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2025-08-19 09:13:22 -07:00
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12
mne/datasets/__init__.py Normal file
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"""Functions for fetching remote datasets.
See :ref:`datasets` for more information.
"""
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import lazy_loader as lazy
(__getattr__, __dir__, __all__) = lazy.attach_stub(__name__, __file__)

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__all__ = [
"_download_all_example_data",
"_fake",
"brainstorm",
"eegbci",
"epilepsy_ecog",
"erp_core",
"eyelink",
"fetch_aparc_sub_parcellation",
"fetch_dataset",
"fetch_fsaverage",
"fetch_hcp_mmp_parcellation",
"fetch_infant_template",
"fetch_phantom",
"fieldtrip_cmc",
"fnirs_motor",
"has_dataset",
"hf_sef",
"kiloword",
"limo",
"misc",
"mtrf",
"multimodal",
"opm",
"phantom_4dbti",
"phantom_kernel",
"phantom_kit",
"refmeg_noise",
"sample",
"sleep_physionet",
"somato",
"spm_face",
"ssvep",
"testing",
"ucl_opm_auditory",
"visual_92_categories",
]
from . import (
_fake,
brainstorm,
eegbci,
epilepsy_ecog,
erp_core,
eyelink,
fieldtrip_cmc,
fnirs_motor,
hf_sef,
kiloword,
limo,
misc,
mtrf,
multimodal,
opm,
phantom_4dbti,
phantom_kernel,
phantom_kit,
refmeg_noise,
sample,
sleep_physionet,
somato,
spm_face,
ssvep,
testing,
ucl_opm_auditory,
visual_92_categories,
)
from ._fetch import fetch_dataset
from ._fsaverage.base import fetch_fsaverage
from ._infant import fetch_infant_template
from ._phantom.base import fetch_phantom
from .utils import (
_download_all_example_data,
fetch_aparc_sub_parcellation,
fetch_hcp_mmp_parcellation,
has_dataset,
)

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mne/datasets/_fake/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Fake dataset for testing."""
from ._fake import data_path, get_version

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mne/datasets/_fake/_fake.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=False, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="fake",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(name="fake", conf="MNE_DATASETS_FAKE_PATH")
def get_version(): # noqa: D103
return _get_version("fake")
get_version.__doc__ = _version_doc.format(name="fake")

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mne/datasets/_fetch.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from __future__ import annotations # only needed for Python ≤ 3.9
import os
import os.path as op
import sys
import time
from pathlib import Path
from shutil import rmtree
from .. import __version__ as mne_version
from ..fixes import _compare_version
from ..utils import _safe_input, logger, warn
from .config import (
MISC_VERSIONED,
RELEASES,
TESTING_VERSIONED,
_bst_license_text,
)
from .utils import (
_dataset_version,
_do_path_update,
_downloader_params,
_get_path,
_log_time_size,
)
_FAKE_VERSION = None # used for monkeypatching while testing versioning
def fetch_dataset(
dataset_params,
processor=None,
path=None,
force_update=False,
update_path=True,
download=True,
check_version=False,
return_version=False,
accept=False,
auth=None,
token=None,
) -> Path | tuple[Path, str]:
"""Fetch an MNE-compatible dataset using pooch.
Parameters
----------
dataset_params : list of dict | dict
The dataset name(s) and corresponding parameters to download the
dataset(s). The dataset parameters that contains the following keys:
``archive_name``, ``url``, ``folder_name``, ``hash``,
``config_key`` (optional). See Notes.
processor : None | "unzip" | "untar" | instance of pooch.Unzip | instance of pooch.Untar
What to do after downloading the file. ``"unzip"`` and ``"untar"`` will
decompress the downloaded file in place; for custom extraction (e.g.,
only extracting certain files from the archive) pass an instance of
``pooch.Unzip`` or ``pooch.Untar``. If ``None`` (the
default), the files are left as-is.
path : None | str
Directory in which to put the dataset. If ``None``, the dataset
location is determined by first checking whether
``dataset_params['config_key']`` is defined, and if so, whether that
config key exists in the MNE-Python config file. If so, the configured
path is used; if not, the location is set to the value of the
``MNE_DATA`` config key (if it exists), or ``~/mne_data`` otherwise.
force_update : bool
Force update of the dataset even if a local copy exists.
Default is False.
update_path : bool | None
If True (default), set the mne-python config to the given
path. If None, the user is prompted.
download : bool
If False and the dataset has not been downloaded yet, it will not be
downloaded and the path will be returned as ``''`` (empty string). This
is mostly used for testing purposes and can be safely ignored by most
users.
check_version : bool
Whether to check the version of the dataset or not. Each version
of the dataset is stored in the root with a ``version.txt`` file.
return_version : bool
Whether or not to return the version of the dataset or not.
Defaults to False.
accept : bool
Some MNE-supplied datasets require acceptance of an additional license.
Default is ``False``.
auth : tuple | None
Optional authentication tuple containing the username and
password/token, passed to ``pooch.HTTPDownloader`` (e.g.,
``auth=('foo', 012345)``).
token : str | None
Optional authentication token passed to ``pooch.HTTPDownloader``.
Returns
-------
data_path : instance of Path
The path to the fetched dataset.
version : str
Only returned if ``return_version`` is True.
See Also
--------
mne.get_config
mne.set_config
mne.datasets.has_dataset
Notes
-----
The ``dataset_params`` argument must contain the following keys:
- ``archive_name``: The name of the (possibly compressed) file to download
- ``url``: URL from which the file can be downloaded
- ``folder_name``: the subfolder within the ``MNE_DATA`` folder in which to
save and uncompress (if needed) the file(s)
- ``hash``: the cryptographic hash type of the file followed by a colon and
then the hash value (examples: "sha256:19uheid...", "md5:upodh2io...")
- ``config_key`` (optional): key passed to :func:`mne.set_config` to store
the on-disk location of the downloaded dataset (e.g.,
``"MNE_DATASETS_EEGBCI_PATH"``). This will only work for the provided
datasets listed :ref:`here <datasets>`; do not use for user-defined
datasets.
An example would look like::
{'dataset_name': 'sample',
'archive_name': 'MNE-sample-data-processed.tar.gz',
'hash': 'md5:12b75d1cb7df9dfb4ad73ed82f61094f',
'url': 'https://osf.io/86qa2/download?version=5',
'folder_name': 'MNE-sample-data',
'config_key': 'MNE_DATASETS_SAMPLE_PATH'}
For datasets where a single (possibly compressed) file must be downloaded,
pass a single :class:`dict` as ``dataset_params``. For datasets where
multiple files must be downloaded and (optionally) uncompressed separately,
pass a list of dicts.
""" # noqa E501
import pooch
t0 = time.time()
if auth is not None:
if len(auth) != 2:
raise RuntimeError(
"auth should be a 2-tuple consisting "
"of a username and password/token."
)
# processor to uncompress files
if processor == "untar":
processor = pooch.Untar(extract_dir=path)
elif processor == "unzip":
processor = pooch.Unzip(extract_dir=path)
if isinstance(dataset_params, dict):
dataset_params = [dataset_params]
# extract configuration parameters
names = [params["dataset_name"] for params in dataset_params]
name = names[0]
dataset_dict = dataset_params[0]
config_key = dataset_dict.get("config_key", None)
folder_name = dataset_dict["folder_name"]
# get download path for specific dataset
path = _get_path(path=path, key=config_key, name=name)
# get the actual path to each dataset folder name
final_path = op.join(path, folder_name)
# handle BrainStorm datasets with nested folders for datasets
if name.startswith("bst_"):
final_path = op.join(final_path, name)
final_path = Path(final_path)
# additional condition: check for version.txt and parse it
# check if testing or misc data is outdated; if so, redownload it
want_version = RELEASES.get(name, None)
want_version = _FAKE_VERSION if name == "fake" else want_version
# get the version of the dataset and then check if the version is outdated
data_version = _dataset_version(final_path, name)
outdated = want_version is not None and _compare_version(
want_version, ">", data_version
)
if outdated:
logger.info(
f"Dataset {name} version {data_version} out of date, "
f"latest version is {want_version}"
)
empty = Path("")
# return empty string if outdated dataset and we don't want to download
if (not force_update) and outdated and not download:
logger.info(
"Dataset out of date but force_update=False and download=False, "
"returning empty data_path"
)
return (empty, data_version) if return_version else empty
# reasons to bail early (hf_sef has separate code for this):
if (not force_update) and (not outdated) and (not name.startswith("hf_sef_")):
# ...if target folder exists (otherwise pooch downloads every
# time because we don't save the archive files after unpacking, so
# pooch can't check its checksum)
if op.isdir(final_path):
if config_key is not None:
_do_path_update(path, update_path, config_key, name)
return (final_path, data_version) if return_version else final_path
# ...if download=False (useful for debugging)
elif not download:
return (empty, data_version) if return_version else empty
# ...if user didn't accept the license
elif name.startswith("bst_"):
if accept or "--accept-brainstorm-license" in sys.argv:
answer = "y"
else:
# If they don't have stdin, just accept the license
# https://github.com/mne-tools/mne-python/issues/8513#issuecomment-726823724 # noqa: E501
answer = _safe_input(f"{_bst_license_text}Agree (y/[n])? ", use="y")
if answer.lower() != "y":
raise RuntimeError("You must agree to the license to use this dataset")
# downloader & processors
download_params = _downloader_params(auth=auth, token=token)
if name == "fake":
download_params["progressbar"] = False
downloader = pooch.HTTPDownloader(**download_params)
# make mappings from archive names to urls and to checksums
urls = dict()
registry = dict()
for idx, this_name in enumerate(names):
this_dataset = dataset_params[idx]
archive_name = this_dataset["archive_name"]
dataset_url = this_dataset["url"]
dataset_hash = this_dataset["hash"]
urls[archive_name] = dataset_url
registry[archive_name] = dataset_hash
# create the download manager
use_path = final_path if processor is None else Path(path)
fetcher = pooch.create(
path=str(use_path),
base_url="", # Full URLs are given in the `urls` dict.
version=None, # Data versioning is decoupled from MNE-Python version.
urls=urls,
registry=registry,
retry_if_failed=2, # 2 retries = 3 total attempts
)
# use our logger level for pooch's logger too
pooch.get_logger().setLevel(logger.getEffectiveLevel())
sz = 0
for idx in range(len(names)):
# fetch and unpack the data
archive_name = dataset_params[idx]["archive_name"]
try:
fetcher.fetch(
fname=archive_name, downloader=downloader, processor=processor
)
except ValueError as err:
err = str(err)
if "hash of downloaded file" in str(err):
raise ValueError(
f"{err} Consider using force_update=True to force "
"the dataset to be downloaded again."
) from None
else:
raise
fname = use_path / archive_name
sz += fname.stat().st_size
# after unpacking, remove the archive file
if processor is not None:
fname.unlink()
# remove version number from "misc" and "testing" datasets folder names
if name == "misc":
rmtree(final_path, ignore_errors=True)
os.replace(op.join(path, MISC_VERSIONED), final_path)
elif name == "testing":
rmtree(final_path, ignore_errors=True)
os.replace(op.join(path, TESTING_VERSIONED), final_path)
# maybe update the config
if config_key is not None:
old_name = "brainstorm" if name.startswith("bst_") else name
_do_path_update(path, update_path, config_key, old_name)
# compare the version of the dataset and mne
data_version = _dataset_version(path, name)
# 0.7 < 0.7.git should be False, therefore strip
if check_version and (
_compare_version(data_version, "<", mne_version.strip(".git"))
):
# OK to `nosec` because it's false positive (misidentified as SQL)
warn(
f"The {name} dataset (version {data_version}) is older than "
f"mne-python (version {mne_version}). If the examples fail, "
f"you may need to update the {name} dataset by using "
f"mne.datasets.{name}.data_path(force_update=True)" # nosec B608
)
_log_time_size(t0, sz)
return (final_path, data_version) if return_version else final_path

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mne/datasets/_fsaverage/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

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mne/datasets/_fsaverage/base.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from pathlib import Path
from ...utils import get_subjects_dir, set_config, verbose
from ..utils import _get_path, _manifest_check_download
FSAVERAGE_MANIFEST_PATH = Path(__file__).parent
@verbose
def fetch_fsaverage(subjects_dir=None, *, verbose=None):
"""Fetch and update fsaverage.
Parameters
----------
subjects_dir : str | None
The path to use as the subjects directory in the MNE-Python
config file. None will use the existing config variable (i.e.,
will not change anything), and if it does not exist, will use
``~/mne_data/MNE-fsaverage-data``.
%(verbose)s
Returns
-------
fs_dir : Path
The fsaverage directory.
(essentially ``subjects_dir / 'fsaverage'``).
.. versionchanged:: 1.8
A :class:`pathlib.Path` object is returned instead of a string.
Notes
-----
This function is designed to provide
1. All modern (Freesurfer 6) fsaverage subject files
2. All MNE fsaverage parcellations
3. fsaverage head surface, fiducials, head<->MRI trans, 1- and 3-layer
BEMs (and surfaces)
This function will compare the contents of ``subjects_dir/fsaverage``
to the ones provided in the remote zip file. If any are missing,
the zip file is downloaded and files are updated. No files will
be overwritten.
.. versionadded:: 0.18
"""
# Code used to create the BEM (other files taken from MNE-sample-data):
#
# $ mne watershed_bem -s fsaverage -d $PWD --verbose info --copy
# $ python
# >>> bem = mne.make_bem_model('fsaverage', subjects_dir='.', verbose=True)
# >>> mne.write_bem_surfaces(
# ... 'fsaverage/bem/fsaverage-5120-5120-5120-bem.fif', bem)
# >>> sol = mne.make_bem_solution(bem, verbose=True)
# >>> mne.write_bem_solution(
# ... 'fsaverage/bem/fsaverage-5120-5120-5120-bem-sol.fif', sol)
# >>> import os
# >>> import os.path as op
# >>> names = sorted(op.join(r, f)
# ... for r, d, files in os.walk('fsaverage')
# ... for f in files)
# with open('fsaverage.txt', 'w') as fid:
# fid.write('\n'.join(names))
#
subjects_dir = _set_montage_coreg_path(subjects_dir)
subjects_dir = subjects_dir.expanduser().absolute()
fs_dir = subjects_dir / "fsaverage"
fs_dir.mkdir(parents=True, exist_ok=True)
_manifest_check_download(
manifest_path=FSAVERAGE_MANIFEST_PATH / "root.txt",
destination=subjects_dir,
url="https://osf.io/3bxqt/download?version=2",
hash_="5133fe92b7b8f03ae19219d5f46e4177",
)
_manifest_check_download(
manifest_path=FSAVERAGE_MANIFEST_PATH / "bem.txt",
destination=subjects_dir / "fsaverage",
url="https://osf.io/7ve8g/download?version=4",
hash_="b31509cdcf7908af6a83dc5ee8f49fb1",
)
return fs_dir
def _get_create_subjects_dir(subjects_dir):
subjects_dir = get_subjects_dir(subjects_dir, raise_error=False)
if subjects_dir is None:
subjects_dir = _get_path(None, "MNE_DATA", "montage coregistration")
subjects_dir = subjects_dir / "MNE-fsaverage-data"
subjects_dir.mkdir(parents=True, exist_ok=True)
return subjects_dir
def _set_montage_coreg_path(subjects_dir=None):
"""Set a subject directory suitable for montage(-only) coregistration."""
subjects_dir = _get_create_subjects_dir(subjects_dir)
old_subjects_dir = get_subjects_dir(None, raise_error=False)
if old_subjects_dir is None:
set_config("SUBJECTS_DIR", subjects_dir)
return subjects_dir

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mne/datasets/_fsaverage/bem.txt Normal file
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bem/fsaverage-fiducials.fif
bem/fsaverage-5120-5120-5120-bem.fif
bem/fsaverage-head.fif
bem/outer_skin.surf
bem/brain.surf
bem/fsaverage-trans.fif
bem/fsaverage-ico-5-src.fif
bem/fsaverage-vol-5-src.fif
bem/outer_skull.surf
bem/inner_skull.surf
bem/fsaverage-5120-5120-5120-bem-sol.fif
bem/fsaverage-inner_skull-bem.fif

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mne/datasets/_fsaverage/root.txt Normal file
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fsaverage/bem/fsaverage-head-dense.fif
fsaverage/bem/fsaverage-head-medium.fif
fsaverage/bem/fsaverage-head.fif
fsaverage/bem/fsaverage-ico-5-src.fif
fsaverage/label/lh.BA1.label
fsaverage/label/lh.BA2.label
fsaverage/label/lh.BA3a.label
fsaverage/label/lh.BA3b.label
fsaverage/label/lh.BA44.label
fsaverage/label/lh.BA45.label
fsaverage/label/lh.BA4a.label
fsaverage/label/lh.BA4p.label
fsaverage/label/lh.BA6.label
fsaverage/label/lh.HCPMMP1.annot
fsaverage/label/lh.HCPMMP1_combined.annot
fsaverage/label/lh.MT.label
fsaverage/label/lh.Medial_wall.label
fsaverage/label/lh.PALS_B12.labels.gii
fsaverage/label/lh.PALS_B12_Brodmann.annot
fsaverage/label/lh.PALS_B12_Lobes.annot
fsaverage/label/lh.PALS_B12_OrbitoFrontal.annot
fsaverage/label/lh.PALS_B12_Visuotopic.annot
fsaverage/label/lh.V1.label
fsaverage/label/lh.V2.label
fsaverage/label/lh.Yeo2011_17Networks_N1000.annot
fsaverage/label/lh.Yeo2011_7Networks_N1000.annot
fsaverage/label/lh.aparc.a2005s.annot
fsaverage/label/lh.aparc.a2009s.annot
fsaverage/label/lh.aparc.annot
fsaverage/label/lh.aparc.label
fsaverage/label/lh.aparc_sub.annot
fsaverage/label/lh.cortex.label
fsaverage/label/lh.entorhinal.label
fsaverage/label/lh.oasis.chubs.annot
fsaverage/label/rh.BA1.label
fsaverage/label/rh.BA2.label
fsaverage/label/rh.BA3a.label
fsaverage/label/rh.BA3b.label
fsaverage/label/rh.BA44.label
fsaverage/label/rh.BA45.label
fsaverage/label/rh.BA4a.label
fsaverage/label/rh.BA4p.label
fsaverage/label/rh.BA6.label
fsaverage/label/rh.HCPMMP1.annot
fsaverage/label/rh.HCPMMP1_combined.annot
fsaverage/label/rh.MT.label
fsaverage/label/rh.Medial_wall.label
fsaverage/label/rh.PALS_B12.labels.gii
fsaverage/label/rh.PALS_B12_Brodmann.annot
fsaverage/label/rh.PALS_B12_Lobes.annot
fsaverage/label/rh.PALS_B12_OrbitoFrontal.annot
fsaverage/label/rh.PALS_B12_Visuotopic.annot
fsaverage/label/rh.V1.label
fsaverage/label/rh.V2.label
fsaverage/label/rh.Yeo2011_17Networks_N1000.annot
fsaverage/label/rh.Yeo2011_7Networks_N1000.annot
fsaverage/label/rh.aparc.a2005s.annot
fsaverage/label/rh.aparc.a2009s.annot
fsaverage/label/rh.aparc.annot
fsaverage/label/rh.aparc.label
fsaverage/label/rh.aparc_sub.annot
fsaverage/label/rh.cortex.label
fsaverage/label/rh.entorhinal.label
fsaverage/label/rh.oasis.chubs.annot
fsaverage/mri.2mm/README
fsaverage/mri.2mm/T1.mgz
fsaverage/mri.2mm/aseg.mgz
fsaverage/mri.2mm/brain.mgz
fsaverage/mri.2mm/brainmask.mgz
fsaverage/mri.2mm/mni305.cor.mgz
fsaverage/mri.2mm/orig.mgz
fsaverage/mri.2mm/reg.2mm.dat
fsaverage/mri.2mm/reg.2mm.mni152.dat
fsaverage/mri.2mm/subcort.mask.mgz
fsaverage/mri.2mm/subcort.prob.mgz
fsaverage/mri/T1.mgz
fsaverage/mri/aparc+aseg.mgz
fsaverage/mri/aparc.a2005s+aseg.mgz
fsaverage/mri/aparc.a2009s+aseg.mgz
fsaverage/mri/aseg.mgz
fsaverage/mri/brain.mgz
fsaverage/mri/brainmask.mgz
fsaverage/mri/lh.ribbon.mgz
fsaverage/mri/mni305.cor.mgz
fsaverage/mri/orig.mgz
fsaverage/mri/p.aseg.mgz
fsaverage/mri/rh.ribbon.mgz
fsaverage/mri/ribbon.mgz
fsaverage/mri/seghead.mgz
fsaverage/mri/subcort.prob.log
fsaverage/mri/subcort.prob.mgz
fsaverage/mri/transforms/reg.mni152.2mm.dat
fsaverage/mri/transforms/talairach.xfm
fsaverage/scripts/build-stamp.txt
fsaverage/scripts/csurfdir
fsaverage/scripts/make_average_surface.log
fsaverage/scripts/make_average_volume.log
fsaverage/scripts/mkheadsurf.log
fsaverage/scripts/mris_inflate.log
fsaverage/scripts/mris_inflate_lh.log
fsaverage/scripts/mris_inflate_rh.log
fsaverage/scripts/recon-all-status.log
fsaverage/scripts/recon-all.cmd
fsaverage/scripts/recon-all.done
fsaverage/scripts/recon-all.env
fsaverage/scripts/recon-all.env.bak
fsaverage/scripts/recon-all.local-copy
fsaverage/scripts/recon-all.log
fsaverage/surf/lh.area
fsaverage/surf/lh.area.seghead
fsaverage/surf/lh.avg_curv
fsaverage/surf/lh.avg_sulc
fsaverage/surf/lh.avg_thickness
fsaverage/surf/lh.cortex.patch.3d
fsaverage/surf/lh.cortex.patch.flat
fsaverage/surf/lh.curv
fsaverage/surf/lh.curv.seghead
fsaverage/surf/lh.fsaverage_sym.sphere.reg
fsaverage/surf/lh.inflated
fsaverage/surf/lh.inflated.H
fsaverage/surf/lh.inflated.K
fsaverage/surf/lh.inflated_avg
fsaverage/surf/lh.inflated_pre
fsaverage/surf/lh.orig
fsaverage/surf/lh.orig.avg.area.mgh
fsaverage/surf/lh.orig_avg
fsaverage/surf/lh.pial
fsaverage/surf/lh.pial.avg.area.mgh
fsaverage/surf/lh.pial_avg
fsaverage/surf/lh.pial_semi_inflated
fsaverage/surf/lh.seghead
fsaverage/surf/lh.seghead.inflated
fsaverage/surf/lh.smoothwm
fsaverage/surf/lh.sphere
fsaverage/surf/lh.sphere.left_right
fsaverage/surf/lh.sphere.reg
fsaverage/surf/lh.sphere.reg.avg
fsaverage/surf/lh.sulc
fsaverage/surf/lh.sulc.seghead
fsaverage/surf/lh.thickness
fsaverage/surf/lh.white
fsaverage/surf/lh.white.avg.area.mgh
fsaverage/surf/lh.white_avg
fsaverage/surf/lh.white_avg.H
fsaverage/surf/lh.white_avg.K
fsaverage/surf/mris_preproc.surface.lh.log
fsaverage/surf/mris_preproc.surface.rh.log
fsaverage/surf/rh.area
fsaverage/surf/rh.avg_curv
fsaverage/surf/rh.avg_sulc
fsaverage/surf/rh.avg_thickness
fsaverage/surf/rh.cortex.patch.3d
fsaverage/surf/rh.cortex.patch.flat
fsaverage/surf/rh.curv
fsaverage/surf/rh.fsaverage_sym.sphere.reg
fsaverage/surf/rh.inflated
fsaverage/surf/rh.inflated.H
fsaverage/surf/rh.inflated.K
fsaverage/surf/rh.inflated_avg
fsaverage/surf/rh.inflated_pre
fsaverage/surf/rh.orig
fsaverage/surf/rh.orig.avg.area.mgh
fsaverage/surf/rh.orig_avg
fsaverage/surf/rh.pial
fsaverage/surf/rh.pial.avg.area.mgh
fsaverage/surf/rh.pial_avg
fsaverage/surf/rh.pial_semi_inflated
fsaverage/surf/rh.smoothwm
fsaverage/surf/rh.sphere
fsaverage/surf/rh.sphere.left_right
fsaverage/surf/rh.sphere.reg
fsaverage/surf/rh.sphere.reg.avg
fsaverage/surf/rh.sulc
fsaverage/surf/rh.thickness
fsaverage/surf/rh.white
fsaverage/surf/rh.white.avg.area.mgh
fsaverage/surf/rh.white_avg
fsaverage/surf/rh.white_avg.H
fsaverage/surf/rh.white_avg.K

117
mne/datasets/_infant/ANTS1-0Months3T.txt Normal file
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bem/ANTS1-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS1-0Months3T-5120-5120-5120-bem.fif
bem/ANTS1-0Months3T-fiducials.fif
bem/ANTS1-0Months3T-head.fif
bem/ANTS1-0Months3T-oct-6-src.fif
bem/ANTS1-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

115
mne/datasets/_infant/ANTS10-5Months3T.txt Normal file
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@@ -0,0 +1,115 @@
bem/ANTS10-5Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS10-5Months3T-5120-5120-5120-bem.fif
bem/ANTS10-5Months3T-fiducials.fif
bem/ANTS10-5Months3T-head.fif
bem/ANTS10-5Months3T-oct-6-src.fif
bem/ANTS10-5Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS12-0Months3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS12-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS12-0Months3T-5120-5120-5120-bem.fif
bem/ANTS12-0Months3T-fiducials.fif
bem/ANTS12-0Months3T-head.fif
bem/ANTS12-0Months3T-oct-6-src.fif
bem/ANTS12-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS15-0Months3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS15-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS15-0Months3T-5120-5120-5120-bem.fif
bem/ANTS15-0Months3T-fiducials.fif
bem/ANTS15-0Months3T-head.fif
bem/ANTS15-0Months3T-oct-6-src.fif
bem/ANTS15-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS18-0Months3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS18-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS18-0Months3T-5120-5120-5120-bem.fif
bem/ANTS18-0Months3T-fiducials.fif
bem/ANTS18-0Months3T-head.fif
bem/ANTS18-0Months3T-oct-6-src.fif
bem/ANTS18-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

117
mne/datasets/_infant/ANTS2-0Months3T.txt Normal file
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bem/ANTS2-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS2-0Months3T-5120-5120-5120-bem.fif
bem/ANTS2-0Months3T-fiducials.fif
bem/ANTS2-0Months3T-head.fif
bem/ANTS2-0Months3T-oct-6-src.fif
bem/ANTS2-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

117
mne/datasets/_infant/ANTS2-0Weeks3T.txt Normal file
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bem/ANTS2-0Weeks3T-5120-5120-5120-bem-sol.fif
bem/ANTS2-0Weeks3T-5120-5120-5120-bem.fif
bem/ANTS2-0Weeks3T-fiducials.fif
bem/ANTS2-0Weeks3T-head.fif
bem/ANTS2-0Weeks3T-oct-6-src.fif
bem/ANTS2-0Weeks3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS2-0Years3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS2-0Years3T-5120-5120-5120-bem-sol.fif
bem/ANTS2-0Years3T-5120-5120-5120-bem.fif
bem/ANTS2-0Years3T-fiducials.fif
bem/ANTS2-0Years3T-head.fif
bem/ANTS2-0Years3T-oct-6-src.fif
bem/ANTS2-0Years3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS3-0Months3T.txt Normal file
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atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS3-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS3-0Months3T-5120-5120-5120-bem.fif
bem/ANTS3-0Months3T-fiducials.fif
bem/ANTS3-0Months3T-head.fif
bem/ANTS3-0Months3T-oct-6-src.fif
bem/ANTS3-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS4-5Months3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS4-5Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS4-5Months3T-5120-5120-5120-bem.fif
bem/ANTS4-5Months3T-fiducials.fif
bem/ANTS4-5Months3T-head.fif
bem/ANTS4-5Months3T-oct-6-src.fif
bem/ANTS4-5Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS6-0Months3T.txt Normal file
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@@ -0,0 +1,121 @@
atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS6-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS6-0Months3T-5120-5120-5120-bem.fif
bem/ANTS6-0Months3T-fiducials.fif
bem/ANTS6-0Months3T-head.fif
bem/ANTS6-0Months3T-oct-6-src.fif
bem/ANTS6-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS7-5Months3T.txt Normal file
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atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS7-5Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS7-5Months3T-5120-5120-5120-bem.fif
bem/ANTS7-5Months3T-fiducials.fif
bem/ANTS7-5Months3T-head.fif
bem/ANTS7-5Months3T-oct-6-src.fif
bem/ANTS7-5Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

121
mne/datasets/_infant/ANTS9-0Months3T.txt Normal file
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atlases/brain_ANTS_IXI_atlas_head_space.nii.gz
atlases/brain_ANTS_LPBA40_atlas_head_space.nii.gz
atlases/brain_IXI_atlas_head_space.nii.gz
atlases/brain_LPBA40_atlas_head_space.nii.gz
bem/ANTS9-0Months3T-5120-5120-5120-bem-sol.fif
bem/ANTS9-0Months3T-5120-5120-5120-bem.fif
bem/ANTS9-0Months3T-fiducials.fif
bem/ANTS9-0Months3T-head.fif
bem/ANTS9-0Months3T-oct-6-src.fif
bem/ANTS9-0Months3T-vol-5-src.fif
bem/inner_skull.surf
bem/inner_skull_large.surf
bem/outer_skin.surf
bem/outer_skin_large.surf
bem/outer_skull.surf
bem/outer_skull_large.surf
flirt_trans_mat.txt
label/aparc.annot.ctab
label/lh.aparc.a2009s.annot
label/lh.aparc.annot
label/lh.cortex.label
label/rh.aparc.a2009s.annot
label/rh.aparc.annot
label/rh.cortex.label
montages/10-10-montage.fif
montages/10-10_electrodes.tsv
montages/10-20-montage.fif
montages/10-20_electrodes.tsv
montages/10-5-montage.fif
montages/10-5_electrodes.tsv
montages/HGSN128-montage.fif
montages/HGSN128_electrodes.tsv
montages/HGSN129-montage.fif
montages/HGSN129_electrodes.tsv
mprage.nii.gz
mri/T1.mgz
mri/aparc+aseg.mgz
mri/aseg.count.txt
mri/aseg.mgz
mri/aseg.nii.gz
mri/aseg.presurf.mgz
mri/brain.finalsurfs.mgz
mri/brain.mgz
mri/brain.nii.gz
mri/brainmask.mgz
mri/brainmask.nii.gz
mri/filled.mgz
mri/lh.dpial.ribbon.mgz
mri/lh.dwhite.ribbon.mgz
mri/lh.ribbon.mgz
mri/norm.mgz
mri/norm.nii.gz
mri/rh.dpial.ribbon.mgz
mri/rh.dwhite.ribbon.mgz
mri/rh.ribbon.mgz
mri/ribbon.mgz
mri/transforms/niftyreg_affine.lta
mri/transforms/niftyreg_affine.nii.gz
mri/transforms/niftyreg_affine.txt
mri/transforms/niftyreg_affine.xfm
mri/transforms/talairach.auto.xfm
mri/transforms/talairach.xfm
mri/wm.mgz
surf/10-10-montage.obj
surf/10-10-montage.surf
surf/10-20-montage.obj
surf/10-20-montage.surf
surf/10-5-montage.obj
surf/10-5-montage.surf
surf/HGSN128-montage.obj
surf/HGSN128-montage.surf
surf/fiducials.obj
surf/fiducials.surf
surf/lh.area
surf/lh.curv
surf/lh.defects
surf/lh.inflated
surf/lh.inflated.H
surf/lh.inflated.K
surf/lh.input
surf/lh.orig
surf/lh.orig.euler.txt
surf/lh.orig_corrected
surf/lh.pial
surf/lh.qsphere
surf/lh.qsphere.nofix
surf/lh.smoothwm
surf/lh.smoothwm1
surf/lh.smoothwm2
surf/lh.smoothwm3
surf/lh.smoothwm4
surf/lh.smoothwm5
surf/lh.sphere
surf/lh.sphere.reg
surf/lh.sulc
surf/lh.thickness
surf/lh.white
surf/rh.area
surf/rh.curv
surf/rh.defects
surf/rh.inflated
surf/rh.inflated.H
surf/rh.inflated.K
surf/rh.input
surf/rh.orig
surf/rh.orig.euler.txt
surf/rh.orig_corrected
surf/rh.pial
surf/rh.qsphere
surf/rh.qsphere.nofix
surf/rh.smoothwm
surf/rh.smoothwm1
surf/rh.smoothwm2
surf/rh.smoothwm3
surf/rh.smoothwm4
surf/rh.smoothwm5
surf/rh.sphere
surf/rh.sphere.reg
surf/rh.sulc
surf/rh.thickness
surf/rh.white

