code: def lowpass_fir(self, frequency, order, beta=5.0, remove_corrupted=True):
          
          from pycbc.filter import lowpass_fir
          ts = lowpass_fir(self, frequency, order, beta=beta)
          if remove_corrupted:
              ts = ts[order:len(ts)-order]
          return ts
docs: Lowpass filter the time series using an FIR filtered generated from
          the ideal response passed through a kaiser window (beta = 5.0)
  
          Parameters
          ----------
          Time Series: TimeSeries
              The time series to be low-passed.
          frequency: float
              The frequency below which is suppressed.
          order: int
              Number of corrupted samples on each side of the time series
          beta: float
              Beta parameter of the kaiser window that sets the side lobe attenuation.
          remove_corrupted : {True, boolean}
              If True, the region of the time series corrupted by the filtering
              is excised before returning. If false, the corrupted regions
              are not excised and the full time series is returned.