def norm_write(self, in_prefix='', out_prefix='w'): sess_scans = scans_for_fnames( fnames_presuffix(self.data_def['functionals'], in_prefix)) matname = fname_presuffix(self.data_def['anatomical'], suffix='_seg_sn.mat', use_ext=False) subj = { 'matname': np.zeros((1,), dtype=object), 'resample': np.vstack(sess_scans.flat), } subj['matname'][0] = matname roptions = { 'preserve':False, 'bb':np.array([[-78,-112, -50],[78,76,85.0]]), 'vox':fltcols([2.0,2.0,2.0]), 'interp':1.0, 'wrap':[0.0,0.0,0.0], 'prefix': out_prefix, } nwinfo = make_job('spatial', 'normalise', [{ 'write':{ 'subj': subj, 'roptions': roptions, } }]) run_jobdef(nwinfo) # knock out the list of images, replacing with only one subj['resample'] = np.zeros((1,), dtype=object) subj['resample'][0] = self.data_def['anatomical'] roptions['interp'] = 4.0 run_jobdef(nwinfo) return out_prefix + in_prefix
def coregister(self, in_prefix=''): func1 = self.data_def['functionals'][0] mean_fname = fname_presuffix(func1, 'mean' + in_prefix) crinfo = make_job('spatial', 'coreg', [{ 'estimate':{ 'ref': np.asarray(mean_fname, dtype=object), 'source': np.asarray(self.data_def['anatomical'], dtype=object), 'other': [''], 'eoptions':{ 'cost_fun':'nmi', 'sep':[4.0, 2.0], 'tol':np.array( [0.02,0.02,0.02, 0.001,0.001,0.001, 0.01,0.01,0.01, 0.001,0.001,0.001]).reshape(1,12), 'fwhm':[7.0, 7.0] } } }]) run_jobdef(crinfo) return in_prefix