@pimms.value<EOL><INDENT>def images(filemap):<DEDENT>
imgmap = filemap['<STR_LIT>']<EOL>def _img_loader(k): return lambda:imgmap[k]<EOL>imgs = {k:_img_loader(k) for k in six.iterkeys(imgmap)}<EOL>def _make_mask(val, eq=True):<EOL><INDENT>rib = imgmap['<STR_LIT>']<EOL>arr = (rib.get_data() == val) if eq else (rib.get_data() != val)<EOL>arr.setflags(write=False)<EOL>return type(rib)(arr, rib.affine, rib.header)<EOL><DEDENT>imgs['<STR_LIT>']  = lambda:_make_mask(<NUM_LIT:3>)<EOL>imgs['<STR_LIT>'] = lambda:_make_mask(<NUM_LIT:2>)<EOL>imgs['<STR_LIT>']  = lambda:_make_mask(<NUM_LIT>)<EOL>imgs['<STR_LIT>'] = lambda:_make_mask(<NUM_LIT>)<EOL>imgs['<STR_LIT>']    = lambda:_make_mask(<NUM_LIT:0>, False)<EOL>return pimms.lazy_map(imgs)<EOL>
sub.images is a persistent map of MRImages tracked by the given subject sub; in HCP subjects
these are renamed and converted from their typical HCP filenames (such as 'ribbon') to forms
that conform to the neuropythy naming conventions (such as 'gray_mask').