def seg_norm(self, in_prefix=''):
def_tpms = np.zeros((3,1), dtype=np.object)
spm_path = spm_info.spm_path
def_tpms[0] = pjoin(spm_path, 'tpm', 'grey.nii'),
def_tpms[1] = pjoin(spm_path, 'tpm', 'white.nii'),
def_tpms[2] = pjoin(spm_path, 'tpm', 'csf.nii')
data = np.zeros((1,), dtype=object)
data[0] = self.data_def['anatomical']
sninfo = make_job('spatial', 'preproc', {
'data': data,
'output':{
'GM':fltcols([0,0,1]),
'WM':fltcols([0,0,1]),
'CSF':fltcols([0,0,0]),
'biascor':1.0,
'cleanup':False,
},
'opts':{
'tpm':def_tpms,
'ngaus':fltcols([2,2,2,4]),
'regtype':'mni',
'warpreg':1.0,
'warpco':25.0,
'biasreg':0.0001,
'biasfwhm':60.0,
'samp':3.0,
'msk':np.array([], dtype=object),
}
})
run_jobdef(sninfo)
return in_prefix
def segnorm(data_def):
def_tpms = np.zeros((3,1), dtype=np.object)
spm_path = spm_info.spm_path
def_tpms[0] = pjoin(spm_path, 'tpm', 'grey.nii'),
def_tpms[1] = pjoin(spm_path, 'tpm', 'white.nii'),
def_tpms[2] = pjoin(spm_path, 'tpm', 'csf.nii')
data = np.zeros((1,), dtype=object)
data[0] = data_def['anatomical']
sninfo = make_job('spatial', 'preproc', {
'data': data,
'output':{
'GM':fltcols([0,0,1]),
'WM':fltcols([0,0,1]),
'CSF':fltcols([0,0,0]),
'biascor':1.0,
'cleanup':False,
},
'opts':{
'tpm':def_tpms,
'ngaus':fltcols([2,2,2,4]),
'regtype':'mni',
'warpreg':1.0,
'warpco':25.0,
'biasreg':0.0001,
'biasfwhm':60.0,
'samp':3.0,
'msk':np.array([], dtype=object),
}
})
run_jobdef(sninfo)
def seg_norm(self, in_prefix=""):
def_tpms = np.zeros((3, 1), dtype=np.object)
spm_path = spm_info.spm_path
def_tpms[0] = (pjoin(spm_path, "tpm", "grey.nii"),)
def_tpms[1] = (pjoin(spm_path, "tpm", "white.nii"),)
def_tpms[2] = pjoin(spm_path, "tpm", "csf.nii")
data = np.zeros((1,), dtype=object)
data[0] = self.data_def["anatomical"]
sninfo = make_job(
"spatial",
"preproc",
{
"data": data,
"output": {
"GM": fltcols([0, 0, 1]),
"WM": fltcols([0, 0, 1]),
"CSF": fltcols([0, 0, 0]),
"biascor": 1.0,
"cleanup": False,
},
"opts": {
"tpm": def_tpms,
"ngaus": fltcols([2, 2, 2, 4]),
"regtype": "mni",
"warpreg": 1.0,
"warpco": 25.0,
"biasreg": 0.0001,
"biasfwhm": 60.0,
"samp": 3.0,
"msk": np.array([], dtype=object),
},
},
)
run_jobdef(sninfo)
return in_prefix
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 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