针对pulse-transit的工具

dist/client/mne/io/kit/__init__.py

@@ -0,0 +1,8 @@
+"""KIT module for reading raw data."""
+
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+from .kit import read_raw_kit, read_epochs_kit
+from .coreg import read_mrk

dist/client/mne/io/kit/constants.py

@@ -0,0 +1,259 @@
+"""KIT constants."""
+
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+from ..._fiff.constants import FIFF
+from ...utils import BunchConst
+
+
+KIT = BunchConst()
+
+# byte values
+KIT.SHORT = 2
+KIT.INT = 4
+KIT.DOUBLE = 8
+
+# channel parameters
+KIT.CALIB_FACTOR = 1.0  # mne_manual p.272
+KIT.RANGE = 1.0  # mne_manual p.272
+KIT.UNIT_MUL = FIFF.FIFF_UNITM_NONE  # default is 0 mne_manual p.273
+KIT.GAINS = [1, 2, 5, 10, 20, 50, 100, 200]
+
+KIT.HPFS = {
+    1: (0, 1, 3, 3),
+    2: (0, 0.03, 0.1, 0.3, 1, 3, 10, 30),
+    3: (0, 0.03, 0.1, 0.3, 1, 3, 10, 30),
+    4: (0, 1, 3, 10, 30, 100, 200, 500),
+}
+KIT.LPFS = {
+    1: (10, 20, 50, 100, 200, 500, 1000, 2000),
+    2: (10, 20, 50, 100, 200, 500, 1000, 2000),
+    3: (10, 20, 50, 100, 200, 500, 1000, 10000),
+    4: (10, 30, 100, 300, 1000, 2000, 5000, 10000),
+}
+KIT.BEFS = {
+    1: (0, 50, 60, 60),
+    2: (0, 0, 0),
+    3: (0, 60, 50, 50),
+}
+
+# Map FLL-Type to filter options (high, low, band)
+KIT.FLL_SETTINGS = {
+    0: (1, 1, 1),  # Hanger Type #1
+    10: (1, 1, 1),  # Hanger Type #2
+    20: (1, 1, 1),  # Hanger Type #2
+    50: (2, 1, 1),  # Hanger Type #3
+    60: (2, 1, 1),  # Hanger Type #3
+    100: (3, 3, 3),  # Low Band Kapper Type
+    101: (1, 3, 2),  # Berlin (DC, 200 Hz, Through)
+    120: (3, 3, 3),  # Low Band Kapper Type
+    200: (4, 4, 3),  # High Band Kapper Type
+    300: (2, 2, 2),  # Kapper Type
+}
+
+# channel types
+KIT.CHANNEL_MAGNETOMETER = 1
+KIT.CHANNEL_MAGNETOMETER_REFERENCE = 0x101
+KIT.CHANNEL_AXIAL_GRADIOMETER = 2
+KIT.CHANNEL_AXIAL_GRADIOMETER_REFERENCE = 0x102
+KIT.CHANNEL_PLANAR_GRADIOMETER = 3
+KIT.CHANNEL_PLANAR_GRADIOMETER_REFERENCE = 0x103
+KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER = 4
+KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER_REFERENCE = 0x104
+KIT.CHANNEL_TRIGGER = -1
+KIT.CHANNEL_EEG = -2
+KIT.CHANNEL_ECG = -3
+KIT.CHANNEL_ETC = -4
+KIT.CHANNEL_NULL = 0
+KIT.CHANNELS_MEG = (
+    KIT.CHANNEL_MAGNETOMETER,
+    KIT.CHANNEL_MAGNETOMETER_REFERENCE,
+    KIT.CHANNEL_AXIAL_GRADIOMETER,
+    KIT.CHANNEL_AXIAL_GRADIOMETER_REFERENCE,
+    KIT.CHANNEL_PLANAR_GRADIOMETER,
+    KIT.CHANNEL_PLANAR_GRADIOMETER_REFERENCE,
