external/CBraMod/preprocessing/ISRUC/edf_.py

"""Reading tools from EDF, EDF+, BDF, and GDF."""

# Authors: Teon Brooks <teon.brooks@gmail.com>
#          Martin Billinger <martin.billinger@tugraz.at>
#          Nicolas Barascud <nicolas.barascud@ens.fr>
#          Stefan Appelhoff <stefan.appelhoff@mailbox.org>
#          Joan Massich <mailsik@gmail.com>
#          Clemens Brunner <clemens.brunner@gmail.com>
#          Jeroen Van Der Donckt (IDlab - imec) <jeroen.vanderdonckt@ugent.be>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import os
import re
from datetime import datetime, timedelta, timezone

import numpy as np
from scipy.interpolate import interp1d

from mne._fiff.constants import FIFF
from mne._fiff.meas_info import _empty_info, _unique_channel_names
from mne._fiff.utils import _blk_read_lims, _mult_cal_one
from mne.annotations import Annotations
from mne.filter import resample
from mne.utils import _validate_type, fill_doc, logger, verbose, warn
from mne.io.base import BaseRaw, _get_scaling

# common channel type names mapped to internal ch types
CH_TYPE_MAPPING = {
    "EEG": FIFF.FIFFV_EEG_CH,
    "SEEG": FIFF.FIFFV_SEEG_CH,
    "ECOG": FIFF.FIFFV_ECOG_CH,
    "DBS": FIFF.FIFFV_DBS_CH,
    "EOG": FIFF.FIFFV_EOG_CH,
    "ECG": FIFF.FIFFV_ECG_CH,
    "EMG": FIFF.FIFFV_EMG_CH,
    "BIO": FIFF.FIFFV_BIO_CH,
    "RESP": FIFF.FIFFV_RESP_CH,
    "TEMP": FIFF.FIFFV_TEMPERATURE_CH,
    "MISC": FIFF.FIFFV_MISC_CH,
    "SAO2": FIFF.FIFFV_BIO_CH,
}


@fill_doc
class RawEDF(BaseRaw):
    """Raw object from EDF, EDF+ or BDF file.

    Parameters
    ----------
    input_fname : path-like
        Path to the EDF, EDF+ or BDF file.
    eog : list or tuple
        Names of channels or list of indices that should be designated EOG
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    misc : list or tuple
        Names of channels or list of indices that should be designated MISC
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    stim_channel : ``'auto'`` | str | list of str | int | list of int
        Defaults to ``'auto'``, which means that channels named ``'status'`` or
        ``'trigger'`` (case insensitive) are set to STIM. If str (or list of
        str), all channels matching the name(s) are set to STIM. If int (or
        list of ints), the channels corresponding to the indices are set to
        STIM.
    exclude : list of str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling.
    infer_types : bool
        If True, try to infer channel types from channel labels. If a channel
        label starts with a known type (such as 'EEG') followed by a space and
        a name (such as 'Fp1'), the channel type will be set accordingly, and
        the channel will be renamed to the original label without the prefix.
        For unknown prefixes, the type will be 'EEG' and the name will not be
        modified. If False, do not infer types and assume all channels are of
        type 'EEG'.

        .. versionadded:: 0.24.1
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.

        .. versionadded:: 1.1
    %(preload)s
    %(units_edf_bdf_io)s
    %(encoding_edf)s
    %(verbose)s

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods.
    mne.io.read_raw_edf : Recommended way to read EDF/EDF+ files.
    mne.io.read_raw_bdf : Recommended way to read BDF files.

    Notes
    -----
    %(edf_resamp_note)s

    Biosemi devices trigger codes are encoded in 16-bit format, whereas system
    codes (CMS in/out-of range, battery low, etc.) are coded in bits 16-23 of
    the status channel (see http://www.biosemi.com/faq/trigger_signals.htm).
    To retrieve correct event values (bits 1-16), one could do:

        >>> events = mne.find_events(...)  # doctest:+SKIP
        >>> events[:, 2] &= (2**16 - 1)  # doctest:+SKIP

    The above operation can be carried out directly in :func:`mne.find_events`
    using the ``mask`` and ``mask_type`` parameters (see
    :func:`mne.find_events` for more details).

    It is also possible to retrieve system codes, but no particular effort has
    been made to decode these in MNE. In case it is necessary, for instance to
    check the CMS bit, the following operation can be carried out:

        >>> cms_bit = 20  # doctest:+SKIP
        >>> cms_high = (events[:, 2] & (1 << cms_bit)) != 0  # doctest:+SKIP

    It is worth noting that in some special cases, it may be necessary to shift
    event values in order to retrieve correct event triggers. This depends on
    the triggering device used to perform the synchronization. For instance, in
    some files events need to be shifted by 8 bits:

        >>> events[:, 2] >>= 8  # doctest:+SKIP

    TAL channels called 'EDF Annotations' or 'BDF Annotations' are parsed and
    extracted annotations are stored in raw.annotations. Use
    :func:`mne.events_from_annotations` to obtain events from these
    annotations.

    If channels named 'status' or 'trigger' are present, they are considered as
    STIM channels by default. Use func:`mne.find_events` to parse events
    encoded in such analog stim channels.
    """

    @verbose
    def __init__(
        self,
        input_fname,
        eog=None,
        misc=None,
        stim_channel="auto",
        exclude=(),
        infer_types=False,
        preload=False,
        include=None,
        units=None,
        encoding="utf8",
        *,
        verbose=None,
    ):
        logger.info("Extracting EDF parameters from {}...".format(input_fname))
        input_fname = os.path.abspath(input_fname)
        info, edf_info, orig_units = _get_info(
            input_fname, stim_channel, eog, misc, exclude, infer_types, preload, include
        )
        logger.info("Creating raw.info structure...")

        _validate_type(units, (str, None, dict), "units")
        if units is None:
            units = dict()
        elif isinstance(units, str):
            units = {ch_name: units for ch_name in info["ch_names"]}

        for k, (this_ch, this_unit) in enumerate(orig_units.items()):
            if this_ch not in units:
                continue
            if this_unit not in ("", units[this_ch]):
                raise ValueError(
                    f"Unit for channel {this_ch} is present in the file as "
                    f"{repr(this_unit)}, cannot overwrite it with the units "
                    f"argument {repr(units[this_ch])}."
                )
            if this_unit == "":
                orig_units[this_ch] = units[this_ch]
                ch_type = edf_info["ch_types"][k]
                scaling = _get_scaling(ch_type.lower(), orig_units[this_ch])
                edf_info["units"][k] /= scaling

        # Raw attributes
        last_samps = [edf_info["nsamples"] - 1]
        super().__init__(
            info,
            preload,
            filenames=[input_fname],
            raw_extras=[edf_info],
            last_samps=last_samps,
            orig_format="int",
            orig_units=orig_units,
            verbose=verbose,
        )

        # Read annotations from file and set it
        if len(edf_info["tal_idx"]) > 0:
            # Read TAL data exploiting the header info (no regexp)
            idx = np.empty(0, int)
            tal_data = self._read_segment_file(
                np.empty((0, self.n_times)),
                idx,
                0,
                0,
                int(self.n_times),
                np.ones((len(idx), 1)),
                None,
            )
            annotations = _read_annotations_edf(
                tal_data[0],
                ch_names=info["ch_names"],
                encoding=encoding,
            )
            self.set_annotations(annotations, on_missing="warn")

    def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
        """Read a chunk of raw data."""
        return _read_segment_file(
            data,
            idx,
            fi,
            start,
            stop,
            self._raw_extras[fi],
            self._filenames[fi],
            cals,
            mult,
        )


@fill_doc
class RawGDF(BaseRaw):
    """Raw object from GDF file.

    Parameters
    ----------
    input_fname : path-like
        Path to the GDF file.
    eog : list or tuple
        Names of channels or list of indices that should be designated EOG
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    misc : list or tuple
        Names of channels or list of indices that should be designated MISC
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    stim_channel : ``'auto'`` | str | list of str | int | list of int
        Defaults to 'auto', which means that channels named 'status' or
        'trigger' (case insensitive) are set to STIM. If str (or list of str),
        all channels matching the name(s) are set to STIM. If int (or list of
        ints), channels corresponding to the indices are set to STIM.
    exclude : list of str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling.

        .. versionadded:: 0.24.1
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.

        .. versionadded:: 1.1
    %(preload)s
    %(verbose)s

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods.
    mne.io.read_raw_gdf : Recommended way to read GDF files.

    Notes
    -----
    If channels named 'status' or 'trigger' are present, they are considered as
    STIM channels by default. Use func:`mne.find_events` to parse events
    encoded in such analog stim channels.
    """

    @verbose
    def __init__(
        self,
        input_fname,
        eog=None,
        misc=None,
        stim_channel="auto",
        exclude=(),
        preload=False,
        include=None,
        verbose=None,
    ):
        logger.info("Extracting EDF parameters from {}...".format(input_fname))
        input_fname = os.path.abspath(input_fname)
        info, edf_info, orig_units = _get_info(
            input_fname, stim_channel, eog, misc, exclude, True, preload, include
        )
        logger.info("Creating raw.info structure...")

