from scipy.signal.spectral import spectrogram
from scipy.signal.waveforms import chirp

import matplotlib.animation as animation
import matplotlib.pyplot as plt
import numpy as np

fs = 8000 # sampling rate
t = np.arange(0, 5.0, 1/fs) # 5 seconds of time

fig, axes = plt.subplots(nrows=1)

def signal(t): # generates a "chirp" signal
    return chirp(t = t, f0 = 100.0,  f1 = 3000.0, t1 = 10.0)

def spect(x): # computes spectrogram
    freq, time, Sxx = spectrogram(x, fs = fs, nfft = 1024, noverlap = 512, nperseg = 1024)
    Z = 10 * np.log10(Sxx)
    return freq, time, Z

y = signal(t)
freq, time, Z = spect(y)
im = axes.pcolorfast(time, freq, Z)
axes.set_xlabel('t [s]')
axes.set_ylabel('f [Hz]')
axes.set_yscale('log') # <-- exposes the leak
axes.set_ylim(0, fs/2)

def animate(i):
    y = signal(t + i/30.0) # sliding temporal window
    freq, time, Z = spect(y)
    im.set_data(Z)
    return [ im ]

ani = animation.FuncAnimation(fig, animate, np.arange(1, 200), 
                              interval=0, blit=True)
plt.show()