5
mne/datasets/_infant/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from .base import fetch_infant_template

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mne/datasets/_infant/base.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from pathlib import Path
from ...utils import _check_option, _validate_type, get_subjects_dir, verbose
from ..utils import _manifest_check_download
_AGES = "2wk 1mo 2mo 3mo 4.5mo 6mo 7.5mo 9mo 10.5mo 12mo 15mo 18mo 2yr"
# https://github.com/christian-oreilly/infant_template_paper/releases
_ORIGINAL_URL = "https://github.com/christian-oreilly/infant_template_paper/releases/download/v0.1-alpha/{subject}.zip" # noqa: E501
# Formatted the same way as md5sum *.zip on Ubuntu:
_ORIGINAL_HASHES = """
851737d5f8f246883f2aef9819c6ec29 ANTS10-5Months3T.zip
32ab6d025f4311433a82e81374f1a045 ANTS1-0Months3T.zip
48ef349e7cc542fdf63ff36d7958ab57 ANTS12-0Months3T.zip
bba22c95aa97988c6e8892d6169ed317 ANTS15-0Months3T.zip
e1bfe5e3ef380592822ced446a4008c7 ANTS18-0Months3T.zip
fa7bee6c0985b9cd15ba53820cd72ccd ANTS2-0Months3T.zip
2ad90540cdf42837c09f8ce829458a35 ANTS2-0Weeks3T.zip
73e6a8b2579b7959a96f7d294ffb7393 ANTS2-0Years3T.zip
cb7b9752894e16a4938ddfe220f6286a ANTS3-0Months3T.zip
16b2a6804c7d5443cfba2ad6f7d4ac6a ANTS4-5Months3T.zip
dbdf2a9976121f2b106da96775690da3 ANTS6-0Months3T.zip
75fe37a1bc80ed6793a8abb47681d5ab ANTS7-5Months3T.zip
790f7dba0a264262e6c1c2dfdf216215 ANTS9-0Months3T.zip
"""
_MANIFEST_PATH = Path(__file__).parent
@verbose
def fetch_infant_template(age, subjects_dir=None, *, verbose=None):
"""Fetch and update an infant MRI template.
Parameters
----------
age : str
Age to download. Can be one of ``{'2wk', '1mo', '2mo', '3mo', '4.5mo',
'6mo', '7.5mo', '9mo', '10.5mo', '12mo', '15mo', '18mo', '2yr'}``.
subjects_dir : str | None
The path to download the template data to.
%(verbose)s
Returns
-------
subject : str
The standard subject name, e.g. ``ANTS4-5Month3T``.
Notes
-----
If you use these templates in your work, please cite
:footcite:`OReillyEtAl2021` and :footcite:`RichardsEtAl2016`.
.. versionadded:: 0.23
References
----------
.. footbibliography::
"""
# Code used to create the lists:
#
# $ for name in 2-0Weeks 1-0Months 2-0Months 3-0Months 4-5Months 6-0Months 7-5Months 9-0Months 10-5Months 12-0Months 15-0Months 18-0Months 2-0Years; do wget https://github.com/christian-oreilly/infant_template_paper/releases/download/v0.1-alpha/ANTS${name}3T.zip; done # noqa: E501
# $ md5sum ANTS*.zip
# $ python
# >>> import os.path as op
# >>> import zipfile
# >>> names = [f'ANTS{name}3T' for name in '2-0Weeks 1-0Months 2-0Months 3-0Months 4-5Months 6-0Months 7-5Months 9-0Months 10-5Months 12-0Months 15-0Months 18-0Months 2-0Years'.split()] # noqa: E501
# >>> for name in names:
# ... with zipfile.ZipFile(f'{name}.zip', 'r') as zip:
# ... names = sorted(name for name in zip.namelist() if not zipfile.Path(zip, name).is_dir()) # noqa: E501
# ... with open(f'{name}.txt', 'w') as fid:
# ... fid.write('\n'.join(names))
_validate_type(age, str, "age")
_check_option("age", age, _AGES.split())
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
unit = dict(wk="Weeks", mo="Months", yr="Years")[age[-2:]]
first = age[:-2].split(".")[0]
dash = "-5" if ".5" in age else "-0"
subject = f"ANTS{first}{dash}{unit}3T"
# Actually get and create the files
subject_dir = subjects_dir / subject
subject_dir.mkdir(parents=True, exist_ok=True)
# .zip -> hash mapping
orig_hashes = dict(
line.strip().split()[::-1] for line in _ORIGINAL_HASHES.strip().splitlines()
)
_manifest_check_download(
manifest_path=_MANIFEST_PATH / f"{subject}.txt",
destination=subject_dir,
url=_ORIGINAL_URL.format(subject=subject),
hash_=orig_hashes[f"{subject}.zip"],
)
return subject

3
mne/datasets/_phantom/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

63
mne/datasets/_phantom/base.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from pathlib import Path
from ...utils import _check_option, _validate_type, get_subjects_dir, verbose
from ..utils import _manifest_check_download
PHANTOM_MANIFEST_PATH = Path(__file__).parent
@verbose
def fetch_phantom(kind, subjects_dir=None, *, verbose=None):
"""Fetch and update a phantom subject.
Parameters
----------
kind : str
The kind of phantom to fetch. Can only be ``'otaniemi'`` (default).
%(subjects_dir)s
%(verbose)s
Returns
-------
subject_dir : pathlib.Path
The resulting phantom subject directory.
See Also
--------
mne.dipole.get_phantom_dipoles
Notes
-----
This function is designed to provide a head surface and T1.mgz for
the 32-dipole Otaniemi phantom. The VectorView/TRIUX phantom has the same
basic outside geometry, but different internal dipole positions.
Unlike most FreeSurfer subjects, the Otaniemi phantom scan was aligned
to the "head" coordinate frame, so an identity head<->MRI :term:`trans`
is appropriate.
.. versionadded:: 0.24
"""
phantoms = dict(
otaniemi=dict(
url="https://osf.io/j5czy/download?version=1",
hash="42d17db5b1db3e30327ffb4cf2649de8",
),
)
_validate_type(kind, str, "kind")
_check_option("kind", kind, list(phantoms))
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
subject = f"phantom_{kind}"
subject_dir = subjects_dir / subject
subject_dir.mkdir(parents=True, exist_ok=True)
_manifest_check_download(
manifest_path=PHANTOM_MANIFEST_PATH / f"{subject}.txt",
destination=subjects_dir,
url=phantoms[kind]["url"],
hash_=phantoms[kind]["hash"],
)
return subject_dir

3
mne/datasets/_phantom/phantom_otaniemi.txt Normal file
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phantom_otaniemi/bem/phantom_otaniemi-fiducials.fif
phantom_otaniemi/mri/T1.mgz
phantom_otaniemi/surf/lh.seghead

7
mne/datasets/brainstorm/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Brainstorm datasets."""
from . import bst_raw, bst_resting, bst_auditory, bst_phantom_ctf, bst_phantom_elekta

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mne/datasets/brainstorm/bst_auditory.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import (
_data_path_doc_accept,
_download_mne_dataset,
_get_version,
_version_doc,
)
_description = """
URL: http://neuroimage.usc.edu/brainstorm/DatasetAuditory
- One subject, two acquisition runs of 6 minutes each
- Subject stimulated binaurally with intra-aural earphones
(air tubes+transducers)
- Each run contains:
- 200 regular beeps (440Hz)
- 40 easy deviant beeps (554.4Hz, 4 semitones higher)
- Random inter-stimulus interval: between 0.7s and 1.7s seconds, uniformly
distributed
- The subject presses a button when detecting a deviant with the right
index finger
- Auditory stimuli generated with the Matlab Psychophysics toolbox
"""
@verbose
def data_path(
path=None,
force_update=False,
update_path=True,
download=True,
accept=False,
*,
verbose=None,
): # noqa: D103
return _download_mne_dataset(
name="bst_auditory",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
)
_data_path_doc = _data_path_doc_accept.format(
name="brainstorm", conf="MNE_DATASETS_BRAINSTORM_DATA_PATH"
)
_data_path_doc = _data_path_doc.replace(
"brainstorm dataset", "brainstorm (bst_auditory) dataset"
)
data_path.__doc__ = _data_path_doc
def get_version(): # noqa: D103
return _get_version("bst_auditory")
get_version.__doc__ = _version_doc.format(name="brainstorm")
def description():
"""Get description of brainstorm (bst_auditory) dataset."""
for desc in _description.splitlines():
print(desc)

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mne/datasets/brainstorm/bst_phantom_ctf.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import (
_data_path_doc_accept,
_download_mne_dataset,
_get_version,
_version_doc,
)
_description = """
URL: http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomCtf
"""
@verbose
def data_path(
path=None,
force_update=False,
update_path=True,
download=True,
accept=False,
*,
verbose=None,
): # noqa: D103
return _download_mne_dataset(
name="bst_phantom_ctf",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
)
_data_path_doc = _data_path_doc_accept.format(
name="brainstorm", conf="MNE_DATASETS_BRAINSTORM_DATA_PATH"
)
_data_path_doc = _data_path_doc.replace(
"brainstorm dataset", "brainstorm (bst_phantom_ctf) dataset"
)
data_path.__doc__ = _data_path_doc
def get_version(): # noqa: D103
return _get_version("bst_phantom_ctf")
get_version.__doc__ = _version_doc.format(name="brainstorm")
def description():
"""Get description of brainstorm (bst_phantom_ctf) dataset."""
for desc in _description.splitlines():
print(desc)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import (
_data_path_doc_accept,
_download_mne_dataset,
_get_version,
_version_doc,
)
_description = """
URL: http://neuroimage.usc.edu/brainstorm/Tutorials/PhantomElekta
"""
@verbose
def data_path(
path=None,
force_update=False,
update_path=True,
download=True,
accept=False,
*,
verbose=None,
): # noqa: D103
return _download_mne_dataset(
name="bst_phantom_elekta",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
)
_data_path_doc = _data_path_doc_accept.format(
name="brainstorm", conf="MNE_DATASETS_BRAINSTORM_DATA_PATH"
)
_data_path_doc = _data_path_doc.replace(
"brainstorm dataset", "brainstorm (bst_phantom_elekta) dataset"
)
data_path.__doc__ = _data_path_doc
def get_version(): # noqa: D103
return _get_version("bst_phantom_elekta")
get_version.__doc__ = _version_doc.format(name="brainstorm")
def description():
"""Get description of brainstorm (bst_phantom_elekta) dataset."""
for desc in _description.splitlines():
print(desc)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from functools import partial
from ...utils import get_config, verbose
from ..utils import (
_data_path_doc_accept,
_download_mne_dataset,
_get_version,
_version_doc,
has_dataset,
)
has_brainstorm_data = partial(has_dataset, name="bst_raw")
_description = """
URL: http://neuroimage.usc.edu/brainstorm/DatasetMedianNerveCtf
- One subject, one acquisition run of 6 minutes
- Subject stimulated using Digitimer Constant Current Stimulator
(model DS7A)
- The run contains 200 electric stimulations randomly distributed between
left and right:
- 102 stimulations of the left hand
- 98 stimulations of the right hand
- Inter-stimulus interval: jittered between [1500, 2000]ms
- Stimuli generated using PsychToolBox on Windows PC (TTL pulse generated
with the parallel port connected to the Digitimer via the rear panel BNC)
"""
@verbose
def data_path(
path=None,
force_update=False,
update_path=True,
download=True,
accept=False,
*,
verbose=None,
): # noqa: D103
return _download_mne_dataset(
name="bst_raw",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
)
_data_path_doc = _data_path_doc_accept.format(
name="brainstorm", conf="MNE_DATASETS_BRAINSTORM_DATA_PATH"
)
_data_path_doc = _data_path_doc.replace(
"brainstorm dataset", "brainstorm (bst_raw) dataset"
)
data_path.__doc__ = _data_path_doc
def get_version(): # noqa: D103
return _get_version("bst_raw")
get_version.__doc__ = _version_doc.format(name="brainstorm")
def description(): # noqa: D103
"""Get description of brainstorm (bst_raw) dataset."""
for desc in _description.splitlines():
print(desc)
def _skip_bstraw_data():
skip_testing = get_config("MNE_SKIP_TESTING_DATASET_TESTS", "false") == "true"
skip = skip_testing or not has_brainstorm_data()
return skip
def requires_bstraw_data(func):
"""Skip testing data test."""
import pytest
return pytest.mark.skipif(
_skip_bstraw_data(), reason="Requires brainstorm dataset"
)(func)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import (
_data_path_doc_accept,
_download_mne_dataset,
_get_version,
_version_doc,
)
_description = """
URL: http://neuroimage.usc.edu/brainstorm/DatasetResting
- One subject
- Two runs of 10 min of resting state recordings
- Eyes open
"""
@verbose
def data_path(
path=None,
force_update=False,
update_path=True,
download=True,
accept=False,
*,
verbose=None,
): # noqa: D103
return _download_mne_dataset(
name="bst_resting",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
)
_data_path_doc = _data_path_doc_accept.format(
name="brainstorm", conf="MNE_DATASETS_BRAINSTORM_DATA_PATH"
)
_data_path_doc = _data_path_doc.replace(
"brainstorm dataset", "brainstorm (bst_resting) dataset"
)
data_path.__doc__ = _data_path_doc
def get_version(): # noqa: D103
return _get_version("bst_resting")
get_version.__doc__ = _version_doc.format(name="brainstorm")
def description():
"""Get description of brainstorm (bst_resting) dataset."""
for desc in _description.splitlines():
print(desc)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
_bst_license_text = """
License
-------
This tutorial dataset (EEG and MRI data) remains a property of the MEG Lab,
McConnell Brain Imaging Center, Montreal Neurological Institute,
McGill University, Canada. Its use and transfer outside the Brainstorm
tutorial, e.g. for research purposes, is prohibited without written consent
from the MEG Lab.
If you reference this dataset in your publications, please:
1) acknowledge its authors: Elizabeth Bock, Esther Florin, Francois Tadel
and Sylvain Baillet, and
2) cite Brainstorm as indicated on the website:
http://neuroimage.usc.edu/brainstorm
For questions, please contact Francois Tadel (francois.tadel@mcgill.ca).
"""
_hcp_mmp_license_text = """
License
-------
I request access to data collected by the Washington University - University
of Minnesota Consortium of the Human Connectome Project (WU-Minn HCP), and
I agree to the following:
1. I will not attempt to establish the identity of or attempt to contact any
of the included human subjects.
2. I understand that under no circumstances will the code that would link
these data to Protected Health Information be given to me, nor will any
additional information about individual human subjects be released to me
under these Open Access Data Use Terms.
3. I will comply with all relevant rules and regulations imposed by my
institution. This may mean that I need my research to be approved or
declared exempt by a committee that oversees research on human subjects,
e.g. my IRB or Ethics Committee. The released HCP data are not considered
de-identified, insofar as certain combinations of HCP Restricted Data
(available through a separate process) might allow identification of
individuals. Different committees operate under different national, state
and local laws and may interpret regulations differently, so it is
important to ask about this. If needed and upon request, the HCP will
provide a certificate stating that you have accepted the HCP Open Access
Data Use Terms.
4. I may redistribute original WU-Minn HCP Open Access data and any derived
data as long as the data are redistributed under these same Data Use Terms.
5. I will acknowledge the use of WU-Minn HCP data and data derived from
WU-Minn HCP data when publicly presenting any results or algorithms
that benefitted from their use.
1. Papers, book chapters, books, posters, oral presentations, and all
other printed and digital presentations of results derived from HCP
data should contain the following wording in the acknowledgments
section: "Data were provided [in part] by the Human Connectome
Project, WU-Minn Consortium (Principal Investigators: David Van Essen
and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and
Centers that support the NIH Blueprint for Neuroscience Research; and
by the McDonnell Center for Systems Neuroscience at Washington
University."
2. Authors of publications or presentations using WU-Minn HCP data
should cite relevant publications describing the methods used by the
HCP to acquire and process the data. The specific publications that
are appropriate to cite in any given study will depend on what HCP
data were used and for what purposes. An annotated and appropriately
up-to-date list of publications that may warrant consideration is
available at http://www.humanconnectome.org/about/acknowledgehcp.html
3. The WU-Minn HCP Consortium as a whole should not be included as an
author of publications or presentations if this authorship would be
based solely on the use of WU-Minn HCP data.
6. Failure to abide by these guidelines will result in termination of my
privileges to access WU-Minn HCP data.
"""
# To update the `testing` or `misc` datasets, push or merge commits to their
# respective repos, and make a new release of the dataset on GitHub. Then
# update the checksum in the MNE_DATASETS dict below, and change version
# here: ↓↓↓↓↓↓↓↓
RELEASES = dict(
testing="0.156",
misc="0.27",
phantom_kit="0.2",
ucl_opm_auditory="0.2",
)
TESTING_VERSIONED = f'mne-testing-data-{RELEASES["testing"]}'
MISC_VERSIONED = f'mne-misc-data-{RELEASES["misc"]}'
# To update any other dataset besides `testing` or `misc`, upload the new
# version of the data archive itself (e.g., to https://osf.io or wherever) and
# then update the corresponding checksum in the MNE_DATASETS dict entry below.
MNE_DATASETS = dict()
# MANDATORY KEYS:
# - archive_name : the name of the compressed file that is downloaded
# - hash : the checksum type followed by a colon and then the checksum value
# (examples: "sha256:19uheid...", "md5:upodh2io...")
# - url : URL from which the file can be downloaded
# - folder_name : the subfolder within the MNE data folder in which to save and
# uncompress (if needed) the file(s)
#
# OPTIONAL KEYS:
# - config_key : key to use with `mne.set_config` to store the on-disk location
# of the downloaded dataset (ex: "MNE_DATASETS_EEGBCI_PATH").
# Testing and misc are at the top as they're updated most often
MNE_DATASETS["testing"] = dict(
archive_name=f"{TESTING_VERSIONED}.tar.gz",
hash="md5:d94fe9f3abe949a507eaeb865fb84a3f",
url=(
"https://codeload.github.com/mne-tools/mne-testing-data/"
f'tar.gz/{RELEASES["testing"]}'
),
# In case we ever have to resort to osf.io again...
# archive_name='mne-testing-data.tar.gz',
# hash='md5:c805a5fed8ca46f723e7eec828d90824',
# url='https://osf.io/dqfgy/download?version=1', # 0.136
folder_name="MNE-testing-data",
config_key="MNE_DATASETS_TESTING_PATH",
)
MNE_DATASETS["misc"] = dict(
archive_name=f"{MISC_VERSIONED}.tar.gz", # 'mne-misc-data',
hash="md5:e343d3a00cb49f8a2f719d14f4758afe",
url=(
"https://codeload.github.com/mne-tools/mne-misc-data/tar.gz/"
f'{RELEASES["misc"]}'
),
folder_name="MNE-misc-data",
config_key="MNE_DATASETS_MISC_PATH",
)
MNE_DATASETS["fnirs_motor"] = dict(
archive_name="MNE-fNIRS-motor-data.tgz",
hash="md5:c4935d19ddab35422a69f3326a01fef8",
url="https://osf.io/dj3eh/download?version=1",
folder_name="MNE-fNIRS-motor-data",
config_key="MNE_DATASETS_FNIRS_MOTOR_PATH",
)
MNE_DATASETS["ucl_opm_auditory"] = dict(
archive_name="auditory_OPM_stationary.zip",
hash="md5:b2d69aa2d656b960bd0c18968dc1a14d",
url="https://osf.io/download/tp324/?version=1", # original is mwrt3
folder_name="auditory_OPM_stationary",
config_key="MNE_DATASETS_UCL_OPM_AUDITORY_PATH",
)
MNE_DATASETS["kiloword"] = dict(
archive_name="MNE-kiloword-data.tar.gz",
hash="md5:3a124170795abbd2e48aae8727e719a8",
url="https://osf.io/qkvf9/download?version=1",
folder_name="MNE-kiloword-data",
config_key="MNE_DATASETS_KILOWORD_PATH",
)
MNE_DATASETS["multimodal"] = dict(
archive_name="MNE-multimodal-data.tar.gz",
hash="md5:26ec847ae9ab80f58f204d09e2c08367",
url="https://ndownloader.figshare.com/files/5999598",
folder_name="MNE-multimodal-data",
config_key="MNE_DATASETS_MULTIMODAL_PATH",
)
MNE_DATASETS["opm"] = dict(
archive_name="MNE-OPM-data.tar.gz",
hash="md5:370ad1dcfd5c47e029e692c85358a374",
url="https://osf.io/p6ae7/download?version=2",
folder_name="MNE-OPM-data",
config_key="MNE_DATASETS_OPM_PATH",
)
MNE_DATASETS["phantom_kit"] = dict(
archive_name="MNE-phantom-KIT-data.tar.gz",
hash="md5:7bfdf40bbeaf17a66c99c695640e0740",
url="https://osf.io/fb6ya/download?version=1",
folder_name="MNE-phantom-KIT-data",
config_key="MNE_DATASETS_PHANTOM_KIT_PATH",
)
MNE_DATASETS["phantom_4dbti"] = dict(
archive_name="MNE-phantom-4DBTi.zip",
hash="md5:938a601440f3ffa780d20a17bae039ff",
url="https://osf.io/v2brw/download?version=2",
folder_name="MNE-phantom-4DBTi",
config_key="MNE_DATASETS_PHANTOM_4DBTI_PATH",
)
MNE_DATASETS["phantom_kernel"] = dict(
archive_name="MNE-phantom-kernel.tar.gz",
hash="md5:4e2ad987dac1a20f95bae8ffeb2d41d6",
url="https://osf.io/dj7wz/download?version=1",
folder_name="MNE-phantom-kernel-data",
config_key="MNE_DATASETS_PHANTOM_KERNEL_PATH",
)
MNE_DATASETS["sample"] = dict(
archive_name="MNE-sample-data-processed.tar.gz",
hash="md5:e8f30c4516abdc12a0c08e6bae57409c",
url="https://osf.io/86qa2/download?version=6",
folder_name="MNE-sample-data",
config_key="MNE_DATASETS_SAMPLE_PATH",
)
MNE_DATASETS["somato"] = dict(
archive_name="MNE-somato-data.tar.gz",
hash="md5:32fd2f6c8c7eb0784a1de6435273c48b",
url="https://osf.io/tp4sg/download?version=7",
folder_name="MNE-somato-data",
config_key="MNE_DATASETS_SOMATO_PATH",
)
MNE_DATASETS["spm"] = dict(
archive_name="MNE-spm-face.tar.gz",
hash="md5:9f43f67150e3b694b523a21eb929ea75",
url="https://osf.io/je4s8/download?version=2",
folder_name="MNE-spm-face",
config_key="MNE_DATASETS_SPM_FACE_PATH",
)
# Visual 92 categories has the dataset split into 2 files.
# We define a dictionary holding the items with the same
# value across both files: folder name and configuration key.
MNE_DATASETS["visual_92_categories"] = dict(
folder_name="MNE-visual_92_categories-data",
config_key="MNE_DATASETS_VISUAL_92_CATEGORIES_PATH",
)
MNE_DATASETS["visual_92_categories_1"] = dict(
archive_name="MNE-visual_92_categories-data-part1.tar.gz",
hash="md5:74f50bbeb65740903eadc229c9fa759f",
url="https://osf.io/8ejrs/download?version=1",
folder_name="MNE-visual_92_categories-data",
config_key="MNE_DATASETS_VISUAL_92_CATEGORIES_PATH",
)
MNE_DATASETS["visual_92_categories_2"] = dict(
archive_name="MNE-visual_92_categories-data-part2.tar.gz",
hash="md5:203410a98afc9df9ae8ba9f933370e20",
url="https://osf.io/t4yjp/download?version=1",
folder_name="MNE-visual_92_categories-data",
config_key="MNE_DATASETS_VISUAL_92_CATEGORIES_PATH",
)
MNE_DATASETS["mtrf"] = dict(
archive_name="mTRF_1.5.zip",
hash="md5:273a390ebbc48da2c3184b01a82e4636",
url="https://osf.io/h85s2/download?version=1",
folder_name="mTRF_1.5",
config_key="MNE_DATASETS_MTRF_PATH",
)
MNE_DATASETS["refmeg_noise"] = dict(
archive_name="sample_reference_MEG_noise-raw.zip",
hash="md5:779fecd890d98b73a4832e717d7c7c45",
url="https://osf.io/drt6v/download?version=1",
folder_name="MNE-refmeg-noise-data",
config_key="MNE_DATASETS_REFMEG_NOISE_PATH",
)
MNE_DATASETS["ssvep"] = dict(
archive_name="ssvep_example_data.zip",
hash="md5:af866bbc0f921114ac9d683494fe87d6",
url="https://osf.io/z8h6k/download?version=5",
folder_name="ssvep-example-data",
config_key="MNE_DATASETS_SSVEP_PATH",
)
MNE_DATASETS["erp_core"] = dict(
archive_name="MNE-ERP-CORE-data.tar.gz",
hash="md5:5866c0d6213bd7ac97f254c776f6c4b1",
url="https://osf.io/rzgba/download?version=1",
folder_name="MNE-ERP-CORE-data",
config_key="MNE_DATASETS_ERP_CORE_PATH",
)
MNE_DATASETS["epilepsy_ecog"] = dict(
archive_name="MNE-epilepsy-ecog-data.tar.gz",
hash="md5:ffb139174afa0f71ec98adbbb1729dea",
url="https://osf.io/z4epq/download?version=1",
folder_name="MNE-epilepsy-ecog-data",
config_key="MNE_DATASETS_EPILEPSY_ECOG_PATH",
)
# Fieldtrip CMC dataset
MNE_DATASETS["fieldtrip_cmc"] = dict(
archive_name="SubjectCMC.zip",
hash="md5:6f9fd6520f9a66e20994423808d2528c",
url="https://osf.io/j9b6s/download?version=1",
folder_name="MNE-fieldtrip_cmc-data",
config_key="MNE_DATASETS_FIELDTRIP_CMC_PATH",
)
# brainstorm datasets:
MNE_DATASETS["bst_auditory"] = dict(
archive_name="bst_auditory.tar.gz",
hash="md5:fa371a889a5688258896bfa29dd1700b",
url="https://osf.io/5t9n8/download?version=1",
folder_name="MNE-brainstorm-data",
config_key="MNE_DATASETS_BRAINSTORM_PATH",
)
MNE_DATASETS["bst_phantom_ctf"] = dict(
archive_name="bst_phantom_ctf.tar.gz",
hash="md5:80819cb7f5b92d1a5289db3fb6acb33c",
url="https://osf.io/sxr8y/download?version=1",
folder_name="MNE-brainstorm-data",
config_key="MNE_DATASETS_BRAINSTORM_PATH",
)
MNE_DATASETS["bst_phantom_elekta"] = dict(
archive_name="bst_phantom_elekta.tar.gz",
hash="md5:1badccbe17998d18cc373526e86a7aaf",
url="https://osf.io/dpcku/download?version=1",
folder_name="MNE-brainstorm-data",
config_key="MNE_DATASETS_BRAINSTORM_PATH",
)
MNE_DATASETS["bst_raw"] = dict(
archive_name="bst_raw.tar.gz",
hash="md5:fa2efaaec3f3d462b319bc24898f440c",
url="https://osf.io/9675n/download?version=2",
folder_name="MNE-brainstorm-data",
config_key="MNE_DATASETS_BRAINSTORM_PATH",
)
MNE_DATASETS["bst_resting"] = dict(
archive_name="bst_resting.tar.gz",
hash="md5:70fc7bf9c3b97c4f2eab6260ee4a0430",
url="https://osf.io/m7bd3/download?version=3",
folder_name="MNE-brainstorm-data",
config_key="MNE_DATASETS_BRAINSTORM_PATH",
)
# HF-SEF
MNE_DATASETS["hf_sef_raw"] = dict(
archive_name="hf_sef_raw.tar.gz",
hash="md5:33934351e558542bafa9b262ac071168",
url="https://zenodo.org/record/889296/files/hf_sef_raw.tar.gz",
folder_name="hf_sef",
config_key="MNE_DATASETS_HF_SEF_PATH",
)
MNE_DATASETS["hf_sef_evoked"] = dict(
archive_name="hf_sef_evoked.tar.gz",
hash="md5:13d34cb5db584e00868677d8fb0aab2b",
# Zenodo can be slow, so we use the OSF mirror
# url=('https://zenodo.org/record/3523071/files/'
# 'hf_sef_evoked.tar.gz'),
url="https://osf.io/25f8d/download?version=2",
folder_name="hf_sef",
config_key="MNE_DATASETS_HF_SEF_PATH",
)
# "fake" dataset (for testing)
MNE_DATASETS["fake"] = dict(
archive_name="foo.tgz",
hash="md5:3194e9f7b46039bb050a74f3e1ae9908",
url="https://github.com/mne-tools/mne-testing-data/raw/master/datasets/foo.tgz",
folder_name="foo",
config_key="MNE_DATASETS_FAKE_PATH",
)
# eyelink dataset
MNE_DATASETS["eyelink"] = dict(
archive_name="MNE-eyelink-data.zip",
hash="md5:68a6323ef17d655f1a659c3290ee1c3f",
url=("https://osf.io/xsu4g/download?version=1"),
folder_name="MNE-eyelink-data",
config_key="MNE_DATASETS_EYELINK_PATH",
)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""EEG Motor Movement/Imagery Dataset."""
from .eegbci import data_path, load_data, standardize

