artemis123.py

# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

import calendar
import datetime
import os.path as op

import numpy as np
from scipy.spatial.distance import cdist

from ..._fiff._digitization import DigPoint, _make_dig_points
from ..._fiff.constants import FIFF
from ..._fiff.meas_info import _empty_info
from ..._fiff.utils import _read_segments_file
from ...transforms import Transform, apply_trans, get_ras_to_neuromag_trans
from ...utils import _check_fname, logger, verbose, warn
from ..base import BaseRaw
from .utils import _load_mne_locs, _read_pos


@verbose
def read_raw_artemis123(
    input_fname, preload=False, verbose=None, pos_fname=None, add_head_trans=True
) -> "RawArtemis123":
    """Read Artemis123 data as raw object.

    Parameters
    ----------
    input_fname : path-like
        Path to the data file (extension ``.bin``). The header file with the
        same file name stem and an extension ``.txt`` is expected to be found
        in the same directory.
    %(preload)s
    %(verbose)s
    pos_fname : path-like | None
        If not None, load digitized head points from this file.
    add_head_trans : bool (default True)
        If True attempt to perform initial head localization. Compute initial
        device to head coordinate transform using HPI coils. If no
        HPI coils are in info['dig'] hpi coils are assumed to be in canonical
        order of fiducial points (nas, rpa, lpa).

    Returns
    -------
    raw : instance of Raw
        A Raw object containing the data.

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods.
    """
    return RawArtemis123(
        input_fname,
        preload=preload,
        verbose=verbose,
        pos_fname=pos_fname,
        add_head_trans=add_head_trans,
    )


def _get_artemis123_info(fname, pos_fname=None):
    """Generate info struct from artemis123 header file."""
    fname = op.splitext(fname)[0]
    header = fname + ".txt"

    logger.info("Reading header...")

    # key names for artemis channel info...
    chan_keys = [
        "name",
        "scaling",
        "FLL_Gain",
        "FLL_Mode",
        "FLL_HighPass",
        "FLL_AutoReset",
        "FLL_ResetLock",
    ]

    header_info = dict()
    header_info["filter_hist"] = []
    header_info["comments"] = ""
    header_info["channels"] = []

    with open(header) as fid:
        # section flag
        # 0 - None
        # 1 - main header
        # 2 - channel header
        # 3 - comments
        # 4 - length
        # 5 - filtering History
        sectionFlag = 0
        for line in fid:
            # skip emptylines or header line for channel info
            if (not line.strip()) or (sectionFlag == 2 and line.startswith("DAQ Map")):
                continue

            # set sectionFlag
            if line.startswith("<end"):
                sectionFlag = 0
            elif line.startswith("<start main header>"):
                sectionFlag = 1
            elif line.startswith("<start per channel header>"):
                sectionFlag = 2
            elif line.startswith("<start comments>"):
                sectionFlag = 3
            elif line.startswith("<start length>"):
                sectionFlag = 4
            elif line.startswith("<start filtering history>"):
                sectionFlag = 5
            else:
                # parse header info lines
                # part of main header - lines are name value pairs
                if sectionFlag == 1:
                    values = line.strip().split("\t")
                    if len(values) == 1:
                        values.append("")
                    header_info[values[0]] = values[1]
                # part of channel header - lines are Channel Info
                elif sectionFlag == 2:
                    values = line.strip().split("\t")
                    if len(values) != 7:
                        raise OSError(
                            f"Error parsing line \n\t:{line}\nfrom file {header}"
                        )
                    tmp = dict()
                    for k, v in zip(chan_keys, values):
                        tmp[k] = v
                    header_info["channels"].append(tmp)
                elif sectionFlag == 3:
                    header_info["comments"] = f"{header_info['comments']}{line.strip()}"
                elif sectionFlag == 4:
                    header_info["num_samples"] = int(line.strip())
                elif sectionFlag == 5:
                    header_info["filter_hist"].append(line.strip())

    for k in [
        "Temporal Filter Active?",
        "Decimation Active?",
        "Spatial Filter Active?",
    ]:
        if header_info[k] != "FALSE":
            warn(f"{k} - set to but is not supported")
    if header_info["filter_hist"]:
        warn("Non-Empty Filter history found, BUT is not supported")

    # build mne info struct
    info = _empty_info(float(header_info["DAQ Sample Rate"]))

    # Attempt to get time/date from fname
    # Artemis123 files saved from the scanner observe the following
    # naming convention 'Artemis_Data_YYYY-MM-DD-HHh-MMm_[chosen by user].bin'
    try:
        date = datetime.datetime.strptime(
            op.basename(fname).split("_")[2], "%Y-%m-%d-%Hh-%Mm"
        )
        meas_date = (calendar.timegm(date.utctimetuple()), 0)
    except Exception:
        meas_date = None

