def predicted_retinotopy_data(hemi, retino_type):
dat = _predicted_retinotopy_names[retino_type.lower()]<EOL>hdat = {s.lower(): s for s in six.iterkeys(hemi.properties)}<EOL>return next((hemi.prop(hdat[s]) for s in dat if s.lower() in hdat), None)<EOL>
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'.