def test_calculate_chpi_positions():
"""Test calculation of cHPI positions
"""
trans, rot, t = head_pos_to_trans_rot_t(read_head_pos(pos_fname))
with warnings.catch_warnings(record=True):
raw = Raw(chpi_fif_fname, allow_maxshield=True, preload=True)
t -= raw.first_samp / raw.info['sfreq']
quats = _calculate_chpi_positions(raw, verbose='debug')
trans_est, rot_est, t_est = head_pos_to_trans_rot_t(quats)
_compare_positions((trans, rot, t), (trans_est, rot_est, t_est), 0.003)
# degenerate conditions
raw_no_chpi = Raw(test_fif_fname)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = 999
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['r'] = np.ones(3)
raw_bad.crop(0, 1., copy=False)
with warnings.catch_warnings(record=True): # bad pos
with catch_logging() as log_file:
_calculate_chpi_positions(raw_bad, verbose=True)
# ignore HPI info header and [done] footer
for line in log_file.getvalue().strip().split('\n')[4:-1]:
assert_true('0/5 good' in line)
def test_compute_proj_eog():
"""Test computation of EOG SSP projectors"""
raw = Raw(raw_fname).crop(0, 10, False)
raw.preload_data()
for average in [False, True]:
n_projs_init = len(raw.info['projs'])
projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
bads=['MEG 2443'], average=average,
avg_ref=True, no_proj=False,
l_freq=None, h_freq=None,
reject=None, tmax=dur_use)
assert_true(len(projs) == (7 + n_projs_init))
assert_true(np.abs(events.shape[0] -
np.sum(np.less(eog_times, dur_use))) <= 1)
# XXX: better tests
# This will throw a warning b/c simplefilter('always')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs, events = compute_proj_eog(raw, n_mag=2, n_grad=2, n_eeg=2,
average=average, bads=[],
avg_ref=True, no_proj=False,
l_freq=None, h_freq=None,
tmax=dur_use)
assert_equal(len(w), 1)
assert_equal(projs, None)
def test_compute_proj_ecg():
"""Test computation of ECG SSP projectors"""
raw = Raw(raw_fname).crop(0, 10, False)
raw.preload_data()
for average in [False, True]:
# For speed, let's not filter here (must also not reject then)
projs, events = compute_proj_ecg(raw, n_mag=2, n_grad=2, n_eeg=2,
ch_name='MEG 1531', bads=['MEG 2443'],
average=average, avg_ref=True,
no_proj=True, l_freq=None,
h_freq=None, reject=None,
tmax=dur_use, qrs_threshold=0.5)
assert_true(len(projs) == 7)
# heart rate at least 0.5 Hz, but less than 3 Hz
assert_true(events.shape[0] > 0.5 * dur_use and
events.shape[0] < 3 * dur_use)
# XXX: better tests
# without setting a bad channel, this should throw a warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs, events = compute_proj_ecg(raw, n_mag=2, n_grad=2, n_eeg=2,
ch_name='MEG 1531', bads=[],
average=average, avg_ref=True,
no_proj=True, l_freq=None,
h_freq=None, tmax=dur_use)
assert_equal(len(w), 1)
assert_equal(projs, None)
def test_cov_estimation_on_raw():
"""Test estimation from raw (typically empty room)"""
tempdir = _TempDir()
raw = Raw(raw_fname, preload=False)
cov_mne = read_cov(erm_cov_fname)
cov = compute_raw_covariance(raw, tstep=None)
assert_equal(cov.ch_names, cov_mne.ch_names)
assert_equal(cov.nfree, cov_mne.nfree)
assert_snr(cov.data, cov_mne.data, 1e4)
cov = compute_raw_covariance(raw) # tstep=0.2 (default)
assert_equal(cov.nfree, cov_mne.nfree - 119) # cutoff some samples
assert_snr(cov.data, cov_mne.data, 1e2)
# test IO when computation done in Python
cov.save(op.join(tempdir, 'test-cov.fif')) # test saving
cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
assert_true(cov_read.ch_names == cov.ch_names)
assert_true(cov_read.nfree == cov.nfree)
assert_array_almost_equal(cov.data, cov_read.data)
# test with a subset of channels
picks = pick_channels(raw.ch_names, include=raw.ch_names[:5])
cov = compute_raw_covariance(raw, picks=picks, tstep=None)
assert_true(cov_mne.ch_names[:5] == cov.ch_names)
assert_snr(cov.data, cov_mne.data[picks][:, picks], 1e4)
cov = compute_raw_covariance(raw, picks=picks)
assert_snr(cov.data, cov_mne.data[picks][:, picks], 90) # cutoff samps
# make sure we get a warning with too short a segment
raw_2 = raw.crop(0, 1)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
cov = compute_raw_covariance(raw_2)
assert_true(any('Too few samples' in str(ww.message) for ww in w))
