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# -*- 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>
#
# License: BSD (3-clause)
import os.path as op
import inspect
import pytest
import numpy as np
from numpy.testing import (assert_array_almost_equal, assert_array_equal,
assert_equal)
from scipy.io import loadmat
from mne import pick_types
from mne.datasets import testing
from mne.externals.six import iterbytes
from mne.utils import run_tests_if_main, requires_pandas, _TempDir
from mne.io import read_raw_edf
from mne.io.base import _RawShell
from mne.io.meas_info import _empty_info
from mne.io.tests.test_raw import _test_raw_reader
from mne.io.pick import channel_type
from mne.io.edf.edf import find_edf_events, _read_annot, _read_annotations_edf
from mne.io.edf.edf import _get_edf_default_event_id
from mne.io.edf.edf import _read_edf_header
from mne.event import find_events
from mne.annotations import events_from_annotations, read_annotations
FILE = inspect.getfile(inspect.currentframe())
data_dir = op.join(op.dirname(op.abspath(FILE)), 'data')
montage_path = op.join(data_dir, 'biosemi.hpts')
bdf_path = op.join(data_dir, 'test.bdf')
edf_path = op.join(data_dir, 'test.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')
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, stim_channel='auto', preload=True)
# Test original units
orig_units = raw._orig_units
assert len(orig_units) == 140
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_edf, input_fname=bdf_path,
eog=eog, misc=misc,
exclude=['M2', 'IEOG'], stim_channel=None)
assert len(raw_py.ch_names) == 71
raw_py = _test_raw_reader(read_raw_edf, input_fname=bdf_path,
montage=montage_path, eog=eog, misc=misc,
exclude=['M2', 'IEOG'], stim_channel=-1)
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_stim_channel():
"""Test BDF stim channel."""
# test if last channel is detected as STIM by default
raw_py = _test_raw_reader(read_raw_edf, input_fname=bdf_path,
stim_channel='auto')
assert channel_type(raw_py.info, raw_py.info["nchan"] - 1) == 'stim'
# test BDF file with wrong scaling info in header - this should be ignored
# for BDF stim channels
events = [[242, 0, 4],
[310, 0, 2],
[952, 0, 1],
[1606, 0, 1],
[2249, 0, 1],
[2900, 0, 1],
[3537, 0, 1],
[4162, 0, 1],
[4790, 0, 1]]
with pytest.deprecated_call(match='stim_channel'):
raw = read_raw_edf(bdf_stim_channel_path, preload=True)
bdf_events = find_events(raw)
assert_array_equal(events, bdf_events)
raw = read_raw_edf(bdf_stim_channel_path, preload=False,
stim_channel='auto')
bdf_events = find_events(raw)
assert_array_equal(events, bdf_events)
@testing.requires_testing_data
def test_edf_overlapping_annotations():
"""Test EDF with overlapping annotations."""
with pytest.warns(RuntimeWarning, match='overlapping.* not fully support'):
read_raw_edf(edf_overlap_annot_path, preload=True, stim_channel='auto',
verbose=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, stim_channel=None,
verbose='error')
def test_edf_data():
"""Test edf files."""
raw = _test_raw_reader(read_raw_edf, input_fname=edf_path,
stim_channel=None, exclude=['Ergo-Left', 'H10'],
verbose='error')
raw_py = read_raw_edf(edf_path, stim_channel='auto', preload=True)
assert_equal(len(raw.ch_names) + 2, len(raw_py.ch_names))
# Test saving and loading when annotations were parsed.
edf_events = find_events(raw_py, output='step', shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2],
[0.3904, 1.0000, 2],
[2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
# 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(bytes('-1 '.encode()))
fid_out.write(rbytes[244:])
with pytest.warns(RuntimeWarning,
match='records .* not match the file size'):
raw = read_raw_edf(broken_fname, preload=True, stim_channel='auto')
read_raw_edf(broken_fname, exclude=raw.ch_names[:132], preload=True,
stim_channel='auto')
@testing.requires_testing_data
def test_stim_channel():
"""Test reading raw edf files with stim channel."""
raw_py = read_raw_edf(edf_path, misc=range(-4, 0), stim_channel=139,
preload=True)
picks = pick_types(raw_py.info, meg=False, eeg=True,
exclude=['EDF Annotations'])
data_py, _ = raw_py[picks]
print(raw_py) # to test repr
print(raw_py.info) # to test Info repr
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = loadmat(edf_eeglab_path)
raw_eeglab = raw_eeglab['data'] * 1e-6 # data are stored in microvolts
data_eeglab = raw_eeglab[picks]
assert_array_almost_equal(data_py, data_eeglab, 10)
events = find_edf_events(raw_py)
assert len(events) - 1 == len(find_events(raw_py)) # start not found
# Test uneven sampling
raw_py = read_raw_edf(edf_uneven_path, stim_channel=None)
data_py, _ = raw_py[0]
# this .mat was generated using the EEG Lab Biosemi Reader
raw_eeglab = loadmat(edf_uneven_eeglab_path)
raw_eeglab = raw_eeglab['data']
data_eeglab = raw_eeglab[0]
# match upsampling
upsample = len(data_eeglab) / len(raw_py)
data_py = np.repeat(data_py, repeats=upsample)
assert_array_equal(data_py, data_eeglab)
pytest.raises(RuntimeError, read_raw_edf, edf_path, preload=False,
stim_channel=-1)
with pytest.warns(RuntimeWarning,
match='Interpolating stim .* Events may jitter'):
raw = read_raw_edf(edf_stim_resamp_path, verbose=True, stim_channel=-1)
with pytest.warns(None) as w:
raw[:]
assert len(w) == 0
events = raw_py.find_edf_events()
assert len(events) == 0
def test_parse_annotation():
"""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 = [a for a in iterbytes(annot)]
annot[1::2] = [a * 256 for a in annot[1::2]]
tal_channel = map(sum, zip(annot[0::2], annot[1::2]))
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_edf_annotations():
"""Test if events are detected correctly in a typical MNE workflow."""
