def create_betas_per_trial_with_pymvpa_roni(study_path, subj, conf, mask_name, flavor, TR):
dhandle = OpenFMRIDataset(study_path)
model = 1
task = 1
# Do this for other tasks as well. not only the first
mask_fname = _opj(study_path, "sub{:0>3d}".format(subj), "masks", conf.mvpa_tasks[0], "{}.nii.gz".format(mask_name))
print mask_fname
run_datasets = []
for run_id in dhandle.get_task_bold_run_ids(task)[subj]:
if type(run_id) == str:
continue
# all_events = dhandle.get_bold_run_model(model, subj, run_id)
all_events = get_bold_run_model(dhandle, 2, subj, run_id)
run_events = []
i = 0
for event in all_events:
if event["task"] == task:
event["condition"] = "{}-{}".format(event["condition"], event["id"])
run_events.append(event)
i += 1
# load BOLD data for this run (with masking); add 0-based chunk ID
run_ds = dhandle.get_bold_run_dataset(subj, task, run_id, flavor=flavor, chunks=run_id - 1, mask=mask_fname)
# convert event info into a sample attribute and assign as 'targets'
run_ds.sa.time_coords = run_ds.sa.time_indices * TR
run_ds.sa["targets"] = events2sample_attr(run_events, run_ds.sa.time_coords, noinfolabel="rest")
# additional time series preprocessing can go here
poly_detrend(run_ds, polyord=1, chunks_attr="chunks")
zscore(run_ds, chunks_attr="chunks", param_est=("targets", ["rest"]), dtype="float32")
glm_dataset = fit_event_hrf_model(run_ds, run_events, time_attr="time_coords", condition_attr="condition")
glm_dataset.sa["targets"] = [x[: x.find("-")] for x in glm_dataset.sa.condition]
glm_dataset.sa["id"] = [x[x.find("-") + 1 :] for x in glm_dataset.sa.condition]
glm_dataset.sa.condition = glm_dataset.sa["targets"]
glm_dataset.sa["chunks"] = [run_id - 1] * len(glm_dataset.samples)
# If a trial was dropped (the subject pressed on a button) than the counter trial from the
# other condition should also be dropped
for pair in conf.conditions_to_compare:
cond_bool = np.array([c in pair for c in glm_dataset.sa["condition"]])
sub_dataset = glm_dataset[cond_bool]
c = Counter(sub_dataset.sa.id)
for value in c:
if c[value] < 2:
id_bool = np.array([value in cond_id for cond_id in glm_dataset.sa["id"]])
glm_dataset = glm_dataset[np.bitwise_not(np.logical_and(id_bool, cond_bool))]
run_datasets.append(glm_dataset)
return vstack(run_datasets, 0)
def test_openfmri_dataset():
of = OpenFMRIDataset(os.path.join(pymvpa_dataroot, 'openfmri'))
sub_ids = of.get_subj_ids()
assert_equal(sub_ids, [1, 'phantom'])
assert_equal(of.get_scan_properties(), {'TR': '2.5'})
tasks = of.get_task_descriptions()
assert_equal(tasks, {1: 'object viewing'})
task = tasks.keys()[0]
run_ids = of.get_bold_run_ids(sub_ids[0], task)
assert_equal(run_ids, range(1, 13))
task_runs = of.get_task_bold_run_ids(task)
assert_equal(task_runs, {1: range(1, 13)})
orig_attrs = SampleAttributes(os.path.join(pymvpa_dataroot,
'attributes_literal.txt'))
for subj, runs in task_runs.iteritems():
for run in runs:
# load single run
ds = of.get_bold_run_dataset(subj, task, run, flavor='1slice',
mask=os.path.join(pymvpa_dataroot,
'mask.nii.gz'),
add_sa='bold_moest.txt')
# basic shape
assert_equal(len(ds), 121)
assert_equal(ds.nfeatures, 530)
# functional mapper
assert_equal(ds.O.shape, (121, 40, 20, 1))
# additional attributes present
moest = of.get_bold_run_motion_estimates(subj, task, run)
for i in range(6):
moest_attr = 'bold_moest.txt_%i' % (i,)
assert_true(moest_attr in ds.sa)
assert_array_equal(moest[:,i], ds.sa[moest_attr].value)
# check conversion of model into sample attribute
events = of.get_bold_run_model(subj, task, run)
targets = events2sample_attr(events,
ds.sa.time_coords,
noinfolabel='rest')
assert_array_equal(
orig_attrs['targets'][(run - 1) * 121: run * len(ds)], targets)
assert_equal(ds.sa['subj'][0], subj)
# more basic access
motion = of.get_task_bold_attributes(1, 'bold_moest.txt', np.loadtxt)
assert_equal(len(motion), 12) # one per run
# one per subject, per volume, 6 estimates
assert_equal([m.shape for m in motion], [(1, 121, 6)] * 12)
def create_betas_per_trial_with_pymvpa(study_path, subj, conf, mask_name, flavor, TR):
dhandle = OpenFMRIDataset(study_path)
model = 1
task = 1
# Do this for other tasks as well. not only the first
mask_fname = _opj(study_path, "sub{:0>3d}".format(subj), "masks", conf.mvpa_tasks[0], "{}.nii.gz".format(mask_name))
print mask_fname
run_datasets = []
for run_id in dhandle.get_task_bold_run_ids(task)[subj]:
if type(run_id) == str:
continue
all_events = dhandle.get_bold_run_model(model, subj, run_id)
run_events = []
i = 0
for event in all_events:
if event["task"] == task:
event["condition"] = "{}-{}".format(event["condition"], i)
run_events.append(event)
i += 1
# load BOLD data for this run (with masking); add 0-based chunk ID
run_ds = dhandle.get_bold_run_dataset(subj, task, run_id, flavor=flavor, chunks=run_id - 1, mask=mask_fname)
# convert event info into a sample attribute and assign as 'targets'
run_ds.sa.time_coords = run_ds.sa.time_indices * TR
print run_id
run_ds.sa["targets"] = events2sample_attr(run_events, run_ds.sa.time_coords, noinfolabel="rest")
# additional time series preprocessing can go here
poly_detrend(run_ds, polyord=1, chunks_attr="chunks")
zscore(run_ds, chunks_attr="chunks", param_est=("targets", ["rest"]), dtype="float32")
glm_dataset = fit_event_hrf_model(run_ds, run_events, time_attr="time_coords", condition_attr="condition")
glm_dataset.sa["targets"] = [x[: x.find("-")] for x in glm_dataset.sa.condition]
glm_dataset.sa.condition = glm_dataset.sa["targets"]
glm_dataset.sa["chunks"] = [run_id - 1] * len(glm_dataset.samples)
run_datasets.append(glm_dataset)
return vstack(run_datasets, 0)
