def subject_factory():
for subject_id in subject_ids:
if subject_id in exclusions:
continue
# construct subject data structure
subject_data = nipype_preproc_spm_utils.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"))
# 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" % 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 subject_factory():
"""producer for subject (input) data"""
for subject_id, sd in haxby_data.iteritems():
subject_data = nipype_preproc_spm_utils.SubjectData()
subject_data.session_id = "haxby2001"
subject_data.subject_id = subject_id
unzip_nii_gz(sd.subject_dir)
subject_data.anat = sd.anat.replace(".gz", "")
subject_data.func = sd.bold.replace(".gz", "")
subject_data.output_dir = os.path.join(
OUTPUT_DIR, subject_data.subject_id)
yield subject_data
def subject_factory(session_output_dir, session):
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]):
# check that subject is not condemned
if subject_id in BAD_SUBJECTS:
continue
# instantiate subject_data object
subject_data = nipype_preproc_spm_utils.SubjectData()
subject_data.subject_id = subject_id
subject_data.session_id = session
# set func
subject_data.func = [
x.replace(".gz", "") for x in session_func if subject_id in x]
assert len(subject_data.func) == 1
subject_data.func = subject_data.func[0]
unzip_nii_gz(os.path.dirname(subject_data.func))
# set anat
subject_data.anat = [
x.replace(".gz", "") for x in session_anat if subject_id in x]
assert len(subject_data.anat) == 1
subject_data.anat = subject_data.anat[0]
unzip_nii_gz(os.path.dirname(subject_data.anat))
# set subject output directory
subject_data.output_dir = os.path.join(
session_output_dir, subject_data.subject_id)
yield subject_data
DATASET_DESCRIPTION = "FSL FEADS example data (single-subject)"
"""sanitize cmd line"""
if len(sys.argv) < 3:
print ("\r\nUsage: python %s <path to FSL feeds data directory>"
" <output_dir>\r\n") % sys.argv[0]
print ("Example:\r\npython %s /usr/share/fsl-feeds/data/"
" fsl_feeds_fmri_runs") % sys.argv[0]
sys.exit(1)
"""set data dir"""
data_dir = os.path.abspath(sys.argv[1])
"""set output dir"""
output_dir = os.path.abspath(sys.argv[2])
unzip_nii_gz(data_dir)
"""experimental setup"""
stats_start_time = time.ctime()
n_scans = 180
TR = 3.
EV1_epoch_duration = 2 * 30
EV2_epoch_duration = 2 * 45
TA = TR * n_scans
EV1_epochs = TA / EV1_epoch_duration
EV1_epochs = int(TA / EV1_epoch_duration)
EV2_epochs = int(TA / EV2_epoch_duration)
EV1_onset = np.linspace(0, EV1_epoch_duration * (EV1_epochs - 1), EV1_epochs)
EV2_onset = np.linspace(0, EV2_epoch_duration * (EV2_epochs - 1), EV2_epochs)
EV1_on = 30
EV2_on = 45