new_contents: import numpy as np
  def normalize_data(data, zero_id = '0', one_id = '1'):
      metadata_str = [f for f in data.dtype.fields.keys() if 'metadata' in f]
      if len(metadata_str)!=1:
          #error
          return
      #find indeces for calibration points
      zero_cal_idx = [i for i, x in enumerate(data[metadata_str[0]]) if x.decode() == zero_id]
      one_cal_idx = [i for i, x in enumerate(data[metadata_str[0]]) if x.decode() == one_id]
      #find values for calibrated 0 and 1
      zero_cal = np.mean(data['Data'][zero_cal_idx])
      one_cal = np.mean(data['Data'][one_cal_idx])
  
      #normalize
      scale_factor = (zero_cal - one_cal)/2
      norm_data = (data['Data']-zero_cal)/scale_factor + 1
      #remove calibration points
      norm_data = [d for ind, d in enumerate(norm_data) if ind not in zero_cal_idx + one_cal_idx]
      return norm_data
  
subject: Normalize data using metadata