# .. note: If the keyword argument include is left out of
# ``openneuro.download``, the whole dataset will be downloaded.
# We're just using data from one subject to reduce the time
# it takes to run the example.
dataset = 'ds002778'
subject = 'pd6'
# Download one subject's data from each dataset
bids_root = op.join(op.dirname(sample.data_path()), dataset)
if not op.isdir(bids_root):
os.makedirs(bids_root)
openneuro.download(dataset=dataset,
target_dir=bids_root,
include=[f'sub-{subject}'])
# %%
# Explore the dataset contents
# ----------------------------
#
# We can use MNE-BIDS to print a tree of all
# included files and folders. We pass the ``max_depth`` parameter to
# `mne_bids.print_dir_tree` to the output to four levels of folders, for
# better readability in this example.
print_dir_tree(bids_root, max_depth=4)
# %%
# We can even ask MNE-BIDS to produce a human-readbale summary report
# We will do this using ``openneuro-py`` which can be installed using pip
# (``pip install openneuro-py``).
import os
import openneuro
import autoreject
dataset = 'ds000117' # The id code on OpenNeuro for this example dataset
subject_id = 16 # OpenfMRI format of subject numbering
target_dir = os.path.join(
os.path.dirname(autoreject.__file__), '..', 'examples', dataset)
if not os.path.isdir(target_dir):
os.makedirs(target_dir)
openneuro.download(dataset=dataset, target_dir=target_dir,
include=[f'sub-{subject_id}/ses-meg/'])
# %%
# We will create epochs with data starting 200 ms before trigger onset
# and continuing up to 800 ms after that. The data contains visual stimuli for
# famous faces, unfamiliar faces, as well as scrambled faces.
tmin, tmax = -0.2, 0.8
events_id = {'famous/first': 5, 'famous/immediate': 6, 'famous/long': 7}
# %%
# Let us now load all the epochs into memory and concatenate them
import mne # noqa
epochs = list()
def _download_via_openneuro(*, ds_name: str, ds_path: Path):
openneuro.download(dataset=DATASET_OPTIONS[ds_name]['openneuro'],
target_dir=ds_path,
include=DATASET_OPTIONS[ds_name]['include'],
exclude=DATASET_OPTIONS[ds_name]['exclude'])