/
boxy.py
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/
boxy.py
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# Authors: Kyle Mathewson, Jonathan Kuziek <kuziek@ualberta.ca>
#
# License: BSD (3-clause)
import re as re
import numpy as np
from ..base import BaseRaw
from ..meas_info import create_info
from ..utils import _mult_cal_one
from ...utils import logger, verbose, fill_doc, _check_fname
from ...annotations import Annotations
@fill_doc
def read_raw_boxy(fname, preload=False, verbose=None):
"""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 : str
Path to the BOXY data file.
%(preload)s
%(verbose)s
Returns
-------
raw : instance of RawBOXY
A Raw object containing BOXY data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
return RawBOXY(fname, preload, verbose)
@fill_doc
class RawBOXY(BaseRaw):
"""Raw object from a BOXY optical imaging file.
Parameters
----------
fname : str
Path to the BOXY data file.
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
@verbose
def __init__(self, fname, preload=False, verbose=None):
logger.info('Loading %s' % 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()
raw_extras['offsets'] = list() # keep track of our offsets
sfreq = None
fname = _check_fname(fname, 'read', True, 'fname')
with open(fname, 'r') 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:
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('MNE has not been tested with BOXY '
'version (%s)' % 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. if i_type == 'Ph' else 1.)
# 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.
delta = end_line - start_line
assert len(raw_extras['offsets']) == delta + 1
if filetype == 'non-parsed':
delta //= (raw_extras['source_num'])
super(RawBOXY, self).__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, 'r') 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)