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 demo_nyu_rest(data_dir="/tmp/nyu_data", output_dir="/tmp/nyu_mrimc_output"):
"""Demo for FSL Feeds data.
Parameters
----------
data_dir: string, optional
where the data is located on your disk, where it will be
downloaded to
output_dir: string, optional
where output will be written to
"""
# fetch data
nyu_data = fetch_nyu_rest(data_dir=data_dir)
# subject data factory
def subject_factory(session=1):
session_func = [x for x in nyu_data.func 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]):
# set func
func = [x for x in session_func if subject_id in x]
assert len(func) == 1
func = func[0]
yield SubjectData(
subject_id=subject_id, func=func, output_dir=os.path.join(output_dir, "session%i" % session, subject_id)
)
# invoke demon to run de demo
_demo_runner(subject_factory(), "NYU resting state")
def demo_nyu_rest(data_dir="/tmp/nyu_data",
n_subjects=1,
output_dir="/tmp/nyu_rest_output",
):
"""Demo for FSL Feeds data.
Parameters
----------
data_dir: string, optional
where the data is located on your disk, where it will be
downloaded to
output_dir: string, optional
where output will be written to
"""
# fetch data
nyu_data = fetch_nyu_rest(data_dir=data_dir, n_subjects=1)
# subject data factory
def subject_factory(session=1):
session_func = [x for x in nyu_data.func 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]):
# set func
func = [
x for x in session_func if subject_id in x]
assert len(func) == 1
func = func[0]
yield SubjectData(subject_id=subject_id, func=func,
output_dir=os.path.join(
output_dir,
"session%i" % session, subject_id))
# invoke demon to run de demo
_fmri_demo_runner(subject_factory(), "NYU Resting State")
def _nyu_rest_factory(session=1):
from pypreprocess.nipype_preproc_spm_utils import SubjectData
nyu_data = fetch_nyu_rest(data_dir=os.path.join(
os.environ['HOME'], "CODE/datasets/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(do_deleteorient=True, do_niigz2nii=False)
yield (subject_data.subject_id, subject_data.func[0],
subject_data.anat)
"""
# standard imports
import sys
import os
# import API for preprocessing business
from pypreprocess.nipype_preproc_spm_utils import do_subjects_preproc
# input data-grabber for SPM Auditory (single-subject) data
from pypreprocess.datasets import fetch_nyu_rest
# file containing configuration for preprocessing the data
this_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
jobfile = os.path.join(os.path.dirname(sys.argv[0]), "nyu_rest_preproc.ini")
# set dataset dir
if len(sys.argv) > 1:
dataset_dir = sys.argv[1]
else:
dataset_dir = os.path.join(this_dir, "nyu_rest")
# fetch spm auditory data
fetch_nyu_rest(data_dir=dataset_dir)
# preprocess the data
results = do_subjects_preproc(jobfile,
dataset_dir=os.path.join(dataset_dir,
"nyu_rest"))
assert len(results) == 1
"""
Preprocessing of NYU rest data.
"""
# standard imports
import sys
import os
# import API for preprocessing business
from pypreprocess.nipype_preproc_spm_utils import do_subjects_preproc
# input data-grabber for SPM Auditory (single-subject) data
from pypreprocess.datasets import fetch_nyu_rest
# file containing configuration for preprocessing the data
jobfile = os.path.join(os.path.dirname(sys.argv[0]),
"nyu_rest_preproc.ini")
# fetch spm auditory data
sd = fetch_nyu_rest()
# preprocess the data
dataset_dir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(
sd.anat_skull[0]))))
results = do_subjects_preproc(jobfile, dataset_dir=dataset_dir)
