-
Notifications
You must be signed in to change notification settings - Fork 1.3k
/
_snirf.py
484 lines (413 loc) · 20.7 KB
/
_snirf.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD-3-Clause
import re
import numpy as np
import datetime
from ..base import BaseRaw
from ..meas_info import create_info, _format_dig_points
from ..utils import _mult_cal_one
from ...annotations import Annotations
from ...utils import (logger, verbose, fill_doc, warn, _check_fname,
_import_h5py)
from ..constants import FIFF
from .._digitization import _make_dig_points
from ...transforms import _frame_to_str, apply_trans
from ..nirx.nirx import _convert_fnirs_to_head
from ..._freesurfer import get_mni_fiducials
@fill_doc
def read_raw_snirf(fname, optode_frame="unknown", preload=False, verbose=None):
"""Reader for a continuous wave SNIRF data.
.. note:: This reader supports the .snirf file type only,
not the .jnirs version.
Files with either 3D or 2D locations can be read.
However, we strongly recommend using 3D positions.
If 2D positions are used the behaviour of MNE functions
can not be guaranteed.
Parameters
----------
fname : str
Path to the SNIRF data file.
optode_frame : str
Coordinate frame used for the optode positions. The default is unknown,
in which case the positions are not modified. If a known coordinate
frame is provided (head, meg, mri), then the positions are transformed
in to the Neuromag head coordinate frame (head).
%(preload)s
%(verbose)s
Returns
-------
raw : instance of RawSNIRF
A Raw object containing fNIRS data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
return RawSNIRF(fname, optode_frame, preload, verbose)
def _open(fname):
return open(fname, 'r', encoding='latin-1')
@fill_doc
class RawSNIRF(BaseRaw):
"""Raw object from a continuous wave SNIRF file.
Parameters
----------
fname : str
Path to the SNIRF data file.
optode_frame : str
Coordinate frame used for the optode positions. The default is unknown,
in which case the positions are not modified. If a known coordinate
frame is provided (head, meg, mri), then the positions are transformed
in to the Neuromag head coordinate frame (head).
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
@verbose
def __init__(self, fname, optode_frame="unknown",
preload=False, verbose=None):
# Must be here due to circular import error
from ...preprocessing.nirs import _validate_nirs_info
h5py = _import_h5py()
fname = _check_fname(fname, 'read', True, 'fname')
logger.info('Loading %s' % fname)
with h5py.File(fname, 'r') as dat:
if 'data2' in dat['nirs']:
warn("File contains multiple recordings. "
"MNE does not support this feature. "
"Only the first dataset will be processed.")
manafacturer = _get_metadata_str(dat, "ManufacturerName")
if (optode_frame == "unknown") & (manafacturer == "Gowerlabs"):
optode_frame = "head"
snirf_data_type = np.array(dat.get('nirs/data1/measurementList1'
'/dataType')).item()
if snirf_data_type not in [1, 99999]:
# 1 = Continuous Wave
# 99999 = Processed
raise RuntimeError('MNE only supports reading continuous'
' wave amplitude and processed haemoglobin'
' SNIRF files. Expected type'
' code 1 or 99999 but received type '
f'code {snirf_data_type}')
last_samps = dat.get('/nirs/data1/dataTimeSeries').shape[0] - 1
sampling_rate = _extract_sampling_rate(dat)
if sampling_rate == 0:
warn("Unable to extract sample rate from SNIRF file.")
# Extract wavelengths
fnirs_wavelengths = np.array(dat.get('nirs/probe/wavelengths'))
fnirs_wavelengths = [int(w) for w in fnirs_wavelengths]
if len(fnirs_wavelengths) != 2:
raise RuntimeError(f'The data contains '
f'{len(fnirs_wavelengths)}'
f' wavelengths: {fnirs_wavelengths}. '
f'MNE only supports reading continuous'
' wave amplitude SNIRF files '
'with two wavelengths.')
