## 2/26/2024

### 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}")

..