def test_ica_reset():
"""Test ICA resetting"""
raw = Raw(raw_fname).crop(0.5, stop, False)
raw.load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
run_time_attrs = (
'_pre_whitener',
'unmixing_matrix_',
'mixing_matrix_',
'n_components_',
'n_samples_',
'pca_components_',
'pca_explained_variance_',
'pca_mean_'
)
with warnings.catch_warnings(record=True):
ica = ICA(
n_components=3, max_pca_components=3, n_pca_components=3,
method='fastica', max_iter=1).fit(raw, picks=picks)
assert_true(all(hasattr(ica, attr) for attr in run_time_attrs))
ica._reset()
assert_true(not any(hasattr(ica, attr) for attr in run_time_attrs))
def test_calculate_chpi_positions():
"""Test calculation of cHPI positions
"""
trans, rot, t = get_chpi_positions(pos_fname)
with warnings.catch_warnings(record=True):
raw = Raw(raw_fif_fname, allow_maxshield=True, preload=True)
t -= raw.first_samp / raw.info['sfreq']
trans_est, rot_est, t_est = _calculate_chpi_positions(raw, verbose='debug')
_compare_positions((trans, rot, t), (trans_est, rot_est, t_est))
# degenerate conditions
raw_no_chpi = Raw(test_fif_fname)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = 999
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['r'] = np.ones(3)
raw_bad.crop(0, 1., copy=False)
with catch_logging() as log_file:
_calculate_chpi_positions(raw_bad)
for line in log_file.getvalue().split('\n')[:-1]:
assert_true('0/5 acceptable' in line)
def test_clean_info_bads():
"""Test cleaning info['bads'] when bad_channels are excluded """
raw_file = op.join(op.dirname(__file__), "io", "tests", "data", "test_raw.fif")
raw = Raw(raw_file)
# select eeg channels
picks_eeg = pick_types(raw.info, meg=False, eeg=True)
# select 3 eeg channels as bads
idx_eeg_bad_ch = picks_eeg[[1, 5, 14]]
eeg_bad_ch = [raw.info["ch_names"][k] for k in idx_eeg_bad_ch]
# select meg channels
picks_meg = pick_types(raw.info, meg=True, eeg=False)
# select randomly 3 meg channels as bads
idx_meg_bad_ch = picks_meg[[0, 15, 34]]
meg_bad_ch = [raw.info["ch_names"][k] for k in idx_meg_bad_ch]
# simulate the bad channels
raw.info["bads"] = eeg_bad_ch + meg_bad_ch
# simulate the call to pick_info excluding the bad eeg channels
info_eeg = pick_info(raw.info, picks_eeg)
# simulate the call to pick_info excluding the bad meg channels
info_meg = pick_info(raw.info, picks_meg)
assert_equal(info_eeg["bads"], eeg_bad_ch)
assert_equal(info_meg["bads"], meg_bad_ch)
def test_hilbert():
"""Test computation of analytic signal using hilbert
"""
raw = Raw(fif_fname, preload=True)
picks_meg = pick_types(raw.info, meg=True, exclude='bads')
picks = picks_meg[:4]
raw_filt = raw.copy()
raw_filt.filter(10, 20)
raw_filt_2 = raw_filt.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw.apply_hilbert(picks)
raw2.apply_hilbert(picks, envelope=True, n_jobs=2)
# Test custom n_fft
raw_filt.apply_hilbert(picks)
raw_filt_2.apply_hilbert(picks, n_fft=raw_filt_2.n_times + 1000)
assert_equal(raw_filt._data.shape, raw_filt_2._data.shape)
assert_allclose(raw_filt._data[:, 50:-50], raw_filt_2._data[:, 50:-50],
atol=1e-13, rtol=1e-2)
assert_raises(ValueError, raw3.apply_hilbert, picks,
n_fft=raw3.n_times - 100)
env = np.abs(raw._data[picks, :])
assert_allclose(env, raw2._data[picks, :], rtol=1e-2, atol=1e-13)
def _get_data():
# Read raw data
raw = Raw(raw_fname)
raw.info['bads'] = ['MEG 2443', 'EEG 053'] # 2 bads channels
# Set picks
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
# Read several epochs
event_id, tmin, tmax = 1, -0.2, 0.5
events = mne.read_events(event_fname)[0:100]
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True,
picks=picks, baseline=(None, 0), preload=True,
reject=dict(grad=4000e-13, mag=4e-12))
# Create an epochs object with one epoch and one channel of artificial data
event_id, tmin, tmax = 1, 0.0, 1.0
epochs_sin = mne.Epochs(raw, events[0:5], event_id, tmin, tmax, proj=True,
picks=[0], baseline=(None, 0), preload=True,
reject=dict(grad=4000e-13))
freq = 10
epochs_sin._data = np.sin(2 * np.pi * freq
* epochs_sin.times)[None, None, :]
return epochs, epochs_sin
def test_set_eeg_reference():
"""Test rereference eeg data"""
raw = Raw(fif_fname, preload=True)
raw.info['projs'] = []
