"""
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, and if so
    how long it 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: anchor on the activity point nearest the segment start, then the nearest
    point (at/after it) to the segment end, then verify a few intermediate segment
    points are each passed within tolerance between those anchors. Returns the time
    between the start and end anchors, or None if the activity doesn't follow the segment.
    """
    n = len(act_coords)
    if n < 2 or len(seg_coords) < 2:
        return None

    start_pt, end_pt = seg_coords[0], seg_coords[-1]

    si, sd = None, tol_m
    for i in range(n):
        d = haversine_m(act_coords[i], start_pt)
        if d < sd:
            sd, si = d, i
    if si is None:
        return None

    ei, ed = None, tol_m
    for i in range(si + 1, n):
        d = haversine_m(act_coords[i], end_pt)
        if d < ed:
            ed, ei = d, i
    if ei is None or ei <= si:
        return None

    # Verify the activity actually follows the segment shape between the anchors.
    for frac in (0.25, 0.5, 0.75):
        sp = seg_coords[int(frac * (len(seg_coords) - 1))]
        if not any(haversine_m(act_coords[i], sp) <= tol_m for i in range(si, ei + 1)):
            return None

    dur = (act_times[ei] - act_times[si]).total_seconds()
    return dur if dur > 0 else None


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