"""Normal-segment classification + success-ratio for FT-P-04 (AZ-412 / AC-2.1a).

The SUT exposes a per-frame ``registration_success`` boolean (either via
``NAMED_VALUE_FLOAT`` MAVLink messages or via the post-run FDR archive).
This helper:

1. Reads the Derkachi ``data_imu.csv`` (SCALED_IMU2 + GLOBAL_POSITION_INT
   columns) and derives a per-row attitude approximation from accelerometer
   readings (the spec's AC-1 explicitly says attitude is
   ``SCALED_IMU2``-derived, NOT internal SUT state).
2. Classifies each video frame as **normal** when both:
   * bank/pitch within ±10° of nadir, AND
   * inferred prior-frame overlap ≥40 % (heuristic from translation magnitude).
3. Computes the success ratio over the **normal** set only — sharp-turn
   frames are excluded from the denominator per AC-3.
4. Asserts the ratio meets the AC-2 budget (≥0.95).

The video is 30 fps; the IMU/telemetry CSV is 10 Hz (one row per 100 ms,
i.e. 3 video frames per row). The classifier expands each telemetry row
to 3 video-frame indices (the same row drives 3 consecutive frames).

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

from __future__ import annotations

import csv
import math
from dataclasses import dataclass, field
from pathlib import Path
from typing import Iterable, Mapping, Sequence

ATTITUDE_LIMIT_DEG = 10.0
TARGET_OVERLAP_FRACTION = 0.40
SUCCESS_RATIO_REQUIRED = 0.95
VIDEO_FPS = 30
IMU_HZ = 10
VIDEO_FRAMES_PER_IMU_ROW = VIDEO_FPS // IMU_HZ
# Derkachi nadir camera: the camera_info.md fixture records ~141 m altitude
# AGL and ~55° horizontal FOV. The "ground footprint width" at nadir is
# 2 * alt * tan(FOV/2) ≈ 2 * 141 * tan(27.5°) ≈ 147 m. We use a single
# scenario-wide ground footprint to keep the heuristic transparent.
DEFAULT_GROUND_FOOTPRINT_M = 147.0


@dataclass(frozen=True)
class ImuTelemetryRow:
    """One row of ``data_imu.csv`` distilled to the columns the classifier needs.

    Velocity fields are floats (cm/s) because the shipped ``data_imu.csv``
    stores them in scientific notation (e.g. ``-4.44E-16`` near hover).
    Acceleration fields stay int per the SCALED_IMU2 wire format.
    """

    timestamp_ms: float
    time_s: float
    xacc: int
    yacc: int
    zacc: int
    vx_cms: float
    vy_cms: float
    vz_cms: float


@dataclass(frozen=True)
class FrameAttitude:
    """Bank + pitch derived from accel; used for the ±10° gate.

    Accel-only attitude assumes the platform is in near-equilibrium
    flight (the dominant accel is gravity). Valid for the cropped
    nadir-cruise segments AC-2.1a targets; explicitly NOT valid during
    aggressive manoeuvres — but those are exactly the frames AC-2.1a
    wants to EXCLUDE from the denominator. So the limitation matches
    the AC intent.
    """

    bank_deg: float
    pitch_deg: float


@dataclass(frozen=True)
class FrameClassification:
    """Per-video-frame classification used to build the FT-P-04 denominator."""

    frame_index: int
    imu_row_index: int
    attitude: FrameAttitude
    translation_m: float
    overlap_fraction: float
    is_normal: bool
    excluded_reason: str = ""


@dataclass(frozen=True)
class SuccessReport:
    """Aggregate report consumed by the scenario assertion.

    ``ratio = success_count / denominator`` where ``denominator`` is the
    count of normal frames (sharp-turn / low-overlap frames are excluded
    per AC-3 — they are counted in ``excluded_count`` for diagnostic
    clarity).
    """

    success_count: int
    denominator: int
    ratio: float
    excluded_count: int
    excluded_by_attitude: int
    excluded_by_overlap: int
    excluded_by_missing_metric: int
    ratio_required: float = SUCCESS_RATIO_REQUIRED

    @property
    def passes(self) -> bool:
        return self.denominator > 0 and self.ratio >= self.ratio_required


def load_imu_telemetry(csv_path: Path) -> list[ImuTelemetryRow]:
    """Read ``data_imu.csv`` and return one row per non-blank entry.