        # Raw attributes
        last_samps = [edf_info["nsamples"] - 1]
        super().__init__(
            info,
            preload,
            filenames=[input_fname],
            raw_extras=[edf_info],
            last_samps=last_samps,
            orig_format="int",
            orig_units=orig_units,
            verbose=verbose,
        )

        # Read annotations from file and set it
        onset, duration, desc = _get_annotations_gdf(edf_info, self.info["sfreq"])

        self.set_annotations(
            Annotations(
                onset=onset, duration=duration, description=desc, orig_time=None
            )
        )

    def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
        """Read a chunk of raw data."""
        return _read_segment_file(
            data,
            idx,
            fi,
            start,
            stop,
            self._raw_extras[fi],
            self._filenames[fi],
            cals,
            mult,
        )


def _read_ch(fid, subtype, samp, dtype_byte, dtype=None):
    """Read a number of samples for a single channel."""
    # BDF
    if subtype == "bdf":
        ch_data = np.fromfile(fid, dtype=dtype, count=samp * dtype_byte)
        ch_data = ch_data.reshape(-1, 3).astype(INT32)
        ch_data = (ch_data[:, 0]) + (ch_data[:, 1] << 8) + (ch_data[:, 2] << 16)
        # 24th bit determines the sign
        ch_data[ch_data >= (1 << 23)] -= 1 << 24

    # GDF data and EDF data
    else:
        ch_data = np.fromfile(fid, dtype=dtype, count=samp)

    return ch_data


def _read_segment_file(data, idx, fi, start, stop, raw_extras, filenames, cals, mult):
    """Read a chunk of raw data."""
    n_samps = raw_extras["n_samps"]
    buf_len = int(raw_extras["max_samp"])
    dtype = raw_extras["dtype_np"]
    dtype_byte = raw_extras["dtype_byte"]
    data_offset = raw_extras["data_offset"]
    stim_channel_idxs = raw_extras["stim_channel_idxs"]
    orig_sel = raw_extras["sel"]
    tal_idx = raw_extras.get("tal_idx", np.empty(0, int))
    subtype = raw_extras["subtype"]
    cal = raw_extras["cal"]
    offsets = raw_extras["offsets"]
    gains = raw_extras["units"]

    read_sel = np.concatenate([orig_sel[idx], tal_idx])
    tal_data = []

    # only try to read the stim channel if it's not None and it's
    # actually one of the requested channels
    idx_arr = np.arange(idx.start, idx.stop) if isinstance(idx, slice) else idx

    # We could read this one EDF block at a time, which would be this:
    ch_offsets = np.cumsum(np.concatenate([[0], n_samps]), dtype=np.int64)
    block_start_idx, r_lims, d_lims = _blk_read_lims(start, stop, buf_len)
    # But to speed it up, we really need to read multiple blocks at once,
    # Otherwise we can end up with e.g. 18,181 chunks for a 20 MB file!
    # Let's do ~10 MB chunks:
    n_per = max(10 * 1024 * 1024 // (ch_offsets[-1] * dtype_byte), 1)
    with open(filenames, "rb", buffering=0) as fid:
        # Extract data
        start_offset = data_offset + block_start_idx * ch_offsets[-1] * dtype_byte

        # first read everything into the `ones` array. For channels with
        # lower sampling frequency, there will be zeros left at the end of the
        # row. Ignore TAL/annotations channel and only store `orig_sel`
        ones = np.zeros((len(orig_sel), data.shape[-1]), dtype=data.dtype)
        # save how many samples have already been read per channel
        n_smp_read = [0 for _ in range(len(orig_sel))]

        # read data in chunks
        for ai in range(0, len(r_lims), n_per):
            block_offset = ai * ch_offsets[-1] * dtype_byte
            n_read = min(len(r_lims) - ai, n_per)
            fid.seek(start_offset + block_offset, 0)
            # Read and reshape to (n_chunks_read, ch0_ch1_ch2_ch3...)
            many_chunk = _read_ch(
                fid, subtype, ch_offsets[-1] * n_read, dtype_byte, dtype
            ).reshape(n_read, -1)
            r_sidx = r_lims[ai][0]
            r_eidx = buf_len * (n_read - 1) + r_lims[ai + n_read - 1][1]

            # loop over selected channels, ci=channel selection
            for ii, ci in enumerate(read_sel):
                # This now has size (n_chunks_read, n_samp[ci])
                ch_data = many_chunk[:, ch_offsets[ci] : ch_offsets[ci + 1]].copy()

                # annotation channel has to be treated separately
                if ci in tal_idx:
                    tal_data.append(ch_data)
                    continue

                orig_idx = idx_arr[ii]
                ch_data = ch_data * cal[orig_idx]
                ch_data += offsets[orig_idx]
                ch_data *= gains[orig_idx]

                assert ci == orig_sel[orig_idx]

                if n_samps[ci] != buf_len:
                    if orig_idx in stim_channel_idxs:
                        # Stim channel will be interpolated
                        old = np.linspace(0, 1, n_samps[ci] + 1, True)
                        new = np.linspace(0, 1, buf_len, False)
                        ch_data = np.append(ch_data, np.zeros((len(ch_data), 1)), -1)
                        ch_data = interp1d(old, ch_data, kind="zero", axis=-1)(new)
                elif orig_idx in stim_channel_idxs:
                    ch_data = np.bitwise_and(ch_data.astype(int), 2**17 - 1)

                one_i = ch_data.ravel()[r_sidx:r_eidx]

                # note how many samples have been read
                smp_read = n_smp_read[orig_idx]
                ones[orig_idx, smp_read : smp_read + len(one_i)] = one_i
                n_smp_read[orig_idx] += len(one_i)

        # skip if no data was requested, ie. only annotations were read
        if sum(n_smp_read) > 0:
            # expected number of samples, equals maximum sfreq
            smp_exp = data.shape[-1]
            assert max(n_smp_read) == smp_exp

            # resample data after loading all chunks to prevent edge artifacts
            resampled = False
            for i, smp_read in enumerate(n_smp_read):
                # nothing read, nothing to resample
                if smp_read == 0:
                    continue
                # upsample if n_samples is lower than from highest sfreq
                if smp_read != smp_exp:
                    assert (ones[i, smp_read:] == 0).all()  # sanity check
                    ones[i, :] = resample(
                        ones[i, :smp_read].astype(np.float64),
                        smp_exp,
                        smp_read,
                        npad=0,
                        axis=-1,
                    )
                    resampled = True

            # give warning if we resampled a subselection
            if resampled and raw_extras["nsamples"] != (stop - start):
                warn(
                    "Loading an EDF with mixed sampling frequencies and "
                    "preload=False will result in edge artifacts. "
                    "It is recommended to use preload=True."
                    "See also https://github.com/mne-tools/mne-python/issues/10635"
                )

            _mult_cal_one(data[:, :], ones, idx, cals, mult)

    if len(tal_data) > 1:
        tal_data = np.concatenate([tal.ravel() for tal in tal_data])
        tal_data = tal_data[np.newaxis, :]
    return tal_data


def _read_header(fname, exclude, infer_types, include=None):
    """Unify EDF, BDF and GDF _read_header call.

    Parameters
    ----------
    fname : str
        Path to the EDF+, BDF, or GDF file.
    exclude : list of str | str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling. A str is
        interpreted as a regular expression.
    infer_types : bool
        If True, try to infer channel types from channel labels. If a channel
        label starts with a known type (such as 'EEG') followed by a space and
        a name (such as 'Fp1'), the channel type will be set accordingly, and
        the channel will be renamed to the original label without the prefix.
        For unknown prefixes, the type will be 'EEG' and the name will not be
        modified. If False, do not infer types and assume all channels are of
        type 'EEG'.
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.

