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