# Test setting an average reference
assert_true(not _has_eeg_average_ref_proj(raw.info['projs']))
reref, ref_data = set_eeg_reference(raw)
assert_true(_has_eeg_average_ref_proj(reref.info['projs']))
assert_true(ref_data is None)
# Test setting an average reference when one was already present
with warnings.catch_warnings(record=True): # weight tables
reref, ref_data = set_eeg_reference(raw, copy=False)
assert_true(ref_data is None)
# Rereference raw data by creating a copy of original data
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'], copy=True)
assert_true(reref.info['custom_ref_applied'])
_test_reference(raw, reref, ref_data, ['EEG 001', 'EEG 002'])
# Test that data is modified in place when copy=False
reref, ref_data = set_eeg_reference(raw, ['EEG 001', 'EEG 002'],
copy=False)
assert_true(raw is reref)
def test_split_files():
"""Test writing and reading of split raw files
"""
tempdir = _TempDir()
raw_1 = Raw(fif_fname, preload=True)
assert_allclose(raw_1.info['buffer_size_sec'], 10., atol=1e-2) # samp rate
split_fname = op.join(tempdir, 'split_raw.fif')
raw_1.save(split_fname, buffer_size_sec=1.0, split_size='10MB')
raw_2 = Raw(split_fname)
assert_allclose(raw_2.info['buffer_size_sec'], 1., atol=1e-2) # samp rate
data_1, times_1 = raw_1[:, :]
data_2, times_2 = raw_2[:, :]
assert_array_equal(data_1, data_2)
assert_array_equal(times_1, times_2)
# test the case where the silly user specifies the split files
fnames = [split_fname]
fnames.extend(sorted(glob.glob(op.join(tempdir, 'split_raw-*.fif'))))
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
raw_2 = Raw(fnames)
data_2, times_2 = raw_2[:, :]
assert_array_equal(data_1, data_2)
assert_array_equal(times_1, times_2)
def test_ica_rank_reduction():
"""Test recovery of full data when no source is rejected"""
# Most basic recovery
raw = Raw(raw_fname).crop(0.5, stop, False)
raw.load_data()
picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
eog=False, exclude='bads')[:10]
n_components = 5
max_pca_components = len(picks)
for n_pca_components in [6, 10]:
with warnings.catch_warnings(record=True): # non-convergence
warnings.simplefilter('always')
ica = ICA(n_components=n_components,
max_pca_components=max_pca_components,
n_pca_components=n_pca_components,
method='fastica', max_iter=1).fit(raw, picks=picks)
rank_before = raw.estimate_rank(picks=picks)
assert_equal(rank_before, len(picks))
raw_clean = ica.apply(raw, copy=True)
rank_after = raw_clean.estimate_rank(picks=picks)
# interaction between ICA rejection and PCA components difficult
# to preduct. Rank_after often seems to be 1 higher then
# n_pca_components
assert_true(n_components < n_pca_components <= rank_after <=
rank_before)
def test_preload_modify():
"""Test preloading and modifying data
"""
tempdir = _TempDir()
for preload in [False, True, 'memmap.dat']:
raw = Raw(fif_fname, preload=preload)
nsamp = raw.last_samp - raw.first_samp + 1
picks = pick_types(raw.info, meg='grad', exclude='bads')
data = rng.randn(len(picks), nsamp // 2)
try:
raw[picks, :nsamp // 2] = data
except RuntimeError as err:
if not preload:
continue
else:
raise err
tmp_fname = op.join(tempdir, 'raw.fif')
raw.save(tmp_fname, overwrite=True)
raw_new = Raw(tmp_fname)
data_new, _ = raw_new[picks, :nsamp / 2]
assert_allclose(data, data_new)
def test_subject_info():
"""Test reading subject information
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 1, False)
assert_true(raw.info['subject_info'] is None)
# fake some subject data
keys = ['id', 'his_id', 'last_name', 'first_name', 'birthday', 'sex',
'hand']
vals = [1, 'foobar', 'bar', 'foo', (1901, 2, 3), 0, 1]
subject_info = dict()
for key, val in zip(keys, vals):
subject_info[key] = val
raw.info['subject_info'] = subject_info
out_fname = op.join(tempdir, 'test_subj_info_raw.fif')
raw.save(out_fname, overwrite=True)
raw_read = Raw(out_fname)
for key in keys:
assert_equal(subject_info[key], raw_read.info['subject_info'][key])
raw_read.anonymize()
assert_true(raw_read.info.get('subject_info') is None)
out_fname_anon = op.join(tempdir, 'test_subj_info_anon_raw.fif')
raw_read.save(out_fname_anon, overwrite=True)
raw_read = Raw(out_fname_anon)
assert_true(raw_read.info.get('subject_info') is None)
