def _get_version(name):
"""Get a dataset version."""
from mne.datasets._fetch import fetch_dataset
if not has_dataset(name):
return None
dataset_params = MNE_DATASETS[name]
dataset_params['dataset_name'] = name
config_key = MNE_DATASETS[name]['config_key']
# get download path for specific dataset
path = _get_path(path=None, key=config_key, name=name)
return fetch_dataset(dataset_params, path=path, return_version=True)[1]
def _download_mne_dataset(name,
processor,
path,
force_update,
update_path,
download,
accept=False):
"""Aux function for downloading internal MNE datasets."""
from mne.datasets._fetch import fetch_dataset
# import pooch library for handling the dataset downloading
pooch = _soft_import('pooch', 'dataset downloading', strict=True)
dataset_params = MNE_DATASETS[name]
dataset_params['dataset_name'] = name
config_key = MNE_DATASETS[name]['config_key']
folder_name = MNE_DATASETS[name]['folder_name']
# get download path for specific dataset
path = _get_path(path=path, key=config_key, name=name)
# instantiate processor that unzips file
if processor == 'nested_untar':
processor_ = pooch.Untar(extract_dir=op.join(path, folder_name))
elif processor == 'nested_unzip':
processor_ = pooch.Unzip(extract_dir=op.join(path, folder_name))
else:
processor_ = processor
# handle case of multiple sub-datasets with different urls
if name == 'visual_92_categories':
dataset_params = []
for name in ['visual_92_categories_1', 'visual_92_categories_2']:
this_dataset = MNE_DATASETS[name]
this_dataset['dataset_name'] = name
dataset_params.append(this_dataset)
return fetch_dataset(dataset_params=dataset_params,
processor=processor_,
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept)
def has_dataset(name):
"""Check for presence of a dataset.
Parameters
----------
name : str | dict
The dataset to check. Strings refer to one of the supported datasets
listed :ref:`here <datasets>`. A :class:`dict` can be used to check for
user-defined datasets (see the Notes section of :func:`fetch_dataset`),
and must contain keys ``dataset_name``, ``archive_name``, ``url``,
``folder_name``, ``hash``.
Returns
-------
has : bool
True if the dataset is present.
"""
from mne.datasets._fetch import fetch_dataset
if isinstance(name, dict):
dataset_name = name['dataset_name']
dataset_params = name
else:
dataset_name = 'spm' if name == 'spm_face' else name
dataset_params = MNE_DATASETS[dataset_name]
dataset_params['dataset_name'] = dataset_name
config_key = dataset_params['config_key']
# get download path for specific dataset
path = _get_path(path=None, key=config_key, name=dataset_name)
dp = fetch_dataset(dataset_params,
path=path,
download=False,
check_version=False)
if dataset_name.startswith('bst_'):
check = dataset_name
else:
check = MNE_DATASETS[dataset_name]['folder_name']
return dp.endswith(check)