"""SGP4 counterfactual propagation — "what if no maneuver?" simulation.

For each likely-avoidance maneuver, propagates the pre-maneuver TLE forward
to estimate whether a close approach would have occurred. This generates
counterfactual "would-have-collided" labels for training enrichment.

Uses the sgp4 library for efficient satellite propagation.
"""

import math
import numpy as np
from datetime import datetime, timedelta, timezone

try:
    from sgp4.api import Satrec, WGS72
    from sgp4 import exporter
    SGP4_AVAILABLE = True
except ImportError:
    SGP4_AVAILABLE = False

# Earth parameters
EARTH_RADIUS_KM = 6378.137

# Counterfactual thresholds
COLLISION_THRESHOLD_KM = 1.0    # "Would have collided" if closer than this
NEARBY_ALT_BAND_KM = 50.0      # Altitude proximity for neighbor selection
NEARBY_RAAN_BAND_DEG = 30.0    # RAAN proximity for neighbor selection


def celestrak_json_to_satrec(tle_json: dict) -> "Satrec":
    """Convert a CelesTrak GP JSON record to an sgp4 Satrec object.

    CelesTrak JSON includes TLE_LINE1/TLE_LINE2 when available. Falls
    back to constructing from orbital elements via sgp4init().

    Args:
        tle_json: CelesTrak GP JSON dict with orbital elements.

    Returns:
        sgp4 Satrec object ready for propagation.

    Raises:
        ImportError: If sgp4 is not installed.
        ValueError: If TLE data is insufficient.
    """
    if not SGP4_AVAILABLE:
        raise ImportError("sgp4 library is required: pip install sgp4")

    # Prefer TLE lines if available (most reliable)
    line1 = tle_json.get("TLE_LINE1", "")
    line2 = tle_json.get("TLE_LINE2", "")
    if line1 and line2:
        return Satrec.twoline2rv(line1, line2)

    # Construct from JSON orbital elements using sgp4init
    satrec = Satrec()

    # Parse epoch
    epoch_str = tle_json.get("EPOCH", "")
    if not epoch_str:
        raise ValueError("No EPOCH in TLE JSON")

    epoch_dt = datetime.fromisoformat(epoch_str.replace("Z", "+00:00"))
    if epoch_dt.tzinfo is None:
        epoch_dt = epoch_dt.replace(tzinfo=timezone.utc)

    # Convert to Julian date pair for sgp4
    year = epoch_dt.year
    mon = epoch_dt.month
    day = epoch_dt.day
    hr = epoch_dt.hour
    minute = epoch_dt.minute
    sec = epoch_dt.second + epoch_dt.microsecond / 1e6

    # sgp4init expects elements in specific units
    no_kozai = float(tle_json.get("MEAN_MOTION", 0)) * (2.0 * math.pi / 1440.0)  # rev/day -> rad/min
    ecco = float(tle_json.get("ECCENTRICITY", 0))
    inclo = math.radians(float(tle_json.get("INCLINATION", 0)))
    nodeo = math.radians(float(tle_json.get("RA_OF_ASC_NODE", 0)))
    argpo = math.radians(float(tle_json.get("ARG_OF_PERICENTER", 0)))
    mo = math.radians(float(tle_json.get("MEAN_ANOMALY", 0)))
    bstar = float(tle_json.get("BSTAR", 0))
    norad_id = int(tle_json.get("NORAD_CAT_ID", 0))

    # Epoch in Julian date
    jd_base = _datetime_to_jd(epoch_dt)
    epoch_jd = jd_base
    # sgp4init epoch is minutes since 1949-12-31 00:00 UTC
    # But the Python API uses (jdsatepoch, jdsatepochF) pair
    jd_whole = int(epoch_jd)
    jd_frac = epoch_jd - jd_whole

    satrec.sgp4init(
        WGS72,           # gravity model
        'i',             # 'a' = old AFSPC mode, 'i' = improved
        norad_id,        # NORAD catalog number
        (epoch_jd - 2433281.5),  # epoch in days since 1949 Dec 31 00:00 UT
        bstar,           # BSTAR drag term
        0.0,             # ndot (not used in sgp4init 'i' mode)
        0.0,             # nddot (not used)
        ecco,            # eccentricity
        argpo,           # argument of perigee (radians)
        inclo,           # inclination (radians)
        mo,              # mean anomaly (radians)
        no_kozai,        # mean motion (radians/minute)
        nodeo,           # RAAN (radians)
    )

