mirror of
https://github.com/alexbelgium/hassio-addons.git
synced 2026-01-15 17:08:19 +01:00
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
import numpy as np
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import scipy.io.wavfile as wavfile
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import matplotlib.pyplot as plt
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import os
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import glob
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import sys # Import the sys module
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from utils.helpers import get_settings
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# Dependencies /usr/bin/pip install numpy scipy matplotlib
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# Define the directory containing the WAV files
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conf = get_settings()
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input_directory = os.path.join(conf['RECS_DIR'], 'StreamData')
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output_directory = os.path.join(conf['RECS_DIR'], 'Extracted/Charts')
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# Ensure the output directory exists
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if not os.path.exists(output_directory):
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os.makedirs(output_directory)
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# Check if a command-line argument is provided
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if len(sys.argv) > 1:
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# If an argument is provided, use it as the file to analyze
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wav_files = [sys.argv[1]]
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else:
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# If no argument is provided, analyze all WAV files in the directory
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wav_files = glob.glob(os.path.join(input_directory, '*.wav'))
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# Process each file
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for file_path in wav_files:
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# Load the WAV file
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sample_rate, audio_data = wavfile.read(file_path)
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# If stereo, select only one channel
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if len(audio_data.shape) > 1:
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audio_data = audio_data[:, 0]
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# Apply the Hamming window to the audio data
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hamming_window = np.hamming(len(audio_data))
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windowed_data = audio_data * hamming_window
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# Compute the FFT of the windowed audio data
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audio_fft = np.fft.fft(windowed_data)
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audio_fft = np.abs(audio_fft)
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# Compute the frequencies associated with the FFT values
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frequencies = np.fft.fftfreq(len(windowed_data), d=1/sample_rate)
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# Select the range of interest
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idx = np.where((frequencies >= 150) & (frequencies <= 15000))
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# Calculate the saturation threshold based on the bit depth
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bit_depth = audio_data.dtype.itemsize * 8
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max_amplitude = 2**(bit_depth - 1) - 1
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saturation_threshold = 0.8 * max_amplitude
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# Plot the spectrum with a logarithmic Y-axis
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plt.figure(figsize=(10, 6))
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plt.semilogy(frequencies[idx], audio_fft[idx], label='Spectrum')
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plt.axhline(y=saturation_threshold, color='r', linestyle='--', label='Saturation Threshold')
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plt.xlabel("Frequency (Hz)")
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plt.ylabel("Amplitude (Logarithmic)")
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plt.title(f"Frequency Spectrum (150 - 15000 Hz) - {os.path.basename(file_path)}")
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plt.legend()
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plt.grid(True)
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# Save the plot as a PNG file
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output_filename = os.path.basename(file_path).replace('.wav', '_spectrum.png')
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plt.savefig(os.path.join(output_directory, output_filename))
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plt.close() # Close the figure to free memory
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