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import re
import time
from importlib.resources import files
from os import path as op
from pathlib import Path
from ...utils import _url_to_local_path, logger, verbose, warn
from ..utils import _do_path_update, _downloader_params, _get_path, _log_time_size
EEGMI_URL = "https://physionet.org/files/eegmmidb/1.0.0/"
@verbose
def data_path(url, path=None, force_update=False, update_path=None, *, verbose=None):
"""Get path to local copy of EEGMMI dataset URL.
This is a low-level function useful for getting a local copy of a remote EEGBCI
dataset :footcite:`SchalkEtAl2004`, which is also available at PhysioNet
:footcite:`GoldbergerEtAl2000`. Metadata, such as the meaning of event markers
may be obtained from the
`PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
Parameters
----------
url : str
The dataset to use.
path : None | path-like
Location of where to look for the EEGBCI data. If ``None``, the environment
variable or config parameter ``MNE_DATASETS_EEGBCI_PATH`` is used. If neither
exists, the ``~/mne_data`` directory is used. If the EEGBCI 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 ``MNE_DATASETS_EEGBCI_PATH`` in the configuration to the given
path. If ``None``, the user is prompted.
%(verbose)s
Returns
-------
path : list of Path
Local path to the given data file. This path is contained inside a list of
length one for compatibility.
Notes
-----
For example, one could do:
>>> from mne.datasets import eegbci
>>> url = "http://www.physionet.org/physiobank/database/eegmmidb/"
>>> eegbci.data_path(url, "~/datasets") # doctest:+SKIP
This would download the given EEGBCI data file to the ``~/datasets`` folder and
prompt the user to store this path in the config (if it does not already exist).
References
----------
.. footbibliography::
"""
import pooch
key = "MNE_DATASETS_EEGBCI_PATH"
name = "EEGBCI"
path = _get_path(path, key, name)
fname = "MNE-eegbci-data"
destination = _url_to_local_path(url, op.join(path, fname))
destinations = [destination]
# fetch the file
downloader = pooch.HTTPDownloader(**_downloader_params())
if not op.isfile(destination) or force_update:
if op.isfile(destination):
os.remove(destination)
if not op.isdir(op.dirname(destination)):
os.makedirs(op.dirname(destination))
pooch.retrieve(
url=url,
path=destination,
downloader=downloader,
fname=fname,
)
# offer to update the path
_do_path_update(path, update_path, key, name)
destinations = [Path(dest) for dest in destinations]
return destinations
@verbose
def load_data(
subjects=None,
runs=None,
*,
subject=None,
path=None,
force_update=False,
update_path=None,
base_url=EEGMI_URL,
verbose=None,
): # noqa: D301
"""Get paths to local copies of EEGBCI dataset files.
This will fetch data for the EEGBCI dataset :footcite:`SchalkEtAl2004`, which is
also available at PhysioNet :footcite:`GoldbergerEtAl2000`. Metadata, such as the
meaning of event markers may be obtained from the
`PhysioNet documentation page <https://physionet.org/content/eegmmidb/1.0.0/>`_.
Parameters
----------
subjects : int | list of int
The subjects to use. Can be in the range of 1-109 (inclusive).
runs : int | list of int
The runs to use (see Notes for details).
subject : int
This parameter is deprecated and will be removed in mne version 1.9.
Please use ``subjects`` instead.
path : None | path-like
Location of where to look for the EEGBCI data. If ``None``, the environment
variable or config parameter ``MNE_DATASETS_EEGBCI_PATH`` is used. If neither
exists, the ``~/mne_data`` directory is used. If the EEGBCI 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 ``MNE_DATASETS_EEGBCI_PATH`` in the configuration to the given
path. If ``None``, the user is prompted.
base_url : str
The URL root for the data.
%(verbose)s
Returns
-------
paths : list
List of local data paths of the given type.
Notes
-----
The run numbers correspond to:
========= ===================================
run task
========= ===================================
1 Baseline, eyes open
2 Baseline, eyes closed
3, 7, 11 Motor execution: left vs right hand
4, 8, 12 Motor imagery: left vs right hand
5, 9, 13 Motor execution: hands vs feet
6, 10, 14 Motor imagery: hands vs feet
========= ===================================
For example, one could do::
>>> from mne.datasets import eegbci
>>> eegbci.load_data([1, 2], [6, 10, 14], "~/datasets") # doctest:+SKIP
This would download runs 6, 10, and 14 (hand/foot motor imagery) runs from subjects
1 and 2 in the EEGBCI dataset to "~/datasets" and prompt the user to store this path
in the config (if it does not already exist).
References
----------
.. footbibliography::
"""
import pooch
# XXX: Remove this with mne 1.9 ↓↓↓
# Also remove the subject parameter at that point.
# Also remove the `None` default for subjects and runs params at that point.
if subject is not None:
subjects = subject
warn(
"The ``subject`` parameter is deprecated and will be removed in version "
"1.9. Use the ``subjects`` parameter (note the `s`) to suppress this "
"warning.",
FutureWarning,
)
del subject
if subjects is None or runs is None:
raise ValueError("You must pass the parameters ``subjects`` and ``runs``.")
# ↑↑↑
t0 = time.time()
if not hasattr(subjects, "__iter__"):
subjects = [subjects]
if not hasattr(runs, "__iter__"):
runs = [runs]
# get local storage path
config_key = "MNE_DATASETS_EEGBCI_PATH"
folder = "MNE-eegbci-data"
name = "EEGBCI"
path = _get_path(path, config_key, name)
# extract path parts
pattern = r"(?:https?://.*)(files)/(eegmmidb)/(\d+\.\d+\.\d+)/?"
match = re.compile(pattern).match(base_url)
if match is None:
raise ValueError(
"base_url does not match the expected EEGMI folder "
"structure. Please notify MNE-Python developers."
)
base_path = op.join(path, folder, *match.groups())
# create the download manager
fetcher = pooch.create(
path=base_path,
base_url=base_url,
version=None, # data versioning is decoupled from MNE-Python version
registry=None, # registry is loaded from file (below)
retry_if_failed=2, # 2 retries = 3 total attempts
)
# load the checksum registry
registry = files("mne").joinpath("data", "eegbci_checksums.txt")
fetcher.load_registry(registry)
# fetch the file(s)
data_paths = []
sz = 0
for subject in subjects:
for run in runs:
file_part = f"S{subject:03d}/S{subject:03d}R{run:02d}.edf"
destination = Path(base_path, file_part)
data_paths.append(destination)
if destination.exists():
if force_update:
destination.unlink()
else:
continue
if sz == 0: # log once
logger.info("Downloading EEGBCI data")
fetcher.fetch(file_part)
# update path in config if desired
sz += destination.stat().st_size
_do_path_update(path, update_path, config_key, name)
if sz > 0:
_log_time_size(t0, sz)
return data_paths
def standardize(raw):
"""Standardize channel positions and names.
Parameters
----------
raw : instance of Raw
The raw data to standardize. Operates in-place.
"""
rename = dict()
for name in raw.ch_names:
std_name = name.strip(".")
std_name = std_name.upper()
if std_name.endswith("Z"):
std_name = std_name[:-1] + "z"
if std_name.startswith("FP"):
std_name = "Fp" + std_name[2:]
rename[name] = std_name
raw.rename_channels(rename)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Clinical epilepsy datasets."""
from ._data import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="epilepsy_ecog",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="epilepsy_ecog", conf="MNE_DATASETS_EPILEPSY_ECOG_PATH"
)
def get_version(): # noqa: D103
return _get_version("epilepsy_ecog")
get_version.__doc__ = _version_doc.format(name="epilepsy_ecog")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""ERP-CORE EEG dataset."""
from .erp_core import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="erp_core",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="erp_core", conf="MNE_DATASETS_ERP_CORE_PATH"
)
def get_version(): # noqa: D103
return _get_version("erp_core")
get_version.__doc__ = _version_doc.format(name="erp_core")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Eyelink test dataset."""
from .eyelink import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="eyelink",
processor="unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="eyelink", conf="MNE_DATASETS_EYELINK_PATH"
)
def get_version(): # noqa: D103
return _get_version("eyelink")
get_version.__doc__ = _version_doc.format(name="eyelink")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""fieldtrip Cortico-Muscular Coherence (CMC) Dataset."""
from .fieldtrip_cmc import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="fieldtrip_cmc",
processor="nested_unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="fieldtrip_cmc", conf="MNE_DATASETS_FIELDTRIP_CMC_PATH"
)
def get_version(): # noqa: D103
return _get_version("fieldtrip_cmc")
get_version.__doc__ = _version_doc.format(name="fieldtrip_cmc")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""fNIRS motor dataset."""
from .fnirs_motor import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="fnirs_motor",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="fnirs_motor", conf="MNE_DATASETS_FNIRS_MOTOR_PATH"
)
def get_version(): # noqa: D103
return _get_version("fnirs_motor")
get_version.__doc__ = _version_doc.format(name="fnirs_motor")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""HF-SEF dataset."""
from .hf_sef import data_path

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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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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE visual_92_categories dataset."""
from .kiloword import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
):
"""Get path to local copy of the kiloword dataset.
This is the dataset from :footcite:`DufauEtAl2015`.
Parameters
----------
path : None | str
Location of where to look for the kiloword data storing
location. If None, the environment variable or config parameter
MNE_DATASETS_KILOWORD_PATH is used. If it doesn't exist,
the "mne-python/examples" directory is used. If the
kiloword dataset is not found under the given path (e.g.,
as "mne-python/examples/MNE-kiloword-data"), 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_KILOWORD_PATH in mne-python
config to the given path. If None, the user is prompted.
download : bool
If False and the kiloword dataset has not been downloaded yet,
it will not be downloaded and the path will be returned as
'' (empty string). This is mostly used for debugging purposes
and can be safely ignored by most users.
%(verbose)s
Returns
-------
path : list of Path
Local path to the given data file. This path is contained inside a list
of length one, for compatibility.
References
----------
.. footbibliography::
"""
return _download_mne_dataset(
name="kiloword",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
def get_version():
"""Get dataset version."""
return _get_version("kiloword")
get_version.__doc__ = _version_doc.format(name="kiloword")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""LIMO Dataset."""
from .limo import data_path, load_data

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
import time
from pathlib import Path
import numpy as np
from scipy.io import loadmat
from ..._fiff.meas_info import create_info
from ...channels import make_standard_montage
from ...epochs import EpochsArray
from ...utils import _check_pandas_installed, logger, verbose
from ..utils import _do_path_update, _downloader_params, _get_path, _log_time_size
# root url for LIMO files
root_url = "https://files.de-1.osf.io/v1/resources/52rea/providers/osfstorage/"
@verbose
def data_path(
subject, path=None, force_update=False, update_path=None, *, verbose=None
):
"""Get path to local copy of LIMO dataset URL.
This is a low-level function useful for getting a local copy of the
remote LIMO dataset :footcite:`Rousselet2016`. The complete dataset is
available at datashare.is.ed.ac.uk/.
Parameters
----------
subject : int
Subject to download. Must be of :class:`ìnt` in the range from 1
to 18 (inclusive).
path : None | str
Location of where to look for the LIMO data storing directory.
If None, the environment variable or config parameter
``MNE_DATASETS_LIMO_PATH`` is used. If it doesn't exist, the
"~/mne_data" directory is used. If the LIMO 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_LIMO_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
path : str
Local path to the given data file.
Notes
-----
For example, one could do:
>>> from mne.datasets import limo
>>> limo.data_path(subject=1, path=os.getenv('HOME') + '/datasets') # doctest:+SKIP
This would download the LIMO data file to the 'datasets' folder,
and prompt the user to save the 'datasets' path to the mne-python config,
if it isn't there already.
References
----------
.. footbibliography::
""" # noqa: E501
import pooch
t0 = time.time()
downloader = pooch.HTTPDownloader(**_downloader_params())
# local storage patch
config_key = "MNE_DATASETS_LIMO_PATH"
name = "LIMO"
subj = f"S{subject}"
path = _get_path(path, config_key, name)
base_path = op.join(path, "MNE-limo-data")
subject_path = op.join(base_path, subj)
# the remote URLs are in the form of UUIDs:
urls = dict(
S18={
"Yr.mat": "5cf839833a4d9500178a6ff8",
"LIMO.mat": "5cf83907e650a2001ad592e4",
},
S17={
"Yr.mat": "5cf838e83a4d9500168aeb76",
"LIMO.mat": "5cf83867a542b80019c87602",
},
S16={
"Yr.mat": "5cf83857e650a20019d5778f",
"LIMO.mat": "5cf837dc3a4d9500188a64fe",
},
S15={
"Yr.mat": "5cf837cce650a2001ad591e8",
"LIMO.mat": "5cf83758a542b8001ac7d11d",
},
S14={
"Yr.mat": "5cf837493a4d9500198a938f",
"LIMO.mat": "5cf836e4a542b8001bc7cc53",
},
S13={
"Yr.mat": "5cf836d23a4d9500178a6df7",
"LIMO.mat": "5cf836543a4d9500168ae7cb",
},
S12={
"Yr.mat": "5cf83643d4c7d700193e5954",
"LIMO.mat": "5cf835193a4d9500178a6c92",
},
S11={
"Yr.mat": "5cf8356ea542b8001cc81517",
"LIMO.mat": "5cf834f7d4c7d700163daab8",
},
S10={
"Yr.mat": "5cf833b0e650a20019d57454",
"LIMO.mat": "5cf83204e650a20018d59eb2",
},
S9={
"Yr.mat": "5cf83201a542b8001cc811cf",
"LIMO.mat": "5cf8316c3a4d9500168ae13b",
},
S8={
"Yr.mat": "5cf8326ce650a20017d60373",
"LIMO.mat": "5cf8316d3a4d9500198a8dc5",
},
S7={
"Yr.mat": "5cf834a03a4d9500168ae59b",
"LIMO.mat": "5cf83069e650a20017d600d7",
},
S6={
"Yr.mat": "5cf830e6a542b80019c86a70",
"LIMO.mat": "5cf83057a542b80019c869ca",
},
S5={
"Yr.mat": "5cf8115be650a20018d58041",
"LIMO.mat": "5cf80c0bd4c7d700193e213c",
},
S4={
"Yr.mat": "5cf810c9a542b80019c8450a",
"LIMO.mat": "5cf80bf83a4d9500198a6eb4",
},
S3={
"Yr.mat": "5cf80c55d4c7d700163d8f52",
"LIMO.mat": "5cf80bdea542b80019c83cab",
},
S2={
"Yr.mat": "5cde827123fec40019e01300",
"LIMO.mat": "5cde82682a50c4001677c259",
},
S1={
"Yr.mat": "5d6d3071536cf5001a8b0c78",
"LIMO.mat": "5d6d305f6f41fc001a3151d8",
},
)
# these can't be in the registry file (mne/data/dataset_checksums.txt)
# because of filename duplication
hashes = dict(
S18={
"Yr.mat": "md5:87f883d442737971a80fc0a35d057e51",
"LIMO.mat": "md5:8b4879646f65d7876fa4adf2e40162c5",
},
S17={
"Yr.mat": "md5:7b667ec9eefd7a9996f61ae270e295ee",
"LIMO.mat": "md5:22eaca4e6fad54431fd61b307fc426b8",
},
S16={
"Yr.mat": "md5:c877afdb4897426421577e863a45921a",
"LIMO.mat": "md5:86672d7afbea1e8c39305bc3f852c8c2",
},
S15={
"Yr.mat": "md5:eea9e0140af598fefc08c886a6f05de5",
"LIMO.mat": "md5:aed5cb71ddbfd27c6a3ac7d3e613d07f",
},
S14={
"Yr.mat": "md5:8bd842cfd8588bd5d32e72fdbe70b66e",
"LIMO.mat": "md5:1e07d1f36f2eefad435a77530daf2680",
},
S13={
"Yr.mat": "md5:d7925d2af7288b8a5186dfb5dbb63d34",
"LIMO.mat": "md5:ba891015d2f9e447955fffa9833404ca",
},
S12={
"Yr.mat": "md5:0e1d05beaa4bf2726e0d0671b78fe41e",
"LIMO.mat": "md5:423fd479d71097995b6614ecb11df9ad",
},
S11={
"Yr.mat": "md5:1b0016fb9832e43b71f79c1992fcbbb1",
"LIMO.mat": "md5:1a281348c2a41ee899f42731d30cda70",
},
S10={
"Yr.mat": "md5:13c66f60e241b9a9cc576eaf1b55a417",
"LIMO.mat": "md5:3c4b41e221eb352a21bbef1a7e006f06",
},
S9={
"Yr.mat": "md5:3ae1d9c3a1d9325deea2f2dddd1ab507",
"LIMO.mat": "md5:5e204e2a4bcfe4f535b4b1af469b37f7",
},
S8={
"Yr.mat": "md5:7e9adbca4e03d8d7ce8ea07ccecdc8fd",
"LIMO.mat": "md5:88313c21d34428863590e586b2bc3408",
},
S7={
"Yr.mat": "md5:6b5290a6725ecebf1022d5d2789b186d",
"LIMO.mat": "md5:8c769219ebc14ce3f595063e84bfc0a9",
},
S6={
"Yr.mat": "md5:420c858a8340bf7c28910b7b0425dc5d",
"LIMO.mat": "md5:9cf4e1a405366d6bd0cc6d996e32fd63",
},
S5={
"Yr.mat": "md5:946436cfb474c8debae56ffb1685ecf3",
"LIMO.mat": "md5:241fac95d3a79d2cea081391fb7078bd",
},
S4={
"Yr.mat": "md5:c8216af78ac87b739e86e57b345cafdd",
"LIMO.mat": "md5:8e10ef36c2e075edc2f787581ba33459",
},
S3={
"Yr.mat": "md5:ff02e885b65b7b807146f259a30b1b5e",
"LIMO.mat": "md5:59b5fb3a9749003133608b5871309e2c",
},
S2={
"Yr.mat": "md5:a4329022e57fd07ceceb7d1735fd2718",
"LIMO.mat": "md5:98b284b567f2dd395c936366e404f2c6",
},
S1={
"Yr.mat": "md5:076c0ae78fb71d43409c1877707df30e",
"LIMO.mat": "md5:136c8cf89f8f111a11f531bd9fa6ae69",
},
)
# create the download manager
fetcher = pooch.create(
path=subject_path,
base_url="",
version=None, # Data versioning is decoupled from MNE-Python version.
registry=hashes[subj],
urls={key: f"{root_url}{uuid}" for key, uuid in urls[subj].items()},
retry_if_failed=2, # 2 retries = 3 total attempts
)
# use our logger level for pooch's logger too
pooch.get_logger().setLevel(logger.getEffectiveLevel())
# fetch the data
sz = 0
for fname in ("LIMO.mat", "Yr.mat"):
destination = Path(subject_path, fname)
if destination.exists():
if force_update:
destination.unlink()
else:
continue
if sz == 0: # log once
logger.info("Downloading LIMO data")
# fetch the remote file (if local file missing or has hash mismatch)
fetcher.fetch(fname=fname, downloader=downloader)
sz += destination.stat().st_size
# update path in config if desired
_do_path_update(path, update_path, config_key, name)
if sz > 0:
_log_time_size(t0, sz)
return base_path
@verbose
def load_data(subject, path=None, force_update=False, update_path=None, verbose=None):
"""Fetch subjects epochs data for the LIMO data set.
Parameters
----------
subject : int
Subject to use. Must be of class ìnt in the range from 1 to 18.
path : str
Location of where to look for the LIMO data.
If None, the environment variable or config parameter
``MNE_DATASETS_LIMO_PATH`` is used. If it doesn't exist, the
"~/mne_data" directory is used.
force_update : bool
Force update of the dataset even if a local copy exists.
update_path : bool | None
If True, set the MNE_DATASETS_LIMO_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
epochs : instance of Epochs
The epochs.
""" # noqa: E501
pd = _check_pandas_installed()
# subject in question
if isinstance(subject, int) and 1 <= subject <= 18:
subj = f"S{subject}"
else:
raise ValueError("subject must be an int in the range from 1 to 18")
# set limo path, download and decompress files if not found
limo_path = data_path(subject, path, force_update, update_path)
# -- 1) import .mat files
# epochs info
fname_info = op.join(limo_path, subj, "LIMO.mat")
data_info = loadmat(fname_info)
# number of epochs per condition
design = data_info["LIMO"]["design"][0][0]["X"][0][0]
data_info = data_info["LIMO"]["data"][0][0][0][0]
# epochs data
fname_eeg = op.join(limo_path, subj, "Yr.mat")
data = loadmat(fname_eeg)
# -- 2) get epochs information from structure
# sampling rate
sfreq = data_info["sampling_rate"][0][0]
# tmin and tmax
tmin = data_info["start"][0][0]
# create events matrix
sample = np.arange(len(design))
prev_id = np.zeros(len(design))
ev_id = design[:, 1]
events = np.array([sample, prev_id, ev_id]).astype(int).T
# event ids, such that Face B == 1
event_id = {"Face/A": 0, "Face/B": 1}
# -- 3) extract channel labels from LIMO structure
# get individual labels
labels = data_info["chanlocs"]["labels"]
labels = [label for label, *_ in labels[0]]
# get montage
montage = make_standard_montage("biosemi128")
# add external electrodes (e.g., eogs)
ch_names = montage.ch_names + ["EXG1", "EXG2", "EXG3", "EXG4"]
# match individual labels to labels in montage
found_inds = [ind for ind, name in enumerate(ch_names) if name in labels]
missing_chans = [name for name in ch_names if name not in labels]
assert labels == [ch_names[ind] for ind in found_inds]
# -- 4) extract data from subjects Yr structure
# data is stored as channels x time points x epochs
# data['Yr'].shape # <-- see here
# transpose to epochs x channels time points
data = np.transpose(data["Yr"], (2, 0, 1))
# initialize data in expected order
temp_data = np.empty((data.shape[0], len(ch_names), data.shape[2]))
# copy over the non-missing data
for source, target in enumerate(found_inds):
# avoid copy when fancy indexing
temp_data[:, target, :] = data[:, source, :]
# data to V (to match MNE's format)
data = temp_data / 1e6
# create list containing channel types
types = ["eog" if ch.startswith("EXG") else "eeg" for ch in ch_names]
# -- 5) Create custom info for mne epochs structure
# create info
info = create_info(ch_names, sfreq, types).set_montage(montage)
# get faces and noise variables from design matrix
event_list = list(events[:, 2])
faces = ["B" if event else "A" for event in event_list]
noise = list(design[:, 2])
# create epochs metadata
metadata = {"face": faces, "phase-coherence": noise}
metadata = pd.DataFrame(metadata)
# -- 6) Create custom epochs array
epochs = EpochsArray(
data, info, events, tmin, event_id, metadata=metadata, verbose=False
)
epochs.info["bads"] = missing_chans # missing channels are marked as bad.
return epochs

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE misc dataset."""
from ._misc import data_path, _pytest_mark

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, has_dataset
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="misc",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
def _pytest_mark():
import pytest
return pytest.mark.skipif(
not has_dataset(name="misc"), reason="Requires misc dataset"
)
data_path.__doc__ = _data_path_doc.format(name="misc", conf="MNE_DATASETS_MISC_PATH")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""mTRF Dataset."""
from .mtrf import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
data_name = "mtrf"
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name=data_name,
processor="unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(name=data_name, conf="MNE_DATASETS_MTRF_PATH")
def get_version(): # noqa: D103
return _get_version(data_name)
get_version.__doc__ = _version_doc.format(name=data_name)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Multimodal dataset."""
from .multimodal import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="multimodal",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="multimodal", conf="MNE_DATASETS_MULTIMODAL_PATH"
)
def get_version(): # noqa: D103
return _get_version("multimodal")
get_version.__doc__ = _version_doc.format(name="multimodal")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""OPM dataset."""
from .opm import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="opm",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(name="opm", conf="MNE_DATASETS_OPML_PATH")
def get_version(): # noqa: D103
return _get_version("opm")
get_version.__doc__ = _version_doc.format(name="opm")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Multimodal dataset."""
from .phantom_4dbti import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="phantom_4dbti",
processor="unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="phantom_4dbti", conf="MNE_DATASETS_PHANTOM_4DBTI_PATH"
)
def get_version(): # noqa: D103
return _get_version("phantom_4dbti")
get_version.__doc__ = _version_doc.format(name="phantom_4dbti")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Multimodal dataset."""
from .phantom_kernel import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="phantom_kernel",
processor="nested_untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="phantom_kernel", conf="MNE_DATASETS_PHANTOM_KERNEL_PATH"
)
def get_version(): # noqa: D103
return _get_version("phantom_kernel")
get_version.__doc__ = _version_doc.format(name="phantom_kernel")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""KIT phantom dataset."""
from .phantom_kit import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="phantom_kit",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="phantom_kit", conf="MNE_DATASETS_PHANTOM_KIT_PATH"
)
def get_version(): # noqa: D103
return _get_version("phantom_kit")
get_version.__doc__ = _version_doc.format(name="phantom_kit")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MEG reference-noise data set."""
from .refmeg_noise import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="refmeg_noise",
processor="unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="refmeg_noise", conf="MNE_DATASETS_REFMEG_NOISE_PATH"
)
def get_version(): # noqa: D103
return _get_version("refmeg_noise")
get_version.__doc__ = _version_doc.format(name="refmeg_noise")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE sample dataset."""
from .sample import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="sample",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="sample", conf="MNE_DATASETS_SAMPLE_PATH"
)
def get_version(): # noqa: D103
return _get_version("sample")
get_version.__doc__ = _version_doc.format(name="sample")

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83493e1c32d441c9e5ee3de6a024bfb5e7ab9f5f SC4141E0-PSG.edf
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5c5c63016b43421a523d1efcb34247e90aa6318b SC4202EC-Hypnogram.edf
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211410fab6381da0dfaef4134d5a05eec935a4ec SC4222E0-PSG.edf
1488fbfbc149499dafa8dafff4f7504053af429f SC4222EC-Hypnogram.edf
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9f6df70676d6cddcf069ceb7f408a7989af99ce2 SC4231EJ-Hypnogram.edf
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7dbc0289707ff70662d367d65de7bec188484d1b SC4251E0-PSG.edf
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4cb7a383736e09125a82ef7e4f17b41130c7ac00 SC4252EU-Hypnogram.edf
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501eda59557bb99d530d01bdad3579f1e1158991 SC4261FM-Hypnogram.edf
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20994715d34edb26113180ee330ce287dbf57b60 SC4271F0-PSG.edf
26c5c7f3a5c350d3505af2857835ce81252c5990 SC4271FC-Hypnogram.edf
9e79eb465e34b7eb6fe27ae3ce35d28d6693d44b SC4272F0-PSG.edf
956fe4b45d29a8999faf280a6168e332afab6abc SC4272FM-Hypnogram.edf
51811913d7854f95c319076e670d988687ca667c SC4281G0-PSG.edf
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12d777787dd1975eef9015329fd774b2bfa1d53a SC4282GC-Hypnogram.edf
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577c1345f6d070d975db5016048722f78b1b414e SC4291GA-Hypnogram.edf
7416f44a3b149b4ca1fc3e53d546a093a7333bb5 SC4292G0-PSG.edf
6e111a15160a31609761f742315df800b1311b3b SC4292GC-Hypnogram.edf
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812c34844e834b97949019741fa7f835d973725d SC4311EC-Hypnogram.edf
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335381ae310e9f1f053c37763eeee74d7d873471 SC4321E0-PSG.edf
67ba7d3b97354deb31db095e748ea3a4014fae2c SC4321EC-Hypnogram.edf
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40cf9a6397a52c7deda693ca596e928cc2b9f4e9 SC4322EC-Hypnogram.edf
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ca943e2b73c6404f929c372ebd817b7b3b71b4dd SC4331FV-Hypnogram.edf
5bce6ea9b2d6c9bfb41065e92bf9cc05a11b5b75 SC4332F0-PSG.edf
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17de25c8f023fe632aa403a6d9525c1cde8eaef5 SC4341F0-PSG.edf
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325423a85890dcc921253bde7c7027d66f14033e SC4352F0-PSG.edf
1e0583b2a58432c964506ff44752d597753658c9 SC4352FV-Hypnogram.edf
30b90aaf965938d569ea362f66e2afa0c08c7017 SC4362F0-PSG.edf
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0dc56fce13b6317f197d0b17c04f5be4af1c964f SC4371F0-PSG.edf
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97b2915a8a343efc7b785998c0532beaea2fbe91 SC4372FC-Hypnogram.edf
6098d2b501b82ca0ddc8893547c6990e204e8ba6 SC4381F0-PSG.edf
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40ce0168d5f546fcd445996ab614f43823a7c2b1 SC4382F0-PSG.edf
796f8507254c2d8d345171c077dbd855e112eb47 SC4382FW-Hypnogram.edf
28fd8ad1aee307847e2eb579763ebca18e56f540 SC4401E0-PSG.edf
65b5671a89871351ee3da7ea800aad276a445b2a SC4401EC-Hypnogram.edf
3d4bafa57933cfb20c342e8cc54c15916a621454 SC4402E0-PSG.edf
037efea0fc8a6dfa8f85fa1f2fa6fd9a19f2c830 SC4402EW-Hypnogram.edf
30a533b67fdb2adac6a4e83088a07fe1bbaddb6c SC4411E0-PSG.edf
5df1bf20d4f29b95a2bdde853b2a157dd9530a8a SC4411EJ-Hypnogram.edf
bc8e6ea829f14da5396a4b250394c1b72d6631c3 SC4412E0-PSG.edf
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80f246adffb92a3785f91368a77b0250aa040462 SC4422E0-PSG.edf
709251cc7ae6556544c153caf9dac7f82bba113b SC4422EA-Hypnogram.edf
194ae942cf80764e81b4cdabeed9e5a57916aab3 SC4431E0-PSG.edf
497ad7e671edab6e7adc9d35a6aa45b7fd9a706b SC4431EM-Hypnogram.edf
c45a66d27ea03bf448903fe30f17838e9a0fa0de SC4432E0-PSG.edf
10fe276e215f9406c0ddedaa48651cf480892476 SC4432EM-Hypnogram.edf
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68d4e44ad54069701972df66d8a81b4ca434bf2f SC4441EC-Hypnogram.edf
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315db0f9d91988ddc2b198f89cc22f96190eff71 SC4451F0-PSG.edf
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0286d52cdf898ed8e3b17bb26b9c50ef512daf4d SC4452FW-Hypnogram.edf
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53b69cb41339bc69144eaa5a5a42c2937f237fc9 SC4462F0-PSG.edf
0c6d3974e140c1e62ed2cadaed395781575af042 SC4462FJ-Hypnogram.edf
05d71b55de4c86791195391b1cec8b35e447922d SC4471F0-PSG.edf
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7a12c0d6f3005998472b128e06dd645a8619dae7 SC4472F0-PSG.edf
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50fce6396aceaf35d9d7e16175053a3b78f214d0 SC4481FV-Hypnogram.edf
34d71530fd1da925ba20b4c48a07f7b18153e0c7 SC4482F0-PSG.edf
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23ea1f5f299c6cd99d434f014d7490621dbbc854 SC4491G0-PSG.edf
36c6c8112524c7bc9553db37601b38984946209b SC4491GJ-Hypnogram.edf
02c975bfc0773928095239b80d00ac5a7ea5880f SC4492G0-PSG.edf
3673eaad8396ef0ec36cb4299541c30653b72e1f SC4492GJ-Hypnogram.edf
1c31fc02412029bc7369979b8c9f5956420748f5 SC4501E0-PSG.edf
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53c5d982139d248736f6dd7ff3f97f635647eacd SC4512E0-PSG.edf
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71222ac5b7784ed1d3a79ee3e9036431d6eba9bd SC4531E0-PSG.edf
934dbfeb29f4f4db4b61e36fb8ddab4ddbf4ff94 SC4531EM-Hypnogram.edf
2d472fb64da5d05a546f780da876b90ad26208f9 SC4532E0-PSG.edf
708b43e7d43a6f5719f48c11bd6a81b037aabfc4 SC4532EV-Hypnogram.edf
4d3ec2f85149bb10fed1013831c3aa1f58049229 SC4541F0-PSG.edf
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2909f5b0d3fdb89e19d42b406798e9cbb4615bb6 SC4542F0-PSG.edf
9548ed641fb961fa46706339891a9453b731369f SC4542FW-Hypnogram.edf
0bf97e463cbcefb7df48bca712f29dcc74223330 SC4551F0-PSG.edf
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7380403f8d72fa4c30013cd026cc1dad23ac2b3e SC4552FW-Hypnogram.edf
1a9baf1b072ca9d2784a404292169ff3177ea83f SC4561F0-PSG.edf
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4c7081edf572cadee51d30174cd65aa6c658f5a9 SC4562F0-PSG.edf
676ab92dbc6532f67d672f80337c71f817fd3a6d SC4562FJ-Hypnogram.edf
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08ee39eb94d819968512297ca883f9bca046de9c SC4571FV-Hypnogram.edf
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7a38cbe581167dfec27a15935e6d386b228616fa SC4572FC-Hypnogram.edf
16a1edbd6a089386fd7de72aef802182d0a2959d SC4581G0-PSG.edf
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9da93f4c2459dd4fe2e5ee6a171904d4f604cd6e SC4582G0-PSG.edf
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017793b040df8a860df0e43e3e0a496e2cb3f9c1 SC4591G0-PSG.edf
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1e284bddd7952862327c83092db21805e6ab6c38 SC4592G0-PSG.edf
58d1678e9ec9f49c9c6a15031dee26d802026851 SC4592GY-Hypnogram.edf
ece6d6ce09fac6fc521cf3f1b536f1ea2a8a1778 SC4601E0-PSG.edf
8f77b05fe58f43cdfdcdba7cc3d27abcac7d37f2 SC4601EC-Hypnogram.edf
0e50df304ced29651267f43689ce49e063f808d6 SC4602E0-PSG.edf
1c52de92668fe4c89cd5e270e17017ef47880991 SC4602EJ-Hypnogram.edf
2cc6e418c0b7af472aa34d2bbd5ece85bdb6a879 SC4611E0-PSG.edf
f5715ab48f24221c28c1d5c45508c8bb58c912ec SC4611EG-Hypnogram.edf
6593e1af07101fa4c5bce8984296858be17e7d4f SC4612E0-PSG.edf
cedb61bbe7a273b12f45579963d5a84f2ab21811 SC4612EA-Hypnogram.edf
31cd2cae56977c6b872311f2a6e60827748b973d SC4621E0-PSG.edf
7acc5296b33ca4eee8d6577064c8c651ee96e527 SC4621EV-Hypnogram.edf
7a7e226d47dccd959305e3f633686335c8e66557 SC4622E0-PSG.edf
9957c9c9e0c705aac0f7125f411b2531a722601c SC4622EJ-Hypnogram.edf
6dfb32aa4c94968a52d61b90a38573d178669bfb SC4631E0-PSG.edf
48e28f93fc71ffc539776196f9d9d1365415e0b4 SC4631EM-Hypnogram.edf
3baa8081b30cc3dfece9d550289dfc94812530d5 SC4632E0-PSG.edf
cd2765ebdabc66cb4ac2320d02e3b7ab0340ede4 SC4632EA-Hypnogram.edf
0e5d109a929490cbecf59573577a97df07a05cd0 SC4641E0-PSG.edf
7b896dc5b34d71381d8462001dc3e05b145cf48c SC4641EP-Hypnogram.edf
03169b7ee9de83b2e17e9bd0d6274965e9518b37 SC4642E0-PSG.edf
d8a870d26e468a643eaebe3275e5e2912690c0d8 SC4642EP-Hypnogram.edf
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6ed9c4f66c03e56f86730ddd8986f3600c040d4a SC4652EG-Hypnogram.edf
c6057505d2acf7b08371e266cf0fca1bfeb1e4e1 SC4661E0-PSG.edf
06474e72126d2a00c1968e70730e1deac060f94e SC4661EJ-Hypnogram.edf
24d278194360dc78ebd0cfe940fb4d5f7f93ccbc SC4662E0-PSG.edf
07ca0fbfb6030289a089f84e50d7bbfd043f31ad SC4662EJ-Hypnogram.edf
4357aa9fedf0b53896d41e5dccd7b525f7212177 SC4671G0-PSG.edf
459889157743c434933194446af5168cb145dfcb SC4671GJ-Hypnogram.edf
fd86b31a5c22176e1887e2fac460edce42bd2fdf SC4672G0-PSG.edf
dedb182b8c063cefabf1763eb19cd26d0608017f SC4672GV-Hypnogram.edf
3f60b5ad5e1092e90c38f2072b3c041bd7313550 SC4701E0-PSG.edf
196a388f60ee4aecfa982f89e2db03ff91e906e7 SC4701EC-Hypnogram.edf
a6853fee26b1541f85be7ddc3f42f06ccfe2fcfc SC4702E0-PSG.edf
464f7382ec11703b5bc6512930fdfbb1ab6d030a SC4702EA-Hypnogram.edf
e97d691bfecf770ca4e47289b846886c16ef19fb SC4711E0-PSG.edf
81ec5d0288f36c4368e5f06f21980f99774bf533 SC4711EC-Hypnogram.edf
9b99be6cb45af22bdbead7ea01f1375631c9b365 SC4712E0-PSG.edf
66b121441a45ae19852b7002fd78c2caf236631a SC4712EA-Hypnogram.edf
5c9caa01cc1f8065f87195c9f2dc2aeebf83c03d SC4721E0-PSG.edf
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mne/datasets/sleep_physionet/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from . import age, temazepam, _utils

