def _check_scaling_inputs(data, picks_list, scalings):
 rescale_dict_ = dict(mag=1000000000000000.0, grad=10000000000000.0, eeg=1000000.0)
 scalings_ = None
 if (isinstance(scalings, string_types) and (scalings == 'norm')):
  scalings_ = (1.0 / _compute_row_norms(data))
 elif isinstance(scalings, dict):
  rescale_dict_.update(scalings)
  scalings_ = rescale_dict_
 elif isinstance(scalings, np.ndarray):
  scalings_ = scalings
 elif (scalings is None):
  pass
 else:
  raise NotImplementedError(("No way! That's not a rescaling option: %s" % scalings))
 return scalings_

def _check_scaling_inputs ( data , picks_list , scalings ) : rescale_dict_ = dict ( mag = 1000000000000000.0 , grad = 10000000000000.0 , eeg = 1000000.0 ) scalings_ = None if ( isinstance ( scalings , string_types ) and ( scalings == 'norm' ) ) : scalings_ = ( 1.0 / _compute_row_norms ( data ) ) elif isinstance ( scalings , dict ) : rescale_dict_ . update ( scalings ) scalings_ = rescale_dict_ elif isinstance ( scalings , np . ndarray ) : scalings_ = scalings elif ( scalings is None ) : pass else : raise NotImplementedError ( ( "No way! That's not a rescaling option: %s" % scalings ) ) return scalings_
aux function .