"""Rebuild `data/site_centroids.parquet` from the files in this repository, and check it matches.

Standalone. It needs Python 3.10+ and two packages, and nothing else from this project:

    pip install numpy polars

    python verify_bundle.py --sample 0      # every site (4,466)
    python verify_bundle.py --sample 300    # a random 300

`site_centroids.parquet` is the localization ground truth, in the frame of the folds in
`structures/`. This script rebuilds it from the frame-independent inputs in `projection/` and
the folds alone. For each site, it takes every member observation and:

1. reads the observation's contact-residue CA coordinates in its own deposited-entry frame, each
   tagged with the canonical sequence position it maps to (`projection/contact_residues.parquet`);
2. reads the fold's CA at those same positions, dropping positions outside 1..L or with no CA;
3. finds the least-squares rigid transform (Kabsch, with the reflection correction) carrying the
   entry CAs onto the fold CAs, provided at least 3 positions matched;
4. applies that transform to the observation's entry-frame ligand centroid
   (`projection/ligand_centroids.parquet`).

The site centroid is the mean of its members' transformed centroids. The script checks the
rebuilt centroid, member count, spread and median fit RMSD of every sampled site against the
shipped table, and each member's fit RMSD against `projection/ligand_centroids.parquet`.

The fold reader is a minimal mmCIF `_atom_site` parser. It reads the first model, requires a
single chain whose residues are numbered exactly 1..L against `data/sequences.parquet`, and
keeps each residue's first CA as float32, matching how the centroids were built.
"""

import argparse
import gzip
import logging
import pathlib
import re

import numpy as np
import polars as pl

logger = logging.getLogger("verify")
HERE = pathlib.Path(__file__).resolve().parent

# Below three matched CA atoms a rigid transform is underdetermined in at least one axis.
MIN_MATCHED_CA = 3
_TOKEN = re.compile(r"'[^']*'|\"[^\"]*\"|\S+")
_HYDROGEN = {"H", "D"}


def parse_args(argv=None):
    parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
    parser.add_argument("--sample", type=int, default=300, help="sites to check; 0 for all")
    parser.add_argument("--seed", type=int, default=0)
    parser.add_argument("--tolerance", type=float, default=1e-4, help="angstrom")
    return parser.parse_args(argv)


def _atom_site_rows(text: str) -> tuple[list[str], list[list[str]]]:
    """Column names and rows of the `_atom_site` loop."""
    lines = text.splitlines()
    for start, line in enumerate(lines):
        if line.startswith("_atom_site."):
            break
    else:
        raise ValueError("no _atom_site loop")
    columns = []
    index = start
    while index < len(lines) and lines[index].startswith("_atom_site."):
        columns.append(lines[index].strip()[len("_atom_site."):])
        index += 1
    tokens: list[str] = []
    for line in lines[index:]:
        if not line.strip():
            continue
        if line.startswith(("#", "loop_", "_", "data_")):
            break
        tokens.extend(line.split() if "'" not in line and '"' not in line else _TOKEN.findall(line))
    if len(tokens) % len(columns):
        raise ValueError("_atom_site loop has a ragged final row")
    width = len(columns)
    rows = [tokens[i:i + width] for i in range(0, len(tokens), width)]
    return columns, rows


def _value(token: str) -> str:
    return token[1:-1] if len(token) >= 2 and token[0] == token[-1] and token[0] in "'\"" else token


def read_ca(path: pathlib.Path, expected_length: int) -> np.ndarray:
    """Per-residue CA coordinates of a fold, indexed by canonical position.

    Args:
        path: The fold's mmCIF, gzipped or not.
        expected_length: Canonical sequence length.

    Returns:
        ``(L, 3)`` float32, NaN where the fold has no CA for a position.