+    KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER,
+    KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER_REFERENCE,
+)
+KIT.CHANNELS_REFERENCE = (
+    KIT.CHANNEL_MAGNETOMETER_REFERENCE,
+    KIT.CHANNEL_AXIAL_GRADIOMETER_REFERENCE,
+    KIT.CHANNEL_PLANAR_GRADIOMETER_REFERENCE,
+    KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER_REFERENCE,
+)
+KIT.CHANNELS_MISC = (
+    KIT.CHANNEL_TRIGGER,
+    KIT.CHANNEL_EEG,
+    KIT.CHANNEL_ECG,
+    KIT.CHANNEL_ETC,
+)
+KIT.CHANNEL_NAME_NCHAR = {
+    KIT.CHANNEL_MAGNETOMETER: 6,
+    KIT.CHANNEL_AXIAL_GRADIOMETER: 6,
+    KIT.CHANNEL_TRIGGER: 32,
+    KIT.CHANNEL_EEG: 8,
+    KIT.CHANNEL_ECG: 32,
+    KIT.CHANNEL_ETC: 32,
+}
+KIT.CH_TO_FIFF_COIL = {
+    # KIT.CHANNEL_MAGNETOMETER: FIFF.???,
+    KIT.CHANNEL_MAGNETOMETER_REFERENCE: FIFF.FIFFV_COIL_KIT_REF_MAG,
+    KIT.CHANNEL_AXIAL_GRADIOMETER: FIFF.FIFFV_COIL_KIT_GRAD,
+    # KIT.CHANNEL_AXIAL_GRADIOMETER_REFERENCE: FIFF.???,
+    # KIT.CHANNEL_PLANAR_GRADIOMETER: FIFF.???,
+    # KIT.CHANNEL_PLANAR_GRADIOMETER_REFERENCE: FIFF.???,
+    # KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER: FIFF.???,
+    # KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER_REFERENCE: FIFF.???,
+    KIT.CHANNEL_TRIGGER: FIFF.FIFFV_COIL_NONE,
+    KIT.CHANNEL_EEG: FIFF.FIFFV_COIL_EEG,
+    KIT.CHANNEL_ECG: FIFF.FIFFV_COIL_NONE,
+    KIT.CHANNEL_ETC: FIFF.FIFFV_COIL_NONE,
+    KIT.CHANNEL_NULL: FIFF.FIFFV_COIL_NONE,
+}
+KIT.CH_TO_FIFF_KIND = {
+    KIT.CHANNEL_MAGNETOMETER: FIFF.FIFFV_MEG_CH,
+    KIT.CHANNEL_MAGNETOMETER_REFERENCE: FIFF.FIFFV_REF_MEG_CH,
+    KIT.CHANNEL_AXIAL_GRADIOMETER: FIFF.FIFFV_MEG_CH,
+    KIT.CHANNEL_AXIAL_GRADIOMETER_REFERENCE: FIFF.FIFFV_REF_MEG_CH,
+    KIT.CHANNEL_PLANAR_GRADIOMETER: FIFF.FIFFV_MEG_CH,
+    KIT.CHANNEL_PLANAR_GRADIOMETER_REFERENCE: FIFF.FIFFV_REF_MEG_CH,
+    KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER: FIFF.FIFFV_MEG_CH,
+    KIT.CHANNEL_2ND_ORDER_AXIAL_GRADIOMETER_REFERENCE: FIFF.FIFFV_REF_MEG_CH,
+    KIT.CHANNEL_TRIGGER: FIFF.FIFFV_MISC_CH,
+    KIT.CHANNEL_EEG: FIFF.FIFFV_EEG_CH,
+    KIT.CHANNEL_ECG: FIFF.FIFFV_ECG_CH,
+    KIT.CHANNEL_ETC: FIFF.FIFFV_MISC_CH,
+    KIT.CHANNEL_NULL: FIFF.FIFFV_MISC_CH,
+}
+KIT.CH_LABEL = {
+    KIT.CHANNEL_TRIGGER: "TRIGGER",
+    KIT.CHANNEL_EEG: "EEG",
+    KIT.CHANNEL_ECG: "ECG",
+    KIT.CHANNEL_ETC: "MISC",
+    KIT.CHANNEL_NULL: "MISC",
+}
+
+# Acquisition modes
+KIT.CONTINUOUS = 1
+KIT.EVOKED = 2
+KIT.EPOCHS = 3
+
+# coreg constants
