"""Explicit pilot diagnostics for association-only corruption and abstention.

These metrics are defined in the benchmark specification, not replacements for
canonical HOTA/IDF1. Detections stay fixed. One global identity alignment is allowed.
"""

from __future__ import annotations

import itertools

import numpy as np
import pandas as pd
from scipy.optimize import linear_sum_assignment
from sklearn.metrics import average_precision_score, roc_auc_score

from cell_tracking.data.wormtrack_verify.adapters import TRUSTED, XYZ, validate_tracks


def corrupt_tracks(tracks, *, kind, seed, start_frame, stop_frame=None, pair=None):
    """Deterministic swaps/fragmentation with row-level error labels and fixed observations.

    Labels mark correspondence to the original reference identity. Purely global
    renaming is not an error; start_frame must follow the first reference frame.
    Original images, detection IDs, and coordinates are preserved.
    """
    validate_tracks(tracks)
    if not set(tracks.annotation_type).issubset(TRUSTED):
        raise ValueError("Corruptions require independently trusted source annotations")
    if tracks.recording_id.nunique() != 1 or tracks.label_set.nunique() != 1:
        raise ValueError("Corrupt one recording and one label set at a time")
    frames = sorted(tracks.frame_index.unique())
    if start_frame <= frames[0] or start_frame > frames[-1]:
        raise ValueError("Corruption must follow the identity anchor and lie inside the recording")
    stop_frame = frames[-1] + 1 if stop_frame is None else stop_frame
    if stop_frame <= start_frame or stop_frame > frames[-1] + 1:
        raise ValueError("Invalid corruption interval")
    rng = np.random.default_rng(seed)
    ids = sorted(tracks.track_id.unique())
    if pair is None:
        if len(ids) < 2:
            raise ValueError("Need two tracks")
        pair = list(rng.choice(ids, 2, replace=False))
    if len(pair) != 2 or pair[0] == pair[1] or not set(pair).issubset(ids):
        raise ValueError("Pair must identify two distinct source tracks")
    df = tracks.copy()
    df["reference_track_id"] = df.track_id
    interval = (df.frame_index >= start_frame) & (df.frame_index < stop_frame)
    affected = interval & df.track_id.isin(pair)
    if not affected.any():
        raise ValueError("No observations in the selected corruption interval")
    if kind in {"single_frame_swap", "persistent_swap", "swap_recovery", "nearby_swap"}:
        if kind == "single_frame_swap":
            affected &= df.frame_index == start_frame
        df.loc[affected, "track_id"] = df.loc[affected, "track_id"].map(
            {pair[0]: pair[1], pair[1]: pair[0]}
        )
    elif kind == "fragmentation":
        affected = interval & (df.track_id == pair[0])
        df.loc[affected, "track_id"] = pair[0] + f":fragment:{seed}"
    elif kind == "dropout":
        affected = interval & (df.track_id == pair[0])
        df.loc[affected, "validity"] = False
    else:
        raise ValueError(f"Unsupported pilot corruption: {kind}")
    df["is_error"] = affected
    df["corruption_type"] = kind
    df["seed"] = seed
    df["scenario_id"] = f"{kind}:seed{seed}"
    return df


def association_metrics(reference, prediction):
    """Score shared oracle detections with one recording-wide identity permutation.

    Missing predictions count against coverage and correct-observation rate.
    Switches are changes in predicted identity along each reference trajectory;
    fragmented runs also count reappearance after a missing prediction. Evaluation
    covers only source-supported reference observations, not unannotated frames.
    """
    validate_tracks(reference)
    validate_tracks(prediction)
    if reference.recording_id.nunique() != 1 or prediction.recording_id.nunique() != 1:
        raise ValueError("Evaluate each independent recording separately")
    if reference.recording_id.iloc[0] != prediction.recording_id.iloc[0]:
        raise ValueError("Mismatched recording")
    key = ["frame_index", "detection_id"]
    valid_prediction = prediction.loc[prediction.validity]
    joined = reference.loc[reference.validity].merge(
        valid_prediction[key + ["track_id"]],
        on=key,
        how="left",
        suffixes=("_gt", "_pred"),
        validate="one_to_one",
    )
    gt = sorted(joined.track_id_gt.unique())
    pred = sorted(valid_prediction.track_id.unique())
    counts = (
        pd.crosstab(joined.track_id_gt, joined.track_id_pred)
        .reindex(index=gt, columns=pred, fill_value=0)
        .to_numpy()
    )
    mapping = {}
    if counts.size:
        rows, cols = linear_sum_assignment(-counts)
        mapping = {pred[c]: gt[r] for r, c in zip(rows, cols, strict=True)}
    correct = joined.track_id_pred.map(mapping).eq(joined.track_id_gt)
    switches = fragments = 0
    for _, group in joined.sort_values("frame_index").groupby("track_id_gt"):
        ids = group.track_id_pred.tolist()
        observed = [i for i in ids if pd.notna(i)]
        switches += sum(a != b for a, b in itertools.pairwise(observed))
        fragments += max(
            0,
            sum(
                pd.notna(v) and (j == 0 or pd.isna(ids[j - 1]) or v != ids[j - 1])
                for j, v in enumerate(ids)
            )
            - 1,
        )
    extra = len(
        valid_prediction.merge(joined[key], on=key, how="left", indicator=True).query(
            '_merge == "left_only"'
        )
    )
    return {
        "reference_observations": len(joined),
        "predicted_observations": len(valid_prediction),
        "coverage": float(joined.track_id_pred.notna().mean()),
        "correct_observation_rate": float(correct.mean()),
        "identity_switches": int(switches),
        "fragmentations": int(fragments),
        "extra_observations": int(extra),
    }


def reference_residual_scores(tracks, reference_cloud, *, physical=True):
    """Identity-sensitive distance from the fixed first-frame reference.