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import os.path as op
import numpy as np
from ...utils import _check_pandas_installed, _on_missing, _TempDir, verbose
from ..utils import _downloader_params, _get_path
AGE_SLEEP_RECORDS = op.join(op.dirname(__file__), "age_records.csv")
TEMAZEPAM_SLEEP_RECORDS = op.join(op.dirname(__file__), "temazepam_records.csv")
TEMAZEPAM_RECORDS_URL = (
"https://physionet.org/physiobank/database/sleep-edfx/ST-subjects.xls" # noqa: E501
)
TEMAZEPAM_RECORDS_URL_SHA1 = "f52fffe5c18826a2bd4c5d5cb375bb4a9008c885"
AGE_RECORDS_URL = "https://physionet.org/physiobank/database/sleep-edfx/SC-subjects.xls"
AGE_RECORDS_URL_SHA1 = "0ba6650892c5d33a8e2b3f62ce1cc9f30438c54f"
sha1sums_fname = op.join(op.dirname(__file__), "SHA1SUMS")
def _fetch_one(fname, hashsum, path, force_update, base_url):
import pooch
# Fetch the file
url = base_url + "/" + fname
destination = op.join(path, fname)
if op.isfile(destination) and not force_update:
return destination, False
if op.isfile(destination):
os.remove(destination)
if not op.isdir(op.dirname(destination)):
os.makedirs(op.dirname(destination))
downloader = pooch.HTTPDownloader(**_downloader_params())
pooch.retrieve(
url=url,
known_hash=f"sha1:{hashsum}",
path=path,
downloader=downloader,
fname=fname,
)
return destination, True
@verbose
def _data_path(path=None, verbose=None):
"""Get path to local copy of EEG Physionet age Polysomnography dataset URL.
This is a low-level function useful for getting a local copy of a
remote Polysomnography dataset :footcite:`KempEtAl2000` which is available
at PhysioNet :footcite:`GoldbergerEtAl2000`.
Parameters
----------
path : None | str
Location of where to look for the data storing location.
If None, the environment variable or config parameter
``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data"
directory is used. If the dataset is not found under the given path,
the data will be automatically downloaded to the specified folder.
%(verbose)s
Returns
-------
path : list of Path
Local path to the given data file. This path is contained inside a list
of length one, for compatibility.
References
----------
.. footbibliography::
""" # noqa: E501
key = "PHYSIONET_SLEEP_PATH"
name = "PHYSIONET_SLEEP"
path = _get_path(path, key, name)
return op.join(path, "physionet-sleep-data")
def _update_sleep_temazepam_records(fname=TEMAZEPAM_SLEEP_RECORDS):
"""Help function to download Physionet's temazepam dataset records."""
import pooch
pd = _check_pandas_installed()
tmp = _TempDir()
# Download subjects info.
subjects_fname = op.join(tmp, "ST-subjects.xls")
downloader = pooch.HTTPDownloader(**_downloader_params())
pooch.retrieve(
url=TEMAZEPAM_RECORDS_URL,
known_hash=f"sha1:{TEMAZEPAM_RECORDS_URL_SHA1}",
path=tmp,
downloader=downloader,
fname=op.basename(subjects_fname),
)
# Load and Massage the checksums.
sha1_df = pd.read_csv(
sha1sums_fname, sep=" ", header=None, names=["sha", "fname"], engine="python"
)
select_age_records = sha1_df.fname.str.startswith(
"ST"
) & sha1_df.fname.str.endswith("edf")
sha1_df = sha1_df[select_age_records]
sha1_df["id"] = [name[:6] for name in sha1_df.fname]
# Load and massage the data.
data = pd.read_excel(subjects_fname, header=[0, 1])
data = data.set_index(("Subject - age - sex", "Nr"))
data.index.name = "subject"
data.columns.names = [None, None]
data = (
data.set_index(
[("Subject - age - sex", "Age"), ("Subject - age - sex", "M1/F2")],
append=True,
)
.stack(level=0)
.reset_index()
)
data = data.rename(
columns={
("Subject - age - sex", "Age"): "age",
("Subject - age - sex", "M1/F2"): "sex",
"level_3": "drug",
}
)
data["id"] = [f"ST7{s:02d}{n:1d}" for s, n in zip(data.subject, data["night nr"])]
data = pd.merge(sha1_df, data, how="outer", on="id")
data["record type"] = (
data.fname.str.split("-", expand=True)[1]
.str.split(".", expand=True)[0]
.astype("category")
)
data = data.set_index(
["id", "subject", "age", "sex", "drug", "lights off", "night nr", "record type"]
).unstack()
data.columns = [l1 + "_" + l2 for l1, l2 in data.columns]
data = data.reset_index().drop(columns=["id"])
data["sex"] = data.sex.astype("category").cat.rename_categories(
{1: "male", 2: "female"}
)
data["drug"] = data["drug"].str.split(expand=True)[0]
data["subject_orig"] = data["subject"]
data["subject"] = data.index // 2 # to make sure index is from 0 to 21
# Save the data.
data.to_csv(fname, index=False)
def _update_sleep_age_records(fname=AGE_SLEEP_RECORDS):
"""Help function to download Physionet's age dataset records."""
import pooch
pd = _check_pandas_installed()
tmp = _TempDir()
# Download subjects info.
subjects_fname = op.join(tmp, "SC-subjects.xls")
downloader = pooch.HTTPDownloader(**_downloader_params())
pooch.retrieve(
url=AGE_RECORDS_URL,
known_hash=f"sha1:{AGE_RECORDS_URL_SHA1}",
path=tmp,
downloader=downloader,
fname=op.basename(subjects_fname),
)
# Load and Massage the checksums.
sha1_df = pd.read_csv(
sha1sums_fname, sep=" ", header=None, names=["sha", "fname"], engine="python"
)
select_age_records = sha1_df.fname.str.startswith(
"SC"
) & sha1_df.fname.str.endswith("edf")
sha1_df = sha1_df[select_age_records]
sha1_df["id"] = [name[:6] for name in sha1_df.fname]
# Load and massage the data.
data = pd.read_excel(subjects_fname)
data = data.rename(
index=str, columns={"sex (F=1)": "sex", "LightsOff": "lights off"}
)
data["sex"] = data.sex.astype("category").cat.rename_categories(
{1: "female", 2: "male"}
)
data["id"] = [f"SC4{s:02d}{n:1d}" for s, n in zip(data.subject, data.night)]
data = data.set_index("id").join(sha1_df.set_index("id")).dropna()
data["record type"] = (
data.fname.str.split("-", expand=True)[1]
.str.split(".", expand=True)[0]
.astype("category")
)
data = data.reset_index().drop(columns=["id"])
data = data[
["subject", "night", "record type", "age", "sex", "lights off", "sha", "fname"]
]
# Save the data.
data.to_csv(fname, index=False)
def _check_subjects(subjects, n_subjects, missing=None, on_missing="raise"):
"""Check whether subjects are available.
Parameters
----------
subjects : list
Subject numbers to be checked.
n_subjects : int
Number of subjects available.
missing : list | None
Subject numbers that are missing.
on_missing : 'raise' | 'warn' | 'ignore'
What to do if one or several subjects are not available. Valid keys
are 'raise' | 'warn' | 'ignore'. Default is 'error'. If on_missing
is 'warn' it will proceed but warn, if 'ignore' it will proceed
silently.
"""
valid_subjects = np.arange(n_subjects)
if missing is not None:
valid_subjects = np.setdiff1d(valid_subjects, missing)
unknown_subjects = np.setdiff1d(subjects, valid_subjects)
if unknown_subjects.size > 0:
subjects_list = ", ".join([str(s) for s in unknown_subjects])
msg = (
f"This dataset contains subjects 0 to {n_subjects - 1} with "
f"missing subjects {missing}. Unknown subjects: "
f"{subjects_list}."
)
_on_missing(on_missing, msg)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import time
import numpy as np
from ...utils import verbose
from ..utils import _log_time_size
from ._utils import (
AGE_SLEEP_RECORDS,
_check_subjects,
_data_path,
_fetch_one,
_on_missing,
)
data_path = _data_path # expose _data_path(..) as data_path(..)
BASE_URL = "https://physionet.org/physiobank/database/sleep-edfx/sleep-cassette/"
@verbose
def fetch_data(
subjects,
recording=(1, 2),
path=None,
force_update=False,
base_url=BASE_URL,
on_missing="raise",
*,
verbose=None,
): # noqa: D301, E501
"""Get paths to local copies of PhysioNet Polysomnography dataset files.
This will fetch data from the publicly available subjects from PhysioNet's
study of age effects on sleep in healthy subjects
:footcite:`MourtazaevEtAl1995,GoldbergerEtAl2000`. This
corresponds to a subset of 153 recordings from 37 males and 41 females that
were 25-101 years old at the time of the recordings. There are two night
recordings per subject except for subjects 13, 36 and 52 which have one
record missing each due to missing recording hardware.
See more details in
`physionet website <https://physionet.org/physiobank/database/sleep-edfx/sleep-cassette/>`_.
Parameters
----------
subjects : list of int
The subjects to use. Can be in the range of 0-82 (inclusive), however
the following subjects are not available: 39, 68, 69, 78 and 79.
recording : list of int
The night recording indices. Valid values are : [1], [2], or [1, 2].
The following recordings are not available: recording 1 for subject 36
and 52, and recording 2 for subject 13.
path : None | str
Location of where to look for the PhysioNet data storing location.
If None, the environment variable or config parameter
``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data"
directory is used. If the Polysomnography 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.
base_url : str
The URL root.
on_missing : 'raise' | 'warn' | 'ignore'
What to do if one or several recordings are not available. Valid keys
are 'raise' | 'warn' | 'ignore'. Default is 'error'. If on_missing
is 'warn' it will proceed but warn, if 'ignore' it will proceed
silently.
%(verbose)s
Returns
-------
paths : list
List of local data paths of the given type.
See Also
--------
mne.datasets.sleep_physionet.temazepam.fetch_data
Notes
-----
For example, one could do:
>>> from mne.datasets import sleep_physionet
>>> sleep_physionet.age.fetch_data(subjects=[0]) # doctest: +SKIP
This would download data for subject 0 if it isn't there already.
References
----------
.. footbibliography::
""" # noqa: E501
t0 = time.time()
records = np.loadtxt(
AGE_SLEEP_RECORDS,
skiprows=1,
delimiter=",",
usecols=(0, 1, 2, 6, 7),
dtype={
"names": ("subject", "record", "type", "sha", "fname"),
"formats": ("<i2", "i1", "<S9", "S40", "<S22"),
},
)
psg_records = records[np.where(records["type"] == b"PSG")]
hyp_records = records[np.where(records["type"] == b"Hypnogram")]
path = data_path(path=path)
params = [path, force_update, base_url]
_check_subjects(subjects, 83, missing=[39, 68, 69, 78, 79], on_missing=on_missing)
# Check for missing recordings
if set(subjects) & {36, 52} and 1 in recording:
msg = (
"Requested recording 1 for subject 36 and/or 52, but it is not "
"available in corpus."
)
_on_missing(on_missing, msg)
if 13 in subjects and 2 in recording:
msg = (
"Requested recording 2 for subject 13, but it is not available "
"in corpus."
)
_on_missing(on_missing, msg)
fnames = []
sz = 0
for subject in subjects:
for idx in np.where(psg_records["subject"] == subject)[0]:
if psg_records["record"][idx] in recording:
psg_fname, pdl = _fetch_one(
psg_records["fname"][idx].decode(),
psg_records["sha"][idx].decode(),
*params,
)
hyp_fname, hdl = _fetch_one(
hyp_records["fname"][idx].decode(),
hyp_records["sha"][idx].decode(),
*params,
)
fnames.append([psg_fname, hyp_fname])
if pdl:
sz += os.path.getsize(psg_fname)
if hdl:
sz += os.path.getsize(hyp_fname)
if sz > 0:
_log_time_size(t0, sz)
return fnames

