"""926/B — aggregate per-subject diffusion runs into results.json / summary.md."""
import json
from pathlib import Path

import numpy as np

B = Path("/home/hubin/926/results/B")
METRICS = ["chamfer_l2", "chamfer_l1", "fscore_0.1", "fscore_0.05", "emd_256"]
LOWER_BETTER = {"chamfer_l2": True, "chamfer_l1": True, "fscore_0.1": False, "fscore_0.05": False, "emd_256": True}
REF = "paired_4avg"


def boot(d, n=10000, seed=0):
    rng = np.random.default_rng(seed)
    m = d[rng.integers(0, len(d), (n, len(d)))].mean(1)
    return [float(np.quantile(m, 0.025)), float(np.quantile(m, 0.975))]


def main():
    subs = {}
    for p in sorted((B / "per_subject").glob("sub*.json")):
        d = json.load(open(p))
        by = {}
        for r in d["rows"]:
            by.setdefault(r["condition"], []).append(r)
        subs[d["subject"]] = {c: {k: float(np.mean([r[k] for r in rs])) for k in METRICS} | {"n": len(rs)}
                              for c, rs in by.items()}
    conds = [c for c in json.load(open(next((B / "per_subject").glob("sub*.json"))))["conditions"]] \
        if "conditions" in json.load(open(next((B / "per_subject").glob("sub*.json")))) else list(next(iter(subs.values())))
    complete = [s for s in subs if all(c in subs[s] and subs[s][c]["n"] == 144 for c in conds)]
    S = sorted(complete)

    def delta(c, ref, k):
        d = np.array([subs[s][c][k] - subs[s][ref][k] for s in S])
        return {"delta": float(d.mean()), "ci95_subject": boot(d), "per_subject": dict(zip(S, d.tolist())),
                "n_worse": int((d > 0).sum() if LOWER_BETTER[k] else (d < 0).sum())}

    table = {c: {k: {"mean": float(np.mean([subs[s][c][k] for s in S])),
                     "sd_subject": float(np.std([subs[s][c][k] for s in S], ddof=1)) if len(S) > 1 else None}
                 for k in METRICS} for c in conds}
    deltas = {c: {k: delta(c, REF, k) for k in METRICS} for c in conds if c != REF}
    deltas_single = {c: delta(c, "paired_single", "chamfer_l2") for c in conds if c != "paired_single"}

    checks = []

    def chk(name, c, ref, want):
        d = delta(c, ref, "chamfer_l2")
        lo, hi = d["ci95_subject"]
        ok = hi < 0 if want == "better" else lo > 0
        checks.append({"check": name, "input": c, "reference": ref, "want": f"{want} than reference (Chamfer-L2)",
                       "delta": d["delta"], "ci95_subject": d["ci95_subject"], "pass": bool(ok)})

    chk("trial-matched target mean", "target_trialmatched4", REF, "better")
    chk("trial-matched wrong mean", "wrong_trialmatched4", REF, "worse")
    chk("within-subject target mean", "target_within", REF, "better")
    chk("within-subject wrong mean", "wrong_within", REF, "worse")
    for m in ("varnorm", "spectrum", "cov"):
        chk(f"target mean vs moment-matched paired ({m})", "target_within", f"paired_match_{m}", "better")
        chk(f"wrong mean vs moment-matched paired ({m})", "wrong_within", f"paired_match_{m}", "worse")
    passed = len(S) > 1 and all(c["pass"] for c in checks)
    meta = json.load(open(next((B / "per_subject").glob("sub*.json"))))
    res = {"experiment": "926_B_mean_input_controls",
           "decoder": "released Neuro-3D point diffusion, 200k steps, frozen; 200 sampling steps; batch 48; "
                      "seed = 20260722 + batch start (identical across inputs)",
           "checkpoint_step": meta.get("checkpoint_step"), "subjects_complete": S, "n_subjects": len(S),
           "objects_per_subject": 144, "reference": REF,
           "note": "diffusion-to-target distances (~0.4) live in their own column and are NOT the main endpoint (A)",
           "wrong_category": "(c + 1) mod 72", "per_condition": table, "delta_vs_paired_4avg": deltas,
           "delta_vs_paired_single_chamfer_l2": deltas_single, "pass_checks": checks,
           "decision": "keep" if passed else "drop", "test_status": "secondary (main endpoint is A)",
           "per_subject": subs}
    json.dump(res, open(B / "results.json", "w"), indent=1)

    L = [f"# B — mean inputs to the frozen diffusion decoder\n",
         f"**decision: {res['decision']}** (secondary test; {len(S)} subjects complete: {', '.join(S)})\n",
         "Released Neuro-3D point diffusion (200k), frozen; same targets, weights and sampling seed for every input. "
         "Wrong category = (c+1) mod 72. Statistical unit = subject; 95% CI = bootstrap over subjects. "
         "These diffusion-to-target distances (~0.4) are a separate column and are not the main endpoint.\n",
         "## Per input (subject means)\n",
         "| input | Chamfer-L2 | Δ vs paired 4-avg [95% CI] | F@0.1 | Δ F@0.1 | EMD | Δ EMD |", "|---|---|---|---|---|---|---|"]
    for c in conds:
        t = table[c]
        if c == REF:
            L.append(f"| {c} | {t['chamfer_l2']['mean']:.4f} | — | {t['fscore_0.1']['mean']:.4f} | — | {t['emd_256']['mean']:.4f} | — |")
            continue
        d = deltas[c]
        f = lambda k: f"{d[k]['delta']:+.4f} [{d[k]['ci95_subject'][0]:+.4f}, {d[k]['ci95_subject'][1]:+.4f}]"
        L.append(f"| {c} | {t['chamfer_l2']['mean']:.4f} | {f('chamfer_l2')} | {t['fscore_0.1']['mean']:.4f} | "
                 f"{f('fscore_0.1')} | {t['emd_256']['mean']:.4f} | {f('emd_256')} |")
    L += ["\n## Pass checks (Chamfer-L2, CI must exclude 0)\n", "| check | Δ [95% CI] | pass |", "|---|---|---|"]
    for c in checks:
        L.append(f"| {c['check']} (`{c['input']}` − `{c['reference']}`) | {c['delta']:+.4f} "
                 f"[{c['ci95_subject'][0]:+.4f}, {c['ci95_subject'][1]:+.4f}] | {'yes' if c['pass'] else 'no'} |")
    (B / "summary.md").write_text("\n".join(L) + "\n")
    print("\n".join(L))


if __name__ == "__main__":
    main()