+KIT.DIG_POINTS = 10000
+
+# Known KIT systems
+# -----------------
+# KIT recording system is encoded in the SQD file as integer:
+KIT.SYSTEM_MQ_ADULT = 345  # Macquarie Dept of Cognitive Science, 2006 -
+KIT.SYSTEM_MQ_CHILD = 403  # Macquarie Dept of Cognitive Science, 2006 -
+KIT.SYSTEM_AS = 260  # Academia Sinica at Taiwan
+KIT.SYSTEM_AS_2008 = 261  # Academia Sinica, 2008 or 2009 -
+KIT.SYSTEM_NYU_2008 = 32  # NYU-NY, July 7, 2008 -
+KIT.SYSTEM_NYU_2009 = 33  # NYU-NY, January 24, 2009 -
+KIT.SYSTEM_NYU_2010 = 34  # NYU-NY, January 22, 2010 -
+KIT.SYSTEM_NYU_2019 = 35  # NYU-NY, September 18, 2019 -
+KIT.SYSTEM_NYUAD_2011 = 440  # NYU-AD initial launch May 20, 2011 -
+KIT.SYSTEM_NYUAD_2012 = 441  # NYU-AD more channels July 11, 2012 -
+KIT.SYSTEM_NYUAD_2014 = 442  # NYU-AD move to NYUAD campus Nov 20, 2014 -
+KIT.SYSTEM_UMD_2004 = 51  # UMD Marie Mount Hall, October 1, 2004 -
+KIT.SYSTEM_UMD_2014_07 = 52  # UMD update to 16 bit ADC, July 4, 2014 -
+KIT.SYSTEM_UMD_2014_12 = 53  # UMD December 4, 2014 -
+KIT.SYSTEM_UMD_2019_09 = 54  # UMD September 3, 2019 -
+KIT.SYSTEM_YOKOGAWA_2017_01 = 1001  # Kanazawa (until 2017)
+KIT.SYSTEM_YOKOGAWA_2018_01 = 10020  # Kanazawa (since 2018)
+KIT.SYSTEM_YOKOGAWA_2020_08 = 10021  # Kanazawa (since August 2020)
+KIT.SYSTEM_EAGLE_TECHNOLOGY_PTB_2008 = 124
+
+# Sensor layouts for plotting
+KIT_LAYOUT = {
+    KIT.SYSTEM_AS: None,
+    KIT.SYSTEM_AS_2008: "KIT-AS-2008",
+    KIT.SYSTEM_MQ_ADULT: "KIT-160",
+    KIT.SYSTEM_MQ_CHILD: "KIT-125",
+    KIT.SYSTEM_NYU_2008: "KIT-157",
+    KIT.SYSTEM_NYU_2009: "KIT-157",
+    KIT.SYSTEM_NYU_2010: "KIT-157",
+    KIT.SYSTEM_NYU_2019: None,
+    KIT.SYSTEM_NYUAD_2011: "KIT-AD",
+    KIT.SYSTEM_NYUAD_2012: "KIT-AD",
+    KIT.SYSTEM_NYUAD_2014: "KIT-AD",
+    KIT.SYSTEM_UMD_2004: None,
+    KIT.SYSTEM_UMD_2014_07: None,
+    KIT.SYSTEM_UMD_2014_12: "KIT-UMD-3",
+    KIT.SYSTEM_UMD_2019_09: None,
+    KIT.SYSTEM_YOKOGAWA_2017_01: None,
+    KIT.SYSTEM_YOKOGAWA_2018_01: None,
+    KIT.SYSTEM_YOKOGAWA_2020_08: None,
+    KIT.SYSTEM_EAGLE_TECHNOLOGY_PTB_2008: None,
+}
+# Sensor neighbor definitions
+KIT_NEIGHBORS = {
+    KIT.SYSTEM_AS: None,
+    KIT.SYSTEM_AS_2008: None,
+    KIT.SYSTEM_MQ_ADULT: None,
+    KIT.SYSTEM_MQ_CHILD: None,
+    KIT.SYSTEM_NYU_2008: "KIT-157",
+    KIT.SYSTEM_NYU_2009: "KIT-157",
+    KIT.SYSTEM_NYU_2010: "KIT-157",
+    KIT.SYSTEM_NYU_2019: "KIT-NYU-2019",
+    KIT.SYSTEM_NYUAD_2011: "KIT-208",
+    KIT.SYSTEM_NYUAD_2012: "KIT-208",
+    KIT.SYSTEM_NYUAD_2014: "KIT-208",
