"""Downsample a list of DataPoints to at most 1 sample/second and build
the BAS timeseries object (parallel arrays)."""

from datetime import datetime
from math import atan2, cos, radians, sin, sqrt
from typing import Optional

from bincio.extract.models import DataPoint


def _haversine_m(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
    """Great-circle distance in metres between two GPS points."""
    dlat = radians(lat2 - lat1)
    dlon = radians(lon2 - lon1)
    a = sin(dlat / 2) ** 2 + cos(radians(lat1)) * cos(radians(lat2)) * sin(dlon / 2) ** 2
    return 2 * 6_371_000.0 * atan2(sqrt(a), sqrt(1 - a))


_SPATIAL_RESOLUTION_M = 10.0


def _spatial_downsample(
    sampled: list[DataPoint],
    resolution_m: float = _SPATIAL_RESOLUTION_M,
) -> list[DataPoint]:
    """Keep one sample per `resolution_m` of cumulative distance traveled.

    Distance source priority:
      1. GPS haversine (lat/lon present on both consecutive points)
      2. speed_kmh × Δt (fallback when GPS absent or gapped)
    If neither source is available (indoor, no speed data), returns `sampled`
    unchanged. Always retains the first and last points.
    """
    if len(sampled) < 2:
        return sampled

    has_gps   = any(p.lat is not None and p.lon is not None for p in sampled)
    has_speed = any(p.speed_kmh is not None for p in sampled)
    if not has_gps and not has_speed:
        return sampled

    result: list[DataPoint] = [sampled[0]]
    cum_dist   = 0.0
    last_kept  = 0.0
    prev_speed = 0.0

    for i in range(1, len(sampled)):
        prev, cur = sampled[i - 1], sampled[i]
        dt = (cur.timestamp - prev.timestamp).total_seconds()

        if (has_gps
                and prev.lat is not None and prev.lon is not None
                and cur.lat  is not None and cur.lon  is not None):
            dist_m = _haversine_m(prev.lat, prev.lon, cur.lat, cur.lon)
        else:
            spd = cur.speed_kmh if cur.speed_kmh is not None else prev_speed
            dist_m = (spd / 3.6) * max(dt, 0)

        if cur.speed_kmh is not None:
            prev_speed = cur.speed_kmh

        cum_dist += dist_m
        if cum_dist - last_kept >= resolution_m:
            result.append(cur)
            last_kept = cum_dist

    if result[-1] is not sampled[-1]:
        result.append(sampled[-1])

    return result


def _gps_speed_kmh(
    lat_vals: list[Optional[float]],
    lon_vals: list[Optional[float]],
    ts_vals: list[int],
) -> list[Optional[float]]:
    """Compute speed (km/h) from consecutive GPS coordinates via haversine.
    Applies a 5-point centred moving-average to reduce GPS noise.
    """
    n = len(ts_vals)
    raw: list[Optional[float]] = [None] * n
    for i in range(1, n):
        la0, lo0 = lat_vals[i - 1], lon_vals[i - 1]
        la1, lo1 = lat_vals[i], lon_vals[i]
        dt = ts_vals[i] - ts_vals[i - 1]
        if la0 is None or lo0 is None or la1 is None or lo1 is None or dt <= 0:
            continue
        d_km = _haversine_m(la0, lo0, la1, lo1) / 1000.0
        raw[i] = d_km / dt * 3600.0

    # 5-point centred moving average (skip None anchors)
    half = 2
    smoothed: list[Optional[float]] = [None] * n
    for i in range(n):
        vals = [raw[j] for j in range(max(0, i - half), min(n, i + half + 1)) if raw[j] is not None]
        if vals:
            smoothed[i] = round(sum(vals) / len(vals), 2)

    return smoothed


def build_timeseries(
    points: list[DataPoint],
    started_at: datetime,
    privacy: str = "public",
) -> dict:
    """Return the BAS `timeseries` object.

    privacy='no_gps' → lat/lon set to null. All other privacy levels
    (including 'unlisted') retain GPS in the timeseries.
    Downsamples so at most one point per second is emitted.
    """
    if not points:
        return {"t": []}

    include_gps = privacy not in ("no_gps", "private")  # "private" = legacy alias for "unlisted"

    # Downsample: keep at most one point per second
    sampled: list[DataPoint] = []
    last_t: Optional[int] = None
    for p in points:
        t = int((p.timestamp - started_at).total_seconds())
        if t < 0:
            continue
        if last_t is not None and t <= last_t:
            continue  # skip sub-second duplicates and non-monotonic points
        sampled.append(p)
        last_t = t

    sampled = _spatial_downsample(sampled)

    ts_vals   = [int((p.timestamp - started_at).total_seconds()) for p in sampled]
    lat_vals  = [round(p.lat, 7) if p.lat is not None else None for p in sampled] if include_gps else None
    lon_vals  = [round(p.lon, 7) if p.lon is not None else None for p in sampled] if include_gps else None
    ele_vals  = [round(p.elevation_m, 1) if p.elevation_m is not None else None for p in sampled]
    spd_vals  = [round(p.speed_kmh, 2) if p.speed_kmh is not None else None for p in sampled]

    # Derive speed from GPS when the device didn't record per-second speed.
    if include_gps and lat_vals and lon_vals and all(v is None for v in spd_vals):
        spd_vals = _gps_speed_kmh(lat_vals, lon_vals, ts_vals)

    hr_vals   = [p.hr_bpm for p in sampled]
    cad_vals  = [p.cadence_rpm for p in sampled]
    pwr_vals  = [p.power_w for p in sampled]
    tmp_vals  = [round(p.temperature_c, 1) if p.temperature_c is not None else None for p in sampled]

    result: dict = {
        "t":             ts_vals,
        "lat":           lat_vals,
        "lon":           lon_vals,
        "elevation_m":   ele_vals,
        "speed_kmh":     spd_vals,
        "hr_bpm":        hr_vals,
        "cadence_rpm":   cad_vals,
        "power_w":       pwr_vals,
        "temperature_c": tmp_vals,
    }
    return result