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 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 smooth(self, in_prefix='', out_prefix='s'):
fwhm = self.ana_def['fwhm']
try:
len(fwhm)
except TypeError:
fwhm = [fwhm] * 3
fwhm = np.asarray(fwhm, dtype=np.float).reshape(1,3)
sess_scans = scans_for_fnames(
fnames_presuffix(self.data_def['functionals'], in_prefix))
sinfo = make_job('spatial', 'smooth',
{'data':np.vstack(sess_scans.flat),
'fwhm':fwhm,
'dtype':0})
run_jobdef(sinfo)
return out_prefix + in_prefix
def slicetime(self, in_prefix='', out_prefix='a'):
sess_scans = scans_for_fnames(
fnames_presuffix(self.data_def['functionals'], in_prefix))
sdef = self.study_def
stinfo = make_job('temporal', 'st', {
'scans': sess_scans,
'so': sdef['time_to_space'],
'tr': sdef['TR'],
'ta': sdef['TA'],
'nslices': float(sdef['n_slices']),
'refslice':1,
'prefix': out_prefix,
})
run_jobdef(stinfo)
return out_prefix + in_prefix
def realign(self, in_prefix=''):
sess_scans = scans_for_fnames(
fnames_presuffix(self.data_def['functionals'], in_prefix))
rinfo = make_job('spatial', 'realign', [{
'estimate':{
'data':sess_scans,
'eoptions':{
'quality': 0.9,
'sep': 4.0,
'fwhm': 5.0,
'rtm': True,
'interp': 2.0,
'wrap': [0.0,0.0,0.0],
'weight': []
}
}
}])
run_jobdef(rinfo)
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
def reslice(self, in_prefix='', out_prefix='r', out=('1..n', 'mean')):
which = [0, 0]
if 'mean' in out:
which[1] = 1
if '1..n' in out or 'all' in out:
which[0] = 2
elif '2..n' in out:
which[0] = 1
sess_scans = scans_for_fnames(
fnames_presuffix(self.data_def['functionals'], in_prefix))
rsinfo = make_job('spatial', 'realign', [{
'write':{
'data': np.vstack(sess_scans.flat),
'roptions':{
'which': which,
'interp':4.0,
'wrap':[0.0,0.0,0.0],
'mask':True,
'prefix': out_prefix
}
}
}])
run_jobdef(rsinfo)
return out_prefix + in_prefix