+    KIT.SYSTEM_UMD_2004: "KIT-UMD-1",
+    KIT.SYSTEM_UMD_2014_07: "KIT-UMD-2",
+    KIT.SYSTEM_UMD_2014_12: "KIT-UMD-3",
+    KIT.SYSTEM_UMD_2019_09: "KIT-UMD-4",
+    KIT.SYSTEM_YOKOGAWA_2017_01: None,
+    KIT.SYSTEM_YOKOGAWA_2018_01: None,
+    KIT.SYSTEM_YOKOGAWA_2020_08: None,
+    KIT.SYSTEM_EAGLE_TECHNOLOGY_PTB_2008: None,
+}
+# Names displayed in the info dict description
+KIT_SYSNAMES = {
+    KIT.SYSTEM_MQ_ADULT: "Macquarie Dept of Cognitive Science (Adult), 2006-",
+    KIT.SYSTEM_MQ_CHILD: "Macquarie Dept of Cognitive Science (Child), 2006-",
+    KIT.SYSTEM_AS: "Academia Sinica, -2008",
+    KIT.SYSTEM_AS_2008: "Academia Sinica, 2008-",
+    KIT.SYSTEM_NYU_2008: "NYU New York, 2008-9",
+    KIT.SYSTEM_NYU_2009: "NYU New York, 2009-10",
+    KIT.SYSTEM_NYU_2010: "NYU New York, 2010-",
+    KIT.SYSTEM_NYUAD_2011: "New York University Abu Dhabi, 2011-12",
+    KIT.SYSTEM_NYUAD_2012: "New York University Abu Dhabi, 2012-14",
+    KIT.SYSTEM_NYUAD_2014: "New York University Abu Dhabi, 2014-",
+    KIT.SYSTEM_UMD_2004: "University of Maryland, 2004-14",
+    KIT.SYSTEM_UMD_2014_07: "University of Maryland, 2014",
+    KIT.SYSTEM_UMD_2014_12: "University of Maryland, 2014-",
+    KIT.SYSTEM_UMD_2019_09: "University of Maryland, 2019-",
+    KIT.SYSTEM_YOKOGAWA_2017_01: "Yokogawa of Kanazawa (until 2017)",
+    KIT.SYSTEM_YOKOGAWA_2018_01: "Yokogawa of Kanazawa (since 2018)",
+    KIT.SYSTEM_YOKOGAWA_2020_08: "Yokogawa of Kanazawa (since August 2020)",
+    KIT.SYSTEM_EAGLE_TECHNOLOGY_PTB_2008: "Eagle Technology MEG (KIT/Yokogawa style) at PTB (since 2008, software upgrade in 2018)",  # noqa: E501
+}
+
+LEGACY_AMP_PARAMS = {
+    KIT.SYSTEM_NYU_2008: (5.0, 11.0),
+    KIT.SYSTEM_NYU_2009: (5.0, 11.0),
+    KIT.SYSTEM_NYU_2010: (5.0, 11.0),
+    KIT.SYSTEM_UMD_2004: (5.0, 11.0),
+}
+
+# Ones that we don't use are commented out
+KIT.DIR_INDEX_DIR = 0
+KIT.DIR_INDEX_SYSTEM = 1
+KIT.DIR_INDEX_CHANNELS = 4
+KIT.DIR_INDEX_CALIBRATION = 5
+# FLL = 6
+KIT.DIR_INDEX_AMP_FILTER = 7
+KIT.DIR_INDEX_ACQ_COND = 8
+KIT.DIR_INDEX_RAW_DATA = 9
+# AVERAGED_DATA = 10
+# MRI = 11
+KIT.DIR_INDEX_COREG = 12
+# MAGNETIC_SOURCE = 13
+# TRIGGER = 14
+# BOOKMARKS = 15
+# DIGITIZER = 25
+KIT.DIR_INDEX_DIG_POINTS = 26
+KIT.DIR_INDEX_CHPI_DATA = 29

dist/client/mne/io/kit/coreg.py

@@ -0,0 +1,233 @@
+"""Coordinate Point Extractor for KIT system."""
+
+# Authors: The MNE-Python contributors.
+# License: BSD-3-Clause
+# Copyright the MNE-Python contributors.
