#!/usr/bin/env python3
import os
import datetime
import numpy as np
import matplotlib.pyplot as plt
import soundfile as sf
import scipy.signal
from datetime import datetime
import shutil

OUTDIR = "chunks_unprocessed"
PROCESSED_DIR = "chunks_processed"
CHUNK_SECONDS = 1
TOLERANCE = 1
OVERTONE_TOLERANCE = TOLERANCE * 2
THRESHOLD_BASE = 0.5
THRESHOLD_OCT = THRESHOLD_BASE / 10
CLIP_PADDING_BEFORE = 1
CLIP_PADDING_AFTER = 6
TARGET_FREQ = 211
OVERTONE_FREQ = TARGET_FREQ * 2
NFFT = 32768
SKIP_SECONDS = 10

def detect_event(chunk):
    freqs, times, Sxx = scipy.signal.spectrogram(chunk, samplerate, nperseg=NFFT)
    idx_base = np.where((freqs >= TARGET_FREQ - TOLERANCE) & (freqs <= TARGET_FREQ + TOLERANCE))[0]
    idx_oct = np.where((freqs >= OVERTONE_FREQ - OVERTONE_TOLERANCE) & (freqs <= OVERTONE_FREQ + OVERTONE_TOLERANCE))[0]
    if len(idx_base) == 0 or len(idx_oct) == 0:
        return False
    base_energy = np.mean(Sxx[idx_base])
    oct_energy = np.mean(Sxx[idx_oct])
    total_energy = np.mean(Sxx, axis=0).max()
    return base_energy > THRESHOLD_BASE * total_energy and oct_energy > THRESHOLD_OCT * total_energy

def process_chunk(filename):
    input_path = os.path.join(OUTDIR, filename)
    print(f"🔍 Verarbeite {input_path}...")

    # Frequenzanalyse und Event-Erkennung
    data, samplerate = sf.read(input_path)
    if data.ndim > 1:
        data = data[:, 0]  # nur Kanal 1

    chunk_samples = int(CHUNK_SECONDS * samplerate)
    skip_samples = int(SKIP_SECONDS * samplerate)
    padding_before = int(CLIP_PADDING_BEFORE * samplerate)
    padding_after = int(CLIP_PADDING_AFTER * samplerate)

    i = 0
    last_event = -skip_samples
    while i + chunk_samples <= len(data):
        chunk = data[i:i+chunk_samples]
        if i - last_event >= skip_samples and detect_event(chunk):
            clip_start = max(0, i - padding_before)
            clip_end = min(len(data), i + chunk_samples + padding_after)
            clip = data[clip_start:clip_end]

            event_time = datetime.now().strftime("%Y%m%d-%H%M%S")
            base_name = os.path.splitext(filename)[0]
            wav_out = os.path.join(PROCESSED_DIR, f"{base_name}_{event_time}.wav")
            png_out = os.path.join(PROCESSED_DIR, f"{base_name}_{event_time}.png")
            sf.write(wav_out, clip, samplerate)

            plt.figure()
            plt.specgram(clip, Fs=samplerate, NFFT=NFFT, noverlap=NFFT//2, cmap='inferno', vmin=-90, vmax=-20)
            plt.title(f"Spectrogram: {base_name}_{event_time}")
            plt.xlabel("Time (s)")
            plt.ylabel("Frequency (Hz)")
            plt.colorbar(label="dB")
            plt.savefig(png_out)
            plt.close()

            print(f"🎯 Ereignis erkannt bei {event_time}, gespeichert: {wav_out}, {png_out}")
            last_event = i
            i += skip_samples
        else:
            i += chunk_samples

    # Datei verschieben
    output_path = os.path.join(PROCESSED_DIR, filename)
    shutil.move(input_path, output_path)
    print(f"✅ Verschoben nach {output_path}")


def main():
    for filename in os.listdir(OUTDIR):
        if filename.endswith(".flac"):
            process_chunk(filename)

if __name__ == "__main__":
    main()