def test_add_channels():
"""Test raw splitting / re-appending channel types
"""
raw = Raw(test_fif_fname).crop(0, 1).load_data()
raw_nopre = Raw(test_fif_fname, preload=False)
raw_eeg_meg = raw.pick_types(meg=True, eeg=True, copy=True)
raw_eeg = raw.pick_types(meg=False, eeg=True, copy=True)
raw_meg = raw.pick_types(meg=True, eeg=False, copy=True)
raw_stim = raw.pick_types(meg=False, eeg=False, stim=True, copy=True)
raw_new = raw_meg.add_channels([raw_eeg, raw_stim], copy=True)
assert_true(
all(ch in raw_new.ch_names
for ch in list(raw_stim.ch_names) + list(raw_meg.ch_names))
)
raw_new = raw_meg.add_channels([raw_eeg], copy=True)
assert_true(ch in raw_new.ch_names for ch in raw.ch_names)
assert_array_equal(raw_new[:, :][0], raw_eeg_meg[:, :][0])
assert_array_equal(raw_new[:, :][1], raw[:, :][1])
assert_true(all(ch not in raw_new.ch_names for ch in raw_stim.ch_names))
# Now test errors
raw_badsf = raw_eeg.copy()
raw_badsf.info['sfreq'] = 3.1415927
raw_eeg = raw_eeg.crop(.5)
assert_raises(AssertionError, raw_meg.add_channels, [raw_nopre])
assert_raises(RuntimeError, raw_meg.add_channels, [raw_badsf])
assert_raises(AssertionError, raw_meg.add_channels, [raw_eeg])
assert_raises(ValueError, raw_meg.add_channels, [raw_meg])
assert_raises(AssertionError, raw_meg.add_channels, raw_badsf)
def test_calculate_chpi_positions():
"""Test calculation of cHPI positions
"""
trans, rot, t = head_pos_to_trans_rot_t(read_head_pos(pos_fname))
raw = Raw(chpi_fif_fname, allow_maxshield="yes", preload=True)
t -= raw.first_samp / raw.info["sfreq"]
quats = _calculate_chpi_positions(raw, verbose="debug")
trans_est, rot_est, t_est = head_pos_to_trans_rot_t(quats)
_compare_positions((trans, rot, t), (trans_est, rot_est, t_est), 0.003)
# degenerate conditions
raw_no_chpi = Raw(test_fif_fname)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
for d in raw_bad.info["dig"]:
if d["kind"] == FIFF.FIFFV_POINT_HPI:
d["coord_frame"] = 999
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info["dig"]:
if d["kind"] == FIFF.FIFFV_POINT_HPI:
d["r"] = np.ones(3)
raw_bad.crop(0, 1.0, copy=False)
with warnings.catch_warnings(record=True): # bad pos
with catch_logging() as log_file:
_calculate_chpi_positions(raw_bad, verbose=True)
# ignore HPI info header and [done] footer
for line in log_file.getvalue().strip().split("\n")[4:-1]:
assert_true("0/5 good" in line)
# half the rate cuts off cHPI coils
with warnings.catch_warnings(record=True): # uint cast suggestion
raw.resample(300.0, npad="auto")
assert_raises_regex(RuntimeError, "above the", _calculate_chpi_positions, raw)
def test_chpi_subtraction():
"""Test subtraction of cHPI signals"""
raw = Raw(chpi_fif_fname, allow_maxshield="yes", preload=True)
with catch_logging() as log:
filter_chpi(raw, include_line=False, verbose=True)
assert_true("5 cHPI" in log.getvalue())
# MaxFilter doesn't do quite as well as our algorithm with the last bit
raw.crop(0, 16, copy=False)
# remove cHPI status chans
raw_c = Raw(sss_hpisubt_fname).crop(0, 16, copy=False).load_data()
raw_c.pick_types(meg=True, eeg=True, eog=True, ecg=True, stim=True, misc=True)
assert_meg_snr(raw, raw_c, 143, 624)
# Degenerate cases
raw_nohpi = Raw(test_fif_fname, preload=True)
assert_raises(RuntimeError, filter_chpi, raw_nohpi)
# When MaxFliter downsamples, like::
# $ maxfilter -nosss -ds 2 -f test_move_anon_raw.fif \
# -o test_move_anon_ds2_raw.fif
# it can strip out some values of info, which we emulate here:
raw = Raw(chpi_fif_fname, allow_maxshield="yes")
with warnings.catch_warnings(record=True): # uint cast suggestion
raw = raw.crop(0, 1).load_data().resample(600.0, npad="auto")
raw.info["buffer_size_sec"] = np.float64(2.0)
raw.info["lowpass"] = 200.0
del raw.info["maxshield"]
del raw.info["hpi_results"][0]["moments"]
del raw.info["hpi_subsystem"]["event_channel"]
with catch_logging() as log:
filter_chpi(raw, verbose=True)
assert_true("2 cHPI" in log.getvalue())
def test_bads_reconstruction():
"""Test Maxwell filter reconstruction of bad channels"""
raw = Raw(raw_fname, allow_maxshield='yes').crop(0., 1., False)
raw.info['bads'] = bads
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, Raw(sss_bad_recon_fname), 300.)