307
mne/datasets/sleep_physionet/age_records.csv Normal file
View File

@@ -0,0 +1,307 @@
subject,night,record type,age,sex,lights off,sha,fname
0,1,PSG,33,female,00:38:00,adabd3b01fc7bb75c523a974f38ee3ae4e57b40f,SC4001E0-PSG.edf
0,1,Hypnogram,33,female,00:38:00,21c998eadc8b1e3ea6727d3585186b8f76e7e70b,SC4001EC-Hypnogram.edf
0,2,PSG,33,female,21:57:00,c6b6d7a8605cc7e7602b6028ee77f6fbf5f7581d,SC4002E0-PSG.edf
0,2,Hypnogram,33,female,21:57:00,386230188a3552b1fc90bba0fb7476ceaca174b6,SC4002EC-Hypnogram.edf
1,1,PSG,33,female,22:44:00,4d17451f7847355bcab17584de05e7e1df58c660,SC4011E0-PSG.edf
1,1,Hypnogram,33,female,22:44:00,d582a3cbe2db481a362af890bc5a2f5ca7c878dc,SC4011EH-Hypnogram.edf
1,2,PSG,33,female,22:15:00,a47d525f5147904b6890231e2ad338359c7ab94c,SC4012E0-PSG.edf
1,2,Hypnogram,33,female,22:15:00,fa99f60d7f54617cdd1128aff4f21c4daed763c7,SC4012EC-Hypnogram.edf
2,1,PSG,26,female,22:50:00,8b135afa7fb93bb5f1998fda50355944777c245e,SC4021E0-PSG.edf
2,1,Hypnogram,26,female,22:50:00,91043cfe46695088b17b6a02937b25efd674c3fb,SC4021EH-Hypnogram.edf
2,2,PSG,26,female,22:57:00,d739e142b3b328c71b4752149901805dcd6d7e19,SC4022E0-PSG.edf
2,2,Hypnogram,26,female,22:57:00,0c46a03699dd00e8f92a7edff99ebc4642cb3d48,SC4022EJ-Hypnogram.edf
3,1,PSG,26,female,00:02:00,85e58dc1e3303537dade8c5827ab58328239c384,SC4031E0-PSG.edf
3,1,Hypnogram,26,female,00:02:00,6363d8b0fdc48cf396c9abf054bb4a9696d38bdb,SC4031EC-Hypnogram.edf
3,2,PSG,26,female,00:24:00,43963d300642b3aa840e8c468f321b8162601772,SC4032E0-PSG.edf
3,2,Hypnogram,26,female,00:24:00,7925514bc8d2ef3f1103130f08f7b3afd2136b88,SC4032EP-Hypnogram.edf
4,1,PSG,34,female,23:12:00,04d2b88d25f2ae4a65ba44cd9145bd12800a0e20,SC4041E0-PSG.edf
4,1,Hypnogram,34,female,23:12:00,f148821669bd3588187b3b430bd79adf569f86d1,SC4041EC-Hypnogram.edf
4,2,PSG,34,female,23:35:00,76253d964d7797540ffd791e6e136023ed67a485,SC4042E0-PSG.edf
4,2,Hypnogram,34,female,23:35:00,9873df429f971f8a4b720a454f6c0472b8a25ebb,SC4042EC-Hypnogram.edf
5,1,PSG,28,female,01:22:00,ea073451b65ce8a6f1a02a8cc2b89d1a162ca0ae,SC4051E0-PSG.edf
5,1,Hypnogram,28,female,01:22:00,4159ef8a3e119d6dcc1bede806f6fbc017b27a0f,SC4051EC-Hypnogram.edf
5,2,PSG,28,female,00:35:00,5a2efbd21be9b745fd534394eb2503caca7dc53f,SC4052E0-PSG.edf
5,2,Hypnogram,28,female,00:35:00,0e96482d44762df4da65dc4fdb970b342264d22a,SC4052EC-Hypnogram.edf
6,1,PSG,31,female,00:16:00,1736736e585807c14f1ae8bc87a94cae222c5170,SC4061E0-PSG.edf
6,1,Hypnogram,31,female,00:16:00,4bf99622c67c281b25ceccd35e7050328a2946e8,SC4061EC-Hypnogram.edf
6,2,PSG,31,female,22:44:00,763c7ac059f1771a0165e5cb351b176afb1cfe15,SC4062E0-PSG.edf
6,2,Hypnogram,31,female,22:44:00,14f07411cd04d3b4b522d37c129334955287ff5f,SC4062EC-Hypnogram.edf
7,1,PSG,30,female,00:36:00,1374b34f6139b6ff7e865d8243eef39ba334ef50,SC4071E0-PSG.edf
7,1,Hypnogram,30,female,00:36:00,608024fd19a140ad233a4680e07c2495a74b69c2,SC4071EC-Hypnogram.edf
7,2,PSG,30,female,00:41:00,1c570644243d79396df612fa2b9bc027b24430e4,SC4072E0-PSG.edf
7,2,Hypnogram,30,female,00:41:00,a8da6c20b9b48189f05ab537886b59dd141374d2,SC4072EH-Hypnogram.edf
8,1,PSG,25,female,23:35:00,0e1cc2c4e1da14ab94515e3e7e75e8ad30ec99cb,SC4081E0-PSG.edf
8,1,Hypnogram,25,female,23:35:00,9ec663ffa5c17afcaca59d7829d77b9165102237,SC4081EC-Hypnogram.edf
8,2,PSG,25,female,23:37:00,d57d4aa7cbc5045f611a3a3e342b501e086ea426,SC4082E0-PSG.edf
8,2,Hypnogram,25,female,23:37:00,d43c785dba43063d7baa332671c6bac9c832b5b7,SC4082EP-Hypnogram.edf
9,1,PSG,25,female,23:02:00,b3502e0bd54683e973182c791aa962b804e79633,SC4091E0-PSG.edf
9,1,Hypnogram,25,female,23:02:00,7aa63b408c769a4a983a908b6ba41d87dd743c6e,SC4091EC-Hypnogram.edf
9,2,PSG,25,female,23:01:00,246e35852119b33d197db2f7bcfb1b46a5270a03,SC4092E0-PSG.edf
9,2,Hypnogram,25,female,23:01:00,9d85766a83231b1c6076cb293367ccc354c57eeb,SC4092EC-Hypnogram.edf
10,1,PSG,26,male,22:59:00,3ae168ff2c9c0c56f51205fdb10f05a4c6b2064e,SC4101E0-PSG.edf
10,1,Hypnogram,26,male,22:59:00,60d9c3913881e11b06ad99e9870bd1ca4d93c952,SC4101EC-Hypnogram.edf
10,2,PSG,26,male,23:07:00,86f307190961eaab0214fdc0213f8fe05812c7a5,SC4102E0-PSG.edf
10,2,Hypnogram,26,male,23:07:00,8072e2d52bc6c19b45fbd921550e5243bc5a1de7,SC4102EC-Hypnogram.edf
11,1,PSG,26,male,23:00:00,e490956b4dce01c46ba88a2b847f091bb54ea16e,SC4111E0-PSG.edf
11,1,Hypnogram,26,male,23:00:00,12db1920e2f6083c8ab1f2c24fe35dfa03715e4a,SC4111EC-Hypnogram.edf
11,2,PSG,26,male,01:14:00,ca24dc464df61144627588b29d35a85fcc7ac984,SC4112E0-PSG.edf
11,2,Hypnogram,26,male,01:14:00,54dbc39015b0a445b51189987a00e08cc27d8f0c,SC4112EC-Hypnogram.edf
12,1,PSG,26,male,00:50:00,33c72025a7a215ea5e255f4254cb0f93b1313369,SC4121E0-PSG.edf
12,1,Hypnogram,26,male,00:50:00,daa57ece807cb5325c6d1ce059f0e8a8d1c85391,SC4121EC-Hypnogram.edf
12,2,PSG,26,male,01:03:00,34f5145ab62dcc5a53ba18735519e5bb2b13841a,SC4122E0-PSG.edf
12,2,Hypnogram,26,male,01:03:00,b7af1a32d8ca15e8185e4c94213ffc18ad7f6e8a,SC4122EV-Hypnogram.edf
13,1,PSG,27,male,00:14:00,42ff97035aae6dd34ca9437857c48ac6f2ab97df,SC4131E0-PSG.edf
13,1,Hypnogram,27,male,00:14:00,5beef85170bdbb5cf2eea24a79f0f5c2c3975c4b,SC4131EC-Hypnogram.edf
14,1,PSG,27,male,22:55:00,83493e1c32d441c9e5ee3de6a024bfb5e7ab9f5f,SC4141E0-PSG.edf
14,1,Hypnogram,27,male,22:55:00,511d398f22b9b2b304de27c40740a41584ff6af2,SC4141EU-Hypnogram.edf
14,2,PSG,27,male,23:22:00,63d13828b7ebe0d2ed7f491d2b5520e928b9b55d,SC4142E0-PSG.edf
14,2,Hypnogram,27,male,23:22:00,6f123e6fdc90a01b83e694d9744a6d27f3c87b25,SC4142EU-Hypnogram.edf
15,1,PSG,31,male,23:56:00,5a92d49699d4de369d66d9462e91b0dcb3312649,SC4151E0-PSG.edf
15,1,Hypnogram,31,male,23:56:00,37dcbd339c95322d028b3a5466812697041cc373,SC4151EC-Hypnogram.edf
15,2,PSG,31,male,23:38:00,778626489bc4fe2c9137d2d361876d97dce97e5e,SC4152E0-PSG.edf
15,2,Hypnogram,31,male,23:38:00,294cdc47cd3d165031f7041c17f18dd013d216cb,SC4152EC-Hypnogram.edf
16,1,PSG,32,male,22:16:00,e56ff3aa366fe9a04a0fdfdd4cd862e77e8ac807,SC4161E0-PSG.edf
16,1,Hypnogram,32,male,22:16:00,56711b1bfed292032491f5cce57494629286a131,SC4161EC-Hypnogram.edf
16,2,PSG,32,male,23:16:00,722692f9940f3a1bccb9b4488c4477edf7fb128f,SC4162E0-PSG.edf
16,2,Hypnogram,32,male,23:16:00,c85647fb4bc1f382fe46bf9aaf579dc483115885,SC4162EC-Hypnogram.edf
17,1,PSG,31,male,23:58:00,f1a65522cb7d6c71ac47742535a12c88e2019dad,SC4171E0-PSG.edf
17,1,Hypnogram,31,male,23:58:00,dd257c8d922f08c2c8ca5236c9bf54da887c68e5,SC4171EU-Hypnogram.edf
17,2,PSG,31,male,00:37:00,572b81bc24c2c9482e6fc7ba9202a7bf253655e1,SC4172E0-PSG.edf
17,2,Hypnogram,31,male,00:37:00,c9a3b590748d7d6c7ad97c62222bd53d8ebaf630,SC4172EC-Hypnogram.edf
18,1,PSG,28,male,23:25:00,23674d20572853eb6d988d24378c52123f66500c,SC4181E0-PSG.edf
18,1,Hypnogram,28,male,23:25:00,51fc3df2df7d4da654f3e18ed1b233d0c60cfa80,SC4181EC-Hypnogram.edf
18,2,PSG,28,male,23:45:00,83e8cbe882ba863da9fd3c11393c95b6fec5b7a5,SC4182E0-PSG.edf
18,2,Hypnogram,28,male,23:45:00,43c487955edddb4ee2f60193a097c68c25c5dd4d,SC4182EC-Hypnogram.edf
19,1,PSG,28,male,01:50:00,d6da621dbb20dec3494a38c7d2a0363793ac5ebe,SC4191E0-PSG.edf
19,1,Hypnogram,28,male,01:50:00,defc7b9368c2d3c4ab4a294757843825a83cdb5d,SC4191EP-Hypnogram.edf
19,2,PSG,28,male,00:57:00,941353118732321d0246a1d58d72e903bd2f0d8f,SC4192E0-PSG.edf
19,2,Hypnogram,28,male,00:57:00,97b91b3067c5ecde766042fc2cff9e22f8023371,SC4192EV-Hypnogram.edf
20,1,PSG,51,female,23:10:00,38a0be6e45ddd9b1f17d09964a32e005dc5a6519,SC4201E0-PSG.edf
20,1,Hypnogram,51,female,23:10:00,83822f9970d3959ad2e0613492ae39bd0fae6068,SC4201EC-Hypnogram.edf
20,2,PSG,51,female,23:15:00,aa69f5bd47c2ae03c9d38bfe6d0e58408744b885,SC4202E0-PSG.edf
20,2,Hypnogram,51,female,23:15:00,5c5c63016b43421a523d1efcb34247e90aa6318b,SC4202EC-Hypnogram.edf
21,1,PSG,51,female,23:28:00,c106ad072dbc975a3742f7eff151219870f0c794,SC4211E0-PSG.edf
21,1,Hypnogram,51,female,23:28:00,9126937ea8a414d6ae9bc4a4194d841a891fa8a8,SC4211EC-Hypnogram.edf
21,2,PSG,51,female,23:59:00,a06ecb3f0a7b2c306f5ae4dbd83685f877cd945b,SC4212E0-PSG.edf
21,2,Hypnogram,51,female,23:59:00,a85f178b69a1cda47d11dd1e5394dfdcb58de1d4,SC4212EC-Hypnogram.edf
22,1,PSG,56,female,23:47:00,8733ea022d3778259a436507156cf3360ad8be06,SC4221E0-PSG.edf
22,1,Hypnogram,56,female,23:47:00,b158eda4f81772095c129be77f8e60ec9d81b884,SC4221EJ-Hypnogram.edf
22,2,PSG,56,female,23:14:00,211410fab6381da0dfaef4134d5a05eec935a4ec,SC4222E0-PSG.edf
22,2,Hypnogram,56,female,23:14:00,1488fbfbc149499dafa8dafff4f7504053af429f,SC4222EC-Hypnogram.edf
23,1,PSG,50,female,00:51:00,d96f1f35b2f77c7de706036c6e4114139e07b307,SC4231E0-PSG.edf
23,1,Hypnogram,50,female,00:51:00,9f6df70676d6cddcf069ceb7f408a7989af99ce2,SC4231EJ-Hypnogram.edf
23,2,PSG,50,female,00:32:00,6b493fa424c1329ea1c13543d08ba82a9f1e85b6,SC4232E0-PSG.edf
23,2,Hypnogram,50,female,00:32:00,d8ca7d694b3c48ab9d983b9cf67e17744c6b50fb,SC4232EV-Hypnogram.edf
24,1,PSG,54,female,23:22:00,58719e53fe18d2fc4cb1776ab5d43306beb1325d,SC4241E0-PSG.edf
24,1,Hypnogram,54,female,23:22:00,fb1432e303a8f99a2256ce682db95d88772c479f,SC4241EC-Hypnogram.edf
24,2,PSG,54,female,22:50:00,5a6277972c5f03572ed99d9ff63fb637945be778,SC4242E0-PSG.edf
24,2,Hypnogram,54,female,22:50:00,bbbf097f4cc6560fc20c903fba2c7055e1549f85,SC4242EA-Hypnogram.edf
25,1,PSG,56,female,00:32:00,7dbc0289707ff70662d367d65de7bec188484d1b,SC4251E0-PSG.edf
25,1,Hypnogram,56,female,00:32:00,e38be8134e4a36eb418ca1f06a1fe02b52d0ebf1,SC4251EP-Hypnogram.edf
25,2,PSG,56,female,23:49:00,cb3922910ea03d06c1fc5c8f15b71339dc26bc9d,SC4252E0-PSG.edf
25,2,Hypnogram,56,female,23:49:00,4cb7a383736e09125a82ef7e4f17b41130c7ac00,SC4252EU-Hypnogram.edf
26,1,PSG,51,female,23:39:00,b81c9bd1875b33713b5eb56b58f1e120841b507f,SC4261F0-PSG.edf
26,1,Hypnogram,51,female,23:39:00,501eda59557bb99d530d01bdad3579f1e1158991,SC4261FM-Hypnogram.edf
26,2,PSG,51,female,00:20:00,c9f9ad7cd751d5be91396886a2b64a7c1de564ee,SC4262F0-PSG.edf
26,2,Hypnogram,51,female,00:20:00,7ccd12803c5fc602ac1929ff3afd914b894b9143,SC4262FC-Hypnogram.edf
27,1,PSG,54,female,23:41:00,20994715d34edb26113180ee330ce287dbf57b60,SC4271F0-PSG.edf
27,1,Hypnogram,54,female,23:41:00,26c5c7f3a5c350d3505af2857835ce81252c5990,SC4271FC-Hypnogram.edf
27,2,PSG,54,female,22:58:00,9e79eb465e34b7eb6fe27ae3ce35d28d6693d44b,SC4272F0-PSG.edf
27,2,Hypnogram,54,female,22:58:00,956fe4b45d29a8999faf280a6168e332afab6abc,SC4272FM-Hypnogram.edf
28,1,PSG,56,female,23:55:00,51811913d7854f95c319076e670d988687ca667c,SC4281G0-PSG.edf
28,1,Hypnogram,56,female,23:55:00,d188150831e912081dbeda2695231177200c39f9,SC4281GC-Hypnogram.edf
28,2,PSG,56,female,00:13:00,e9f080a766a9b7a247f228e44e9c4ec67e571c95,SC4282G0-PSG.edf
28,2,Hypnogram,56,female,00:13:00,12d777787dd1975eef9015329fd774b2bfa1d53a,SC4282GC-Hypnogram.edf
29,1,PSG,51,female,22:38:00,f81c7574a5e5829e006d0b705bf5208a3349c9c7,SC4291G0-PSG.edf
29,1,Hypnogram,51,female,22:38:00,577c1345f6d070d975db5016048722f78b1b414e,SC4291GA-Hypnogram.edf
29,2,PSG,51,female,23:04:00,7416f44a3b149b4ca1fc3e53d546a093a7333bb5,SC4292G0-PSG.edf
29,2,Hypnogram,51,female,23:04:00,6e111a15160a31609761f742315df800b1311b3b,SC4292GC-Hypnogram.edf
30,1,PSG,50,male,00:09:00,7818e5a02afa89e913111d91ecd651aa3e786e5d,SC4301E0-PSG.edf
30,1,Hypnogram,50,male,00:09:00,d49df84bfea28bb241c09b922cd2dc64f57c5ae5,SC4301EC-Hypnogram.edf
30,2,PSG,50,male,00:20:00,d52859ba6a7ded3364b0d8ef2b722e1d3edda060,SC4302E0-PSG.edf
30,2,Hypnogram,50,male,00:20:00,b3d6f687831ee32f6df1da59f2d568c13f9c09d0,SC4302EV-Hypnogram.edf
31,1,PSG,54,male,23:44:00,b62f5104bddf452f4700c85997e51bec17f0243b,SC4311E0-PSG.edf
31,1,Hypnogram,54,male,23:44:00,812c34844e834b97949019741fa7f835d973725d,SC4311EC-Hypnogram.edf
31,2,PSG,54,male,23:14:00,b0a9b4922665734773abbaba06e7aab32010b862,SC4312E0-PSG.edf
31,2,Hypnogram,54,male,23:14:00,fca1935a8974eac27803e3125cea177995deca11,SC4312EM-Hypnogram.edf
32,1,PSG,57,male,00:48:00,335381ae310e9f1f053c37763eeee74d7d873471,SC4321E0-PSG.edf
32,1,Hypnogram,57,male,00:48:00,67ba7d3b97354deb31db095e748ea3a4014fae2c,SC4321EC-Hypnogram.edf
32,2,PSG,57,male,00:15:00,c9fdcfcce7e603b3289b7417891987fd67f6d921,SC4322E0-PSG.edf
32,2,Hypnogram,57,male,00:15:00,40cf9a6397a52c7deda693ca596e928cc2b9f4e9,SC4322EC-Hypnogram.edf
33,1,PSG,60,male,22:58:00,f37cb4df27286e38c604cae943169ff29b1473fc,SC4331F0-PSG.edf
33,1,Hypnogram,60,male,22:58:00,ca943e2b73c6404f929c372ebd817b7b3b71b4dd,SC4331FV-Hypnogram.edf
33,2,PSG,60,male,22:55:00,5bce6ea9b2d6c9bfb41065e92bf9cc05a11b5b75,SC4332F0-PSG.edf
33,2,Hypnogram,60,male,22:55:00,e4595b0313d5320b0bffefa43260485e19977e3c,SC4332FC-Hypnogram.edf
34,1,PSG,54,male,23:03:00,17de25c8f023fe632aa403a6d9525c1cde8eaef5,SC4341F0-PSG.edf
34,1,Hypnogram,54,male,23:03:00,81ba3c0d8320c9ee306f678b4bc9e6e266165886,SC4341FA-Hypnogram.edf
34,2,PSG,54,male,22:30:00,b659037447a1871f4ba72bbe496cfbe507330530,SC4342F0-PSG.edf
34,2,Hypnogram,54,male,22:30:00,e8e74c0905e89a59022ce0814ca9a050748ec9ae,SC4342FA-Hypnogram.edf
35,1,PSG,57,male,00:02:00,631900bef36d359a0f5807a7e1b202f80b0427ac,SC4351F0-PSG.edf
35,1,Hypnogram,57,male,00:02:00,a15cdf3973b77198d8276dc505dbb35cb39a9b4a,SC4351FA-Hypnogram.edf
35,2,PSG,57,male,23:30:00,325423a85890dcc921253bde7c7027d66f14033e,SC4352F0-PSG.edf
35,2,Hypnogram,57,male,23:30:00,1e0583b2a58432c964506ff44752d597753658c9,SC4352FV-Hypnogram.edf
36,2,PSG,51,male,23:59:00,30b90aaf965938d569ea362f66e2afa0c08c7017,SC4362F0-PSG.edf
36,2,Hypnogram,51,male,23:59:00,fb870d50ce3f4d961d8b061a83d21e5467e4ae6c,SC4362FC-Hypnogram.edf
37,1,PSG,52,male,23:03:00,0dc56fce13b6317f197d0b17c04f5be4af1c964f,SC4371F0-PSG.edf
37,1,Hypnogram,52,male,23:03:00,c19b6cbfdf3a33169ce9b4a5dc94f93b696a21ba,SC4371FA-Hypnogram.edf
37,2,PSG,52,male,23:05:00,c024c491dd836ed0169300e7171c276fd14b1c44,SC4372F0-PSG.edf
37,2,Hypnogram,52,male,23:05:00,97b2915a8a343efc7b785998c0532beaea2fbe91,SC4372FC-Hypnogram.edf
38,1,PSG,51,male,23:12:00,6098d2b501b82ca0ddc8893547c6990e204e8ba6,SC4381F0-PSG.edf
38,1,Hypnogram,51,male,23:12:00,fdbf653a4a675843c97d0a76ef5e4cebf5d2dbcb,SC4381FC-Hypnogram.edf
38,2,PSG,51,male,23:47:00,40ce0168d5f546fcd445996ab614f43823a7c2b1,SC4382F0-PSG.edf
38,2,Hypnogram,51,male,23:47:00,796f8507254c2d8d345171c077dbd855e112eb47,SC4382FW-Hypnogram.edf
40,1,PSG,67,female,23:30:00,28fd8ad1aee307847e2eb579763ebca18e56f540,SC4401E0-PSG.edf
40,1,Hypnogram,67,female,23:30:00,65b5671a89871351ee3da7ea800aad276a445b2a,SC4401EC-Hypnogram.edf
40,2,PSG,67,female,23:55:00,3d4bafa57933cfb20c342e8cc54c15916a621454,SC4402E0-PSG.edf
40,2,Hypnogram,67,female,23:55:00,037efea0fc8a6dfa8f85fa1f2fa6fd9a19f2c830,SC4402EW-Hypnogram.edf
41,1,PSG,66,female,23:28:00,30a533b67fdb2adac6a4e83088a07fe1bbaddb6c,SC4411E0-PSG.edf
41,1,Hypnogram,66,female,23:28:00,5df1bf20d4f29b95a2bdde853b2a157dd9530a8a,SC4411EJ-Hypnogram.edf
41,2,PSG,66,female,23:30:00,bc8e6ea829f14da5396a4b250394c1b72d6631c3,SC4412E0-PSG.edf
41,2,Hypnogram,66,female,23:30:00,f46b1dcfe4f4e3c9d4d4c8516dab9759f9c1224e,SC4412EM-Hypnogram.edf
42,1,PSG,69,female,01:30:00,e8a5d9e0f160ae7bd0b35d75d77b4c872daa30f8,SC4421E0-PSG.edf
42,1,Hypnogram,69,female,01:30:00,d2e34f9bcaac7af23da4448f742ac6ea3c895ed9,SC4421EA-Hypnogram.edf
42,2,PSG,69,female,00:22:00,80f246adffb92a3785f91368a77b0250aa040462,SC4422E0-PSG.edf
42,2,Hypnogram,69,female,00:22:00,709251cc7ae6556544c153caf9dac7f82bba113b,SC4422EA-Hypnogram.edf
43,1,PSG,73,female,01:30:00,194ae942cf80764e81b4cdabeed9e5a57916aab3,SC4431E0-PSG.edf
43,1,Hypnogram,73,female,01:30:00,497ad7e671edab6e7adc9d35a6aa45b7fd9a706b,SC4431EM-Hypnogram.edf
43,2,PSG,73,female,00:47:00,c45a66d27ea03bf448903fe30f17838e9a0fa0de,SC4432E0-PSG.edf
43,2,Hypnogram,73,female,00:47:00,10fe276e215f9406c0ddedaa48651cf480892476,SC4432EM-Hypnogram.edf
44,1,PSG,74,female,00:18:00,e3a09d832cb79b0095d7a311ef1b6ed7c569b79d,SC4441E0-PSG.edf
44,1,Hypnogram,74,female,00:18:00,68d4e44ad54069701972df66d8a81b4ca434bf2f,SC4441EC-Hypnogram.edf
44,2,PSG,74,female,23:55:00,fe51d45e9f3e64a61fa8a5e5274b2e4951a9de43,SC4442E0-PSG.edf
44,2,Hypnogram,74,female,23:55:00,efc2b86bb796b0143f61667402612dfbb85cbb78,SC4442EV-Hypnogram.edf
45,1,PSG,66,female,00:26:00,315db0f9d91988ddc2b198f89cc22f96190eff71,SC4451F0-PSG.edf
45,1,Hypnogram,66,female,00:26:00,bc1f755c3367e378091c44481948a72fc7a928e5,SC4451FY-Hypnogram.edf
45,2,PSG,66,female,00:34:00,a06350e1c85b61c30c3d7d5dc640121b416fe30d,SC4452F0-PSG.edf
45,2,Hypnogram,66,female,00:34:00,0286d52cdf898ed8e3b17bb26b9c50ef512daf4d,SC4452FW-Hypnogram.edf
46,1,PSG,66,female,00:30:00,e4295014c6d4474d8f7f7792c2ea088eb9e43e9f,SC4461F0-PSG.edf
46,1,Hypnogram,66,female,00:30:00,8980e770e58e5704bd36124f6b6bd8d5e3506e12,SC4461FA-Hypnogram.edf
46,2,PSG,66,female,00:38:00,53b69cb41339bc69144eaa5a5a42c2937f237fc9,SC4462F0-PSG.edf
46,2,Hypnogram,66,female,00:38:00,0c6d3974e140c1e62ed2cadaed395781575af042,SC4462FJ-Hypnogram.edf
47,1,PSG,73,female,22:36:00,05d71b55de4c86791195391b1cec8b35e447922d,SC4471F0-PSG.edf
47,1,Hypnogram,73,female,22:36:00,ee235454dbfe947432f3f813c9a6384f6e42d36a,SC4471FA-Hypnogram.edf
47,2,PSG,73,female,22:25:00,7a12c0d6f3005998472b128e06dd645a8619dae7,SC4472F0-PSG.edf
47,2,Hypnogram,73,female,22:25:00,d234d5d6c396bf7ef0a2106a59ee8204429aa3c5,SC4472FA-Hypnogram.edf
48,1,PSG,67,female,23:04:00,c15f6a0e1802dcf74ecec41745677a4932375faf,SC4481F0-PSG.edf
48,1,Hypnogram,67,female,23:04:00,50fce6396aceaf35d9d7e16175053a3b78f214d0,SC4481FV-Hypnogram.edf
48,2,PSG,67,female,23:57:00,34d71530fd1da925ba20b4c48a07f7b18153e0c7,SC4482F0-PSG.edf
48,2,Hypnogram,67,female,23:57:00,e3c48563e63eed27b071d4a7b37c45a0f9dc7eef,SC4482FJ-Hypnogram.edf
49,1,PSG,67,female,23:26:00,23ea1f5f299c6cd99d434f014d7490621dbbc854,SC4491G0-PSG.edf
49,1,Hypnogram,67,female,23:26:00,36c6c8112524c7bc9553db37601b38984946209b,SC4491GJ-Hypnogram.edf
49,2,PSG,67,female,00:13:00,02c975bfc0773928095239b80d00ac5a7ea5880f,SC4492G0-PSG.edf
49,2,Hypnogram,67,female,00:13:00,3673eaad8396ef0ec36cb4299541c30653b72e1f,SC4492GJ-Hypnogram.edf
50,1,PSG,71,male,22:07:00,1c31fc02412029bc7369979b8c9f5956420748f5,SC4501E0-PSG.edf
50,1,Hypnogram,71,male,22:07:00,eb2621c1670a42eb38dfa86a9bc3326818365f3d,SC4501EW-Hypnogram.edf
50,2,PSG,71,male,23:40:00,ff9eae25afa73115e2b184a68e3a72a39efd37e6,SC4502E0-PSG.edf
50,2,Hypnogram,71,male,23:40:00,7605a1893701925ea0fdd047926bbd6c7c043875,SC4502EM-Hypnogram.edf
51,1,PSG,70,male,23:10:00,e12eb259c2894d45b8d0b2f0e75810c2de02237d,SC4511E0-PSG.edf
51,1,Hypnogram,70,male,23:10:00,e549275e9182b9e36ade5abb721098e235ecb164,SC4511EJ-Hypnogram.edf
51,2,PSG,70,male,00:03:00,53c5d982139d248736f6dd7ff3f97f635647eacd,SC4512E0-PSG.edf
51,2,Hypnogram,70,male,00:03:00,e22966c263f6ae7444704881f5249f6fb5dee0c1,SC4512EW-Hypnogram.edf
52,2,PSG,69,male,23:53:00,af70ffdbd3012615923f6a4901e7c0dd3a0fd8ca,SC4522E0-PSG.edf
52,2,Hypnogram,69,male,23:53:00,57af3eaed541229dcb2478c6050f0582e020f878,SC4522EM-Hypnogram.edf
53,1,PSG,67,male,23:49:00,71222ac5b7784ed1d3a79ee3e9036431d6eba9bd,SC4531E0-PSG.edf
53,1,Hypnogram,67,male,23:49:00,934dbfeb29f4f4db4b61e36fb8ddab4ddbf4ff94,SC4531EM-Hypnogram.edf
53,2,PSG,67,male,23:53:00,2d472fb64da5d05a546f780da876b90ad26208f9,SC4532E0-PSG.edf
53,2,Hypnogram,67,male,23:53:00,708b43e7d43a6f5719f48c11bd6a81b037aabfc4,SC4532EV-Hypnogram.edf
54,1,PSG,73,male,23:00:00,4d3ec2f85149bb10fed1013831c3aa1f58049229,SC4541F0-PSG.edf
54,1,Hypnogram,73,male,23:00:00,a301385e6fbde02c83f2545f17cdf75d594d37ce,SC4541FA-Hypnogram.edf
54,2,PSG,73,male,23:30:00,2909f5b0d3fdb89e19d42b406798e9cbb4615bb6,SC4542F0-PSG.edf
54,2,Hypnogram,73,male,23:30:00,9548ed641fb961fa46706339891a9453b731369f,SC4542FW-Hypnogram.edf
55,1,PSG,71,male,22:40:00,0bf97e463cbcefb7df48bca712f29dcc74223330,SC4551F0-PSG.edf
55,1,Hypnogram,71,male,22:40:00,e50b44e6b049baaeb528c31563642b2a2b933834,SC4551FC-Hypnogram.edf
55,2,PSG,71,male,22:29:00,dfa0adaae50110bdd0077483c31d57956020fcb9,SC4552F0-PSG.edf
55,2,Hypnogram,71,male,22:29:00,7380403f8d72fa4c30013cd026cc1dad23ac2b3e,SC4552FW-Hypnogram.edf
56,1,PSG,72,male,23:14:00,1a9baf1b072ca9d2784a404292169ff3177ea83f,SC4561F0-PSG.edf
56,1,Hypnogram,72,male,23:14:00,b31a2dfe652508df46f6afe03ab904c333f7b818,SC4561FJ-Hypnogram.edf
56,2,PSG,72,male,23:22:00,4c7081edf572cadee51d30174cd65aa6c658f5a9,SC4562F0-PSG.edf
56,2,Hypnogram,72,male,23:22:00,676ab92dbc6532f67d672f80337c71f817fd3a6d,SC4562FJ-Hypnogram.edf
57,1,PSG,66,male,23:02:00,e67f3bd381ddfb96d584f6c6d6f6762087d6553d,SC4571F0-PSG.edf
57,1,Hypnogram,66,male,23:02:00,08ee39eb94d819968512297ca883f9bca046de9c,SC4571FV-Hypnogram.edf
57,2,PSG,66,male,23:51:00,deb2aef7a6a4b502c819345a7151ffc2529d4ba7,SC4572F0-PSG.edf
57,2,Hypnogram,66,male,23:51:00,7a38cbe581167dfec27a15935e6d386b228616fa,SC4572FC-Hypnogram.edf
58,1,PSG,67,male,22:36:00,16a1edbd6a089386fd7de72aef802182d0a2959d,SC4581G0-PSG.edf
58,1,Hypnogram,67,male,22:36:00,bfc729575cfdf5f409be2de47dad4e00d43195bf,SC4581GM-Hypnogram.edf
58,2,PSG,67,male,23:04:00,9da93f4c2459dd4fe2e5ee6a171904d4f604cd6e,SC4582G0-PSG.edf
58,2,Hypnogram,67,male,23:04:00,acbade13cfae4fc5fbda2d0766feea83d114aa23,SC4582GP-Hypnogram.edf
59,1,PSG,67,male,23:25:00,017793b040df8a860df0e43e3e0a496e2cb3f9c1,SC4591G0-PSG.edf
59,1,Hypnogram,67,male,23:25:00,f3bb949a7f82acb7fd3d8f35e92efee1402a383f,SC4591GY-Hypnogram.edf
59,2,PSG,67,male,00:14:00,1e284bddd7952862327c83092db21805e6ab6c38,SC4592G0-PSG.edf
59,2,Hypnogram,67,male,00:14:00,58d1678e9ec9f49c9c6a15031dee26d802026851,SC4592GY-Hypnogram.edf
60,1,PSG,89,female,21:35:00,ece6d6ce09fac6fc521cf3f1b536f1ea2a8a1778,SC4601E0-PSG.edf
60,1,Hypnogram,89,female,21:35:00,8f77b05fe58f43cdfdcdba7cc3d27abcac7d37f2,SC4601EC-Hypnogram.edf
60,2,PSG,89,female,23:00:00,0e50df304ced29651267f43689ce49e063f808d6,SC4602E0-PSG.edf
60,2,Hypnogram,89,female,23:00:00,1c52de92668fe4c89cd5e270e17017ef47880991,SC4602EJ-Hypnogram.edf
61,1,PSG,101,female,00:20:00,2cc6e418c0b7af472aa34d2bbd5ece85bdb6a879,SC4611E0-PSG.edf
61,1,Hypnogram,101,female,00:20:00,f5715ab48f24221c28c1d5c45508c8bb58c912ec,SC4611EG-Hypnogram.edf
61,2,PSG,101,female,01:00:00,6593e1af07101fa4c5bce8984296858be17e7d4f,SC4612E0-PSG.edf
61,2,Hypnogram,101,female,01:00:00,cedb61bbe7a273b12f45579963d5a84f2ab21811,SC4612EA-Hypnogram.edf
62,1,PSG,95,female,21:00:00,31cd2cae56977c6b872311f2a6e60827748b973d,SC4621E0-PSG.edf
62,1,Hypnogram,95,female,21:00:00,7acc5296b33ca4eee8d6577064c8c651ee96e527,SC4621EV-Hypnogram.edf
62,2,PSG,95,female,21:00:00,7a7e226d47dccd959305e3f633686335c8e66557,SC4622E0-PSG.edf
62,2,Hypnogram,95,female,21:00:00,9957c9c9e0c705aac0f7125f411b2531a722601c,SC4622EJ-Hypnogram.edf
63,1,PSG,91,female,00:15:00,6dfb32aa4c94968a52d61b90a38573d178669bfb,SC4631E0-PSG.edf
63,1,Hypnogram,91,female,00:15:00,48e28f93fc71ffc539776196f9d9d1365415e0b4,SC4631EM-Hypnogram.edf
63,2,PSG,91,female,23:39:00,3baa8081b30cc3dfece9d550289dfc94812530d5,SC4632E0-PSG.edf
63,2,Hypnogram,91,female,23:39:00,cd2765ebdabc66cb4ac2320d02e3b7ab0340ede4,SC4632EA-Hypnogram.edf
64,1,PSG,85,female,22:30:00,0e5d109a929490cbecf59573577a97df07a05cd0,SC4641E0-PSG.edf
64,1,Hypnogram,85,female,22:30:00,7b896dc5b34d71381d8462001dc3e05b145cf48c,SC4641EP-Hypnogram.edf
64,2,PSG,85,female,22:17:00,03169b7ee9de83b2e17e9bd0d6274965e9518b37,SC4642E0-PSG.edf
64,2,Hypnogram,85,female,22:17:00,d8a870d26e468a643eaebe3275e5e2912690c0d8,SC4642EP-Hypnogram.edf
65,1,PSG,88,female,23:10:00,f2134a2ad001bc146f3e2d9d76cb7f00f03bbe52,SC4651E0-PSG.edf
65,1,Hypnogram,88,female,23:10:00,fad4311c7e11a9aa9a73a8e48d6fa966db61e71d,SC4651EP-Hypnogram.edf
65,2,PSG,88,female,23:00:00,aa66553cb0132634d7d11ffe7fab80aa5119b3d7,SC4652E0-PSG.edf
65,2,Hypnogram,88,female,23:00:00,6ed9c4f66c03e56f86730ddd8986f3600c040d4a,SC4652EG-Hypnogram.edf
66,1,PSG,88,female,21:52:00,c6057505d2acf7b08371e266cf0fca1bfeb1e4e1,SC4661E0-PSG.edf
66,1,Hypnogram,88,female,21:52:00,06474e72126d2a00c1968e70730e1deac060f94e,SC4661EJ-Hypnogram.edf
66,2,PSG,88,female,21:56:00,24d278194360dc78ebd0cfe940fb4d5f7f93ccbc,SC4662E0-PSG.edf
66,2,Hypnogram,88,female,21:56:00,07ca0fbfb6030289a089f84e50d7bbfd043f31ad,SC4662EJ-Hypnogram.edf
67,1,PSG,87,female,22:49:00,4357aa9fedf0b53896d41e5dccd7b525f7212177,SC4671G0-PSG.edf
67,1,Hypnogram,87,female,22:49:00,459889157743c434933194446af5168cb145dfcb,SC4671GJ-Hypnogram.edf
67,2,PSG,87,female,23:52:00,fd86b31a5c22176e1887e2fac460edce42bd2fdf,SC4672G0-PSG.edf
67,2,Hypnogram,87,female,23:52:00,dedb182b8c063cefabf1763eb19cd26d0608017f,SC4672GV-Hypnogram.edf
70,1,PSG,89,male,21:40:00,3f60b5ad5e1092e90c38f2072b3c041bd7313550,SC4701E0-PSG.edf
70,1,Hypnogram,89,male,21:40:00,196a388f60ee4aecfa982f89e2db03ff91e906e7,SC4701EC-Hypnogram.edf
70,2,PSG,89,male,21:39:00,a6853fee26b1541f85be7ddc3f42f06ccfe2fcfc,SC4702E0-PSG.edf
70,2,Hypnogram,89,male,21:39:00,464f7382ec11703b5bc6512930fdfbb1ab6d030a,SC4702EA-Hypnogram.edf
71,1,PSG,88,male,21:15:00,e97d691bfecf770ca4e47289b846886c16ef19fb,SC4711E0-PSG.edf
71,1,Hypnogram,88,male,21:15:00,81ec5d0288f36c4368e5f06f21980f99774bf533,SC4711EC-Hypnogram.edf
71,2,PSG,88,male,23:18:00,9b99be6cb45af22bdbead7ea01f1375631c9b365,SC4712E0-PSG.edf
71,2,Hypnogram,88,male,23:18:00,66b121441a45ae19852b7002fd78c2caf236631a,SC4712EA-Hypnogram.edf
72,1,PSG,88,male,23:04:00,5c9caa01cc1f8065f87195c9f2dc2aeebf83c03d,SC4721E0-PSG.edf
72,1,Hypnogram,88,male,23:04:00,efe62b1e8bac1ea08dbf12374ca6812a6f271d5e,SC4721EC-Hypnogram.edf
72,2,PSG,88,male,23:09:00,a473f32a6075e9ed830a8e9a246129e05959e8b7,SC4722E0-PSG.edf
72,2,Hypnogram,88,male,23:09:00,efb2358de27da4219f64f7bfb37912dc9efb0281,SC4722EM-Hypnogram.edf
73,1,PSG,97,male,22:30:00,b03e4a2df4d086778f3426ed7b6c5bf800cbfe92,SC4731E0-PSG.edf
73,1,Hypnogram,97,male,22:30:00,eb3dc65d7184d676a6678a70b18730d11a414588,SC4731EM-Hypnogram.edf
73,2,PSG,97,male,22:00:00,574ff5c0634137f7d5c51eb5f7626b451f1f9b9d,SC4732E0-PSG.edf
73,2,Hypnogram,97,male,22:00:00,77a523ca9ef4698885b681bf4e27d28dc5c58424,SC4732EJ-Hypnogram.edf
74,1,PSG,92,male,23:14:00,e6ff7462f4ce401e9aff9b3d9c93f0710bc37678,SC4741E0-PSG.edf
74,1,Hypnogram,92,male,23:14:00,bda4d1ab190f4160ec7a3f4420e30d718f02369e,SC4741EA-Hypnogram.edf
74,2,PSG,92,male,23:06:00,2b09f78a2f276061c8758a55585fae7355b38111,SC4742E0-PSG.edf
74,2,Hypnogram,92,male,23:06:00,d4bb4266859c2f92ae8ba96111d59d8ab467f6a0,SC4742EC-Hypnogram.edf
75,1,PSG,96,male,22:58:00,17c356a283b026e507331209512453573bcfebe5,SC4751E0-PSG.edf
75,1,Hypnogram,96,male,22:58:00,d35737e86979127ea01b95dcecea018dd2e44f45,SC4751EC-Hypnogram.edf
75,2,PSG,96,male,23:00:00,b650a49d6e3bb81971e4689c720ee079404857e6,SC4752E0-PSG.edf
75,2,Hypnogram,96,male,23:00:00,3d1c86d8d7ecb6ff79ee12cb950690e929394161,SC4752EM-Hypnogram.edf
76,1,PSG,90,male,23:28:00,8bde3f0d5ab6a592f229dfd7886341b3f800bdb3,SC4761E0-PSG.edf
76,1,Hypnogram,90,male,23:28:00,3dbf15f28a293ac89dcf458d844a8c6443aaf1e6,SC4761EP-Hypnogram.edf
76,2,PSG,90,male,01:29:00,7bdc8eacf1a6502c8f007b08556b7e8b52180d44,SC4762E0-PSG.edf
76,2,Hypnogram,90,male,01:29:00,f6ae10f082a10ead671bfd5fdc50f62c42b9f10d,SC4762EG-Hypnogram.edf
77,1,PSG,85,male,23:23:00,ac8c2be9175cb02e00cccb5d5df2acfaf05971cc,SC4771G0-PSG.edf
77,1,Hypnogram,85,male,23:23:00,09e80b973502d89368d7823ad4aec7417b735f6e,SC4771GC-Hypnogram.edf
77,2,PSG,85,male,00:10:00,eea8671791936358037e5d096491865069989a85,SC4772G0-PSG.edf
77,2,Hypnogram,85,male,00:10:00,25a3b8859091a70ca0cff9ebb777879aa156689e,SC4772GC-Hypnogram.edf
80,1,PSG,54,female,23:05:00,0ce00a144dd9bc1b0e20cd30e6501a3852e4dbef,SC4801G0-PSG.edf
80,1,Hypnogram,54,female,23:05:00,f82d2b8e45723f2a69f8c30286cc68486b0792a6,SC4801GC-Hypnogram.edf
80,2,PSG,54,female,23:18:00,8959ada929c07945757bd6c9ef0267e7c9427a66,SC4802G0-PSG.edf
80,2,Hypnogram,54,female,23:18:00,41ff2d1118425f5828342c07aa58b9d346755b1a,SC4802GV-Hypnogram.edf
81,1,PSG,57,female,22:00:00,dcae3307af54ccf5349945e2fa493464de0a5da2,SC4811G0-PSG.edf
81,1,Hypnogram,57,female,22:00:00,2406ce37b86fc3c7492a3ebe89ae58d15686b33d,SC4811GG-Hypnogram.edf
81,2,PSG,57,female,21:56:00,fd93757cf6bcf45854fca960a067612352e05547,SC4812G0-PSG.edf
81,2,Hypnogram,57,female,21:56:00,244b3bbb4987db0a9cef85950d14899ab9a3aec4,SC4812GV-Hypnogram.edf
82,1,PSG,56,female,23:59:00,9008c6ffc917fb90a3d399e768fe3c563a144a2f,SC4821G0-PSG.edf
82,1,Hypnogram,56,female,23:59:00,59534244c603cd5c3c27db26ae2f014983ec6c9b,SC4821GC-Hypnogram.edf
82,2,PSG,56,female,00:05:00,84f9a60f6b0e7ac33388d8f6492096bcfa60bc18,SC4822G0-PSG.edf
82,2,Hypnogram,56,female,00:05:00,8d14c371bc290658469729addee4461866bb67e2,SC4822GC-Hypnogram.edf
1 subject night record type age sex lights off sha fname
2 0 1 PSG 33 female 00:38:00 adabd3b01fc7bb75c523a974f38ee3ae4e57b40f SC4001E0-PSG.edf
3 0 1 Hypnogram 33 female 00:38:00 21c998eadc8b1e3ea6727d3585186b8f76e7e70b SC4001EC-Hypnogram.edf
4 0 2 PSG 33 female 21:57:00 c6b6d7a8605cc7e7602b6028ee77f6fbf5f7581d SC4002E0-PSG.edf
5 0 2 Hypnogram 33 female 21:57:00 386230188a3552b1fc90bba0fb7476ceaca174b6 SC4002EC-Hypnogram.edf
6 1 1 PSG 33 female 22:44:00 4d17451f7847355bcab17584de05e7e1df58c660 SC4011E0-PSG.edf
7 1 1 Hypnogram 33 female 22:44:00 d582a3cbe2db481a362af890bc5a2f5ca7c878dc SC4011EH-Hypnogram.edf
8 1 2 PSG 33 female 22:15:00 a47d525f5147904b6890231e2ad338359c7ab94c SC4012E0-PSG.edf
9 1 2 Hypnogram 33 female 22:15:00 fa99f60d7f54617cdd1128aff4f21c4daed763c7 SC4012EC-Hypnogram.edf
10 2 1 PSG 26 female 22:50:00 8b135afa7fb93bb5f1998fda50355944777c245e SC4021E0-PSG.edf
11 2 1 Hypnogram 26 female 22:50:00 91043cfe46695088b17b6a02937b25efd674c3fb SC4021EH-Hypnogram.edf
12 2 2 PSG 26 female 22:57:00 d739e142b3b328c71b4752149901805dcd6d7e19 SC4022E0-PSG.edf
13 2 2 Hypnogram 26 female 22:57:00 0c46a03699dd00e8f92a7edff99ebc4642cb3d48 SC4022EJ-Hypnogram.edf
14 3 1 PSG 26 female 00:02:00 85e58dc1e3303537dade8c5827ab58328239c384 SC4031E0-PSG.edf
15 3 1 Hypnogram 26 female 00:02:00 6363d8b0fdc48cf396c9abf054bb4a9696d38bdb SC4031EC-Hypnogram.edf
16 3 2 PSG 26 female 00:24:00 43963d300642b3aa840e8c468f321b8162601772 SC4032E0-PSG.edf
17 3 2 Hypnogram 26 female 00:24:00 7925514bc8d2ef3f1103130f08f7b3afd2136b88 SC4032EP-Hypnogram.edf
18 4 1 PSG 34 female 23:12:00 04d2b88d25f2ae4a65ba44cd9145bd12800a0e20 SC4041E0-PSG.edf
19 4 1 Hypnogram 34 female 23:12:00 f148821669bd3588187b3b430bd79adf569f86d1 SC4041EC-Hypnogram.edf
20 4 2 PSG 34 female 23:35:00 76253d964d7797540ffd791e6e136023ed67a485 SC4042E0-PSG.edf
21 4 2 Hypnogram 34 female 23:35:00 9873df429f971f8a4b720a454f6c0472b8a25ebb SC4042EC-Hypnogram.edf
22 5 1 PSG 28 female 01:22:00 ea073451b65ce8a6f1a02a8cc2b89d1a162ca0ae SC4051E0-PSG.edf
23 5 1 Hypnogram 28 female 01:22:00 4159ef8a3e119d6dcc1bede806f6fbc017b27a0f SC4051EC-Hypnogram.edf
24 5 2 PSG 28 female 00:35:00 5a2efbd21be9b745fd534394eb2503caca7dc53f SC4052E0-PSG.edf
25 5 2 Hypnogram 28 female 00:35:00 0e96482d44762df4da65dc4fdb970b342264d22a SC4052EC-Hypnogram.edf
26 6 1 PSG 31 female 00:16:00 1736736e585807c14f1ae8bc87a94cae222c5170 SC4061E0-PSG.edf
27 6 1 Hypnogram 31 female 00:16:00 4bf99622c67c281b25ceccd35e7050328a2946e8 SC4061EC-Hypnogram.edf
28 6 2 PSG 31 female 22:44:00 763c7ac059f1771a0165e5cb351b176afb1cfe15 SC4062E0-PSG.edf
29 6 2 Hypnogram 31 female 22:44:00 14f07411cd04d3b4b522d37c129334955287ff5f SC4062EC-Hypnogram.edf
30 7 1 PSG 30 female 00:36:00 1374b34f6139b6ff7e865d8243eef39ba334ef50 SC4071E0-PSG.edf
31 7 1 Hypnogram 30 female 00:36:00 608024fd19a140ad233a4680e07c2495a74b69c2 SC4071EC-Hypnogram.edf
32 7 2 PSG 30 female 00:41:00 1c570644243d79396df612fa2b9bc027b24430e4 SC4072E0-PSG.edf
33 7 2 Hypnogram 30 female 00:41:00 a8da6c20b9b48189f05ab537886b59dd141374d2 SC4072EH-Hypnogram.edf
34 8 1 PSG 25 female 23:35:00 0e1cc2c4e1da14ab94515e3e7e75e8ad30ec99cb SC4081E0-PSG.edf
35 8 1 Hypnogram 25 female 23:35:00 9ec663ffa5c17afcaca59d7829d77b9165102237 SC4081EC-Hypnogram.edf
36 8 2 PSG 25 female 23:37:00 d57d4aa7cbc5045f611a3a3e342b501e086ea426 SC4082E0-PSG.edf
37 8 2 Hypnogram 25 female 23:37:00 d43c785dba43063d7baa332671c6bac9c832b5b7 SC4082EP-Hypnogram.edf
38 9 1 PSG 25 female 23:02:00 b3502e0bd54683e973182c791aa962b804e79633 SC4091E0-PSG.edf
39 9 1 Hypnogram 25 female 23:02:00 7aa63b408c769a4a983a908b6ba41d87dd743c6e SC4091EC-Hypnogram.edf
40 9 2 PSG 25 female 23:01:00 246e35852119b33d197db2f7bcfb1b46a5270a03 SC4092E0-PSG.edf
41 9 2 Hypnogram 25 female 23:01:00 9d85766a83231b1c6076cb293367ccc354c57eeb SC4092EC-Hypnogram.edf
42 10 1 PSG 26 male 22:59:00 3ae168ff2c9c0c56f51205fdb10f05a4c6b2064e SC4101E0-PSG.edf
43 10 1 Hypnogram 26 male 22:59:00 60d9c3913881e11b06ad99e9870bd1ca4d93c952 SC4101EC-Hypnogram.edf
44 10 2 PSG 26 male 23:07:00 86f307190961eaab0214fdc0213f8fe05812c7a5 SC4102E0-PSG.edf
45 10 2 Hypnogram 26 male 23:07:00 8072e2d52bc6c19b45fbd921550e5243bc5a1de7 SC4102EC-Hypnogram.edf
46 11 1 PSG 26 male 23:00:00 e490956b4dce01c46ba88a2b847f091bb54ea16e SC4111E0-PSG.edf
47 11 1 Hypnogram 26 male 23:00:00 12db1920e2f6083c8ab1f2c24fe35dfa03715e4a SC4111EC-Hypnogram.edf
48 11 2 PSG 26 male 01:14:00 ca24dc464df61144627588b29d35a85fcc7ac984 SC4112E0-PSG.edf
49 11 2 Hypnogram 26 male 01:14:00 54dbc39015b0a445b51189987a00e08cc27d8f0c SC4112EC-Hypnogram.edf
50 12 1 PSG 26 male 00:50:00 33c72025a7a215ea5e255f4254cb0f93b1313369 SC4121E0-PSG.edf
51 12 1 Hypnogram 26 male 00:50:00 daa57ece807cb5325c6d1ce059f0e8a8d1c85391 SC4121EC-Hypnogram.edf
52 12 2 PSG 26 male 01:03:00 34f5145ab62dcc5a53ba18735519e5bb2b13841a SC4122E0-PSG.edf
53 12 2 Hypnogram 26 male 01:03:00 b7af1a32d8ca15e8185e4c94213ffc18ad7f6e8a SC4122EV-Hypnogram.edf
54 13 1 PSG 27 male 00:14:00 42ff97035aae6dd34ca9437857c48ac6f2ab97df SC4131E0-PSG.edf
55 13 1 Hypnogram 27 male 00:14:00 5beef85170bdbb5cf2eea24a79f0f5c2c3975c4b SC4131EC-Hypnogram.edf
56 14 1 PSG 27 male 22:55:00 83493e1c32d441c9e5ee3de6a024bfb5e7ab9f5f SC4141E0-PSG.edf
57 14 1 Hypnogram 27 male 22:55:00 511d398f22b9b2b304de27c40740a41584ff6af2 SC4141EU-Hypnogram.edf
58 14 2 PSG 27 male 23:22:00 63d13828b7ebe0d2ed7f491d2b5520e928b9b55d SC4142E0-PSG.edf
59 14 2 Hypnogram 27 male 23:22:00 6f123e6fdc90a01b83e694d9744a6d27f3c87b25 SC4142EU-Hypnogram.edf
60 15 1 PSG 31 male 23:56:00 5a92d49699d4de369d66d9462e91b0dcb3312649 SC4151E0-PSG.edf
61 15 1 Hypnogram 31 male 23:56:00 37dcbd339c95322d028b3a5466812697041cc373 SC4151EC-Hypnogram.edf
62 15 2 PSG 31 male 23:38:00 778626489bc4fe2c9137d2d361876d97dce97e5e SC4152E0-PSG.edf
63 15 2 Hypnogram 31 male 23:38:00 294cdc47cd3d165031f7041c17f18dd013d216cb SC4152EC-Hypnogram.edf
64 16 1 PSG 32 male 22:16:00 e56ff3aa366fe9a04a0fdfdd4cd862e77e8ac807 SC4161E0-PSG.edf
65 16 1 Hypnogram 32 male 22:16:00 56711b1bfed292032491f5cce57494629286a131 SC4161EC-Hypnogram.edf
66 16 2 PSG 32 male 23:16:00 722692f9940f3a1bccb9b4488c4477edf7fb128f SC4162E0-PSG.edf
67 16 2 Hypnogram 32 male 23:16:00 c85647fb4bc1f382fe46bf9aaf579dc483115885 SC4162EC-Hypnogram.edf
68 17 1 PSG 31 male 23:58:00 f1a65522cb7d6c71ac47742535a12c88e2019dad SC4171E0-PSG.edf
69 17 1 Hypnogram 31 male 23:58:00 dd257c8d922f08c2c8ca5236c9bf54da887c68e5 SC4171EU-Hypnogram.edf
70 17 2 PSG 31 male 00:37:00 572b81bc24c2c9482e6fc7ba9202a7bf253655e1 SC4172E0-PSG.edf
71 17 2 Hypnogram 31 male 00:37:00 c9a3b590748d7d6c7ad97c62222bd53d8ebaf630 SC4172EC-Hypnogram.edf
72 18 1 PSG 28 male 23:25:00 23674d20572853eb6d988d24378c52123f66500c SC4181E0-PSG.edf
73 18 1 Hypnogram 28 male 23:25:00 51fc3df2df7d4da654f3e18ed1b233d0c60cfa80 SC4181EC-Hypnogram.edf
74 18 2 PSG 28 male 23:45:00 83e8cbe882ba863da9fd3c11393c95b6fec5b7a5 SC4182E0-PSG.edf
75 18 2 Hypnogram 28 male 23:45:00 43c487955edddb4ee2f60193a097c68c25c5dd4d SC4182EC-Hypnogram.edf
76 19 1 PSG 28 male 01:50:00 d6da621dbb20dec3494a38c7d2a0363793ac5ebe SC4191E0-PSG.edf
77 19 1 Hypnogram 28 male 01:50:00 defc7b9368c2d3c4ab4a294757843825a83cdb5d SC4191EP-Hypnogram.edf
78 19 2 PSG 28 male 00:57:00 941353118732321d0246a1d58d72e903bd2f0d8f SC4192E0-PSG.edf
79 19 2 Hypnogram 28 male 00:57:00 97b91b3067c5ecde766042fc2cff9e22f8023371 SC4192EV-Hypnogram.edf
80 20 1 PSG 51 female 23:10:00 38a0be6e45ddd9b1f17d09964a32e005dc5a6519 SC4201E0-PSG.edf
81 20 1 Hypnogram 51 female 23:10:00 83822f9970d3959ad2e0613492ae39bd0fae6068 SC4201EC-Hypnogram.edf
82 20 2 PSG 51 female 23:15:00 aa69f5bd47c2ae03c9d38bfe6d0e58408744b885 SC4202E0-PSG.edf
83 20 2 Hypnogram 51 female 23:15:00 5c5c63016b43421a523d1efcb34247e90aa6318b SC4202EC-Hypnogram.edf
84 21 1 PSG 51 female 23:28:00 c106ad072dbc975a3742f7eff151219870f0c794 SC4211E0-PSG.edf
85 21 1 Hypnogram 51 female 23:28:00 9126937ea8a414d6ae9bc4a4194d841a891fa8a8 SC4211EC-Hypnogram.edf
86 21 2 PSG 51 female 23:59:00 a06ecb3f0a7b2c306f5ae4dbd83685f877cd945b SC4212E0-PSG.edf
87 21 2 Hypnogram 51 female 23:59:00 a85f178b69a1cda47d11dd1e5394dfdcb58de1d4 SC4212EC-Hypnogram.edf
88 22 1 PSG 56 female 23:47:00 8733ea022d3778259a436507156cf3360ad8be06 SC4221E0-PSG.edf
89 22 1 Hypnogram 56 female 23:47:00 b158eda4f81772095c129be77f8e60ec9d81b884 SC4221EJ-Hypnogram.edf
90 22 2 PSG 56 female 23:14:00 211410fab6381da0dfaef4134d5a05eec935a4ec SC4222E0-PSG.edf
91 22 2 Hypnogram 56 female 23:14:00 1488fbfbc149499dafa8dafff4f7504053af429f SC4222EC-Hypnogram.edf
92 23 1 PSG 50 female 00:51:00 d96f1f35b2f77c7de706036c6e4114139e07b307 SC4231E0-PSG.edf
93 23 1 Hypnogram 50 female 00:51:00 9f6df70676d6cddcf069ceb7f408a7989af99ce2 SC4231EJ-Hypnogram.edf
94 23 2 PSG 50 female 00:32:00 6b493fa424c1329ea1c13543d08ba82a9f1e85b6 SC4232E0-PSG.edf
95 23 2 Hypnogram 50 female 00:32:00 d8ca7d694b3c48ab9d983b9cf67e17744c6b50fb SC4232EV-Hypnogram.edf
96 24 1 PSG 54 female 23:22:00 58719e53fe18d2fc4cb1776ab5d43306beb1325d SC4241E0-PSG.edf
97 24 1 Hypnogram 54 female 23:22:00 fb1432e303a8f99a2256ce682db95d88772c479f SC4241EC-Hypnogram.edf
98 24 2 PSG 54 female 22:50:00 5a6277972c5f03572ed99d9ff63fb637945be778 SC4242E0-PSG.edf
99 24 2 Hypnogram 54 female 22:50:00 bbbf097f4cc6560fc20c903fba2c7055e1549f85 SC4242EA-Hypnogram.edf
100 25 1 PSG 56 female 00:32:00 7dbc0289707ff70662d367d65de7bec188484d1b SC4251E0-PSG.edf
101 25 1 Hypnogram 56 female 00:32:00 e38be8134e4a36eb418ca1f06a1fe02b52d0ebf1 SC4251EP-Hypnogram.edf
102 25 2 PSG 56 female 23:49:00 cb3922910ea03d06c1fc5c8f15b71339dc26bc9d SC4252E0-PSG.edf
103 25 2 Hypnogram 56 female 23:49:00 4cb7a383736e09125a82ef7e4f17b41130c7ac00 SC4252EU-Hypnogram.edf
104 26 1 PSG 51 female 23:39:00 b81c9bd1875b33713b5eb56b58f1e120841b507f SC4261F0-PSG.edf
105 26 1 Hypnogram 51 female 23:39:00 501eda59557bb99d530d01bdad3579f1e1158991 SC4261FM-Hypnogram.edf
106 26 2 PSG 51 female 00:20:00 c9f9ad7cd751d5be91396886a2b64a7c1de564ee SC4262F0-PSG.edf
107 26 2 Hypnogram 51 female 00:20:00 7ccd12803c5fc602ac1929ff3afd914b894b9143 SC4262FC-Hypnogram.edf
108 27 1 PSG 54 female 23:41:00 20994715d34edb26113180ee330ce287dbf57b60 SC4271F0-PSG.edf
109 27 1 Hypnogram 54 female 23:41:00 26c5c7f3a5c350d3505af2857835ce81252c5990 SC4271FC-Hypnogram.edf
110 27 2 PSG 54 female 22:58:00 9e79eb465e34b7eb6fe27ae3ce35d28d6693d44b SC4272F0-PSG.edf
111 27 2 Hypnogram 54 female 22:58:00 956fe4b45d29a8999faf280a6168e332afab6abc SC4272FM-Hypnogram.edf
112 28 1 PSG 56 female 23:55:00 51811913d7854f95c319076e670d988687ca667c SC4281G0-PSG.edf
113 28 1 Hypnogram 56 female 23:55:00 d188150831e912081dbeda2695231177200c39f9 SC4281GC-Hypnogram.edf
114 28 2 PSG 56 female 00:13:00 e9f080a766a9b7a247f228e44e9c4ec67e571c95 SC4282G0-PSG.edf
115 28 2 Hypnogram 56 female 00:13:00 12d777787dd1975eef9015329fd774b2bfa1d53a SC4282GC-Hypnogram.edf
116 29 1 PSG 51 female 22:38:00 f81c7574a5e5829e006d0b705bf5208a3349c9c7 SC4291G0-PSG.edf
117 29 1 Hypnogram 51 female 22:38:00 577c1345f6d070d975db5016048722f78b1b414e SC4291GA-Hypnogram.edf
118 29 2 PSG 51 female 23:04:00 7416f44a3b149b4ca1fc3e53d546a093a7333bb5 SC4292G0-PSG.edf
119 29 2 Hypnogram 51 female 23:04:00 6e111a15160a31609761f742315df800b1311b3b SC4292GC-Hypnogram.edf
120 30 1 PSG 50 male 00:09:00 7818e5a02afa89e913111d91ecd651aa3e786e5d SC4301E0-PSG.edf
121 30 1 Hypnogram 50 male 00:09:00 d49df84bfea28bb241c09b922cd2dc64f57c5ae5 SC4301EC-Hypnogram.edf
122 30 2 PSG 50 male 00:20:00 d52859ba6a7ded3364b0d8ef2b722e1d3edda060 SC4302E0-PSG.edf
123 30 2 Hypnogram 50 male 00:20:00 b3d6f687831ee32f6df1da59f2d568c13f9c09d0 SC4302EV-Hypnogram.edf
124 31 1 PSG 54 male 23:44:00 b62f5104bddf452f4700c85997e51bec17f0243b SC4311E0-PSG.edf
125 31 1 Hypnogram 54 male 23:44:00 812c34844e834b97949019741fa7f835d973725d SC4311EC-Hypnogram.edf
126 31 2 PSG 54 male 23:14:00 b0a9b4922665734773abbaba06e7aab32010b862 SC4312E0-PSG.edf
127 31 2 Hypnogram 54 male 23:14:00 fca1935a8974eac27803e3125cea177995deca11 SC4312EM-Hypnogram.edf
128 32 1 PSG 57 male 00:48:00 335381ae310e9f1f053c37763eeee74d7d873471 SC4321E0-PSG.edf
129 32 1 Hypnogram 57 male 00:48:00 67ba7d3b97354deb31db095e748ea3a4014fae2c SC4321EC-Hypnogram.edf
130 32 2 PSG 57 male 00:15:00 c9fdcfcce7e603b3289b7417891987fd67f6d921 SC4322E0-PSG.edf
131 32 2 Hypnogram 57 male 00:15:00 40cf9a6397a52c7deda693ca596e928cc2b9f4e9 SC4322EC-Hypnogram.edf
132 33 1 PSG 60 male 22:58:00 f37cb4df27286e38c604cae943169ff29b1473fc SC4331F0-PSG.edf
133 33 1 Hypnogram 60 male 22:58:00 ca943e2b73c6404f929c372ebd817b7b3b71b4dd SC4331FV-Hypnogram.edf
134 33 2 PSG 60 male 22:55:00 5bce6ea9b2d6c9bfb41065e92bf9cc05a11b5b75 SC4332F0-PSG.edf
135 33 2 Hypnogram 60 male 22:55:00 e4595b0313d5320b0bffefa43260485e19977e3c SC4332FC-Hypnogram.edf
136 34 1 PSG 54 male 23:03:00 17de25c8f023fe632aa403a6d9525c1cde8eaef5 SC4341F0-PSG.edf
137 34 1 Hypnogram 54 male 23:03:00 81ba3c0d8320c9ee306f678b4bc9e6e266165886 SC4341FA-Hypnogram.edf
138 34 2 PSG 54 male 22:30:00 b659037447a1871f4ba72bbe496cfbe507330530 SC4342F0-PSG.edf
139 34 2 Hypnogram 54 male 22:30:00 e8e74c0905e89a59022ce0814ca9a050748ec9ae SC4342FA-Hypnogram.edf
140 35 1 PSG 57 male 00:02:00 631900bef36d359a0f5807a7e1b202f80b0427ac SC4351F0-PSG.edf
141 35 1 Hypnogram 57 male 00:02:00 a15cdf3973b77198d8276dc505dbb35cb39a9b4a SC4351FA-Hypnogram.edf
142 35 2 PSG 57 male 23:30:00 325423a85890dcc921253bde7c7027d66f14033e SC4352F0-PSG.edf
143 35 2 Hypnogram 57 male 23:30:00 1e0583b2a58432c964506ff44752d597753658c9 SC4352FV-Hypnogram.edf
144 36 2 PSG 51 male 23:59:00 30b90aaf965938d569ea362f66e2afa0c08c7017 SC4362F0-PSG.edf
145 36 2 Hypnogram 51 male 23:59:00 fb870d50ce3f4d961d8b061a83d21e5467e4ae6c SC4362FC-Hypnogram.edf
146 37 1 PSG 52 male 23:03:00 0dc56fce13b6317f197d0b17c04f5be4af1c964f SC4371F0-PSG.edf
147 37 1 Hypnogram 52 male 23:03:00 c19b6cbfdf3a33169ce9b4a5dc94f93b696a21ba SC4371FA-Hypnogram.edf
148 37 2 PSG 52 male 23:05:00 c024c491dd836ed0169300e7171c276fd14b1c44 SC4372F0-PSG.edf
149 37 2 Hypnogram 52 male 23:05:00 97b2915a8a343efc7b785998c0532beaea2fbe91 SC4372FC-Hypnogram.edf
150 38 1 PSG 51 male 23:12:00 6098d2b501b82ca0ddc8893547c6990e204e8ba6 SC4381F0-PSG.edf
151 38 1 Hypnogram 51 male 23:12:00 fdbf653a4a675843c97d0a76ef5e4cebf5d2dbcb SC4381FC-Hypnogram.edf
152 38 2 PSG 51 male 23:47:00 40ce0168d5f546fcd445996ab614f43823a7c2b1 SC4382F0-PSG.edf
153 38 2 Hypnogram 51 male 23:47:00 796f8507254c2d8d345171c077dbd855e112eb47 SC4382FW-Hypnogram.edf
154 40 1 PSG 67 female 23:30:00 28fd8ad1aee307847e2eb579763ebca18e56f540 SC4401E0-PSG.edf
155 40 1 Hypnogram 67 female 23:30:00 65b5671a89871351ee3da7ea800aad276a445b2a SC4401EC-Hypnogram.edf
156 40 2 PSG 67 female 23:55:00 3d4bafa57933cfb20c342e8cc54c15916a621454 SC4402E0-PSG.edf
157 40 2 Hypnogram 67 female 23:55:00 037efea0fc8a6dfa8f85fa1f2fa6fd9a19f2c830 SC4402EW-Hypnogram.edf
158 41 1 PSG 66 female 23:28:00 30a533b67fdb2adac6a4e83088a07fe1bbaddb6c SC4411E0-PSG.edf
159 41 1 Hypnogram 66 female 23:28:00 5df1bf20d4f29b95a2bdde853b2a157dd9530a8a SC4411EJ-Hypnogram.edf
160 41 2 PSG 66 female 23:30:00 bc8e6ea829f14da5396a4b250394c1b72d6631c3 SC4412E0-PSG.edf
161 41 2 Hypnogram 66 female 23:30:00 f46b1dcfe4f4e3c9d4d4c8516dab9759f9c1224e SC4412EM-Hypnogram.edf
162 42 1 PSG 69 female 01:30:00 e8a5d9e0f160ae7bd0b35d75d77b4c872daa30f8 SC4421E0-PSG.edf
163 42 1 Hypnogram 69 female 01:30:00 d2e34f9bcaac7af23da4448f742ac6ea3c895ed9 SC4421EA-Hypnogram.edf
164 42 2 PSG 69 female 00:22:00 80f246adffb92a3785f91368a77b0250aa040462 SC4422E0-PSG.edf
165 42 2 Hypnogram 69 female 00:22:00 709251cc7ae6556544c153caf9dac7f82bba113b SC4422EA-Hypnogram.edf
166 43 1 PSG 73 female 01:30:00 194ae942cf80764e81b4cdabeed9e5a57916aab3 SC4431E0-PSG.edf
167 43 1 Hypnogram 73 female 01:30:00 497ad7e671edab6e7adc9d35a6aa45b7fd9a706b SC4431EM-Hypnogram.edf
168 43 2 PSG 73 female 00:47:00 c45a66d27ea03bf448903fe30f17838e9a0fa0de SC4432E0-PSG.edf
169 43 2 Hypnogram 73 female 00:47:00 10fe276e215f9406c0ddedaa48651cf480892476 SC4432EM-Hypnogram.edf
170 44 1 PSG 74 female 00:18:00 e3a09d832cb79b0095d7a311ef1b6ed7c569b79d SC4441E0-PSG.edf
171 44 1 Hypnogram 74 female 00:18:00 68d4e44ad54069701972df66d8a81b4ca434bf2f SC4441EC-Hypnogram.edf
172 44 2 PSG 74 female 23:55:00 fe51d45e9f3e64a61fa8a5e5274b2e4951a9de43 SC4442E0-PSG.edf
173 44 2 Hypnogram 74 female 23:55:00 efc2b86bb796b0143f61667402612dfbb85cbb78 SC4442EV-Hypnogram.edf
174 45 1 PSG 66 female 00:26:00 315db0f9d91988ddc2b198f89cc22f96190eff71 SC4451F0-PSG.edf
175 45 1 Hypnogram 66 female 00:26:00 bc1f755c3367e378091c44481948a72fc7a928e5 SC4451FY-Hypnogram.edf
176 45 2 PSG 66 female 00:34:00 a06350e1c85b61c30c3d7d5dc640121b416fe30d SC4452F0-PSG.edf
177 45 2 Hypnogram 66 female 00:34:00 0286d52cdf898ed8e3b17bb26b9c50ef512daf4d SC4452FW-Hypnogram.edf
178 46 1 PSG 66 female 00:30:00 e4295014c6d4474d8f7f7792c2ea088eb9e43e9f SC4461F0-PSG.edf
179 46 1 Hypnogram 66 female 00:30:00 8980e770e58e5704bd36124f6b6bd8d5e3506e12 SC4461FA-Hypnogram.edf
180 46 2 PSG 66 female 00:38:00 53b69cb41339bc69144eaa5a5a42c2937f237fc9 SC4462F0-PSG.edf
181 46 2 Hypnogram 66 female 00:38:00 0c6d3974e140c1e62ed2cadaed395781575af042 SC4462FJ-Hypnogram.edf
182 47 1 PSG 73 female 22:36:00 05d71b55de4c86791195391b1cec8b35e447922d SC4471F0-PSG.edf
183 47 1 Hypnogram 73 female 22:36:00 ee235454dbfe947432f3f813c9a6384f6e42d36a SC4471FA-Hypnogram.edf
184 47 2 PSG 73 female 22:25:00 7a12c0d6f3005998472b128e06dd645a8619dae7 SC4472F0-PSG.edf
185 47 2 Hypnogram 73 female 22:25:00 d234d5d6c396bf7ef0a2106a59ee8204429aa3c5 SC4472FA-Hypnogram.edf
186 48 1 PSG 67 female 23:04:00 c15f6a0e1802dcf74ecec41745677a4932375faf SC4481F0-PSG.edf
187 48 1 Hypnogram 67 female 23:04:00 50fce6396aceaf35d9d7e16175053a3b78f214d0 SC4481FV-Hypnogram.edf
188 48 2 PSG 67 female 23:57:00 34d71530fd1da925ba20b4c48a07f7b18153e0c7 SC4482F0-PSG.edf
189 48 2 Hypnogram 67 female 23:57:00 e3c48563e63eed27b071d4a7b37c45a0f9dc7eef SC4482FJ-Hypnogram.edf
190 49 1 PSG 67 female 23:26:00 23ea1f5f299c6cd99d434f014d7490621dbbc854 SC4491G0-PSG.edf
191 49 1 Hypnogram 67 female 23:26:00 36c6c8112524c7bc9553db37601b38984946209b SC4491GJ-Hypnogram.edf
192 49 2 PSG 67 female 00:13:00 02c975bfc0773928095239b80d00ac5a7ea5880f SC4492G0-PSG.edf
193 49 2 Hypnogram 67 female 00:13:00 3673eaad8396ef0ec36cb4299541c30653b72e1f SC4492GJ-Hypnogram.edf
194 50 1 PSG 71 male 22:07:00 1c31fc02412029bc7369979b8c9f5956420748f5 SC4501E0-PSG.edf
195 50 1 Hypnogram 71 male 22:07:00 eb2621c1670a42eb38dfa86a9bc3326818365f3d SC4501EW-Hypnogram.edf
196 50 2 PSG 71 male 23:40:00 ff9eae25afa73115e2b184a68e3a72a39efd37e6 SC4502E0-PSG.edf
197 50 2 Hypnogram 71 male 23:40:00 7605a1893701925ea0fdd047926bbd6c7c043875 SC4502EM-Hypnogram.edf
198 51 1 PSG 70 male 23:10:00 e12eb259c2894d45b8d0b2f0e75810c2de02237d SC4511E0-PSG.edf
199 51 1 Hypnogram 70 male 23:10:00 e549275e9182b9e36ade5abb721098e235ecb164 SC4511EJ-Hypnogram.edf
200 51 2 PSG 70 male 00:03:00 53c5d982139d248736f6dd7ff3f97f635647eacd SC4512E0-PSG.edf
201 51 2 Hypnogram 70 male 00:03:00 e22966c263f6ae7444704881f5249f6fb5dee0c1 SC4512EW-Hypnogram.edf
202 52 2 PSG 69 male 23:53:00 af70ffdbd3012615923f6a4901e7c0dd3a0fd8ca SC4522E0-PSG.edf
203 52 2 Hypnogram 69 male 23:53:00 57af3eaed541229dcb2478c6050f0582e020f878 SC4522EM-Hypnogram.edf
204 53 1 PSG 67 male 23:49:00 71222ac5b7784ed1d3a79ee3e9036431d6eba9bd SC4531E0-PSG.edf
205 53 1 Hypnogram 67 male 23:49:00 934dbfeb29f4f4db4b61e36fb8ddab4ddbf4ff94 SC4531EM-Hypnogram.edf
206 53 2 PSG 67 male 23:53:00 2d472fb64da5d05a546f780da876b90ad26208f9 SC4532E0-PSG.edf
207 53 2 Hypnogram 67 male 23:53:00 708b43e7d43a6f5719f48c11bd6a81b037aabfc4 SC4532EV-Hypnogram.edf
208 54 1 PSG 73 male 23:00:00 4d3ec2f85149bb10fed1013831c3aa1f58049229 SC4541F0-PSG.edf
209 54 1 Hypnogram 73 male 23:00:00 a301385e6fbde02c83f2545f17cdf75d594d37ce SC4541FA-Hypnogram.edf
210 54 2 PSG 73 male 23:30:00 2909f5b0d3fdb89e19d42b406798e9cbb4615bb6 SC4542F0-PSG.edf
211 54 2 Hypnogram 73 male 23:30:00 9548ed641fb961fa46706339891a9453b731369f SC4542FW-Hypnogram.edf
212 55 1 PSG 71 male 22:40:00 0bf97e463cbcefb7df48bca712f29dcc74223330 SC4551F0-PSG.edf
213 55 1 Hypnogram 71 male 22:40:00 e50b44e6b049baaeb528c31563642b2a2b933834 SC4551FC-Hypnogram.edf
214 55 2 PSG 71 male 22:29:00 dfa0adaae50110bdd0077483c31d57956020fcb9 SC4552F0-PSG.edf
215 55 2 Hypnogram 71 male 22:29:00 7380403f8d72fa4c30013cd026cc1dad23ac2b3e SC4552FW-Hypnogram.edf
216 56 1 PSG 72 male 23:14:00 1a9baf1b072ca9d2784a404292169ff3177ea83f SC4561F0-PSG.edf
217 56 1 Hypnogram 72 male 23:14:00 b31a2dfe652508df46f6afe03ab904c333f7b818 SC4561FJ-Hypnogram.edf
218 56 2 PSG 72 male 23:22:00 4c7081edf572cadee51d30174cd65aa6c658f5a9 SC4562F0-PSG.edf
219 56 2 Hypnogram 72 male 23:22:00 676ab92dbc6532f67d672f80337c71f817fd3a6d SC4562FJ-Hypnogram.edf
220 57 1 PSG 66 male 23:02:00 e67f3bd381ddfb96d584f6c6d6f6762087d6553d SC4571F0-PSG.edf
221 57 1 Hypnogram 66 male 23:02:00 08ee39eb94d819968512297ca883f9bca046de9c SC4571FV-Hypnogram.edf
222 57 2 PSG 66 male 23:51:00 deb2aef7a6a4b502c819345a7151ffc2529d4ba7 SC4572F0-PSG.edf
223 57 2 Hypnogram 66 male 23:51:00 7a38cbe581167dfec27a15935e6d386b228616fa SC4572FC-Hypnogram.edf
224 58 1 PSG 67 male 22:36:00 16a1edbd6a089386fd7de72aef802182d0a2959d SC4581G0-PSG.edf
225 58 1 Hypnogram 67 male 22:36:00 bfc729575cfdf5f409be2de47dad4e00d43195bf SC4581GM-Hypnogram.edf
226 58 2 PSG 67 male 23:04:00 9da93f4c2459dd4fe2e5ee6a171904d4f604cd6e SC4582G0-PSG.edf
227 58 2 Hypnogram 67 male 23:04:00 acbade13cfae4fc5fbda2d0766feea83d114aa23 SC4582GP-Hypnogram.edf
228 59 1 PSG 67 male 23:25:00 017793b040df8a860df0e43e3e0a496e2cb3f9c1 SC4591G0-PSG.edf
229 59 1 Hypnogram 67 male 23:25:00 f3bb949a7f82acb7fd3d8f35e92efee1402a383f SC4591GY-Hypnogram.edf
230 59 2 PSG 67 male 00:14:00 1e284bddd7952862327c83092db21805e6ab6c38 SC4592G0-PSG.edf
231 59 2 Hypnogram 67 male 00:14:00 58d1678e9ec9f49c9c6a15031dee26d802026851 SC4592GY-Hypnogram.edf
232 60 1 PSG 89 female 21:35:00 ece6d6ce09fac6fc521cf3f1b536f1ea2a8a1778 SC4601E0-PSG.edf
233 60 1 Hypnogram 89 female 21:35:00 8f77b05fe58f43cdfdcdba7cc3d27abcac7d37f2 SC4601EC-Hypnogram.edf
234 60 2 PSG 89 female 23:00:00 0e50df304ced29651267f43689ce49e063f808d6 SC4602E0-PSG.edf
235 60 2 Hypnogram 89 female 23:00:00 1c52de92668fe4c89cd5e270e17017ef47880991 SC4602EJ-Hypnogram.edf
236 61 1 PSG 101 female 00:20:00 2cc6e418c0b7af472aa34d2bbd5ece85bdb6a879 SC4611E0-PSG.edf
237 61 1 Hypnogram 101 female 00:20:00 f5715ab48f24221c28c1d5c45508c8bb58c912ec SC4611EG-Hypnogram.edf
238 61 2 PSG 101 female 01:00:00 6593e1af07101fa4c5bce8984296858be17e7d4f SC4612E0-PSG.edf
239 61 2 Hypnogram 101 female 01:00:00 cedb61bbe7a273b12f45579963d5a84f2ab21811 SC4612EA-Hypnogram.edf
240 62 1 PSG 95 female 21:00:00 31cd2cae56977c6b872311f2a6e60827748b973d SC4621E0-PSG.edf
241 62 1 Hypnogram 95 female 21:00:00 7acc5296b33ca4eee8d6577064c8c651ee96e527 SC4621EV-Hypnogram.edf
242 62 2 PSG 95 female 21:00:00 7a7e226d47dccd959305e3f633686335c8e66557 SC4622E0-PSG.edf
243 62 2 Hypnogram 95 female 21:00:00 9957c9c9e0c705aac0f7125f411b2531a722601c SC4622EJ-Hypnogram.edf
244 63 1 PSG 91 female 00:15:00 6dfb32aa4c94968a52d61b90a38573d178669bfb SC4631E0-PSG.edf
245 63 1 Hypnogram 91 female 00:15:00 48e28f93fc71ffc539776196f9d9d1365415e0b4 SC4631EM-Hypnogram.edf
246 63 2 PSG 91 female 23:39:00 3baa8081b30cc3dfece9d550289dfc94812530d5 SC4632E0-PSG.edf
247 63 2 Hypnogram 91 female 23:39:00 cd2765ebdabc66cb4ac2320d02e3b7ab0340ede4 SC4632EA-Hypnogram.edf
248 64 1 PSG 85 female 22:30:00 0e5d109a929490cbecf59573577a97df07a05cd0 SC4641E0-PSG.edf
249 64 1 Hypnogram 85 female 22:30:00 7b896dc5b34d71381d8462001dc3e05b145cf48c SC4641EP-Hypnogram.edf
250 64 2 PSG 85 female 22:17:00 03169b7ee9de83b2e17e9bd0d6274965e9518b37 SC4642E0-PSG.edf
251 64 2 Hypnogram 85 female 22:17:00 d8a870d26e468a643eaebe3275e5e2912690c0d8 SC4642EP-Hypnogram.edf
252 65 1 PSG 88 female 23:10:00 f2134a2ad001bc146f3e2d9d76cb7f00f03bbe52 SC4651E0-PSG.edf
253 65 1 Hypnogram 88 female 23:10:00 fad4311c7e11a9aa9a73a8e48d6fa966db61e71d SC4651EP-Hypnogram.edf
254 65 2 PSG 88 female 23:00:00 aa66553cb0132634d7d11ffe7fab80aa5119b3d7 SC4652E0-PSG.edf
255 65 2 Hypnogram 88 female 23:00:00 6ed9c4f66c03e56f86730ddd8986f3600c040d4a SC4652EG-Hypnogram.edf
256 66 1 PSG 88 female 21:52:00 c6057505d2acf7b08371e266cf0fca1bfeb1e4e1 SC4661E0-PSG.edf
257 66 1 Hypnogram 88 female 21:52:00 06474e72126d2a00c1968e70730e1deac060f94e SC4661EJ-Hypnogram.edf
258 66 2 PSG 88 female 21:56:00 24d278194360dc78ebd0cfe940fb4d5f7f93ccbc SC4662E0-PSG.edf
259 66 2 Hypnogram 88 female 21:56:00 07ca0fbfb6030289a089f84e50d7bbfd043f31ad SC4662EJ-Hypnogram.edf
260 67 1 PSG 87 female 22:49:00 4357aa9fedf0b53896d41e5dccd7b525f7212177 SC4671G0-PSG.edf
261 67 1 Hypnogram 87 female 22:49:00 459889157743c434933194446af5168cb145dfcb SC4671GJ-Hypnogram.edf
262 67 2 PSG 87 female 23:52:00 fd86b31a5c22176e1887e2fac460edce42bd2fdf SC4672G0-PSG.edf
263 67 2 Hypnogram 87 female 23:52:00 dedb182b8c063cefabf1763eb19cd26d0608017f SC4672GV-Hypnogram.edf
264 70 1 PSG 89 male 21:40:00 3f60b5ad5e1092e90c38f2072b3c041bd7313550 SC4701E0-PSG.edf
265 70 1 Hypnogram 89 male 21:40:00 196a388f60ee4aecfa982f89e2db03ff91e906e7 SC4701EC-Hypnogram.edf
266 70 2 PSG 89 male 21:39:00 a6853fee26b1541f85be7ddc3f42f06ccfe2fcfc SC4702E0-PSG.edf
267 70 2 Hypnogram 89 male 21:39:00 464f7382ec11703b5bc6512930fdfbb1ab6d030a SC4702EA-Hypnogram.edf
268 71 1 PSG 88 male 21:15:00 e97d691bfecf770ca4e47289b846886c16ef19fb SC4711E0-PSG.edf
269 71 1 Hypnogram 88 male 21:15:00 81ec5d0288f36c4368e5f06f21980f99774bf533 SC4711EC-Hypnogram.edf
270 71 2 PSG 88 male 23:18:00 9b99be6cb45af22bdbead7ea01f1375631c9b365 SC4712E0-PSG.edf
271 71 2 Hypnogram 88 male 23:18:00 66b121441a45ae19852b7002fd78c2caf236631a SC4712EA-Hypnogram.edf
272 72 1 PSG 88 male 23:04:00 5c9caa01cc1f8065f87195c9f2dc2aeebf83c03d SC4721E0-PSG.edf
273 72 1 Hypnogram 88 male 23:04:00 efe62b1e8bac1ea08dbf12374ca6812a6f271d5e SC4721EC-Hypnogram.edf
274 72 2 PSG 88 male 23:09:00 a473f32a6075e9ed830a8e9a246129e05959e8b7 SC4722E0-PSG.edf
275 72 2 Hypnogram 88 male 23:09:00 efb2358de27da4219f64f7bfb37912dc9efb0281 SC4722EM-Hypnogram.edf
276 73 1 PSG 97 male 22:30:00 b03e4a2df4d086778f3426ed7b6c5bf800cbfe92 SC4731E0-PSG.edf
277 73 1 Hypnogram 97 male 22:30:00 eb3dc65d7184d676a6678a70b18730d11a414588 SC4731EM-Hypnogram.edf
278 73 2 PSG 97 male 22:00:00 574ff5c0634137f7d5c51eb5f7626b451f1f9b9d SC4732E0-PSG.edf
279 73 2 Hypnogram 97 male 22:00:00 77a523ca9ef4698885b681bf4e27d28dc5c58424 SC4732EJ-Hypnogram.edf
280 74 1 PSG 92 male 23:14:00 e6ff7462f4ce401e9aff9b3d9c93f0710bc37678 SC4741E0-PSG.edf
281 74 1 Hypnogram 92 male 23:14:00 bda4d1ab190f4160ec7a3f4420e30d718f02369e SC4741EA-Hypnogram.edf
282 74 2 PSG 92 male 23:06:00 2b09f78a2f276061c8758a55585fae7355b38111 SC4742E0-PSG.edf
283 74 2 Hypnogram 92 male 23:06:00 d4bb4266859c2f92ae8ba96111d59d8ab467f6a0 SC4742EC-Hypnogram.edf
284 75 1 PSG 96 male 22:58:00 17c356a283b026e507331209512453573bcfebe5 SC4751E0-PSG.edf
285 75 1 Hypnogram 96 male 22:58:00 d35737e86979127ea01b95dcecea018dd2e44f45 SC4751EC-Hypnogram.edf
286 75 2 PSG 96 male 23:00:00 b650a49d6e3bb81971e4689c720ee079404857e6 SC4752E0-PSG.edf
287 75 2 Hypnogram 96 male 23:00:00 3d1c86d8d7ecb6ff79ee12cb950690e929394161 SC4752EM-Hypnogram.edf
288 76 1 PSG 90 male 23:28:00 8bde3f0d5ab6a592f229dfd7886341b3f800bdb3 SC4761E0-PSG.edf
289 76 1 Hypnogram 90 male 23:28:00 3dbf15f28a293ac89dcf458d844a8c6443aaf1e6 SC4761EP-Hypnogram.edf
290 76 2 PSG 90 male 01:29:00 7bdc8eacf1a6502c8f007b08556b7e8b52180d44 SC4762E0-PSG.edf
291 76 2 Hypnogram 90 male 01:29:00 f6ae10f082a10ead671bfd5fdc50f62c42b9f10d SC4762EG-Hypnogram.edf
292 77 1 PSG 85 male 23:23:00 ac8c2be9175cb02e00cccb5d5df2acfaf05971cc SC4771G0-PSG.edf
293 77 1 Hypnogram 85 male 23:23:00 09e80b973502d89368d7823ad4aec7417b735f6e SC4771GC-Hypnogram.edf
294 77 2 PSG 85 male 00:10:00 eea8671791936358037e5d096491865069989a85 SC4772G0-PSG.edf
295 77 2 Hypnogram 85 male 00:10:00 25a3b8859091a70ca0cff9ebb777879aa156689e SC4772GC-Hypnogram.edf
296 80 1 PSG 54 female 23:05:00 0ce00a144dd9bc1b0e20cd30e6501a3852e4dbef SC4801G0-PSG.edf
297 80 1 Hypnogram 54 female 23:05:00 f82d2b8e45723f2a69f8c30286cc68486b0792a6 SC4801GC-Hypnogram.edf
298 80 2 PSG 54 female 23:18:00 8959ada929c07945757bd6c9ef0267e7c9427a66 SC4802G0-PSG.edf
299 80 2 Hypnogram 54 female 23:18:00 41ff2d1118425f5828342c07aa58b9d346755b1a SC4802GV-Hypnogram.edf
300 81 1 PSG 57 female 22:00:00 dcae3307af54ccf5349945e2fa493464de0a5da2 SC4811G0-PSG.edf
301 81 1 Hypnogram 57 female 22:00:00 2406ce37b86fc3c7492a3ebe89ae58d15686b33d SC4811GG-Hypnogram.edf
302 81 2 PSG 57 female 21:56:00 fd93757cf6bcf45854fca960a067612352e05547 SC4812G0-PSG.edf
303 81 2 Hypnogram 57 female 21:56:00 244b3bbb4987db0a9cef85950d14899ab9a3aec4 SC4812GV-Hypnogram.edf
304 82 1 PSG 56 female 23:59:00 9008c6ffc917fb90a3d399e768fe3c563a144a2f SC4821G0-PSG.edf
305 82 1 Hypnogram 56 female 23:59:00 59534244c603cd5c3c27db26ae2f014983ec6c9b SC4821GC-Hypnogram.edf
306 82 2 PSG 56 female 00:05:00 84f9a60f6b0e7ac33388d8f6492096bcfa60bc18 SC4822G0-PSG.edf
307 82 2 Hypnogram 56 female 00:05:00 8d14c371bc290658469729addee4461866bb67e2 SC4822GC-Hypnogram.edf

