"""
Route matching: identifies when multiple activities were on the same route.
Uses a bounding-box pre-filter + dynamic time warping (DTW) for GPS track similarity.
"""
import math
from typing import Optional
import polyline as polyline_lib
import numpy as np


def decode_polyline_to_coords(encoded: str) -> list[tuple[float, float]]:
    return polyline_lib.decode(encoded)


def bounding_boxes_overlap(bb1: dict, bb2: dict, tolerance_deg: float = 0.005) -> bool:
    """Quick check: do two bounding boxes overlap (with a tolerance margin)?"""
    return (
        bb1["min_lat"] - tolerance_deg <= bb2["max_lat"] + tolerance_deg and
        bb1["max_lat"] + tolerance_deg >= bb2["min_lat"] - tolerance_deg and
        bb1["min_lon"] - tolerance_deg <= bb2["max_lon"] + tolerance_deg and
        bb1["max_lon"] + tolerance_deg >= bb2["min_lon"] - tolerance_deg
    )


def sample_coords(coords: list[tuple], n: int = 100) -> list[tuple]:
    """Downsample a track to n evenly-spaced points for DTW efficiency."""
    if len(coords) <= n:
        return coords
    indices = [int(i * (len(coords) - 1) / (n - 1)) for i in range(n)]
    return [coords[i] for i in indices]


def dtw_distance(track1: list[tuple], track2: list[tuple]) -> float:
    """
    Compute DTW distance between two GPS tracks.
    Each point is (lat, lon). Returns average distance in metres per matched pair.
    """
    n, m = len(track1), len(track2)
    dtw = np.full((n + 1, m + 1), np.inf)
    dtw[0][0] = 0.0

    for i in range(1, n + 1):
        for j in range(1, m + 1):
            cost = haversine_m(track1[i-1], track2[j-1])
            dtw[i][j] = cost + min(dtw[i-1][j], dtw[i][j-1], dtw[i-1][j-1])

    return dtw[n][m] / max(n, m)


def haversine_m(p1: tuple, p2: tuple) -> float:
    R = 6371000
    lat1, lon1 = math.radians(p1[0]), math.radians(p1[1])
    lat2, lon2 = math.radians(p2[0]), math.radians(p2[1])
    dlat = lat2 - lat1
    dlon = lon2 - lon1
    a = math.sin(dlat/2)**2 + math.cos(lat1)*math.cos(lat2)*math.sin(dlon/2)**2
    return 2 * R * math.asin(math.sqrt(a))


def routes_are_similar(
    poly1: str,
    poly2: str,
    bb1: Optional[dict],
    bb2: Optional[dict],
    dtw_threshold_m: float = 80.0,
    dist1: Optional[float] = None,
    dist2: Optional[float] = None,
) -> bool:
    """
    Returns True if two activities are on sufficiently similar routes.
    First does a cheap bounding box check, then DTW on downsampled tracks.
    When dist1/dist2 are provided:
    - Rejects if distance differs by more than 2.5%
    - Uses 3% of route distance as the DTW threshold (capped at 300m)
    """
    if dist1 and dist2 and dist1 > 0 and dist2 > 0:
        if abs(dist1 - dist2) / max(dist1, dist2) > 0.025:
            return False
        dtw_threshold_m = min(max(dist1, dist2) * 0.03, 300.0)

    if bb1 and bb2:
        if not bounding_boxes_overlap(bb1, bb2):
            return False

    try:
        coords1 = sample_coords(decode_polyline_to_coords(poly1), 60)
        coords2 = sample_coords(decode_polyline_to_coords(poly2), 60)
    except Exception:
        return False

    if not coords1 or not coords2:
        return False

    dist = dtw_distance(coords1, coords2)
    return dist < dtw_threshold_m


def match_segment_in_activity(
    seg_coords: list[tuple],
    act_coords: list[tuple],
    act_times: list,
    tol_m: float = 30.0,
) -> Optional[float]:
    """
    Determine whether an activity track traverses a segment's GPS geometry in the
    segment's own direction, and if so how long the fastest such traversal took.
    Works even when the activity's overall route differs — only the overlapping
    stretch matters.

    seg_coords: [(lat, lon), ...] segment geometry (start → end).
    act_coords: [(lat, lon), ...] activity track, in time order.
    act_times:  parallel list of datetimes for act_coords.

