TimeSignals.py 文件源码

def derivFFT(df, n=1  ) :
   """ Deriv a signal trought FFT, warning, edge can be a bit noisy...
   indexList : channel to derive
   n : order of derivation
   """
   deriv = []
   for iSig in range(df.shape[1]) :
      fft = np.fft.fft( df.values[:,iSig] )   #FFT
      freq = np.fft.fftfreq( df.shape[0] , dx(df) )

      from copy import deepcopy
      fft0 = deepcopy(fft)
      if n>0 :
         fft *= (1j * 2*pi* freq[:])**n                    #Derivation in frequency domain
      else :
         fft[-n:] *= (1j * 2*pi* freq[-n:])**n
         fft[0:-n] = 0.

      tts = np.real(np.fft.ifft(fft))
      tts -= tts[0]
      deriv.append( tts )    #Inverse FFT

   return pd.DataFrame( data = np.transpose(deriv), index = df.index , columns = [ "DerivFFT("+ x +")" for x in df.columns ]  )