92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""verify_lut4.py — тонкая подгонка узлов LUT под все 36 точек."""
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import numpy as np
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from scipy.interpolate import PchipInterpolator
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from scipy.optimize import least_squares
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FS = 44100.0
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DEPTH = 0.8639736175537109
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QS = [0.1, 0.2, 0.3, 0.5, 0.7, 1.0, 1.5, 2.0, 3.0, 5.0, 10.0]
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DUAL = np.array([(10.220, 15.224), (10.219, 13.963), (10.219, 12.947),
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(10.219, 11.725), (10.218, 11.119), (10.216, 10.689),
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(10.210, 10.412), (10.203, 10.305), (10.182, 10.225),
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(10.113, 10.183), (9.822, 10.165)])
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FCS = [800.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1200.0]
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T1KQ = np.array([7.868, 8.536, 8.726, 8.788, 8.729, 8.575, 8.115])
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T1K = np.array([14.548, 15.332, 15.553, 15.626, 15.557, 15.378, 14.840])
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L_DUAL = 10 ** (-7.142 / 20)
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L_T1KQ = 10 ** (-18.063 / 20)
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L_T1K = 1.0
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TILT = {500: 1.414, 1000: 1.454, 2000: 1.795}
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Q, G = 0.900, 4.132
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LUTX = np.array([-0.75, -0.50, -0.25, 0.00, 0.25, 0.50, 0.574, 0.61, 0.75, 1.00])
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LUTY0 = np.array([0.4453, 0.4551, 0.4784, 0.5041, 0.5331, 0.5715, 0.5660, 0.6470, 0.6574, 0.6636])
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def res_at(ft, fc, Q, g):
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w0 = fc * 2 * np.pi / FS
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c, s = np.cos(w0), np.sin(w0)
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p = (s * 0.5) / Q
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a, a2 = p * g, p / g
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A = [a + 1, -2 * c, 1 - a]
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B = [a2 + 1, -2 * c, 1 - a2]
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w = 2 * np.pi * ft / FS
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z = np.exp(-1j * w)
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return np.abs(2.0 * (B[0] + B[1] * z + B[2] * z * z) /
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(A[0] + A[1] * z + A[2] * z * z))
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def build_data():
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xs, meas = [], []
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for i, q in enumerate(QS):
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for f, m in ((500, DUAL[i, 0]), (2000, DUAL[i, 1])):
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xs.append(np.log10(L_DUAL / res_at(f, 500, q, G)))
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meas.append(m)
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for i, fc in enumerate(FCS):
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r = res_at(1000, fc, 0.9999978, G)
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xs.append(np.log10(L_T1KQ / r)); meas.append(T1KQ[i])
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xs.append(np.log10(L_T1K / r)); meas.append(T1K[i])
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return np.array(xs), np.array(meas)
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XS, MEAS = build_data()
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TILTS = np.array([1.414 if x < 0.35 else (1.795 if x < -0.05 else 1.454) for x in XS])
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# уточнение: tilt по тегу. пересоберём аккуратно
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def tags():
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ts = []
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for i, q in enumerate(QS):
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for f in (500.0, 2000.0):
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ts.append(TILT[f])
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for _ in range(7):
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ts.append(TILT[1000]); ts.append(TILT[1000])
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return np.array(ts)
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TT = tags()
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def model(ly):
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lut = PchipInterpolator(LUTX, ly)
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out = []
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for x, tilt in zip(XS, TT):
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C = DEPTH * tilt * lut(x)
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out.append(-20 * np.log10(max(1 - C, 1e-9)))
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return np.array(out)
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def run():
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r = least_squares(lambda ly: model(ly) - MEAS, LUTY0, max_nfev=30000, xtol=1e-13, ftol=1e-13)
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ly = r.x
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pred = model(ly)
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rmse = np.sqrt(np.mean((pred - MEAS) ** 2))
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print(f'LUT-knot fit rmse={rmse:.4f} dB')
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for i, (x, m, p, t) in enumerate(zip(XS, MEAS, pred, TT)):
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if abs(p - m) > 0.1:
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print(f' x={x:+.3f} tilt={t:.3f} {m:7.3f}/{p:7.3f} ({p - m:+.3f})')
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print('knots:')
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for xx, yy in zip(LUTX, ly):
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print(f' ({xx:+.3f}, {yy:.4f}),')
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if __name__ == '__main__':
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run()
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