    Strategy: for every pass of the activity near the segment START, walk forward
    accumulating path length; accept the traversal only if the activity reaches the
    segment END after covering roughly the segment's own length (so an out-and-back
    route can't match an early start to a late finish), and the intermediate segment
    points are passed in order. Returns the shortest valid traversal time, or None.
    """
    n = len(act_coords)
    m = len(seg_coords)
    if n < 2 or m < 2:
        return None

    start_pt, end_pt = seg_coords[0], seg_coords[-1]
    seg_len = sum(haversine_m(seg_coords[k], seg_coords[k + 1]) for k in range(m - 1))
    if seg_len <= 0:
        return None

    near_start = lambda i: haversine_m(act_coords[i], start_pt) <= tol_m

    # One candidate entry per pass through the start region (first point of each run).
    entries = [i for i in range(n) if near_start(i) and (i == 0 or not near_start(i - 1))]

    best = None
    for si in entries:
        path = 0.0
        ei = None
        for i in range(si + 1, n):
            path += haversine_m(act_coords[i - 1], act_coords[i])
            if path > seg_len * 1.5:          # wandered too far without finishing → wrong pass/direction
                break
            if path >= seg_len * 0.6 and haversine_m(act_coords[i], end_pt) <= tol_m:
                ei = i
                break
        if ei is None:
            continue

        # Confirm the activity follows the segment shape in order between the anchors.
        ok = True
        for frac in (0.25, 0.5, 0.75):
            sp = seg_coords[int(frac * (m - 1))]
            if not any(haversine_m(act_coords[k], sp) <= tol_m for k in range(si, ei + 1)):
                ok = False
                break
        if not ok:
            continue

        dur = (act_times[ei] - act_times[si]).total_seconds()
        if dur > 0 and (best is None or dur < best):
            best = dur

    return best


def find_best_split_time(
    data_points: list[dict],
    target_distance_m: float,
) -> Optional[float]:
    """
    Find the best (fastest) time over any target_distance_m window within an activity.
    E.g. fastest 1km split in a 10km run.
    Returns duration in seconds.
    """
    points_with_dist = [
        p for p in data_points
        if p.get("distance_m") is not None and p.get("timestamp") is not None
    ]

    if not points_with_dist:
        return None

    best = None
    j = 0

    for i, start_p in enumerate(points_with_dist):
        start_dist = start_p["distance_m"]
        start_ts = start_p["timestamp"]

        # Advance j until distance covered >= target
        while j < len(points_with_dist):
            end_p = points_with_dist[j]
            covered = end_p["distance_m"] - start_dist
            if covered >= target_distance_m:
                from datetime import datetime
                t1 = datetime.fromisoformat(start_ts) if isinstance(start_ts, str) else start_ts
                t2 = datetime.fromisoformat(end_p["timestamp"]) if isinstance(end_p["timestamp"], str) else end_p["timestamp"]
                duration = (t2 - t1).total_seconds()
                if best is None or duration < best:
                    best = duration
                break
            j += 1

        if j >= len(points_with_dist):
            break

    return best


STANDARD_DISTANCES = [
    (400, "400m"),
    (800, "800m"),
    (1000, "1k"),
    (1609.34, "1 mile"),
    (3000, "3k"),
    (5000, "5k"),
    (10000, "10k"),
    (21097.5, "Half marathon"),
    (42195, "Marathon"),
    (50000, "50k"),
    (100000, "100k"),
]


def compute_best_splits(data_points: list[dict], total_distance_m: float) -> dict[str, float]:
    """Compute best split times for all standard distances that fit within the activity."""
    results = {}
    for dist_m, label in STANDARD_DISTANCES:
        if total_distance_m >= dist_m * 0.95:  # allow 5% tolerance
            best = find_best_split_time(data_points, dist_m)
            if best:
                results[label] = best
    return results