#!/usr/bin/env python3
import argparse
import json
from pathlib import Path

import mdtraj as md
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from tqdm import tqdm
from sklearn.decomposition import PCA
from sklearn.neighbors import NearestNeighbors
from sklearn.metrics import pairwise_distances
from scipy.linalg import eigh


def load_replicates(entry_dir: Path):
    entry = entry_dir.name
    top = entry_dir / f"{entry}.pdb"
    xtcs = sorted(entry_dir.glob(f"{entry}_R*.xtc"))

    if not top.exists():
        raise FileNotFoundError(f"Missing pdb: {top}")
    if len(xtcs) == 0:
        raise FileNotFoundError(f"Missing xtc files in {entry_dir}")

    trajs = []
    for xtc in xtcs:
        traj = md.load(str(xtc), top=str(top))
        trajs.append(traj)

    return entry, top, trajs


def select_ca_pairs(traj, cutoff_nm=1.2, min_seq_sep=4, max_pairs=4000, seed=0):
    ca_idx = traj.topology.select("name CA")
    if len(ca_idx) < 10:
        raise ValueError("Too few CA atoms")

    xyz = traj.xyz[0, ca_idx]
    n = len(ca_idx)

    pairs = []
    for i in range(n):
        for j in range(i + min_seq_sep, n):
            d = np.linalg.norm(xyz[i] - xyz[j])
            if d <= cutoff_nm:
                pairs.append((ca_idx[i], ca_idx[j]))

    pairs = np.asarray(pairs, dtype=np.int32)

    if len(pairs) == 0:
        raise ValueError("No CA pairs selected")

    if len(pairs) > max_pairs:
        rng = np.random.default_rng(seed)
        idx = rng.choice(len(pairs), size=max_pairs, replace=False)
        pairs = pairs[idx]

    return ca_idx, pairs


def compute_features(trajs, pairs):
    feats = []
    for traj in trajs:
        x = md.compute_distances(traj, pairs).astype(np.float32)
        feats.append(x)
    return feats


def standardize(feats, eps=1e-6):
    all_x = np.concatenate(feats, axis=0)
    mean = all_x.mean(axis=0, keepdims=True)
    std = all_x.std(axis=0, keepdims=True)
    std = np.maximum(std, eps)
    return [(x - mean) / std for x in feats]


def fit_pca(feats, pca_dim=50, seed=0):
    all_x = np.concatenate(feats, axis=0)
    pca_dim = min(pca_dim, all_x.shape[0] - 1, all_x.shape[1])

    pca = PCA(n_components=pca_dim, random_state=seed)
    all_y = pca.fit_transform(all_x).astype(np.float32)

    outs = []
    start = 0
    for x in feats:
        n = x.shape[0]
        outs.append(all_y[start:start + n])
        start += n

    return outs


def fit_tica(feats, lag=10, tica_dim=3, reg=1e-6):
    d = feats[0].shape[1]

    C00 = np.zeros((d, d), dtype=np.float64)
    C0t = np.zeros((d, d), dtype=np.float64)
    count = 0

    for x in feats:
        if x.shape[0] <= lag:
            continue

        x0 = x[:-lag].astype(np.float64)
        xt = x[lag:].astype(np.float64)

        C00 += x0.T @ x0
        C00 += xt.T @ xt
        C0t += x0.T @ xt
        count += x0.shape[0]

    if count == 0:
        raise ValueError("lag too large")

    C00 = C00 / (2.0 * count)
    C0t = C0t / count

    C00 = 0.5 * (C00 + C00.T)
    C0t = 0.5 * (C0t + C0t.T)

