"""Audio feature extraction: onsets, chroma, RMS energy (Phase 6).

extract_features(path) -> {
    "onset_times": [float, ...],
    "rms_1s": [float, ...],
    "chroma_1s": [int, ...],
    "duration": float,
    "sr": int,
}
"""
from __future__ import annotations
import asyncio
from pathlib import Path
from typing import Any


def extract_features(path: str) -> dict[str, Any]:
    """Extract onsets, chroma, RMS from audio file.

    Args:
        path: Absolute path to audio file.

    Returns:
        Dictionary with onset_times (seconds), rms_1s (per-second mean RMS),
        chroma_1s (per-second dominant pitch class 0-11), duration, sr.
    """
    import librosa
    import numpy as np

    p = Path(path)
    if not p.exists():
        raise FileNotFoundError(f"Audio file not found: {path}")

    y, sr = librosa.load(path, sr=None, mono=True)

    # Onsets — note attacks / transients
    onset_frames = librosa.onset.onset_detect(y=y, sr=sr, units='frames')
    onset_times = librosa.frames_to_time(onset_frames, sr=sr).tolist()

    # Chroma — tonal/harmonic content (CQT-based)
    chroma = librosa.feature.chroma_cqt(y=y, sr=sr)  # shape (12, T)

    # RMS energy — loudness envelope
    rms = librosa.feature.rms(y=y)[0]  # shape (T,)

    # Reduce to 1s resolution for downstream consumers (heuristic + LLM)
    hop_length = 512
    frame_rate = sr / hop_length
    duration = len(y) / sr

    n_seconds = int(duration)
    rms_1s = []
    chroma_1s = []
    for i in range(n_seconds):
        start_frame = int(i * frame_rate)
        end_frame = int((i + 1) * frame_rate)
        # RMS mean for this second
        rms_slice = rms[start_frame:min(end_frame, len(rms))]
        rms_1s.append(float(np.mean(rms_slice)) if len(rms_slice) > 0 else 0.0)
        # Dominant pitch class for this second
        chroma_slice = chroma[:, start_frame:min(end_frame, chroma.shape[1])]
        if chroma_slice.shape[1] > 0:
            chroma_1s.append(int(np.argmax(np.mean(chroma_slice, axis=1))))
        else:
            chroma_1s.append(0)

    return {
        "onset_times": onset_times,
        "rms_1s": rms_1s,
        "chroma_1s": chroma_1s,
        "duration": duration,
        "sr": sr,
    }


async def extract_features_async(path: str) -> dict[str, Any]:
    """Async wrapper — runs in thread pool."""
    return await asyncio.to_thread(extract_features, path)