    Returns
    -------
    (edf_info, orig_units) : tuple
    """
    ext = os.path.splitext(fname)[1][1:].lower()
    logger.info("%s file detected" % ext.upper())
    if ext in ("bdf", "edf", "rec"):
        return _read_edf_header(fname, exclude, infer_types, include)
    elif ext == "gdf":
        return _read_gdf_header(fname, exclude, include), None
    else:
        raise NotImplementedError(
            f"Only GDF, EDF, and BDF files are supported, got {ext}."
        )


def _get_info(
    fname, stim_channel, eog, misc, exclude, infer_types, preload, include=None
):
    """Extract information from EDF+, BDF or GDF file."""
    eog = eog if eog is not None else []
    misc = misc if misc is not None else []

    edf_info, orig_units = _read_header(fname, exclude, infer_types, include)

    # XXX: `tal_ch_names` to pass to `_check_stim_channel` should be computed
    #      from `edf_info['ch_names']` and `edf_info['tal_idx']` but 'tal_idx'
    #      contains stim channels that are not TAL.
    stim_channel_idxs, _ = _check_stim_channel(stim_channel, edf_info["ch_names"])

    sel = edf_info["sel"]  # selection of channels not excluded
    ch_names = edf_info["ch_names"]  # of length len(sel)
    if "ch_types" in edf_info:
        ch_types = edf_info["ch_types"]  # of length len(sel)
    else:
        ch_types = [None] * len(sel)
    if len(sel) == 0:  # only want stim channels
        n_samps = edf_info["n_samps"][[0]]
    else:
        n_samps = edf_info["n_samps"][sel]
    nchan = edf_info["nchan"]
    physical_ranges = edf_info["physical_max"] - edf_info["physical_min"]
    cals = edf_info["digital_max"] - edf_info["digital_min"]
    bad_idx = np.where((~np.isfinite(cals)) | (cals == 0))[0]
    if len(bad_idx) > 0:
        warn(
            "Scaling factor is not defined in following channels:\n"
            + ", ".join(ch_names[i] for i in bad_idx)
        )
        cals[bad_idx] = 1
    bad_idx = np.where(physical_ranges == 0)[0]
    if len(bad_idx) > 0:
        warn(
            "Physical range is not defined in following channels:\n"
            + ", ".join(ch_names[i] for i in bad_idx)
        )
        physical_ranges[bad_idx] = 1

    # Creates a list of dicts of eeg channels for raw.info
    logger.info("Setting channel info structure...")
    chs = list()
    pick_mask = np.ones(len(ch_names))

    chs_without_types = list()

    for idx, ch_name in enumerate(ch_names):
        chan_info = {}
        chan_info["cal"] = 1.0
        chan_info["logno"] = idx + 1
        chan_info["scanno"] = idx + 1
        chan_info["range"] = 1.0
        chan_info["unit_mul"] = FIFF.FIFF_UNITM_NONE
        chan_info["ch_name"] = ch_name
        chan_info["unit"] = FIFF.FIFF_UNIT_V
        chan_info["coord_frame"] = FIFF.FIFFV_COORD_HEAD
        chan_info["coil_type"] = FIFF.FIFFV_COIL_EEG
        chan_info["kind"] = FIFF.FIFFV_EEG_CH
        # montage can't be stored in EDF so channel locs are unknown:
        chan_info["loc"] = np.full(12, np.nan)

        # if the edf info contained channel type information
        # set it now
        ch_type = ch_types[idx]
        if ch_type is not None and ch_type in CH_TYPE_MAPPING:
            chan_info["kind"] = CH_TYPE_MAPPING.get(ch_type)
            if ch_type not in ["EEG", "ECOG", "SEEG", "DBS"]:
                chan_info["coil_type"] = FIFF.FIFFV_COIL_NONE
            pick_mask[idx] = False
        # if user passes in explicit mapping for eog, misc and stim
        # channels set them here
        if ch_name in eog or idx in eog or idx - nchan in eog:
            chan_info["coil_type"] = FIFF.FIFFV_COIL_NONE
            chan_info["kind"] = FIFF.FIFFV_EOG_CH
            pick_mask[idx] = False
        elif ch_name in misc or idx in misc or idx - nchan in misc:
            chan_info["coil_type"] = FIFF.FIFFV_COIL_NONE
            chan_info["kind"] = FIFF.FIFFV_MISC_CH
            pick_mask[idx] = False
        elif idx in stim_channel_idxs:
            chan_info["coil_type"] = FIFF.FIFFV_COIL_NONE
            chan_info["unit"] = FIFF.FIFF_UNIT_NONE
            chan_info["kind"] = FIFF.FIFFV_STIM_CH
            pick_mask[idx] = False
            chan_info["ch_name"] = ch_name
            ch_names[idx] = chan_info["ch_name"]
            edf_info["units"][idx] = 1
        elif ch_type not in CH_TYPE_MAPPING:
            chs_without_types.append(ch_name)
        chs.append(chan_info)

    # warn if channel type was not inferable
    if len(chs_without_types):
        msg = (
            "Could not determine channel type of the following channels, "
            f'they will be set as EEG:\n{", ".join(chs_without_types)}'
        )
        logger.info(msg)

    edf_info["stim_channel_idxs"] = stim_channel_idxs
    if any(pick_mask):
        picks = [item for item, mask in zip(range(nchan), pick_mask) if mask]
        edf_info["max_samp"] = max_samp = n_samps[picks].max()
    else:
        edf_info["max_samp"] = max_samp = n_samps.max()

    # Info structure
    # -------------------------------------------------------------------------

    not_stim_ch = [x for x in range(n_samps.shape[0]) if x not in stim_channel_idxs]
    if len(not_stim_ch) == 0:  # only loading stim channels
        not_stim_ch = list(range(len(n_samps)))
    sfreq = (
        np.take(n_samps, not_stim_ch).max()
        * edf_info["record_length"][1]
        / edf_info["record_length"][0]
    )
    del n_samps
    info = _empty_info(sfreq)
    info["meas_date"] = edf_info["meas_date"]
    info["chs"] = chs
    info["ch_names"] = ch_names

    # Subject information
    info["subject_info"] = {}

    # String subject identifier
    if edf_info["subject_info"].get("id") is not None:
        info["subject_info"]["his_id"] = edf_info["subject_info"]["id"]
    # Subject sex (0=unknown, 1=male, 2=female)
    if edf_info["subject_info"].get("sex") is not None:
        if edf_info["subject_info"]["sex"] == "M":
            info["subject_info"]["sex"] = 1
        elif edf_info["subject_info"]["sex"] == "F":
            info["subject_info"]["sex"] = 2
        else:
            info["subject_info"]["sex"] = 0
    # Subject names (first, middle, last).
    if edf_info["subject_info"].get("name") is not None:
        sub_names = edf_info["subject_info"]["name"].split("_")
        if len(sub_names) < 2 or len(sub_names) > 3:
            info["subject_info"]["last_name"] = edf_info["subject_info"]["name"]
        elif len(sub_names) == 2:
            info["subject_info"]["first_name"] = sub_names[0]
            info["subject_info"]["last_name"] = sub_names[1]
        else:
            info["subject_info"]["first_name"] = sub_names[0]
            info["subject_info"]["middle_name"] = sub_names[1]
            info["subject_info"]["last_name"] = sub_names[2]
    # Birthday in (year, month, day) format.
    if isinstance(edf_info["subject_info"].get("birthday"), datetime):
        info["subject_info"]["birthday"] = (
            edf_info["subject_info"]["birthday"].year,
            edf_info["subject_info"]["birthday"].month,
            edf_info["subject_info"]["birthday"].day,
        )
    # Handedness (1=right, 2=left, 3=ambidextrous).
    if edf_info["subject_info"].get("hand") is not None:
        info["subject_info"]["hand"] = int(edf_info["subject_info"]["hand"])
    # Height in meters.
    if edf_info["subject_info"].get("height") is not None:
        info["subject_info"]["height"] = float(edf_info["subject_info"]["height"])
    # Weight in kilograms.
    if edf_info["subject_info"].get("weight") is not None:
        info["subject_info"]["weight"] = float(edf_info["subject_info"]["weight"])

    # Filter settings
    highpass = edf_info["highpass"]
    lowpass = edf_info["lowpass"]
    if highpass.size == 0:
        pass
    elif all(highpass):
        if highpass[0] == "NaN":
            # Placeholder for future use. Highpass set in _empty_info.
            pass
        elif highpass[0] == "DC":
            info["highpass"] = 0.0
        else:
            hp = highpass[0]
            try:
                hp = float(hp)
            except Exception:
                hp = 0.0
            info["highpass"] = hp
    else:
        info["highpass"] = float(np.max(highpass))
        warn(
            "Channels contain different highpass filters. Highest filter "
            "setting will be stored."
        )
    if np.isnan(info["highpass"]):
        info["highpass"] = 0.0
    if lowpass.size == 0:
        # Placeholder for future use. Lowpass set in _empty_info.
        pass
    elif all(lowpass):
        if lowpass[0] in ("NaN", "0", "0.0"):
            # Placeholder for future use. Lowpass set in _empty_info.
            pass
        else:
            info["lowpass"] = float(lowpass[0])
    else:
        info["lowpass"] = float(np.min(lowpass))
        warn(
            "Channels contain different lowpass filters. Lowest filter "
            "setting will be stored."
        )
    if np.isnan(info["lowpass"]):
        info["lowpass"] = info["sfreq"] / 2.0

    if info["highpass"] > info["lowpass"]:
        warn(
            f'Highpass cutoff frequency {info["highpass"]} is greater '
            f'than lowpass cutoff frequency {info["lowpass"]}, '
            "setting values to 0 and Nyquist."
        )
        info["highpass"] = 0.0
        info["lowpass"] = info["sfreq"] / 2.0

