"""EEG (electroencephalography) pipeline.

Loads raw recordings (FIF/EDF), bandpass-filters, removes EOG artifacts via
ICA, slices into fixed-duration epochs, computes per-band PSD + statistical
features, flattens to a 2D table, and writes a model-ready Parquet at
`data/processed/eeg_features.parquet`.

Follows the Data Readiness contract in AGENTS.md §4 and the Parquet storage
convention in §6: schema validity, domain validity (drop NaN/inf epochs with
a logged WARNING), determinism (seeded ICA + sklearn RNG), traceability
(in/out/dropped counts at INFO), and idempotent overwrite output.
"""
from __future__ import annotations

import time
from pathlib import Path
from typing import Callable

import mne
import numpy as np
import pandas as pd
from mne.preprocessing import ICA
from scipy import signal as scipy_signal
from scipy import stats as scipy_stats

from src.core.determinism import pin_threads
from src.core.logger import get_logger
from src.core.storage import write_parquet
from src.core.tracking import track_pipeline_run

logger = get_logger(__name__)

# Pin BLAS / OpenMP / pyarrow to single-threaded mode so byte-determinism
# (AGENTS.md §4 rule 3) holds across hardware. See src.core.determinism.
pin_threads()

# Pearson-correlation threshold for EOG-component rejection in ICA.
# Real-world EOG components typically score 0.8-0.95 against the EOG channel;
# 0.9 is a conservative floor that avoids false positives at the cost of
# missing weak artifacts. Lower (0.7-0.8) for noisier recordings.
_EOG_CORR_THRESHOLD: float = 0.9


# Default I/O paths for the EEG pipeline. Override via run_pipeline() args.
DEFAULT_INPUT = Path("data/raw/eeg.fif")
DEFAULT_OUTPUT = Path("data/processed/eeg_features.parquet")


def is_valid_epoch(epoch: np.ndarray | None) -> bool:
    """Return True iff `epoch` is a non-empty 2-D numeric array with no NaN/inf.

    The annotation is the *expected* input class; the implementation defensively
    rejects any other garbage (lists, scalars, string dtypes, zero-sized arrays)
    without raising — matching the BBB pipeline's `is_valid_smiles` pattern.
    """
    if not isinstance(epoch, np.ndarray):
        return False
    if epoch.ndim != 2:
        return False
    if epoch.size == 0:
        return False
    if not np.issubdtype(epoch.dtype, np.number):
        return False
    if not np.all(np.isfinite(epoch)):
        return False
    return True


def bandpass_filter(
    raw: mne.io.BaseRaw,
    l_freq: float = 1.0,
    h_freq: float = 40.0,
) -> mne.io.BaseRaw:
    """Apply a non-mutating bandpass filter to an MNE Raw.

    Default 1-40 Hz removes drift below 1 Hz and high-frequency noise / line
    artifacts above 40 Hz. Returns a copy; the input `raw` is unchanged.

    Args:
        raw: Loaded `mne.io.BaseRaw` (call `.load_data()` first if from disk).
        l_freq: Low-cut frequency in Hz. Must be strictly less than `h_freq`.
        h_freq: High-cut frequency in Hz.

    Returns:
        A filtered copy of `raw`.

    Raises:
        ValueError: if `l_freq >= h_freq`. MNE silently produces a corrupted
            band-stop-like result on inverted inputs, so we guard up front.
    """
    if l_freq >= h_freq:
        raise ValueError(
            f"l_freq ({l_freq}) must be strictly less than h_freq ({h_freq})"
        )

    out = raw.copy()
    # picks="all" includes the EOG channel so the ICA step in
    # remove_artifacts_with_ica sees a consistently-filtered EOG reference.
    out.filter(l_freq=l_freq, h_freq=h_freq, picks="all", verbose="ERROR")
    logger.info("Bandpass filter applied: %.1f-%.1f Hz", l_freq, h_freq)
    return out


def remove_artifacts_with_ica(
    raw: mne.io.BaseRaw,
    eog_ch_name: str | None = None,
    n_components: int = 15,
    random_state: int = 97,
) -> mne.io.BaseRaw:
    """Remove EOG-like artifacts using MNE's ICA + EOG correlation.