    # build subject info must be an integer (as per FIFF)
    try:
        subject_info = {"id": int(header_info["Subject ID"])}
    except ValueError:
        subject_info = {"id": 0}

    # build description
    desc = ""
    for k in ["Purpose", "Notes"]:
        desc += f"{k} : {header_info[k]}\n"
    desc += f"Comments : {header_info['comments']}"

    info.update(
        {
            "meas_date": meas_date,
            "description": desc,
            "subject_info": subject_info,
            "proj_name": header_info["Project Name"],
        }
    )

    # Channel Names by type
    ref_mag_names = ["REF_001", "REF_002", "REF_003", "REF_004", "REF_005", "REF_006"]

    ref_grad_names = ["REF_007", "REF_008", "REF_009", "REF_010", "REF_011", "REF_012"]

    # load mne loc dictionary
    loc_dict = _load_mne_locs()
    info["chs"] = []
    bads = []

    for i, chan in enumerate(header_info["channels"]):
        # build chs struct
        t = {
            "cal": float(chan["scaling"]),
            "ch_name": chan["name"],
            "logno": i + 1,
            "scanno": i + 1,
            "range": 1.0,
            "unit_mul": FIFF.FIFF_UNITM_NONE,
            "coord_frame": FIFF.FIFFV_COORD_DEVICE,
        }
        # REF_018 has a zero cal which can cause problems. Let's set it to
        # a value of another ref channel to make writers/readers happy.
        if t["cal"] == 0:
            t["cal"] = 4.716e-10
            bads.append(t["ch_name"])
        t["loc"] = loc_dict.get(chan["name"], np.zeros(12))

        if chan["name"].startswith("MEG"):
            t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_GRAD
            t["kind"] = FIFF.FIFFV_MEG_CH
            # While gradiometer units are T/m, the meg sensors referred to as
            # gradiometers report the field difference between 2 pick-up coils.
            # Therefore the units of the measurements should be T
            # *AND* the baseline (difference between pickup coils)
            # should not be used in leadfield / forwardfield computations.
            t["unit"] = FIFF.FIFF_UNIT_T
            t["unit_mul"] = FIFF.FIFF_UNITM_F

        # 3 axis reference magnetometers
        elif chan["name"] in ref_mag_names:
            t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_REF_MAG
            t["kind"] = FIFF.FIFFV_REF_MEG_CH
            t["unit"] = FIFF.FIFF_UNIT_T
            t["unit_mul"] = FIFF.FIFF_UNITM_F

        # reference gradiometers
        elif chan["name"] in ref_grad_names:
            t["coil_type"] = FIFF.FIFFV_COIL_ARTEMIS123_REF_GRAD
            t["kind"] = FIFF.FIFFV_REF_MEG_CH
            # While gradiometer units are T/m, the meg sensors referred to as
            # gradiometers report the field difference between 2 pick-up coils.
            # Therefore the units of the measurements should be T
            # *AND* the baseline (difference between pickup coils)
            # should not be used in leadfield / forwardfield computations.
            t["unit"] = FIFF.FIFF_UNIT_T
            t["unit_mul"] = FIFF.FIFF_UNITM_F

        # other reference channels are unplugged and should be ignored.
        elif chan["name"].startswith("REF"):
            t["coil_type"] = FIFF.FIFFV_COIL_NONE
            t["kind"] = FIFF.FIFFV_MISC_CH
            t["unit"] = FIFF.FIFF_UNIT_V
            bads.append(t["ch_name"])

        elif chan["name"].startswith(("AUX", "TRG", "MIO")):
            t["coil_type"] = FIFF.FIFFV_COIL_NONE
            t["unit"] = FIFF.FIFF_UNIT_V
            if chan["name"].startswith("TRG"):
                t["kind"] = FIFF.FIFFV_STIM_CH
            else:
                t["kind"] = FIFF.FIFFV_MISC_CH
        else:
            raise ValueError(
                f'Channel does not match expected channel Types:"{chan["name"]}"'
            )

        # incorporate multiplier (unit_mul) into calibration
        t["cal"] *= 10 ** t["unit_mul"]
        t["unit_mul"] = FIFF.FIFF_UNITM_NONE

