"""926/A — target geometry of fixed-budget hypothesis sets.

Members are train category medoids (results/p0_category_geometry, objects 00-07),
never new samples. Geometry follows the existing category-anchor evaluation
(scripts/p0_prepare_anchor_errors.py): 512 points by linspace, centred, unit
sphere, Chamfer-L2 = sum of mean squared nearest distances. Fill geometry is
the existing train-medoid matrix results/geometry_risk/category_medoid_chamfer.npy.

Per object and set S we report
  recall              true category in S
  train_medoid        min_{s in S} D[true, s]  (medoid-to-medoid, the quantity the fill optimises)
  target_*            best-of-set Chamfer-L2 / F@0.1 / EMD-256 of the held-out target vs the set medoids
  proto_*             same, but each set member is represented by a held-out geometry prototype:
                      the medoid of the other 7 train objects of that category (never the fill medoid)
Main endpoint: per-subject mean target Chamfer of FL 8+2 minus 10+0 (top-10),
95% CI by resampling subjects.
"""
import argparse
import csv
import json
import os
import sys
from pathlib import Path

import numpy as np
import torch
from scipy.optimize import linear_sum_assignment
from scipy.spatial import distance_matrix

REFS = Path("/home/hubin/workspace/July/brain3d_refs")
CACHE = Path("/home/hubin/workspace/code/cache")
sys.path.insert(0, str(REFS))
sys.path.insert(0, str(REFS / "scripts"))
from gaussian_mvp.catalog import point_cloud_stem  # noqa: E402

PC = REFS / "data" / "EEG-3D" / "point_cloud_simple"
N_POINTS = 512
KS = [int(k) for k in os.environ["A_KS"].split(",")] if os.environ.get("A_KS") else [1, 3, 5, 10, 20]
M_DEP = ["fl", "random", "kcenter", "farthest", "dpp", "fl_calibrated", "learned_fl"]


# ----------------------------------------------------------------------------- geometry
def normalize(p):
    p = p - p.mean(0, keepdims=True)
    return p / max(float(np.linalg.norm(p, axis=1).max()), 1e-8)


def load_obj(name, n=N_POINTS):
    p = np.load(PC / f"{point_cloud_stem(name)}.npy")[:, :3]
    return normalize(p[np.linspace(0, len(p) - 1, n).round().astype(np.int64)].astype(np.float32))


def chamfer_matrix(A, B):
    """A [n, P, 3], B [m, P, 3] -> Chamfer-L2 [n, m] (anchor convention), F@0.1 [n, m]."""
    A, B = torch.from_numpy(np.asarray(A)), torch.from_numpy(np.asarray(B))
    ch, fs = np.zeros((len(A), len(B))), np.zeros((len(A), len(B)))
    for i in range(len(A)):
        d = torch.cdist(A[i].unsqueeze(0).expand(len(B), -1, -1), B)  # [m, P, P]; A rows = target
        t2p, p2t = d.min(2).values, d.min(1).values
        ch[i] = (t2p.square().mean(1) + p2t.square().mean(1)).numpy()
        prec, rec = (p2t < 0.1).float().mean(1), (t2p < 0.1).float().mean(1)
        fs[i] = torch.where(prec + rec > 0, 2 * prec * rec / (prec + rec + 1e-12), torch.zeros_like(prec)).numpy()
    return ch, fs


def emd_matrix(A, B):
    ia, ib = np.linspace(0, A.shape[1] - 1, 256).astype(int), np.linspace(0, B.shape[1] - 1, 256).astype(int)
    out = np.zeros((len(A), len(B)))
    for i in range(len(A)):
        for j in range(len(B)):
            c = distance_matrix(B[j][ib], A[i][ia])
            r, cc = linear_sum_assignment(c)
            out[i, j] = c[r, cc].mean()
    return out


