# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import re as re
from pathlib import Path
from typing import Any
import numpy as np
from ..._fiff.meas_info import create_info
from ..._fiff.utils import _mult_cal_one
from ...annotations import Annotations
from ...utils import _check_fname, _verbose_control, fill_doc_static, logger
from ..base import BaseRaw
[docs]
@fill_doc_static("preload", "verbose")
def read_raw_boxy(
fname: Path | str,
preload: bool | str = False,
verbose: bool | str | int | None = None,
) -> "RawBOXY":
"""Reader for an optical imaging recording.
This function has been tested using the ISS Imagent I and II systems
and versions 0.40/0.84 of the BOXY recording software.
Parameters
----------
fname : path-like
Path to the BOXY data file.
preload : bool | str
Preload data into memory for data manipulation and faster indexing.
If True, the data will be preloaded into memory (fast, requires
large amount of memory). If preload is a string, it is the name of a
freshly created memory-mapped file used to store the data on the hard
drive (slower, requires less memory). An existing file is overwritten.
The caller owns the file and is responsible for removing it after the
Raw object is no longer in use. For supported Raw readers, the exact string
``"auto"`` instead reuses decoded data below the directory configured by
:func:`mne.set_cache_dir`. Entries persist without a size limit and are mapped
copy-on-write. Use ``Path("auto")`` for a literal filename.
.. versionchanged:: 1.13
Support for the ``"auto"`` decoded-data cache was added.
verbose : bool | str | int | None
Control verbosity of the logging output. If ``None``, use the default
verbosity level. See the :ref:`logging documentation <tut-logging>` and
:func:`mne.verbose` for details. Should only be passed as a keyword
argument.
Returns
-------
raw : instance of RawBOXY
A Raw object containing BOXY data.
See :class:`mne.io.Raw` for documentation of attributes and methods.
See Also
--------
mne.io.Raw : Documentation of attributes and methods of RawBOXY.
"""
return RawBOXY(fname, preload, verbose)
@fill_doc_static("preload", "verbose")
class RawBOXY(BaseRaw):
"""Raw object from a BOXY optical imaging file.
Parameters
----------
fname : path-like
Path to the BOXY data file.
preload : bool | str
Preload data into memory for data manipulation and faster indexing.
If True, the data will be preloaded into memory (fast, requires
large amount of memory). If preload is a string, it is the name of a
freshly created memory-mapped file used to store the data on the hard
drive (slower, requires less memory). An existing file is overwritten.
The caller owns the file and is responsible for removing it after the
Raw object is no longer in use. For supported Raw readers, the exact string
``"auto"`` instead reuses decoded data below the directory configured by
:func:`mne.set_cache_dir`. Entries persist without a size limit and are mapped
copy-on-write. Use ``Path("auto")`` for a literal filename.
.. versionchanged:: 1.13
Support for the ``"auto"`` decoded-data cache was added.
verbose : bool | str | int | None
Control verbosity of the logging output. If ``None``, use the default
verbosity level. See the :ref:`logging documentation <tut-logging>` and
:func:`mne.verbose` for details. Should only be passed as a keyword
argument.
See Also
--------
mne.io.Raw : Documentation of attributes and methods.
"""
@_verbose_control
def __init__(self, fname, preload=False, verbose=None):
logger.info(f"Loading {fname}")
# Read header file and grab some info.
start_line = np.inf
col_names = mrk_col = filetype = mrk_data = end_line = None
raw_extras: dict[str, Any] = dict()
raw_extras["offsets"] = list() # keep track of our offsets
sfreq = None
fname = str(_check_fname(fname, "read", True, "fname"))
with open(fname) as fid:
line_num = 0
i_line = fid.readline()
while i_line:
# most of our lines will be data lines, so check that first
if line_num >= start_line:
assert col_names is not None
assert filetype is not None
if "#DATA ENDS" in i_line:
# Data ends just before this.
end_line = line_num
break
if mrk_col is not None:
assert mrk_data is not None
if filetype == "non-parsed":
# Non-parsed files have different lines lengths.
crnt_line = i_line.rsplit(" ")[0]
temp_data = re.findall(r"[-+]?\d*\.?\d+", crnt_line)
if len(temp_data) == len(col_names):
mrk_data.append(
float(
re.findall(r"[-+]?\d*\.?\d+", crnt_line)[
mrk_col
]
)
)
else:
crnt_line = i_line.rsplit(" ")[0]
mrk_data.append(
float(re.findall(r"[-+]?\d*\.?\d+", crnt_line)[mrk_col])
)
raw_extras["offsets"].append(fid.tell())
# now proceed with more standard header parsing
elif "BOXY.EXE:" in i_line:
boxy_ver = re.findall(r"\d*\.\d+", i_line.rsplit(" ")[-1])[0]
# Check that the BOXY version is supported
if boxy_ver not in ["0.40", "0.84"]:
raise RuntimeError(
f"MNE has not been tested with BOXY version ({boxy_ver})"
)
elif "Detector Channels" in i_line:
raw_extras["detect_num"] = int(i_line.rsplit(" ")[0])
elif "External MUX Channels" in i_line:
raw_extras["source_num"] = int(i_line.rsplit(" ")[0])
elif "Update Rate (Hz)" in i_line or "Updata Rate (Hz)" in i_line:
# Version 0.40 of the BOXY recording software
# (and possibly other versions lower than 0.84) contains a
# typo in the raw data file where 'Update Rate' is spelled
# "Updata Rate. This will account for this typo.
sfreq = float(i_line.rsplit(" ")[0])
elif "#DATA BEGINS" in i_line:
