def _check_epochs_input(epochs, y, picks=None):
 if (y is None):
  y = epochs.events[:, 2]
 elif isinstance(y, list):
  y = np.array(y)
 X = epochs.get_data()
 if (picks is None):
  picks = _pick_data_channels(epochs.info, with_ref_meg=False)
 if isinstance(picks, (list, np.ndarray)):
  picks = np.array(picks, dtype=np.int)
 else:
  raise ValueError('picks must be a list or a numpy.ndarray of int')
 X = X[:, picks, :]
 assert (X.shape[0] == y.shape[0])
 return (X, y, picks)

def _check_epochs_input ( epochs , y , picks = None ) : if ( y is None ) : y = epochs . events [ : , 2 ] elif isinstance ( y , list ) : y = np . array ( y ) X = epochs . get_data ( ) if ( picks is None ) : picks = _pick_data_channels ( epochs . info , with_ref_meg = False ) if isinstance ( picks , ( list , np . ndarray ) ) : picks = np . array ( picks , dtype = np . int ) else : raise ValueError ( 'picks must be a list or a numpy.ndarray of int' ) X = X [ : , picks , : ] assert ( X . shape [ 0 ] == y . shape [ 0 ] ) return ( X , y , picks )
aux function of generalizationacrosstime .