def build_geometry(work):
    p = work / "geometry.npz"
    if p.exists():
        z = np.load(p, allow_pickle=True)
        return {k: z[k] for k in z.files}
    anchor = torch.load(REFS / "results/p0_anchor_validation_safe/anchor_errors.pt", map_location="cpu",
                        weights_only=False)
    names = list(anchor["test_names"])
    target = np.stack([load_obj(n) for n in names])
    med, proto, med05, info = [], [], [], {"medoid": [], "proto": [], "medoid05": []}
    for c in range(72):
        st = torch.load(REFS / f"results/p0_category_geometry/category_{c:02d}.pt", map_location="cpu",
                        weights_only=False)
        m = st["medoid"].float().numpy()
        med.append(m[np.linspace(0, len(m) - 1, N_POINTS).round().astype(np.int64)])
        tn, pw = list(st["train_names"]), st["pairwise"].double().numpy()
        k = tn.index(st["medoid_name"])
        keep = [j for j in range(len(tn)) if j != k]
        pk = keep[int(pw[np.ix_(keep, keep)].sum(1).argmin())]
        proto.append(load_obj(tn[pk]))
        t05 = [n for n in tn if int(n.rsplit("_", 1)[1]) <= 5]
        pts = np.stack([load_obj(n) for n in t05])
        ch05, _ = chamfer_matrix(pts, pts)
        k05 = int(ch05.sum(1).argmin())
        med05.append(pts[k05])
        info["medoid"].append(st["medoid_name"]); info["proto"].append(tn[pk]); info["medoid05"].append(t05[k05])
    med, proto, med05 = np.stack(med), np.stack(proto), np.stack(med05)
    g = {"names": np.array(names), "true": anchor["true_category"].long().numpy()}
    for tag, M in (("target", med), ("proto", proto), ("m05", med05)):
        ch, fs = chamfer_matrix(target, M)
        g[f"{tag}_ch"], g[f"{tag}_f"] = ch, fs
        g[f"{tag}_emd"] = emd_matrix(target, M)
        print(f"A geometry {tag} done", flush=True)
    g["D05"] = chamfer_matrix(med05, med05)[0]
    np.fill_diagonal(g["D05"], 0.0)
    g["anchor_ch"] = anchor["category_errors"].double().numpy()
    g["info"] = np.array(json.dumps(info))
    np.savez(p, **g)
    return g


# ----------------------------------------------------------------------------- set constructions
def topk(q, k):
    return list(np.argsort(-q)[:k])


def fill_fl(q, D, K, m, w=None):
    w = q if w is None else w
    S = topk(q, m)
    cur = D[:, S].min(1) if S else np.full(len(q), np.inf)
    while len(S) < K:
        cand = np.setdiff1d(np.arange(len(q)), S)
        risk = (w[:, None] * np.minimum(cur[:, None], D[:, cand])).sum(0)
        b = int(cand[np.argmin(risk)])
        S.append(b); cur = np.minimum(cur, D[:, b])
    return S


def fill_kcenter(q, D, K, m):
    S = topk(q, m)
    cur = D[:, S].min(1) if S else np.full(len(q), np.inf)
    while len(S) < K:
        cand = np.setdiff1d(np.arange(len(q)), S)
        risk = (q[:, None] * np.minimum(cur[:, None], D[:, cand])).max(0)
        b = int(cand[np.argmin(risk)])
        S.append(b); cur = np.minimum(cur, D[:, b])
    return S


def fill_farthest(q, D, K, m):
    S = topk(q, max(m, 1)) if K else []
    S = S[:K]
    while len(S) < K:
        cand = np.setdiff1d(np.arange(len(q)), S)
        b = int(cand[np.argmax(D[np.ix_(cand, S)].min(1))])
        S.append(b)
    return S


def fill_random(q, K, m, rng):
    S = topk(q, m)
    return S + list(rng.choice(np.setdiff1d(np.arange(len(q)), S), K - m, replace=False))


def fill_dpp(q, Ssim, K, m):
    """Greedy MAP of L = diag(sqrt q) Ssim diag(sqrt q), with the top-m categories forced first."""
    n = len(q)
    qq = np.sqrt(q)
    L = qq[:, None] * Ssim * qq[None, :]
    forced = topk(q, m)
    cis, d2 = np.zeros((K, n)), np.diag(L).copy()
    S = []
    for t in range(K):
        if t < len(forced):
            j = forced[t]
        else:
            s = d2.copy(); s[S] = -np.inf
            j = int(np.argmax(s))
            if s[j] <= 1e-12:
                rest = [c for c in np.argsort(-q) if c not in S]
                S += rest[:K - len(S)]
                return S
        S.append(j)
        if t == K - 1:
            break
        if d2[j] <= 1e-12:
            continue
        dj = np.sqrt(d2[j])
        e = (L[j] - cis[:t, j] @ cis[:t]) / dj
        cis[t] = e
        d2 = d2 - e ** 2
    return S


