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tyler
2025-08-19 09:13:22 -07:00
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#
# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
import re
from collections import namedtuple
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
from ..._fiff._digitization import _make_dig_points
from ..._fiff.constants import FIFF
from ..._fiff.meas_info import create_info
from ..._fiff.tag import _coil_trans_to_loc
from ..._fiff.utils import _mult_cal_one, _read_segments_file
from ...annotations import Annotations
from ...surface import _normal_orth
from ...transforms import (
Transform,
_angle_between_quats,
apply_trans,
combine_transforms,
get_ras_to_neuromag_trans,
invert_transform,
rot_to_quat,
)
from ...utils import _check_fname, check_fname, logger, verbose
from ..base import BaseRaw
from ..ctf.trans import _quaternion_align
FILE_EXTENSIONS = {
"Curry 7": {
"info": ".dap",
"data": ".dat",
"labels": ".rs3",
"events_cef": ".cef",
"events_ceo": ".ceo",
"hpi": ".hpi",
},
"Curry 8": {
"info": ".cdt.dpa",
"data": ".cdt",
"labels": ".cdt.dpa",
"events_cef": ".cdt.cef",
"events_ceo": ".cdt.ceo",
"hpi": ".cdt.hpi",
},
}
CHANTYPES = {"meg": "_MAG1", "eeg": "", "misc": "_OTHERS"}
FIFFV_CHANTYPES = {
"meg": FIFF.FIFFV_MEG_CH,
"eeg": FIFF.FIFFV_EEG_CH,
"misc": FIFF.FIFFV_MISC_CH,
}
FIFFV_COILTYPES = {
"meg": FIFF.FIFFV_COIL_CTF_GRAD,
"eeg": FIFF.FIFFV_COIL_EEG,
"misc": FIFF.FIFFV_COIL_NONE,
}
SI_UNITS = dict(V=FIFF.FIFF_UNIT_V, T=FIFF.FIFF_UNIT_T)
SI_UNIT_SCALE = dict(c=1e-2, m=1e-3, u=1e-6, µ=1e-6, n=1e-9, p=1e-12, f=1e-15)
CurryParameters = namedtuple(
"CurryParameters",
"n_samples, sfreq, is_ascii, unit_dict, n_chans, dt_start, chanidx_in_file",
)
def _get_curry_version(file_extension):
"""Check out the curry file version."""
return "Curry 8" if "cdt" in file_extension else "Curry 7"
def _get_curry_file_structure(fname, required=()):
"""Store paths to a dict and check for required files."""
_msg = (
"The following required files cannot be found: {0}.\nPlease make "
"sure all required files are located in the same directory as {1}."
)
fname = Path(_check_fname(fname, "read", True, "fname"))
# we don't use os.path.splitext to also handle extensions like .cdt.dpa
# this won't handle a dot in the filename, but it should handle it in
# the parent directories
fname_base = fname.name.split(".", maxsplit=1)[0]
ext = fname.name[len(fname_base) :]
fname_base = str(fname)
fname_base = fname_base[: len(fname_base) - len(ext)]
del fname
version = _get_curry_version(ext)
my_curry = dict()
for key in ("info", "data", "labels", "events_cef", "events_ceo", "hpi"):
fname = fname_base + FILE_EXTENSIONS[version][key]
if op.isfile(fname):
_key = "events" if key.startswith("events") else key
my_curry[_key] = fname
missing = [field for field in required if field not in my_curry]
if missing:
raise FileNotFoundError(_msg.format(np.unique(missing), fname))
return my_curry
def _read_curry_lines(fname, regex_list):
"""Read through the lines of a curry parameter files and save data.
Parameters
----------
fname : path-like
Path to a curry file.
regex_list : list of str
A list of strings or regular expressions to search within the file.
Each element `regex` in `regex_list` must be formulated so that
`regex + " START_LIST"` initiates the start and `regex + " END_LIST"`
initiates the end of the elements that should be saved.
Returns
-------
data_dict : dict
A dictionary containing the extracted data. For each element `regex`
in `regex_list` a dictionary key `data_dict[regex]` is created, which
contains a list of the according data.