    This intentionally simple consistency diagnostic includes genuine tissue motion.
    It is not an independent deformation model or a certificate of correctness.
    Reference cloud must come from an independently trusted reference observation.
    """
    cols = [f"{a}_um" for a in "xyz"] if physical else XYZ
    ref = reference_cloud.set_index("track_id")
    if ref.index.duplicated().any():
        raise ValueError("Reference identities must be unique")
    a = tracks[cols].to_numpy(dtype=float)
    b = ref.reindex(tracks.track_id)[cols].to_numpy(dtype=float)
    scores = np.linalg.norm(a - b, axis=1)
    scores[~tracks.validity.to_numpy()] = np.inf
    # Unseen fragment IDs are unsupported; preserve absence rather than fit a new anatomy.
    scores[np.isnan(scores)] = np.inf
    return scores


def verifier_metrics(scores, is_error, *, threshold):
    """Fixed-population selective risk/coverage; ties enter a risk curve together.

    False certification = accepted errors / all labeled errors. Selective risk =
    accepted errors / accepted observations. Unsupported scores abstain. Threshold
    must be specified before evaluation; this function does not select it.
    """
    scores = np.asarray(scores, dtype=float)
    labels = np.asarray(is_error, dtype=bool)
    if len(scores) != len(labels) or not len(scores) or np.isnan(scores).any():
        raise ValueError("Scores and labels must be aligned, nonempty, and non-NaN")
    if np.isnan(threshold):
        raise ValueError("Threshold cannot be NaN")
    accept = np.isfinite(scores) & (scores <= threshold)
    # Infinity denotes abstention; map it above all finite scores for ranking metrics.
    finite = scores[np.isfinite(scores)]
    ranked = np.where(np.isfinite(scores), scores, (finite.max() + 1 if len(finite) else 1))
    both = labels.any() and not labels.all()
    curve = []
    order = np.flatnonzero(np.isfinite(scores))
    order = order[np.argsort(scores[order], kind="stable")]
    if len(order):
        ordered_scores = scores[order]
        cumulative_errors = np.cumsum(labels[order])
        ends = np.r_[np.flatnonzero(np.diff(ordered_scores) != 0), len(order) - 1]
        curve = [
            {
                "threshold": float(ordered_scores[i]),
                "coverage": float((i + 1) / len(scores)),
                "risk": float(cumulative_errors[i] / (i + 1)),
            }
            for i in ends
        ]
    return {
        "observations": len(scores),
        "error_prevalence": float(labels.mean()),
        "auroc": float(roc_auc_score(labels, ranked)) if both else None,
        "auprc": float(average_precision_score(labels, ranked)) if both else None,
        "false_certification_rate": float((accept & labels).sum() / labels.sum())
        if labels.any()
        else None,
        "certified_coverage": float(accept.mean()),
        "selective_risk": float(labels[accept].mean()) if accept.any() else None,
        "correct_certified_coverage": float((accept & ~labels).mean()),
        "threshold": float(threshold),
        "risk_coverage": curve,
    }


def oracle_association_baseline(detections, *, method="hungarian", physical=True):
    """Untrained Euclidean nearest-neighbor or bipartite association on fixed detections."""
    if method not in {"nearest_neighbor", "hungarian"}:
        raise ValueError(method)
    cols = [f"{a}_um" for a in "xyz"] if physical else XYZ
    output = []
    previous = None
    next_id = 0
    for frame, points in detections.sort_values("frame_index").groupby("frame_index"):
        points = points.copy().reset_index(drop=True)
        xyz = points[cols].to_numpy(dtype=float)
        if not np.isfinite(xyz).all():
            raise ValueError("Baseline requires valid coordinates in the selected units")
        ids = [None] * len(points)
        if previous is not None:
            old_xyz, old_ids = previous
            distance = np.linalg.norm(old_xyz[:, None] - xyz[None], axis=2)
            if method == "hungarian":
                rows, columns = linear_sum_assignment(distance)
            else:
                pairs = sorted((distance[i, j], i, j) for i, j in enumerate(distance.argmin(1)))
                chosen = set()
                matches = []
                for _, i, j in pairs:
                    if j not in chosen:
                        matches.append((i, j))
                        chosen.add(j)
                rows = [p[0] for p in matches]
                columns = [p[1] for p in matches]
            for i, j in zip(rows, columns, strict=True):
                ids[j] = old_ids[i]
        for i, identity in enumerate(ids):
            if identity is None:
                ids[i] = f"baseline:{next_id}"
                next_id += 1
        points["track_id"] = ids
        points["annotation_type"] = "algorithm_prediction"
        points["label_set"] = "prediction"
        output.append(points)
        previous = xyz, ids
    return pd.concat(output, ignore_index=True)