def lowpass(self, frequency, gpass=<NUM_LIT:2>, gstop=<NUM_LIT:30>, fstop=None, type='<STR_LIT>',<EOL>filtfilt=True, **kwargs):
<EOL>filt = filter_design.lowpass(frequency, self.sample_rate,<EOL>fstop=fstop, gpass=gpass, gstop=gstop,<EOL>analog=False, type=type, **kwargs)<EOL>return self.filter(*filt, filtfilt=filtfilt)<EOL>
Filter this `TimeSeries` with a Butterworth low-pass filter.

        Parameters
        ----------
        frequency : `float`
            low-pass corner frequency

        gpass : `float`
            the maximum loss in the passband (dB).

        gstop : `float`
            the minimum attenuation in the stopband (dB).

        fstop : `float`
            stop-band edge frequency, defaults to `frequency * 1.5`

        type : `str`
            the filter type, either ``'iir'`` or ``'fir'``

        **kwargs
            other keyword arguments are passed to
            :func:`gwpy.signal.filter_design.lowpass`

        Returns
        -------
        lpseries : `TimeSeries`
            a low-passed version of the input `TimeSeries`

        See Also
        --------
        gwpy.signal.filter_design.lowpass
            for details on the filter design
        TimeSeries.filter
            for details on how the filter is applied

        .. note::

           When using `scipy < 0.16.0` some higher-order filters may be
           unstable. With `scipy >= 0.16.0` higher-order filters are
           decomposed into second-order-sections, and so are much more stable.