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os
import time
import numpy as np
from ...utils import verbose
from ..utils import _log_time_size
from ._utils import TEMAZEPAM_SLEEP_RECORDS, _check_subjects, _data_path, _fetch_one
data_path = _data_path # expose _data_path(..) as data_path(..)
BASE_URL = "https://physionet.org/physiobank/database/sleep-edfx/sleep-telemetry/" # noqa: E501
@verbose
def fetch_data(
subjects, path=None, force_update=False, base_url=BASE_URL, *, verbose=None
):
"""Get paths to local copies of PhysioNet Polysomnography dataset files.
This will fetch data from the publicly available subjects from PhysioNet's
study of Temazepam effects on sleep :footcite:`KempEtAl2000`. This
corresponds to a set of 22 subjects. Subjects had mild difficulty falling
asleep but were otherwise healthy.
See more details in the `physionet website
<https://physionet.org/physiobank/database/sleep-edfx/>`_
:footcite:`GoldbergerEtAl2000`.
Parameters
----------
subjects : list of int
The subjects to use. Can be in the range of 0-21 (inclusive).
path : None | str
Location of where to look for the PhysioNet data storing location.
If None, the environment variable or config parameter
``PHYSIONET_SLEEP_PATH`` is used. If it doesn't exist, the "~/mne_data"
directory is used. If the Polysomnography 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.
base_url : str
The base URL to download from.
%(verbose)s
Returns
-------
paths : list
List of local data paths of the given type.
See Also
--------
mne.datasets.sleep_physionet.age.fetch_data
Notes
-----
For example, one could do:
>>> from mne.datasets import sleep_physionet
>>> sleep_physionet.temazepam.fetch_data(subjects=[1]) # doctest: +SKIP
This would download data for subject 0 if it isn't there already.
References
----------
.. footbibliography::
"""
t0 = time.time()
records = np.loadtxt(
TEMAZEPAM_SLEEP_RECORDS,
skiprows=1,
delimiter=",",
usecols=(0, 3, 6, 7, 8, 9),
dtype={
"names": (
"subject",
"record",
"hyp sha",
"psg sha",
"hyp fname",
"psg fname",
),
"formats": ("<i2", "<S15", "S40", "S40", "<S22", "<S16"),
},
)
_check_subjects(subjects, 22)
path = data_path(path=path)
params = [path, force_update, base_url]
fnames = []
sz = 0
for subject in subjects: # all the subjects are present at this point
for idx in np.where(records["subject"] == subject)[0]:
if records["record"][idx] == b"Placebo":
psg_fname, pdl = _fetch_one(
records["psg fname"][idx].decode(),
records["psg sha"][idx].decode(),
*params,
)
hyp_fname, hdl = _fetch_one(
records["hyp fname"][idx].decode(),
records["hyp sha"][idx].decode(),
*params,
)
fnames.append([psg_fname, hyp_fname])
if pdl:
sz += os.path.getsize(psg_fname)
if hdl:
sz += os.path.getsize(hyp_fname)
if sz > 0:
_log_time_size(t0, sz)
return fnames

