def disk_vmag(hemi, retinotopy='any', to=None, **kw):
    '''
    disk_vmag(mesh) yields the visual magnification based on the projection of disks on the cortical
      surface into the visual field.

    All options accepted by mag_data() are accepted by disk_vmag().
    '''
    mdat = mag_data(hemi, retinotopy=retinotopy, **kw)
    if pimms.is_vector(mdat): return tuple([face_vmag(m, to=to) for m in mdat])
    elif pimms.is_vector(mdat.keys(), 'int'):
        return pimms.lazy_map({k: curry(lambda k: face_vmag(mdat[k], to=to), k)
                               for k in six.iterkeys(mdat)})
    #TODO: implement the disk_vmag calculation using mdat
    # convert to the appropriate type according to the to param
    raise NotImplementedError()
def disk_vmag ( hemi , retinotopy = 'any' , to = None , ** kw ) : mdat = mag_data ( hemi , retinotopy = retinotopy , ** kw ) if pimms . is_vector ( mdat ) : return tuple ( [ face_vmag ( m , to = to ) for m in mdat ] ) elif pimms . is_vector ( mdat . keys ( ) , 'int' ) : return pimms . lazy_map ( { k : curry ( lambda k : face_vmag ( mdat [ k ] , to = to ) , k ) for k in six . iterkeys ( mdat ) } ) raise NotImplementedError ( )
disk_vmag(mesh) yields the visual magnification based on the projection of disks on the cortical
      surface into the visual field.

    All options accepted by mag_data() are accepted by disk_vmag().