code: def bartlett(timeseries, segmentlength, noverlap=None, window=None, plan=None):
      # pylint: disable=unused-argument
      
      return _lal_spectrum(timeseries, segmentlength, noverlap=0,
                           method='welch', window=window, plan=plan)
docs: Calculate an PSD of this `TimeSeries` using Bartlett's method
  
      Parameters
      ----------
      timeseries : `~gwpy.timeseries.TimeSeries`
          input `TimeSeries` data.
  
      segmentlength : `int`
          number of samples in single average.
  
      noverlap : `int`
          number of samples to overlap between segments, defaults to 50%.
  
      window : `tuple`, `str`, optional
          window parameters to apply to timeseries prior to FFT
  
      plan : `REAL8FFTPlan`, optional
          LAL FFT plan to use when generating average spectrum
  
      Returns
      -------
      spectrum : `~gwpy.frequencyseries.FrequencySeries`
          average power `FrequencySeries`
  
      See also
      --------
      lal.REAL8AverageSpectrumWelch