"""Outlier-tolerance evaluation for FT-N-01 (AZ-424 / AC-3.1).

Consumes the AZ-408 ``outlier`` injector's ``manifest.csv`` (which
frames were replaced + the geodesic offset) and the SUT's outbound
estimate stream, and validates:

* AC-1: at least ``MIN_OUTLIER_COUNT`` outlier frames were injected
  over the replay.
* AC-2: for every outlier event,
  ``error_after_outlier ≤ error_before_outlier + DRIFT_BUDGET_M``.
* AC-3: ``cov_semi_major_m`` is non-decreasing across the 3-frame
  window centred on the outlier (frame before, outlier, frame after).

The injector's ``geodesic_offset_m`` column verifies the
RESTRICT-CAM-1 / AC-3.1 threshold (>350 m) per-row — the AC-1 count
check here is a coarser invariant that does not duplicate the
per-row geodesic gate.

Public-boundary discipline: does NOT import any
``src/gps_denied_onboard`` symbol.
"""

from __future__ import annotations

import csv
from dataclasses import dataclass
from pathlib import Path
from typing import Sequence

from .geo import distance_m

DRIFT_BUDGET_M = 50.0  # AC-2
COVARIANCE_WINDOW_FRAMES = 3  # AC-3: 1 before + 1 outlier + 1 after
MIN_OUTLIER_COUNT = 10  # AC-1: ~10 over Derkachi 8-min replay


@dataclass(frozen=True)
class GtPose:
    """One ground-truth pose for a video frame, keyed by frame index."""

    frame_idx: int
    lat_deg: float
    lon_deg: float


@dataclass(frozen=True)
class OutboundEstimate:
    """One outbound estimate with covariance + label, keyed by frame index."""

    frame_idx: int
    monotonic_ms: int
    lat_deg: float
    lon_deg: float
    cov_semi_major_m: float
    source_label: str


@dataclass(frozen=True)
class OutlierEvent:
    """One row from the injector's manifest.csv."""

    frame_idx: int
    geodesic_offset_m: float
    src_jpeg_path: str


@dataclass(frozen=True)
class OutlierEventReport:
    """AC-2 + AC-3 evaluation for one outlier event."""

    frame_idx: int
    error_before_m: float | None
    error_outlier_m: float | None
    error_after_m: float | None
    drift_m: float | None  # error_after - error_before; AC-2 budget
    cov_before: float | None
    cov_outlier: float | None
    cov_after: float | None
    cov_non_decreasing: bool

    @property
    def passes_drift(self) -> bool:
        return (
            self.drift_m is not None
            and self.drift_m <= DRIFT_BUDGET_M
        )

    @property
    def passes_covariance(self) -> bool:
        return self.cov_non_decreasing

    @property
    def passes(self) -> bool:
        return self.passes_drift and self.passes_covariance


@dataclass(frozen=True)
class OutlierToleranceReport:
    """Aggregate report for all outlier events in the replay."""

    events: tuple[OutlierEventReport, ...]
    total_outliers: int

    @property
    def passes_count(self) -> bool:
        return self.total_outliers >= MIN_OUTLIER_COUNT

    @property
    def failed_event_count(self) -> int:
        return sum(1 for e in self.events if not e.passes)

    @property
    def passes(self) -> bool:
        return self.passes_count and self.failed_event_count == 0


def load_outlier_manifest(manifest_path: Path) -> list[OutlierEvent]:
    """Read ``outlier/manifest.csv`` into typed events.

    Schema (AZ-408): ``frame_idx, src_jpeg_path, replacement_tile_x,
    replacement_tile_y, geodesic_offset_m, seed``.
    """
    if not manifest_path.exists():
        raise FileNotFoundError(
            f"outlier manifest not found: {manifest_path} — run the "
            "outlier injector first (AZ-408 / runner/helpers/injector_fixtures)"
        )
    events: list[OutlierEvent] = []
    with manifest_path.open() as fh:
        reader = csv.DictReader(fh)
        required = {"frame_idx", "src_jpeg_path", "geodesic_offset_m"}
        missing = required - set(reader.fieldnames or [])
        if missing:
            raise ValueError(
                f"outlier manifest {manifest_path} missing required columns: "
                f"{sorted(missing)}"
            )
        for row in reader:
            events.append(
                OutlierEvent(
                    frame_idx=int(row["frame_idx"]),
                    geodesic_offset_m=float(row["geodesic_offset_m"]),
                    src_jpeg_path=row["src_jpeg_path"],
                )
            )
    return events