"""
save_lines = {}
data_dict = {}
for regex in regex_list:
save_lines[regex] = False
data_dict[regex] = []
with open(fname) as fid:
for line in fid:
for regex in regex_list:
if re.match(regex + " END_LIST", line):
save_lines[regex] = False
if save_lines[regex] and line != "\n":
result = line.replace("\n", "")
if "\t" in result:
result = result.split("\t")
data_dict[regex].append(result)
if re.match(regex + " START_LIST", line):
save_lines[regex] = True
return data_dict
def _read_curry_parameters(fname):
"""Extract Curry params from a Curry info file."""
_msg_match = (
"The sampling frequency and the time steps extracted from "
"the parameter file do not match."
)
_msg_invalid = "sfreq must be greater than 0. Got sfreq = {0}"
var_names = [
"NumSamples",
"SampleFreqHz",
"DataFormat",
"SampleTimeUsec",
"NumChannels",
"StartYear",
"StartMonth",
"StartDay",
"StartHour",
"StartMin",
"StartSec",
"StartMillisec",
"NUM_SAMPLES",
"SAMPLE_FREQ_HZ",
"DATA_FORMAT",
"SAMPLE_TIME_USEC",
"NUM_CHANNELS",
"START_YEAR",
"START_MONTH",
"START_DAY",
"START_HOUR",
"START_MIN",
"START_SEC",
"START_MILLISEC",
]
param_dict = dict()
unit_dict = dict()
with open(fname) as fid:
for line in iter(fid):
if any(var_name in line for var_name in var_names):
key, val = line.replace(" ", "").replace("\n", "").split("=")
param_dict[key.lower().replace("_", "")] = val
for key, type_ in CHANTYPES.items():
if f"DEVICE_PARAMETERS{type_} START" in line:
data_unit = next(fid)
unit_dict[key] = (
data_unit.replace(" ", "").replace("\n", "").split("=")[-1]
)
# look for CHAN_IN_FILE sections, which may or may not exist; issue #8391
types = ["meg", "eeg", "misc"]
chanidx_in_file = _read_curry_lines(
fname, ["CHAN_IN_FILE" + CHANTYPES[key] for key in types]
)
n_samples = int(param_dict["numsamples"])
sfreq = float(param_dict["samplefreqhz"])
time_step = float(param_dict["sampletimeusec"]) * 1e-6
is_ascii = param_dict["dataformat"] == "ASCII"
n_channels = int(param_dict["numchannels"])
try:
dt_start = datetime(
int(param_dict["startyear"]),
int(param_dict["startmonth"]),
int(param_dict["startday"]),
int(param_dict["starthour"]),
int(param_dict["startmin"]),
int(param_dict["startsec"]),
int(param_dict["startmillisec"]) * 1000,
timezone.utc,
)
# Note that the time zone information is not stored in the Curry info
# file, and it seems the start time info is in the local timezone
# of the acquisition system (which is unknown); therefore, just set
# the timezone to be UTC. If the user knows otherwise, they can
# change it later. (Some Curry files might include StartOffsetUTCMin,
# but its presence is unpredictable, so we won't rely on it.)
except (ValueError, KeyError):
dt_start = None # if missing keywords or illegal values, don't set
if time_step == 0:
true_sfreq = sfreq
elif sfreq == 0:
true_sfreq = 1 / time_step
elif not np.isclose(sfreq, 1 / time_step):
raise ValueError(_msg_match)
else: # they're equal and != 0
true_sfreq = sfreq
if true_sfreq <= 0:
raise ValueError(_msg_invalid.format(true_sfreq))
return CurryParameters(
n_samples,
true_sfreq,
is_ascii,
unit_dict,
n_channels,
dt_start,
chanidx_in_file,
)
def _read_curry_info(curry_paths):
"""Extract info from curry parameter files."""
