import os
import gc
from pathlib import Path
from typing import Tuple, List, Optional, Union, Dict, Any

import h5py
import numpy as np
import pandas as pd
import scipy.signal as signal
from joblib import Parallel, delayed
from scipy.signal import iirnotch
from tqdm import tqdm

def sequence_to_seconds(seq_len: int, fs: float) -> float:
    """Converts a sequence length in samples to time in seconds.

    Args:
        seq_len (int): The number of samples in the sequence.
        fs (float): The sampling frequency in Hz.

    Returns:
        float: The duration of the sequence in seconds.
    """
    return seq_len / fs


def notch_filter(data: np.ndarray, notch_freq: float = 50.0, Q: float = 30.0, fs: float = 2000.0) -> np.ndarray:
    """Applies a notch filter to every channel of the input data independently.

    Args:
        data (np.ndarray): The input signal array of shape (T, D).
        notch_freq (float, optional): The frequency to be removed in Hz. Defaults to 50.0.
        Q (float, optional): The quality factor. Defaults to 30.0.
        fs (float, optional): The sampling frequency in Hz. Defaults to 2000.0.

    Returns:
        np.ndarray: The filtered signal array.
    """
    b, a = iirnotch(notch_freq, Q, fs)
    out = np.zeros_like(data)
    for ch in range(data.shape[1]):
        out[:, ch] = signal.filtfilt(b, a, data[:, ch])
    return out


def bandpass_filter_emg(
    emg: np.ndarray,
    lowcut: float = 20.0,
    highcut: float = 90.0,
    fs: float = 2000.0,
    order: int = 4
) -> np.ndarray:
    """Applies a Butterworth bandpass filter to the EMG signal.

    Args:
        emg (np.ndarray): The input signal array of shape (T, D).
        lowcut (float, optional): Lower bound of the passband in Hz. Defaults to 20.0.
        highcut (float, optional): Upper bound of the passband in Hz. Defaults to 90.0.
        fs (float, optional): The sampling frequency in Hz. Defaults to 2000.0.
        order (int, optional): The order of the filter. Defaults to 4.

    Returns:
        np.ndarray: The filtered signal array.
    """
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = signal.butter(order, [low, high], btype="bandpass")
    out = np.zeros_like(emg)
    for c in range(emg.shape[1]):
        out[:, c] = signal.filtfilt(b, a, emg[:, c])
    return out


def process_emg_features(emg: np.ndarray, window_size: int = 1000, stride: int = 500) -> np.ndarray:
    """Segments raw EMG signals into overlapping windows.

    Args:
        emg (np.ndarray): Raw EMG data of shape (T, n_ch).
        window_size (int, optional): Number of samples per window. Defaults to 1000.
        stride (int, optional): Number of samples to shift between windows. Defaults to 500.

    Returns:
        np.ndarray: Segmented data of shape (N, window_size, n_ch).
    """
    segs = []
    N = len(emg)
    for start in range(0, N, stride):
        end = start + window_size
        if end > N:  # skip the last segment if it is not complete
            continue
        win = emg[start:end]
        segs.append(win)
    return np.array(segs)


def process_one_recording(file_path: str, fs: float = 2000.0, window_size: int = 1000, stride: int = 500) -> np.ndarray:
    """Processes a single EMG2Pose recording file.

    Loads HDF5 timeseries, filters EMG, normalizes (Z-score), and segments.

    Args:
        file_path (str): Absolute path to the .h5 recording file.
        fs (float, optional): Sampling frequency in Hz. Defaults to 2000.0.
        window_size (int, optional): Temporal window size in samples. Defaults to 1000.
        stride (int, optional): Stride between windows in samples. Defaults to 500.

