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66 lines
1.5 KiB
Python
66 lines
1.5 KiB
Python
import numpy as np
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from numpy.linalg import norm
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from amodem import dsp
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from amodem import config
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from amodem import sampling
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def test_linreg():
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x = np.array([1, 3, 2, 8, 4, 6, 9, 7, 0, 5])
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a, b = 12.3, 4.56
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y = a * x + b
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a_, b_ = dsp.linear_regression(x, y)
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assert abs(a - a_) < 1e-10
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assert abs(b - b_) < 1e-10
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def test_filter():
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x = range(10)
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y = dsp.lfilter(b=[1], a=[1], x=x)
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assert (np.array(x) == y).all()
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x = [1] + [0] * 10
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y = dsp.lfilter(b=[0.5], a=[1, -0.5], x=x)
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assert list(y) == [0.5 ** (i+1) for i in range(len(x))]
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def test_estimate():
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r = np.random.RandomState(seed=0)
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x = r.uniform(-1, 1, [1000])
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x[:10] = 0
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x[len(x)-10:] = 0
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c = 1.23
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y = c * x
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c_, = dsp.estimate(x=x, y=y, order=1)
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assert abs(c - c_) < 1e-12
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h = [1, 1]
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y = dsp.lfilter(b=h, a=[1], x=x)
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h_ = dsp.estimate(x=x, y=y, order=len(h))
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assert norm(h - h_) < 1e-12
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h = [0.1, 0.6, 0.9, 0.7, -0.2]
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L = len(h) // 2
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y = dsp.lfilter(b=h, a=[1], x=x)
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h_ = dsp.estimate(
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x=x[:len(x)-L], y=y[L:],
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order=len(h), lookahead=L
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)
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assert norm(h - h_) < 1e-12
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y_ = dsp.lfilter(b=h_, a=[1], x=x)
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assert norm(y - y_) < 1e-12
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def test_demux():
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freqs = [1e3, 2e3]
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carriers = [dsp.exp_iwt(f, config.Nsym) for f in freqs]
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syms = [3, 2j]
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sig = np.dot(syms, carriers)
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res = dsp.Demux(sampling.Sampler(sig.real), freqs)
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res = np.array(list(res))
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assert np.max(np.abs(res - syms)) < 1e-12
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