    # Some keys to be consistent with FIF measurement info
    info["description"] = None
    edf_info["nsamples"] = int(edf_info["n_records"] * max_samp)

    info._unlocked = False
    info._update_redundant()

    # Later used for reading
    edf_info["cal"] = physical_ranges / cals

    # physical dimension in µV
    edf_info["offsets"] = (
        edf_info["physical_min"] - edf_info["digital_min"] * edf_info["cal"]
    )
    del edf_info["physical_min"]
    del edf_info["digital_min"]

    if edf_info["subtype"] == "bdf":
        edf_info["cal"][stim_channel_idxs] = 1
        edf_info["offsets"][stim_channel_idxs] = 0
        edf_info["units"][stim_channel_idxs] = 1

    return info, edf_info, orig_units


def _parse_prefilter_string(prefiltering):
    """Parse prefilter string from EDF+ and BDF headers."""
    highpass = np.array(
        [
            v
            for hp in [
                re.findall(r"HP:\s*([0-9]+[.]*[0-9]*)", filt) for filt in prefiltering
            ]
            for v in hp
        ]
    )
    lowpass = np.array(
        [
            v
            for hp in [
                re.findall(r"LP:\s*([0-9]+[.]*[0-9]*)", filt) for filt in prefiltering
            ]
            for v in hp
        ]
    )
    return highpass, lowpass


def _edf_str(x):
    return x.decode("latin-1").split("\x00")[0]


def _edf_str_num(x):
    return _edf_str(x).replace(",", ".")


def _read_edf_header(fname, exclude, infer_types, include=None):
    """Read header information from EDF+ or BDF file."""
    edf_info = {"events": []}

    with open(fname, "rb") as fid:
        fid.read(8)  # version (unused here)

        # patient ID
        patient = {}
        id_info = fid.read(80).decode("latin-1").rstrip()
        id_info = id_info.split(" ")
        if len(id_info):
            patient["id"] = id_info[0]
            if len(id_info) >= 4:
                try:
                    birthdate = datetime.strptime(id_info[2], "%d-%b-%Y")
                except ValueError:
                    birthdate = "X"
                patient["sex"] = id_info[1]
                patient["birthday"] = birthdate
                patient["name"] = id_info[3]
                if len(id_info) > 4:
                    for info in id_info[4:]:
                        if "=" in info:
                            key, value = info.split("=")
                            if key in ["weight", "height"]:
                                patient[key] = float(value)
                            elif key in ["hand"]:
                                patient[key] = int(value)
                            else:
                                warn(f"Invalid patient information {key}")

        # Recording ID
        meas_id = {}
        rec_info = fid.read(80).decode("latin-1").rstrip().split(" ")
        valid_startdate = False
        if len(rec_info) == 5:
            try:
                startdate = datetime.strptime(rec_info[1], "%d-%b-%Y")
            except ValueError:
                startdate = "X"
            else:
                valid_startdate = True
            meas_id["startdate"] = startdate
            meas_id["study_id"] = rec_info[2]
            meas_id["technician"] = rec_info[3]
            meas_id["equipment"] = rec_info[4]

        # If startdate available in recording info, use it instead of the
        # file's meas_date since it contains all 4 digits of the year
        if valid_startdate:
            day = meas_id["startdate"].day
            month = meas_id["startdate"].month
            year = meas_id["startdate"].year
            fid.read(8)  # skip file's meas_date
        else:
            meas_date = fid.read(8).decode("latin-1")
            day, month, year = [int(x) for x in meas_date.split(".")]
            year = year + 2000 if year < 85 else year + 1900

        meas_time = fid.read(8).decode("latin-1")
        hour, minute, sec = [int(x) for x in meas_time.split(".")]
        try:
            meas_date = datetime(
                year, month, day, hour, minute, sec, tzinfo=timezone.utc
            )
        except ValueError:
            warn(
                f"Invalid date encountered ({year:04d}-{month:02d}-"
                f"{day:02d} {hour:02d}:{minute:02d}:{sec:02d})."
            )
            meas_date = None

        header_nbytes = int(_edf_str(fid.read(8)))

        # The following 44 bytes sometimes identify the file type, but this is
        # not guaranteed. Therefore, we skip this field and use the file
        # extension to determine the subtype (EDF or BDF, which differ in the
        # number of bytes they use for the data records; EDF uses 2 bytes
        # whereas BDF uses 3 bytes).
        fid.read(44)
        subtype = os.path.splitext(fname)[1][1:].lower()

        n_records = int(_edf_str(fid.read(8)))
        record_length = float(_edf_str(fid.read(8)))
        record_length = np.array([record_length, 1.0])  # in seconds
        if record_length[0] == 0:
            record_length[0] = 1.0
            warn(
                "Header information is incorrect for record length. Default "
                "record length set to 1.\nIt is possible that this file only"
                " contains annotations and no signals. In that case, please "
                "use mne.read_annotations() to load these annotations."
            )

        nchan = int(_edf_str(fid.read(4)))
        channels = list(range(nchan))

        # read in 16 byte labels and strip any extra spaces at the end
        ch_labels = [fid.read(16).strip().decode("latin-1") for _ in channels]

        # get channel names and optionally channel type
        # EDF specification contains 16 bytes that encode channel names,
        # optionally prefixed by a string representing channel type separated
        # by a space
        if infer_types:
            ch_types, ch_names = [], []
            for ch_label in ch_labels:
                ch_type, ch_name = "EEG", ch_label  # default to EEG
                parts = ch_label.split(" ")
                if len(parts) > 1:
                    if parts[0].upper() in CH_TYPE_MAPPING:
                        ch_type = parts[0].upper()
                        ch_name = " ".join(parts[1:])
                        logger.info(
                            f"Channel '{ch_label}' recognized as type "
                            f"{ch_type} (renamed to '{ch_name}')."
                        )
                ch_types.append(ch_type)
                ch_names.append(ch_name)
        else:
            ch_types, ch_names = ["EEG"] * nchan, ch_labels

        exclude = _find_exclude_idx(ch_names, exclude, include)
        tal_idx = _find_tal_idx(ch_names)
        exclude = np.concatenate([exclude, tal_idx])
        sel = np.setdiff1d(np.arange(len(ch_names)), exclude)
        for ch in channels:
            fid.read(80)  # transducer
        units = [fid.read(8).strip().decode("latin-1") for ch in channels]
        edf_info["units"] = list()
        for i, unit in enumerate(units):
            if i in exclude:
                continue
            # allow μ (greek mu), µ (micro symbol) and μ (sjis mu) codepoints
            if unit in ("\u03BCV", "\u00B5V", "\x83\xCAV", "uV"):
                edf_info["units"].append(1e-6)
            elif unit == "mV":
                edf_info["units"].append(1e-3)
            else:
                edf_info["units"].append(1)
        edf_info["units"] = np.array(edf_info["units"], float)

        ch_names = [ch_names[idx] for idx in sel]
        units = [units[idx] for idx in sel]

        # make sure channel names are unique
        ch_names = _unique_channel_names(ch_names)
        orig_units = dict(zip(ch_names, units))

        physical_min = np.array([float(_edf_str_num(fid.read(8))) for ch in channels])[
            sel
        ]
        physical_max = np.array([float(_edf_str_num(fid.read(8))) for ch in channels])[
            sel
        ]
        digital_min = np.array([float(_edf_str_num(fid.read(8))) for ch in channels])[
            sel
        ]
        digital_max = np.array([float(_edf_str_num(fid.read(8))) for ch in channels])[
            sel
        ]
        prefiltering = [_edf_str(fid.read(80)).strip() for ch in channels][:-1]
        highpass, lowpass = _parse_prefilter_string(prefiltering)

        # number of samples per record
        n_samps = np.array([int(_edf_str(fid.read(8))) for ch in channels])

        # Populate edf_info
        edf_info.update(
            ch_names=ch_names,
            ch_types=ch_types,
            data_offset=header_nbytes,
            digital_max=digital_max,
            digital_min=digital_min,
            highpass=highpass,
            sel=sel,
            lowpass=lowpass,
            meas_date=meas_date,
            n_records=n_records,
            n_samps=n_samps,
            nchan=nchan,
            subject_info=patient,
            physical_max=physical_max,
            physical_min=physical_min,
            record_length=record_length,
            subtype=subtype,
            tal_idx=tal_idx,
        )

        fid.read(32 * nchan).decode()  # reserved
        assert fid.tell() == header_nbytes

        fid.seek(0, 2)
        n_bytes = fid.tell()
        n_data_bytes = n_bytes - header_nbytes
        total_samps = n_data_bytes // 3 if subtype == "bdf" else n_data_bytes // 2
        read_records = total_samps // np.sum(n_samps)
        if n_records != read_records:
            warn(
                "Number of records from the header does not match the file "
                "size (perhaps the recording was not stopped before exiting)."
                " Inferring from the file size."
            )
            edf_info["n_records"] = read_records
        del n_records