def test_cross_talk():
"""Test Maxwell filter cross-talk cancellation"""
raw = Raw(raw_fname, allow_maxshield='yes').crop(0., 1., False)
raw.info['bads'] = bads
sss_ctc = Raw(sss_ctc_fname)
raw_sss = maxwell_filter(raw, cross_talk=ctc_fname,
origin=mf_head_origin, regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_ctc, 275.)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_true(len(py_ctc) > 0)
assert_raises(ValueError, maxwell_filter, raw, cross_talk=raw)
assert_raises(ValueError, maxwell_filter, raw, cross_talk=raw_fname)
mf_ctc = sss_ctc.info['proc_history'][0]['max_info']['sss_ctc']
del mf_ctc['block_id'] # we don't write this
assert_equal(object_diff(py_ctc, mf_ctc), '')
raw_ctf = Raw(fname_ctf_raw)
assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04))
_assert_n_free(raw_sss, 68)
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 70)
raw_missing = raw.crop(0, 0.1, copy=True).load_data().pick_channels(
[raw.ch_names[pi] for pi in pick_types(raw.info, meg=True,
exclude=())[3:]])
with warnings.catch_warnings(record=True) as w:
maxwell_filter(raw_missing, cross_talk=ctc_fname)
assert_equal(len(w), 1)
assert_true('Not all cross-talk channels in raw' in str(w[0].message))
# MEG channels not in cross-talk
assert_raises(RuntimeError, maxwell_filter, raw_ctf, origin=(0., 0., 0.04),
cross_talk=ctc_fname)
def test_make_eeg_layout():
""" Test creation of EEG layout """
tmp_name = 'foo'
lout_name = 'test_raw'
lout_orig = read_layout(kind=lout_name, path=lout_path)
info = Raw(fif_fname).info
layout = make_eeg_layout(info)
layout.save(op.join(tempdir, tmp_name + '.lout'))
lout_new = read_layout(kind=tmp_name, path=tempdir, scale=False)
assert_array_equal(lout_new.kind, tmp_name)
assert_allclose(layout.pos, lout_new.pos, atol=0.1)
assert_array_equal(lout_orig.names, lout_new.names)
# Test input validation
assert_raises(ValueError, make_eeg_layout, info, radius=-0.1)
assert_raises(ValueError, make_eeg_layout, info, radius=0.6)
assert_raises(ValueError, make_eeg_layout, info, width=-0.1)
assert_raises(ValueError, make_eeg_layout, info, width=1.1)
assert_raises(ValueError, make_eeg_layout, info, height=-0.1)
assert_raises(ValueError, make_eeg_layout, info, height=1.1)
bad_info = info.copy()
bad_info['dig'] = None
assert_raises(RuntimeError, make_eeg_layout, bad_info)
bad_info['dig'] = []
assert_raises(RuntimeError, make_eeg_layout, bad_info)
def perform_detrending(fname_raw, save=True):
from mne.io import Raw
from numpy import poly1d, polyfit
fnraw = get_files_from_list(fname_raw)
# loop across all filenames
for fname in fnraw:
# read data in
raw = Raw(fname, preload=True)
# get channels
picks = mne.pick_types(raw.info, meg='mag', ref_meg=True, eeg=False, stim=False,
eog=False, exclude='bads')
xval = np.arange(raw._data.shape[1])
# loop over all channels
for ipick in picks:
coeff = polyfit(xval, raw._data[ipick, :], deg=1)
trend = poly1d(coeff)
raw._data[ipick, :] -= trend(xval)
# save detrended data
if save:
fnout = fname_raw[:fname_raw.rfind('-raw.fif')] + ',dt-raw.fif'
raw.save(fnout, overwrite=True)
return raw
def test_ch_loc():
"""Test raw kit loc
"""
raw_py = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path, stim='<')
raw_bin = Raw(op.join(data_dir, 'test_bin_raw.fif'))
ch_py = raw_py._raw_extras[0]['sensor_locs'][:, :5]
# ch locs stored as m, not mm
ch_py[:, :3] *= 1e3
ch_sns = read_sns(op.join(data_dir, 'sns.txt'))
assert_array_almost_equal(ch_py, ch_sns, 2)
assert_array_almost_equal(raw_py.info['dev_head_t']['trans'],
raw_bin.info['dev_head_t']['trans'], 4)
for py_ch, bin_ch in zip(raw_py.info['chs'], raw_bin.info['chs']):
if bin_ch['ch_name'].startswith('MEG'):
# the stored ch locs have more precision than the sns.txt
assert_array_almost_equal(py_ch['loc'], bin_ch['loc'], decimal=2)
# test when more than one marker file provided
mrks = [mrk_path, mrk2_path, mrk3_path]
read_raw_kit(sqd_path, mrks, elp_path, hsp_path, preload=False)
# this dataset does not have the equivalent set of points :(
raw_bin.info['dig'] = raw_bin.info['dig'][:8]
raw_py.info['dig'] = raw_py.info['dig'][:8]
assert_dig_allclose(raw_py.info, raw_bin.info)
