old_contents: import numpy as np
  import pylab as plt
  from numpy import fft
  import src.mylib.mlstm as mlstm
  
  def bandpass_filter(x, freq, frequency_of_signal=0, band=0.1):
      if (frequency_of_signal - band) < abs(freq) < (frequency_of_signal + band):
          return x
      else:
          return 0
  
  ds = mlstm.loadFXData('JPY=X', '../db/forex.db', 9000)
  x = ds[['Close']].values
  x = x.flatten()
  L = []
  for i in range(1000):
      x = x[i:]
      N = len(x)
      spectrum = fft.fft(x)
      feq = fft.fftfreq(N)            # frequencies
      ampli = np.absolute(spectrum)   # amplitude
      phase = np.angle(spectrum)      # phase
      #print(phase)
      index = np.argsort(-ampli)
      sfreq = feq[index]
      sampl = ampli[index]
      #sfreq = np.where(sfreq > 0)
      #big = list(zip(*sfreq))
      print(sfreq[1:10] * N)
      #plt.semilogy(sfreq * N, sampl, 'o')
  #F_filtered = np.asanyarray([bandpass_filter(x, freq) for x, freq in zip(spectrum, feq)])
  #filtered_signal = np.fft.ifft(F_filtered)
  
  #plt.semilogy(feq[1:], ampli[1:]), 'o') #zero feq is very large
  #plt.semilogy(ampli[1:])
  plt.legend()
  plt.show()
  
new_contents: import numpy as np
  import pylab as plt
  from numpy import fft
  import src.mylib.mlstm as mlstm
  
  def bandpass_filter(x, freq, frequency_of_signal=0, band=0.1):
      if (frequency_of_signal - band) < abs(freq) < (frequency_of_signal + band):
          return x
      else:
          return 0
  
  ds = mlstm.loadFXData('JPY=X', '../db/forex.db', 3500)
  y = ds[['Close']].values
  y = y.flatten()
  for k in range(5, 30):
      for i in range(0, 3000, 1):
          x = y[i:i+k]
          N = len(x)
          spectrum = fft.fft(x)
          feq = fft.fftfreq(N)            # frequencies
          ampli = np.absolute(spectrum)   # amplitude
          phase = np.angle(spectrum)      # phase
          #print(phase)
          index = np.argsort(-ampli)
          sfreq = feq[index]
          sampl = ampli[index]
          sfreq = sfreq[sfreq > 0]
          #big = list(zip(*sfreq))
          if sfreq[0] * N >= k*0.5 - 1:
              print(N, sfreq[:2] * N)
          #plt.semilogy(ampli, 'o')
          #F_filtered = np.asanyarray([bandpass_filter(x, freq) for x, freq in zip(spectrum, feq)])
          #filtered_signal = np.fft.ifft(F_filtered)
  
          #plt.semilogy(feq[1:], ampli[1:]), 'o') #zero feq is very large
          #plt.semilogy(ampli[1:])
          #plt.title(str(N))
          #plt.legend()
          #plt.show()
subject: Add Tensor Flow