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
|
# -*- coding: utf-8 -*-
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Martin Billinger <martin.billinger@tugraz.at>
# Alan Leggitt <alan.leggitt@ucsf.edu>
# Alexandre Barachant <alexandre.barachant@gmail.com>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Joan Massich <mailsik@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import inspect
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from scipy.io import loadmat
import pytest
from mne import pick_types, Annotations
from mne.datasets import testing
from mne.utils import run_tests_if_main, requires_pandas, _TempDir
from mne.io import read_raw_edf, read_raw_bdf
from mne.io.tests.test_raw import _test_raw_reader
from mne.io.edf.edf import _get_edf_default_event_id
from mne.io.edf.edf import _read_annotations_edf
from mne.io.edf.edf import _read_ch
from mne.io.pick import channel_indices_by_type
from mne.annotations import events_from_annotations, read_annotations
from mne.io.meas_info import _kind_dict as _KIND_DICT
FILE = inspect.getfile(inspect.currentframe())
data_dir = op.join(op.dirname(op.abspath(FILE)), 'data')
montage_path = op.join(data_dir, 'biosemi.hpts') # XXX: missing reader
bdf_path = op.join(data_dir, 'test.bdf')
edf_path = op.join(data_dir, 'test.edf')
duplicate_channel_labels_path = op.join(data_dir,
'duplicate_channel_labels.edf')
edf_uneven_path = op.join(data_dir, 'test_uneven_samp.edf')
bdf_eeglab_path = op.join(data_dir, 'test_bdf_eeglab.mat')
edf_eeglab_path = op.join(data_dir, 'test_edf_eeglab.mat')
edf_uneven_eeglab_path = op.join(data_dir, 'test_uneven_samp.mat')
edf_stim_channel_path = op.join(data_dir, 'test_edf_stim_channel.edf')
edf_txt_stim_channel_path = op.join(data_dir, 'test_edf_stim_channel.txt')
data_path = testing.data_path(download=False)
edf_stim_resamp_path = op.join(data_path, 'EDF', 'test_edf_stim_resamp.edf')
edf_overlap_annot_path = op.join(data_path, 'EDF',
'test_edf_overlapping_annotations.edf')
edf_reduced = op.join(data_path, 'EDF', 'test_reduced.edf')
bdf_stim_channel_path = op.join(data_path, 'BDF', 'test_bdf_stim_channel.bdf')
test_generator_bdf = op.join(data_path, 'BDF', 'test_generator_2.bdf')
test_generator_edf = op.join(data_path, 'EDF', 'test_generator_2.edf')
eog = ['REOG', 'LEOG', 'IEOG']
misc = ['EXG1', 'EXG5', 'EXG8', 'M1', 'M2']
def test_orig_units():
"""Test exposure of original channel units."""
raw = read_raw_edf(edf_path, preload=True)
# Test original units
orig_units = raw._orig_units
assert len(orig_units) == len(raw.ch_names)
assert orig_units['A1'] == u'µV' # formerly 'uV' edit by _check_orig_units
def test_bdf_data():
"""Test reading raw bdf files."""
raw_py = _test_raw_reader(read_raw_bdf, input_fname=bdf_path,
eog=eog, misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
raw_py = _test_raw_reader(read_raw_bdf, input_fname=bdf_path,
montage='biosemi64', eog=eog, misc=misc,
exclude=['M2', 'IEOG'])
assert len(raw_py.ch_names) == 71
assert 'RawEDF' in repr(raw_py)
picks = pick_types(raw_py.info, meg=False, eeg=True, exclude='bads')
data_py, _ = raw_py[picks]
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = loadmat(bdf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
# bdf saved as a single, resolution to seven decimal points in matlab
assert_array_almost_equal(data_py, data_eeglab, 8)
# Manually checking that float coordinates are imported
assert (raw_py.info['chs'][0]['loc']).any()
assert (raw_py.info['chs'][25]['loc']).any()
assert (raw_py.info['chs'][63]['loc']).any()
@testing.requires_testing_data
def test_bdf_crop_save_stim_channel(tmpdir):
"""Test EDF with various sampling rates."""
raw = read_raw_bdf(bdf_stim_channel_path)
raw.save(tmpdir.join('test-raw.fif'), tmin=1.2, tmax=4.0, overwrite=True)
@testing.requires_testing_data
def test_edf_reduced():
"""Test EDF with various sampling rates."""
