def _nyu_rest_factory(session=1):
from pypreprocess.nipype_preproc_spm_utils import SubjectData
nyu_data = fetch_nyu_rest(sessions=[session], n_subjects=7)
session_func = [x for x in nyu_data.func if "session%i" % session in x]
session_anat = [
x for x in nyu_data.anat_skull if "session%i" % session in x]
for subject_id in set([os.path.basename(os.path.dirname
(os.path.dirname(x)))
for x in session_func]):
# instantiate subject_data object
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = session
# set func
subject_data.func = [x for x in session_func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in session_anat if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = "/tmp/%s" % subject_id
subject_data.sanitize(deleteorient=True, niigz2nii=False)
yield (subject_data.subject_id, subject_data.func[0],
subject_data.anat)
def subject_factory():
"""
Producer for HAXBY subject data.
"""
for subject_id in set(
[os.path.basename(os.path.dirname(x)) for x in haxby_data.func]):
# instantiate subject_data object
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = "haxby2001"
# set func
subject_data.func = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = os.path.join(OUTPUT_DIR,
subject_data.subject_id)
yield subject_data
def _nyu_rest_factory(session=1):
from pypreprocess.nipype_preproc_spm_utils import SubjectData
nyu_data = fetch_nyu_rest(sessions=[session], n_subjects=7)
session_func = [x for x in nyu_data.func if "session%i" % session in x]
session_anat = [
x for x in nyu_data.anat_skull if "session%i" % session in x]
for subject_id in set([os.path.basename(os.path.dirname
(os.path.dirname(x)))
for x in session_func]):
# instantiate subject_data object
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = session
# set func
subject_data.func = [x for x in session_func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in session_anat if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = "/tmp/%s" % subject_id
subject_data.sanitize(deleteorient=True, niigz2nii=False)
yield (subject_data.subject_id, subject_data.func[0],
subject_data.anat)
def subject_factory():
"""
Producer for HAXBY subject data.
"""
for subject_id in set([os.path.basename(
os.path.dirname(x))
for x in haxby_data.func]):
# instantiate subject_data object
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = "haxby2001"
# set func
subject_data.func = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = os.path.join(OUTPUT_DIR,
subject_data.subject_id)
yield subject_data
def subject_factory():
for subject_id in subject_ids:
if subject_id in exclusions:
continue
# construct subject data structure
subject_data = SubjectData()
subject_data.session_id = session_ids
subject_data.subject_id = subject_id
subject_data.func = []
# glob for bold data
has_bad_sessions = False
for session_id in subject_data.session_id:
bold_dir = os.path.join(
data_dir,
"%s/BOLD/%s" % (subject_id, session_id))
# # extract .nii.gz to .nii
# unzip_nii_gz(bold_dir)
# glob bold data for this session
func = glob.glob(os.path.join(bold_dir, "bold.nii.gz"))
# check that this session is OK (has bold data, etc.)
if not func:
has_bad_sessions = True
break
subject_data.func.append(func[0])
# exclude subject if necessary
if has_bad_sessions:
continue
# glob for anatomical data
# anat_dir = os.path.join(
# data_dir,
# "%s/anatomy" % subject_id)
# # extract .nii.gz to .ni
# unzip_nii_gz(anat_dir)
# glob anatomical data proper
subject_data.anat = glob.glob(
os.path.join(
data_dir,
"%s/anatomy/highres001_brain.nii.gz" % subject_id))[0]
# set subject output dir (all calculations for
# this subject go here)
subject_data.output_dir = os.path.join(
output_dir,
subject_id)
yield subject_data
def to_T2space(t2_img, t1_img, output_dir):
"""
Wrapper for pypreprocess's coregister function,
used here to coregister T1 image to T2 image
"""
data = SubjectData()
data.anat = t1_img
data.func = [t2_img]
