def demo_fsl_feeds(data_dir="/tmp/fsl-feeds-data", output_dir="/tmp/fsl_feeds_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
fsl_feeds_data = fetch_fsl_feeds_data(data_dir=data_dir)
# subject data factory
def subject_factory():
subject_id = "sub001"
yield SubjectData(
subject_id=subject_id, func=fsl_feeds_data.func, output_dir=os.path.join(output_dir, subject_id)
)
# invoke demon to run de demo
_demo_runner(subject_factory(), "FSL FEEDS")
def demo_fsl_feeds(data_dir="/tmp/fsl-feeds-data",
output_dir="/tmp/fsl_feeds_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
fsl_feeds_data = fetch_fsl_feeds_data(data_dir=data_dir)
# subject data factory
def subject_factory():
subject_id = "sub001"
yield SubjectData(subject_id=subject_id,
func=fsl_feeds_data.func,
output_dir=os.path.join(output_dir, subject_id))
# invoke demon to run de demo
_demo_runner(subject_factory(), "FSL FEEDS")
onset = list(EV1_onset) + list(EV2_onset)
duration = [EV1_on] * EV1_epochs + [EV2_on] * EV2_epochs
paradigm = BlockParadigm(con_id=conditions, onset=onset, duration=duration)
frametimes = np.linspace(0, (n_scans - 1) * TR, n_scans)
maximum_epoch_duration = max(EV1_epoch_duration, EV2_epoch_duration)
hfcut = 1.5 * maximum_epoch_duration # why ?
"""construct design matrix"""
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
design_matrix = make_dmtx(frametimes,
paradigm, hrf_model=hrf_model,
drift_model=drift_model, hfcut=hfcut)
"""fetch input data"""
_subject_data = fetch_fsl_feeds_data(data_dir)
subject_data = SubjectData()
subject_data.subject_id = "sub001"
subject_data.func = _subject_data.func
subject_data.anat = _subject_data.anat
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,
fwhm=8,
dataset_id="FSL FEEDS single-subject",
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
onset = list(EV1_onset) + list(EV2_onset)
duration = [EV1_on] * EV1_epochs + [EV2_on] * EV2_epochs
paradigm = BlockParadigm(con_id=conditions, onset=onset, duration=duration)
frametimes = np.linspace(0, (n_scans - 1) * TR, n_scans)
maximum_epoch_duration = max(EV1_epoch_duration, EV2_epoch_duration)
hfcut = 1.5 * maximum_epoch_duration # why ?
"""construct design matrix"""
drift_model = 'Cosine'
hrf_model = 'Canonical With Derivative'
design_matrix = make_dmtx(frametimes,
paradigm,
hrf_model=hrf_model,
drift_model=drift_model,
hfcut=hfcut)
"""fetch input data"""
_subject_data = fetch_fsl_feeds_data(data_dir)
subject_data = SubjectData()
subject_data.subject_id = "sub001"
subject_data.func = _subject_data.func
subject_data.anat = _subject_data.anat
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,
fwhm=8,
dataset_id="FSL FEEDS single-subject",
dataset_description=DATASET_DESCRIPTION,
do_shutdown_reloaders=False,
)
"""collect preprocessed data"""