def fit_temperature(p, y):
    lp = np.log(np.clip(p, 1e-12, 1))
    best, bt = np.inf, 1.0
    for T in np.exp(np.linspace(np.log(0.05), np.log(20), 400)):
        z = lp / T; z -= z.max(1, keepdims=True)
        nll = -(z[np.arange(len(y)), y] - np.log(np.exp(z).sum(1))).mean()
        if nll < best:
            best, bt = nll, T
    return bt


def temper(p, T):
    z = np.log(np.clip(p, 1e-12, 1)) / T
    z -= z.max(-1, keepdims=True)
    e = np.exp(z)
    return e / e.sum(-1, keepdims=True)


def learned_features(Q, D):
    """Per (object, candidate) features from the posterior only."""
    lq = np.log(np.clip(Q, 1e-12, 1))
    rank = np.argsort(np.argsort(-Q, 1), 1)
    exp_d = Q @ D                                   # expected train-medoid distance of c under q
    return np.stack([lq - lq.mean(1, keepdims=True), np.log1p(rank), exp_d - exp_d.mean(1, keepdims=True)], -1)


def fit_learned(Qc, yc, D):
    from sklearn.linear_model import LogisticRegression
    X = learned_features(Qc, D).reshape(-1, 3)
    Y = (np.arange(72)[None, :] == yc[:, None]).reshape(-1)
    return LogisticRegression(C=1.0, max_iter=2000, class_weight="balanced").fit(X, Y)


# ----------------------------------------------------------------------------- evaluation
def evaluate(sets, g, D, true):
    """sets: list (per object) of index lists -> dict of per-object arrays."""
    r = {"recall": np.array([t in s for s, t in zip(sets, true)], float),
         "size": np.array([len(s) for s in sets], float),
         "train_medoid": np.array([D[t, s].min() for s, t in zip(sets, true)])}
    for tag in ("target", "proto"):
        ch, f, e = g[f"{tag}_ch"], g[f"{tag}_f"], g[f"{tag}_emd"]
        r[f"{tag}_chamfer"] = np.array([ch[i, s].min() for i, s in enumerate(sets)])
        r[f"{tag}_f01"] = np.array([f[i, s].max() for i, s in enumerate(sets)])
        r[f"{tag}_emd"] = np.array([e[i, s].min() for i, s in enumerate(sets)])
    return r


def subject_ci(x, n_boot, rng):
    """x [n_subjects] -> mean and 95% CI by resampling subjects."""
    idx = rng.integers(0, len(x), (n_boot, len(x)))
    d = x[idx].mean(1)
    return float(x.mean()), [float(np.quantile(d, 0.025)), float(np.quantile(d, 0.975))]


def reproduce_existing(g, D, out_dir):
    """Re-run the existing collective sweep and compare with results/geometry_risk_sweep_v2_validation_safe_full."""
    ens = json.load(open(REFS / "results/p0_anchor_validation_safe/ensemble12.json"))
    Q = np.array([r["category_probabilities"] for r in ens["per_sample"]], float)
    true = np.array([r["true_category"] for r in ens["per_sample"]])
    E = g["anchor_ch"]
    rows, worst = [], 0.0
    for f in sorted((REFS / "results/geometry_risk_sweep_v2_validation_safe_full").glob("K*_M*.json")):
        ref = json.load(open(f))
        K, M = ref["set_size"], ref["mandatory_probability_top"]
        P = [topk(q, K) for q in Q]
        R = [fill_fl(q, D, K, M) for q in Q]
        mine = {"topk_recall": np.mean([t in s for s, t in zip(P, true)]),
                "topk_chamfer": np.mean([E[i, s].min() for i, s in enumerate(P)]),
                "set_recall": np.mean([t in s for s, t in zip(R, true)]),
                "set_chamfer": np.mean([E[i, s].min() for i, s in enumerate(R)])}
        theirs = {"topk_recall": ref["probability_topk"]["category_recall"],
                  "topk_chamfer": ref["probability_topk"]["mean_chamfer"],
                  "set_recall": ref["geometry_risk_set"]["category_recall"],
                  "set_chamfer": ref["geometry_risk_set"]["mean_chamfer"]}
        diff = max(abs(mine[k] - theirs[k]) for k in mine)
        same_sets = all(sorted(a) == sorted(b) for a, b in zip(R, ref["geometry_risk_sets"]))
        worst = max(worst, diff)
        rows.append({"K": K, "M": M, **{f"mine_{k}": v for k, v in mine.items()},
                     **{f"existing_{k}": v for k, v in theirs.items()}, "max_abs_diff": diff, "same_sets": same_sets})
    anchor_diff = float(np.abs(g["target_ch"] - g["anchor_ch"]).max())
    rep = {"cells": rows, "max_abs_diff": worst, "all_sets_identical": all(r["same_sets"] for r in rows),
           "target_matrix_vs_anchor_errors_max_abs_diff": anchor_diff,
           "ok": bool(worst < 1e-5 and all(r["same_sets"] for r in rows) and anchor_diff < 1e-4)}
    json.dump(rep, open(out_dir / "reproduction.json", "w"), indent=1, default=float)
    return rep


