scripts/selcal-fft.py

@@ -1,147 +0,0 @@
-#! /usr/bin/env python3
-
-
-import numpy as np
-from scipy.io import wavfile
-from scipy.fft import fft
-from scipy.signal import butter, lfilter, decimate
-import matplotlib.pyplot as plt
-import sys
-
-frequencies = np.array([10 ** ((n + 22) * 0.0225 + 2) for n in range(32)])
-widths = np.array([6.25 * 10 ** (n * 0.0225) for n in range(32)])
-widths = np.array([6.25 for n in range(32)])
-
-def butter_bandpass(lowcut, highcut, fs, order=5):
-    nyquist = 0.5 * fs
-    low = lowcut / nyquist
-    high = highcut / nyquist
-    b, a = butter(order, [low, high], btype='band')
-    return b, a
-
-def bandpass_filter(data, lowcut, highcut, fs, order=5):
-    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
-    y = lfilter(b, a, data)
-    return y
-
-def butter_lowpass(cutoff, fs, order=5):
-    nyquist = 0.5 * fs
-    normal_cutoff = cutoff / nyquist
-    b, a = butter(order, normal_cutoff, btype='low', analog=False)
-    return b, a
-
-def lowpass_filter(data, cutoff, fs, order=5):
-    b, a = butter_lowpass(cutoff, fs, order=order)
-    y = lfilter(b, a, data)
-    return y
-
-def get_largest_two_indices(numbers, threshold):
-    # Check if the list has at least three numbers
-    if len(numbers) < 3:
-        return None
-
-    # Find the indices of the three largest numbers in the list
-    indices = sorted(range(len(numbers)), key=lambda i: numbers[i], reverse=True)
-    largest_index = indices[0]
-    second_largest_index = indices[1]
-    third_largest_index = indices[2]
-
-    # Check if the largest and second largest numbers are at least threshold larger than the third largest
-    if numbers[largest_index] - numbers[third_largest_index] >= threshold and \
-       numbers[second_largest_index] - numbers[third_largest_index] >= threshold:
-        return largest_index, second_largest_index
-    else:
-        return None
-
-# Step 1: Read the WAV file
-sample_rate, data = wavfile.read(sys.argv[1])
-
-# Handle stereo audio by converting to mono if needed
-if len(data.shape) == 2:
-    data = data.mean(axis=1)
-
-# Define the maximum frequency of interest and Nyquist rate
-max_freq = 1600.0  # 2000 Hz
-nyquist_rate = 2 * max_freq  # Nyquist rate to prevent aliasing
-
-# If the sample rate is higher than the Nyquist rate, downsample
-if sample_rate > nyquist_rate:
-    # Apply a lowpass filter before downsampling
-    cutoff = max_freq #+ 500  # Lowpass filter cutoff slightly above max_freq to prevent aliasing
-    filtered_data = lowpass_filter(data, cutoff, sample_rate)
-
-    # Calculate the downsampling factor
-    downsample_factor = int(sample_rate / nyquist_rate)
-
-    # Downsample the filtered signal
-    filtered_data = decimate(filtered_data, downsample_factor)
-    sample_rate = sample_rate // downsample_factor
-else:
-    filtered_data = data
-
-# Step 2: Define the increment (0.1 seconds) and segment size
-#increment = 0.2  # in seconds
-#segment_size = int(sample_rate * increment)
-segment_size = 1024
-increment = segment_size / sample_rate
-print(f"Segment size: {segment_size}")
-
-# Step 3: Process each segment
-num_segments = len(filtered_data) // segment_size
-
-# Calculate the frequency resolution
-delta_f = sample_rate / segment_size
-
-# Determine the bin range for desired frequency range (100 Hz to 2000 Hz)
-high_bin = int(max_freq / delta_f)
-
-seg_off = int(sample_rate * 0.1)
-act_segs = len(filtered_data) // seg_off
-
-# Initialize a 2D array to store DFT results (magnitude spectrum)
-# Only store the bins within the desired frequency range
-dft_results = np.zeros((act_segs, high_bin))
-
-for i in range(act_segs):
-    end = (i+1) * seg_off
-    start = end - segment_size
-
-    try:
-        segment = filtered_data[start:end]
-
-        # Step 4: Apply the DFT
-        dft_result = fft(segment)
-
-        magnitudes = np.abs(dft_result)
-        total_energy = np.sum(magnitudes ** 2)
-        normalized_magnitudes = magnitudes / np.sqrt(total_energy)
-        normalized_magnitude = np.mean(normalized_magnitudes)
-
-        # Store the magnitude spectrum in the 2D array, only for the desired frequency range
-        dft_results[i, :] = normalized_magnitudes[:high_bin]
-
-        scores = [
-            10 * np.log10(np.sum(normalized_magnitudes[int((f-w)/delta_f):int((f+w)/delta_f)]))
-            for f,w in zip(frequencies, widths)
-        ]
-
-        codes = get_largest_two_indices(scores, 3.0)
-        if codes:
-            print([frequencies[code] for code in sorted(codes)])
-    except:
-        pass
-
-
-# Step 5: Plot the spectrogram
-plt.figure(figsize=(12, 8))
-extent = [0, num_segments * increment, 0, max_freq]
-plt.imshow(dft_results.T, aspect='auto', origin='lower', extent=extent, cmap='viridis')
-plt.colorbar(label='Magnitude')
-plt.title('Spectrogram (100 Hz to 2000 Hz)')
-plt.xlabel('Time (s)')
-plt.ylabel('Frequency (Hz)')
-
-
-for freq in frequencies:
-    plt.axhline(y=freq, color='r', linestyle='--', linewidth=0.5)
-plt.show()