code: def normal(start, end, seed=0):
      
      # This is reproduceable because we used fixed seeds from known values
      s = int(start / BLOCK_SIZE)
      e = int(end / BLOCK_SIZE)
  
      # The data evenly divides so the last block would be superfluous
      if end % BLOCK_SIZE == 0:
          e -= 1
  
      sv = RandomState(seed).randint(-2**50, 2**50)
      data = numpy.concatenate([block(i + sv) for i in numpy.arange(s, e + 1, 1)])
      ts = TimeSeries(data, delta_t=1.0 / SAMPLE_RATE, epoch=start)
      return ts.time_slice(start, end)
docs: Generate data with a white Gaussian (normal) distribution
  
      Parameters
      ----------
      start_time : int
          Start time in GPS seconds to generate noise
      end_time : int
          End time in GPS seconds to generate nosie
      seed : {None, int}
          The seed to generate the noise.
  
      Returns
      --------
      noise : TimeSeries
          A TimeSeries containing gaussian noise