# Extract channels
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
return [atoi(c) for c in re.split(r'(\d+)', text)]
channels = np.array([name for name in dat['nirs']['data1'].keys()])
channels_idx = np.array(['measurementList' in n for n in channels])
channels = channels[channels_idx]
channels = sorted(channels, key=natural_keys)
# Source and detector labels are optional fields.
# Use S1, S2, S3, etc if not specified.
if 'sourceLabels_disabled' in dat['nirs/probe']:
# This is disabled as
# MNE-Python does not currently support custom source names.
# Instead, sources must be integer values.
sources = np.array(dat.get('nirs/probe/sourceLabels'))
sources = [s.decode('UTF-8') for s in sources]
else:
sources = np.unique([_correct_shape(np.array(dat.get(
'nirs/data1/' + c + '/sourceIndex')))[0]
for c in channels])
sources = [f"S{int(s)}" for s in sources]
if 'detectorLabels_disabled' in dat['nirs/probe']:
# This is disabled as
# MNE-Python does not currently support custom detector names.
# Instead, detector must be integer values.
detectors = np.array(dat.get('nirs/probe/detectorLabels'))
detectors = [d.decode('UTF-8') for d in detectors]
else:
detectors = np.unique([_correct_shape(np.array(dat.get(
'nirs/data1/' + c + '/detectorIndex')))[0]
for c in channels])
detectors = [f"D{int(d)}" for d in detectors]
# Extract source and detector locations
# 3D positions are optional in SNIRF,
# but highly recommended in MNE.
if ('detectorPos3D' in dat['nirs/probe']) &\
('sourcePos3D' in dat['nirs/probe']):
# If 3D positions are available they are used even if 2D exists
detPos3D = np.array(dat.get('nirs/probe/detectorPos3D'))
srcPos3D = np.array(dat.get('nirs/probe/sourcePos3D'))
elif ('detectorPos2D' in dat['nirs/probe']) &\
('sourcePos2D' in dat['nirs/probe']):
warn('The data only contains 2D location information for the '
'optode positions. '
'It is highly recommended that data is used '
'which contains 3D location information for the '
'optode positions. With only 2D locations it can not be '
'guaranteed that MNE functions will behave correctly '
'and produce accurate results. If it is not possible to '
'include 3D positions in your data, please consider '
'using the set_montage() function.')
detPos2D = np.array(dat.get('nirs/probe/detectorPos2D'))
srcPos2D = np.array(dat.get('nirs/probe/sourcePos2D'))
# Set the third dimension to zero. See gh#9308
detPos3D = np.append(detPos2D,
np.zeros((detPos2D.shape[0], 1)), axis=1)
srcPos3D = np.append(srcPos2D,
np.zeros((srcPos2D.shape[0], 1)), axis=1)
else:
raise RuntimeError('No optode location information is '
'provided. MNE requires at least 2D '
'location information')
assert len(sources) == srcPos3D.shape[0]
assert len(detectors) == detPos3D.shape[0]
chnames = []
ch_types = []
for chan in channels:
src_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' +
chan + '/sourceIndex')))[0])
det_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' +
chan + '/detectorIndex')))[0])
if snirf_data_type == 1:
wve_idx = int(_correct_shape(np.array(
dat.get('nirs/data1/' + chan +
'/wavelengthIndex')))[0])
ch_name = sources[src_idx - 1] + '_' +\
detectors[det_idx - 1] + ' ' +\
str(fnirs_wavelengths[wve_idx - 1])
chnames.append(ch_name)
ch_types.append('fnirs_cw_amplitude')
elif snirf_data_type == 99999:
dt_id = _correct_shape(
np.array(dat.get('nirs/data1/' + chan +
'/dataTypeLabel')))[0].decode('UTF-8')
# Convert between SNIRF processed names and MNE type names
dt_id = dt_id.lower().replace("dod", "fnirs_od")
ch_name = sources[src_idx - 1] + '_' + \
detectors[det_idx - 1]
if dt_id == "fnirs_od":
wve_idx = int(_correct_shape(np.array(
dat.get('nirs/data1/' + chan +
'/wavelengthIndex')))[0])
suffix = ' ' + str(fnirs_wavelengths[wve_idx - 1])
else:
suffix = ' ' + dt_id.lower()
ch_name = ch_name + suffix
chnames.append(ch_name)
ch_types.append(dt_id)
# Create mne structure
info = create_info(chnames,
sampling_rate,
ch_types=ch_types)
subject_info = {}
names = np.array(dat.get('nirs/metaDataTags/SubjectID'))
subject_info['first_name'] = \
_correct_shape(names)[0].decode('UTF-8')
# Read non standard (but allowed) custom metadata tags
if 'lastName' in dat.get('nirs/metaDataTags/'):
ln = dat.get('/nirs/metaDataTags/lastName')[0].decode('UTF-8')
subject_info['last_name'] = ln
if 'middleName' in dat.get('nirs/metaDataTags/'):
m = dat.get('/nirs/metaDataTags/middleName')[0].decode('UTF-8')
subject_info['middle_name'] = m
if 'sex' in dat.get('nirs/metaDataTags/'):
s = dat.get('/nirs/metaDataTags/sex')[0].decode('UTF-8')
if s in {'M', 'Male', '1', 'm'}:
subject_info['sex'] = FIFF.FIFFV_SUBJ_SEX_MALE
elif s in {'F', 'Female', '2', 'f'}:
subject_info['sex'] = FIFF.FIFFV_SUBJ_SEX_FEMALE
elif s in {'0', 'u'}:
subject_info['sex'] = FIFF.FIFFV_SUBJ_SEX_UNKNOWN
# End non standard name reading
# Update info
info.update(subject_info=subject_info)
length_unit = _get_metadata_str(dat, "LengthUnit")
length_scaling = _get_lengthunit_scaling(length_unit)
srcPos3D /= length_scaling
detPos3D /= length_scaling
if optode_frame in ["mri", "meg"]:
# These are all in MNI or MEG coordinates, so let's transform
# them to the Neuromag head coordinate frame
srcPos3D, detPos3D, _, head_t = _convert_fnirs_to_head(
'fsaverage', optode_frame, 'head', srcPos3D, detPos3D, [])
else:
head_t = np.eye(4)
if optode_frame in ["head", "mri", "meg"]:
# Then the transformation to head was performed above
coord_frame = FIFF.FIFFV_COORD_HEAD
elif 'MNE_coordFrame' in dat.get('nirs/metaDataTags/'):
coord_frame = int(dat.get('/nirs/metaDataTags/MNE_coordFrame')
[0])
else:
coord_frame = FIFF.FIFFV_COORD_UNKNOWN
for idx, chan in enumerate(channels):
src_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' +
chan + '/sourceIndex')))[0])
det_idx = int(_correct_shape(np.array(dat.get('nirs/data1/' +
chan + '/detectorIndex')))[0])
info['chs'][idx]['loc'][3:6] = srcPos3D[src_idx - 1, :]
info['chs'][idx]['loc'][6:9] = detPos3D[det_idx - 1, :]
# Store channel as mid point
midpoint = (info['chs'][idx]['loc'][3:6] +
info['chs'][idx]['loc'][6:9]) / 2
info['chs'][idx]['loc'][0:3] = midpoint
info['chs'][idx]['coord_frame'] = coord_frame
if (snirf_data_type in [1]) or \
((snirf_data_type == 99999) and
(ch_types[idx] == "fnirs_od")):
wve_idx = int(_correct_shape(np.array(dat.get(
'nirs/data1/' + chan + '/wavelengthIndex')))[0])
info['chs'][idx]['loc'][9] = fnirs_wavelengths[wve_idx - 1]
if 'landmarkPos3D' in dat.get('nirs/probe/'):
diglocs = np.array(dat.get('/nirs/probe/landmarkPos3D'))
diglocs /= length_scaling
digname = np.array(dat.get('/nirs/probe/landmarkLabels'))
nasion, lpa, rpa, hpi = None, None, None, None
extra_ps = dict()
for idx, dign in enumerate(digname):
dign = dign.lower()
if dign in [b'lpa', b'al']:
lpa = diglocs[idx, :3]
elif dign in [b'nasion']:
nasion = diglocs[idx, :3]
elif dign in [b'rpa', b'ar']:
rpa = diglocs[idx, :3]
else:
extra_ps[f'EEG{len(extra_ps) + 1:03d}'] = \
diglocs[idx, :3]
dig = _make_dig_points(
nasion=nasion, lpa=lpa, rpa=rpa, hpi=hpi,
dig_ch_pos=extra_ps,
coord_frame=_frame_to_str[coord_frame])
else:
ch_locs = [info['chs'][idx]['loc'][0:3]
for idx in range(len(channels))]
# Set up digitization
dig = get_mni_fiducials('fsaverage', verbose=False)
for fid in dig:
fid['r'] = apply_trans(head_t, fid['r'])
fid['coord_frame'] = FIFF.FIFFV_COORD_HEAD
for ii, ch_loc in enumerate(ch_locs, 1):
dig.append(dict(
kind=FIFF.FIFFV_POINT_EEG, # misnomer prob okay
r=ch_loc,
ident=ii,
coord_frame=FIFF.FIFFV_COORD_HEAD,
))
dig = _format_dig_points(dig)
del head_t
with info._unlock():
info['dig'] = dig