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--out", default="/home/hubin/926/results/A")
    ap.add_argument("--seed", type=int, default=20260722)
    ap.add_argument("--posterior-seed", type=int, default=0)
    ap.add_argument("--random-draws", type=int, default=50)
    ap.add_argument("--bootstrap", type=int, default=10000)
    a = ap.parse_args()
    out = Path(a.out); out.mkdir(parents=True, exist_ok=True)
    work = Path("/home/hubin/926/cache"); work.mkdir(parents=True, exist_ok=True)
    g = build_geometry(work)
    D = np.load(REFS / "results/geometry_risk/category_medoid_chamfer.npy").astype(np.float64)

    rep = reproduce_existing(g, D, out)
    print(f"A reproduction: max diff {rep['max_abs_diff']:.2e}, sets identical {rep['all_sets_identical']}, "
          f"target-vs-anchor {rep['target_matrix_vs_anchor_errors_max_abs_diff']:.2e} -> ok={rep['ok']}", flush=True)
    if not rep["ok"]:
        (out / "BLOCKED.md").write_text(
            "# A blocked\n\nThe re-implemented train-medoid set scores do not match the existing sweep "
            "(`results/geometry_risk_sweep_v2_validation_safe_full`). See `reproduction.json`.\n")
        raise SystemExit("A blocked: reproduction failed")

    z = np.load(CACHE / f"posteriors_seed{a.posterior_seed}.npz")
    Qt, Qc, yt, yc = z["test"].astype(np.float64), z["calib"].astype(np.float64), z["test_target"], z["calib_target"]
    true = g["true"]
    assert np.array_equal(yt, true), "posterior test order differs from anchor order"
    rng = np.random.default_rng(a.seed)
    Ssim = np.exp(-D / np.median(D[np.triu_indices(72, 1)]))
    w_, v_ = np.linalg.eigh(Ssim)
    Ssim = (v_ * np.clip(w_, 0, None)) @ v_.T + 1e-6 * np.eye(72)
    learned = fit_learned(Qc.reshape(-1, 72), np.tile(yc, 12), D)
    uniform = np.full(72, 1.0 / 72)

    import pickle
    pkl = work / f"a_per_obj_seed{a.posterior_seed}_K{'-'.join(map(str, KS))}_r{a.random_draws}.pkl"
    per_obj = pickle.load(open(pkl, "rb")) if pkl.exists() else {}   # (method, K, m) -> {metric: [12, 144]}

    def add(key, sets_per_subject):
        ev = [evaluate(s, g, D, true) for s in sets_per_subject]
        per_obj[key] = {k: np.stack([e[k] for e in ev]) for k in ev[0]}

    for K in ([] if per_obj else KS):
        for m in range(K + 1):
            add(("fl", K, m), [[fill_fl(q, D, K, m) for q in Qt[s]] for s in range(12)])
            add(("kcenter", K, m), [[fill_kcenter(q, D, K, m) for q in Qt[s]] for s in range(12)])
            add(("farthest", K, m), [[fill_farthest(q, D, K, m) for q in Qt[s]] for s in range(12)])
            add(("dpp", K, m), [[fill_dpp(q, Ssim, K, m) for q in Qt[s]] for s in range(12)])
            draws = [[[fill_random(q, K, m, rng) for q in Qt[s]] for s in range(12)] for _ in range(a.random_draws)]
            evs = [[evaluate(d[s], g, D, true) for s in range(12)] for d in draws]
            per_obj[("random", K, m)] = {k: np.mean([np.stack([e[s][k] for s in range(12)]) for e in evs], 0)
                                         for k in evs[0][0]}
        print(f"A K={K} base constructions done", flush=True)

