Initial commit: soothe2 RE workspace + roadmap

- Инфраструктура: RTTI-дампы (rtti_dsp/full.json), декомпиляции DSP-классов (decomp_*.txt),
  Ghidra-скрипты (Dump*.java, ImportRtti*.java, Diag.java), depthcurve/curve_fits LUT.
- Поведенческая модель sim_v5.py + sim.py (RMSE ~0.3dB), утилиты (measure, tt_sweep, patchparam,
  sweep, harness_*, verify_sim).
- summary.md + roadmap.md (контракт из manual M1-M12, топология пайплайна из OCR, фазы A-C).
- pipeline_ocr.txt: OCR диаграммы Appendix A (mid/side ручка, trim/mix/bypass порядок).

fit_curves.py

@@ -0,0 +1,52 @@
+#!/usr/bin/env python3
+"""Fit parametric curves (v2): amount_total = A(depth)*S(sens)*H(sharp).
+S normalized so S(12)=1, H(10)=1. A→1 as depth→large.
+Saturation form: f(x)=1-exp(-(x/k)^m).
+"""
+import numpy as np
+from scipy.optimize import curve_fit
+
+def sat(x, k, m):
+    return 1 - np.exp(-(x / k) ** m)
+
+def to_amount(red):
+    return 1 - 10 ** (-red / 20.0)
+
+def fit(name, x, y, p0):
+    popt, _ = curve_fit(sat, x, y, p0=p0, maxfev=50000)
+    pred = sat(x, *popt)
+    print(f"{name}: k={popt[0]:.4f} m={popt[1]:.3f}  RMSE={np.sqrt(np.mean((pred-y)**2)):.5f}")
+    for xi, yi, pi in zip(x, y, pred):
+        print(f"   x={xi:6.2f}  meas={yi:.4f}  pred={pi:.4f}")
+    return popt
+
+A_def = to_amount(7.70)
+
+# A: depth sweep at S=H=1 (sens12 sharp10)
+x = np.array([0.5, 0.864, 1.0, 2.0, 3.0, 5.0, 10.0, 20.0], float)
+y = np.array([to_amount(d) for d in [7.14, 7.70, 7.91, 9.56, 11.29, 14.89, 24.25, 39.41]])
+ka = fit('A(depth)', x, y, [3.0, 0.8])
+
+# S: sens at depth0.864 (S saturates -> S(12)=1). amount = A_def * S
+# S has a floor at sens=0 (still 2.89dB): S(x) = s0 + (1-s0)*sat(x)
+def S(x, s0, k, m):
+    return s0 + (1 - s0) * sat(x, k, m)
+x = np.array([0.0, 6.0, 12.0, 24.0, 48.0], float)
+y = np.array([to_amount(d) for d in [2.89, 5.14, 7.70, 7.70, 7.70]]) / A_def
+popt, _ = curve_fit(S, x, y, p0=[0.4, 3.0, 3.0], maxfev=50000)
+pred = S(x, *popt)
+print(f"S(sens): s0={popt[0]:.4f} k={popt[1]:.4f} m={popt[2]:.3f}  RMSE={np.sqrt(np.mean((pred-y)**2)):.5f}")
+for xi, yi, pi in zip(x, y, pred):
+    print(f"   x={xi:6.2f}  meas={yi:.4f}  pred={pi:.4f}")
+ks = popt
+
+# H: sharp at depth0.864 -> H(10)=1
+x = np.array([1.0, 3.0, 5.0, 10.0, 20.0], float)
+y = np.array([to_amount(d) for d in [1.11, 3.71, 5.90, 7.70, 7.70]]) / A_def
+kh = fit('H(sharp)/norm', x, y, [3.0, 1.5])
+
+print("\ncross-check amount_total = A*S*H at default:")
+print(f"  A(0.864)*S(12)*H(10) = {sat(0.864,*ka):.4f}*1*1 -> red {(-20*np.log10(1-sat(0.864,*ka))):.2f} dB (meas 7.70)")
+print(f"  depth=10 sens=0:      A(10)*S(0) = {sat(10,*ka):.4f}*{sat(0,*ks):.4f} = {sat(10,*ka)*sat(0,*ks):.4f} -> {( -20*np.log10(1-sat(10,*ka)*sat(0,*ks))):.2f} dB")
+np.savez('/home/m/re-tools/curve_fits.npz',
+         k_depth=ka, k_sens=ks, k_sharp=kh, A_def=A_def)