        if subtype == "bdf":
            edf_info["dtype_byte"] = 3  # 24-bit (3 byte) integers
            edf_info["dtype_np"] = UINT8
        else:
            edf_info["dtype_byte"] = 2  # 16-bit (2 byte) integers
            edf_info["dtype_np"] = INT16

    return edf_info, orig_units


INT8 = "<i1"
UINT8 = "<u1"
INT16 = "<i2"
UINT16 = "<u2"
INT32 = "<i4"
UINT32 = "<u4"
INT64 = "<i8"
UINT64 = "<u8"
FLOAT32 = "<f4"
FLOAT64 = "<f8"
GDFTYPE_NP = (
    None,
    INT8,
    UINT8,
    INT16,
    UINT16,
    INT32,
    UINT32,
    INT64,
    UINT64,
    None,
    None,
    None,
    None,
    None,
    None,
    None,
    FLOAT32,
    FLOAT64,
)
GDFTYPE_BYTE = tuple(np.dtype(x).itemsize if x is not None else 0 for x in GDFTYPE_NP)


def _check_dtype_byte(types):
    assert sum(GDFTYPE_BYTE) == 42
    dtype_byte = [GDFTYPE_BYTE[t] for t in types]
    dtype_np = [GDFTYPE_NP[t] for t in types]
    if len(np.unique(dtype_byte)) > 1:
        # We will not read it properly, so this should be an error
        raise RuntimeError("Reading multiple data types not supported")
    return dtype_np[0], dtype_byte[0]


def _read_gdf_header(fname, exclude, include=None):
    """Read GDF 1.x and GDF 2.x header info."""
    edf_info = dict()
    events = None
    with open(fname, "rb") as fid:
        version = fid.read(8).decode()
        edf_info["type"] = edf_info["subtype"] = version[:3]
        edf_info["number"] = float(version[4:])
        meas_date = None

        # GDF 1.x
        # ---------------------------------------------------------------------
        if edf_info["number"] < 1.9:
            # patient ID
            pid = fid.read(80).decode("latin-1")
            pid = pid.split(" ", 2)
            patient = {}
            if len(pid) >= 2:
                patient["id"] = pid[0]
                patient["name"] = pid[1]

            # Recording ID
            meas_id = {}
            meas_id["recording_id"] = _edf_str(fid.read(80)).strip()

            # date
            tm = _edf_str(fid.read(16)).strip()
            try:
                if tm[14:16] == "  ":
                    tm = tm[:14] + "00" + tm[16:]
                meas_date = datetime(
                    int(tm[0:4]),
                    int(tm[4:6]),
                    int(tm[6:8]),
                    int(tm[8:10]),
                    int(tm[10:12]),
                    int(tm[12:14]),
                    int(tm[14:16]) * pow(10, 4),
                    tzinfo=timezone.utc,
                )
            except Exception:
                pass

            header_nbytes = np.fromfile(fid, INT64, 1)[0]
            meas_id["equipment"] = np.fromfile(fid, UINT8, 8)[0]
            meas_id["hospital"] = np.fromfile(fid, UINT8, 8)[0]
            meas_id["technician"] = np.fromfile(fid, UINT8, 8)[0]
            fid.seek(20, 1)  # 20bytes reserved

            n_records = np.fromfile(fid, INT64, 1)[0]
            # record length in seconds
            record_length = np.fromfile(fid, UINT32, 2)
            if record_length[0] == 0:
                record_length[0] = 1.0
                warn(
                    "Header information is incorrect for record length. "
                    "Default record length set to 1."
                )
            nchan = int(np.fromfile(fid, UINT32, 1)[0])
            channels = list(range(nchan))
            ch_names = [_edf_str(fid.read(16)).strip() for ch in channels]
            exclude = _find_exclude_idx(ch_names, exclude, include)
            sel = np.setdiff1d(np.arange(len(ch_names)), exclude)
            fid.seek(80 * len(channels), 1)  # transducer
            units = [_edf_str(fid.read(8)).strip() for ch in channels]
            edf_info["units"] = list()
            for i, unit in enumerate(units):
                if i in exclude:
                    continue
                if unit[:2] == "uV":
                    edf_info["units"].append(1e-6)
                else:
                    edf_info["units"].append(1)
            edf_info["units"] = np.array(edf_info["units"], float)

            ch_names = [ch_names[idx] for idx in sel]
            physical_min = np.fromfile(fid, FLOAT64, len(channels))
            physical_max = np.fromfile(fid, FLOAT64, len(channels))
            digital_min = np.fromfile(fid, INT64, len(channels))
            digital_max = np.fromfile(fid, INT64, len(channels))
            prefiltering = [_edf_str(fid.read(80)) for ch in channels][:-1]
            highpass, lowpass = _parse_prefilter_string(prefiltering)

            # n samples per record
            n_samps = np.fromfile(fid, INT32, len(channels))

            # channel data type
            dtype = np.fromfile(fid, INT32, len(channels))

            # total number of bytes for data
            bytes_tot = np.sum(
                [GDFTYPE_BYTE[t] * n_samps[i] for i, t in enumerate(dtype)]
            )

            # Populate edf_info
            dtype_np, dtype_byte = _check_dtype_byte(dtype)
            edf_info.update(
                bytes_tot=bytes_tot,
                ch_names=ch_names,
                data_offset=header_nbytes,
                digital_min=digital_min,
                digital_max=digital_max,
                dtype_byte=dtype_byte,
                dtype_np=dtype_np,
                exclude=exclude,
                highpass=highpass,
                sel=sel,
                lowpass=lowpass,
                meas_date=meas_date,
                meas_id=meas_id,
                n_records=n_records,
                n_samps=n_samps,
                nchan=nchan,
                subject_info=patient,
                physical_max=physical_max,
                physical_min=physical_min,
                record_length=record_length,
            )

            fid.seek(32 * edf_info["nchan"], 1)  # reserved
            assert fid.tell() == header_nbytes

            # Event table
            # -----------------------------------------------------------------
            etp = header_nbytes + n_records * edf_info["bytes_tot"]
            # skip data to go to event table
            fid.seek(etp)
            etmode = np.fromfile(fid, UINT8, 1)[0]
            if etmode in (1, 3):
                sr = np.fromfile(fid, UINT8, 3).astype(np.uint32)
                event_sr = sr[0]
                for i in range(1, len(sr)):
                    event_sr = event_sr + sr[i] * 2 ** (i * 8)
                n_events = np.fromfile(fid, UINT32, 1)[0]
                pos = np.fromfile(fid, UINT32, n_events) - 1  # 1-based inds
                typ = np.fromfile(fid, UINT16, n_events)

                if etmode == 3:
                    chn = np.fromfile(fid, UINT16, n_events)
                    dur = np.fromfile(fid, UINT32, n_events)
                else:
                    chn = np.zeros(n_events, dtype=np.int32)
                    dur = np.ones(n_events, dtype=UINT32)
                np.maximum(dur, 1, out=dur)
                events = [n_events, pos, typ, chn, dur]

        # GDF 2.x
        # ---------------------------------------------------------------------
        else:
            # FIXED HEADER
            handedness = ("Unknown", "Right", "Left", "Equal")
            gender = ("Unknown", "Male", "Female")
            scale = ("Unknown", "No", "Yes", "Corrected")

            # date
            pid = fid.read(66).decode()
            pid = pid.split(" ", 2)
            patient = {}
            if len(pid) >= 2:
                patient["id"] = pid[0]
                patient["name"] = pid[1]
            fid.seek(10, 1)  # 10bytes reserved

            # Smoking / Alcohol abuse / drug abuse / medication
            sadm = np.fromfile(fid, UINT8, 1)[0]
            patient["smoking"] = scale[sadm % 4]
            patient["alcohol_abuse"] = scale[(sadm >> 2) % 4]
            patient["drug_abuse"] = scale[(sadm >> 4) % 4]
            patient["medication"] = scale[(sadm >> 6) % 4]
            patient["weight"] = np.fromfile(fid, UINT8, 1)[0]
            if patient["weight"] == 0 or patient["weight"] == 255:
                patient["weight"] = None
            patient["height"] = np.fromfile(fid, UINT8, 1)[0]
            if patient["height"] == 0 or patient["height"] == 255:
                patient["height"] = None

            # Gender / Handedness / Visual Impairment
            ghi = np.fromfile(fid, UINT8, 1)[0]
            patient["sex"] = gender[ghi % 4]
            patient["handedness"] = handedness[(ghi >> 2) % 4]
            patient["visual"] = scale[(ghi >> 4) % 4]