    Only the columns the classifier needs are kept. Other columns are
    ignored to keep the classifier independent of upstream column churn.
    """
    if not csv_path.exists():
        raise FileNotFoundError(
            f"data_imu.csv not found at {csv_path} — bind-mount the Derkachi fixture"
        )
    needed = {
        "timestamp(ms)",
        "Time",
        "SCALED_IMU2.xacc",
        "SCALED_IMU2.yacc",
        "SCALED_IMU2.zacc",
        "GLOBAL_POSITION_INT.vx",
        "GLOBAL_POSITION_INT.vy",
        "GLOBAL_POSITION_INT.vz",
    }
    rows: list[ImuTelemetryRow] = []
    with csv_path.open() as fh:
        reader = csv.DictReader(fh)
        missing = needed - set(reader.fieldnames or [])
        if missing:
            raise ValueError(f"data_imu.csv missing columns: {sorted(missing)}")
        for raw in reader:
            if not raw["timestamp(ms)"].strip():
                continue
            rows.append(
                ImuTelemetryRow(
                    timestamp_ms=float(raw["timestamp(ms)"]),
                    time_s=float(raw["Time"]),
                    xacc=int(float(raw["SCALED_IMU2.xacc"])),
                    yacc=int(float(raw["SCALED_IMU2.yacc"])),
                    zacc=int(float(raw["SCALED_IMU2.zacc"])),
                    vx_cms=float(raw["GLOBAL_POSITION_INT.vx"]),
                    vy_cms=float(raw["GLOBAL_POSITION_INT.vy"]),
                    vz_cms=float(raw["GLOBAL_POSITION_INT.vz"]),
                )
            )
    return rows


def compute_attitude(row: ImuTelemetryRow) -> FrameAttitude:
    """Derive bank + pitch from accelerometer (gravity-as-down assumption).

    SCALED_IMU2 acc components are in mg (milli-g). Sign convention:
    body-frame +x forward, +y right, +z down. With dominant gravity on
    +z the resting attitude has xacc=0, yacc=0, zacc=-1000 (negative
    because the body frame measures the reaction force pointing UP).

    pitch = atan2(-xacc, sqrt(yacc² + zacc²))
    bank  = atan2(yacc, zacc)
    """
    x = float(row.xacc)
    y = float(row.yacc)
    z = float(row.zacc)
    pitch_rad = math.atan2(-x, math.sqrt(y * y + z * z))
    bank_rad = math.atan2(y, z)
    # The atan2(y, z) convention puts level flight at ±π (since z is
    # negative gravity); we want level = 0, so subtract π and wrap.
    bank_deg_raw = math.degrees(bank_rad)
    if bank_deg_raw > 90.0:
        bank_deg = bank_deg_raw - 180.0
    elif bank_deg_raw < -90.0:
        bank_deg = bank_deg_raw + 180.0
    else:
        bank_deg = bank_deg_raw
    return FrameAttitude(bank_deg=bank_deg, pitch_deg=math.degrees(pitch_rad))


def compute_translation_m(row: ImuTelemetryRow, prev_row: ImuTelemetryRow | None) -> float:
    """Ground-plane translation between consecutive frames in meters.

    Uses the GLOBAL_POSITION_INT velocity (vx, vy in cm/s); vz is
    excluded because vertical motion mostly affects scale, not overlap.
    Per-frame dt = 1/30 s. With telemetry at 10 Hz, the same velocity
    drives 3 consecutive frames.
    """
    vx_ms = row.vx_cms / 100.0
    vy_ms = row.vy_cms / 100.0
    horizontal_speed = math.hypot(vx_ms, vy_ms)
    dt_s = 1.0 / VIDEO_FPS
    return horizontal_speed * dt_s


def compute_overlap_fraction(
    translation_m: float, ground_footprint_m: float
) -> float:
    """Fraction of ground footprint that overlaps with the prior frame.

    Approximation: assume a square ground footprint of side
    ``ground_footprint_m``. After translating by ``translation_m`` in
    the horizontal plane, the overlap is
    ``max(0, 1 - translation_m / ground_footprint_m)``.

    This is an upper bound — diagonal motion or rotation eats more
    overlap. The ±10° attitude gate rules out the rotation-heavy
    frames; pure translation is what survives, and this approximation
    is tight for cruise flight.
    """
    if ground_footprint_m <= 0:
        raise ValueError(f"ground_footprint_m must be > 0, got {ground_footprint_m}")
    fraction = 1.0 - translation_m / ground_footprint_m
    return max(0.0, min(1.0, fraction))


def classify_frames(
    imu_rows: Sequence[ImuTelemetryRow],
    *,
    attitude_limit_deg: float = ATTITUDE_LIMIT_DEG,
    min_overlap_fraction: float = TARGET_OVERLAP_FRACTION,
    ground_footprint_m: float = DEFAULT_GROUND_FOOTPRINT_M,
    video_frames_per_imu_row: int = VIDEO_FRAMES_PER_IMU_ROW,
) -> list[FrameClassification]:
    """Build the per-video-frame classification list.

    Each ``ImuTelemetryRow`` drives ``video_frames_per_imu_row``
    consecutive video frames (3 frames per IMU row by default). Frame
    indices are 0-based and contiguous.

