def combine_sessions(sessions, **kwargs):
""" Merge session data sets in single data set.
"""
# make a copy of the sessions, just to be safe
sessions_ = list(sessions)
# define dataset based on first session
dataset_ = None
# append data from other sessions
for i, session_ in enumerate(sessions_):
print("[+] session: {}, file: {}".format(
i, session_.meta.reset_index(drop=False).session[0])
)
if dataset_ is None:
dataset_ = Bunch(**dict(session_))
else:
dataset_.data = dataset_.data.append(session_.data, ignore_index=True, sort=False)
dataset_.meta = dataset_.meta.append(session_.meta, ignore_index=False, sort=False)
dataset_.tmask = dataset_.tmask.append(session_.tmask, ignore_index=False, sort=False)
# clean
if kwargs.get('clean_meta'):
dataset_.meta = clean_meta(dataset_.meta, **kwargs).reset_index(drop=False)
# set X, y
dataset_.X = dataset_.data.values.reshape(-1, dataset_.data.shape[-1])
dataset_.y = dataset_.meta.values.reshape(-1, dataset_.meta.shape[-1])
# cache sessions
dataset_.sessions = list(sessions)
return dataset_
def load_subject_data(dataset,
index=0,
mask='mask_vt',
sample_mask=None,
smoothing_fwhm=4,
**kwargs):
""" Load functional data for a single haxby subject. """
# extract relevant files
func_fn = dataset.func[index]
mask_fn = dataset.get(mask)
if not isinstance(mask_fn, str):
mask_fn = mask_fn[index]
# extract data from func using mask_vt
masker = NiftiMasker(
mask_img=mask_fn,
sample_mask=sample_mask,
standardize=True,
detrend=True,
smoothing_fwhm=smoothing_fwhm,
low_pass=0.09,
high_pass=0.008,
t_r=2.5,
memory="nilearn_cache",
)
X = masker.fit_transform(func_fn)
data = pd.DataFrame(X)
# return as bunch
subject = Bunch()
subject.data = data
subject.X = X
subject.masker = masker
subject.mask = mask_fn
subject.func = func_fn
subject.subject_code = os.path.basename(os.path.dirname(func_fn))
return subject
