def transform(self, fseries, **kwargs):
weight = <NUM_LIT:1> + numpy.log10(self.qrange[<NUM_LIT:1>]/self.qrange[<NUM_LIT:0>]) / numpy.sqrt(<NUM_LIT:2>)<EOL>nind, nplanes, peak, result = (<NUM_LIT:0>, <NUM_LIT:0>, <NUM_LIT:0>, None)<EOL>for plane in self:<EOL><INDENT>nplanes += <NUM_LIT:1><EOL>nind += sum([<NUM_LIT:1> + row.ntiles * row.deltam for row in plane])<EOL>result = plane.transform(fseries, **kwargs)<EOL>if result.peak['<STR_LIT>'] > peak:<EOL><INDENT>out = result<EOL>peak = out.peak['<STR_LIT>']<EOL><DEDENT><DEDENT>return (out, nind * weight / nplanes)<EOL>
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