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
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 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 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
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 slicetime(data_def):
sess_scans = scans_for_fnames(data_def['functionals'])
stinfo = make_job('temporal', 'st', {
'scans': sess_scans,
'so':range(1,31,2) + range(2,31,2),
'tr':2.5,
'ta':2.407,
'nslices':float(30),
'refslice':1
})
run_jobdef(stinfo)
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 smooth(data_def, fwhm=8.0):
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(data_def['functionals'], 'wa'))
sinfo = make_job('spatial', 'smooth',
{'data':np.vstack(sess_scans.flat),
'fwhm':fwhm,
'dtype':0})
run_jobdef(sinfo)
def slicetime(data_def):
sess_scans = scans_for_fnames(data_def['functionals'])
stinfo = make_job(
'temporal', 'st', {
'scans': sess_scans,
'so': range(1, 31, 2) + range(2, 31, 2),
'tr': 2.5,
'ta': 2.407,
'nslices': float(30),
'refslice': 1
})
run_jobdef(stinfo)
def reslice(data_def):
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a'))
rsinfo = make_job('spatial', 'realign', [{
'write':{
'data': np.vstack(sess_scans.flat),
'roptions':{
'which':[2, 1],
'interp':4.0,
'wrap':[0.0,0.0,0.0],
'mask':True,
}
}
}])
run_jobdef(rsinfo)
def smooth(data_def, fwhm=8.0):
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(data_def['functionals'], 'wa'))
sinfo = make_job('spatial', 'smooth', {
'data': np.vstack(sess_scans.flat),
'fwhm': fwhm,
'dtype': 0
})
run_jobdef(sinfo)
def reslice(data_def):
sess_scans = scans_for_fnames(
fnames_presuffix(data_def['functionals'], 'a'))
rsinfo = make_job('spatial', 'realign', [{
'write': {
'data': np.vstack(sess_scans.flat),
'roptions': {
'which': [2, 1],
'interp': 4.0,
'wrap': [0.0, 0.0, 0.0],
'mask': True,
}
}
}])
run_jobdef(rsinfo)
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 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 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 realign(data_def):
sess_scans = scans_for_fnames(fnames_presuffix(data_def['functionals'], 'a'))
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)
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(data_def):
sess_scans = scans_for_fnames(
fnames_presuffix(data_def['functionals'], 'a'))
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)
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(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
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
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
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