    C00 += reg * np.eye(d)

    vals, vecs = eigh(C0t, C00)
    order = np.argsort(np.abs(vals))[::-1]
    vals = vals[order]
    vecs = vecs[:, order]

    tica_dim = min(tica_dim, vecs.shape[1])
    W = vecs[:, :tica_dim]

    qs = [(x @ W).astype(np.float32) for x in feats]
    return qs, vals[:tica_dim]


def build_affinity(q_all, k_scale=30, eps=1e-8):
    D = pairwise_distances(q_all, metric="euclidean").astype(np.float32)

    k_scale = min(k_scale, len(q_all) - 1)
    nn = NearestNeighbors(n_neighbors=k_scale + 1)
    nn.fit(q_all)
    dist, _ = nn.kneighbors(q_all)

    sigma = dist[:, -1].astype(np.float32)
    sigma = np.maximum(sigma, eps)

    A = np.exp(-(D ** 2) / (sigma[:, None] * sigma[None, :])).astype(np.float32)
    np.fill_diagonal(A, 1.0)

    return A, sigma


def make_metadata(trajs):
    rep_ids = []
    local_ids = []

    for r, traj in enumerate(trajs):
        n = traj.n_frames
        rep_ids.extend([r] * n)
        local_ids.extend(list(range(n)))

    return np.asarray(rep_ids), np.asarray(local_ids)


def kabsch_rmsd(P, Q):
    P = P - P.mean(axis=0, keepdims=True)
    Q = Q - Q.mean(axis=0, keepdims=True)

    H = P.T @ Q
    U, S, Vt = np.linalg.svd(H)
    R = Vt.T @ U.T

    if np.linalg.det(R) < 0:
        Vt[-1] *= -1
        R = Vt.T @ U.T

    P_rot = P @ R
    return np.sqrt(np.mean(np.sum((P_rot - Q) ** 2, axis=1)))


def sample_groups(A, rep_ids, mode="all", n_pairs=3000, n_candidates=500000, seed=0):
    rng = np.random.default_rng(seed)
    n = A.shape[0]

    i = rng.integers(0, n, size=n_candidates)
    j = rng.integers(0, n, size=n_candidates)

    mask = i != j

    if mode == "cross":
        mask &= rep_ids[i] != rep_ids[j]
    elif mode == "same":
        mask &= rep_ids[i] == rep_ids[j]

    i = i[mask]
    j = j[mask]

    vals = A[i, j]

    q10 = np.quantile(vals, 0.10)
    q45 = np.quantile(vals, 0.45)
    q55 = np.quantile(vals, 0.55)
    q90 = np.quantile(vals, 0.90)

    idx_high = np.where(vals >= q90)[0]
    idx_mid = np.where((vals >= q45) & (vals <= q55))[0]
    idx_low = np.where(vals <= q10)[0]

    groups = {}
    for name, idx in [
        (f"{mode}_high", idx_high),
        (f"{mode}_mid", idx_mid),
        (f"{mode}_low", idx_low),
    ]:
        if len(idx) == 0:
            groups[name] = (np.array([], dtype=int), np.array([], dtype=int))
            continue

        take = rng.choice(idx, size=min(n_pairs, len(idx)), replace=False)
        groups[name] = (i[take], j[take])

    return groups, {
        f"{mode}_q10": float(q10),
        f"{mode}_q45": float(q45),
        f"{mode}_q55": float(q55),
        f"{mode}_q90": float(q90),
    }


def evaluate_groups(groups, ca_xyz_all, dist_feat_all, rep_ids, local_ids):
    rows = []

    for group, (ii, jj) in groups.items():
        rmsds = []
        contact_diffs = []
        same_rep = []
        time_gaps = []

        for a, b in tqdm(list(zip(ii, jj)), desc=f"eval {group}", leave=False):
            rmsd = kabsch_rmsd(ca_xyz_all[a], ca_xyz_all[b]) * 10.0
            cdiff = np.mean(np.abs(dist_feat_all[a] - dist_feat_all[b])) * 10.0

            sr = rep_ids[a] == rep_ids[b]
            gap = abs(int(local_ids[a]) - int(local_ids[b])) if sr else np.nan

            rmsds.append(rmsd)
            contact_diffs.append(cdiff)
            same_rep.append(sr)
            time_gaps.append(gap)

        rmsds = np.asarray(rmsds, dtype=np.float32)
        contact_diffs = np.asarray(contact_diffs, dtype=np.float32)
        same_rep = np.asarray(same_rep, dtype=bool)
        time_gaps = np.asarray(time_gaps, dtype=np.float32)

