import numpy as np
import h5py
import logging
from dataclasses import dataclass
from typing import List, Tuple, Optional, Literal, Union
import os
import glob
import tyro

def setup_logging(name):
    logging.basicConfig(level=logging.INFO, format='%(name)s - %(levelname)s - %(message)s')
    return logging.getLogger(name)

log = setup_logging("Sync")

def _load_h5_dataset_1d(h5_path: str, dataset: str) -> np.ndarray:
    """
    Load a 1D dataset from an HDF5 file.
    """
    with h5py.File(h5_path, 'r') as f:
        if dataset not in f:
            raise ValueError(f"Dataset not found in HDF5 file: {dataset!r} (file: {h5_path})")
        dset = f[dataset]
        # Many recorders preallocate more slots than actual frames; honor num_frames attr if present.
        num_frames_attr = f.attrs.get("num_frames")
        if isinstance(num_frames_attr, (int, np.integer)) and 0 < num_frames_attr <= dset.shape[0]:
            arr = dset[: int(num_frames_attr)]
        else:
            arr = dset[:]

    arr = np.asarray(arr, dtype=np.float64)
    if arr.ndim != 1:
        raise ValueError(f"Expected 1D dataset {dataset!r}, got shape {arr.shape} (file: {h5_path})")
    # Preallocated trailing zeros may be absent from num_frames metadata. Never
    # remove an interior slot: that would shift every later video frame index.
    valid = np.isfinite(arr) & (arr > 0)
    if not np.all(valid):
        first_invalid = int(np.flatnonzero(~valid)[0])
        if np.any(valid[first_invalid:]):
            raise ValueError(f"Invalid timestamp inside recorded frames: {h5_path}")
        arr = arr[:first_invalid]
    if arr.size and np.any(np.diff(arr) <= 0):
        raise ValueError(f"Timestamps are not strictly increasing: {h5_path}")
    return arr


def load_camera_timestamps_h5(camera_h5_path: str, frame_count: Optional[int] = None) -> np.ndarray:
    """Load H5 camera timestamps, restricted to exported video/depth frames."""
    timestamps = _load_h5_dataset_1d(camera_h5_path, "timestamps_ms")
    if frame_count is not None:
        if frame_count <= 0 or len(timestamps) < frame_count:
            raise ValueError(f"Expected {frame_count} ZED timestamps, found {len(timestamps)}")
        timestamps = timestamps[:frame_count]
    return timestamps


def load_radar_timestamps_h5(radar_h5_path: str) -> np.ndarray:
    """
    Load radar timestamps from `src/radarRecorder.py` output HDF5.

    Expected dataset: 'timestamps_ms' (milliseconds since Unix epoch).
    """
    return _load_h5_dataset_1d(radar_h5_path, "timestamps_ms")


def load_dji_timestamps_h5(dji_h5_path: str) -> np.ndarray:
    """
    Load DJI timestamps from `src/djiRecorder.py` output HDF5.

    Expected dataset: 'timestamps_ms' (milliseconds since Unix epoch).
    """
    return _load_h5_dataset_1d(dji_h5_path, "timestamps_ms")


@dataclass(frozen=True)
class SyncResult:
    """
    Output of 2-way timestamp synchronization (camera + radar).

    - `pairs`: list of (camera_frame_idx, radar_frame_idx)
    - `diffs_ms`: signed difference in ms for each pair: camera_ts - radar_ts
    """

    pairs: List[Tuple[int, int]]
    diffs_ms: np.ndarray
    camera_count: int
    radar_count: int
    tolerance_ms: float


@dataclass(frozen=True)
class SyncResult3Way:
    """
    Output of 3-way timestamp synchronization (radar + ZED depth + DJI RGB).

    - `triples`: list of (radar_frame_idx, zed_frame_idx, dji_frame_idx)
    - `diffs_radar_zed_ms`: signed difference radar_ts - zed_ts
    - `diffs_radar_dji_ms`: signed difference radar_ts - dji_ts
    """

    triples: List[Tuple[int, int, int]]
    diffs_radar_zed_ms: np.ndarray
    diffs_radar_dji_ms: np.ndarray
    radar_count: int
    zed_count: int
    dji_count: int
    tolerance_ms: float


def save_sync_csv(out_csv_path: str, result: Union[SyncResult, SyncResult3Way]) -> None:
    """
    Save sync pairs/triples to CSV.
    