    return satrec


def _datetime_to_jd(dt: datetime) -> float:
    """Convert datetime to Julian Date."""
    if dt.tzinfo is not None:
        dt = dt.astimezone(timezone.utc).replace(tzinfo=None)
    a = (14 - dt.month) // 12
    y = dt.year + 4800 - a
    m = dt.month + 12 * a - 3
    jdn = dt.day + (153 * m + 2) // 5 + 365 * y + y // 4 - y // 100 + y // 400 - 32045
    jd = jdn + (dt.hour - 12) / 24.0 + dt.minute / 1440.0 + dt.second / 86400.0
    return jd


def _propagate_positions(satrec: "Satrec", start_jd: float, hours: float, step_min: float) -> np.ndarray:
    """Propagate a satellite and return position array (N x 3) in km.

    Returns empty array if propagation fails.
    """
    n_steps = int(hours * 60 / step_min) + 1
    positions = []

    for i in range(n_steps):
        minutes_since_epoch = (start_jd - satrec.jdsatepoch - satrec.jdsatepochF) * 1440.0 + i * step_min
        e, r, v = satrec.sgp4(satrec.jdsatepoch, satrec.jdsatepochF + minutes_since_epoch / 1440.0)
        if e != 0:
            continue
        positions.append(r)

    if not positions:
        return np.array([]).reshape(0, 3)
    return np.array(positions)


def find_nearby_satellites(
    maneuvered_tle: dict,
    all_tles: list[dict],
    alt_band_km: float = NEARBY_ALT_BAND_KM,
    raan_band_deg: float = NEARBY_RAAN_BAND_DEG,
) -> list[dict]:
    """Find satellites in similar orbital shell to the maneuvered object."""
    from src.data.maneuver_detector import mean_motion_to_sma, sma_to_altitude

    norad_id = int(maneuvered_tle.get("NORAD_CAT_ID", 0))
    mm = float(maneuvered_tle.get("MEAN_MOTION", 0))
    target_alt = sma_to_altitude(mean_motion_to_sma(mm))
    target_raan = float(maneuvered_tle.get("RA_OF_ASC_NODE", 0))

    nearby = []
    for tle in all_tles:
        tid = int(tle.get("NORAD_CAT_ID", 0))
        if tid == norad_id or tid <= 0:
            continue

        t_mm = float(tle.get("MEAN_MOTION", 0))
        t_alt = sma_to_altitude(mean_motion_to_sma(t_mm))
        t_raan = float(tle.get("RA_OF_ASC_NODE", 0))

        alt_diff = abs(t_alt - target_alt)
        raan_diff = abs(t_raan - target_raan)
        raan_diff = min(raan_diff, 360.0 - raan_diff)

        if alt_diff < alt_band_km and raan_diff < raan_band_deg:
            nearby.append(tle)

    return nearby


def propagate_counterfactual(
    pre_maneuver_tle: dict,
    nearby_tles: list[dict],
    hours_forward: float = 24.0,
    step_minutes: float = 10.0,
) -> dict:
    """Simulate "what if no maneuver?" using SGP4 propagation.

    Propagates the pre-maneuver TLE (before orbit change) forward and
    checks for close approaches with nearby satellites.

    Args:
        pre_maneuver_tle: Yesterday's TLE for the maneuvered satellite.
        nearby_tles: Current TLEs for nearby satellites.
        hours_forward: How far to propagate (hours).
        step_minutes: Time step for propagation (minutes).

