"""Compute aggregated metrics from a ParsedActivity.

All calculations are self-contained — no external state needed.
Uses inline haversine rather than geopy.geodesic to keep the hot path fast.
"""

import math
from dataclasses import dataclass
from datetime import datetime
from typing import Optional

from bincio.extract.models import DataPoint, ParsedActivity

# Standard MMP durations (seconds). Log-spaced so the curve looks good on a log-x axis.
MMP_DURATIONS_S = [1, 2, 5, 10, 15, 20, 30, 60, 120, 180, 300, 600, 1200, 1800, 3600]

# Speed below which we consider the athlete stopped (km/h)
_STOPPED_THRESHOLD_KMH = 1.0
_EARTH_R = 6_371_000.0  # metres


def _haversine_m(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
    """Great-circle distance in metres. ~10x faster than geopy.geodesic."""
    phi1 = math.radians(lat1)
    phi2 = math.radians(lat2)
    dphi = phi2 - phi1
    dlam = math.radians(lon2 - lon1)
    a = math.sin(dphi * 0.5) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlam * 0.5) ** 2
    return 2.0 * _EARTH_R * math.asin(math.sqrt(min(a, 1.0)))


@dataclass
class ComputedMetrics:
    distance_m: Optional[float]
    duration_s: Optional[int]
    moving_time_s: Optional[int]
    elevation_gain_m: Optional[float]
    elevation_loss_m: Optional[float]
    avg_speed_kmh: Optional[float]
    max_speed_kmh: Optional[float]
    avg_hr_bpm: Optional[int]
    max_hr_bpm: Optional[int]
    avg_cadence_rpm: Optional[int]
    avg_power_w: Optional[int]
    max_power_w: Optional[int]
    bbox: Optional[tuple[float, float, float, float]]   # min_lon, min_lat, max_lon, max_lat
    start_latlng: Optional[tuple[float, float]]
    end_latlng: Optional[tuple[float, float]]
    mmp: Optional[list[list[int]]]  # [[duration_s, avg_watts], ...] — None if no power data


def compute(activity: ParsedActivity) -> ComputedMetrics:
    pts = activity.points
    if not pts:
        return _empty()

    duration_s = _duration(pts)
    distance_m, moving_time_s, avg_speed_kmh, max_speed_kmh = _gps_stats(pts)
    gain, loss = _elevation(pts)
    avg_hr, max_hr = _hr_stats(pts)
    avg_cad = _avg_nonnull([p.cadence_rpm for p in pts])
    avg_pow = _avg_nonnull([p.power_w for p in pts])
    max_pow = _max_nonnull([p.power_w for p in pts])
    bbox = _bbox(pts)
    start_ll, end_ll = _endpoints(pts)
    mmp = compute_mmp(pts, activity.started_at)

    return ComputedMetrics(
        distance_m=distance_m,
        duration_s=duration_s,
        moving_time_s=moving_time_s,
        elevation_gain_m=round(gain, 1) if gain is not None else None,
        elevation_loss_m=round(abs(loss), 1) if loss is not None else None,
        avg_speed_kmh=round(avg_speed_kmh, 2) if avg_speed_kmh else None,
        max_speed_kmh=round(max_speed_kmh, 2) if max_speed_kmh else None,
        avg_hr_bpm=avg_hr,
        max_hr_bpm=max_hr,
        avg_cadence_rpm=avg_cad,
        avg_power_w=avg_pow,
        max_power_w=max_pow,
        bbox=bbox,
        start_latlng=start_ll,
        end_latlng=end_ll,
        mmp=mmp,
    )


# ── mean maximal power ────────────────────────────────────────────────────────

def compute_mmp(pts: list[DataPoint], started_at: datetime) -> Optional[list[list[int]]]:
    """Compute Mean Maximal Power curve at the standard MMP_DURATIONS_S.

    Builds a 1 Hz power series (same downsampling as timeseries.py), then uses
    a O(n) sliding-window sum for each duration.  Returns a list of
    [duration_s, avg_watts] pairs (integers), or None when the activity has no
    power data.  Only durations shorter than the total activity are included.
    """
    # 1 Hz downsample: at most one sample per second, skip sub-second duplicates.
    # Seconds without a recorded sample are omitted (not zero-filled) so that
    # paused-recording gaps don't silently lower power averages.
    power_1hz: list[int] = []
    last_t = -1
    for p in pts:
        t = int((p.timestamp - started_at).total_seconds())
        if t < 0 or t == last_t:
            continue
        last_t = t
        if p.power_w is not None:
            power_1hz.append(p.power_w)

    if len(power_1hz) < 2:
        return None

    n = len(power_1hz)
    results: list[list[int]] = []

    for d in MMP_DURATIONS_S:
        if d > n:
            break  # activity shorter than this duration — stop (durations are sorted)

        # Sliding window of exactly d samples = d seconds at 1 Hz.
        window_sum = sum(power_1hz[:d])
        best = window_sum

        for i in range(1, n - d + 1):
            window_sum += power_1hz[i + d - 1] - power_1hz[i - 1]
            if window_sum > best:
                best = window_sum

        results.append([d, round(best / d)])

    return results if results else None


# ── single-pass GPS stats ──────────────────────────────────────────────────────
# distance, moving time, avg speed, and max speed are all derived from the same
# per-segment loop, so we compute them in one pass instead of four.

def _gps_stats(
    pts: list[DataPoint],
) -> tuple[Optional[float], Optional[int], Optional[float], Optional[float]]:
    """Return (distance_m, moving_time_s, avg_speed_kmh, max_speed_kmh)."""