def test_bads_reconstruction():
"""Test Maxwell filter reconstruction of bad channels"""
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, allow_maxshield=True).crop(0.0, 1.0, False)
raw.info["bads"] = bads
raw_sss = maxwell_filter(raw, origin=mf_head_origin, regularize=None, bad_condition="ignore")
assert_meg_snr(raw_sss, Raw(sss_bad_recon_fname), 300.0)
def test_set_channel_types():
"""Test set_channel_types
"""
raw = Raw(raw_fname)
# Error Tests
# Test channel name exists in ch_names
mapping = {'EEG 160': 'EEG060'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test change to illegal channel type
mapping = {'EOG 061': 'xxx'}
assert_raises(ValueError, raw.set_channel_types, mapping)
# Test type change
raw2 = Raw(raw_fname)
raw2.info['bads'] = ['EEG 059', 'EEG 060', 'EOG 061']
mapping = {'EEG 060': 'eog', 'EEG 059': 'ecg', 'EOG 061': 'seeg'}
raw2.set_channel_types(mapping)
info = raw2.info
assert_true(info['chs'][374]['ch_name'] == 'EEG 060')
assert_true(info['chs'][374]['kind'] == FIFF.FIFFV_EOG_CH)
assert_true(info['chs'][374]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][374]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][373]['ch_name'] == 'EEG 059')
assert_true(info['chs'][373]['kind'] == FIFF.FIFFV_ECG_CH)
assert_true(info['chs'][373]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][373]['coil_type'] == FIFF.FIFFV_COIL_NONE)
assert_true(info['chs'][375]['ch_name'] == 'EOG 061')
assert_true(info['chs'][375]['kind'] == FIFF.FIFFV_SEEG_CH)
assert_true(info['chs'][375]['unit'] == FIFF.FIFF_UNIT_V)
assert_true(info['chs'][375]['coil_type'] == FIFF.FIFFV_COIL_EEG)
def test_io_complex():
"""Test IO with complex data types
"""
rng = np.random.RandomState(0)
tempdir = _TempDir()
dtypes = [np.complex64, np.complex128]
raw = Raw(fif_fname, preload=True)
picks = np.arange(5)
start, stop = raw.time_as_index([0, 5])
data_orig, _ = raw[picks, start:stop]
for di, dtype in enumerate(dtypes):
imag_rand = np.array(1j * rng.randn(data_orig.shape[0], data_orig.shape[1]), dtype)
raw_cp = raw.copy()
raw_cp._data = np.array(raw_cp._data, dtype)
raw_cp._data[picks, start:stop] += imag_rand
# this should throw an error because it's complex
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_cp.save(op.join(tempdir, "raw.fif"), picks, tmin=0, tmax=5, overwrite=True)
# warning gets thrown on every instance b/c simplifilter('always')
assert_equal(len(w), 1)
raw2 = Raw(op.join(tempdir, "raw.fif"))
raw2_data, _ = raw2[picks, :]
n_samp = raw2_data.shape[1]
assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])
# with preloading
raw2 = Raw(op.join(tempdir, "raw.fif"), preload=True)
raw2_data, _ = raw2[picks, :]
n_samp = raw2_data.shape[1]
assert_allclose(raw2_data[:, :n_samp], raw_cp._data[picks, :n_samp])
def test_calculate_chpi_positions():
"""Test calculation of cHPI positions
"""
trans, rot, t = get_chpi_positions(pos_fname)
with warnings.catch_warnings(record=True):
raw = Raw(raw_fif_fname, allow_maxshield=True, preload=True)
t -= raw.first_samp / raw.info['sfreq']
trans_est, rot_est, t_est = _calculate_chpi_positions(raw, verbose='debug')
_compare_positions((trans, rot, t), (trans_est, rot_est, t_est))
# degenerate conditions
raw_no_chpi = Raw(test_fif_fname)
assert_raises(RuntimeError, _calculate_chpi_positions, raw_no_chpi)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['coord_frame'] = 999
break
assert_raises(RuntimeError, _calculate_chpi_positions, raw_bad)
raw_bad = raw.copy()
for d in raw_bad.info['dig']:
if d['kind'] == FIFF.FIFFV_POINT_HPI:
d['r'] = np.ones(3)
raw_bad.crop(0, 1., copy=False)
tempdir = _TempDir()
log_file = op.join(tempdir, 'temp_log.txt')
set_log_file(log_file, overwrite=True)
try:
_calculate_chpi_positions(raw_bad)
finally:
set_log_file()
with open(log_file, 'r') as fid:
for line in fid:
assert_true('0/5 acceptable' in line)
def manual_filter():
fname='ec_rest_before_tsss_mc_rsl.fif'
raw = Raw(fname, preload=False)
raw.preload_data() # data becomes numpy.float64
_mmap_raw(raw)
copy = False # filter in-place
zero_phase = True
n_jobs = 1
picks = pick_types(raw.info, exclude=[], meg=True)
x = raw._data # pointer?
Fs = 1000.
Fp = 20.
