tx_audio, gain=0.5)
data = tx_audio.getvalue()
data = common.loads(data)
if chan is not None:
data = chan(data)
if df:
sampler = sampling.Sampler(data, sampling.Interpolator())
sampler.freq += df
data = sampler.take(len(data))
data = common.dumps(data)
rx_audio = BytesIO(data)
rx_data = BytesIO()
dump = BytesIO()
try:
result = main.recv(config=cfg,
src=rx_audio,
dst=rx_data,
dump_audio=dump,
pylab=None)
finally:
rx_audio.close()
rx_data = rx_data.getvalue()
assert data.startswith(dump.getvalue())
assert result == success
if success:
assert rx_data == tx_data
def resample(src, dst, df=0.0):
x = common.load(src)
sampler = Sampler(x, Interpolator())
sampler.freq += df
y = sampler.take(len(x))
dst.write(common.dumps(y))
def test_dumps_loads():
x = np.array([.1, .4, .2, .6, .3, .5])
y = common.loads(common.dumps(x))
assert all(x == y)