        rows.append({
            "group": group,
            "n_pairs": len(rmsds),
            "rmsd_A_mean": float(np.nanmean(rmsds)),
            "rmsd_A_std": float(np.nanstd(rmsds)),
            "contact_diff_A_mean": float(np.nanmean(contact_diffs)),
            "contact_diff_A_std": float(np.nanstd(contact_diffs)),
            "same_rep_frac": float(np.mean(same_rep)) if len(same_rep) else np.nan,
            "time_gap_mean_same_rep": float(np.nanmean(time_gaps)),
            "near_time_frac_gap_le_5": float(np.nanmean(time_gaps <= 5)),
            "near_time_frac_gap_le_20": float(np.nanmean(time_gaps <= 20)),
        })

    return pd.DataFrame(rows)


def plot_full_heatmap(A, frames_per_rep, out_file):
    plt.figure(figsize=(6, 5))
    plt.imshow(A, aspect="auto", vmin=0, vmax=1)
    plt.colorbar(label="Affinity")

    boundaries = np.cumsum(frames_per_rep)[:-1]
    for b in boundaries:
        plt.axhline(b, color="white", linewidth=1)
        plt.axvline(b, color="white", linewidth=1)

    plt.title("Full affinity heatmap with replicate boundaries")
    plt.xlabel("Frame index")
    plt.ylabel("Frame index")
    plt.tight_layout()
    plt.savefig(out_file, dpi=200)
    plt.close()


def plot_q(q_all, rep_ids, out_file):
    plt.figure(figsize=(5, 4))

    if q_all.shape[1] >= 2:
        plt.scatter(q_all[:, 0], q_all[:, 1], c=rep_ids, s=4, alpha=0.75)
        plt.xlabel("TIC 1")
        plt.ylabel("TIC 2")
    else:
        plt.scatter(np.arange(len(q_all)), q_all[:, 0], c=rep_ids, s=4, alpha=0.75)
        plt.xlabel("Frame")
        plt.ylabel("TIC 1")

    plt.title("Slow coordinates colored by replicate")
    plt.tight_layout()
    plt.savefig(out_file, dpi=200)
    plt.close()


def process_entry(entry_dir: Path, out_root: Path, args):
    entry, top, trajs = load_replicates(entry_dir)
    out_dir = out_root / entry
    out_dir.mkdir(parents=True, exist_ok=True)

    ca_idx, pairs = select_ca_pairs(
        trajs[0],
        cutoff_nm=args.contact_cutoff_nm,
        min_seq_sep=args.min_seq_sep,
        max_pairs=args.max_ca_pairs,
        seed=args.seed,
    )

    raw_feats = compute_features(trajs, pairs)
    feats = standardize(raw_feats)

    pca_feats = fit_pca(feats, pca_dim=args.pca_dim, seed=args.seed)
    qs, tica_vals = fit_tica(pca_feats, lag=args.lag, tica_dim=args.tica_dim)

    q_all = np.concatenate(qs, axis=0)
    A, sigma = build_affinity(q_all, k_scale=args.k_scale)

    rep_ids, local_ids = make_metadata(trajs)

    ca_xyz_all = np.concatenate([t.xyz[:, ca_idx, :] for t in trajs], axis=0)
    dist_feat_all = np.concatenate(raw_feats, axis=0)

    all_groups, all_thr = sample_groups(
        A, rep_ids, mode="all",
        n_pairs=args.sample_pairs,
        n_candidates=args.candidate_pairs,
        seed=args.seed,
    )

    cross_groups, cross_thr = sample_groups(
        A, rep_ids, mode="cross",
        n_pairs=args.sample_pairs,
        n_candidates=args.candidate_pairs,
        seed=args.seed,
    )

    same_groups, same_thr = sample_groups(
        A, rep_ids, mode="same",
        n_pairs=args.sample_pairs,
        n_candidates=args.candidate_pairs,
        seed=args.seed,
    )