_test_raw_reader(read_raw_edf, input_fname=edf_reduced, verbose='error')
def test_edf_data():
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf, input_fname=edf_path,
exclude=['Ergo-Left', 'H10'], verbose='error')
raw_py = read_raw_edf(edf_path, preload=True)
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test with number of records not in header (-1).
tempdir = _TempDir()
broken_fname = op.join(tempdir, 'broken.edf')
with open(edf_path, 'rb') as fid_in:
fid_in.seek(0, 2)
n_bytes = fid_in.tell()
fid_in.seek(0, 0)
rbytes = fid_in.read(int(n_bytes * 0.4))
with open(broken_fname, 'wb') as fid_out:
fid_out.write(rbytes[:236])
fid_out.write(b'-1 ')
fid_out.write(rbytes[244:])
with pytest.warns(RuntimeWarning,
match='records .* not match the file size'):
raw = read_raw_edf(broken_fname, preload=True)
read_raw_edf(broken_fname, exclude=raw.ch_names[:132], preload=True)
def test_duplicate_channel_labels_edf():
"""Test reading edf file with duplicate channel names."""
EXPECTED_CHANNEL_NAMES = ['EEG F1-Ref-0', 'EEG F2-Ref', 'EEG F1-Ref-1']
with pytest.warns(RuntimeWarning, match='Channel names are not unique'):
raw = read_raw_edf(duplicate_channel_labels_path, preload=False)
assert raw.ch_names == EXPECTED_CHANNEL_NAMES
def test_parse_annotation(tmpdir):
"""Test parsing the tal channel."""
# test the parser
annot = (b'+180\x14Lights off\x14Close door\x14\x00\x00\x00\x00\x00'
b'+180\x14Lights off\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+180\x14Close door\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+3.14\x1504.20\x14nothing\x14\x00\x00\x00\x00'
b'+1800.2\x1525.5\x14Apnea\x14\x00\x00\x00\x00\x00\x00\x00'
b'+123\x14\x14\x00\x00\x00\x00\x00\x00\x00')
annot_file = tmpdir.join('annotations.txt')
annot_file.write(annot)
annot = [a for a in bytes(annot)]
annot[1::2] = [a * 256 for a in annot[1::2]]
tal_channel_A = np.array(list(map(sum, zip(annot[0::2], annot[1::2]))),
dtype=np.int64)
with open(str(annot_file), 'rb') as fid:
# ch_data = np.fromfile(fid, dtype=np.int16, count=len(annot))
tal_channel_B = _read_ch(fid, subtype='EDF', dtype=np.int16,
samp=(len(annot) - 1) // 2,
dtype_byte='This_parameter_is_not_used')
for tal_channel in [tal_channel_A, tal_channel_B]:
onset, duration, description = _read_annotations_edf([tal_channel])
assert_equal(np.column_stack((onset, duration, description)),
[[180., 0., 'Lights off'], [180., 0., 'Close door'],
[180., 0., 'Lights off'], [180., 0., 'Close door'],
[3.14, 4.2, 'nothing'], [1800.2, 25.5, 'Apnea']])
def test_find_events_backward_compatibility():
"""Test if events are detected correctly in a typical MNE workflow."""
EXPECTED_EVENTS = [[68, 0, 2],
[199, 0, 2],
[1024, 0, 3],
[1280, 0, 2]]
# test an actual file
raw = read_raw_edf(edf_path, preload=True)
event_id = _get_edf_default_event_id(raw.annotations.description)
event_id.pop('start')
events_from_EFA, _ = events_from_annotations(raw, event_id=event_id,
use_rounding=False)
assert_array_equal(events_from_EFA, EXPECTED_EVENTS)
@requires_pandas
@pytest.mark.parametrize('fname', [edf_path, bdf_path])
def test_to_data_frame(fname):
"""Test EDF/BDF Raw Pandas exporter."""
ext = op.splitext(fname)[1][1:].lower()
if ext == 'edf':
raw = read_raw_edf(fname, preload=True, verbose='error')
elif ext == 'bdf':
raw = read_raw_bdf(fname, preload=True, verbose='error')
_, times = raw[0, :10]
df = raw.to_data_frame()
assert (df.columns == raw.ch_names).all()
assert_array_equal(np.round(times * 1e3), df.index.values[:10])
df = raw.to_data_frame(index=None, scalings={'eeg': 1e13})
assert 'time' in df.index.names
assert_array_equal(df.values[:, 0], raw._data[0] * 1e13)
def test_read_raw_edf_stim_channel_input_parameters():
"""Test edf raw reader deprecation."""
_MSG = "`read_raw_edf` is not supposed to trigger a deprecation warning"
with pytest.warns(None) as recwarn:
read_raw_edf(edf_path)
assert all([w.category != DeprecationWarning for w in recwarn.list]), _MSG
for invalid_stim_parameter in ['EDF Annotations', 'BDF Annotations']:
with pytest.raises(ValueError,
match="stim channel is not supported"):
read_raw_edf(edf_path, stim_channel=invalid_stim_parameter)
def _assert_annotations_equal(a, b):
assert_array_equal(a.onset, b.onset)
assert_array_equal(a.duration, b.duration)
assert_array_equal(a.description, b.description)
assert a.orig_time == b.orig_time
def test_read_annot(tmpdir):
"""Test parsing the tal channel."""