    Returns:
        Dict with: min_distance_km, time_of_closest_approach,
                   would_have_collided, closest_norad_id, n_neighbors_checked.
    """
    if not SGP4_AVAILABLE:
        return {
            "min_distance_km": None,
            "would_have_collided": False,
            "error": "sgp4 not installed",
        }

    try:
        target_sat = celestrak_json_to_satrec(pre_maneuver_tle)
    except (ValueError, Exception) as e:
        return {
            "min_distance_km": None,
            "would_have_collided": False,
            "error": f"target TLE parse failed: {e}",
        }

    # Use current time as propagation start
    now = datetime.now(timezone.utc)
    start_jd = _datetime_to_jd(now)

    # Propagate maneuvered satellite (pre-maneuver orbit)
    target_positions = _propagate_positions(target_sat, start_jd, hours_forward, step_minutes)
    if len(target_positions) == 0:
        return {
            "min_distance_km": None,
            "would_have_collided": False,
            "error": "target propagation failed",
        }

    global_min_dist = float("inf")
    closest_norad = 0
    closest_time_offset_min = 0.0
    n_checked = 0

    for neighbor_tle in nearby_tles:
        try:
            neighbor_sat = celestrak_json_to_satrec(neighbor_tle)
        except (ValueError, Exception):
            continue

        neighbor_positions = _propagate_positions(neighbor_sat, start_jd, hours_forward, step_minutes)
        if len(neighbor_positions) == 0:
            continue

        n_checked += 1

        # Compute distances at each timestep (use min of overlapping steps)
        n_common = min(len(target_positions), len(neighbor_positions))
        diffs = target_positions[:n_common] - neighbor_positions[:n_common]
        distances = np.linalg.norm(diffs, axis=1)
        min_idx = np.argmin(distances)
        min_dist = distances[min_idx]

        if min_dist < global_min_dist:
            global_min_dist = min_dist
            closest_norad = int(neighbor_tle.get("NORAD_CAT_ID", 0))
            closest_time_offset_min = min_idx * step_minutes

    if global_min_dist == float("inf"):
        return {
            "min_distance_km": None,
            "would_have_collided": False,
            "n_neighbors_checked": n_checked,
            "error": "no valid neighbors propagated",
        }

    tca_dt = now + timedelta(minutes=closest_time_offset_min)

    return {
        "min_distance_km": round(global_min_dist, 3),
        "time_of_closest_approach": tca_dt.isoformat(),
        "would_have_collided": global_min_dist < COLLISION_THRESHOLD_KM,
        "closest_norad_id": closest_norad,
        "n_neighbors_checked": n_checked,
    }


def compute_forward_trajectory(
    tle_1: dict,
    tle_2: dict,
    hours_forward: float = 120.0,
    step_minutes: float = 20.0,
) -> list[dict] | None:
    """Compute full trajectory time series for two satellites.

    Returns list of trajectory points with ECI positions and separation
    distance, suitable for baking into the webapp alerts JSON so the
    frontend doesn't need to do SGP4 propagation or load TLE data.

    Args:
        tle_1: CelesTrak GP JSON for satellite 1.
        tle_2: CelesTrak GP JSON for satellite 2.
        hours_forward: How far to propagate (default 120h = 5 days).
        step_minutes: Time step for propagation (minutes).

    Returns:
        List of dicts with: h (hours from start), d (distance km),
        s1 [x,y,z] ECI km, s2 [x,y,z] ECI km. None if propagation fails.
    """
    if not SGP4_AVAILABLE:
        return None

    try:
        sat1 = celestrak_json_to_satrec(tle_1)
        sat2 = celestrak_json_to_satrec(tle_2)
    except (ValueError, Exception):
        return None

    now = datetime.now(timezone.utc)
    start_jd = _datetime_to_jd(now)

    n_steps = int(hours_forward * 60 / step_minutes) + 1
    points = []

    for i in range(n_steps):
        mins = i * step_minutes
        target_jd = start_jd + mins / 1440.0
        jd_whole = int(target_jd)
        jd_frac = target_jd - jd_whole

        e1, r1, _ = sat1.sgp4(jd_whole, jd_frac)
        e2, r2, _ = sat2.sgp4(jd_whole, jd_frac)

        if e1 != 0 or e2 != 0:
            continue
        if not all(math.isfinite(v) for v in r1 + r2):
            continue

        dx = r1[0] - r2[0]
        dy = r1[1] - r2[1]
        dz = r1[2] - r2[2]
        dist = math.sqrt(dx * dx + dy * dy + dz * dz)

        points.append({
            "h": round(mins / 60.0, 2),
            "d": round(dist, 1),
            "s1": [round(r1[0], 1), round(r1[1], 1), round(r1[2], 1)],
            "s2": [round(r2[0], 1), round(r2[1], 1), round(r2[2], 1)],
        })

    return points if points else None


def compute_tca_trail(
    tle_1: dict,
    tle_2: dict,
    tca_hours: float,
    half_window_min: float = 30.0,
    step_minutes: float = 0.25,
) -> list[dict] | None:
    """Compute dense trail around TCA for globe orbital path visualization.