    summary_all = evaluate_groups(all_groups, ca_xyz_all, dist_feat_all, rep_ids, local_ids)
    summary_cross = evaluate_groups(cross_groups, ca_xyz_all, dist_feat_all, rep_ids, local_ids)
    summary_same = evaluate_groups(same_groups, ca_xyz_all, dist_feat_all, rep_ids, local_ids)

    summary = pd.concat([summary_all, summary_cross, summary_same], axis=0)
    summary.insert(0, "entry", entry)

    summary.to_csv(out_dir / "affinity_sanity_all_cross_same.tsv", sep="\t", index=False)

    frames_per_rep = [int(t.n_frames) for t in trajs]

    np.save(out_dir / "q.npy", q_all)
    np.save(out_dir / "affinity.float16.npy", A.astype(np.float16))

    plot_q(q_all, rep_ids, out_dir / "q_by_replicate.png")
    plot_full_heatmap(A, frames_per_rep, out_dir / "full_affinity_heatmap.png")

    meta = {
        "entry": entry,
        "topology": str(top),
        "frames_per_replicate": frames_per_rep,
        "total_frames": int(sum(frames_per_rep)),
        "n_CA": int(len(ca_idx)),
        "n_CA_pairs": int(len(pairs)),
        "lag": args.lag,
        "pca_dim": args.pca_dim,
        "tica_dim": args.tica_dim,
        "tica_eigenvalues": [float(x) for x in tica_vals],
        "sigma_mean": float(np.mean(sigma)),
        "sigma_std": float(np.std(sigma)),
        "thresholds": {**all_thr, **cross_thr, **same_thr},
    }

    with open(out_dir / "meta.json", "w") as f:
        json.dump(meta, f, indent=2)

    return summary


def main():
    parser = argparse.ArgumentParser()

    parser.add_argument("--atlas_root", default="/raid_zoe/home/lr/wangyi/p/atlas_1000_analysis")
    parser.add_argument("--out_root", default="/raid_zoe/home/lr/wangyi/p/affinity_crossrep_check")
    parser.add_argument("--entry", default="16pk_A")
    parser.add_argument("--max_proteins", type=int, default=None)

    parser.add_argument("--lag", type=int, default=10)
    parser.add_argument("--pca_dim", type=int, default=50)
    parser.add_argument("--tica_dim", type=int, default=3)

    parser.add_argument("--contact_cutoff_nm", type=float, default=1.2)
    parser.add_argument("--min_seq_sep", type=int, default=4)
    parser.add_argument("--max_ca_pairs", type=int, default=4000)

    parser.add_argument("--k_scale", type=int, default=30)
    parser.add_argument("--sample_pairs", type=int, default=3000)
    parser.add_argument("--candidate_pairs", type=int, default=500000)

    parser.add_argument("--seed", type=int, default=0)

    args = parser.parse_args()

    atlas_root = Path(args.atlas_root)
    out_root = Path(args.out_root)
    out_root.mkdir(parents=True, exist_ok=True)

    if args.entry.lower() == "all":
        entry_dirs = sorted([
            p for p in atlas_root.iterdir()
            if p.is_dir() and not p.name.startswith("_")
        ])
        if args.max_proteins is not None:
            entry_dirs = entry_dirs[:args.max_proteins]
    else:
        entry_dirs = [atlas_root / args.entry]

    all_summaries = []

    for entry_dir in tqdm(entry_dirs, desc="proteins"):
        try:
            summary = process_entry(entry_dir, out_root, args)
            all_summaries.append(summary)
            print(summary)
        except Exception as e:
            print(f"[FAILED] {entry_dir.name}: {e}")
            with open(out_root / "failed.txt", "a") as f:
                f.write(f"{entry_dir.name}\t{e}\n")

    if all_summaries:
        df = pd.concat(all_summaries, axis=0)
        df.to_csv(out_root / "all_affinity_sanity_all_cross_same.tsv", sep="\t", index=False)

    print(f"Saved to: {out_root}")


if __name__ == "__main__":
    main()