    Fits an ICA decomposition on `raw`, finds components whose time courses
    correlate (Pearson) with the named EOG channel via `find_bads_eog` using
    `measure="correlation"`, marks them as "bad" and reconstructs the signal
    without them. Returns a copy; the input `raw` is unchanged.

    If `eog_ch_name` is None or not present in the recording's channels,
    ICA is skipped entirely and a copy of `raw` is returned unchanged.

    Args:
        raw: Loaded, ideally bandpass-filtered, `mne.io.BaseRaw`.
        eog_ch_name: Name of the EOG channel for correlation-based detection.
            None disables auto-rejection; a string that is not in the recording's
            channel list logs a WARNING and skips ICA.
        n_components: Cap on ICA components. If this exceeds the number of EEG
            channels, MNE raises ValueError, so the implementation internally
            caps it at `max(n_eeg - 1, 1)` before fitting.
        random_state: Seed for ICA's underlying solver. Required for §4
            Determinism.

    Returns:
        A copy of `raw` with EOG-correlated ICA components removed (or an
        unchanged copy if ICA was skipped).

    Raises:
        ValueError: if the EEG data is rank-deficient (all-zero or constant
            channels) and `mne.preprocessing.ICA.fit` cannot converge.
    """
    out = raw.copy()
    if eog_ch_name is None:
        logger.info("ICA skipped: eog_ch_name not provided")
        return out
    if eog_ch_name not in out.ch_names:
        logger.warning(
            "ICA skipped: eog_ch_name=%r not found in channels %s",
            eog_ch_name, out.ch_names,
        )
        return out

    # Guard: ICA.fit cannot handle NaN/inf in the data (scipy SVD will raise).
    # If the raw contains non-finite samples, skip ICA so the NaN propagates
    # to the epoch-level validity check in extract_features_from_recording
    # where it will be cleanly dropped with a WARNING.
    eeg_picks_check = mne.pick_types(out.info, eeg=True, meg=False)
    if not np.all(np.isfinite(out.get_data(picks=eeg_picks_check))):
        logger.warning(
            "ICA skipped: EEG data contains NaN/inf values; "
            "invalid epochs will be dropped downstream"
        )
        return out

    # Cap n_components at rank-1. Average reference (if applied) reduces rank
    # to n_eeg - 1; using that as the ceiling is safe for both referenced and
    # unreferenced data and avoids ValueError from ICA.fit on small recordings.
    n_eeg = len(mne.pick_types(out.info, eeg=True, meg=False))
    safe_n = min(n_components, max(n_eeg - 1, 1))

    ica = ICA(
        n_components=safe_n,
        random_state=random_state,
        max_iter="auto",
        method="fastica",
        verbose="ERROR",
    )
    ica.fit(out, picks="eeg", verbose="ERROR")
    # Use raw correlation (not z-score) so we can reliably flag artifact
    # components on small recordings where n_components < 10 makes the
    # default z-score threshold algebraically unreachable.
    bad_idx, _ = ica.find_bads_eog(
        out,
        ch_name=eog_ch_name,
        measure="correlation",
        threshold=_EOG_CORR_THRESHOLD,
        verbose="ERROR",
    )
    ica.exclude = list(bad_idx)
    logger.info(
        "ICA fit: n_components=%d, EOG-correlated rejected=%d (indices=%s)",
        safe_n, len(ica.exclude), ica.exclude,
    )
    ica.apply(out, verbose="ERROR")
    return out


EEG_BANDS: dict[str, tuple[float, float]] = {
    "delta": (1.0, 4.0),
    "theta": (4.0, 8.0),
    "alpha": (8.0, 13.0),
    "beta":  (13.0, 30.0),
    "gamma": (30.0, 40.0),
}


def _band_power(freqs: np.ndarray, psd: np.ndarray, lo: float, hi: float) -> float:
    """Mean PSD value within the [lo, hi) frequency band."""
    mask = (freqs >= lo) & (freqs < hi)
    if not mask.any():
        return 0.0
    return float(psd[mask].mean())


