#!/usr/bin/env python3
"""Phase B step 2: temporal detector-dynamics scan (offline).

Variants with time memory applied to FULL trajectory (state settles before
metric window), then validated pipeline (window, -6dB core, median/rms).
"""
import numpy as np

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

FULL = {}
for name, traj, dump, bm in [
        ('res_500','traj_res500.bin','dump_res_new.bin',85),
        ('al_12','traj_al12.bin','dump_t1k.bin',85),
        ('al_24','traj_al24.bin','dump_t1k.bin',85),
        ('t1k_1000','traj_t1k.bin','dump_t1k.bin',85)]:
    FULL[name] = load_traj('/tmp/opencode/'+traj)

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 t_hold(T, b):          # linear peak-hold decay
    out = T.copy()
    for t in range(1, len(T)):
        out[t] = np.maximum(T[t], b*out[t-1])
    return out

def t_dbdecay(T, r):       # dB-domain peak decay r dB/frame
    db = np.log10(np.maximum(T, 1e-12))*20.0
    for t in range(1, len(db)):
        db[t] = np.maximum(db[t], db[t-1]-r)
    return 10**(db/20)

def t_ema(T, a):           # EMA in dB domain
    db = np.log10(np.maximum(T, 1e-12))*20.0
    out = db.copy()
    for t in range(1, len(db)):
        out[t] = a*db[t] + (1-a)*out[t-1]
    return 10**(out/20)

def eval_tf(fn):
    out = {}
    for name,(dB,W,bm,_) in DATA.items():
        Tt = fn(FULL[name])[WIN[name][0]:WIN[name][1]]
        dBp = np.log10(np.maximum(Tt, 1e-12))*20.0
        if name != 'res_500':
            lv = dBp[:, bm]; keep = dBp[lv >= lv.max()-6]
        else:
            keep = dBp
        g = gains_batch(np.ascontiguousarray(keep), 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])
    return out

VARS = [('none', lambda T: T)]
for b in [0.8, 0.9, 0.95, 0.99]: VARS.append((f'hold b={b}', lambda T, b=b: t_hold(T, b)))
for r in [0.25, 0.5, 1.0, 2.0]: VARS.append((f'dbdec r={r}', lambda T, r=r: t_dbdecay(T, r)))
for a in [0.3, 0.5, 0.7]: VARS.append((f'ema a={a}', lambda T, a=a: t_ema(T, a)))

print(f'{"variant":>12} ' + ' '.join(f'{n:>9}' for n in DATA) + '   rms')
for lbl, fn in VARS:
    e = eval_tf(fn)
    tot = np.sqrt(sum(v*v for v in e.values())/len(e))
    print(f'{lbl:>12} ' + ' '.join(f'{v:+9.2f}' for v in e.values()) + f'   {tot:.2f}')