def demo_spm_multimodal_fmri(data_dir="/tmp/spm_multimodal_fmri",
output_dir="/tmp/spm_multimodal_fmri_output",
):
"""Demo for SPM multimodal fmri (faces vs scrambled)
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
spm_multimodal_fmri_data = fetch_spm_multimodal_fmri_data(
data_dir)
# subject data factory
def subject_factory():
subject_id = "sub001"
yield SubjectData(subject_id=subject_id,
func=[spm_multimodal_fmri_data.func1,
spm_multimodal_fmri_data.func2],
output_dir=os.path.join(output_dir, subject_id))
# invoke demon to run de demo
_demo_runner(subject_factory(),
"SPM Multimodal fMRI faces vs scrambled", n_sessions=2)
def demo_spm_multimodal_fmri(data_dir="/tmp/spm_multimodal_fmri", output_dir="/tmp/spm_multimodal_fmri_output"):
"""Demo for SPM multimodal fmri (faces vs scrambled)
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
spm_multimodal_fmri_data = fetch_spm_multimodal_fmri_data(data_dir)
# subject data factory
def subject_factory():
subject_id = "sub001"
yield SubjectData(
subject_id=subject_id,
func=[spm_multimodal_fmri_data.func1, spm_multimodal_fmri_data.func2],
output_dir=os.path.join(output_dir, subject_id),
)
# invoke demon to run de demo
_demo_runner(subject_factory(), "SPM Multimodal fMRI faces vs scrambled", n_sessions=2)
)
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
)
from pypreprocess.reporting.glm_reporter import generate_subject_stats_report
from pypreprocess.nipype_preproc_spm_utils import (do_subjects_preproc,
SubjectData)
# file containing configuration for preprocessing the data
this_dir = os.path.abspath(os.path.dirname(sys.argv[0]))
jobfile = os.path.join(this_dir, "multimodal_faces_preproc.ini")
# set dataset dir
if len(sys.argv) > 1:
dataset_dir = sys.argv[1]
else:
dataset_dir = os.path.join(this_dir, "spm_multimodal_faces")
# fetch spm multimodal_faces data
subject_data = fetch_spm_multimodal_fmri_data(dataset_dir)
# preprocess the data
subject_id = "sub001"
subject_data = SubjectData(output_dir=os.path.join(dataset_dir, "pypreprocess",
subject_id),
subject_id=subject_id,
func=[subject_data.func1, subject_data.func2],
anat=subject_data.anat,
trials_ses1=subject_data.trials_ses1,
trials_ses2=subject_data.trials_ses2)
subject_data = do_subjects_preproc([subject_data],
realign=True,
coregister=True,
segment=True,
normalize=True)[0]
# set data and output paths (change as you will)
DATA_DIR = "spm_multimodal_fmri"
OUTPUT_DIR = "spm_multimodal_runs"
if len(sys.argv) > 1:
DATA_DIR = sys.argv[1]
if len(sys.argv) > 2:
OUTPUT_DIR = sys.argv[2]
print "\tDATA_DIR: %s" % DATA_DIR
print "\tOUTPUT_DIR: %s" % OUTPUT_DIR
print
if not os.path.exists(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
# fetch the data
sd = fetch_spm_multimodal_fmri_data(DATA_DIR)
subject_id = "sub001"
subject_data = SubjectData(subject_id=subject_id,
session_id=["Session1", "Session2"],
func=[sd.func1, sd.func2],
anat=sd.anat,
trials_ses1=sd.trials_ses1,
trials_ses2=sd.trials_ses2,
output_dir=os.path.join(OUTPUT_DIR, subject_id)
)
# preprocess the data
subject_data = do_subject_preproc(subject_data, do_normalize=False, fwhm=[8.])
# collect preprocessed data
anat_img = nibabel.load(subject_data.anat)
line.append(1.)
condition, onset, duration, amplitude = line
conditions.append(condition)
onsets.append(float(onset))
durations.append(float(duration))
amplitudes.append(float(amplitude))
fd.close()
if not line_cnt > 0:
raise ValueError(
"Couldn't read any data from onset file: %s" % onset_file)
return map(np.array, [conditions, onsets, durations, amplitudes])
# fetch data
data_dir = "examples/spm_multimodal/"
subject_data = fetch_spm_multimodal_fmri_data(data_dir)
# XXX to be verified
tr = 2.
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
hfcut = 128.
time_units = "tr" # default if 1
if time_units == "tr":
time_units = tr
# re-write onset files into compatible format
for sess in xrange(2):
trials = getattr(subject_data, "trials_ses%i" % (sess + 1))
fd = open(trials.split(".")[0] + ".txt", 'w')
timing = scipy.io.loadmat(trials, squeeze_me=True, struct_as_record=False)
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)