EXPECTED_ANNOTATIONS = [[180.0, 0, 'Lights off'], [180.0, 0, 'Close door'],
[180.0, 0, 'Lights off'], [180.0, 0, 'Close door'],
[3.14, 4.2, 'nothing'], [1800.2, 25.5, 'Apnea']]
EXPECTED_ONSET = [180.0, 180.0, 180.0, 180.0, 3.14, 1800.2]
EXPECTED_DURATION = [0, 0, 0, 0, 4.2, 25.5]
EXPECTED_DESC = ['Lights off', 'Close door', 'Lights off', 'Close door',
'nothing', 'Apnea']
EXPECTED_ANNOTATIONS = Annotations(onset=EXPECTED_ONSET,
duration=EXPECTED_DURATION,
description=EXPECTED_DESC,
orig_time=None)
annot = (b'+180\x14Lights off\x14Close door\x14\x00\x00\x00\x00\x00'
b'+180\x14Lights off\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+180\x14Close door\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+3.14\x1504.20\x14nothing\x14\x00\x00\x00\x00'
b'+1800.2\x1525.5\x14Apnea\x14\x00\x00\x00\x00\x00\x00\x00'
b'+123\x14\x14\x00\x00\x00\x00\x00\x00\x00')
annot_file = tmpdir.join('annotations.txt')
annot_file.write(annot)
onset, duration, desc = _read_annotations_edf(annotations=str(annot_file))
annotation = Annotations(onset=onset, duration=duration, description=desc,
orig_time=None)
_assert_annotations_equal(annotation, EXPECTED_ANNOTATIONS)
# Now test when reading from buffer of data
with open(str(annot_file), 'rb') as fid:
ch_data = np.fromfile(fid, dtype=np.int16, count=len(annot))
onset, duration, desc = _read_annotations_edf([ch_data])
annotation = Annotations(onset=onset, duration=duration, description=desc,
orig_time=None)
_assert_annotations_equal(annotation, EXPECTED_ANNOTATIONS)
@testing.requires_testing_data
@pytest.mark.parametrize('fname', [test_generator_edf, test_generator_bdf])
def test_read_annotations(fname, recwarn):
"""Test IO of annotations from edf and bdf files via regexp."""
annot = read_annotations(fname)
assert len(annot.onset) == 2
@testing.requires_testing_data
@pytest.mark.parametrize('fname', [test_generator_edf, test_generator_bdf])
def test_load_generator(fname, recwarn):
"""Test IO of annotations from edf and bdf files with raw info."""
ext = op.splitext(fname)[1][1:].lower()
if ext == 'edf':
raw = read_raw_edf(fname)
elif ext == 'bdf':
raw = read_raw_bdf(fname)
assert len(raw.annotations.onset) == 2
found_types = [k for k, v in
channel_indices_by_type(raw.info, picks=None).items()
if v]
assert len(found_types) == 1
events, event_id = events_from_annotations(raw)
ch_names = ['squarewave', 'ramp', 'pulse', 'ECG', 'noise', 'sine 1 Hz',
'sine 8 Hz', 'sine 8.5 Hz', 'sine 15 Hz', 'sine 17 Hz',
'sine 50 Hz']
assert raw.get_data().shape == (11, 120000)
assert raw.ch_names == ch_names
assert event_id == {'RECORD START': 2, 'REC STOP': 1}
assert_array_equal(events, [[0, 0, 2], [120000, 0, 1]])
@pytest.mark.parametrize('EXPECTED, test_input', [
pytest.param({'stAtUs': 'stim', 'tRigGer': 'stim', 'sine 1 Hz': 'eeg'},
'auto', id='auto'),
pytest.param({'stAtUs': 'eeg', 'tRigGer': 'eeg', 'sine 1 Hz': 'eeg'},
None, id='None'),
pytest.param({'stAtUs': 'eeg', 'tRigGer': 'eeg', 'sine 1 Hz': 'stim'},
'sine 1 Hz', id='single string'),
pytest.param({'stAtUs': 'eeg', 'tRigGer': 'eeg', 'sine 1 Hz': 'stim'},
2, id='single int'),
pytest.param({'stAtUs': 'eeg', 'tRigGer': 'eeg', 'sine 1 Hz': 'stim'},
-1, id='single int (revers indexing)'),
pytest.param({'stAtUs': 'stim', 'tRigGer': 'stim', 'sine 1 Hz': 'eeg'},
[0, 1], id='int list')])
def test_edf_stim_ch_pick_up(test_input, EXPECTED):
"""Test stim_channel."""
TYPE_LUT = {v[0]: k for k, v in _KIND_DICT.items()}
fname = op.join(data_dir, 'test_stim_channel.edf')
raw = read_raw_edf(fname, stim_channel=test_input)
ch_types = {ch['ch_name']: TYPE_LUT[ch['kind']] for ch in raw.info['chs']}
assert ch_types == EXPECTED
run_tests_if_main()
|