    Determinism: this function depends only on the input rows + tunables
    — same input → same output.
    """
    if attitude_limit_deg <= 0:
        raise ValueError(f"attitude_limit_deg must be > 0, got {attitude_limit_deg}")
    if not 0.0 < min_overlap_fraction < 1.0:
        raise ValueError(
            f"min_overlap_fraction must be in (0, 1), got {min_overlap_fraction}"
        )

    classifications: list[FrameClassification] = []
    prev_row: ImuTelemetryRow | None = None
    frame_index = 0
    for imu_row_index, row in enumerate(imu_rows):
        attitude = compute_attitude(row)
        translation_m = compute_translation_m(row, prev_row)
        overlap_fraction = compute_overlap_fraction(translation_m, ground_footprint_m)
        attitude_ok = (
            abs(attitude.bank_deg) <= attitude_limit_deg
            and abs(attitude.pitch_deg) <= attitude_limit_deg
        )
        overlap_ok = overlap_fraction >= min_overlap_fraction
        is_normal = attitude_ok and overlap_ok
        if not attitude_ok:
            reason = "attitude_exceeds_limit"
        elif not overlap_ok:
            reason = "overlap_below_threshold"
        else:
            reason = ""
        for _ in range(video_frames_per_imu_row):
            classifications.append(
                FrameClassification(
                    frame_index=frame_index,
                    imu_row_index=imu_row_index,
                    attitude=attitude,
                    translation_m=translation_m,
                    overlap_fraction=overlap_fraction,
                    is_normal=is_normal,
                    excluded_reason=reason,
                )
            )
            frame_index += 1
        prev_row = row
    return classifications


def compute_success_ratio(
    classifications: Sequence[FrameClassification],
    registration_success_by_frame: Mapping[int, bool],
) -> SuccessReport:
    """Compute the success ratio over the normal-frame denominator.

    Parameters
    ----------
    classifications : the per-frame classification list (output of
        ``classify_frames``).
    registration_success_by_frame : dict[frame_index, bool] — populated
        from ``NAMED_VALUE_FLOAT`` listener output or post-run FDR read.
        Frames missing from this dict are excluded from the denominator
        and counted in ``excluded_by_missing_metric`` (the SUT failed to
        emit the metric — AC-2 of the AC-NEW-3 FDR-schema spec covers
        the schema; this scenario complains if the metric is absent).

    Returns
    -------
    SuccessReport
    """
    success = 0
    denominator = 0
    excluded_by_attitude = 0
    excluded_by_overlap = 0
    excluded_by_missing_metric = 0
    for cls in classifications:
        if not cls.is_normal:
            if cls.excluded_reason == "attitude_exceeds_limit":
                excluded_by_attitude += 1
            elif cls.excluded_reason == "overlap_below_threshold":
                excluded_by_overlap += 1
            continue
        metric = registration_success_by_frame.get(cls.frame_index)
        if metric is None:
            excluded_by_missing_metric += 1
            continue
        denominator += 1
        if metric:
            success += 1
    excluded_count = excluded_by_attitude + excluded_by_overlap + excluded_by_missing_metric
    ratio = (success / denominator) if denominator > 0 else 0.0
    return SuccessReport(
        success_count=success,
        denominator=denominator,
        ratio=ratio,
        excluded_count=excluded_count,
        excluded_by_attitude=excluded_by_attitude,
        excluded_by_overlap=excluded_by_overlap,
        excluded_by_missing_metric=excluded_by_missing_metric,
    )


def write_csv_evidence(
    out_path: Path,
    classifications: Iterable[FrameClassification],
    registration_success_by_frame: Mapping[int, bool],
) -> Path:
    """Write the FT-P-04 per-frame evidence CSV.

    Header: ``frame_index, imu_row_index, bank_deg, pitch_deg,
    translation_m, overlap_fraction, is_normal, excluded_reason,
    registration_success``.
    """
    out_path.parent.mkdir(parents=True, exist_ok=True)
    with out_path.open("w", newline="") as fh:
        writer = csv.writer(fh)
        writer.writerow(
            [
                "frame_index",
                "imu_row_index",
                "bank_deg",
                "pitch_deg",
                "translation_m",
                "overlap_fraction",
                "is_normal",
                "excluded_reason",
                "registration_success",
            ]
        )
        for cls in classifications:
            metric = registration_success_by_frame.get(cls.frame_index)
            writer.writerow(
                [
                    cls.frame_index,
                    cls.imu_row_index,
                    f"{cls.attitude.bank_deg:.3f}",
                    f"{cls.attitude.pitch_deg:.3f}",
                    f"{cls.translation_m:.4f}",
                    f"{cls.overlap_fraction:.4f}",
                    "true" if cls.is_normal else "false",
                    cls.excluded_reason,
                    "" if metric is None else ("true" if metric else "false"),
                ]
            )
    return out_path