        # append this channel to the info
        info["chs"].append(t)
        if chan["FLL_ResetLock"] == "TRUE":
            bads.append(t["ch_name"])

    # HPI information
    # print header_info.keys()
    hpi_sub = dict()
    # Don't know what event_channel is don't think we have it HPIs are either
    # always on or always off.
    # hpi_sub['event_channel'] = ???
    hpi_sub["hpi_coils"] = [dict(), dict(), dict(), dict()]
    hpi_coils = [dict(), dict(), dict(), dict()]
    drive_channels = ["MIO_001", "MIO_003", "MIO_009", "MIO_011"]
    key_base = "Head Tracking %s %d"

    # set default HPI frequencies
    if info["sfreq"] == 1000:
        default_freqs = [140, 150, 160, 40]
    else:
        default_freqs = [700, 750, 800, 40]

    for i in range(4):
        # build coil structure
        hpi_coils[i]["number"] = i + 1
        hpi_coils[i]["drive_chan"] = drive_channels[i]
        this_freq = header_info.pop(key_base % ("Frequency", i + 1), default_freqs[i])
        hpi_coils[i]["coil_freq"] = this_freq

        # check if coil is on
        if header_info[key_base % ("Channel", i + 1)] == "OFF":
            hpi_sub["hpi_coils"][i]["event_bits"] = [0]
        else:
            hpi_sub["hpi_coils"][i]["event_bits"] = [256]

    info["hpi_subsystem"] = hpi_sub
    info["hpi_meas"] = [{"hpi_coils": hpi_coils}]
    # read in digitized points if supplied
    if pos_fname is not None:
        info["dig"] = _read_pos(pos_fname)
    else:
        info["dig"] = []

    info._unlocked = False
    info._update_redundant()
    # reduce info['bads'] to unique set
    info["bads"] = list(set(bads))
    del bads
    return info, header_info


class RawArtemis123(BaseRaw):
    """Raw object from Artemis123 file.

    Parameters
    ----------
    input_fname : path-like
        Path to the Artemis123 data file (ending in ``'.bin'``).
    %(preload)s
    %(verbose)s

    See Also
    --------
    mne.io.Raw : Documentation of attributes and methods.
    """

    @verbose
    def __init__(
        self,
        input_fname,
        preload=False,
        verbose=None,
        pos_fname=None,
        add_head_trans=True,
    ):
        from ...chpi import (
            _fit_coil_order_dev_head_trans,
            compute_chpi_amplitudes,
            compute_chpi_locs,
        )

        input_fname = str(_check_fname(input_fname, "read", True, "input_fname"))
        fname, ext = op.splitext(input_fname)
        if ext == ".txt":
            input_fname = fname + ".bin"
        elif ext != ".bin":
            raise RuntimeError(
                'Valid artemis123 files must end in "txt"' + ' or ".bin".'
            )

        if not op.exists(input_fname):
            raise RuntimeError(f"{input_fname} - Not Found")

        info, header_info = _get_artemis123_info(input_fname, pos_fname=pos_fname)

        last_samps = [header_info.get("num_samples", 1) - 1]

        super().__init__(
            info,
            preload,
            filenames=[input_fname],
            raw_extras=[header_info],
            last_samps=last_samps,
            orig_format="single",
            verbose=verbose,
        )

        if add_head_trans:
            n_hpis = 0
            for d in info["hpi_subsystem"]["hpi_coils"]:
                if d["event_bits"] == [256]:
                    n_hpis += 1
            if n_hpis < 3:
                warn(
                    f"{n_hpis:d} HPIs active. At least 3 needed to perform"
                    "head localization\n *NO* head localization performed"
                )
            else:
                # Localized HPIs using the 1st 250 milliseconds of data.
                with info._unlock():
                    info["hpi_results"] = [
                        dict(
                            dig_points=[
                                dict(
                                    r=np.zeros(3),
                                    coord_frame=FIFF.FIFFV_COORD_DEVICE,
                                    ident=ii + 1,
                                )
                                for ii in range(n_hpis)
                            ],
                            coord_trans=Transform("meg", "head"),
                        )
                    ]
                coil_amplitudes = compute_chpi_amplitudes(
                    self, tmin=0, tmax=0.25, t_window=0.25, t_step_min=0.25
                )
                assert len(coil_amplitudes["times"]) == 1
                coil_locs = compute_chpi_locs(self.info, coil_amplitudes)
                with info._unlock():
                    info["hpi_results"] = None
                hpi_g = coil_locs["gofs"][0]
                hpi_dev = coil_locs["rrs"][0]