# Statistical-moment functions, bound to their column-label names. The
# `STATS` tuple below is derived from this list so labels and computations
# can never drift out of sync (a class of bug the original parallel-list
# design was vulnerable to).
_StatFn = Callable[[np.ndarray], float]
_STATS_FUNCS: tuple[tuple[str, _StatFn], ...]  # populated below


def _stat_mean(x: np.ndarray) -> float:
    return float(np.mean(x))


def _stat_std(x: np.ndarray) -> float:
    return float(np.std(x))


def _stat_var(x: np.ndarray) -> float:
    return float(np.var(x))


def _stat_skew(x: np.ndarray) -> float:
    return float(scipy_stats.skew(x))


def _stat_kurtosis(x: np.ndarray) -> float:
    return float(scipy_stats.kurtosis(x))


_STATS_FUNCS = (
    ("mean", _stat_mean),
    ("std", _stat_std),
    ("var", _stat_var),
    ("skew", _stat_skew),
    ("kurtosis", _stat_kurtosis),
)
STATS: tuple[str, ...] = tuple(name for name, _ in _STATS_FUNCS)


def compute_features_from_epoch(epoch: np.ndarray, sfreq: float) -> np.ndarray:
    """Compute PSD-band + statistical features for one epoch.

    Per channel, the feature block is:
      [psd_delta, psd_theta, psd_alpha, psd_beta, psd_gamma,
       mean, std, var, skew, kurtosis]
    Channels are stacked in their input order. The resulting 1-D vector has
    length ``n_channels * (len(EEG_BANDS) + len(STATS))``.

    PSD uses Welch's method (`scipy.signal.welch`, `nperseg=min(256, n_samples)`).
    For meaningful Welch averaging, the epoch should contain at least
    `2 * nperseg` samples (e.g. ≥2 seconds at 256 Hz); shorter epochs degrade
    to a single-segment periodogram with high estimation variance.

    Statistical conventions:
      - ``mean``, ``std``, ``var`` use NumPy with ``ddof=0`` (biased / population
        estimators). For sample statistics callers must apply ``ddof=1`` adjustment
        downstream.
      - ``skew`` uses ``scipy.stats.skew(bias=True)`` (biased estimator).
      - ``kurtosis`` uses ``scipy.stats.kurtosis(fisher=True, bias=True)`` —
        Fisher's *excess* kurtosis (Gaussian → 0, not 3). Add 3 if Pearson
        kurtosis is required downstream.
      - For constant-valued channels (zero variance), ``skew`` and
        ``kurtosis`` are mathematically undefined and scipy returns NaN.
        We post-process the feature vector with ``np.nan_to_num`` to map
        any NaN/inf to 0.0, preserving the "no NaN survives" Parquet
        contract from AGENTS.md §6.

    Precondition: `epoch` must be finite (no NaN/inf). Filter via
    `is_valid_epoch` before calling — feature values are NaN-propagating.

    Args:
        epoch: A 2-D array shape (n_channels, n_samples), all-finite.
        sfreq: Sampling rate in Hz.

    Returns:
        A 1-D `np.ndarray` of dtype float64.
    """
    n_channels, n_samples = epoch.shape
    nperseg = min(256, n_samples)
    feats: list[float] = []
    for ch in range(n_channels):
        x = epoch[ch]
        freqs, psd = scipy_signal.welch(x, fs=sfreq, nperseg=nperseg)
        for _band, (lo, hi) in EEG_BANDS.items():
            feats.append(_band_power(freqs, psd, lo, hi))
        for _name, fn in _STATS_FUNCS:
            feats.append(fn(x))
    arr = np.asarray(feats, dtype=np.float64)
    # Constant-valued / zero-variance channels (e.g., disconnected electrodes)
    # make scipy.stats.skew / kurtosis return NaN. Map those to 0.0 so the
    # downstream Parquet contract ("no NaN in feature table") holds.
    return np.nan_to_num(arr, nan=0.0, posinf=0.0, neginf=0.0)


