"""
Real EEG signal processing pipeline.
Parses EEG files and extracts features for fMRI translation.
"""

import numpy as np
from scipy import signal
from scipy.stats import kurtosis, skew
import io
import os
from typing import Optional


# Frequency bands (Hz)
BANDS = {
    "delta": (1, 4),
    "theta": (4, 8),
    "alpha": (8, 13),
    "beta": (13, 30),
    "gamma": (30, 45),
}

BAND_NAMES = list(BANDS.keys())


def parse_eeg_file(file_bytes: bytes, filename: str) -> tuple[np.ndarray, float, list[str]]:
    """
    Parse an EEG file and return (data, sfreq, channel_names).
    data shape: (n_channels, n_samples)

    Supports: .edf, .bdf, .csv, .txt
    """
    ext = os.path.splitext(filename)[1].lower()

    if ext in (".edf", ".bdf"):
        return _parse_edf(file_bytes, ext)
    elif ext in (".csv", ".txt"):
        return _parse_csv(file_bytes)
    else:
        raise ValueError(f"Unsupported file format: {ext}")


def _parse_edf(file_bytes: bytes, ext: str) -> tuple[np.ndarray, float, list[str]]:
    """Parse EDF/BDF files using MNE."""
    import mne
    import tempfile

    suffix = ext
    with tempfile.NamedTemporaryFile(suffix=suffix, delete=False) as tmp:
        tmp.write(file_bytes)
        tmp_path = tmp.name

    try:
        if ext == ".edf":
            raw = mne.io.read_raw_edf(tmp_path, preload=True, verbose=False)
        else:
            raw = mne.io.read_raw_bdf(tmp_path, preload=True, verbose=False)

        # Pick only EEG channels
        raw.pick(picks="eeg", exclude="bads")

        data = raw.get_data()  # (n_channels, n_samples)
        sfreq = raw.info["sfreq"]
        ch_names = raw.ch_names

        return data, sfreq, ch_names
    finally:
        os.unlink(tmp_path)


def _parse_csv(file_bytes: bytes) -> tuple[np.ndarray, float, list[str]]:
    """
    Parse CSV/TXT EEG files.
    Expects: columns = channels, rows = time samples.
    First row can be header (channel names).
    Assumes 256 Hz sampling rate if not specified.
    """
    import pandas as pd

    text = file_bytes.decode("utf-8", errors="ignore")
    df = pd.read_csv(io.StringIO(text))

    # Try to detect if first column is timestamp
    first_col = df.columns[0].lower()
    if "time" in first_col or "stamp" in first_col or "sample" in first_col:
        # Try to infer sample rate from timestamps
        if df[df.columns[0]].dtype in [np.float64, np.float32, np.int64]:
            timestamps = df[df.columns[0]].values
            if len(timestamps) > 1:
                dt = np.median(np.diff(timestamps))
                if dt > 0:
                    sfreq = 1.0 / dt if dt < 1 else 1.0 / (dt / 1000.0)
                else:
                    sfreq = 256.0
            else:
                sfreq = 256.0
        else:
            sfreq = 256.0
        df = df.iloc[:, 1:]  # Remove timestamp column
    else:
        sfreq = 256.0

    ch_names = [str(c) for c in df.columns]
    data = df.values.T.astype(np.float64)  # (n_channels, n_samples)

    return data, sfreq, ch_names


def preprocess(data: np.ndarray, sfreq: float) -> np.ndarray:
    """
    Preprocess raw EEG data:
    1. Bandpass filter 1-45 Hz
    2. Notch filter at 50 Hz and 60 Hz (line noise)
    3. Z-score normalization per channel
    """
    n_channels, n_samples = data.shape

    # Minimum samples check
    min_samples = int(sfreq * 2)  # Need at least 2 seconds
    if n_samples < min_samples:
        raise ValueError(
            f"EEG recording too short: {n_samples/sfreq:.1f}s. Need at least 2 seconds."
        )

    # Bandpass filter 1-45 Hz
    nyq = sfreq / 2.0
    low = 1.0 / nyq
    high = min(45.0 / nyq, 0.99)  # Ensure below Nyquist

    if high <= low:
        raise ValueError(f"Sample rate too low ({sfreq} Hz) for 1-45 Hz bandpass filter.")

    sos_bp = signal.butter(4, [low, high], btype="band", output="sos")
    filtered = signal.sosfiltfilt(sos_bp, data, axis=1)

    # Notch filters for line noise
    for notch_freq in [50.0, 60.0]:
        if notch_freq < nyq:
            b_notch, a_notch = signal.iirnotch(notch_freq, Q=30.0, fs=sfreq)
            filtered = signal.filtfilt(b_notch, a_notch, filtered, axis=1)

    # Z-score normalize each channel
    means = filtered.mean(axis=1, keepdims=True)
    stds = filtered.std(axis=1, keepdims=True)
    stds[stds < 1e-10] = 1.0  # Avoid division by zero
    normalized = (filtered - means) / stds

    return normalized


def extract_psd_features(data: np.ndarray, sfreq: float) -> np.ndarray:
    """
    Extract Power Spectral Density features per channel per band.
    Returns: (n_channels * n_bands,) = typically (8 * 5 = 40,) features
    """
    n_channels = data.shape[0]
    n_bands = len(BANDS)
    features = np.zeros(n_channels * n_bands)

    for ch in range(n_channels):
        # Welch's method for PSD estimation
        freqs, psd = signal.welch(
            data[ch], fs=sfreq, nperseg=min(256, data.shape[1]), noverlap=128
        )

