text: """ convert file format so na > np.nan to be compatible with DeepTCR"""
  
  import glob
  import os
  import pandas as pd
  import numpy as np
  from multiprocessing import Pool
  
  files = '/users/dyao/DeepTCR_COVID19/Data/raw/ImmuneCODE-Review-002/*.tsv'
  #'/users/dyao/DeepTCR_COVID19/Data/raw/ ImmuneCODE-Review-002/*.tsv'
  files = glob.glob(files)
  
  def reform(file):
      try:
          if not os.path.exists(os.path.join('/users/dyao/DeepTCR_COVID19/Data/raw/repertoires',os.path.basename(file))):
              #'/users/dyao/DeepTCR_COVID19/Data/raw/repertoires'
              df = pd.read_csv(file,sep='\t')
              df['amino_acid'][df['amino_acid']=='na'] = np.nan
              df=df.dropna()
              df.to_csv(os.path.join('./users/dyao/DeepTCR_COVID19/Data/raw/repertoires',os.path.basename(file)),sep='\t',index=False)
          return None
      except:
          return file
  
  p = Pool(80)
  out = p.map(reform,files)
  p.join()
  p.close()
  
  file = '/users/dyao/DeepTCR_COVID19/Data/raw/ImmuneCODE-Review-002/INCOV067-AC-3_TCRB.tsv'
  #'/users/dyao/DeepTCR_COVID19/Data/raw/ImmuneCODE-Review-002/INCOV068-AC-3_TCRB.tsv
  df = pd.read_csv(file, sep='\t')
  df_out = pd.DataFrame(columns=range(0,52))
  df_out[3] = df[df.columns[1]]
  df_out[4] = df[df.columns[2]]
  df_out[49] = df[df.columns[7]]
  df_out[35] = df[df.columns[14]]
  df_out[42] = df[df.columns[21]]
  df_out.to_csv(os.path.join('/users/dyao/DeepTCR_COVID19/Data/raw/repertoires', os.path.basename(file)), sep='\t', index=False)
  
  out = [x for x in out if x != None]
  df = pd.read_csv(out[0], sep='\t')
  