code: def _find_cont_running_quantile(wl, fluxes, ivars, q, delta_lambda,
                                  verbose=False):
      
      cont = np.zeros(fluxes.shape)
      nstars = fluxes.shape[0]
      for jj in range(nstars):
          if verbose:
              print("cont_norm_q(): working on star [%s/%s]..." % (jj+1, nstars))
          flux = fluxes[jj,:]
          ivar = ivars[jj,:]
          for ll, lam in enumerate(wl):
              indx = (np.where(abs(wl-lam) < delta_lambda))[0]
              flux_cut = flux[indx]
              ivar_cut = ivar[indx]
              cont[jj, ll] = _weighted_median(flux_cut, ivar_cut, q)
      return cont
docs: Perform continuum normalization using a running quantile
  
      Parameters
      ----------
      wl: numpy ndarray 
          wavelength vector
      fluxes: numpy ndarray of shape (nstars, npixels)
          pixel intensities
      ivars: numpy ndarray of shape (nstars, npixels)
          inverse variances, parallel to fluxes
      q: float
          the desired quantile cut
      delta_lambda: int
          the number of pixels over which the median is calculated
  
      Output
      ------
      norm_fluxes: numpy ndarray of shape (nstars, npixels)
          normalized pixel intensities
      norm_ivars: numpy ndarray of shape (nstars, npixels)
          rescaled pixel invariances