def bandpass(self, flow, fhigh, gpass=<NUM_LIT:2>, gstop=<NUM_LIT:30>, fstop=None, type='<STR_LIT>',<EOL>filtfilt=True, **kwargs):
<EOL>filt = filter_design.bandpass(flow, fhigh, 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 band-pass filter.

        Parameters
        ----------
        flow : `float`
            lower corner frequency of pass band

        fhigh : `float`
            upper corner frequency of pass band

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

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

        fstop : `tuple` of `float`, optional
            `(low, high)` edge-frequencies of stop band

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

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

        Returns
        -------
        bpseries : `TimeSeries`
            a band-passed version of the input `TimeSeries`

        See Also
        --------
        gwpy.signal.filter_design.bandpass
            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.