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#!/usr/bin/env python2
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import os.path as op
from ...utils import _check_option, verbose
from ..config import MNE_DATASETS
from ..utils import _do_path_update, _download_mne_dataset, _get_path
@verbose
def data_path(
dataset="evoked", path=None, force_update=False, update_path=True, *, verbose=None
):
"""Get path to local copy of the high frequency SEF dataset.
Gets a local copy of the high frequency SEF MEG dataset
:footcite:`NurminenEtAl2017`.
Parameters
----------
dataset : 'evoked' | 'raw'
Whether to get the main dataset (evoked, structural and the rest) or
the separate dataset containing raw MEG data only.
path : None | str
Where to look for the HF-SEF data storing location.
If None, the environment variable or config parameter
``MNE_DATASETS_HF_SEF_PATH`` is used. If it doesn't exist, the
"~/mne_data" directory is used. If the HF-SEF dataset
is not found under the given path, the data
will be automatically downloaded to the specified folder.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_HF_SEF_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
path : str
Local path to the directory where the HF-SEF data is stored.
References
----------
.. footbibliography::
"""
_check_option("dataset", dataset, ("evoked", "raw"))
if dataset == "raw":
data_dict = MNE_DATASETS["hf_sef_raw"]
data_dict["dataset_name"] = "hf_sef_raw"
else:
data_dict = MNE_DATASETS["hf_sef_evoked"]
data_dict["dataset_name"] = "hf_sef_evoked"
config_key = data_dict["config_key"]
folder_name = data_dict["folder_name"]
# get download path for specific dataset
path = _get_path(path=path, key=config_key, name=folder_name)
final_path = op.join(path, folder_name)
megdir = op.join(final_path, "MEG", "subject_a")
has_raw = (
dataset == "raw"
and op.isdir(megdir)
and any("raw" in filename for filename in os.listdir(megdir))
)
has_evoked = dataset == "evoked" and op.isdir(op.join(final_path, "subjects"))
# data not there, or force_update requested:
if has_raw or has_evoked and not force_update:
_do_path_update(path, update_path, config_key, folder_name)
return final_path
# instantiate processor that unzips file
data_path = _download_mne_dataset(
name=data_dict["dataset_name"],
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=True,
)
return data_path
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