def _index_by_frame(estimates: Sequence[OutboundEstimate]) -> dict[int, OutboundEstimate]:
    by_frame: dict[int, OutboundEstimate] = {}
    for e in estimates:
        by_frame[e.frame_idx] = e
    return by_frame


def _index_gt(gt: Sequence[GtPose]) -> dict[int, GtPose]:
    by_frame: dict[int, GtPose] = {}
    for g in gt:
        by_frame[g.frame_idx] = g
    return by_frame


def _error_m(est: OutboundEstimate | None, gt: GtPose | None) -> float | None:
    if est is None or gt is None:
        return None
    return distance_m(gt.lat_deg, gt.lon_deg, est.lat_deg, est.lon_deg)


def evaluate_event(
    event: OutlierEvent,
    estimates_by_frame: dict[int, OutboundEstimate],
    gt_by_frame: dict[int, GtPose],
) -> OutlierEventReport:
    """Compute the AC-2 + AC-3 report for one outlier event."""
    before = estimates_by_frame.get(event.frame_idx - 1)
    outlier = estimates_by_frame.get(event.frame_idx)
    after = estimates_by_frame.get(event.frame_idx + 1)

    gt_before = gt_by_frame.get(event.frame_idx - 1)
    gt_outlier = gt_by_frame.get(event.frame_idx)
    gt_after = gt_by_frame.get(event.frame_idx + 1)

    err_before = _error_m(before, gt_before)
    err_outlier = _error_m(outlier, gt_outlier)
    err_after = _error_m(after, gt_after)

    drift: float | None = None
    if err_before is not None and err_after is not None:
        drift = err_after - err_before

    cov_before = before.cov_semi_major_m if before is not None else None
    cov_outlier = outlier.cov_semi_major_m if outlier is not None else None
    cov_after = after.cov_semi_major_m if after is not None else None

    covs = [c for c in (cov_before, cov_outlier, cov_after) if c is not None]
    cov_non_decreasing = all(covs[i + 1] >= covs[i] for i in range(len(covs) - 1))

    return OutlierEventReport(
        frame_idx=event.frame_idx,
        error_before_m=err_before,
        error_outlier_m=err_outlier,
        error_after_m=err_after,
        drift_m=drift,
        cov_before=cov_before,
        cov_outlier=cov_outlier,
        cov_after=cov_after,
        cov_non_decreasing=cov_non_decreasing,
    )


def evaluate(
    events: Sequence[OutlierEvent],
    estimates: Sequence[OutboundEstimate],
    gt: Sequence[GtPose],
) -> OutlierToleranceReport:
    """Aggregate report across all outlier events."""
    by_frame = _index_by_frame(estimates)
    gt_idx = _index_gt(gt)
    reports = tuple(evaluate_event(ev, by_frame, gt_idx) for ev in events)
    return OutlierToleranceReport(events=reports, total_outliers=len(events))


def write_csv_evidence(out_path: Path, report: OutlierToleranceReport) -> Path:
    """Write per-event FT-N-01 evidence CSV."""
    out_path.parent.mkdir(parents=True, exist_ok=True)
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(
            [
                "frame_idx",
                "error_before_m",
                "error_outlier_m",
                "error_after_m",
                "drift_m",
                "cov_before",
                "cov_outlier",
                "cov_after",
                "cov_non_decreasing",
                "passes_drift",
                "passes_covariance",
                "passes",
            ]
        )
        for e in report.events:
            writer.writerow(
                [
                    e.frame_idx,
                    "" if e.error_before_m is None else f"{e.error_before_m:.3f}",
                    "" if e.error_outlier_m is None else f"{e.error_outlier_m:.3f}",
                    "" if e.error_after_m is None else f"{e.error_after_m:.3f}",
                    "" if e.drift_m is None else f"{e.drift_m:.3f}",
                    "" if e.cov_before is None else f"{e.cov_before:.3f}",
                    "" if e.cov_outlier is None else f"{e.cov_outlier:.3f}",
                    "" if e.cov_after is None else f"{e.cov_after:.3f}",
                    "true" if e.cov_non_decreasing else "false",
                    "true" if e.passes_drift else "false",
                    "true" if e.passes_covariance else "false",
                    "true" if e.passes else "false",
                ]
            )
    return out_path