    For SyncResult (2-way):
        camera_frame_idx,radar_frame_idx,diff_ms
    
    For SyncResult3Way (3-way):
        radar_frame_idx,zed_frame_idx,dji_frame_idx,diff_radar_zed_ms,diff_radar_dji_ms
    """
    os.makedirs(os.path.dirname(out_csv_path) or ".", exist_ok=True)
    
    if isinstance(result, SyncResult3Way):
        # 3-way sync
        radar_idx = np.array([t[0] for t in result.triples], dtype=np.int64)
        zed_idx = np.array([t[1] for t in result.triples], dtype=np.int64)
        dji_idx = np.array([t[2] for t in result.triples], dtype=np.int64)
        arr = np.column_stack([
            radar_idx, 
            zed_idx, 
            dji_idx,
            result.diffs_radar_zed_ms.astype(np.float64, copy=False),
            result.diffs_radar_dji_ms.astype(np.float64, copy=False)
        ])
        header = "radar_frame_idx,zed_frame_idx,dji_frame_idx,diff_radar_zed_ms,diff_radar_dji_ms"
        fmt = ["%d", "%d", "%d", "%.6f", "%.6f"]
    else:
        # 2-way sync (backward compatibility)
        cam_idx = np.array([p[0] for p in result.pairs], dtype=np.int64)
        rad_idx = np.array([p[1] for p in result.pairs], dtype=np.int64)
        arr = np.column_stack([cam_idx, rad_idx, result.diffs_ms.astype(np.float64, copy=False)])
        header = "camera_frame_idx,radar_frame_idx,diff_ms"
        fmt = ["%d", "%d", "%.6f"]
    
    np.savetxt(out_csv_path, arr, fmt=fmt, delimiter=",", header=header, comments="")


def _pick_latest_file(directory: str, pattern: str) -> str:
    """
    Pick the latest file (by lexicographic sort) matching a pattern in a directory.

    This works for our timestamped filenames like:
    - camera_timestamps_YYYYmmdd_HHMMSS.h5
    - radar_YYYYmmdd_HHMMSS.h5
    """
    matches = glob.glob(os.path.join(directory, pattern))
    if not matches:
        raise FileNotFoundError(f"No files matching {pattern!r} in directory: {directory}")
    matches.sort()
    return matches[-1]


def _pick_latest_file_multi_ext(directory: str, patterns: list[str]) -> str:
    """
    Pick the latest file matching any of multiple patterns.
    
    Useful for finding files with different extensions (e.g., .mkv or .mp4).
    
    Example:
        path = _pick_latest_file_multi_ext("data", ["dji_*.mkv", "dji_*.mp4"])
    """
    all_matches = []
    for pattern in patterns:
        matches = glob.glob(os.path.join(directory, pattern))
        all_matches.extend(matches)
    
    if not all_matches:
        patterns_str = " or ".join(patterns)
        raise FileNotFoundError(f"No files matching {patterns_str} in directory: {directory}")
    
    all_matches.sort()
    return all_matches[-1]


def cli(
    directory: Optional[str] = None,
    camera_timestamps_h5: Optional[str] = None,
    radar_h5: Optional[str] = None,
    zed_frame_count: Optional[int] = None,
    out_csv: str = os.path.join("data", "sync_pairs.csv"),
    tolerance_ms: float = 50.0,
    enforce_one_to_one: bool = True,
) -> None:
    """
    CLI wrapper for timestamp synchronization.

    Usage patterns:

    1) Directory mode (auto-pick latest files):
       - Provide `directory` to pick H5 files, including exported ZED depth.