data.output_dir = output_dir
coreged = _do_subject_coregister(data,
caching=False,
hardlink_output=False,
coreg_anat_to_func=True)
return coreged
def _abide_factory(institute="KKI"):
for scans in sorted(glob.glob(
"/home/elvis/CODE/datasets/ABIDE/%s_*/%s_*/scans" % (
institute, institute))):
subject_data = SubjectData()
subject_data.subject_id = os.path.basename(os.path.dirname(
os.path.dirname(scans)))
subject_data.func = os.path.join(scans,
"rest/resources/NIfTI/files/rest.nii")
subject_data.anat = os.path.join(
scans, "anat/resources/NIfTI/files/mprage.nii")
subject_data.output_dir = os.path.join(ABIDE_OUTPUT_DIR,
subject_data.subject_id)
yield subject_data
def _abide_factory(institute="KKI"):
for scans in sorted(
glob.glob("/home/elvis/CODE/datasets/ABIDE/%s_*/%s_*/scans" %
(institute, institute))):
subject_data = SubjectData()
subject_data.subject_id = os.path.basename(
os.path.dirname(os.path.dirname(scans)))
subject_data.func = os.path.join(
scans, "rest/resources/NIfTI/files/rest.nii")
subject_data.anat = os.path.join(
scans, "anat/resources/NIfTI/files/mprage.nii")
subject_data.output_dir = os.path.join(ABIDE_OUTPUT_DIR,
subject_data.subject_id)
yield subject_data
def subject_factory():
for subject_id in subject_ids:
subject_data = SubjectData()
subject_data.subject_id = subject_id
try:
subject_data.func = glob.glob(
os.path.join(
abide_data_dir,
"%s/%s/scans/rest*/resources/NIfTI/files/rest.nii" % (
subject_id, subject_id)))[0]
except IndexError:
ignored_because = "no rest data found"
print("Ignoring subject %s (%s)" % (subject_id,)
ignored_because)
ignored_subject_ids.append((subject_id, ignored_because))
continue
try:
subject_data.anat = glob.glob(
os.path.join(
abide_data_dir,
"%s/%s/scans/anat/resources/NIfTI/files/mprage.nii" % (
subject_id, subject_id)))[0]
except IndexError:
if do_dartel:
# can't do DARTEL in under such conditions
continue
try:
subject_data.hires = glob.glob(
os.path.join(
abide_data_dir,
("%s/%s/scans/hires/resources/NIfTI/"
"files/hires.nii") % (subject_id, subject_id)))[0]
except IndexError:
ignored_because = "no anat/hires data found"
print("Ignoring subject %s (%s)" % (subject_id,)
ignored_because)
ignored_subject_ids.append((subject_id, ignored_because))
continue
subject_data.output_dir = os.path.join(
os.path.join(
institute_output_dir, subject_id))
# yield data for this subject
yield subject_data
def subject_factory():
anats = glob.glob(
os.path.join(data_dir, 'sub*', 'ses-*', 'anat',
'sub-*_ses-*_acq-highres_T1w.nii'))
subject_sessions = [(anat.split('/')[-4], anat.split('/')[-3])
for anat in anats]
subject_sessions = [('sub-01', 'ses-12')]
for subject_session in subject_sessions:
subject, session = subject_session
subject_data = SubjectData(isdicom=False,
scratch=scratch,
session_output_dirs=[],
n_sessions=0)
subject_data.subject_id = subject
subject_data.anat = os.path.join(
data_dir, subject, session, 'anat',
'%s_%s_acq-highres_T1w.nii' % (subject, session))
subject_data.func = []
subject_data.output_dir = os.path.join(data_dir, subject, session,
'anat', 'dartel')
# yield data for this subject
yield subject_data
# do_report=False,
# do_dartel=True
)
if 0x0:
for (with_anat, do_segment, do_normalize,
fwhm, hard_link_output) in itertools.product(
[False, True], [False, True], [False, True], [0, 8, [8, 8, 8]],
[False, True]):
# load spm auditory data
sd = fetch_spm_auditory_data(os.path.join(
os.environ['HOME'], 'CODE/datasets/spm_auditory'))
subject_data1 = SubjectData(func=[sd.func],
anat=sd.anat if with_anat else None)
subject_data1.output_dir = "/tmp/kimbo/sub001/"
# load spm multimodal fmri data
sd = fetch_spm_multimodal_fmri_data(os.path.join(
os.environ['HOME'], 'CODE/datasets/spm_multimodal_fmri'))
subject_data2 = SubjectData(func=[sd.func1, sd.func2],
anat=sd.anat if with_anat else None,
session_id=['Session 1', "Session 2"])
subject_data2.output_dir = "/tmp/kiki/sub001/"