45
mne/datasets/sleep_physionet/temazepam_records.csv Normal file
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@@ -0,0 +1,45 @@
subject,age,sex,drug,lights off,night nr,sha_Hypnogram,sha_PSG,fname_Hypnogram,fname_PSG,subject_orig
0,60,male,Placebo,23:01:00,1,ff28e5e01296cefed49ae0c27cfb3ebc42e710bf,b9d11484126ebff1884034396d6a20c62c0ef48d,ST7011JP-Hypnogram.edf,ST7011J0-PSG.edf,1
0,60,male,Temazepam,23:48:00,2,7a98a0ebba9e5e8fc4aac9ab82849385570d7789,b97c67d2ec40721349fd6faea32ea7155a11940a,ST7012JP-Hypnogram.edf,ST7012J0-PSG.edf,1
1,35,female,Temazepam,00:00:00,1,635b07240047ade50649ff0f72ccde792f464f09,552e579d96e6c4ae083c7e1422e11b945ebcdabd,ST7021JM-Hypnogram.edf,ST7021J0-PSG.edf,2
1,35,female,Placebo,23:27:00,2,228c608743abcc28f8c4946e8394ecf8e6ada89c,ebabfa224599201d9baf91311f78f6410971810f,ST7022JM-Hypnogram.edf,ST7022J0-PSG.edf,2
2,18,female,Placebo,23:53:00,1,422655bae4525d121bd45fead048207be9b34c4b,41f8e344b9872d93c8c2f2da283252231584b08f,ST7041JO-Hypnogram.edf,ST7041J0-PSG.edf,4
2,18,female,Temazepam,22:37:00,2,eff297358a0c9d175109ba692ac3f9f4cd2c08ed,229ee3bb4d060332c219c3dc1153732ab5499d57,ST7042JO-Hypnogram.edf,ST7042J0-PSG.edf,4
3,32,female,Temazepam,23:34:00,1,d7696bd1b891dd85e96e20ea727dcebe49ab6dfd,17b186214e8944667571f52098564e377b32d695,ST7051JA-Hypnogram.edf,ST7051J0-PSG.edf,5
3,32,female,Placebo,23:23:00,2,64f2718c004e64ab598979da139b90452febc9bf,489fcb38c07688192d9c0eae5455d95241028ad8,ST7052JA-Hypnogram.edf,ST7052J0-PSG.edf,5
4,35,female,Placebo,23:28:00,1,fd9214d026453fce71efa2975ea732e1c1458f69,9fb2b4ed47a6d4b2f0b60a354123e491e8738b19,ST7061JR-Hypnogram.edf,ST7061J0-PSG.edf,6
4,35,female,Temazepam,23:26:00,2,c2a4abe15f08f230b734a328494ab0d2ae9dc786,afc5599194648da5568dafa1a811818e77df4842,ST7062JR-Hypnogram.edf,ST7062J0-PSG.edf,6
5,51,female,Placebo,00:02:00,1,bc08c797bb7aaf92de1c869d46c6dd4590939996,010a65ad86b79d19c372a421f0e7c975e56278c8,ST7071JA-Hypnogram.edf,ST7071J0-PSG.edf,7
5,51,female,Temazepam,23:24:00,2,1a7813b7a2389c0346e3844835590b9cb2f40f56,15c5aa5591e35d60ba25044cdd4b3d748d3c0cfc,ST7072JA-Hypnogram.edf,ST7072J0-PSG.edf,7
6,66,female,Temazepam,23:53:00,1,8259b52c62203b85268d23b3a2d87605fdcfa2a6,cb66a0493d90d0d1204936e3e7c944ed536265e3,ST7081JW-Hypnogram.edf,ST7081J0-PSG.edf,8
6,66,female,Placebo,23:20:00,2,bc33c3aba61c0fa937ef56d4ce7b1468c80663b5,b1cb29c7a7321b7e628d04a477338c4f62f0c093,ST7082JW-Hypnogram.edf,ST7082J0-PSG.edf,8
7,47,male,Temazepam,23:42:00,1,af845641a8118d004bcfa6b597f23517e3a752e9,b046dd63d92339914eca0489d8a4c566b69e7723,ST7091JE-Hypnogram.edf,ST7091J0-PSG.edf,9
7,47,male,Placebo,00:30:00,2,ec89bb908ff70e123ffa94bc2c11bb1ce54bcb6a,2986f4d64f5118c5e356a2abe6bf86521ffde339,ST7092JE-Hypnogram.edf,ST7092J0-PSG.edf,9
8,20,female,Placebo,23:21:00,1,5919542c566d882fbf947c66f4858ad17199103a,5662b560f095b8397303cced87e43d407a0d18f7,ST7101JE-Hypnogram.edf,ST7101J0-PSG.edf,10
8,20,female,Temazepam,23:28:00,2,1f05e92c9ca076350f981d0ec75ad720606bacbc,f697a140f18d1005107fcbb7c81d85a5e8cb6ec6,ST7102JE-Hypnogram.edf,ST7102J0-PSG.edf,10
9,21,female,Temazepam,23:38:00,1,5964553fe07cbca302526b2153a2507f7d02fab8,e2bf9db482f230a56372603d23fb12f5c56062f7,ST7111JE-Hypnogram.edf,ST7111J0-PSG.edf,11
9,21,female,Placebo,23:52:00,2,e4d8406eaca361d2c5d9953b3c67ed1098dd5925,d3c7907b9b1e4f087f31bd655548b8673b6ec735,ST7112JE-Hypnogram.edf,ST7112J0-PSG.edf,11
10,21,male,Placebo,23:46:00,1,a991ed3d8be6d55ee563545077f3d280466a4989,6e90bac48e48f71e5572944a364009eab6ea818d,ST7121JE-Hypnogram.edf,ST7121J0-PSG.edf,12
10,21,male,Temazepam,23:56:00,2,b6c2c21e3cf17b371b31af78c64f28aa5811e36f,ae7426c464296ec0a839ccaa9763e3f2c57f41f1,ST7122JE-Hypnogram.edf,ST7122J0-PSG.edf,12
11,22,male,Temazepam,00:38:00,1,91ee1bd29b156b33e03cb8c324a8fac15ec06674,d0d6c83b76f627b067e0daac3c181e3666f8ab08,ST7131JR-Hypnogram.edf,ST7131J0-PSG.edf,13
11,22,male,Placebo,00:31:00,2,028a5c4ed911d67a17b45f12966b32c46949d374,54a50dcc40e3d6677b80c629b2f908339d9a7c3e,ST7132JR-Hypnogram.edf,ST7132J0-PSG.edf,13
12,20,male,Placebo,00:40:00,1,203e78e02a92a9f85f07790398f64c66f248e5cc,6bf8feeabc2259d15f1f535abda90caacc8d4a86,ST7141JE-Hypnogram.edf,ST7141J0-PSG.edf,14
12,20,male,Temazepam,00:53:00,2,1f7cc3a1923dd6a3504c82d76f820555ad0b6a1b,b42eb28089bbdcbf3244dead53fd01d5f5ac3ddf,ST7142JE-Hypnogram.edf,ST7142J0-PSG.edf,14
13,66,female,Placebo,23:42:00,1,cfcb0089e22244bc5047f61e72a39735cbdc36cf,c0df1253b6509c4b4ed9e1283f26cf206a8c725c,ST7151JA-Hypnogram.edf,ST7151J0-PSG.edf,15
13,66,female,Temazepam,23:33:00,2,27e9b4527eea33ded9072db3c6626f94a966da58,faefa07a1ca180861d6f26d5f35285c009dca21e,ST7152JA-Hypnogram.edf,ST7152J0-PSG.edf,15
14,79,female,Temazepam,23:18:00,1,5a1ef1d375b01f83264e84db4af58acded68f15e,8a4f1c44a17b5d665cc30f1141d003043274ac2b,ST7161JM-Hypnogram.edf,ST7161J0-PSG.edf,16
14,79,female,Placebo,23:21:00,2,18b3d7eb9685ec8131fc0a8f81ba6205122595dc,66925c8fa9f6da18f8590dcf2a6174cfe46e912d,ST7162JM-Hypnogram.edf,ST7162J0-PSG.edf,16
15,48,female,Placebo,23:40:00,1,13c371fc4384751cc4bdd3044c6a0813ea12816e,67c47cb92de8806c60303a4baa87ca6cf52a2245,ST7171JA-Hypnogram.edf,ST7171J0-PSG.edf,17
15,48,female,Temazepam,23:48:00,2,8de0f3f59dd27d07f5f6a74216814ced08f104b5,a46118a5ca9cfaa62ca11c6a8b079e82877305ef,ST7172JA-Hypnogram.edf,ST7172J0-PSG.edf,17
16,53,female,Temazepam,23:24:00,1,483aa0b448393d61043c98c204c93d4c60abb6bd,501f2f9d9ebe15e6dfc86fda6e90f9a54a39660a,ST7181JR-Hypnogram.edf,ST7181J0-PSG.edf,18
16,53,female,Placebo,23:38:00,2,50efc607882659f8229db773703f5b973b471ed4,0eab40d3687a2cf708e48137eab26c0c43b75773,ST7182JR-Hypnogram.edf,ST7182J0-PSG.edf,18
17,28,female,Temazepam,23:44:00,1,e7fcb89cf0f1484ab114bf40dcf2bf4cd413696b,b1b10cd45a7c0f91286c6fc3f755e59af483bac1,ST7191JR-Hypnogram.edf,ST7191J0-PSG.edf,19
17,28,female,Placebo,23:22:00,2,def09a7d469984005b0c8414b7995ae8e269fd15,e80de913aa41b987a43d94cf8f0106d61e4e883b,ST7192JR-Hypnogram.edf,ST7192J0-PSG.edf,19
18,24,male,Placebo,23:47:00,1,17a0e8aebb885a960a74343bace57d2ab0b6296a,454233ae9e6a948848030c5f4d9e60dfcb0facde,ST7201JO-Hypnogram.edf,ST7201J0-PSG.edf,20
18,24,male,Temazepam,00:01:00,2,ed26efdb6b2d9e815f2a725970262cb9c15c7b98,1e97e392968415da67432842c952344b6d3cdc8c,ST7202JO-Hypnogram.edf,ST7202J0-PSG.edf,20
19,34,female,Temazepam,23:10:00,1,b8756397056f623674c3b03db808b2c8c64b0a0a,c6582cfa8fcf6542a688fa8842011a93d86f2c60,ST7211JJ-Hypnogram.edf,ST7211J0-PSG.edf,21
19,34,female,Placebo,23:44:00,2,e25e47adf0c0f09df542ef061272ed9569fb80ea,389f3920b39b4b9ad4fba6f91198299b7c6f6676,ST7212JJ-Hypnogram.edf,ST7212J0-PSG.edf,21
20,56,male,Placebo,23:22:00,1,7656827835362b7b44b296bad83ff6001e14f489,58315bec82d381dec56bf96924a94014462bb608,ST7221JA-Hypnogram.edf,ST7221J0-PSG.edf,22
20,56,male,Temazepam,23:44:00,2,da840db60086e43a2429fb1322ede5e5976b3cda,4961a08b87416246b8b8186190eca0e96da6a50d,ST7222JA-Hypnogram.edf,ST7222J0-PSG.edf,22
21,48,female,Placebo,23:27:00,1,bbaac4f2c2f330f70583eb179d855fcf42b4fbff,7a850ce4bc6bd14ea072f3a45b002f8015cf2f14,ST7241JO-Hypnogram.edf,ST7241J0-PSG.edf,24
21,48,female,Temazepam,23:36:00,2,f70b3dfce2c14f01221a66a4acb522df1affffdb,5c8bd182bfc9609929094769718b2835fe1099ad,ST7242JO-Hypnogram.edf,ST7242J0-PSG.edf,24
1 subject age sex drug lights off night nr sha_Hypnogram sha_PSG fname_Hypnogram fname_PSG subject_orig
2 0 60 male Placebo 23:01:00 1 ff28e5e01296cefed49ae0c27cfb3ebc42e710bf b9d11484126ebff1884034396d6a20c62c0ef48d ST7011JP-Hypnogram.edf ST7011J0-PSG.edf 1
3 0 60 male Temazepam 23:48:00 2 7a98a0ebba9e5e8fc4aac9ab82849385570d7789 b97c67d2ec40721349fd6faea32ea7155a11940a ST7012JP-Hypnogram.edf ST7012J0-PSG.edf 1
4 1 35 female Temazepam 00:00:00 1 635b07240047ade50649ff0f72ccde792f464f09 552e579d96e6c4ae083c7e1422e11b945ebcdabd ST7021JM-Hypnogram.edf ST7021J0-PSG.edf 2
5 1 35 female Placebo 23:27:00 2 228c608743abcc28f8c4946e8394ecf8e6ada89c ebabfa224599201d9baf91311f78f6410971810f ST7022JM-Hypnogram.edf ST7022J0-PSG.edf 2
6 2 18 female Placebo 23:53:00 1 422655bae4525d121bd45fead048207be9b34c4b 41f8e344b9872d93c8c2f2da283252231584b08f ST7041JO-Hypnogram.edf ST7041J0-PSG.edf 4
7 2 18 female Temazepam 22:37:00 2 eff297358a0c9d175109ba692ac3f9f4cd2c08ed 229ee3bb4d060332c219c3dc1153732ab5499d57 ST7042JO-Hypnogram.edf ST7042J0-PSG.edf 4
8 3 32 female Temazepam 23:34:00 1 d7696bd1b891dd85e96e20ea727dcebe49ab6dfd 17b186214e8944667571f52098564e377b32d695 ST7051JA-Hypnogram.edf ST7051J0-PSG.edf 5
9 3 32 female Placebo 23:23:00 2 64f2718c004e64ab598979da139b90452febc9bf 489fcb38c07688192d9c0eae5455d95241028ad8 ST7052JA-Hypnogram.edf ST7052J0-PSG.edf 5
10 4 35 female Placebo 23:28:00 1 fd9214d026453fce71efa2975ea732e1c1458f69 9fb2b4ed47a6d4b2f0b60a354123e491e8738b19 ST7061JR-Hypnogram.edf ST7061J0-PSG.edf 6
11 4 35 female Temazepam 23:26:00 2 c2a4abe15f08f230b734a328494ab0d2ae9dc786 afc5599194648da5568dafa1a811818e77df4842 ST7062JR-Hypnogram.edf ST7062J0-PSG.edf 6
12 5 51 female Placebo 00:02:00 1 bc08c797bb7aaf92de1c869d46c6dd4590939996 010a65ad86b79d19c372a421f0e7c975e56278c8 ST7071JA-Hypnogram.edf ST7071J0-PSG.edf 7
13 5 51 female Temazepam 23:24:00 2 1a7813b7a2389c0346e3844835590b9cb2f40f56 15c5aa5591e35d60ba25044cdd4b3d748d3c0cfc ST7072JA-Hypnogram.edf ST7072J0-PSG.edf 7
14 6 66 female Temazepam 23:53:00 1 8259b52c62203b85268d23b3a2d87605fdcfa2a6 cb66a0493d90d0d1204936e3e7c944ed536265e3 ST7081JW-Hypnogram.edf ST7081J0-PSG.edf 8
15 6 66 female Placebo 23:20:00 2 bc33c3aba61c0fa937ef56d4ce7b1468c80663b5 b1cb29c7a7321b7e628d04a477338c4f62f0c093 ST7082JW-Hypnogram.edf ST7082J0-PSG.edf 8
16 7 47 male Temazepam 23:42:00 1 af845641a8118d004bcfa6b597f23517e3a752e9 b046dd63d92339914eca0489d8a4c566b69e7723 ST7091JE-Hypnogram.edf ST7091J0-PSG.edf 9
17 7 47 male Placebo 00:30:00 2 ec89bb908ff70e123ffa94bc2c11bb1ce54bcb6a 2986f4d64f5118c5e356a2abe6bf86521ffde339 ST7092JE-Hypnogram.edf ST7092J0-PSG.edf 9
18 8 20 female Placebo 23:21:00 1 5919542c566d882fbf947c66f4858ad17199103a 5662b560f095b8397303cced87e43d407a0d18f7 ST7101JE-Hypnogram.edf ST7101J0-PSG.edf 10
19 8 20 female Temazepam 23:28:00 2 1f05e92c9ca076350f981d0ec75ad720606bacbc f697a140f18d1005107fcbb7c81d85a5e8cb6ec6 ST7102JE-Hypnogram.edf ST7102J0-PSG.edf 10
20 9 21 female Temazepam 23:38:00 1 5964553fe07cbca302526b2153a2507f7d02fab8 e2bf9db482f230a56372603d23fb12f5c56062f7 ST7111JE-Hypnogram.edf ST7111J0-PSG.edf 11
21 9 21 female Placebo 23:52:00 2 e4d8406eaca361d2c5d9953b3c67ed1098dd5925 d3c7907b9b1e4f087f31bd655548b8673b6ec735 ST7112JE-Hypnogram.edf ST7112J0-PSG.edf 11
22 10 21 male Placebo 23:46:00 1 a991ed3d8be6d55ee563545077f3d280466a4989 6e90bac48e48f71e5572944a364009eab6ea818d ST7121JE-Hypnogram.edf ST7121J0-PSG.edf 12
23 10 21 male Temazepam 23:56:00 2 b6c2c21e3cf17b371b31af78c64f28aa5811e36f ae7426c464296ec0a839ccaa9763e3f2c57f41f1 ST7122JE-Hypnogram.edf ST7122J0-PSG.edf 12
24 11 22 male Temazepam 00:38:00 1 91ee1bd29b156b33e03cb8c324a8fac15ec06674 d0d6c83b76f627b067e0daac3c181e3666f8ab08 ST7131JR-Hypnogram.edf ST7131J0-PSG.edf 13
25 11 22 male Placebo 00:31:00 2 028a5c4ed911d67a17b45f12966b32c46949d374 54a50dcc40e3d6677b80c629b2f908339d9a7c3e ST7132JR-Hypnogram.edf ST7132J0-PSG.edf 13
26 12 20 male Placebo 00:40:00 1 203e78e02a92a9f85f07790398f64c66f248e5cc 6bf8feeabc2259d15f1f535abda90caacc8d4a86 ST7141JE-Hypnogram.edf ST7141J0-PSG.edf 14
27 12 20 male Temazepam 00:53:00 2 1f7cc3a1923dd6a3504c82d76f820555ad0b6a1b b42eb28089bbdcbf3244dead53fd01d5f5ac3ddf ST7142JE-Hypnogram.edf ST7142J0-PSG.edf 14
28 13 66 female Placebo 23:42:00 1 cfcb0089e22244bc5047f61e72a39735cbdc36cf c0df1253b6509c4b4ed9e1283f26cf206a8c725c ST7151JA-Hypnogram.edf ST7151J0-PSG.edf 15
29 13 66 female Temazepam 23:33:00 2 27e9b4527eea33ded9072db3c6626f94a966da58 faefa07a1ca180861d6f26d5f35285c009dca21e ST7152JA-Hypnogram.edf ST7152J0-PSG.edf 15
30 14 79 female Temazepam 23:18:00 1 5a1ef1d375b01f83264e84db4af58acded68f15e 8a4f1c44a17b5d665cc30f1141d003043274ac2b ST7161JM-Hypnogram.edf ST7161J0-PSG.edf 16
31 14 79 female Placebo 23:21:00 2 18b3d7eb9685ec8131fc0a8f81ba6205122595dc 66925c8fa9f6da18f8590dcf2a6174cfe46e912d ST7162JM-Hypnogram.edf ST7162J0-PSG.edf 16
32 15 48 female Placebo 23:40:00 1 13c371fc4384751cc4bdd3044c6a0813ea12816e 67c47cb92de8806c60303a4baa87ca6cf52a2245 ST7171JA-Hypnogram.edf ST7171J0-PSG.edf 17
33 15 48 female Temazepam 23:48:00 2 8de0f3f59dd27d07f5f6a74216814ced08f104b5 a46118a5ca9cfaa62ca11c6a8b079e82877305ef ST7172JA-Hypnogram.edf ST7172J0-PSG.edf 17
34 16 53 female Temazepam 23:24:00 1 483aa0b448393d61043c98c204c93d4c60abb6bd 501f2f9d9ebe15e6dfc86fda6e90f9a54a39660a ST7181JR-Hypnogram.edf ST7181J0-PSG.edf 18
35 16 53 female Placebo 23:38:00 2 50efc607882659f8229db773703f5b973b471ed4 0eab40d3687a2cf708e48137eab26c0c43b75773 ST7182JR-Hypnogram.edf ST7182J0-PSG.edf 18
36 17 28 female Temazepam 23:44:00 1 e7fcb89cf0f1484ab114bf40dcf2bf4cd413696b b1b10cd45a7c0f91286c6fc3f755e59af483bac1 ST7191JR-Hypnogram.edf ST7191J0-PSG.edf 19
37 17 28 female Placebo 23:22:00 2 def09a7d469984005b0c8414b7995ae8e269fd15 e80de913aa41b987a43d94cf8f0106d61e4e883b ST7192JR-Hypnogram.edf ST7192J0-PSG.edf 19
38 18 24 male Placebo 23:47:00 1 17a0e8aebb885a960a74343bace57d2ab0b6296a 454233ae9e6a948848030c5f4d9e60dfcb0facde ST7201JO-Hypnogram.edf ST7201J0-PSG.edf 20
39 18 24 male Temazepam 00:01:00 2 ed26efdb6b2d9e815f2a725970262cb9c15c7b98 1e97e392968415da67432842c952344b6d3cdc8c ST7202JO-Hypnogram.edf ST7202J0-PSG.edf 20
40 19 34 female Temazepam 23:10:00 1 b8756397056f623674c3b03db808b2c8c64b0a0a c6582cfa8fcf6542a688fa8842011a93d86f2c60 ST7211JJ-Hypnogram.edf ST7211J0-PSG.edf 21
41 19 34 female Placebo 23:44:00 2 e25e47adf0c0f09df542ef061272ed9569fb80ea 389f3920b39b4b9ad4fba6f91198299b7c6f6676 ST7212JJ-Hypnogram.edf ST7212J0-PSG.edf 21
42 20 56 male Placebo 23:22:00 1 7656827835362b7b44b296bad83ff6001e14f489 58315bec82d381dec56bf96924a94014462bb608 ST7221JA-Hypnogram.edf ST7221J0-PSG.edf 22
43 20 56 male Temazepam 23:44:00 2 da840db60086e43a2429fb1322ede5e5976b3cda 4961a08b87416246b8b8186190eca0e96da6a50d ST7222JA-Hypnogram.edf ST7222J0-PSG.edf 22
44 21 48 female Placebo 23:27:00 1 bbaac4f2c2f330f70583eb179d855fcf42b4fbff 7a850ce4bc6bd14ea072f3a45b002f8015cf2f14 ST7241JO-Hypnogram.edf ST7241J0-PSG.edf 24
45 21 48 female Temazepam 23:36:00 2 f70b3dfce2c14f01221a66a4acb522df1affffdb 5c8bd182bfc9609929094769718b2835fe1099ad ST7242JO-Hypnogram.edf ST7242J0-PSG.edf 24

