How to plot spectrums

Frequency Spectrum

Analyze the strength of each frequency in a signal segment.

Code

from scipy.fftpack import fft,ifft
#dt = tr.stats.npts/tr.stats.sampling_rate # 数据时间长度
df = tr.stats.sampling_rate
def FFT (Fs,data):
    data = data - np.mean(data)
    L = len(data)                          # 信号长度
    N = int(np.power(2,np.ceil(np.log2(L))))    # 下一个最近二次幂
    FFT_y = (abs(fft(data,N)))/L*2                  # N点FFT，但除以实际信号长度 L
    Fre = np.arange(int(N/2))*Fs/N          # 频率坐标
    FFT_y = FFT_y[range(int(N/2))]          # 取一半
    return Fre, FFT_y
#Fre, FFT_y1 = FFT(df, count)
Fre, FFT_y1 = FFT(df, tr.data)
plt.figure(figsize=(8,2),dpi=100)
plt.plot(Fre,FFT_y1)
#plt.plot(1/Fre/3600/24,FFT_y1)
plt.grid()
#plt.xlabel('Period (day)')
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
plt.xscale('log')
#plt.xlim(0,0.00002)
plt.show() 

Output

Time-Frequency Spectrum

Frequency spectrum at different time of the segment.

Code

from scipy.signal import stft
df = tr.stats.sampling_rate
f, t, zxx = stft(tr.data, fs=df, nperseg=512, noverlap=512*0.9, nfft=512)
plt.figure(figsize=(10,10))
plt.imshow(np.abs(zxx), aspect='auto',
           extent = (min(t), max(t), max(f), min(f))
          )
plt.gca().invert_yaxis()
#cb = plt.colorbar()
#cb.ax.tick_params(labelsize = 20)
plt.xticks(size = 20)
plt.yticks(size = 20)
plt.title('STFT Magnitude', size = 30)
plt.xlabel('Time [sec]', size = 30)
plt.ylabel('Frequency [Hz]', size = 30)

Output