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105 lines
2.3 KiB
Python
105 lines
2.3 KiB
Python
import os
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from io import BytesIO
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import numpy as np
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from amodem import send
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from amodem import recv
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from amodem import common
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from amodem import dsp
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from amodem import sampling
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from amodem import config
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config = config.fastest()
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import logging
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s %(levelname)-12s %(message)s')
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import pytest
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class Args(object):
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def __init__(self, **kwargs):
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self.__dict__.update(kwargs)
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def __getattr__(self, name):
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return None
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def run(size, chan=None, df=0, success=True):
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tx_data = os.urandom(size)
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tx_audio = BytesIO()
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send.main(config=config, src=BytesIO(tx_data), dst=tx_audio)
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data = tx_audio.getvalue()
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data = common.loads(data)
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if chan is not None:
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data = chan(data)
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if df:
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sampler = sampling.Sampler(data, sampling.Interpolator())
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sampler.freq += df
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data = sampler.take(len(data))
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data = common.dumps(data)
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rx_audio = BytesIO(data)
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rx_data = BytesIO()
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d = BytesIO()
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result = recv.main(config=config, src=rx_audio, dst=rx_data, dump_audio=d)
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rx_data = rx_data.getvalue()
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assert data.startswith(d.getvalue())
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assert result == success
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if success:
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assert rx_data == tx_data
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@pytest.fixture(params=[0, 1, 3, 10, 42, 123])
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def small_size(request):
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return request.param
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def test_small(small_size):
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run(small_size, chan=lambda x: x)
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def test_error():
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skip = 32000 # remove trailing silence
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run(1024, chan=lambda x: x[:-skip], success=False)
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@pytest.fixture(params=[sign * drift for sign in (+1, -1)
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for drift in (0.1, 1, 10, 100)])
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def freq_err(request):
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return request.param * 1e-6
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def test_timing(freq_err):
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run(8192, df=freq_err)
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def test_lowpass():
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run(1024, chan=lambda x: dsp.lfilter(b=[0.9], a=[1.0, -0.1], x=x))
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def test_highpass():
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run(1024, chan=lambda x: dsp.lfilter(b=[0.9], a=[1.0, 0.1], x=x))
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def test_attenuation():
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run(5120, chan=lambda x: x * 0.1)
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def test_low_noise():
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r = np.random.RandomState(seed=0)
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run(5120, chan=lambda x: x + r.normal(size=len(x), scale=0.0001))
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def test_medium_noise():
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r = np.random.RandomState(seed=0)
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run(5120, chan=lambda x: x + r.normal(size=len(x), scale=0.001))
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def test_large():
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run(54321, chan=lambda x: x)
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