        for bi, (band_name, (fmin, fmax)) in enumerate(BANDS.items()):
            idx = np.where((freqs >= fmin) & (freqs <= fmax))[0]
            if len(idx) > 0:
                # Relative band power (log-transformed)
                band_power = np.trapz(psd[idx], freqs[idx])
                total_power = np.trapz(psd, freqs)
                if total_power > 0:
                    features[ch * n_bands + bi] = np.log1p(
                        band_power / total_power
                    )

    return features


def extract_coherence_features(data: np.ndarray, sfreq: float) -> np.ndarray:
    """
    Extract coherence between all channel pairs across frequency bands.
    For n channels: n*(n-1)/2 pairs * n_bands features.
    """
    n_channels = data.shape[0]
    n_pairs = n_channels * (n_channels - 1) // 2
    n_bands = len(BANDS)
    features = np.zeros(n_pairs * n_bands)

    pair_idx = 0
    for i in range(n_channels):
        for j in range(i + 1, n_channels):
            # Magnitude squared coherence
            freqs, coh = signal.coherence(
                data[i], data[j], fs=sfreq,
                nperseg=min(256, data.shape[1]),
                noverlap=128,
            )

            for bi, (band_name, (fmin, fmax)) in enumerate(BANDS.items()):
                idx = np.where((freqs >= fmin) & (freqs <= fmax))[0]
                if len(idx) > 0:
                    features[pair_idx * n_bands + bi] = np.mean(coh[idx])

            pair_idx += 1

    return features


def extract_hjorth_features(data: np.ndarray) -> np.ndarray:
    """
    Extract Hjorth parameters (Activity, Mobility, Complexity) per channel.
    Returns: (n_channels * 3,) features
    """
    n_channels = data.shape[0]
    features = np.zeros(n_channels * 3)

    for ch in range(n_channels):
        x = data[ch]

        # Activity = variance of the signal
        activity = np.var(x)

        # First derivative
        dx = np.diff(x)
        dx_var = np.var(dx)

        # Second derivative
        ddx = np.diff(dx)
        ddx_var = np.var(ddx)

        # Mobility = sqrt(var(dx) / var(x))
        mobility = np.sqrt(dx_var / activity) if activity > 0 else 0

        # Complexity = mobility(dx) / mobility(x)
        mobility_dx = np.sqrt(ddx_var / dx_var) if dx_var > 0 else 0
        complexity = mobility_dx / mobility if mobility > 0 else 0

        features[ch * 3] = np.log1p(activity)  # Log-transform activity
        features[ch * 3 + 1] = mobility
        features[ch * 3 + 2] = complexity

    return features


def extract_statistical_features(data: np.ndarray) -> np.ndarray:
    """
    Extract statistical features per channel: mean, std, skewness, kurtosis.
    Returns: (n_channels * 4,) features
    """
    n_channels = data.shape[0]
    features = np.zeros(n_channels * 4)

    for ch in range(n_channels):
        x = data[ch]
        features[ch * 4] = np.mean(np.abs(x))
        features[ch * 4 + 1] = np.std(x)
        features[ch * 4 + 2] = skew(x)
        features[ch * 4 + 3] = kurtosis(x)

    return features


def extract_all_features(
    data: np.ndarray, sfreq: float, target_channels: int = 8
) -> np.ndarray:
    """
    Extract all features from preprocessed EEG data.
    Handles variable channel counts by padding or selecting channels.

    Returns feature vector ready for the translation model.
    """
    n_channels = data.shape[0]

    # Handle channel count mismatch
    if n_channels > target_channels:
        # Select evenly spaced channels
        indices = np.linspace(0, n_channels - 1, target_channels, dtype=int)
        data = data[indices]
    elif n_channels < target_channels:
        # Pad with zeros
        padding = np.zeros((target_channels - n_channels, data.shape[1]))
        data = np.vstack([data, padding])

    n_ch = data.shape[0]  # Should be target_channels now

    # Extract all feature types
    psd = extract_psd_features(data, sfreq)           # n_ch * 5 = 40
    coherence = extract_coherence_features(data, sfreq) # n_ch*(n_ch-1)/2 * 5 = 140
    hjorth = extract_hjorth_features(data)              # n_ch * 3 = 24
    stats = extract_statistical_features(data)          # n_ch * 4 = 32

    # Concatenate all features
    all_features = np.concatenate([psd, coherence, hjorth, stats])

    return all_features


def get_feature_dimension(n_channels: int = 8) -> int:
    """Calculate total feature dimension for given channel count."""
    n_bands = 5
    n_pairs = n_channels * (n_channels - 1) // 2

    psd_dim = n_channels * n_bands          # 40
    coh_dim = n_pairs * n_bands             # 140
    hjorth_dim = n_channels * 3             # 24
    stats_dim = n_channels * 4              # 32

    return psd_dim + coh_dim + hjorth_dim + stats_dim  # 236


def process_eeg_file(
    file_bytes: bytes, filename: str, target_channels: int = 8
) -> tuple[np.ndarray, dict]:
    """
    Full pipeline: parse → preprocess → extract features.
    Returns (feature_vector, metadata).
    """
    # Parse
    data, sfreq, ch_names = parse_eeg_file(file_bytes, filename)

    metadata = {
        "filename": filename,
        "original_channels": len(ch_names),
        "channel_names": ch_names[:20],  # Limit for response size
        "sample_rate": sfreq,
        "duration_seconds": round(data.shape[1] / sfreq, 2),
        "n_samples": data.shape[1],
        "target_channels": target_channels,
    }

    # Preprocess
    preprocessed = preprocess(data, sfreq)

    # Extract features
    features = extract_all_features(preprocessed, sfreq, target_channels)

    metadata["feature_dimension"] = len(features)

    return features, metadata