Fstop = 21.
filter_length='10s'
print('x.shape before prepping:', x.shape)
h_fft, n_h, n_edge, orig_shape = _do_prep(Fs, Fp, Fstop, x, copy, picks,
filter_length, zero_phase)
print('x.shape after prepping:', x.shape)
print('Before filtering:')
print(x[0][:10])
for p in picks:
mangle_x(x[p])
# _1d_overlap_filter(x[p], h_fft, n_h, n_edge, zero_phase,
# dict(use_cuda=False))
#x.shape = orig_shape
print('After filtering:')
print(x[0][:10])
print('Data type:', raw._data[0][:5])
print(type(raw._data))
def test_bads_reconstruction():
"""Test Maxwell filter reconstruction of bad channels"""
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, allow_maxshield=True).crop(0., 1., False)
raw.info['bads'] = bads
raw_sss = maxwell_filter(raw)
_assert_snr(raw_sss, Raw(sss_bad_recon_fname), 300.)
def test_find_ecg():
"""Test find ECG peaks"""
raw = Raw(raw_fname)
# once with mag-trick
# once with characteristic channel
for ch_name in ['MEG 1531', None]:
events, ch_ECG, average_pulse, ecg = find_ecg_events(
raw, event_id=999, ch_name=None, return_ecg=True)
assert_equal(len(raw.times), len(ecg))
n_events = len(events)
_, times = raw[0, :]
assert_true(55 < average_pulse < 60)
picks = pick_types(
raw.info, meg='grad', eeg=False, stim=False,
eog=False, ecg=True, emg=False, ref_meg=False,
exclude='bads')
raw.load_data()
ecg_epochs = create_ecg_epochs(raw, picks=picks, keep_ecg=True)
assert_equal(len(ecg_epochs.events), n_events)
assert_true('ECG-SYN' not in raw.ch_names)
assert_true('ECG-SYN' in ecg_epochs.ch_names)
picks = pick_types(
ecg_epochs.info, meg=False, eeg=False, stim=False,
eog=False, ecg=True, emg=False, ref_meg=False,
exclude='bads')
assert_true(len(picks) == 1)
def test_cov_estimation_on_raw_segment():
"""Test estimation from raw on continuous recordings (typically empty room)
"""
tempdir = _TempDir()
raw = Raw(raw_fname, preload=False)
cov = compute_raw_data_covariance(raw)
cov_mne = read_cov(erm_cov_fname)
assert_true(cov_mne.ch_names == cov.ch_names)
assert_true(linalg.norm(cov.data - cov_mne.data, ord='fro')
/ linalg.norm(cov.data, ord='fro') < 1e-4)
# test IO when computation done in Python
cov.save(op.join(tempdir, 'test-cov.fif')) # test saving
cov_read = read_cov(op.join(tempdir, 'test-cov.fif'))
assert_true(cov_read.ch_names == cov.ch_names)
assert_true(cov_read.nfree == cov.nfree)
assert_array_almost_equal(cov.data, cov_read.data)
# test with a subset of channels
picks = pick_channels(raw.ch_names, include=raw.ch_names[:5])
cov = compute_raw_data_covariance(raw, picks=picks)
assert_true(cov_mne.ch_names[:5] == cov.ch_names)
assert_true(linalg.norm(cov.data - cov_mne.data[picks][:, picks],
ord='fro') / linalg.norm(cov.data, ord='fro') < 1e-4)
# make sure we get a warning with too short a segment
raw_2 = raw.crop(0, 1)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
cov = compute_raw_data_covariance(raw_2)
assert_true(len(w) == 1)
def test_simulate_raw_sphere():
"""Test simulation of raw data with sphere model"""
seed = 42
raw, src, stc, trans, sphere = _get_data()
assert_true(len(pick_types(raw.info, meg=False, ecg=True)) == 1)
# head pos
head_pos_sim = dict()
# these will be at 1., 2., ... sec
shifts = [[0.001, 0., -0.001], [-0.001, 0.001, 0.]]
