initial commit

mne/datasets/sleep_physionet/SHA1SUMS

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mne/datasets/sleep_physionet/__init__.py

@@ -0,0 +1,5 @@
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+from . import age, temazepam, _utils

mne/datasets/sleep_physionet/_utils.py

@@ -0,0 +1,244 @@
+# 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)

mne/datasets/sleep_physionet/age.py

@@ -0,0 +1,153 @@
+# 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

mne/datasets/sleep_physionet/age_records.csv

@@ -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
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+19,1,Hypnogram,28,male,01:50:00,defc7b9368c2d3c4ab4a294757843825a83cdb5d,SC4191EP-Hypnogram.edf
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+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
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+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
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+63,2,Hypnogram,91,female,23:39:00,cd2765ebdabc66cb4ac2320d02e3b7ab0340ede4,SC4632EA-Hypnogram.edf
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+65,1,Hypnogram,88,female,23:10:00,fad4311c7e11a9aa9a73a8e48d6fa966db61e71d,SC4651EP-Hypnogram.edf
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+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
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+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
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+81,1,PSG,57,female,22:00:00,dcae3307af54ccf5349945e2fa493464de0a5da2,SC4811G0-PSG.edf
+81,1,Hypnogram,57,female,22:00:00,2406ce37b86fc3c7492a3ebe89ae58d15686b33d,SC4811GG-Hypnogram.edf
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+82,2,Hypnogram,56,female,00:05:00,8d14c371bc290658469729addee4461866bb67e2,SC4822GC-Hypnogram.edf

mne/datasets/sleep_physionet/temazepam.py

@@ -0,0 +1,119 @@
+# 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

mne/datasets/sleep_physionet/temazepam_records.csv

@@ -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