    2) File mode (explicit paths):
       - Provide BOTH `camera_timestamps_h5` and `radar_h5`, optionally `zed_frame_count`.
    """
    if directory is not None:
        if camera_timestamps_h5 is not None or radar_h5 is not None or zed_frame_count is not None:
            raise ValueError("If 'directory' is provided, do not also pass explicit file paths.")
        camera_timestamps_h5 = _pick_latest_file(directory, "camera_timestamps_*.h5")
        radar_h5 = _pick_latest_file(directory, "radar_*.h5")
        zed_depth_h5 = _pick_latest_file(directory, "zed_depth*.h5")
        with h5py.File(zed_depth_h5) as depth_file:
            zed_frame_count = depth_file["depth_mm"].shape[0]
        log.info(f"Auto-picked camera timestamps: {camera_timestamps_h5}")
        log.info(f"Auto-picked radar file: {radar_h5}")
        log.info(f"Auto-picked ZED depth: {zed_depth_h5}")

    if camera_timestamps_h5 is None or radar_h5 is None:
        raise ValueError(
            "Provide either:\n"
            "- directory=<folder containing camera_timestamps_*.h5 and radar_*.h5>, OR\n"
            "- camera_timestamps_h5=<path> AND radar_h5=<path>."
        )

    result = synchronize_timestamps(
        camera_timestamps_h5=camera_timestamps_h5,
        radar_h5=radar_h5,
        tolerance_ms=tolerance_ms,
        enforce_one_to_one=enforce_one_to_one,
        zed_frame_count=zed_frame_count,
    )
    save_sync_csv(out_csv, result)
    log.info(f"Wrote sync CSV: {out_csv}")

def _nearest_unused_camera_index(
    cam_ts: np.ndarray,
    target_t: float,
    used_camera: set[int],
    start_idx: int,
) -> Optional[int]:
    """
    Find nearest unused camera index to `target_t`.

    `start_idx` is the insertion index from `np.searchsorted(cam_ts, target_t)`.
    We expand outward until we find an unused camera frame.
    """
    left = start_idx - 1
    right = start_idx
    while left >= 0 or right < len(cam_ts):
        cand_left = left if left >= 0 else None
        cand_right = right if right < len(cam_ts) else None

        if cand_left is None and cand_right is None:
            return None

        best: Optional[int] = None
        best_abs = float("inf")

        for cand in (cand_left, cand_right):
            if cand is None:
                continue
            if cand in used_camera:
                continue
            abs_diff = abs(float(cam_ts[cand] - target_t))
            if abs_diff < best_abs:
                best_abs = abs_diff
                best = int(cand)

        if best is not None:
            return best

        left -= 1
        right += 1

    return None


def synchronize_timestamps(
    camera_timestamps_h5: str,
    radar_h5: str,
    tolerance_ms: float = 50.0,
    enforce_one_to_one: bool = True,
    strategy: Literal["nearest"] = "nearest",
    zed_frame_count: Optional[int] = None,
) -> SyncResult:
    """
    Synchronize camera and radar timestamps.

    Assumptions (true for the new recorders):
    - Both timestamp streams are in **milliseconds since Unix epoch** (from `time.time()*1000`).
    - Each stream is **monotonically increasing**.

    Parameters:
    - zed_frame_count: Number of exported ZED RGB/depth frames.

    Returns pairs and per-pair signed diffs: Δt = t_cam - t_radar.
    """
    if strategy != "nearest":
        raise ValueError(f"Unsupported strategy: {strategy!r}")

    cam_ts = load_camera_timestamps_h5(camera_timestamps_h5, zed_frame_count)
    rad_ts = load_radar_timestamps_h5(radar_h5)
    
    log.info(f"Camera timestamps: {len(cam_ts)}")
    
    # Ensure float for arithmetic (HDF5 may store float64 already, but keep consistent)
    cam_ts = cam_ts.astype(np.float64, copy=False)
    rad_ts = rad_ts.astype(np.float64, copy=False)