+
+import re
+from collections import OrderedDict
+from os import SEEK_CUR, PathLike
+from pathlib import Path
+
+import numpy as np
+
+from ..._fiff._digitization import _make_dig_points
+from ...channels.montage import (
+    _check_dig_shape,
+    read_custom_montage,
+    read_dig_polhemus_isotrak,
+    read_polhemus_fastscan,
+)
+from ...transforms import (
+    Transform,
+    als_ras_trans,
+    apply_trans,
+    get_ras_to_neuromag_trans,
+)
+from ...utils import _check_fname, _check_option, warn
+from .constants import FIFF, KIT
+
+INT32 = "<i4"
+FLOAT64 = "<f8"
+
+
+def read_mrk(fname):
+    r"""Marker Point Extraction in MEG space directly from sqd.
+
+    Parameters
+    ----------
+    fname : path-like
+        Absolute path to Marker file.
+        File formats allowed: \*.sqd, \*.mrk, \*.txt.
+
+    Returns
+    -------
+    mrk_points : ndarray, shape (n_points, 3)
+        Marker points in MEG space [m].
+    """
+    from .kit import _read_dirs
+
+    fname = Path(_check_fname(fname, "read", must_exist=True, name="mrk file"))
+    _check_option("file extension", fname.suffix, (".sqd", ".mrk", ".txt"))
+    if fname.suffix in (".sqd", ".mrk"):
+        with open(fname, "rb", buffering=0) as fid:
+            dirs = _read_dirs(fid)
+            fid.seek(dirs[KIT.DIR_INDEX_COREG]["offset"])
+            # skips match_done, meg_to_mri and mri_to_meg
+            fid.seek(KIT.INT + (2 * KIT.DOUBLE * 16), SEEK_CUR)
+            mrk_count = np.fromfile(fid, INT32, 1)[0]
+            pts = []
+            for _ in range(mrk_count):
+                # mri_type, meg_type, mri_done, meg_done
+                _, _, _, meg_done = np.fromfile(fid, INT32, 4)
+                _, meg_pts = np.fromfile(fid, FLOAT64, 6).reshape(2, 3)
+                if meg_done:
+                    pts.append(meg_pts)
+            mrk_points = np.array(pts)
+    else:
+        assert fname.suffix == ".txt"
+        mrk_points = _read_dig_kit(fname, unit="m")
+
+    # check output
+    mrk_points = np.asarray(mrk_points)
+    if mrk_points.shape != (5, 3):
+        err = f"{repr(fname)} is no marker file, shape is {mrk_points.shape}"
+        raise ValueError(err)
+    return mrk_points
+
+
+def read_sns(fname):
+    """Sensor coordinate extraction in MEG space.
+
+    Parameters
+    ----------
+    fname : path-like
+        Absolute path to sensor definition file.
+
+    Returns
+    -------
+    locs : numpy.array, shape = (n_points, 3)
+        Sensor coil location.
+    """
+    p = re.compile(
+        r"\d,[A-Za-z]*,([\.\-0-9]+),"
+        + r"([\.\-0-9]+),([\.\-0-9]+),"
+        + r"([\.\-0-9]+),([\.\-0-9]+)"
+    )
+    with open(fname) as fid:
+        locs = np.array(p.findall(fid.read()), dtype=float)
+    return locs
+
+
+def _set_dig_kit(mrk, elp, hsp, eeg, *, bad_coils=()):
+    """Add landmark points and head shape data to the KIT instance.
+
+    Digitizer data (elp and hsp) are represented in [mm] in the Polhemus
+    ALS coordinate system. This is converted to [m].
+
+    Parameters
+    ----------
+    mrk : path-like | array_like, shape (5, 3) | None
+        Marker points representing the location of the marker coils with
+        respect to the MEG Sensors, or path to a marker file.
+    elp : path-like | array_like, shape (8, 3) | None
+        Digitizer points representing the location of the fiducials and the
+        marker coils with respect to the digitized head shape, or path to a
+        file containing these points.
+    hsp : path-like | array, shape (n_points, 3) | None
+        Digitizer head shape points, or path to head shape file. If more
+        than 10`000 points are in the head shape, they are automatically
+        decimated.
+    bad_coils : list
+        Indices of bad marker coils (up to two). Bad coils will be excluded
+        when computing the device-head transformation.
+    eeg : dict
+        Ordered dict of EEG dig points.
+
+    Returns
+    -------
+    dig_points : list
+        List of digitizer points for info['dig'].
+    dev_head_t : Transform
+        A dictionary describing the device-head transformation.
+    hpi_results : list
+        The hpi results.
