def test_export_raw_edf(tmp_path, dataset, format):
"""Test saving a Raw instance to EDF format."""
if dataset == 'test':
fname = _resource_path('mne.io.tests.data', 'test_raw.fif')
raw = read_raw_fif(fname)
elif dataset == 'misc':
fname = op.join(misc.data_path(), 'ecog', 'sample_ecog_ieeg.fif')
raw = read_raw_fif(fname)
# only test with EEG channels
raw.pick_types(eeg=True, ecog=True, seeg=True)
raw.load_data()
orig_ch_names = raw.ch_names
temp_fname = op.join(str(tmp_path), f'test.{format}')
# test runtime errors
with pytest.raises(RuntimeError, match='The maximum'), \
pytest.warns(RuntimeWarning, match='Data has a non-integer'):
raw.export(temp_fname, physical_range=(-1e6, 0))
with pytest.raises(RuntimeError, match='The minimum'), \
pytest.warns(RuntimeWarning, match='Data has a non-integer'):
raw.export(temp_fname, physical_range=(0, 1e6))
if dataset == 'test':
with pytest.warns(RuntimeWarning, match='Data has a non-integer'):
raw.export(temp_fname)
elif dataset == 'misc':
with pytest.warns(RuntimeWarning, match='EDF format requires'):
raw.export(temp_fname)
if 'epoc' in raw.ch_names:
raw.drop_channels(['epoc'])
raw_read = read_raw_edf(temp_fname, preload=True)
assert orig_ch_names == raw_read.ch_names
# only compare the original length, since extra zeros are appended
orig_raw_len = len(raw)
# assert data and times are not different
# Due to the physical range of the data, reading and writing is
# not lossless. For example, a physical min/max of -/+ 3200 uV
# will result in a resolution of 0.09 uV. This resolution
# though is acceptable for most EEG manufacturers.
assert_array_almost_equal(raw.get_data(),
raw_read.get_data()[:, :orig_raw_len],
decimal=4)
# Due to the data record duration limitations of EDF files, one
# cannot store arbitrary float sampling rate exactly. Usually this
# results in two sampling rates that are off by very low number of
# decimal points. This for practical purposes does not matter
# but will result in an error when say the number of time points
# is very very large.
assert_allclose(raw.times,
raw_read.times[:orig_raw_len],
rtol=0,
atol=1e-5)
import numpy as np
from numpy.testing import (assert_allclose, assert_array_almost_equal,
assert_array_equal)
from mne import (read_epochs_eeglab, Epochs, read_evokeds, read_evokeds_mff,
Annotations)
from mne.datasets import testing, misc
from mne.export import export_evokeds, export_evokeds_mff
from mne.io import (read_raw_fif, read_raw_eeglab, read_raw_edf,
read_raw_brainvision)
from mne.utils import (_check_eeglabio_installed, requires_version,
object_diff, _check_edflib_installed, _resource_path,
_check_pybv_installed)
from mne.tests.test_epochs import _get_data
fname_evoked = _resource_path('mne.io.tests.data', 'test-ave.fif')
fname_raw = _resource_path('mne.io.tests.data', 'test_raw.fif')
data_path = testing.data_path(download=False)
egi_evoked_fname = op.join(data_path, 'EGI', 'test_egi_evoked.mff')
misc_path = misc.data_path(download=False)
@pytest.mark.skipif(not _check_pybv_installed(strict=False),
reason='pybv not installed')
@pytest.mark.parametrize(['meas_date', 'orig_time', 'ext'], [
[None, None, '.vhdr'],
[
datetime(2022, 12, 3, 19, 1, 10, 720100, tzinfo=timezone.utc), None,
'.eeg'
],