# test an actual file
raw = read_raw_edf(edf_path, preload=True, stim_channel='auto')
edf_events = find_events(raw, output='step', shortest_event=0,
stim_channel='STI 014')
# onset, duration, id
events = [[0.1344, 0.2560, 2],
[0.3904, 1.0000, 2],
[2.0000, 0.0000, 3],
[2.5000, 2.5000, 2]]
events = np.array(events)
events[:, :2] *= 512 # convert time to samples
events = np.array(events, dtype=int)
events[:, 1] -= 1
events[events[:, 1] <= 0, 1] = 1
events[:, 1] += events[:, 0]
onsets = events[:, [0, 2]]
offsets = events[:, [1, 2]]
events = np.zeros((2 * events.shape[0], 3), dtype=int)
events[0::2, [0, 2]] = onsets
events[1::2, [0, 1]] = offsets
assert_array_equal(edf_events, events)
def test_edf_stim_channel():
"""Test stim channel for edf file."""
# test if stim channel is automatically detected
raw = read_raw_edf(edf_path, preload=True, stim_channel='auto')
assert channel_type(raw.info, raw.info["nchan"] - 1) == 'stim'
raw = read_raw_edf(edf_stim_channel_path, preload=True,
stim_channel=-1)
true_data = np.loadtxt(edf_txt_stim_channel_path).T
# EDF writer pad data if file to small
_, ns = true_data.shape
edf_data = raw._data[:, :ns]
# assert stim channels are equal
assert_array_equal(true_data[-1], edf_data[-1])
# assert data are equal
assert_array_almost_equal(true_data[0:-1] * 1e-6, edf_data[0:-1])
@requires_pandas
def test_to_data_frame():
"""Test edf Raw Pandas exporter."""
for path in [edf_path, bdf_path]:
raw = read_raw_edf(path, stim_channel=None, 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_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']]
SFREQ = 100
DATA_LENGTH = int(EXPECTED_ANNOTATIONS[-1][0] * SFREQ) + 1
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)
annotmap_file = tmpdir.join('annotations_map.txt')
annotmap_file.write('Lights off:1,nothing:2,Apnea:3,Close door:4')
stim_ch = _read_annot(annot=str(annot_file), annotmap=str(annotmap_file),
sfreq=SFREQ, data_length=DATA_LENGTH)
assert stim_ch.shape == (DATA_LENGTH,)
assert_array_equal(np.bincount(stim_ch), [180018, 0, 1, 1, 1])
def test_read_raw_edf_deprecation_of_annot_annotmap(tmpdir):
"""Test deprecation of annot and annotmap."""
annot = (b'+0.1344\x150.2560\x14two\x14\x00\x00\x00\x00'
b'+0.3904\x151.0\x14two\x14\x00\x00\x00\x00'
b'+2.0\x14three\x14\x00\x00\x00\x00\x00\x00\x00\x00'
b'+2.5\x152.5\x14two\x14\x00\x00\x00\x00')
annot_file = tmpdir.join('annotations.txt')
annot_file.write(annot)
annotmap_file = tmpdir.join('annotations_map.txt')
annotmap_file.write('two:2,three:3')
with pytest.warns(DeprecationWarning, match="annot.*annotmap.*"):
read_raw_edf(input_fname=edf_path, annot=str(annot_file),
annotmap=str(annotmap_file), preload=True)
def _compute_sfreq_from_edf_info(edf_info):
# Compute sfreq from edf_info
sel = edf_info['sel']
n_samps = edf_info['n_samps'][sel]
sfreq = n_samps.max() * \
edf_info['record_length'][1] / edf_info['record_length'][0]
return sfreq
def _get_empty_raw_with_valid_annot(fname):
raw = _RawShell()
raw.first_samp = 0
edf_info, orig_units = _read_edf_header(fname=fname, annot=None,
annotmap=None, exclude=())
sfreq = _compute_sfreq_from_edf_info(edf_info)
raw.info = _empty_info(sfreq)
raw.info['meas_date'] = edf_info['meas_date']
def _time_as_index(times, use_rounding, origin):
if use_rounding:
return np.round(np.atleast_1d(times) * sfreq)
else:
return np.floor(np.atleast_1d(times) * sfreq)
raw.time_as_index = _time_as_index
return raw
@testing.requires_testing_data
def test_find_events_and_events_from_annot_are_the_same():
"""Test that old behaviour and new produce the same events."""
EXPECTED_EVENTS = [[68, 0, 2],
[199, 0, 2],
[1024, 0, 3],
[1280, 0, 2]]
raw = read_raw_edf(edf_path, preload=True, stim_channel='auto')
raw_shell = _get_empty_raw_with_valid_annot(edf_path)
assert raw_shell.info['meas_date'] == raw.info['meas_date']
assert raw_shell.info['sfreq'] == raw.info['sfreq']
assert raw_shell.first_samp == raw.first_samp
events_from_find_events = find_events(raw)
assert_array_equal(events_from_find_events, EXPECTED_EVENTS)
annot = read_annotations(edf_path)
raw_shell.set_annotations(annot)
event_id = _get_edf_default_event_id(annot.description)
event_id.pop('start')
events_from_EFA, _ = events_from_annotations(raw_shell, event_id=event_id,
use_rounding=False)
assert_array_equal(events_from_EFA, events_from_find_events)
run_tests_if_main()
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