curry_params = _read_curry_parameters(curry_paths["info"])
R = np.eye(4)
R[[0, 1], [0, 1]] = -1 # rotate 180 deg
# shift down and back
# (chosen by eyeballing to make the CTF helmet look roughly correct)
R[:3, 3] = [0.0, -0.015, -0.12]
curry_dev_dev_t = Transform("ctf_meg", "meg", R)
# read labels from label files
label_fname = curry_paths["labels"]
types = ["meg", "eeg", "misc"]
labels = _read_curry_lines(
label_fname, ["LABELS" + CHANTYPES[key] for key in types]
)
sensors = _read_curry_lines(
label_fname, ["SENSORS" + CHANTYPES[key] for key in types]
)
normals = _read_curry_lines(
label_fname, ["NORMALS" + CHANTYPES[key] for key in types]
)
assert len(labels) == len(sensors) == len(normals)
all_chans = list()
dig_ch_pos = dict()
for key in ["meg", "eeg", "misc"]:
chanidx_is_explicit = (
len(curry_params.chanidx_in_file["CHAN_IN_FILE" + CHANTYPES[key]]) > 0
) # channel index
# position in the datafile may or may not be explicitly declared,
# based on the CHAN_IN_FILE section in info file
for ind, chan in enumerate(labels["LABELS" + CHANTYPES[key]]):
chanidx = len(all_chans) + 1 # by default, just assume the
# channel index in the datafile is in order of the channel
# names as we found them in the labels file
if chanidx_is_explicit: # but, if explicitly declared, use
# that index number
chanidx = int(
curry_params.chanidx_in_file["CHAN_IN_FILE" + CHANTYPES[key]][ind]
)
if chanidx <= 0: # if chanidx was explicitly declared to be ' 0',
# it means the channel is not actually saved in the data file
# (e.g. the "Ref" channel), so don't add it to our list.
# Git issue #8391
continue
ch = {
"ch_name": chan,
"unit": curry_params.unit_dict[key],
"kind": FIFFV_CHANTYPES[key],
"coil_type": FIFFV_COILTYPES[key],
"ch_idx": chanidx,
}
if key == "eeg":
loc = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
# XXX just the sensor, where is ref (next 3)?
assert loc.shape == (3,)
loc /= 1000.0 # to meters
loc = np.concatenate([loc, np.zeros(9)])
ch["loc"] = loc
# XXX need to check/ensure this
ch["coord_frame"] = FIFF.FIFFV_COORD_HEAD
dig_ch_pos[chan] = loc[:3]
elif key == "meg":
pos = np.array(sensors["SENSORS" + CHANTYPES[key]][ind], float)
pos /= 1000.0 # to meters
pos = pos[:3] # just the inner coil
pos = apply_trans(curry_dev_dev_t, pos)
nn = np.array(normals["NORMALS" + CHANTYPES[key]][ind], float)
assert np.isclose(np.linalg.norm(nn), 1.0, atol=1e-4)
nn /= np.linalg.norm(nn)
nn = apply_trans(curry_dev_dev_t, nn, move=False)
trans = np.eye(4)
trans[:3, 3] = pos
trans[:3, :3] = _normal_orth(nn).T
ch["loc"] = _coil_trans_to_loc(trans)
ch["coord_frame"] = FIFF.FIFFV_COORD_DEVICE
all_chans.append(ch)
dig = _make_dig_points(
dig_ch_pos=dig_ch_pos, coord_frame="head", add_missing_fiducials=True
)
del dig_ch_pos
ch_count = len(all_chans)
