74 lines
2.7 KiB
Python
74 lines
2.7 KiB
Python
#!/usr/bin/env python3
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"""extract_lut.py — извлечение LUT-кривой level->mask из коллапса.
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Форма B.10 ЗАФИКСИРОВАНА (Q=0.9, gain=4.13, tilt 1.414/1.454/1.795).
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По каждой из 36 точек вычисляем x=log10(L0/res) и y_lut=(1-10^(-red/20))/(depth*tilt).
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Все точки должны лечь на ОДНУ монотонную кривую y_lut(x). Печатаем кривую.
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"""
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import numpy as np
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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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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 add(pts, L, res, red, tilt, tag):
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y = (1 - 10 ** (-red / 20)) / (DEPTH * tilt)
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pts.append((np.log10(L / res), y, tag))
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pts = []
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for q in QS:
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for f in (500.0, 2000.0):
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add(pts, L_DUAL, res_at(f, 500, q, G), DUAL[QS.index(q)][0 if f < 1000 else 1],
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TILT[f], 'dual')
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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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add(pts, L_T1KQ, r, T1KQ[i], TILT[1000], 't1kq')
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add(pts, L_T1K, r, T1K[i], TILT[1000], 't1k')
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pts.sort()
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# печать всех точек: x, y_lut
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print('x=log10(L0/res) y_lut tag')
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for x, y, tag in pts:
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print(f'{x:+.3f} {y:.4f} {tag}')
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# биннинг для кривой
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import collections
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bins = collections.defaultdict(list)
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for x, y, tag in pts:
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bins[round(x * 4) / 4].append(y)
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print('\nкривая (бин 0.25 по x):')
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xs, ys = [], []
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for bx in sorted(bins):
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v = np.mean(bins[bx])
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xs.append(bx); ys.append(v)
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print(f'x={bx:+.2f} y={v:.4f} (n={len(bins[bx])}, spread={np.std(bins[bx]):.4f})')
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# подгонка свободной кривой к (xs, ys): монотонная интерполяция
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print('\nНЕПАРАМЕТРИЧЕСКАЯ КРИВАЯ (для model):')
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for bx, v in zip(xs, ys):
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print(f' ({bx:+.3f}, {v:.4f}),')
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