            # Recording identification
            meas_id = {}
            meas_id["recording_id"] = _edf_str(fid.read(64)).strip()
            vhsv = np.fromfile(fid, UINT8, 4)
            loc = {}
            if vhsv[3] == 0:
                loc["vertpre"] = 10 * int(vhsv[0] >> 4) + int(vhsv[0] % 16)
                loc["horzpre"] = 10 * int(vhsv[1] >> 4) + int(vhsv[1] % 16)
                loc["size"] = 10 * int(vhsv[2] >> 4) + int(vhsv[2] % 16)
            else:
                loc["vertpre"] = 29
                loc["horzpre"] = 29
                loc["size"] = 29
            loc["version"] = 0
            loc["latitude"] = float(np.fromfile(fid, UINT32, 1)[0]) / 3600000
            loc["longitude"] = float(np.fromfile(fid, UINT32, 1)[0]) / 3600000
            loc["altitude"] = float(np.fromfile(fid, INT32, 1)[0]) / 100
            meas_id["loc"] = loc

            meas_date = np.fromfile(fid, UINT64, 1)[0]
            if meas_date != 0:
                meas_date = datetime(1, 1, 1, tzinfo=timezone.utc) + timedelta(
                    meas_date * pow(2, -32) - 367
                )
            else:
                meas_date = None

            birthday = np.fromfile(fid, UINT64, 1).tolist()[0]
            if birthday == 0:
                birthday = datetime(1, 1, 1, tzinfo=timezone.utc)
            else:
                birthday = datetime(1, 1, 1, tzinfo=timezone.utc) + timedelta(
                    birthday * pow(2, -32) - 367
                )
            patient["birthday"] = birthday
            if patient["birthday"] != datetime(1, 1, 1, 0, 0, tzinfo=timezone.utc):
                today = datetime.now(tz=timezone.utc)
                patient["age"] = today.year - patient["birthday"].year
                today = today.replace(year=patient["birthday"].year)
                if today < patient["birthday"]:
                    patient["age"] -= 1
            else:
                patient["age"] = None

            header_nbytes = np.fromfile(fid, UINT16, 1)[0] * 256

            fid.seek(6, 1)  # 6 bytes reserved
            meas_id["equipment"] = np.fromfile(fid, UINT8, 8)
            meas_id["ip"] = np.fromfile(fid, UINT8, 6)
            patient["headsize"] = np.fromfile(fid, UINT16, 3)
            patient["headsize"] = np.asarray(patient["headsize"], np.float32)
            patient["headsize"] = np.ma.masked_array(
                patient["headsize"], np.equal(patient["headsize"], 0), None
            ).filled()
            ref = np.fromfile(fid, FLOAT32, 3)
            gnd = np.fromfile(fid, FLOAT32, 3)
            n_records = np.fromfile(fid, INT64, 1)[0]

            # record length in seconds
            record_length = np.fromfile(fid, UINT32, 2)
            if record_length[0] == 0:
                record_length[0] = 1.0
                warn(
                    "Header information is incorrect for record length. "
                    "Default record length set to 1."
                )

            nchan = int(np.fromfile(fid, UINT16, 1)[0])
            fid.seek(2, 1)  # 2bytes reserved

            # Channels (variable header)
            channels = list(range(nchan))
            ch_names = [_edf_str(fid.read(16)).strip() for ch in channels]
            exclude = _find_exclude_idx(ch_names, exclude, include)
            sel = np.setdiff1d(np.arange(len(ch_names)), exclude)

            fid.seek(80 * len(channels), 1)  # reserved space
            fid.seek(6 * len(channels), 1)  # phys_dim, obsolete

            """The Physical Dimensions are encoded as int16, according to:
            - Units codes :
            https://sourceforge.net/p/biosig/svn/HEAD/tree/trunk/biosig/doc/units.csv
            - Decimal factors codes:
            https://sourceforge.net/p/biosig/svn/HEAD/tree/trunk/biosig/doc/DecimalFactors.txt
            """  # noqa
            units = np.fromfile(fid, UINT16, len(channels)).tolist()
            unitcodes = np.array(units[:])
            edf_info["units"] = list()
            for i, unit in enumerate(units):
                if i in exclude:
                    continue
                if unit == 4275:  # microvolts
                    edf_info["units"].append(1e-6)
                elif unit == 4274:  # millivolts
                    edf_info["units"].append(1e-3)
                elif unit == 512:  # dimensionless
                    edf_info["units"].append(1)
                elif unit == 0:
                    edf_info["units"].append(1)  # unrecognized
                else:
                    warn(
                        "Unsupported physical dimension for channel %d "
                        "(assuming dimensionless). Please contact the "
                        "MNE-Python developers for support." % i
                    )
                    edf_info["units"].append(1)
            edf_info["units"] = np.array(edf_info["units"], float)

            ch_names = [ch_names[idx] for idx in sel]
            physical_min = np.fromfile(fid, FLOAT64, len(channels))
            physical_max = np.fromfile(fid, FLOAT64, len(channels))
            digital_min = np.fromfile(fid, FLOAT64, len(channels))
            digital_max = np.fromfile(fid, FLOAT64, len(channels))

            fid.seek(68 * len(channels), 1)  # obsolete
            lowpass = np.fromfile(fid, FLOAT32, len(channels))
            highpass = np.fromfile(fid, FLOAT32, len(channels))
            notch = np.fromfile(fid, FLOAT32, len(channels))

            # number of samples per record
            n_samps = np.fromfile(fid, INT32, len(channels))

            # data type
            dtype = np.fromfile(fid, INT32, len(channels))

            channel = {}
            channel["xyz"] = [np.fromfile(fid, FLOAT32, 3)[0] for ch in channels]

            if edf_info["number"] < 2.19:
                impedance = np.fromfile(fid, UINT8, len(channels)).astype(float)
                impedance[impedance == 255] = np.nan
                channel["impedance"] = pow(2, impedance / 8)
                fid.seek(19 * len(channels), 1)  # reserved
            else:
                tmp = np.fromfile(fid, FLOAT32, 5 * len(channels))
                tmp = tmp[::5]
                fZ = tmp[:]
                impedance = tmp[:]
                # channels with no voltage (code 4256) data
                ch = [unitcodes & 65504 != 4256][0]
                impedance[np.where(ch)] = None
                # channel with no impedance (code 4288) data
                ch = [unitcodes & 65504 != 4288][0]
                fZ[np.where(ch)[0]] = None

            assert fid.tell() == header_nbytes

            # total number of bytes for data
            bytes_tot = np.sum(
                [GDFTYPE_BYTE[t] * n_samps[i] for i, t in enumerate(dtype)]
            )

            # Populate edf_info
            dtype_np, dtype_byte = _check_dtype_byte(dtype)
            edf_info.update(
                bytes_tot=bytes_tot,
                ch_names=ch_names,
                data_offset=header_nbytes,
                dtype_byte=dtype_byte,
                dtype_np=dtype_np,
                digital_min=digital_min,
                digital_max=digital_max,
                exclude=exclude,
                gnd=gnd,
                highpass=highpass,
                sel=sel,
                impedance=impedance,
                lowpass=lowpass,
                meas_date=meas_date,
                meas_id=meas_id,
                n_records=n_records,
                n_samps=n_samps,
                nchan=nchan,
                notch=notch,
                subject_info=patient,
                physical_max=physical_max,
                physical_min=physical_min,
                record_length=record_length,
                ref=ref,
            )

            # EVENT TABLE
            # -----------------------------------------------------------------
            etp = (
                edf_info["data_offset"] + edf_info["n_records"] * edf_info["bytes_tot"]
            )
            fid.seek(etp)  # skip data to go to event table
            etmode = fid.read(1).decode()
            if etmode != "":
                etmode = np.fromstring(etmode, UINT8).tolist()[0]

                if edf_info["number"] < 1.94:
                    sr = np.fromfile(fid, UINT8, 3)
                    event_sr = sr[0]
                    for i in range(1, len(sr)):
                        event_sr = event_sr + sr[i] * 2 ** (i * 8)
                    n_events = np.fromfile(fid, UINT32, 1)[0]
                else:
                    ne = np.fromfile(fid, UINT8, 3)
                    n_events = ne[0]
                    for i in range(1, len(ne)):
                        n_events = n_events + ne[i] * 2 ** (i * 8)
                    event_sr = np.fromfile(fid, FLOAT32, 1)[0]

                pos = np.fromfile(fid, UINT32, n_events) - 1  # 1-based inds
                typ = np.fromfile(fid, UINT16, n_events)

                if etmode == 3:
                    chn = np.fromfile(fid, UINT16, n_events)
                    dur = np.fromfile(fid, UINT32, n_events)
                else:
                    chn = np.zeros(n_events, dtype=np.uint32)
                    dur = np.ones(n_events, dtype=np.uint32)
                np.maximum(dur, 1, out=dur)
                events = [n_events, pos, typ, chn, dur]
                edf_info["event_sfreq"] = event_sr

    edf_info.update(events=events, sel=np.arange(len(edf_info["ch_names"])))

    return edf_info


def _check_stim_channel(
    stim_channel, ch_names, tal_ch_names=["EDF Annotations", "BDF Annotations"]
):
    """Check that the stimulus channel exists in the current datafile."""
    DEFAULT_STIM_CH_NAMES = ["status", "trigger"]

    if stim_channel is None or stim_channel is False:
        return [], []

    if stim_channel is True:  # convenient aliases
        stim_channel = "auto"

    elif isinstance(stim_channel, str):
        if stim_channel == "auto":
            if "auto" in ch_names:
                warn(
                    RuntimeWarning,
                    "Using `stim_channel='auto'` when auto"
                    " also corresponds to a channel name is ambiguous."
                    " Please use `stim_channel=['auto']`.",
                )
            else:
                valid_stim_ch_names = DEFAULT_STIM_CH_NAMES
        else:
            valid_stim_ch_names = [stim_channel.lower()]

    elif isinstance(stim_channel, int):
        valid_stim_ch_names = [ch_names[stim_channel].lower()]

    elif isinstance(stim_channel, list):
        if all([isinstance(s, str) for s in stim_channel]):
            valid_stim_ch_names = [s.lower() for s in stim_channel]
        elif all([isinstance(s, int) for s in stim_channel]):
            valid_stim_ch_names = [ch_names[s].lower() for s in stim_channel]
        else:
            raise ValueError("Invalid stim_channel")
    else:
        raise ValueError("Invalid stim_channel")

