code: def transform(self, fseries, **kwargs):
          
          weight = 1 + numpy.log10(self.qrange[1]/self.qrange[0]) / numpy.sqrt(2)
          nind, nplanes, peak, result = (0, 0, 0, None)
          # identify the plane with the loudest tile
          for plane in self:
              nplanes += 1
              nind += sum([1 + row.ntiles * row.deltam for row in plane])
              result = plane.transform(fseries, **kwargs)
              if result.peak['energy'] > peak:
                  out = result
                  peak = out.peak['energy']
          return (out, nind * weight / nplanes)
docs: Compute the time-frequency plane at fixed Q with the most
          significant tile
  
          Parameters
          ----------
          fseries : `~gwpy.timeseries.FrequencySeries`
              the complex FFT of a time-series data set
  
          **kwargs
              other keyword arguments to pass to `QPlane.transform`
  
          Returns
          -------
          out : `QGram`
              signal energies over the time-frequency plane containing the most
              significant tile
  
          N : `int`
              estimated number of statistically independent tiles
  
          See Also
          --------
          QPlane.transform
              compute the Q-transform over a single time-frequency plane