    # Reverse logic: iterate radar frames and pick nearest camera frame.
    # Goal: keep (as many as possible) radar frames, since radar is the lower FPS stream.
    pairs: List[Tuple[int, int]] = []
    diffs: List[float] = []
    used_camera: set[int] = set()

    for rad_idx, r_t in enumerate(rad_ts):
        insert_idx = int(np.searchsorted(cam_ts, r_t))

        if enforce_one_to_one:
            cam_idx = _nearest_unused_camera_index(cam_ts, float(r_t), used_camera, insert_idx)
        else:
            # Nearest of the two immediate neighbors (can reuse camera frames).
            cand0 = insert_idx - 1 if insert_idx > 0 else None
            cand1 = insert_idx if insert_idx < len(cam_ts) else None
            best: Optional[int] = None
            best_abs = float("inf")
            for cand in (cand0, cand1):
                if cand is None:
                    continue
                abs_diff = abs(float(cam_ts[cand] - r_t))
                if abs_diff < best_abs:
                    best_abs = abs_diff
                    best = int(cand)
            cam_idx = best

        if cam_idx is None:
            # No camera frames at all (or no unused ones left).
            break

        signed = float(cam_ts[cam_idx] - r_t)
        abs_diff = abs(signed)

        if abs_diff > tolerance_ms:
            continue

        pairs.append((int(cam_idx), int(rad_idx)))
        diffs.append(signed)
        if enforce_one_to_one:
            used_camera.add(int(cam_idx))

    diffs_ms = np.array(diffs, dtype=np.float64)
    log.info(
        f"Synchronized {len(pairs)} pairs | "
        f"camera={len(cam_ts)} radar={len(rad_ts)} | tol={tolerance_ms}ms | "
        f"one_to_one={enforce_one_to_one}"
    )
    return SyncResult(
        pairs=pairs,
        diffs_ms=diffs_ms,
        camera_count=int(len(cam_ts)),
        radar_count=int(len(rad_ts)),
        tolerance_ms=float(tolerance_ms),
    )

if __name__ == "__main__":
    tyro.cli(cli)


def synchronize_timestamps_3way(
    radar_h5: str,
    zed_timestamps_h5: str,
    dji_timestamps_h5: str,
    tolerance_ms: float = 50.0,
    enforce_one_to_one: bool = True,
    zed_frame_count: Optional[int] = None,
    dji_frame_count: Optional[int] = None,
) -> SyncResult3Way:
    """
    Synchronize 3 timestamp streams: radar (reference), ZED depth, DJI RGB.
    
    Strategy:
    - Radar is the lowest FPS stream (typically ~10 Hz), so we use it as the reference.
    - For each radar frame, find the nearest ZED frame and nearest DJI frame.
    - Both must be within tolerance_ms of the radar timestamp.
    
    Parameters:
    - radar_h5: Path to radar HDF5 file with timestamps_ms
    - zed_timestamps_h5: Path to ZED camera timestamps HDF5
    - dji_timestamps_h5: Path to DJI timestamps HDF5
    - tolerance_ms: Maximum time difference in milliseconds
    - enforce_one_to_one: If True, each ZED/DJI frame can only be used once
    - zed_frame_count: Number of aligned ZED RGB/depth frames
    - dji_frame_count: Number of anonymized DJI video frames
    
    Returns:
    - SyncResult3Way with triples (radar_idx, zed_idx, dji_idx)
    """
    # Load all timestamps
    radar_ts = load_radar_timestamps_h5(radar_h5)
    zed_ts = load_camera_timestamps_h5(zed_timestamps_h5, zed_frame_count)
    dji_ts = load_dji_timestamps_h5(dji_timestamps_h5)
    if dji_frame_count is not None:
        if dji_frame_count <= 0:
            raise ValueError(f"Invalid DJI frame count: {dji_frame_count}")
        if len(dji_ts) == 0:
            raise ValueError(f"No DJI timestamps in {dji_timestamps_h5}")
        if len(dji_ts) < dji_frame_count:
            log.warning(
                "DJI video has %d frames but only %d timestamps; "
                "untimed frames cannot be synchronized",
                dji_frame_count, len(dji_ts),
            )
        dji_ts = dji_ts[:dji_frame_count]
    
    log.info(f"ZED timestamps: {len(zed_ts)}")
    