do_subjects_preproc([subject_data1, subject_data2],
do_dartel=True,
do_segment=do_segment,
do_normalize=do_normalize,
fwhm=fwhm,
hard_link_output=hard_link_output
paradigm=paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
"""fetch input data"""
_subject_data = fetch_fsl_feeds()
subject_data = SubjectData()
subject_data.subject_id = "sub001"
subject_data.func = _subject_data.func
subject_data.anat = _subject_data.anat
output_dir = os.path.join(_subject_data.data_dir, "pypreprocess_output")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
subject_data.output_dir = os.path.join(
output_dir, subject_data.subject_id)
"""preprocess the data"""
results = do_subjects_preproc(
[subject_data],
output_dir=output_dir,
dataset_id="FSL FEEDS single-subject",
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
)
"""collect preprocessed data"""
fmri_files = results[0]['func']
anat_file = results[0]['anat']
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = "haxby2001"
# set func
subject_data.func = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in haxby_data.anat if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = os.path.join(OUTPUT_DIR, subject_data.subject_id)
# add this subject to list
subjects.append(subject_data)
# do preprocessing proper
results = do_subjects_preproc(subjects,
output_dir=OUTPUT_DIR,
dataset_id="HAXBY 2001",
realign=False,
coregister=False,
dartel=DARTEL,
tsdiffana=False,
dataset_description=DATASET_DESCRIPTION)
paradigm=paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
period_cut=hfcut)
"""fetch input data"""
_subject_data = fetch_fsl_feeds()
subject_data = SubjectData()
subject_data.subject_id = "sub001"
subject_data.func = _subject_data.func
subject_data.anat = _subject_data.anat
output_dir = os.path.join(_subject_data.data_dir, "pypreprocess_output")
if not os.path.exists(output_dir):
os.makedirs(output_dir)
subject_data.output_dir = os.path.join(
output_dir, subject_data.subject_id)
"""preprocess the data"""
results = do_subjects_preproc(
[subject_data],
output_dir=output_dir,
dataset_id="FSL FEEDS single-subject",
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
)
"""collect preprocessed data"""
fmri_files = results[0]['func']
anat_file = results[0]['anat']
for x in haxby_data.func]):
# instantiate subject_data object
subject_data = SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = "haxby2001"
# set func
subject_data.func = [x for x in haxby_data.func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
# set anat
subject_data.anat = [x for x in haxby_data.anat if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
# set subject output directory
subject_data.output_dir = os.path.join(OUTPUT_DIR,
subject_data.subject_id)
# add this subject to list
subjects.append(subject_data)
# do preprocessing proper
results = do_subjects_preproc(subjects, output_dir=OUTPUT_DIR,
dataset_id="HAXBY 2001", realign=False,
coregister=False, dartel=DARTEL,
tsdiffana=False,
dataset_description=DATASET_DESCRIPTION)
# do_dartel=True
)
if 0x0:
for (with_anat, do_segment, do_normalize, fwhm,
hard_link_output) in itertools.product([False, True], [False, True],
[False, True],
[0, 8, [8, 8, 8]],
[False, True]):
# load spm auditory data
sd = fetch_spm_auditory_data(
os.path.join(os.environ['HOME'], 'CODE/datasets/spm_auditory'))
subject_data1 = SubjectData(func=[sd.func],
anat=sd.anat if with_anat else None)
subject_data1.output_dir = "/tmp/kimbo/sub001/"
# load spm multimodal fmri data
sd = fetch_spm_multimodal_fmri_data(
os.path.join(os.environ['HOME'],
'CODE/datasets/spm_multimodal_fmri'))
subject_data2 = SubjectData(func=[sd.func1, sd.func2],
anat=sd.anat if with_anat else None,
session_id=['Session 1', "Session 2"])
subject_data2.output_dir = "/tmp/kiki/sub001/"
do_subjects_preproc([subject_data1, subject_data2],
do_dartel=True,
do_segment=do_segment,
do_normalize=do_normalize,
fwhm=fwhm,