        temps = [fit_temperature(Qc[s], yc) for s in range(12)]
        Qcal = np.stack([temper(Qt[s], temps[s]) for s in range(12)])
        for m in range(K + 1):
            add(("fl_calibrated", K, m), [[fill_fl(q, D, K, m) for q in Qcal[s]] for s in range(12)])
        F = learned_features(Qt.reshape(-1, 72), D).reshape(12, 144, 72, 3)
        score = learned.decision_function(F.reshape(-1, 3)).reshape(12, 144, 72)
        for m in range(K + 1):
            add(("learned_fl", K, m), [[fill_fl(sc, D, K, m, w=q) for sc, q in zip(score[s], Qt[s])]
                                       for s in range(12)])
        # entropy-driven slot allocation: more geometry slots for flatter posteriors, mean r ~ 0.2 K
        H = -(Qt * np.log(np.clip(Qt, 1e-12, 1))).sum(-1)
        rbar = max(1, round(0.2 * K)) if K > 1 else 0
        sets = []
        for s in range(12):
            pct = np.argsort(np.argsort(H[s])) / (len(H[s]) - 1)
            r = np.clip(np.round(pct * 2 * rbar), 0, K - 1 if K > 1 else 0).astype(int)
            sets.append([fill_fl(q, D, K, K - ri) for q, ri in zip(Qt[s], r)])
        add(("entropy_alloc", K, -1), sets)
        add(("oracle_category_fl", K, -1),
            [[[int(t)] + [c for c in fill_fl(np.where(np.arange(72) == t, 1.0, q), D, K, 1) if c != t][:K - 1]
              for q, t in zip(Qt[s], true)] for s in range(12)])
        add(("global_prior", K, -1), [[fill_fl(uniform, D, K, 0)] * 144 for _ in range(12)])
        # split-conformal (LAC) on the calibration objects, alpha chosen so the mean calibration size ~ K
        sets, alphas = [], []
        for s in range(12):
            sc_cal = 1 - Qc[s][np.arange(144), yc]
            best = None
            for alpha in np.linspace(0.01, 0.99, 197):
                qh = np.quantile(sc_cal, min(1.0, np.ceil((145) * (1 - alpha)) / 144), method="higher")
                size = ((1 - Qc[s]) <= qh).sum(1).mean()
                if best is None or abs(size - K) < abs(best[1] - K):
                    best = (alpha, size, qh)
            alphas.append(float(best[0]))
            sets.append([list(np.flatnonzero((1 - q) <= best[2])) or topk(q, 1) for q in Qt[s]])
        add(("conformal_size_matched", K, -1), sets)
        per_obj[("conformal_size_matched", K, -1)]["alpha"] = np.array(alphas)
        print(f"A K={K} all constructions done", flush=True)

    pickle.dump(per_obj, open(pkl, "wb"))

    # ------------------------------------------------------------------ tables
    metrics = ["recall", "size", "train_medoid", "target_chamfer", "target_f01", "target_emd",
               "proto_chamfer", "proto_f01", "proto_emd"]
    rows = []
    for (meth, K, m), v in per_obj.items():
        ref = per_obj[("fl", K, K)]
        row = {"method": meth, "K": K, "m": m if m >= 0 else "", "slots_geometry": K - m if m >= 0 else ""}
        for k in metrics:
            row[k] = float(v[k].mean())
        for k in ("target_chamfer", "target_f01", "target_emd", "recall"):
            d = v[k].mean(1) - ref[k].mean(1)
            mu, ci = subject_ci(d, a.bootstrap, rng)
            row[f"delta_{k}_vs_topK"], row[f"delta_{k}_ci_lo"], row[f"delta_{k}_ci_hi"] = mu, ci[0], ci[1]
        rows.append(row)
    with open(out / "sweep.csv", "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=list(rows[0])); w.writeheader(); w.writerows(rows)

