code: def load_h5_data(path='', data_type='LFP', y=None, electrode=None,
                   warmup=0., scaling=1.):
      
      assert y is not None or electrode is not None
      
      if y is not None:
          f = h5py.File(os.path.join(path, '%s_%ss.h5' %(y,data_type)))
          data = f['data'].value[:,:, warmup:]
          if scaling != 1.:
              np.random.shuffle(data)
              num_cells = int(len(data)*scaling)
              data = data[:num_cells,:, warmup:]
      else:
          f = h5py.File(os.path.join(path, '%ssum.h5' %data_type))
          data = f['data'].value[:, warmup:]
  
      return data
docs: Function loading results from hdf5 file
      
      
      Parameters
      ----------
      path : str
          Path to hdf5-file
      data_type : str
          Signal types in ['CSD' , 'LFP', 'CSDsum', 'LFPsum'].
      y : None or str
          Name of population.
      electrode : None or int
          TODO: update, electrode is NOT USED
      warmup : float
          Lower cutoff of time series to remove possible transients
      scaling : float,
          Scaling factor for population size that determines the amount of loaded
          single-cell signals
                  
      
      Returns
      ----------
      numpy.ndarray
          [electrode id, compound signal] if `y` is None
      numpy.ndarray
          [cell id, electrode, single-cell signal] otherwise