    Returns 15-sec resolution positions for ±30 min around TCA.

    Args:
        tle_1: CelesTrak GP JSON for satellite 1.
        tle_2: CelesTrak GP JSON for satellite 2.
        tca_hours: Hours from now to TCA (from compute_forward_tca).
        half_window_min: Half window in minutes around TCA.
        step_minutes: Time step in minutes.

    Returns:
        List of dicts with s1 [x,y,z] and s2 [x,y,z] ECI km. None if fails.
    """
    if not SGP4_AVAILABLE:
        return None

    try:
        sat1 = celestrak_json_to_satrec(tle_1)
        sat2 = celestrak_json_to_satrec(tle_2)
    except (ValueError, Exception):
        return None

    now = datetime.now(timezone.utc)
    start_jd = _datetime_to_jd(now)

    tca_min = tca_hours * 60.0
    t_start = tca_min - half_window_min
    t_end = tca_min + half_window_min
    n_steps = int((t_end - t_start) / step_minutes) + 1

    trail = []
    for i in range(n_steps):
        mins = t_start + i * step_minutes
        target_jd = start_jd + mins / 1440.0
        jd_whole = int(target_jd)
        jd_frac = target_jd - jd_whole

        e1, r1, _ = sat1.sgp4(jd_whole, jd_frac)
        e2, r2, _ = sat2.sgp4(jd_whole, jd_frac)

        if e1 != 0 or e2 != 0:
            continue
        if not all(math.isfinite(v) for v in r1 + r2):
            continue

        dx = r1[0] - r2[0]
        dy = r1[1] - r2[1]
        dz = r1[2] - r2[2]
        dist = math.sqrt(dx * dx + dy * dy + dz * dz)

        trail.append({
            "h": round(mins / 60.0, 3),
            "d": round(dist, 1),
            "s1": [round(r1[0], 1), round(r1[1], 1), round(r1[2], 1)],
            "s2": [round(r2[0], 1), round(r2[1], 1), round(r2[2], 1)],
        })

    return trail if trail else None


def compute_forward_tca(
    tle_1: dict,
    tle_2: dict,
    hours_forward: float = 120.0,
    step_minutes: float = 10.0,
) -> dict:
    """Compute forward Time of Closest Approach between two satellites.

    Propagates both satellites forward using SGP4 and finds the minimum
    separation distance and when it occurs.

    Args:
        tle_1: CelesTrak GP JSON for satellite 1.
        tle_2: CelesTrak GP JSON for satellite 2.
        hours_forward: How far to propagate (default 120h = 5 days).
        step_minutes: Time step for propagation (minutes).

    Returns:
        Dict with: tca_hours, tca_min_distance_km, or error.
    """
    if not SGP4_AVAILABLE:
        return {"tca_hours": None, "tca_min_distance_km": None}

    try:
        sat1 = celestrak_json_to_satrec(tle_1)
        sat2 = celestrak_json_to_satrec(tle_2)
    except (ValueError, Exception) as e:
        return {"tca_hours": None, "tca_min_distance_km": None}

    now = datetime.now(timezone.utc)
    start_jd = _datetime_to_jd(now)

    pos1 = _propagate_positions(sat1, start_jd, hours_forward, step_minutes)
    pos2 = _propagate_positions(sat2, start_jd, hours_forward, step_minutes)

    if len(pos1) == 0 or len(pos2) == 0:
        return {"tca_hours": None, "tca_min_distance_km": None}

    n_common = min(len(pos1), len(pos2))
    diffs = pos1[:n_common] - pos2[:n_common]
    distances = np.linalg.norm(diffs, axis=1)
    min_idx = int(np.argmin(distances))
    min_dist = float(distances[min_idx])
    tca_hours = min_idx * step_minutes / 60.0

    return {
        "tca_hours": round(tca_hours, 1),
        "tca_min_distance_km": round(min_dist, 1),
    }