    # Ensure float64 for arithmetic
    radar_ts = radar_ts.astype(np.float64, copy=False)
    zed_ts = zed_ts.astype(np.float64, copy=False)
    dji_ts = dji_ts.astype(np.float64, copy=False)
    
    log.info(f"Timestamp ranges:")
    log.info(f"  Radar: {len(radar_ts)} frames, {radar_ts[0]:.2f} - {radar_ts[-1]:.2f} ms")
    log.info(f"  ZED:   {len(zed_ts)} frames, {zed_ts[0]:.2f} - {zed_ts[-1]:.2f} ms")
    log.info(f"  DJI:   {len(dji_ts)} frames, {dji_ts[0]:.2f} - {dji_ts[-1]:.2f} ms")
    
    # Iterate through radar frames (lowest FPS, reference stream)
    triples = []
    diffs_radar_zed = []
    diffs_radar_dji = []
    used_zed = set()
    used_dji = set()
    
    for radar_idx, r_t in enumerate(radar_ts):
        # Find nearest ZED frame
        zed_insert_idx = int(np.searchsorted(zed_ts, r_t))
        if enforce_one_to_one:
            zed_idx = _nearest_unused_camera_index(zed_ts, float(r_t), used_zed, zed_insert_idx)
        else:
            cand0 = zed_insert_idx - 1 if zed_insert_idx > 0 else None
            cand1 = zed_insert_idx if zed_insert_idx < len(zed_ts) else None
            zed_idx = None
            best_abs = float("inf")
            for cand in (cand0, cand1):
                if cand is None:
                    continue
                abs_diff = abs(float(zed_ts[cand] - r_t))
                if abs_diff < best_abs:
                    best_abs = abs_diff
                    zed_idx = int(cand)
        
        if zed_idx is None:
            continue
        
        zed_diff = float(r_t - zed_ts[zed_idx])
        if abs(zed_diff) > tolerance_ms:
            continue
        
        # Find nearest DJI frame
        dji_insert_idx = int(np.searchsorted(dji_ts, r_t))
        if enforce_one_to_one:
            dji_idx = _nearest_unused_camera_index(dji_ts, float(r_t), used_dji, dji_insert_idx)
        else:
            cand0 = dji_insert_idx - 1 if dji_insert_idx > 0 else None
            cand1 = dji_insert_idx if dji_insert_idx < len(dji_ts) else None
            dji_idx = None
            best_abs = float("inf")
            for cand in (cand0, cand1):
                if cand is None:
                    continue
                abs_diff = abs(float(dji_ts[cand] - r_t))
                if abs_diff < best_abs:
                    best_abs = abs_diff
                    dji_idx = int(cand)
        
        if dji_idx is None:
            continue
        
        dji_diff = float(r_t - dji_ts[dji_idx])
        if abs(dji_diff) > tolerance_ms:
            continue
        
        # Both ZED and DJI within tolerance - add triple
        triples.append((int(radar_idx), int(zed_idx), int(dji_idx)))
        diffs_radar_zed.append(zed_diff)
        diffs_radar_dji.append(dji_diff)
        
        if enforce_one_to_one:
            used_zed.add(int(zed_idx))
            used_dji.add(int(dji_idx))
    
    diffs_radar_zed_ms = np.array(diffs_radar_zed, dtype=np.float64)
    diffs_radar_dji_ms = np.array(diffs_radar_dji, dtype=np.float64)
    
    log.info(
        f"Synchronized {len(triples)} triples | "
        f"radar={len(radar_ts)} zed={len(zed_ts)} dji={len(dji_ts)} | "
        f"tol={tolerance_ms}ms | one_to_one={enforce_one_to_one}"
    )
    
    return SyncResult3Way(
        triples=triples,
        diffs_radar_zed_ms=diffs_radar_zed_ms,
        diffs_radar_dji_ms=diffs_radar_dji_ms,
        radar_count=int(len(radar_ts)),
        zed_count=int(len(zed_ts)),
        dji_count=int(len(dji_ts)),
        tolerance_ms=float(tolerance_ms),
    )