code: def highpass_fir(timeseries, frequency, order, beta=5.0):
      
      k = frequency / float((int(1.0 / timeseries.delta_t) / 2))
      coeff = scipy.signal.firwin(order * 2 + 1, k, window=('kaiser', beta), pass_zero=False)
      data = fir_zero_filter(coeff, timeseries)
      return TimeSeries(data, epoch=timeseries.start_time, delta_t=timeseries.delta_t)
docs: Highpass 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 high-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.