def predicted_retinotopy_data(hemi, retino_type):
    '''
    predicted_retinotopy_data(hemi, t) yields a numpy array of data for the given cortex object hemi
    and retinotopy type t; it does this by looking at the properties in hemi and picking out any
    combination that is commonly used to denote empirical retinotopy data. These common names are
    stored in _predicted_retintopy_names, in order of preference, which may be modified.
    The argument t should be one of 'polar_angle', 'eccentricity', 'visual_area'.
    '''
    dat = _predicted_retinotopy_names[retino_type.lower()]
    hdat = {s.lower(): s for s in six.iterkeys(hemi.properties)}
    return next((hemi.prop(hdat[s]) for s in dat if s.lower() in hdat), None)
def predicted_retinotopy_data ( hemi , retino_type ) : dat = _predicted_retinotopy_names [ retino_type . lower ( ) ] hdat = { s . lower ( ) : s for s in six . iterkeys ( hemi . properties ) } return next ( ( hemi . prop ( hdat [ s ] ) for s in dat if s . lower ( ) in hdat ) , None )
predicted_retinotopy_data(hemi, t) yields a numpy array of data for the given cortex object hemi
    and retinotopy type t; it does this by looking at the properties in hemi and picking out any
    combination that is commonly used to denote empirical retinotopy data. These common names are
    stored in _predicted_retintopy_names, in order of preference, which may be modified.
    The argument t should be one of 'polar_angle', 'eccentricity', 'visual_area'.