docs: def run(self):
          """ Perform the postprocessing steps, computing compound signals from
          cell-specific output files.
          """
          if RANK == 0:
              if 'LFP' in self.savelist:
                  #get the per population LFPs and total LFP from all populations:
                  self.LFPdict, self.LFPsum = self.calc_lfp()
                  self.LFPdictLayer = self.calc_lfp_layer()
      
                  #save global LFP sum, and from L23E, L4I etc.:
                  f = h5py.File(os.path.join(self.savefolder,
                                             self.compound_file.format('LFP')
                                             ), 'w')
                  f['srate'] = 1E3 / self.dt_output
                  f.create_dataset('data', data=self.LFPsum, compression=4)
                  f.close()
      
                  for key, value in list(self.LFPdictLayer.items()):
                      f = h5py.File(os.path.join(self.populations_path,
                                                 self.output_file.format(key,
                                                                         'LFP.h5')
                                                 ), 'w')
                      f['srate'] = 1E3 / self.dt_output
                      f.create_dataset('data', data=value, compression=4)
                      f.close()
  
              if 'CSD' in self.savelist:
                  #get the per population CSDs and total CSD from all populations:
                  self.CSDdict, self.CSDsum = self.calc_csd()
                  self.CSDdictLayer = self.calc_csd_layer()
      
                  #save global CSD sum, and from L23E, L4I etc.:
                  f = h5py.File(os.path.join(self.savefolder,
                                             self.compound_file.format('CSD')),
                                'w')
                  f['srate'] = 1E3 / self.dt_output
                  f.create_dataset('data', data=self.CSDsum, compression=4)
                  f.close()
      
                  for key, value in list(self.CSDdictLayer.items()):
                      f = h5py.File(os.path.join(self.populations_path,
                                                 self.output_file.format(key,
                                                                         'CSD.h5')
                                                 ), 'w')
                      f['srate'] = 1E3 / self.dt_output
                      f.create_dataset('data', data=value, compression=4)
                      f.close()
  
          else:
              pass