def ifft(self):
from ..timeseries import TimeSeries<EOL>nout = (self.size - <NUM_LIT:1>) * <NUM_LIT:2><EOL>dift = npfft.irfft(self.value * nout) / <NUM_LIT:2><EOL>new = TimeSeries(dift, epoch=self.epoch, channel=self.channel,<EOL>unit=self.unit, dx=<NUM_LIT:1>/self.dx/nout)<EOL>return new<EOL>
Compute the one-dimensional discrete inverse Fourier
        transform of this `FrequencySeries`.

        Returns
        -------
        out : :class:`~gwpy.timeseries.TimeSeries`
            the normalised, real-valued `TimeSeries`.

        See Also
        --------
        :mod:`scipy.fftpack` for the definition of the DFT and conventions
        used.

        Notes
        -----
        This method applies the necessary normalisation such that the
        condition holds:

        >>> timeseries = TimeSeries([1.0, 0.0, -1.0, 0.0], sample_rate=1.0)
        >>> timeseries.fft().ifft() == timeseries