def _build_feature_columns(eeg_ch_names: list[str]) -> list[str]:
    """Generate the deterministic, in-channel-order column ordering."""
    cols: list[str] = []
    for ch in eeg_ch_names:
        for band in EEG_BANDS:
            cols.append(f"feat_{ch}_psd_{band}")
        for stat in STATS:
            cols.append(f"feat_{ch}_{stat}")
    return cols


def extract_features_from_recording(
    raw: mne.io.BaseRaw,
    epoch_duration_s: float = 2.0,
    eog_ch_name: str | None = None,
    n_components: int = 15,
    random_state: int = 97,
) -> pd.DataFrame:
    """Run the EEG pipeline on a Raw and return a 2-D feature DataFrame.

    Steps:
      1. Bandpass filter (1-40 Hz).
      2. ICA-based EOG artifact rejection (skipped if `eog_ch_name` is None).
      3. Slice into fixed-duration epochs.
      4. Drop any epoch with NaN/inf samples (logged WARNING).
      5. Compute features per epoch and stack into a DataFrame whose columns
         are `feat_<channel>_psd_<band>` and `feat_<channel>_<stat>`.

    Args:
        raw: Loaded `mne.io.BaseRaw` (must be `.load_data()`'d).
        epoch_duration_s: Length of each fixed-duration epoch in seconds.
        eog_ch_name: Name of EOG reference channel for ICA. None disables ICA.
        n_components: Cap on ICA components.
        random_state: Seed for ICA's solver (determinism).

    Returns:
        A `pd.DataFrame` with one row per valid epoch and ``n_eeg_channels *
        (len(EEG_BANDS) + len(STATS))`` ``feat_*`` columns.

    Raises:
        ValueError: if `epoch_duration_s * sfreq` rounds to less than 1 sample.
            (Other ValueError sources can propagate from `bandpass_filter`
            and `remove_artifacts_with_ica`; see their respective docstrings.)
    """
    filtered = bandpass_filter(raw, l_freq=1.0, h_freq=40.0)
    cleaned = remove_artifacts_with_ica(
        filtered,
        eog_ch_name=eog_ch_name,
        n_components=n_components,
        random_state=random_state,
    )

    sfreq = float(cleaned.info["sfreq"])
    n_samples_per_epoch = int(round(epoch_duration_s * sfreq))
    if n_samples_per_epoch < 1:
        raise ValueError(
            f"epoch_duration_s={epoch_duration_s!r} at sfreq={sfreq} Hz produces "
            f"{n_samples_per_epoch} samples per epoch (must be >= 1)"
        )
    eeg_picks = mne.pick_types(cleaned.info, eeg=True, meg=False, eog=False)
    eeg_names = [cleaned.ch_names[i] for i in eeg_picks]
    data = cleaned.get_data(picks=eeg_picks)  # shape (n_eeg, n_times)
    _, n_times = data.shape
    n_total_epochs = n_times // n_samples_per_epoch

    feature_cols = _build_feature_columns(eeg_names)
    rows: list[np.ndarray] = []
    invalid_indices: list[int] = []
    for ep in range(n_total_epochs):
        start = ep * n_samples_per_epoch
        end = start + n_samples_per_epoch
        epoch = data[:, start:end]
        if not is_valid_epoch(epoch):
            invalid_indices.append(ep)
            continue
        rows.append(compute_features_from_epoch(epoch, sfreq=sfreq))

    n_dropped = len(invalid_indices)
    if n_dropped:
        display = invalid_indices[:10]
        suffix = (
            f"... (+{n_dropped - 10} more)" if n_dropped > 10 else ""
        )
        logger.warning(
            "Dropping %d/%d epochs with invalid samples (indices=%s%s)",
            n_dropped, n_total_epochs, display, suffix,
        )

    if not rows:
        logger.info(
            "Feature extraction complete: in=%d, out=0, dropped=%d (%.2f%%)",
            n_total_epochs, n_dropped,
            100.0 * n_dropped / max(n_total_epochs, 1),
        )
        return pd.DataFrame(columns=feature_cols).astype(np.float64)