    Raises:
        ValueError: If the first model is not one chain numbered 1..`expected_length`.
    """
    opener = gzip.open if path.suffix == ".gz" else open
    with opener(path, "rt") as handle:
        columns, rows = _atom_site_rows(handle.read())
    col = {name: i for i, name in enumerate(columns)}
    seq_col = col.get("auth_seq_id", col.get("label_seq_id"))
    chain_col = col.get("auth_asym_id", col.get("label_asym_id"))
    model_col = col.get("pdbx_PDB_model_num")
    ins_col = col.get("pdbx_PDB_ins_code")
    alt_col = col.get("label_alt_id")
    comp_col = col["label_comp_id"]
    name_col = col.get("label_atom_id", col.get("auth_atom_id"))
    element_col = col.get("type_symbol")

    first_model = rows[0][model_col] if model_col is not None and rows else None
    chains = []
    residues = []  # (seq_num, ins_code, comp_id) of consecutive atom groups, in file order
    ca = np.full((expected_length, 3), np.nan, dtype=np.float32)
    seen_ca = set()
    first_alt: dict[tuple, str] = {}
    for row in rows:
        if model_col is not None and row[model_col] != first_model:
            continue
        if element_col is not None and _value(row[element_col]).upper() in _HYDROGEN:
            continue
        chain = _value(row[chain_col])
        if chain not in chains:
            chains.append(chain)
        key = (
            int(_value(row[seq_col])),
            _value(row[ins_col]) if ins_col is not None else "?",
            _value(row[comp_col]),
        )
        if not residues or residues[-1] != key:
            residues.append(key)
        # Keep one conformer: the first alternate location seen for each residue.
        if alt_col is not None:
            alt = _value(row[alt_col])
            if alt not in (".", "?"):
                if first_alt.setdefault(key, alt) != alt:
                    continue
        if _value(row[name_col]) == "CA" and key not in seen_ca:
            seen_ca.add(key)
            position = key[0]
            if 1 <= position <= expected_length:
                ca[position - 1] = [float(row[col["Cartn_x"]]), float(row[col["Cartn_y"]]),
                                    float(row[col["Cartn_z"]])]
    if len(chains) != 1:
        raise ValueError(f"{path}: expected one chain in the first model, found {len(chains)}")
    numbers = [r[0] for r in residues]
    if numbers != list(range(1, expected_length + 1)):
        raise ValueError(
            f"{path}: numbered {numbers[:3]}..{numbers[-3:]} over {len(numbers)} residues, "
            f"expected 1..{expected_length}"
        )
    return ca


def kabsch(source: np.ndarray, target: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
    """Rotation and translation with ``source @ rotation.T + translation`` closest to `target`."""
    source = np.asarray(source, dtype=np.float64)
    target = np.asarray(target, dtype=np.float64)
    source_centre, target_centre = source.mean(axis=0), target.mean(axis=0)
    covariance = (source - source_centre).T @ (target - target_centre)
    u, _, vt = np.linalg.svd(covariance)
    # Flip the least significant axis when the naive solution is a reflection.
    sign = np.sign(np.linalg.det(vt.T @ u.T))
    rotation = vt.T @ np.diag([1.0, 1.0, sign]) @ u.T
    return rotation, target_centre - rotation @ source_centre


def project(source_ca: np.ndarray, fold_ca: np.ndarray, centroid: np.ndarray):
    """Carry one ligand centroid from its entry's frame into the fold's.

    Returns:
        ``(centroid, fit_rmsd_angstrom)``, or None when fewer than 3 positions matched.
    """
    if len(source_ca) < MIN_MATCHED_CA:
        return None
    rotation, translation = kabsch(source_ca, fold_ca)
    fitted = source_ca @ rotation.T + translation
    rmsd = float(np.sqrt(((fitted - np.asarray(fold_ca, dtype=np.float64)) ** 2).sum(axis=1).mean()))
    return rotation @ np.asarray(centroid, dtype=np.float64) + translation, rmsd


def main(argv=None) -> None:
    logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s")
    args = parse_args(argv)