# Data should start a couple lines later.
start_line = line_num + 3
elif line_num == start_line - 2:
# Grab names for each column of data.
raw_extras["col_names"] = col_names = re.findall(
r"\w+\-\w+|\w+\-\d+|\w+", i_line.rsplit(" ")[0]
)
if "exmux" in col_names:
# Change filetype based on data organisation.
filetype = "non-parsed"
else:
filetype = "parsed"
if "digaux" in col_names:
mrk_col = col_names.index("digaux")
mrk_data = list()
# raw_extras['offsets'].append(fid.tell())
elif line_num == start_line - 1:
raw_extras["offsets"].append(fid.tell())
line_num += 1
i_line = fid.readline()
assert sfreq is not None
raw_extras.update(filetype=filetype, start_line=start_line, end_line=end_line)
# Label each channel in our data, for each data type (DC, AC, Ph).
# Data is organised by channels x timepoint, where the first
# 'source_num' rows correspond to the first detector, the next
# 'source_num' rows correspond to the second detector, and so on.
ch_names = list()
ch_types = list()
cals = list()
for det_num in range(raw_extras["detect_num"]):
for src_num in range(raw_extras["source_num"]):
for i_type, ch_type in [
("DC", "fnirs_cw_amplitude"),
("AC", "fnirs_fd_ac_amplitude"),
("Ph", "fnirs_fd_phase"),
]:
ch_names.append(f"S{src_num + 1}_D{det_num + 1} {i_type}")
ch_types.append(ch_type)
cals.append(np.pi / 180.0 if i_type == "Ph" else 1.0)
# Create info structure.
info = create_info(ch_names, sfreq, ch_types)
for ch, cal in zip(info["chs"], cals):
ch["cal"] = cal
# Determine how long our data is.
assert end_line is not None
delta = end_line - start_line
assert len(raw_extras["offsets"]) == delta + 1
if filetype == "non-parsed":
delta //= raw_extras["source_num"]
super().__init__(
info,
preload,
filenames=[fname],
first_samps=[0],
last_samps=[delta - 1],
raw_extras=[raw_extras],
verbose=verbose,
)
# Now let's grab our markers, if they are present.
if mrk_data is not None:
mrk_data = np.array(mrk_data, float)
# We only want the first instance of each trigger.
prev_mrk = 0
mrk_idx = list()
duration = list()
tmp_dur = 0
for i_num, i_mrk in enumerate(mrk_data):
if i_mrk != 0 and i_mrk != prev_mrk:
mrk_idx.append(i_num)
if i_mrk != 0 and i_mrk == prev_mrk:
tmp_dur += 1
if i_mrk == 0 and i_mrk != prev_mrk:
duration.append((tmp_dur + 1) / sfreq)
tmp_dur = 0
prev_mrk = i_mrk
onset = np.array(mrk_idx) / sfreq
description = mrk_data[mrk_idx]
annot = Annotations(onset, duration, description)
self.set_annotations(annot)
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a segment of data from a file.
Boxy file organises data in two ways, parsed or un-parsed.
Regardless of type, output has (n_montages x n_sources x n_detectors
+ n_marker_channels) rows, and (n_timepoints x n_blocks) columns.
"""
source_num = self._raw_extras[fi]["source_num"]
detect_num = self._raw_extras[fi]["detect_num"]
start_line = self._raw_extras[fi]["start_line"]
end_line = self._raw_extras[fi]["end_line"]
filetype = self._raw_extras[fi]["filetype"]
col_names = self._raw_extras[fi]["col_names"]
offsets = self._raw_extras[fi]["offsets"]
boxy_file = self.filenames[fi]
# Non-parsed multiplexes sources, so we need source_num times as many
# lines in that case
if filetype == "parsed":
start_read = start_line + start
stop_read = start_read + (stop - start)
else:
assert filetype == "non-parsed"
start_read = start_line + start * source_num
stop_read = start_read + (stop - start) * source_num
assert start_read >= start_line
assert stop_read <= end_line
# Possible detector names.
detectors = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"[:detect_num]
# Loop through our data.
one = np.zeros((len(col_names), stop_read - start_read))
with open(boxy_file) as fid:
# Just a more efficient version of this:
# ii = 0
# for line_num, i_line in enumerate(fid):
# if line_num >= start_read:
# if line_num >= stop_read:
# break
# # Grab actual data.
# i_data = i_line.strip().split()
# one[:len(i_data), ii] = i_data
# ii += 1
fid.seek(offsets[start_read - start_line], 0)
for oo in one.T:
i_data = fid.readline().strip().split()
oo[: len(i_data)] = i_data
# in theory we could index in the loop above, but it's painfully slow,
# so let's just take a hopefully minor memory hit
if filetype == "non-parsed":
ch_idxs = [
col_names.index(f"{det}-{i_type}")
for det in detectors
for i_type in ["DC", "AC", "Ph"]
]
one = (
one[ch_idxs]
.reshape( # each "time point" multiplexes srcs
len(detectors), 3, -1, source_num
)
.transpose( # reorganize into (det, source, DC/AC/Ph, t) order
0, 3, 1, 2
)
.reshape( # reshape the way we store it (det x source x DAP, t)
len(detectors) * source_num * 3, -1
)
)
else:
assert filetype == "parsed"
ch_idxs = [
col_names.index(f"{det}-{i_type}{si + 1}")
for det in detectors
for si in range(source_num)
for i_type in ["DC", "AC", "Ph"]
]
one = one[ch_idxs]
# Place our data into the data object in place.
_mult_cal_one(data, one, idx, cals, mult)