text: # extrat_34_hfs: extract mean and std from LLD of 34 features
  # better to save when extracting LLD
  
  import numpy as np
  feat_pad = np.load('../data/feat_34_float.npy')
  
  feat_pad_float = feat_pad.astype(feat_pad)
  
  feat_pad_float[feat_pad_float==0] =np.nan
  
  # compute mean and std
  mean = np.nanmean(feat_pad_float, axis=1)
  std = np.nanstd(feat_pad_float, axis=1)
  
  # output feature
  feat_hfs = np.hstack([mean, std])
  feat_hst = feat_hfs.reshape(10039, 1, 68)
  np.save('../data/feat_hfs.npy', feat_hfs)
  