code: def notch_fir(self, f1, f2, order, beta=5.0, remove_corrupted=True):
          
          from pycbc.filter import notch_fir
          ts = notch_fir(self, f1, f2, order, beta=beta)
          if remove_corrupted:
              ts = ts[order:len(ts)-order]
          return ts
docs: notch filter the time series using an FIR filtered generated from
          the ideal response passed through a time-domain kaiser
          window (beta = 5.0)
  
          The suppression of the notch filter is related to the bandwidth and
          the number of samples in the filter length. For a few Hz bandwidth,
          a length corresponding to a few seconds is typically
          required to create significant suppression in the notched band.
  
          Parameters
          ----------
          Time Series: TimeSeries
              The time series to be notched.
          f1: float
              The start of the frequency suppression.
          f2: float
              The end of the frequency suppression.
          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.