assert ch_count == curry_params.n_chans # ensure that we have assembled
# the same number of channels as declared in the info (.DAP) file in the
# DATA_PARAMETERS section. Git issue #8391
# sort the channels to assure they are in the order that matches how
# recorded in the datafile. In general they most likely are already in
# the correct order, but if the channel index in the data file was
# explicitly declared we might as well use it.
all_chans = sorted(all_chans, key=lambda ch: ch["ch_idx"])
ch_names = [chan["ch_name"] for chan in all_chans]
info = create_info(ch_names, curry_params.sfreq)
with info._unlock():
info["meas_date"] = curry_params.dt_start # for Git issue #8398
info["dig"] = dig
_make_trans_dig(curry_paths, info, curry_dev_dev_t)
for ind, ch_dict in enumerate(info["chs"]):
all_chans[ind].pop("ch_idx")
ch_dict.update(all_chans[ind])
assert ch_dict["loc"].shape == (12,)
ch_dict["unit"] = SI_UNITS[all_chans[ind]["unit"][1]]
ch_dict["cal"] = SI_UNIT_SCALE[all_chans[ind]["unit"][0]]
return info, curry_params.n_samples, curry_params.is_ascii
_card_dict = {
"Left ear": FIFF.FIFFV_POINT_LPA,
"Nasion": FIFF.FIFFV_POINT_NASION,
"Right ear": FIFF.FIFFV_POINT_RPA,
}
def _make_trans_dig(curry_paths, info, curry_dev_dev_t):
# Coordinate frame transformations and definitions
no_msg = "Leaving device<->head transform as None"
info["dev_head_t"] = None
label_fname = curry_paths["labels"]
key = "LANDMARKS" + CHANTYPES["meg"]
lm = _read_curry_lines(label_fname, [key])[key]
lm = np.array(lm, float)
lm.shape = (-1, 3)
if len(lm) == 0:
# no dig
logger.info(no_msg + " (no landmarks found)")
return
lm /= 1000.0
key = "LM_REMARKS" + CHANTYPES["meg"]
remarks = _read_curry_lines(label_fname, [key])[key]
assert len(remarks) == len(lm)
with info._unlock():
info["dig"] = list()
cards = dict()
for remark, r in zip(remarks, lm):
kind = ident = None
if remark in _card_dict:
kind = FIFF.FIFFV_POINT_CARDINAL
ident = _card_dict[remark]
cards[ident] = r
elif remark.startswith("HPI"):
kind = FIFF.FIFFV_POINT_HPI
ident = int(remark[3:]) - 1
if kind is not None:
info["dig"].append(
dict(kind=kind, ident=ident, r=r, coord_frame=FIFF.FIFFV_COORD_UNKNOWN)
)
with info._unlock():
info["dig"].sort(key=lambda x: (x["kind"], x["ident"]))
has_cards = len(cards) == 3
has_hpi = "hpi" in curry_paths
if has_cards and has_hpi: # have all three
logger.info("Composing device<->head transformation from dig points")
hpi_u = np.array(
[d["r"] for d in info["dig"] if d["kind"] == FIFF.FIFFV_POINT_HPI], float
)
hpi_c = np.ascontiguousarray(_first_hpi(curry_paths["hpi"])[: len(hpi_u), 1:4])
unknown_curry_t = _quaternion_align("unknown", "ctf_meg", hpi_u, hpi_c, 1e-2)
angle = np.rad2deg(
_angle_between_quats(
np.zeros(3), rot_to_quat(unknown_curry_t["trans"][:3, :3])
)
)
dist = 1000 * np.linalg.norm(unknown_curry_t["trans"][:3, 3])
logger.info(f" Fit a {angle:0.1f}° rotation, {dist:0.1f} mm translation")
unknown_dev_t = combine_transforms(
unknown_curry_t, curry_dev_dev_t, "unknown", "meg"
)
unknown_head_t = Transform(
"unknown",
"head",
get_ras_to_neuromag_trans(
*(
cards[key]
for key in (
FIFF.FIFFV_POINT_NASION,
FIFF.FIFFV_POINT_LPA,
FIFF.FIFFV_POINT_RPA,
)
)
),
)
with info._unlock():
info["dev_head_t"] = combine_transforms(
invert_transform(unknown_dev_t), unknown_head_t, "meg", "head"
)
for d in info["dig"]:
d.update(
coord_frame=FIFF.FIFFV_COORD_HEAD,
r=apply_trans(unknown_head_t, d["r"]),
)
else:
if has_cards:
no_msg += " (no .hpi file found)"
elif has_hpi:
no_msg += " (not all cardinal points found)"
else:
no_msg += " (neither cardinal points nor .hpi file found)"
logger.info(no_msg)
def _first_hpi(fname):
# Get the first HPI result
with open(fname) as fid:
for line in fid:
line = line.strip()
if any(x in line for x in ("FileVersion", "NumCoils")) or not line:
continue
hpi = np.array(line.split(), float)
break
else:
raise RuntimeError(f"Could not find valid HPI in {fname}")
# t is the first entry
assert hpi.ndim == 1
hpi = hpi[1:]
hpi.shape = (-1, 5)
hpi /= 1000.0
return hpi
def _read_events_curry(fname):
"""Read events from Curry event files.