str_date = _correct_shape(np.array((dat.get(
'/nirs/metaDataTags/MeasurementDate'))))[0].decode('UTF-8')
str_time = _correct_shape(np.array((dat.get(
'/nirs/metaDataTags/MeasurementTime'))))[0].decode('UTF-8')
str_datetime = str_date + str_time
# Several formats have been observed so we try each in turn
for dt_code in ['%Y-%m-%d%H:%M:%SZ',
'%Y-%m-%d%H:%M:%S']:
try:
meas_date = datetime.datetime.strptime(
str_datetime, dt_code)
except ValueError:
pass
else:
break
else:
warn("Extraction of measurement date from SNIRF file failed. "
"The date is being set to January 1st, 2000, "
f"instead of {str_datetime}")
meas_date = datetime.datetime(2000, 1, 1, 0, 0, 0)
meas_date = meas_date.replace(tzinfo=datetime.timezone.utc)
with info._unlock():
info['meas_date'] = meas_date
if 'DateOfBirth' in dat.get('nirs/metaDataTags/'):
str_birth = np.array((dat.get('/nirs/metaDataTags/'
'DateOfBirth')))[0].decode()
birth_matched = re.fullmatch(r'(\d+)-(\d+)-(\d+)', str_birth)
if birth_matched is not None:
birthday = (int(birth_matched.groups()[0]),
int(birth_matched.groups()[1]),
int(birth_matched.groups()[2]))
with info._unlock():
info["subject_info"]['birthday'] = birthday
super(RawSNIRF, self).__init__(info, preload, filenames=[fname],
last_samps=[last_samps],
verbose=verbose)
# Extract annotations
annot = Annotations([], [], [])
for key in dat['nirs']:
if 'stim' in key:
data = np.atleast_2d(np.array(
dat.get('/nirs/' + key + '/data')))
if data.size > 0:
desc = _correct_shape(np.array(dat.get(
'/nirs/' + key + '/name')))[0]
annot.append(data[:, 0], 1.0, desc.decode('UTF-8'))
self.set_annotations(annot, emit_warning=False)
# Validate that the fNIRS info is correctly formatted
_validate_nirs_info(self.info)
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a segment of data from a file."""
import h5py
with h5py.File(self._filenames[0], 'r') as dat:
one = dat['/nirs/data1/dataTimeSeries'][start:stop].T
_mult_cal_one(data, one, idx, cals, mult)
# Helper function for when the numpy array has shape (), i.e. just one element.
def _correct_shape(arr):
if arr.shape == ():
arr = arr[np.newaxis]
return arr
def _get_timeunit_scaling(time_unit):
"""MNE expects time in seconds, return required scaling."""
scalings = {'ms': 1000, 's': 1, 'unknown': 1}
if time_unit in scalings:
return scalings[time_unit]
else:
raise RuntimeError(f'The time unit {time_unit} is not supported by '
'MNE. Please report this error as a GitHub '
'issue to inform the developers.')
def _get_lengthunit_scaling(length_unit):
"""MNE expects distance in m, return required scaling."""
scalings = {'m': 1, 'cm': 100, 'mm': 1000}
if length_unit in scalings:
return scalings[length_unit]
else:
raise RuntimeError(f'The length unit {length_unit} is not supported '
'by MNE. Please report this error as a GitHub '
'issue to inform the developers.')
def _extract_sampling_rate(dat):
"""Extract the sample rate from the time field."""
time_data = np.array(dat.get('nirs/data1/time'))
sampling_rate = 0
if len(time_data) == 2:
# specified as onset, samplerate
sampling_rate = 1. / (time_data[1] - time_data[0])
else:
# specified as time points
fs_diff = np.around(np.diff(time_data), decimals=4)
if len(np.unique(fs_diff)) == 1:
# Uniformly sampled data
sampling_rate = 1. / np.unique(fs_diff)
else:
warn("MNE does not currently support reading "
"SNIRF files with non-uniform sampled data.")
time_unit = _get_metadata_str(dat, "TimeUnit")
time_unit_scaling = _get_timeunit_scaling(time_unit)
sampling_rate *= time_unit_scaling
return sampling_rate
def _get_metadata_str(dat, field):
if field not in np.array(dat.get('nirs/metaDataTags')):
return None
data = dat.get(f'/nirs/metaDataTags/{field}')
data = _correct_shape(np.array(data))
data = str(data[0], 'utf-8')
return data