def _apply_scaling_array(data, picks_list, scalings):
 scalings = _check_scaling_inputs(data, picks_list, scalings)
 if isinstance(scalings, dict):
  picks_dict = dict(picks_list)
  scalings = [(picks_dict[k], v) for (k, v) in scalings.items() if (k in picks_dict)]
  for (idx, scaling) in scalings:
   data[idx, :] *= scaling
 else:
  data *= scalings[:, np.newaxis]

def _apply_scaling_array ( data , picks_list , scalings ) : scalings = _check_scaling_inputs ( data , picks_list , scalings ) if isinstance ( scalings , dict ) : picks_dict = dict ( picks_list ) scalings = [ ( picks_dict [ k ] , v ) for ( k , v ) in scalings . items ( ) if ( k in picks_dict ) ] for ( idx , scaling ) in scalings : data [ idx , : ] *= scaling else : data *= scalings [ : , np . newaxis ]
scale data type-dependently for estimation .