    truth = pl.read_parquet(HERE / "data/site_centroids.parquet")
    centroids = pl.read_parquet(HERE / "projection/ligand_centroids.parquet")
    residues = pl.read_parquet(HERE / "projection/contact_residues.parquet")
    lengths = dict(
        pl.read_parquet(HERE / "data/sequences.parquet")
        .select("normalized_uniprot_id", "sequence_length")
        .iter_rows()
    )

    if args.sample:
        truth = truth.sample(min(args.sample, truth.height), seed=args.seed, shuffle=True)
    logger.info("checking %d sites", truth.height)

    by_observation = {key[0]: part for key, part in residues.group_by(["pocket_observation_id"])}
    members = {key[0]: part for key, part in centroids.group_by(["pocket_site_id"])}
    folds: dict[str, np.ndarray] = {}

    worst = {"centroid": 0.0, "spread": 0.0, "median_fit_rmsd": 0.0, "member_fit_rmsd": 0.0}
    count_mismatch = checked = skipped = 0
    for row in truth.iter_rows(named=True):
        site, uniprot = row["pocket_site_id"], row["normalized_uniprot_id"]
        path = HERE / f"structures/{uniprot}.cif.gz"
        if not path.exists() or site not in members:
            skipped += 1
            continue
        if uniprot not in folds:
            folds[uniprot] = read_ca(path, lengths[uniprot])
        fold_ca = folds[uniprot]
        projected, fits = [], []
        for member in members[site].iter_rows(named=True):
            part = by_observation.get(member["pocket_observation_id"])
            if part is None:
                continue
            positions = part["canonical_position"].to_numpy()
            source = np.stack(
                [part["ca_x"].to_numpy(), part["ca_y"].to_numpy(), part["ca_z"].to_numpy()], axis=1
            )
            inside = (positions >= 1) & (positions <= len(fold_ca))
            positions, source = positions[inside], source[inside]
            target = fold_ca[positions - 1]
            usable = ~np.isnan(target).any(axis=1)
            result = project(
                source[usable],
                target[usable],
                np.array([member["ligand_centroid_x"], member["ligand_centroid_y"],
                          member["ligand_centroid_z"]]),
            )
            if result is not None:
                projected.append(result[0])
                fits.append(result[1])
                worst["member_fit_rmsd"] = max(
                    worst["member_fit_rmsd"], abs(result[1] - member["fit_rmsd_angstrom"]))
        if not projected:
            skipped += 1
            continue
        stacked = np.stack(projected)
        rebuilt = stacked.mean(axis=0)
        shipped = np.array([row["ligand_centroid_x"], row["ligand_centroid_y"],
                            row["ligand_centroid_z"]])
        worst["centroid"] = max(worst["centroid"], float(np.abs(rebuilt - shipped).max()))
        spread = float(np.linalg.norm(stacked - rebuilt, axis=1).max())
        worst["spread"] = max(worst["spread"], abs(spread - row["centroid_spread_angstrom"]))
        worst["median_fit_rmsd"] = max(
            worst["median_fit_rmsd"], abs(float(np.median(fits)) - row["median_fit_rmsd_angstrom"]))
        count_mismatch += len(projected) != row["centroid_member_count"]
        checked += 1

    logger.info("checked %d sites over %d folds, skipped %d", checked, len(folds), skipped)
    logger.info("worst coordinate disagreement: %.6f A", worst["centroid"])
    logger.info(
        "worst spread / median fit RMSD / member fit RMSD disagreement: %.6f / %.6f / %.6f A",
        worst["spread"], worst["median_fit_rmsd"], worst["member_fit_rmsd"],
    )
    logger.info("sites whose member count differs: %d", count_mismatch)
    failures = [f"{name} {value:.6f} A" for name, value in worst.items() if value > args.tolerance]
    if count_mismatch:
        failures.append(f"{count_mismatch} member-count mismatches")
    if skipped:
        failures.append(f"{skipped} sites skipped")
    if failures:
        raise SystemExit(f"FAILED (tolerance {args.tolerance} A): " + "; ".join(failures))
    logger.info("OK: the shipped files reproduce site_centroids.parquet")


if __name__ == "__main__":
    main()