Dominant frequency extraction.

 

Let's say we have channel x Length signal data ex)EEG (electroencephalogram) or time series data.

We might wonder what dominant Hz is there.

The code analysis this question and return 5 top dominant frequency. 

.

import numpy as np

from collections import Counter

from scipy.signal import welch

def identify_dominant_frequencies(signal, fs, top_n=5):

freqs, psd = welch(signal, fs)

peak_indices = np.argsort(psd)[-top_n:]

dominant_freqs = freqs[peak_indices]

return dominant_freqs

..

dominant_freqs = identify_dominant_frequencies(signal, fs, top_n)

dominant_freqs_summary[channel].extend(dominant_freqs) # Append the frequencies

..

median_dominant_freqs = {channel: np.median(freqs) if freqs else None for channel, freqs in dominant_freqs_summary.items()}

..

def get_top_n_frequencies(freq_list, top_n=5, bin_width=1.0):

# Bin frequencies into discrete intervals

binned_freqs = np.round(np.array(freq_list) / bin_width) * bin_width

# Count the frequency of each binned frequency

freq_counter = Counter(binned_freqs)

# Find the top N most common binned frequencies

top_freqs = freq_counter.most_common(top_n)

# Extract just the frequencies from the top N tuples (freq, count)

top_freqs = [freq for freq, count in top_freqs]

return top_freqs

# Initialize a dictionary to store the top 5 frequencies for each channel

top_5_freqs_all_channels = {}

bin_width = 1.0

# Calculate the top 5 frequencies for each channel

for channel, freqs in dominant_freqs_summary.items():

top_5_freqs = get_top_n_frequencies(freqs, top_n=5, bin_width=bin_width)

top_5_freqs_all_channels[channel] = top_5_freqs

print(f"{channel}: Top 5 Frequencies = {top_5_freqs}")

..