#!/usr/bin/env python3
"""Phase B step 1c: neighborhood MEAN/RMS pooling scan (max already refuted).

Rationale: slide_max raises lvl at tone bins via sidelobes -> over-reduction
(tones broke -2.7..-4.9). Mean/RMS pooling does the opposite for an isolated
narrow peak among quiet neighbours -> less reduction on tones, ~neutral on
wide noise. Pooling in LINEAR lvl domain (am ~ lvl near flat res).
"""
import numpy as np

exec(open('/tmp/opencode/phaseA_grid_fast.py').read().split("# old-law reference gains")[0])

def slide(x, w, op):
    if w <= 1: return x
    h = w // 2
    xp = np.pad(x, ((0, 0), (h, h)), mode='edge')
    win = np.lib.stride_tricks.sliding_window_view(xp, w, axis=1)
    if op == 'mean': return win.mean(axis=-1)
    return np.sqrt((win ** 2).mean(axis=-1))

RAW = {}
for name, traj in [('res_500','traj_res500.bin'), ('al_12','traj_al12.bin'),
                   ('al_24','traj_al24.bin'), ('t1k_1000','traj_t1k.bin')]:
    RAW[name] = load_traj('/tmp/opencode/'+traj)[WIN[name][0]:WIN[name][1]]

GO = {}
for name,(dB,W,bm,_) in DATA.items():
    c_old = np.clip((dB+13.78)/82.07, 0, 1)**0.344*4.2
    g = gains_batch(dB, W, bm, np.array([0.]), np.array([0.]), np.array([99.]), C_pre=c_old)
    GO[name] = float(g[0].mean()) if name=='res_500' else float(np.median(g[0]))

def eval_variant(op, w):
    saved = {n: DATA[n] for n in DATA}
    for name,(dB,W,bm,_) in DATA.items():
        T = slide(RAW[name], w, op)
        dBp = np.log10(np.maximum(T, 1e-12))*20.0
        if name != 'res_500':
            lv = dBp[:, bm]; keep = dBp[lv >= lv.max()-6]
        else:
            keep = dBp
        DATA[name] = (np.ascontiguousarray(keep), W, bm, None)
    out = {}
    for name,(dB,W,bm,_) in DATA.items():
        g = gains_batch(dB, W, bm, np.array([1.8]), np.array([0.11]), np.array([99.]))
        agg = np.sqrt(np.mean(g**2, axis=1)) if name=='res_500' else np.median(g, axis=1)
        out[name] = MEAS_OLD[name] + 20*np.log10(float(agg[0])/GO[name])
    DATA.update(saved)
    return out

print(f'{"op":>4} {"w":>3} ' + ' '.join(f'{n:>9}' for n in DATA) + '   rms')
for op in ['mean', 'rms']:
    for w in [3, 5, 9, 17, 33]:
        e = eval_variant(op, w)
        tot = np.sqrt(sum(v*v for v in e.values())/len(e))
        print(f'{op:>4} {w:>3} ' + ' '.join(f'{v:+9.2f}' for v in e.values()) + f'   {tot:.2f}')