code: def _cont_norm_running_quantile_regions_mp(wl, fluxes, ivars, q, delta_lambda,
                                             ranges, n_proc=2, verbose=False):
      
      print("contnorm.py: continuum norm using running quantile")
      print("Taking spectra in %s chunks" % len(ranges))
      # nstars = fluxes.shape[0]
      nchunks = len(ranges)
      norm_fluxes = np.zeros(fluxes.shape)
      norm_ivars = np.zeros(ivars.shape)
      for i in xrange(nchunks):
          chunk = ranges[i, :]
          start = chunk[0]
          stop = chunk[1]
          if verbose:
              print('@Bo Zhang: Going to normalize Chunk [%d/%d], pixel:[%d, %d] ...'
                    % (i+1, nchunks, start, stop))
          output = _cont_norm_running_quantile_mp(
              wl[start:stop], fluxes[:, start:stop],
              ivars[:, start:stop], q, delta_lambda,
              n_proc=n_proc, verbose=verbose)
          norm_fluxes[:, start:stop] = output[0]
          norm_ivars[:, start:stop] = output[1]
      return norm_fluxes, norm_ivars
docs: Perform continuum normalization using running quantile, for spectrum
      that comes in chunks.
  
      The same as _cont_norm_running_quantile_regions(),
      but using multi-processing.
  
      Bo Zhang (NAOC)