    # Forbid the synthesis of stim channels from TAL Annotations
    tal_ch_names_found = [
        ch for ch in valid_stim_ch_names if ch in [t.lower() for t in tal_ch_names]
    ]
    if len(tal_ch_names_found):
        _msg = (
            "The synthesis of the stim channel is not supported"
            " since 0.18. Please remove {} from `stim_channel`"
            " and use `mne.events_from_annotations` instead"
        ).format(tal_ch_names_found)
        raise ValueError(_msg)

    ch_names_low = [ch.lower() for ch in ch_names]
    found = list(set(valid_stim_ch_names) & set(ch_names_low))

    if not found:
        return [], []
    else:
        stim_channel_idxs = [ch_names_low.index(f) for f in found]
        names = [ch_names[idx] for idx in stim_channel_idxs]
        return stim_channel_idxs, names


def _find_exclude_idx(ch_names, exclude, include=None):
    """Find indices of all channels to exclude.

    If there are several channels called "A" and we want to exclude "A", then
    add (the index of) all "A" channels to the exclusion list.
    """
    if include:  # find other than include channels
        if exclude:
            raise ValueError(
                "'exclude' must be empty if 'include' is assigned. " f"Got {exclude}."
            )
        if isinstance(include, str):  # regex for channel names
            indices_include = []
            for idx, ch in enumerate(ch_names):
                if re.match(include, ch):
                    indices_include.append(idx)
            indices = np.setdiff1d(np.arange(len(ch_names)), indices_include)
            return indices
        # list of channel names
        return [idx for idx, ch in enumerate(ch_names) if ch not in include]

    if isinstance(exclude, str):  # regex for channel names
        indices = []
        for idx, ch in enumerate(ch_names):
            if re.match(exclude, ch):
                indices.append(idx)
        return indices
    # list of channel names
    return [idx for idx, ch in enumerate(ch_names) if ch in exclude]


def _find_tal_idx(ch_names):
    # Annotations / TAL Channels
    accepted_tal_ch_names = ["EDF Annotations", "BDF Annotations"]
    tal_channel_idx = np.where(np.isin(ch_names, accepted_tal_ch_names))[0]
    return tal_channel_idx


@fill_doc
def read_raw_edf(
    input_fname,
    eog=None,
    misc=None,
    stim_channel="auto",
    exclude=(),
    infer_types=False,
    include=None,
    preload=False,
    units=None,
    encoding="utf8",
    *,
    verbose=None,
):
    """Reader function for EDF and EDF+ files.

    Parameters
    ----------
    input_fname : path-like
        Path to the EDF or EDF+ file.
    eog : list or tuple
        Names of channels or list of indices that should be designated EOG
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    misc : list or tuple
        Names of channels or list of indices that should be designated MISC
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    stim_channel : ``'auto'`` | str | list of str | int | list of int
        Defaults to ``'auto'``, which means that channels named ``'status'`` or
        ``'trigger'`` (case insensitive) are set to STIM. If str (or list of
        str), all channels matching the name(s) are set to STIM. If int (or
        list of ints), channels corresponding to the indices are set to STIM.
    exclude : list of str | str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling. A str is
        interpreted as a regular expression.
    infer_types : bool
        If True, try to infer channel types from channel labels. If a channel
        label starts with a known type (such as 'EEG') followed by a space and
        a name (such as 'Fp1'), the channel type will be set accordingly, and
        the channel will be renamed to the original label without the prefix.
        For unknown prefixes, the type will be 'EEG' and the name will not be
        modified. If False, do not infer types and assume all channels are of
        type 'EEG'.

        .. versionadded:: 0.24.1
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.

        .. versionadded:: 1.1
    %(preload)s
    %(units_edf_bdf_io)s
    %(encoding_edf)s
    %(verbose)s

    Returns
    -------
    raw : instance of RawEDF
        The raw instance.
        See :class:`mne.io.Raw` for documentation of attributes and methods.

    See Also
    --------
    mne.io.read_raw_bdf : Reader function for BDF files.
    mne.io.read_raw_gdf : Reader function for GDF files.
    mne.export.export_raw : Export function for EDF files.
    mne.io.Raw : Documentation of attributes and methods of RawEDF.

    Notes
    -----
    %(edf_resamp_note)s

    It is worth noting that in some special cases, it may be necessary to shift
    event values in order to retrieve correct event triggers. This depends on
    the triggering device used to perform the synchronization. For instance, in
    some files events need to be shifted by 8 bits:

        >>> events[:, 2] >>= 8  # doctest:+SKIP

    TAL channels called 'EDF Annotations' are parsed and extracted annotations
    are stored in raw.annotations. Use :func:`mne.events_from_annotations` to
    obtain events from these annotations.

    If channels named 'status' or 'trigger' are present, they are considered as
    STIM channels by default. Use func:`mne.find_events` to parse events
    encoded in such analog stim channels.

    The EDF specification allows optional storage of channel types in the
    prefix of the signal label for each channel. For example, ``EEG Fz``
    implies that ``Fz`` is an EEG channel and ``MISC E`` would imply ``E`` is
    a MISC channel. However, there is no standard way of specifying all
    channel types. MNE-Python will try to infer the channel type, when such a
    string exists, defaulting to EEG, when there is no prefix or the prefix is
    not recognized.

    The following prefix strings are mapped to MNE internal types:

        - 'EEG': 'eeg'
        - 'SEEG': 'seeg'
        - 'ECOG': 'ecog'
        - 'DBS': 'dbs'
        - 'EOG': 'eog'
        - 'ECG': 'ecg'
        - 'EMG': 'emg'
        - 'BIO': 'bio'
        - 'RESP': 'resp'
        - 'MISC': 'misc'
        - 'SAO2': 'bio'

    The EDF specification allows storage of subseconds in measurement date.
    However, this reader currently sets subseconds to 0 by default.
    """
    input_fname = os.path.abspath(input_fname)
    ext = os.path.splitext(input_fname)[1][1:].lower()
    # if ext != "edf":
    #     raise NotImplementedError(f"Only EDF files are supported, got {ext}.")
    return RawEDF(
        input_fname=input_fname,
        eog=eog,
        misc=misc,
        stim_channel=stim_channel,
        exclude=exclude,
        infer_types=infer_types,
        preload=preload,
        include=include,
        units=units,
        encoding=encoding,
        verbose=verbose,
    )


@fill_doc
def read_raw_bdf(
    input_fname,
    eog=None,
    misc=None,
    stim_channel="auto",
    exclude=(),
    infer_types=False,
    include=None,
    preload=False,
    units=None,
    encoding="utf8",
    *,
    verbose=None,
):
    """Reader function for BDF files.

    Parameters
    ----------
    input_fname : path-like
        Path to the BDF file.
    eog : list or tuple
        Names of channels or list of indices that should be designated EOG
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    misc : list or tuple
        Names of channels or list of indices that should be designated MISC
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    stim_channel : ``'auto'`` | str | list of str | int | list of int
        Defaults to ``'auto'``, which means that channels named ``'status'`` or
        ``'trigger'`` (case insensitive) are set to STIM. If str (or list of
        str), all channels matching the name(s) are set to STIM. If int (or
        list of ints), channels corresponding to the indices are set to STIM.
    exclude : list of str | str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling. A str is
        interpreted as a regular expression.
    infer_types : bool
        If True, try to infer channel types from channel labels. If a channel
        label starts with a known type (such as 'EEG') followed by a space and
        a name (such as 'Fp1'), the channel type will be set accordingly, and
        the channel will be renamed to the original label without the prefix.
        For unknown prefixes, the type will be 'EEG' and the name will not be
        modified. If False, do not infer types and assume all channels are of
        type 'EEG'.

        .. versionadded:: 0.24.1
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.

