code: def _parse_01(ofiles, individual=False):
      
  
      ## parse results from outfiles
      cols = []
      dats = []
      for ofile in ofiles:
  
          ## parse file
          with open(ofile) as infile:
              dat  = infile.read()
          lastbits = dat.split(".mcmc.txt\n\n")[1:]
          results = lastbits[0].split("\n\n")[0].split()
  
          ## get shape from ...
          shape = (((len(results) - 3) / 4), 4)
          dat = np.array(results[3:]).reshape(shape)
          cols.append(dat[:, 3].astype(float))
          
      if not individual:
          ## get mean results across reps
          cols = np.array(cols)
          cols = cols.sum(axis=0) / len(ofiles) #10.
          dat[:, 3] = cols.astype(str)
  
          ## format as a DF
          df = pd.DataFrame(dat[:, 1:])
          df.columns = ["delim", "prior", "posterior"]
          nspecies = 1 + np.array([list(i) for i in dat[:, 1]], dtype=int).sum(axis=1)
          df["nspecies"] = nspecies
          return df
      
      else:
          ## get mean results across reps
          #return cols
          res = []
          for i in xrange(len(cols)):
              x = dat
              x[:, 3] = cols[i].astype(str)
              x = pd.DataFrame(x[:, 1:])
              x.columns = ['delim', 'prior', 'posterior']
              nspecies = 1 + np.array([list(i) for i in dat[:, 1]], dtype=int).sum(axis=1)
              x["nspecies"] = nspecies
              res.append(x)
          return res
docs: a subfunction for summarizing results