In [1]:

    

from scipy import signal
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline


    

In [2]:

    

fs = 10e3
N = 1e5
amp = 2 * np.sqrt(2)
noise_power = 0.01 * fs / 2
time = np.arange(N) / float(fs)
mod = 500*np.cos(2*np.pi*0.25*time)
carrier = amp * np.sin(2*np.pi*3e3*time + mod)
noise = np.random.normal(scale=np.sqrt(noise_power), size=time.shape)
noise *= np.exp(-time/5)
x = carrier + noise


    

In [3]:

    

plt.plot(x)


    

    
Out[3]:





[<matplotlib.lines.Line2D at 0x7f02de76f9b0>]






    

In [4]:

    

f, t, Sxx = signal.spectrogram(x, fs)


    

In [5]:

    

print(t.shape) #(446,)
print(f.shape) #(129,)
print(Sxx.shape) #(129, 446)


    

    




(446,)
(129,)
(129, 446)

In [6]:

    

plt.figure()
plt.pcolormesh(t, f, Sxx)
plt.ylabel('Frequency [Hz]')
plt.xlabel('Time [sec]')
plt.show()