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
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