+    """
+    from ...coreg import _decimate_points, fit_matched_points
+
+    if isinstance(hsp, (str, Path, PathLike)):
+        hsp = _read_dig_kit(hsp)
+    n_pts = len(hsp)
+    if n_pts > KIT.DIG_POINTS:
+        hsp = _decimate_points(hsp, res=0.005)
+        n_new = len(hsp)
+        warn(
+            f"The selected head shape contained {n_pts} points, which is more than "
+            f"recommended ({KIT.DIG_POINTS}), and was automatically downsampled to "
+            f"{n_new} points. The preferred way to downsample is using FastScan."
+        )
+
+    if isinstance(elp, (str, Path, PathLike)):
+        elp_points = _read_dig_kit(elp)
+        if len(elp_points) != 8:
+            raise ValueError(
+                f"File {repr(elp)} should contain 8 points; got shape "
+                f"{elp_points.shape}."
+            )
+        elp = elp_points
+        if len(bad_coils) > 0:
+            elp = np.delete(elp, np.array(bad_coils) + 3, 0)
+    # check we have at least 3 marker coils (whether read from file or
+    # passed in directly)
+    if len(elp) not in (6, 7, 8):
+        raise ValueError(f"ELP should contain 6 ~ 8 points; got shape {elp.shape}.")
+    if isinstance(mrk, (str, Path, PathLike)):
+        mrk = read_mrk(mrk)
+        if len(bad_coils) > 0:
+            mrk = np.delete(mrk, bad_coils, 0)
+    if len(mrk) not in (3, 4, 5):
+        raise ValueError(f"MRK should contain 3 ~ 5 points; got shape {mrk.shape}.")
+
+    mrk = apply_trans(als_ras_trans, mrk)
+
+    nasion, lpa, rpa = elp[:3]
+    nmtrans = get_ras_to_neuromag_trans(nasion, lpa, rpa)
+    elp = apply_trans(nmtrans, elp)
+    hsp = apply_trans(nmtrans, hsp)
+    eeg = OrderedDict((k, apply_trans(nmtrans, p)) for k, p in eeg.items())
+
+    # device head transform
+    trans = fit_matched_points(tgt_pts=elp[3:], src_pts=mrk, out="trans")
+
+    nasion, lpa, rpa = elp[:3]
+    elp = elp[3:]
+
+    dig_points = _make_dig_points(nasion, lpa, rpa, elp, hsp, dig_ch_pos=eeg)
+    dev_head_t = Transform("meg", "head", trans)
+
+    hpi_results = [
+        dict(
+            dig_points=[
+                dict(
+                    ident=ci,
+                    r=r,
+                    kind=FIFF.FIFFV_POINT_HPI,
+                    coord_frame=FIFF.FIFFV_COORD_UNKNOWN,
+                )
+                for ci, r in enumerate(mrk)
+            ],
+            coord_trans=dev_head_t,
+        )
+    ]
+
+    return dig_points, dev_head_t, hpi_results
+
+
+def _read_dig_kit(fname, unit="auto"):
+    # Read dig points from a file and return ndarray, using FastSCAN for .txt
+    fname = _check_fname(fname, "read", must_exist=True, name="hsp or elp file")
+    assert unit in ("auto", "m", "mm")
+    _check_option("file extension", fname.suffix, (".hsp", ".elp", ".mat", ".txt"))
+    if fname.suffix == ".txt":
+        unit = "mm" if unit == "auto" else unit
+        out = read_polhemus_fastscan(fname, unit=unit, on_header_missing="ignore")
+    elif fname.suffix in (".hsp", ".elp"):
+        unit = "m" if unit == "auto" else unit
+        mon = read_dig_polhemus_isotrak(fname, unit=unit)
+        if fname.suffix == ".hsp":
+            dig = [d["r"] for d in mon.dig if d["kind"] != FIFF.FIFFV_POINT_CARDINAL]
+        else:
+            dig = [d["r"] for d in mon.dig]
+            if (
+                dig
+                and mon.dig[0]["kind"] == FIFF.FIFFV_POINT_CARDINAL
+                and mon.dig[0]["ident"] == FIFF.FIFFV_POINT_LPA
+            ):
+                # LPA, Nasion, RPA -> NLR
+                dig[:3] = [dig[1], dig[0], dig[2]]
+        out = np.array(dig, float)
+    else:
+        assert fname.suffix == ".mat"
+        out = np.array([d["r"] for d in read_custom_montage(fname).dig])
+    _check_dig_shape(out)
+    return out