English: A power spectrum of two sinusoidal basis functions, calculated by the periodogram method. The significance of basis functions is that there is no spectral leakage into the neighboring frequency bins.
importnumpyasnpfromscipyimportsignalimportmatplotlib.pyplotaspltw_1=30# frequency of the 1st component of the signal (Hz)w_2=50# frequency of the 2nd component of the signal (Hz)a=1.1# magnitude of the 1st component of the signalb=0.6# magnitude of the 2nd component of the signalt=np.array([iforiinrange(1,301)])/1000# time samples (s)fs=1/(t[1]-t[0])# sampling frequency (Hz)x=a*np.cos(2*np.pi*w_1*t)+b*np.sin(2*np.pi*w_2*t)# considered signalf,Pxx_den=signal.periodogram(x,fs=fs,scaling='spectrum')plt.subplots(1,1,figsize=(6,4),dpi=250)plt.stem(f[:50],Pxx_den[:50],'-')plt.ylabel('Spectrum')plt.xlabel('Frequencies (Hz)')plt.title('Periodogram')plt.grid(True)plt.savefig("Periodogram.svg")
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