1.  fs = 100
2.     T = 1
3.     y, _ = sin(f=10.2, T=T, fs=fs)
4.  
5.  
6.     for f_2 in np.linspace(11.4, 15.5, 9):
7.         t = np.arange(0, T, 1 / fs)
8.         y_2, _ = sin(f=f_2, T=T, fs=fs)
9.  
10.         s = y_2 + y
11.         S, F = spect_dB(s, len(s), fs)
12.  
13.         hann_win = signal.windows.hann(len(t))
14.         hann_win = s * hann_win
15.         S_wnd_hann, F_hann = spect_dB(hann_win, len(s), fs)
16.  
17.         hamming_win = signal.windows.hamming(len(t))
18.         hamming_win = s * hamming_win
19.         S_wnd_hamming, F_hamming = spect_dB(hamming_win, len(s), fs)
20.  
21.         blackman_win = signal.windows.blackman(len(t))
22.         blackman_win = s * blackman_win
23.         S_wnd_blackman, F_blackman = spect_dB(blackman_win, len(s), fs)
24.  
25.         plt.figure()
26.         plt.plot(F, S, label='Rectangle')
27.         plt.plot(F_hann, S_wnd_hann, label='Hann')
28.         plt.plot(F_hamming, S_wnd_hamming, label='Hamming')
29.         plt.plot(F_blackman, S_wnd_blackman, label='Blackman')
30.         plt.legend()
31.  
32.  
33.     plt.show()