Parameters
----------
fname : path-like
Path to a curry event file with extensions .cef, .ceo,
.cdt.cef, or .cdt.ceo
Returns
-------
events : ndarray, shape (n_events, 3)
The array of events.
"""
check_fname(
fname,
"curry event",
(".cef", ".ceo", ".cdt.cef", ".cdt.ceo"),
endings_err=(".cef", ".ceo", ".cdt.cef", ".cdt.ceo"),
)
events_dict = _read_curry_lines(fname, ["NUMBER_LIST"])
# The first 3 column seem to contain the event information
curry_events = np.array(events_dict["NUMBER_LIST"], dtype=int)[:, 0:3]
return curry_events
def _read_annotations_curry(fname, sfreq="auto"):
r"""Read events from Curry event files.
Parameters
----------
fname : str
The filename.
sfreq : float | 'auto'
The sampling frequency in the file. If set to 'auto' then the
``sfreq`` is taken from the respective info file of the same name with
according file extension (\*.dap for Curry 7; \*.cdt.dpa for Curry8).
So data.cef looks in data.dap and data.cdt.cef looks in data.cdt.dpa.
Returns
-------
annot : instance of Annotations | None
The annotations.
"""
required = ["events", "info"] if sfreq == "auto" else ["events"]
curry_paths = _get_curry_file_structure(fname, required)
events = _read_events_curry(curry_paths["events"])
if sfreq == "auto":
sfreq = _read_curry_parameters(curry_paths["info"]).sfreq
onset = events[:, 0] / sfreq
duration = np.zeros(events.shape[0])
description = events[:, 2]
return Annotations(onset, duration, description)
@verbose
def read_raw_curry(fname, preload=False, verbose=None) -> "RawCurry":
"""Read raw data from Curry files.
Parameters
----------
fname : path-like
Path to a curry file with extensions ``.dat``, ``.dap``, ``.rs3``,
``.cdt``, ``.cdt.dpa``, ``.cdt.cef`` or ``.cef``.
%(preload)s
%(verbose)s
Returns
-------
raw : instance of RawCurry
A Raw object containing Curry data.
See :class:`mne.io.Raw` for documentation of attributes and methods.
See Also
--------
mne.io.Raw : Documentation of attributes and methods of RawCurry.
"""
return RawCurry(fname, preload, verbose)
class RawCurry(BaseRaw):
"""Raw object from Curry file.
Parameters
----------
fname : path-like
Path to a curry file with extensions ``.dat``, ``.dap``, ``.rs3``,
``.cdt``, ``.cdt.dpa``, ``.cdt.cef`` or ``.cef``.
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attributes and methods.
"""
@verbose
def __init__(self, fname, preload=False, verbose=None):
curry_paths = _get_curry_file_structure(
fname, required=["info", "data", "labels"]
)
data_fname = op.abspath(curry_paths["data"])
info, n_samples, is_ascii = _read_curry_info(curry_paths)
last_samps = [n_samples - 1]
raw_extras = dict(is_ascii=is_ascii)
super().__init__(
info,
preload,
filenames=[data_fname],
last_samps=last_samps,
orig_format="int",
raw_extras=[raw_extras],
verbose=verbose,
)
if "events" in curry_paths:
logger.info(
"Event file found. Extracting Annotations from "
f"{curry_paths['events']}..."
)
annots = _read_annotations_curry(
curry_paths["events"], sfreq=self.info["sfreq"]
)
self.set_annotations(annots)
else:
logger.info("Event file not found. No Annotations set.")
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a chunk of raw data."""
if self._raw_extras[fi]["is_ascii"]:
if isinstance(idx, slice):
idx = np.arange(idx.start, idx.stop)
block = np.loadtxt(
self.filenames[0], skiprows=start, max_rows=stop - start, ndmin=2
).T
_mult_cal_one(data, block, idx, cals, mult)
else:
_read_segments_file(
self, data, idx, fi, start, stop, cals, mult, dtype="<f4"
)