    # Prefer device-recorded cumulative distance (FIT files always have this)
    device_dist = next(
        (p.distance_m for p in reversed(pts) if p.distance_m is not None), None
    )

    moving_s = 0
    moving_dist_m = 0.0
    total_dist_m = 0.0
    max_seg_kmh = 0.0
    has_data = False

    # Device speed values (used for max if present)
    device_max_kmh: Optional[float] = None
    if any(p.speed_kmh is not None for p in pts):
        device_max_kmh = max(p.speed_kmh for p in pts if p.speed_kmh is not None)

    for a, b in zip(pts, pts[1:]):
        dt = (b.timestamp - a.timestamp).total_seconds()
        if dt <= 0:
            continue

        if a.lat is not None and a.lon is not None and b.lat is not None and b.lon is not None:
            seg_m = _haversine_m(a.lat, a.lon, b.lat, b.lon)
            seg_kmh = (seg_m / dt) * 3.6
            has_data = True
        elif a.speed_kmh is not None:
            seg_kmh = a.speed_kmh
            seg_m = (seg_kmh / 3.6) * dt
            has_data = True
        else:
            continue

        total_dist_m += seg_m
        if seg_kmh > max_seg_kmh:
            max_seg_kmh = seg_kmh

        if seg_kmh >= _STOPPED_THRESHOLD_KMH:
            moving_s += int(dt)
            moving_dist_m += seg_m

    if not has_data:
        return device_dist, None, None, None

    # Fall back to haversine distance if device recorded 0 but we computed real GPS distance
    if device_dist is not None and device_dist > 0:
        distance_m = device_dist
    else:
        distance_m = round(total_dist_m, 1) if total_dist_m > 0 else device_dist
    moving_time_s = moving_s if moving_s > 0 else None
    avg_speed_kmh = (moving_dist_m / moving_s) * 3.6 if moving_s > 0 else None
    # Prefer device speed for max (more stable than GPS-derived per-second spikes)
    max_speed_kmh = device_max_kmh if device_max_kmh is not None else (
        max_seg_kmh if max_seg_kmh > 0 else None
    )

    return distance_m, moving_time_s, avg_speed_kmh, max_speed_kmh


# ── remaining helpers ──────────────────────────────────────────────────────────

def _duration(pts: list[DataPoint]) -> Optional[int]:
    if len(pts) < 2:
        return None
    return int((pts[-1].timestamp - pts[0].timestamp).total_seconds())


def _elevation(pts: list[DataPoint]) -> tuple[Optional[float], Optional[float]]:
    elevations = [p.elevation_m for p in pts if p.elevation_m is not None]
    if len(elevations) < 2:
        return None, None
    gain = loss = 0.0
    for a, b in zip(elevations, elevations[1:]):
        diff = b - a
        if diff > 0:
            gain += diff
        else:
            loss += diff
    return gain, loss


def _hr_stats(pts: list[DataPoint]) -> tuple[Optional[int], Optional[int]]:
    hrs = [p.hr_bpm for p in pts if p.hr_bpm is not None]
    if not hrs:
        return None, None
    return int(sum(hrs) / len(hrs)), max(hrs)


def _avg_nonnull(values: list) -> Optional[int]:
    v = [x for x in values if x is not None]
    return int(sum(v) / len(v)) if v else None


def _max_nonnull(values: list) -> Optional[int]:
    v = [x for x in values if x is not None]
    return max(v) if v else None


def _bbox(pts: list[DataPoint]) -> Optional[tuple[float, float, float, float]]:
    lats = [p.lat for p in pts if p.lat is not None]
    lons = [p.lon for p in pts if p.lon is not None]
    if not lats:
        return None
    return (min(lons), min(lats), max(lons), max(lats))


def _endpoints(
    pts: list[DataPoint],
) -> tuple[Optional[tuple[float, float]], Optional[tuple[float, float]]]:
    gps = [(p.lat, p.lon) for p in pts if p.lat is not None and p.lon is not None]
    if not gps:
        return None, None
    return gps[0], gps[-1]


def _empty() -> ComputedMetrics:
    return ComputedMetrics(
        distance_m=None, duration_s=None, moving_time_s=None,
        elevation_gain_m=None, elevation_loss_m=None,
        avg_speed_kmh=None, max_speed_kmh=None,
        avg_hr_bpm=None, max_hr_bpm=None,
        avg_cadence_rpm=None, avg_power_w=None, max_power_w=None,
        bbox=None, start_latlng=None, end_latlng=None,
        mmp=None,
    )