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""Somatosensory dataset."""
from .somato import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="somato",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="somato", conf="MNE_DATASETS_SOMATO_PATH"
)
def get_version(): # noqa: D103
return _get_version("somato")
get_version.__doc__ = _version_doc.format(name="somato")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""SPM face dataset."""
from .spm_data import data_path, has_spm_data, get_version, requires_spm_data

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from functools import partial
from ...utils import get_config, verbose
from ..utils import (
_data_path_doc,
_download_mne_dataset,
_get_version,
_version_doc,
has_dataset,
)
has_spm_data = partial(has_dataset, name="spm")
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="spm",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(name="spm", conf="MNE_DATASETS_SPM_DATA_PATH")
def get_version(): # noqa: D103
return _get_version("spm")
get_version.__doc__ = _version_doc.format(name="spm")
def _skip_spm_data():
skip_testing = get_config("MNE_SKIP_TESTING_DATASET_TESTS", "false") == "true"
skip = skip_testing or not has_spm_data()
return skip
def requires_spm_data(func):
"""Skip testing data test."""
import pytest
return pytest.mark.skipif(_skip_spm_data(), reason="Requires spm dataset")(func)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""SSVEP dataset."""
from .ssvep import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name="ssvep",
processor="unzip",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(name="ssvep", conf="MNE_DATASETS_SSVEP_PATH")
def get_version(): # noqa: D103
return _get_version("ssvep")
get_version.__doc__ = _version_doc.format(name="ssvep")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE testing dataset."""
from ._testing import (
data_path,
requires_testing_data,
get_version,
_pytest_param,
_pytest_mark,
)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from functools import partial
from ...utils import get_config, verbose
from ..utils import (
_data_path_doc,
_download_mne_dataset,
_get_version,
_version_doc,
has_dataset,
)
has_testing_data = partial(has_dataset, name="testing")
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
# Make sure we don't do something stupid
if download and get_config("MNE_SKIP_TESTING_DATASET_TESTS", "false") == "true":
raise RuntimeError("Cannot download data if skipping is forced")
return _download_mne_dataset(
name="testing",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="testing", conf="MNE_DATASETS_TESTING_PATH"
)
def get_version(): # noqa: D103
return _get_version("testing")
get_version.__doc__ = _version_doc.format(name="testing")
# Allow forcing of testing dataset skip (for Debian tests) using:
# `make test-no-testing-data`
def _skip_testing_data():
skip_testing = get_config("MNE_SKIP_TESTING_DATASET_TESTS", "false") == "true"
skip = skip_testing or not has_testing_data()
return skip
def requires_testing_data(func):
"""Skip testing data test."""
return _pytest_mark()(func)
def _pytest_param(*args, **kwargs):
if len(args) == 0:
args = ("testing_data",)
import pytest
# turn anything that uses testing data into an auto-skipper by
# setting params=[testing._pytest_param()], or by parametrizing functions
# with testing._pytest_param(whatever)
kwargs["marks"] = kwargs.get("marks", list()) + [_pytest_mark()]
return pytest.param(*args, **kwargs)
def _pytest_mark():
import pytest
return pytest.mark.skipif(_skip_testing_data(), reason="Requires testing dataset")

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""fNIRS motor dataset."""
from .ucl_opm_auditory import data_path, get_version

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
_NAME = "ucl_opm_auditory"
_PROCESSOR = "unzip"
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
): # noqa: D103
return _download_mne_dataset(
name=_NAME,
processor=_PROCESSOR,
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name=_NAME,
conf=f"MNE_DATASETS_{_NAME.upper()}_PATH",
)
def get_version(): # noqa: D103
return _get_version(_NAME)
get_version.__doc__ = _version_doc.format(name=_NAME)

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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import importlib
import inspect
import logging
import os
import os.path as op
import sys
import tempfile
import time
import zipfile
from collections import OrderedDict
from pathlib import Path
from typing import cast
import numpy as np
from ..label import Label, read_labels_from_annot, write_labels_to_annot
from ..utils import (
_pl,
_safe_input,
_validate_type,
get_config,
get_subjects_dir,
logger,
set_config,
verbose,
)
from ..utils.docs import _docformat, docdict
from .config import MNE_DATASETS, _hcp_mmp_license_text
_data_path_doc = """Get path to local copy of {name} dataset.
Parameters
----------
path : None | str
Location of where to look for the {name} dataset.
If None, the environment variable or config parameter
``{conf}`` is used. If it doesn't exist, the
"~/mne_data" directory is used. If the {name} 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 {name} dataset even if a local copy exists.
Default is False.
update_path : bool | None
If True (default), set the ``{conf}`` in mne-python
config to the given path. If None, the user is prompted.
download : bool
If False and the {name} dataset has not been downloaded yet,
it will not be downloaded and the path will be returned as
'' (empty string). This is mostly used for debugging purposes
and can be safely ignored by most users.
%(verbose)s
Returns
-------
path : instance of Path
Path to {name} dataset directory.
"""
_data_path_doc_accept = _data_path_doc.split("%(verbose)s")
_data_path_doc_accept[-1] = "%(verbose)s" + _data_path_doc_accept[-1]
_data_path_doc_accept.insert(1, " %(accept)s")
_data_path_doc_accept = "".join(_data_path_doc_accept)
_data_path_doc = _docformat(_data_path_doc, docdict)
_data_path_doc_accept = _docformat(_data_path_doc_accept, docdict)
_version_doc = """Get version of the local {name} dataset.
Returns
-------
version : str | None
Version of the {name} local dataset, or None if the dataset
does not exist locally.
"""
def _dataset_version(path, name):
"""Get the version of the dataset."""
ver_fname = op.join(path, "version.txt")
if op.exists(ver_fname):
with open(ver_fname) as fid:
version = fid.readline().strip() # version is on first line
else:
logger.debug(f"Version file missing: {ver_fname}")
# Sample dataset versioning was introduced after 0.3
# SPM dataset was introduced with 0.7
versions = dict(sample="0.7", spm="0.3")
version = versions.get(name, "0.0")
return version
def _get_path(path, key, name):
"""Get a dataset path."""
# 1. Input
_validate_type(path, ("path-like", None), path)
if path is not None:
return Path(path).expanduser()
# 2. get_config(key) — unless key is None or "" (special get_config values)
# 3. get_config('MNE_DATA')
path = get_config(key or "MNE_DATA", get_config("MNE_DATA"))
if path is not None:
path = Path(path).expanduser()
if not path.exists():
msg = (
f"Download location {path} as specified by MNE_DATA does "
f"not exist. Either create this directory manually and try "
f"again, or set MNE_DATA to an existing directory."
)
raise FileNotFoundError(msg)
return path
# 4. ~/mne_data (but use a fake home during testing so we don't
# unnecessarily create ~/mne_data)
logger.info(f"Using default location ~/mne_data for {name}...")
path = Path(os.getenv("_MNE_FAKE_HOME_DIR", "~")).expanduser() / "mne_data"
if not path.is_dir():
logger.info(f"Creating {path}")
try:
path.mkdir()
except OSError:
raise OSError(
"User does not have write permissions "
f"at '{path}', try giving the path as an "
"argument to data_path() where user has "
"write permissions, for ex:data_path"
"('/home/xyz/me2/')"
)
return path
def _do_path_update(path, update_path, key, name):
"""Update path."""
path = op.abspath(path)
identical = get_config(key, "", use_env=False) == path
if not identical:
if update_path is None:
update_path = True
if "--update-dataset-path" in sys.argv:
answer = "y"
else:
msg = (
f"Do you want to set the path:\n {path}\nas the default {name} "
"dataset path in the mne-python config [y]/n? "
)
answer = _safe_input(msg, alt="pass update_path=True")
if answer.lower() == "n":
update_path = False
if update_path:
set_config(key, str(path), set_env=False)
return path
# This is meant to be semi-public: let packages like mne-bids use it to make
# sure they don't accidentally set download=True in their tests, too
_MODULES_TO_ENSURE_DOWNLOAD_IS_FALSE_IN_TESTS = ("mne",)
def _check_in_testing_and_raise(name, download):
"""Check if we're in an MNE test and raise an error if download!=False."""
root_dirs = [
importlib.import_module(ns)
for ns in _MODULES_TO_ENSURE_DOWNLOAD_IS_FALSE_IN_TESTS
]
root_dirs = [str(Path(ns.__file__).parent) for ns in root_dirs]
check = False
func = None
frame = inspect.currentframe()
try:
# First, traverse out of the data_path() call
while frame:
if frame.f_code.co_name in ("data_path", "load_data"):
func = frame.f_code.co_name
frame = frame.f_back.f_back # out of verbose decorator
break
frame = frame.f_back
# Next, see what the caller was
while frame:
fname = frame.f_code.co_filename
if fname is not None:
fname = Path(fname)
# in mne namespace, and
# (can't use is_relative_to here until 3.9)
if any(str(fname).startswith(rd) for rd in root_dirs) and (
# in tests/*.py
fname.parent.stem == "tests"
or
# or in a conftest.py
fname.stem == "conftest.py"
):
check = True
break
frame = frame.f_back
finally:
del frame
if check and download is not False:
raise RuntimeError(
f"Do not download dataset {repr(name)} in tests, pass "
f"{func}(download=False) to prevent accidental downloads"
)
def _download_mne_dataset(
name, processor, path, force_update, update_path, download, accept=False
) -> Path:
"""Aux function for downloading internal MNE datasets."""
import pooch
from mne.datasets._fetch import fetch_dataset
_check_in_testing_and_raise(name, download)
# import pooch library for handling the dataset downloading
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 cast(
Path,
fetch_dataset(
dataset_params=dataset_params,
processor=processor_,
path=path,
force_update=force_update,
update_path=update_path,
download=download,
accept=accept,
),
)
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 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 str(dp).endswith(check)
@verbose
def _download_all_example_data(verbose=True):
"""Download all datasets used in examples and tutorials."""
# This function is designed primarily to be used by CircleCI, to:
#
# 1. Streamline data downloading
# 2. Make CircleCI fail early (rather than later) if some necessary data
# cannot be retrieved.
# 3. Avoid download statuses and timing biases in rendered examples.
#
# verbose=True by default so we get nice status messages.
# Consider adding datasets from here to CircleCI for PR-auto-build
paths = dict()
for kind in (
"sample testing misc spm_face somato hf_sef multimodal "
"fnirs_motor opm mtrf fieldtrip_cmc kiloword phantom_kit phantom_4dbti "
"refmeg_noise ssvep epilepsy_ecog ucl_opm_auditory eyelink "
"erp_core brainstorm.bst_raw brainstorm.bst_auditory "
"brainstorm.bst_resting brainstorm.bst_phantom_ctf "
"brainstorm.bst_phantom_elekta phantom_kernel"
).split():
mod = importlib.import_module(f"mne.datasets.{kind}")
data_path_func = getattr(mod, "data_path")
kwargs = dict()
if "accept" in inspect.getfullargspec(data_path_func).args:
kwargs["accept"] = True
paths[kind] = data_path_func(**kwargs)
logger.info(f"[done {kind}]")
# Now for the exceptions:
from . import (
eegbci,
fetch_fsaverage,
fetch_hcp_mmp_parcellation,
fetch_infant_template,
fetch_phantom,
limo,
sleep_physionet,
)
eegbci.load_data(subjects=1, runs=[6, 10, 14], update_path=True)
eegbci.load_data(subjects=range(1, 5), runs=[3], update_path=True)
logger.info("[done eegbci]")
sleep_physionet.age.fetch_data(subjects=[0, 1], recording=[1])
logger.info("[done sleep_physionet]")
# If the user has SUBJECTS_DIR, respect it, if not, set it to the EEG one
# (probably on CircleCI, or otherwise advanced user)
fetch_fsaverage(subjects_dir=None)
logger.info("[done fsaverage]")
# Now also update the sample dataset path, if not already SUBJECTS_DIR
# (some tutorials make use of these files)
fetch_fsaverage(subjects_dir=paths["sample"] / "subjects")
fetch_infant_template("6mo")
logger.info("[done infant_template]")
fetch_hcp_mmp_parcellation(subjects_dir=paths["sample"] / "subjects", accept=True)
logger.info("[done hcp_mmp_parcellation]")
fetch_phantom("otaniemi", subjects_dir=paths["brainstorm.bst_phantom_elekta"])
logger.info("[done phantom]")
limo.load_data(subject=1, update_path=True)
logger.info("[done limo]")
@verbose
def fetch_aparc_sub_parcellation(subjects_dir=None, verbose=None):
"""Fetch the modified subdivided aparc parcellation.
This will download and install the subdivided aparc parcellation
:footcite:'KhanEtAl2018' files for
FreeSurfer's fsaverage to the specified directory.
Parameters
----------
subjects_dir : path-like | None
The subjects directory to use. The file will be placed in
``subjects_dir + '/fsaverage/label'``.
%(verbose)s
References
----------
.. footbibliography::
"""
import pooch
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
destination = subjects_dir / "fsaverage" / "label"
urls = dict(lh="https://osf.io/p92yb/download", rh="https://osf.io/4kxny/download")
hashes = dict(
lh="9e4d8d6b90242b7e4b0145353436ef77", rh="dd6464db8e7762d969fc1d8087cd211b"
)
downloader = pooch.HTTPDownloader(**_downloader_params())
for hemi in ("lh", "rh"):
fname = f"{hemi}.aparc_sub.annot"
fpath = destination / fname
if not fpath.is_file():
pooch.retrieve(
url=urls[hemi],
known_hash=f"md5:{hashes[hemi]}",
path=destination,
downloader=downloader,
fname=fname,
)
@verbose
def fetch_hcp_mmp_parcellation(
subjects_dir=None, combine=True, *, accept=False, verbose=None
):
"""Fetch the HCP-MMP parcellation.
This will download and install the HCP-MMP parcellation
:footcite:`GlasserEtAl2016` files for FreeSurfer's fsaverage
:footcite:`Mills2016` to the specified directory.
Parameters
----------
subjects_dir : path-like | None
The subjects directory to use. The file will be placed in
``subjects_dir + '/fsaverage/label'``.
combine : bool
If True, also produce the combined/reduced set of 23 labels per
hemisphere as ``HCPMMP1_combined.annot``
:footcite:`GlasserEtAl2016supp`.
%(accept)s
%(verbose)s
Notes
-----
Use of this parcellation is subject to terms of use on the
`HCP-MMP webpage <https://balsa.wustl.edu/WN56>`_.
References
----------
.. footbibliography::
"""
import pooch
subjects_dir = get_subjects_dir(subjects_dir, raise_error=True)
destination = subjects_dir / "fsaverage" / "label"
fnames = [destination / f"{hemi}.HCPMMP1.annot" for hemi in ("lh", "rh")]
urls = dict(
lh="https://ndownloader.figshare.com/files/5528816",
rh="https://ndownloader.figshare.com/files/5528819",
)
hashes = dict(
lh="46a102b59b2fb1bb4bd62d51bf02e975", rh="75e96b331940227bbcb07c1c791c2463"
)
if not all(fname.exists() for fname in fnames):
if accept or "--accept-hcpmmp-license" in sys.argv:
answer = "y"
else:
answer = _safe_input(f"{_hcp_mmp_license_text}\nAgree (y/[n])? ")
if answer.lower() != "y":
raise RuntimeError("You must agree to the license to use this dataset")
downloader = pooch.HTTPDownloader(**_downloader_params())
for hemi, fpath in zip(("lh", "rh"), fnames):
if not op.isfile(fpath):
fname = fpath.name
pooch.retrieve(
url=urls[hemi],
known_hash=f"md5:{hashes[hemi]}",
path=destination,
downloader=downloader,
fname=fname,
)
if combine:
fnames = [
op.join(destination, f"{hemi}.HCPMMP1_combined.annot")
for hemi in ("lh", "rh")
]
if all(op.isfile(fname) for fname in fnames):
return
# otherwise, let's make them
logger.info("Creating combined labels")
groups = OrderedDict(
[
("Primary Visual Cortex (V1)", ("V1",)),
("Early Visual Cortex", ("V2", "V3", "V4")),
(
"Dorsal Stream Visual Cortex",
("V3A", "V3B", "V6", "V6A", "V7", "IPS1"),
),
(
"Ventral Stream Visual Cortex",
("V8", "VVC", "PIT", "FFC", "VMV1", "VMV2", "VMV3"),
),
(
"MT+ Complex and Neighboring Visual Areas",
("V3CD", "LO1", "LO2", "LO3", "V4t", "FST", "MT", "MST", "PH"),
),
("Somatosensory and Motor Cortex", ("4", "3a", "3b", "1", "2")),
(
"Paracentral Lobular and Mid Cingulate Cortex",
(
"24dd",
"24dv",
"6mp",
"6ma",
"SCEF",
"5m",
"5L",
"5mv",
),
),
("Premotor Cortex", ("55b", "6d", "6a", "FEF", "6v", "6r", "PEF")),
(
"Posterior Opercular Cortex",
("43", "FOP1", "OP4", "OP1", "OP2-3", "PFcm"),
),
("Early Auditory Cortex", ("A1", "LBelt", "MBelt", "PBelt", "RI")),
(
"Auditory Association Cortex",
(
"A4",
"A5",
"STSdp",
"STSda",
"STSvp",
"STSva",
"STGa",
"TA2",
),
),
(
"Insular and Frontal Opercular Cortex",
(
"52",
"PI",
"Ig",
"PoI1",
"PoI2",
"FOP2",
"FOP3",
"MI",
"AVI",
"AAIC",
"Pir",
"FOP4",
"FOP5",
),
),
(
"Medial Temporal Cortex",
(
"H",
"PreS",
"EC",
"PeEc",
"PHA1",
"PHA2",
"PHA3",
),
),
(
"Lateral Temporal Cortex",
(
"PHT",
"TE1p",
"TE1m",
"TE1a",
"TE2p",
"TE2a",
"TGv",
"TGd",
"TF",
),
),
(
"Temporo-Parieto-Occipital Junction",
(
"TPOJ1",
"TPOJ2",
"TPOJ3",
"STV",
"PSL",
),
),
(
"Superior Parietal Cortex",
(
"LIPv",
"LIPd",
"VIP",
"AIP",
"MIP",
"7PC",
"7AL",
"7Am",
"7PL",
"7Pm",
),
),
(
"Inferior Parietal Cortex",
(
"PGp",
"PGs",
"PGi",
"PFm",
"PF",
"PFt",
"PFop",
"IP0",
"IP1",
"IP2",
),
),
(
"Posterior Cingulate Cortex",
(
"DVT",
"ProS",
"POS1",
"POS2",
"RSC",
"v23ab",
"d23ab",
"31pv",
"31pd",
"31a",
"23d",
"23c",
"PCV",
"7m",
),
),
(
"Anterior Cingulate and Medial Prefrontal Cortex",
(
"33pr",
"p24pr",
"a24pr",
"p24",
"a24",
"p32pr",
"a32pr",
"d32",
"p32",
"s32",
"8BM",
"9m",
"10v",
"10r",
"25",
),
),
(
"Orbital and Polar Frontal Cortex",
(
"47s",
"47m",
"a47r",
"11l",
"13l",
"a10p",
"p10p",
"10pp",
"10d",
"OFC",
"pOFC",
),
),
(
"Inferior Frontal Cortex",
(
"44",
"45",
"IFJp",
"IFJa",
"IFSp",
"IFSa",
"47l",
"p47r",
),
),
(
"DorsoLateral Prefrontal Cortex",
(
"8C",
"8Av",
"i6-8",
"s6-8",
"SFL",
"8BL",
"9p",
"9a",
"8Ad",
"p9-46v",
"a9-46v",
"46",
"9-46d",
),
),
("???", ("???",)),
]
)
assert len(groups) == 23
labels_out = list()
for hemi in ("lh", "rh"):
labels = read_labels_from_annot(
"fsaverage", "HCPMMP1", hemi=hemi, subjects_dir=subjects_dir, sort=False
)
label_names = [
"???" if label.name.startswith("???") else label.name.split("_")[1]
for label in labels
]
used = np.zeros(len(labels), bool)
for key, want in groups.items():
assert "\t" not in key
these_labels = [
li
for li, label_name in enumerate(label_names)
if label_name in want
]
assert not used[these_labels].any()
assert len(these_labels) == len(want)
used[these_labels] = True
these_labels = [labels[li] for li in these_labels]
# take a weighted average to get the color
# (here color == task activation)
w = np.array([len(label.vertices) for label in these_labels])
w = w / float(w.sum())
color = np.dot(w, [label.color for label in these_labels])
these_labels = sum(
these_labels, Label([], subject="fsaverage", hemi=hemi)
)
these_labels.name = key
these_labels.color = color
labels_out.append(these_labels)
assert used.all()
assert len(labels_out) == 46
for hemi, side in (("lh", "left"), ("rh", "right")):
table_name = f"./{side}.fsaverage164.label.gii"
write_labels_to_annot(
labels_out,
"fsaverage",
"HCPMMP1_combined",
hemi=hemi,
subjects_dir=subjects_dir,
sort=False,
table_name=table_name,
)
def _manifest_check_download(manifest_path, destination, url, hash_):
import pooch
with open(manifest_path) as fid:
names = [name.strip() for name in fid.readlines()]
need = list()
for name in names:
if not (destination / name).is_file():
need.append(name)
logger.info(
"%d file%s missing from %s in %s",
len(need),
_pl(need),
manifest_path.name,
destination,
)
if len(need) > 0:
downloader = pooch.HTTPDownloader(**_downloader_params())
with tempfile.TemporaryDirectory() as path:
logger.info("Downloading missing files remotely")
path = Path(path)
fname_path = path / "temp.zip"
pooch.retrieve(
url=url,
known_hash=f"md5:{hash_}",
path=path,
downloader=downloader,
fname=fname_path.name,
)
logger.info(f"Extracting missing file{_pl(need)}")
with zipfile.ZipFile(fname_path, "r") as ff:
members = set(f for f in ff.namelist() if not f.endswith("/"))
missing = sorted(members.symmetric_difference(set(names)))
if len(missing):
raise RuntimeError(
"Zip file did not have correct names:\n{'\n'.join(missing)}"
)
for name in need:
ff.extract(name, path=destination)
logger.info(f"Successfully extracted {len(need)} file{_pl(need)}")
def _log_time_size(t0, sz):
t = time.time() - t0
fmt = "%Ss"
if t > 60:
fmt = f"%Mm{fmt}"
if t > 3600:
fmt = f"%Hh{fmt}"
sz = sz / 1048576 # 1024 ** 2
t = time.strftime(fmt, time.gmtime(t))
logger.info(f"Download complete in {t} ({sz:.1f} MB)")
def _downloader_params(*, auth=None, token=None):
params = dict(timeout=15)
params["progressbar"] = (
logger.level <= logging.INFO and get_config("MNE_TQDM", "tqdm.auto") != "off"
)
if auth is not None:
params["auth"] = auth
if token is not None:
params["headers"] = {"Authorization": f"token {token}"}
return params

7
mne/datasets/visual_92_categories/__init__.py Normal file
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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
"""MNE visual_92_categories dataset."""
from .visual_92_categories import data_path, get_version

67
mne/datasets/visual_92_categories/visual_92_categories.py Normal file
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@@ -0,0 +1,67 @@
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
from ...utils import verbose
from ..utils import _data_path_doc, _download_mne_dataset, _get_version, _version_doc
@verbose
def data_path(
path=None, force_update=False, update_path=True, download=True, *, verbose=None
):
"""
Get path to local copy of visual_92_categories dataset.
.. note:: The dataset contains four fif-files, the trigger files and the T1
mri image. This dataset is rather big in size (more than 5 GB).
Parameters
----------
path : None | str
Location of where to look for the visual_92_categories data storing
location. If None, the environment variable or config parameter
MNE_DATASETS_VISUAL_92_CATEGORIES_PATH is used. If it doesn't exist,
the "mne-python/examples" directory is used. If the
visual_92_categories dataset is not found under the given path (e.g.,
as "mne-python/examples/MNE-visual_92_categories-data"), 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_VISUAL_92_CATEGORIES_PATH in mne-python
config to the given path. If None, the user is prompted.
%(verbose)s
Returns
-------
path : instance of Path
Local path to the given data file.
Notes
-----
The visual_92_categories dataset is documented in the following publication
Radoslaw M. Cichy, Dimitrios Pantazis, Aude Oliva (2014) Resolving
human object recognition in space and time. doi: 10.1038/NN.3635
"""
return _download_mne_dataset(
name="visual_92_categories",
processor="untar",
path=path,
force_update=force_update,
update_path=update_path,
download=download,
)
data_path.__doc__ = _data_path_doc.format(
name="visual_92_categories", conf="MNE_DATASETS_VISUAL_92_CATEGORIES_PATH"
)
def get_version():
"""Get dataset version."""
return _get_version("visual_92_categories")
get_version.__doc__ = _version_doc.format(name="visual_92_categories")