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 norm_write(data_def): sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a')) matname = fname_presuffix(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], } 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] = data_def['anatomical'] roptions['interp'] = 4.0 run_jobdef(nwinfo)
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
def coregister(data_def): func1 = data_def['functionals'][0] mean_fname = fname_presuffix(func1, 'meana') crinfo = make_job('spatial', 'coreg', [{ 'estimate':{ 'ref': [mean_fname], 'source': [data_def['anatomical']], '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)
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(data_def): func1 = data_def['functionals'][0] mean_fname = fname_presuffix(func1, 'meana') crinfo = make_job('spatial', 'coreg', [{ 'estimate': { 'ref': [mean_fname], 'source': [data_def['anatomical']], '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)
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