docs: def _fourier(self):
          """ 1 side Fourier transform and scale by dt all waveforms in catalog """
  
          freq_bin_upper = 2000
          freq_bin_lower = 40
          fs = self._metadata['fs']
          Y_transformed = {}
          for key in self.Y_dict.keys():
              # normalize by fs, bins have units strain/Hz
              fs = self._metadata["fs"]
              Y_transformed[key] = (1./fs)*np.fft.fft(self.Y_dict[key])[freq_bin_lower:freq_bin_upper]
          self.Y_transformed = Y_transformed
          # add in transform metadata
          self._metadata["dF"] = 1./self._metadata["T"]
  
          # because we are in the fourier domain, we will need the psd
          self.psd = load_psd()[freq_bin_lower:freq_bin_upper]
          dF = self._metadata['dF']
          self.sigma = convert_psd_to_sigma(self.psd, dF)