Peak-n-hold on FFT for IQ data

compute.py

import scipy.io
import matplotlib.pyplot as plt
import numpy as np
from numpy.fft import fft, fftshift

path = '/home/oli/baseband_144288239Hz_17-59-43_08-12-2022.wav'
sr, d = scipy.io.wavfile.read(path)
d = d[...,0] + 1j * d[...,1] # convert to complex array

sta, stop = 30, 60    # time of sweep beginning and end in .wav file

fftsize = 1024

cfreq = 144288.239    # center frequency in kHz
fspan = sr/1000       # span in kHz = sample rate/1000
xlabel = np.linspace(cfreq-fspan/2, cfreq+fspan/2, fftsize)  # label for each bin

win = np.hamming(fftsize)  # hamming window for filtering fft result

maxes = np.zeros(fftsize)-999 # array for recording maximum value of each fft bucket

for s in range(sta*sr, stop*sr, fftsize):
    data = win*d[s:s+fftsize]        # cut out chunk of data
    yf = np.abs(fftshift(fft(data)))  # take fft, reorder the output, get modulus
    
    s_mag = yf * 2 / np.sum(win)
    s_dbfs = 20 * np.log10(s_mag/32768) # normalize and convert to log scale

    maxes = np.maximum(maxes, s_dbfs)  # record peak values

plt.plot(xlabel, maxes)  # plot
plt.show()