    Returns:
        np.ndarray: Array of processed segments (N, window_size, n_ch).
    """
    with h5py.File(file_path, "r") as f:
        grp = f["emg2pose"]
        data = grp["timeseries"]
        emg = data["emg"][:].astype(np.float32)

    # ==== Preprocessing EMG data ====
    emg_filt = bandpass_filter_emg(emg, 20, 450, fs=fs)
    emg_filt = notch_filter(emg_filt, 50, 30, fs=fs)

    # z-score
    mu = emg_filt.mean(axis=0)
    sd = emg_filt.std(axis=0, ddof=1)
    sd[sd == 0] = 1.0
    emg_z = (emg_filt - mu) / sd

    # segment
    segs = process_emg_features(emg_z, window_size, stride)

    return segs


def main():
    import argparse

    args = argparse.ArgumentParser(description="Process EMG data from DB5.")
    args.add_argument("--data_dir", type=str)
    args.add_argument("--save_dir", type=str)
    args.add_argument(
        "--seq_len", type=int, help="Size of the window in samples for segmentation."
    )
    args.add_argument(
        "--stride", type=int, help="Step size between windows in samples for segmentation."
    )
    args.add_argument(
        "--subsample", type=float, default=1.0, help="Whether to subsample the data"
    )
    args.add_argument(
        "--n_jobs",
        type=int,
        default=-1,
        help="Number of parallel jobs to run. -1 means using all available cores.",
    )
    args.add_argument(
        "--group_size",
        type=int,
        default=1000,
        help="Number of samples per group in the output HDF5 file.",
    )
    args.add_argument(
        "--seed", type=int, default=42, help="Random seed for reproducibility."
    )
    args = args.parse_args()

    data_dir = args.data_dir
    save_dir = args.save_dir
    os.makedirs(save_dir, exist_ok=True)

    fs = 2000.0  # original sampling rate
    window_size, stride = args.seq_len, args.stride

    window_seconds = sequence_to_seconds(window_size, fs)
    print(f"Window size: {window_size} samples ({window_seconds:.2f} seconds)")

    df = pd.read_csv(os.path.join(data_dir, "metadata.csv"))
    if args.subsample < 1.0:
        df = df.groupby("split", group_keys=False).sample(
            frac=args.subsample, random_state=args.seed
        )
    df = df.reset_index(drop=True)

    splits = {}
    for split, df_ in df.groupby("split"):
        sessions = list(df_.filename)
        splits[split] =[
            Path(data_dir).expanduser().joinpath(f"{session}.hdf5")
            for session in sessions
        ]

    for split, files in splits.items():
        out_file = os.path.join(save_dir, f"{split}.h5")

        # Remove existing file if it exists so we don't accidentally append to old runs
        if os.path.exists(out_file):
            os.remove(out_file)

        print(f"Processing {split} split ({len(files)} files)...")

        with h5py.File(out_file, "w") as h5f:
            group_idx = 0
            with Parallel(n_jobs=args.n_jobs) as parallel:
                with tqdm(total=len(files), desc=f"Processing & Saving {split}") as pbar:

                    # Iterate files in batches
                    for i in range(0, len(files), args.group_size):
                        batch_files = files[i : i + args.group_size]

                        # Process current batch
                        results = parallel(
                            delayed(process_one_recording)(file_path, fs, window_size, stride)
                            for file_path in batch_files
                        )

                        if results:
                            X_chunk = np.concatenate(results, axis=0)  # [N, window_size, ch]
                            X_chunk = X_chunk.transpose(0, 2, 1)          # [N, ch, window_size]
                            X_chunk = X_chunk.astype(np.float32)

                            # Write each processed batch as a group compatible with HDF5Loader
                            grp = h5f.create_group(f"data_group_{group_idx}")
                            grp.create_dataset("X", data=X_chunk)
                            group_idx += 1

                        # Explicitly clear memory of large numpy arrays
                        del results
                        if 'X_chunk' in locals():
                            del X_chunk
                        gc.collect()

                        pbar.update(len(batch_files))


if __name__ == "__main__":
    main()