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67 lines
1.6 KiB
Python
67 lines
1.6 KiB
Python
from numpy.linalg import norm
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from numpy.random import RandomState
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import numpy as np
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import utils
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from amodem import equalizer
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from amodem import levinson
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from amodem import config
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config = config.fastest()
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def assert_approx(x, y, e=1e-12):
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assert norm(x - y) < e * norm(x)
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def test_training():
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L = 1000
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e = equalizer.Equalizer(config)
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t1 = e.train_symbols(L)
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t2 = e.train_symbols(L)
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assert (t1 == t2).all()
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def test_commutation():
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x = np.random.RandomState(seed=0).normal(size=1000)
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b = [1, 1j, -1, -1j]
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a = [1, 0.1]
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y = utils.lfilter(x=x, b=b, a=a)
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y1 = utils.lfilter(x=utils.lfilter(x=x, b=b, a=[1]), b=[1], a=a)
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y2 = utils.lfilter(x=utils.lfilter(x=x, b=[1], a=a), b=b, a=[1])
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assert_approx(y, y1)
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assert_approx(y, y2)
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z = utils.lfilter(x=y, b=a, a=[1])
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z_ = utils.lfilter(x=x, b=b, a=[1])
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assert_approx(z, z_)
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def test_modem():
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L = 1000
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e = equalizer.Equalizer(config)
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sent = e.train_symbols(L)
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gain = config.Nfreq
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x = e.modulator(sent) * gain
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received = e.demodulator(x, L)
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assert_approx(sent, received)
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def test_signal():
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length = 120
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x = np.sign(RandomState(0).normal(size=length))
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x[-20:] = 0 # make sure the signal has bounded support
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den = np.array([1, -0.6, 0.1])
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num = np.array([0.5])
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y = utils.lfilter(x=x, b=num, a=den)
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lookahead = 2
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h = equalizer.train(
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signal=y, expected=x, order=len(den), lookahead=lookahead)
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assert norm(h[:lookahead]) < 1e-12
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h = h[lookahead:]
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assert_approx(h, den / num)
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x_ = utils.lfilter(x=y, b=h, a=[1])
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assert_approx(x_, x)
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