Add fft_analysis/src/fft_analysis/cli.py
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fft_analysis/src/fft_analysis/cli.py
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fft_analysis/src/fft_analysis/cli.py
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import argparse
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import sys
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import logging
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from pathlib import Path
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from typing import NoReturn
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import numpy as np
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from fft_analysis.io_utils import load_audio_file, save_spectrum_to_json
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from fft_analysis.core import perform_fft
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def main() -> NoReturn:
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"""Command-line interface for running FFT analysis on an audio file."""
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parser = argparse.ArgumentParser(
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description=(
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"Performs FFT analysis on an input audio file and saves the result as a JSON spectrum."
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)
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)
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parser.add_argument(
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"--input",
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required=True,
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help="Pfad zur Eingabe-Audiodatei (WAV/FLAC)",
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)
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parser.add_argument(
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"--output",
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required=True,
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help="Pfad zur Ausgabe-Datei mit dem FFT-Spektrum im JSON-Format",
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)
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args = parser.parse_args()
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# Setup logging for CLI execution
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logging.basicConfig(level=logging.INFO, format="[%(levelname)s] %(message)s")
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logger = logging.getLogger(__name__)
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input_path = Path(args.input)
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output_path = Path(args.output)
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if not input_path.exists() or not input_path.is_file():
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logger.error(f"Input file not found: {input_path}")
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sys.exit(1)
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try:
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logger.info(f"Loading audio file: {input_path}")
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audio_data, _ = load_audio_file(str(input_path))
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if not isinstance(audio_data, np.ndarray):
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raise TypeError("Loaded audio data is not a numpy.ndarray")
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logger.info("Performing FFT analysis...")
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spectrum = perform_fft(audio_data)
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if not isinstance(spectrum, np.ndarray):
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raise TypeError("FFT result is not a numpy.ndarray")
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logger.info(f"Saving FFT spectrum to: {output_path}")
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save_spectrum_to_json(spectrum, str(output_path))
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logger.info("FFT analysis completed successfully.")
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except Exception as exc:
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logger.exception(f"An error occurred during FFT processing: {exc}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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