                # only use HPI coils with localizaton goodness_of_fit > 0.98
                bad_idx = []
                for i, g in enumerate(hpi_g):
                    msg = f"HPI coil {i + 1} - location goodness of fit ({g:0.3f})"
                    if g < 0.98:
                        bad_idx.append(i)
                        msg += " *Removed from coregistration*"
                    logger.info(msg)
                hpi_dev = np.delete(hpi_dev, bad_idx, axis=0)
                hpi_g = np.delete(hpi_g, bad_idx, axis=0)

                if pos_fname is not None:
                    # Digitized HPI points are needed.
                    hpi_head = np.array(
                        [
                            d["r"]
                            for d in self.info.get("dig", [])
                            if d["kind"] == FIFF.FIFFV_POINT_HPI
                        ]
                    )

                    if len(hpi_head) != len(hpi_dev):
                        raise RuntimeError(
                            f"number of digitized ({len(hpi_head)}) and active "
                            f"({len(hpi_dev)}) HPI coils are not the same."
                        )

                    # compute initial head to dev transform and hpi ordering
                    head_to_dev_t, order, trans_g = _fit_coil_order_dev_head_trans(
                        hpi_dev, hpi_head
                    )

                    # set the device to head transform
                    self.info["dev_head_t"] = Transform(
                        FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_COORD_HEAD, head_to_dev_t
                    )

                    # add hpi_meg_dev to dig...
                    for idx, point in enumerate(hpi_dev):
                        d = {
                            "r": point,
                            "ident": idx + 1,
                            "kind": FIFF.FIFFV_POINT_HPI,
                            "coord_frame": FIFF.FIFFV_COORD_DEVICE,
                        }
                        self.info["dig"].append(DigPoint(d))

                    dig_dists = cdist(hpi_head[order], hpi_head[order])
                    dev_dists = cdist(hpi_dev, hpi_dev)
                    tmp_dists = np.abs(dig_dists - dev_dists)
                    dist_limit = tmp_dists.max() * 1.1

                    logger.info(
                        "HPI-Dig corrregsitration\n"
                        f"\tGOF : {trans_g:0.3f}\n"
                        f"\tMax Coil Error : {100 * tmp_dists.max():0.3f} cm\n"
                    )

                else:
                    logger.info("Assuming Cardinal HPIs")
                    nas = hpi_dev[0]
                    lpa = hpi_dev[2]
                    rpa = hpi_dev[1]
                    t = get_ras_to_neuromag_trans(nas, lpa, rpa)
                    with self.info._unlock():
                        self.info["dev_head_t"] = Transform(
                            FIFF.FIFFV_COORD_DEVICE, FIFF.FIFFV_COORD_HEAD, t
                        )

                    # transform fiducial points
                    nas = apply_trans(t, nas)
                    lpa = apply_trans(t, lpa)
                    rpa = apply_trans(t, rpa)

                    hpi = apply_trans(self.info["dev_head_t"], hpi_dev)
                    with self.info._unlock():
                        self.info["dig"] = _make_dig_points(
                            nasion=nas, lpa=lpa, rpa=rpa, hpi=hpi
                        )
                    order = np.array([0, 1, 2])
                    dist_limit = 0.005

                # fill in hpi_results
                hpi_result = dict()

                # add HPI points in device coords...
                dig = []
                for idx, point in enumerate(hpi_dev):
                    dig.append(
                        {
                            "r": point,
                            "ident": idx + 1,
                            "kind": FIFF.FIFFV_POINT_HPI,
                            "coord_frame": FIFF.FIFFV_COORD_DEVICE,
                        }
                    )
                hpi_result["dig_points"] = dig

                # attach Transform
                hpi_result["coord_trans"] = self.info["dev_head_t"]

                # 1 based indexing
                hpi_result["order"] = order + 1
                hpi_result["used"] = np.arange(3) + 1
                hpi_result["dist_limit"] = dist_limit
                hpi_result["good_limit"] = 0.98

                # Warn for large discrepancies between digitized and fit
                # cHPI locations
                if hpi_result["dist_limit"] > 0.005:
                    warn(
                        "Large difference between digitized geometry"
                        " and HPI geometry. Max coil to coil difference"
                        f" is {100.0 * tmp_dists.max():0.2f} cm\n"
                        "beware of *POOR* head localization"
                    )

                # store it
                with self.info._unlock():
                    self.info["hpi_results"] = [hpi_result]

    def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
        """Read a chunk of raw data."""
        _read_segments_file(self, data, idx, fi, start, stop, cals, mult, dtype=">f4")