    main_new, main_ref = per_obj[("fl", 10, 8)], per_obj[("fl", 10, 10)]
    ps = []
    for s in range(12):
        ps.append({"subject": f"sub{s + 1:02d}", **{f"top10_{k}": float(main_ref[k][s].mean()) for k in metrics},
                   **{f"fl8p2_{k}": float(main_new[k][s].mean()) for k in metrics}})
    with open(out / "per_subject.csv", "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=list(ps[0])); w.writeheader(); w.writerows(ps)
    pc = []
    for c in range(72):
        sel = true == c
        pc.append({"category": c, "name": str(g["names"][sel][0]).rsplit("_", 1)[0],
                   **{f"top10_{k}": float(main_ref[k][:, sel].mean()) for k in metrics},
                   **{f"fl8p2_{k}": float(main_new[k][:, sel].mean()) for k in metrics}})
    with open(out / "per_category.csv", "w", newline="") as f:
        w = csv.DictWriter(f, fieldnames=list(pc[0])); w.writeheader(); w.writerows(pc)
    with open(out / "per_object.csv", "w", newline="") as f:
        w = csv.writer(f)
        w.writerow(["subject", "object", "category", "set", *metrics])
        for s in range(12):
            for i in range(144):
                for tag, v in (("top10", main_ref), ("fl8p2", main_new)):
                    w.writerow([f"sub{s + 1:02d}", str(g["names"][i]), int(true[i]), tag, *[float(v[k][s, i]) for k in metrics]])

    def contrast(new, ref):
        o = {}
        for k in metrics:
            d = new[k].mean(1) - ref[k].mean(1)
            mu, ci = subject_ci(d, a.bootstrap, rng)
            o[k] = {"new": float(new[k].mean()), "ref": float(ref[k].mean()), "delta": mu, "ci95_subject": ci,
                    "subjects_improved": int((d < 0).sum() if k not in ("recall", "target_f01", "proto_f01")
                                             else (d > 0).sum())}
        return o

    primary = contrast(main_new, main_ref)
    side = {}
    for K in (3, 5):
        for m in range(K):
            c = contrast(per_obj[("fl", K, m)], per_obj[("fl", K, K)])
            side[f"K={K},{m}+{K - m}"] = c
    side_ok = [k for k, c in side.items()
               if c["target_chamfer"]["delta"] < 0 and c["target_chamfer"]["ci95_subject"][1] < 0
               and c["recall"]["delta"] >= -0.01]
    # robustness: medoids from objects 00-05 only (fill and evaluation)
    g05 = dict(g); g05["target_ch"], g05["target_f"], g05["target_emd"] = g["m05_ch"], g["m05_f"], g["m05_emd"]
    r05 = {}
    for K, m in ((10, 10), (10, 8)):
        ev = [evaluate([fill_fl(q, g["D05"], K, m) for q in Qt[s]], g05, g["D05"], true) for s in range(12)]
        r05[f"{m}+{K - m}"] = {k: np.stack([e[k] for e in ev]) for k in ev[0]}
    robust05 = contrast(r05["8+2"], r05["10+0"])

    p = primary
    passed = bool(p["target_chamfer"]["delta"] < 0 and p["target_chamfer"]["ci95_subject"][1] < 0
                  and p["recall"]["delta"] >= 0 and p["target_f01"]["delta"] > 0 and p["target_emd"]["delta"] < 0
                  and len(side_ok) > 0)
    res = {"experiment": "926_A_target_geometry_sets", "posterior": f"validation-safe per-subject encoders, seed "
           f"{a.posterior_seed} (code/cache/posteriors_seed{a.posterior_seed}.npz); 12 subjects x 144 held-out objects",
           "reproduction": {k: v for k, v in rep.items() if k != "cells"},
           "primary_endpoint": {"contrast": "FL 8+2 minus top-10 (10+0), best-of-set vs held-out target",
                                "statistics_unit": "subject (bootstrap over 12 subjects, 10000)", **p},
           "secondary_K3_K5_cells_same_direction_significant_recall_drop_le_1pp": side_ok,
           "secondary_K3_K5": side,
           "robustness_medoids_objects_00_05": robust05,
           "decision": "keep" if passed else "drop",
           "geometry": {"points": N_POINTS, "normalization": "centred, unit sphere", "chamfer": "Chamfer-L2 (squared)",
                        "f_threshold": 0.1, "emd_points": 256,
                        "medoids": "results/p0_category_geometry (train objects 00-07)",
                        "prototype": "medoid of the other 7 train objects of the category",
                        "fill_distance": "results/geometry_risk/category_medoid_chamfer.npy"},
           "random_draws": a.random_draws, "bootstrap": a.bootstrap, "seed": a.seed,
           "conformal_alpha_per_K": {str(K): per_obj[("conformal_size_matched", K, -1)]["alpha"].tolist() for K in KS}}
    json.dump(res, open(out / "results.json", "w"), indent=1, default=float)
    print(json.dumps({"decision": res["decision"], "primary": {k: p[k] for k in ("recall", "target_chamfer", "target_f01",
                                                                                "target_emd")}}, default=float), flush=True)


if __name__ == "__main__":
    main()