        .. versionadded:: 1.1
    %(preload)s
    %(units_edf_bdf_io)s
    %(encoding_edf)s
    %(verbose)s

    Returns
    -------
    raw : instance of RawEDF
        The raw instance.
        See :class:`mne.io.Raw` for documentation of attributes and methods.

    See Also
    --------
    mne.io.read_raw_edf : Reader function for EDF and EDF+ files.
    mne.io.read_raw_gdf : Reader function for GDF files.
    mne.io.Raw : Documentation of attributes and methods of RawEDF.

    Notes
    -----
    :class:`mne.io.Raw` only stores signals with matching sampling frequencies.
    Therefore, if mixed sampling frequency signals are requested, all signals
    are upsampled to the highest loaded sampling frequency. In this case, using
    preload=True is recommended, as otherwise, edge artifacts appear when
    slices of the signal are requested.

    Biosemi devices trigger codes are encoded in 16-bit format, whereas system
    codes (CMS in/out-of range, battery low, etc.) are coded in bits 16-23 of
    the status channel (see http://www.biosemi.com/faq/trigger_signals.htm).
    To retrieve correct event values (bits 1-16), one could do:

        >>> events = mne.find_events(...)  # doctest:+SKIP
        >>> events[:, 2] &= (2**16 - 1)  # doctest:+SKIP

    The above operation can be carried out directly in :func:`mne.find_events`
    using the ``mask`` and ``mask_type`` parameters (see
    :func:`mne.find_events` for more details).

    It is also possible to retrieve system codes, but no particular effort has
    been made to decode these in MNE. In case it is necessary, for instance to
    check the CMS bit, the following operation can be carried out:

        >>> cms_bit = 20  # doctest:+SKIP
        >>> cms_high = (events[:, 2] & (1 << cms_bit)) != 0  # doctest:+SKIP

    It is worth noting that in some special cases, it may be necessary to shift
    event values in order to retrieve correct event triggers. This depends on
    the triggering device used to perform the synchronization. For instance, in
    some files events need to be shifted by 8 bits:

        >>> events[:, 2] >>= 8  # doctest:+SKIP

    TAL channels called 'BDF Annotations' are parsed and extracted annotations
    are stored in raw.annotations. Use :func:`mne.events_from_annotations` to
    obtain events from these annotations.

    If channels named 'status' or 'trigger' are present, they are considered as
    STIM channels by default. Use func:`mne.find_events` to parse events
    encoded in such analog stim channels.
    """
    input_fname = os.path.abspath(input_fname)
    ext = os.path.splitext(input_fname)[1][1:].lower()
    if ext != "bdf":
        raise NotImplementedError(f"Only BDF files are supported, got {ext}.")
    return RawEDF(
        input_fname=input_fname,
        eog=eog,
        misc=misc,
        stim_channel=stim_channel,
        exclude=exclude,
        infer_types=infer_types,
        preload=preload,
        include=include,
        units=units,
        encoding=encoding,
        verbose=verbose,
    )


@fill_doc
def read_raw_gdf(
    input_fname,
    eog=None,
    misc=None,
    stim_channel="auto",
    exclude=(),
    include=None,
    preload=False,
    verbose=None,
):
    """Reader function for GDF files.

    Parameters
    ----------
    input_fname : path-like
        Path to the GDF file.
    eog : list or tuple
        Names of channels or list of indices that should be designated EOG
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    misc : list or tuple
        Names of channels or list of indices that should be designated MISC
        channels. Values should correspond to the electrodes in the file.
        Default is None.
    stim_channel : ``'auto'`` | str | list of str | int | list of int
        Defaults to ``'auto'``, which means that channels named ``'status'`` or
        ``'trigger'`` (case insensitive) are set to STIM. If str (or list of
        str), all channels matching the name(s) are set to STIM. If int (or
        list of ints), channels corresponding to the indices are set to STIM.
    exclude : list of str | str
        Channel names to exclude. This can help when reading data with
        different sampling rates to avoid unnecessary resampling. A str is
        interpreted as a regular expression.
    include : list of str | str
        Channel names to be included. A str is interpreted as a regular
        expression. 'exclude' must be empty if include is assigned.
    %(preload)s
    %(verbose)s

    Returns
    -------
    raw : instance of RawGDF
        The raw instance.
        See :class:`mne.io.Raw` for documentation of attributes and methods.

    See Also
    --------
    mne.io.read_raw_edf : Reader function for EDF and EDF+ files.
    mne.io.read_raw_bdf : Reader function for BDF files.
    mne.io.Raw : Documentation of attributes and methods of RawGDF.

    Notes
    -----
    If channels named 'status' or 'trigger' are present, they are considered as
    STIM channels by default. Use func:`mne.find_events` to parse events
    encoded in such analog stim channels.
    """
    input_fname = os.path.abspath(input_fname)
    ext = os.path.splitext(input_fname)[1][1:].lower()
    if ext != "gdf":
        raise NotImplementedError(f"Only GDF files are supported, got {ext}.")
    return RawGDF(
        input_fname=input_fname,
        eog=eog,
        misc=misc,
        stim_channel=stim_channel,
        exclude=exclude,
        preload=preload,
        include=include,
        verbose=verbose,
    )


@fill_doc
def _read_annotations_edf(annotations, ch_names=None, encoding="utf8"):
    """Annotation File Reader.

    Parameters
    ----------
    annotations : ndarray (n_chans, n_samples) | str
        Channel data in EDF+ TAL format or path to annotation file.
    ch_names : list of string
        List of channels' names.
    %(encoding_edf)s

    Returns
    -------
    annot : instance of Annotations
        The annotations.
    """
    pat = "([+-]\\d+\\.?\\d*)(\x15(\\d+\\.?\\d*))?(\x14.*?)\x14\x00"
    if isinstance(annotations, str):
        with open(annotations, "rb") as annot_file:
            triggers = re.findall(pat.encode(), annot_file.read())
            triggers = [tuple(map(lambda x: x.decode(encoding), t)) for t in triggers]
    else:
        tals = bytearray()
        annotations = np.atleast_2d(annotations)
        for chan in annotations:
            this_chan = chan.ravel()
            if this_chan.dtype == INT32:  # BDF
                this_chan = this_chan.view(dtype=UINT8)
                this_chan = this_chan.reshape(-1, 4)
                # Why only keep the first 3 bytes as BDF values
                # are stored with 24 bits (not 32)
                this_chan = this_chan[:, :3].ravel()
                # As ravel() returns a 1D array we can add all values at once
                tals.extend(this_chan)
            else:
                this_chan = chan.astype(np.int64)
                # Exploit np vectorized processing
                tals.extend(np.uint8([this_chan % 256, this_chan // 256]).flatten("F"))
        try:
            triggers = re.findall(pat, tals.decode(encoding))
        except UnicodeDecodeError as e:
            raise Exception(
                "Encountered invalid byte in at least one annotations channel."
                " You might want to try setting \"encoding='latin1'\"."
            ) from e

    events = {}
    offset = 0.0
    for k, ev in enumerate(triggers):
        onset = float(ev[0]) + offset
        duration = float(ev[2]) if ev[2] else 0
        for description in ev[3].split("\x14")[1:]:
            if description:
                if (
                    "@@" in description
                    and ch_names is not None
                    and description.split("@@")[1] in ch_names
                ):
                    description, ch_name = description.split("@@")
                    key = f"{onset}_{duration}_{description}"
                else:
                    ch_name = None
                    key = f"{onset}_{duration}_{description}"
                    if key in events:
                        key += f"_{k}"  # make key unique
                if key in events and ch_name:
                    events[key][3] += (ch_name,)
                else:
                    events[key] = [
                        onset,
                        duration,
                        description,
                        (ch_name,) if ch_name else (),
                    ]

            elif k == 0:
                # The startdate/time of a file is specified in the EDF+ header
                # fields 'startdate of recording' and 'starttime of recording'.
                # These fields must indicate the absolute second in which the
                # start of the first data record falls. So, the first TAL in
                # the first data record always starts with +0.X, indicating
                # that the first data record starts a fraction, X, of a second
                # after the startdate/time that is specified in the EDF+
                # header. If X=0, then the .X may be omitted.
                offset = -onset

    if events:
        onset, duration, description, annot_ch_names = zip(*events.values())
    else:
        onset, duration, description, annot_ch_names = list(), list(), list(), list()

    assert len(onset) == len(duration) == len(description) == len(annot_ch_names)

    return Annotations(
        onset=onset,
        duration=duration,
        description=description,
        orig_time=None,
        ch_names=annot_ch_names,
    )


def _get_annotations_gdf(edf_info, sfreq):
    onset, duration, desc = list(), list(), list()
    events = edf_info.get("events", None)
    # Annotations in GDF: events are stored as the following
    # list: `events = [n_events, pos, typ, chn, dur]` where pos is the
    # latency, dur is the duration in samples. They both are
    # numpy.ndarray
    if events is not None and events[1].shape[0] > 0:
        onset = events[1] / sfreq
        duration = events[4] / sfreq
        desc = events[2]

    return onset, duration, desc