    matrix = np.vstack(rows)
    out = pd.DataFrame(matrix, columns=feature_cols, dtype=np.float64)
    logger.info(
        "Feature extraction complete: in=%d, out=%d, dropped=%d (%.2f%%)",
        n_total_epochs, len(out), n_dropped,
        100.0 * n_dropped / max(n_total_epochs, 1),
    )
    return out


def run_pipeline(
    input_path: Path = DEFAULT_INPUT,
    output_path: Path = DEFAULT_OUTPUT,
    epoch_duration_s: float = 2.0,
    eog_ch_name: str | None = None,
    n_components: int = 15,
    random_state: int = 97,
) -> None:
    """Run the EEG pipeline end-to-end: raw FIF/EDF -> processed feature Parquet.

    Reads `input_path` via MNE, applies bandpass + ICA + epoching + feature
    extraction, then writes a model-ready Parquet at `output_path` (preserves
    float64 dtype; satisfies AGENTS.md §6).

    Args:
        input_path: Path to the raw recording (.fif or .edf).
        output_path: Where to write the processed feature Parquet file.
            Parent directory is created if missing.
        epoch_duration_s: Length of each fixed-duration epoch (seconds).
        eog_ch_name: Name of the EOG channel for ICA-based artifact rejection.
            None disables ICA.
        n_components: Cap on ICA components.
        random_state: Seed for ICA's solver. Required for §4 Determinism.

    Raises:
        FileNotFoundError: if `input_path` does not exist.
        IsADirectoryError: if `output_path` resolves to an existing directory.
    """
    input_path = Path(input_path)
    output_path = Path(output_path)
    if not input_path.exists():
        raise FileNotFoundError(f"Raw EEG file not found: {input_path}")

    started = time.perf_counter()
    logger.info("Reading raw EEG from %s", input_path)
    # Format dispatch: .edf via read_raw_edf, anything else (FIF, gzipped FIF)
    # via read_raw_fif. .bdf / .set / .vhdr support can be added here.
    if input_path.suffix.lower() == ".edf":
        raw = mne.io.read_raw_edf(input_path, preload=True, verbose="ERROR")
    else:
        raw = mne.io.read_raw_fif(input_path, preload=True, verbose="ERROR")
    logger.info(
        "Loaded %d channels, sfreq=%.1f Hz, n_times=%d",
        len(raw.ch_names), raw.info["sfreq"], raw.n_times,
    )

    features = extract_features_from_recording(
        raw,
        epoch_duration_s=epoch_duration_s,
        eog_ch_name=eog_ch_name,
        n_components=n_components,
        random_state=random_state,
    )

    # Parquet preserves dtypes (float64 features stay float64) and is
    # byte-deterministic with single-threaded snappy. AGENTS.md §6.
    write_parquet(features, output_path)
    logger.info(
        "Wrote processed features to %s (rows=%d, cols=%d)",
        output_path, len(features), features.shape[1],
    )

    duration_sec = time.perf_counter() - started

    with track_pipeline_run(
        experiment_name="eeg_pipeline",
        params={
            "input_path": str(input_path),
            "output_path": str(output_path),
            "epoch_duration_s": epoch_duration_s,
            "eog_ch_name": str(eog_ch_name) if eog_ch_name is not None else "None",
            "n_components": n_components,
            "random_state": random_state,
        },
        metrics={
            "rows_out": float(len(features)),
            "duration_sec": duration_sec,
        },
        artifact_path=output_path,
    ):
        pass


if __name__ == "__main__":
    # Day-2 CLI entrypoint — runs with default paths against `data/raw/eeg.fif`.
    # Defaults to `eog_ch_name=None` (ICA disabled). Pass an EOG channel
    # name programmatically via run_pipeline(eog_ch_name=...) to enable
    # artifact rejection. Argument parsing (argparse / click) lands later.
    #   python -m src.pipelines.eeg_pipeline
    run_pipeline()