for time_key, shift in enumerate(shifts):
# Create 4x4 matrix transform and normalize
temp_trans = deepcopy(raw.info['dev_head_t'])
temp_trans['trans'][:3, 3] += shift
head_pos_sim[time_key + 1.] = temp_trans['trans']
#
# Test raw simulation with basic parameters
#
raw_sim = simulate_raw(raw, stc, trans, src, sphere, read_cov(cov_fname),
head_pos=head_pos_sim,
blink=True, ecg=True, random_state=seed)
raw_sim_2 = simulate_raw(raw, stc, trans_fname, src_fname, sphere,
cov_fname, head_pos=head_pos_sim,
blink=True, ecg=True, random_state=seed)
assert_array_equal(raw_sim_2[:][0], raw_sim[:][0])
# Test IO on processed data
tempdir = _TempDir()
test_outname = op.join(tempdir, 'sim_test_raw.fif')
raw_sim.save(test_outname)
raw_sim_loaded = Raw(test_outname, preload=True, proj=False)
assert_allclose(raw_sim_loaded[:][0], raw_sim[:][0], rtol=1e-6, atol=1e-20)
del raw_sim, raw_sim_2
# with no cov (no noise) but with artifacts, most time periods should match
# but the EOG/ECG channels should not
for ecg, eog in ((True, False), (False, True), (True, True)):
raw_sim_3 = simulate_raw(raw, stc, trans, src, sphere,
cov=None, head_pos=head_pos_sim,
blink=eog, ecg=ecg, random_state=seed)
raw_sim_4 = simulate_raw(raw, stc, trans, src, sphere,
cov=None, head_pos=head_pos_sim,
blink=False, ecg=False, random_state=seed)
picks = np.arange(len(raw.ch_names))
diff_picks = pick_types(raw.info, meg=False, ecg=ecg, eog=eog)
these_picks = np.setdiff1d(picks, diff_picks)
close = isclose(raw_sim_3[these_picks][0],
raw_sim_4[these_picks][0], atol=1e-20)
assert_true(np.mean(close) > 0.7)
far = ~isclose(raw_sim_3[diff_picks][0],
raw_sim_4[diff_picks][0], atol=1e-20)
assert_true(np.mean(far) > 0.99)
del raw_sim_3, raw_sim_4
# make sure it works with EEG-only and MEG-only
raw_sim_meg = simulate_raw(raw.copy().pick_types(meg=True, eeg=False),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
raw_sim_eeg = simulate_raw(raw.copy().pick_types(meg=False, eeg=True),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
raw_sim_meeg = simulate_raw(raw.copy().pick_types(meg=True, eeg=True),
stc, trans, src, sphere, cov=None,
ecg=True, blink=True, random_state=seed)
assert_allclose(np.concatenate((raw_sim_meg[:][0], raw_sim_eeg[:][0])),
raw_sim_meeg[:][0], rtol=1e-7, atol=1e-20)
del raw_sim_meg, raw_sim_eeg, raw_sim_meeg
# check that different interpolations are similar given small movements
raw_sim_cos = simulate_raw(raw, stc, trans, src, sphere,
head_pos=head_pos_sim,
random_state=seed)
raw_sim_lin = simulate_raw(raw, stc, trans, src, sphere,
head_pos=head_pos_sim, interp='linear',
random_state=seed)
assert_allclose(raw_sim_cos[:][0], raw_sim_lin[:][0],
rtol=1e-5, atol=1e-20)
del raw_sim_cos, raw_sim_lin
# Make impossible transform (translate up into helmet) and ensure failure
head_pos_sim_err = deepcopy(head_pos_sim)
head_pos_sim_err[1.][2, 3] -= 0.1 # z trans upward 10cm
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
ecg=False, blink=False, head_pos=head_pos_sim_err)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src,
bem_fname, ecg=False, blink=False,
head_pos=head_pos_sim_err)
# other degenerate conditions
assert_raises(TypeError, simulate_raw, 'foo', stc, trans, src, sphere)
assert_raises(TypeError, simulate_raw, raw, 'foo', trans, src, sphere)
assert_raises(ValueError, simulate_raw, raw, stc.copy().crop(0, 0),
trans, src, sphere)
stc_bad = stc.copy()
stc_bad.tstep += 0.1
assert_raises(ValueError, simulate_raw, raw, stc_bad, trans, src, sphere)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
chpi=True) # no cHPI info
assert_raises(ValueError, simulate_raw, raw, stc, trans, src, sphere,
interp='foo')
assert_raises(TypeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=1.)
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=pos_fname) # ends up with t>t_end
head_pos_sim_err = deepcopy(head_pos_sim)
head_pos_sim_err[-1.] = head_pos_sim_err[1.] # negative time
assert_raises(RuntimeError, simulate_raw, raw, stc, trans, src, sphere,
head_pos=head_pos_sim_err)
raw_bad = raw.copy()
raw_bad.info['dig'] = None
assert_raises(RuntimeError, simulate_raw, raw_bad, stc, trans, src, sphere,
blink=True)
import numpy as np
import mne
from mne.io import Raw
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample
print(__doc__)
###############################################################################
# Setup paths and prepare epochs data
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = Raw(raw_fname, preload=True)
raw.filter(1, 30, method='iir')
picks = mne.pick_types(raw.info,
meg=True,
eeg=False,
eog=True,
ecg=True,
stim=False,
exclude='bads')
# longer + more epochs for more artifact exposure
events = mne.find_events(raw, stim_channel='STI 014')
event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)
reject = dict(eog=250e-6)
tmin, tmax = -0.5, 0.5
epochs = mne.Epochs(raw,
def test_proj():
"""Test SSP proj operations
"""
tempdir = _TempDir()
for proj in [True, False]:
raw = Raw(fif_fname, preload=False, proj=proj)
assert_true(all(p['active'] == proj for p in raw.info['projs']))
data, times = raw[0:2, :]
data1, times1 = raw[0:2]
assert_array_equal(data, data1)
assert_array_equal(times, times1)
# test adding / deleting proj
if proj:
assert_raises(ValueError, raw.add_proj, [],
{'remove_existing': True})
assert_raises(ValueError, raw.del_proj, 0)
else:
projs = deepcopy(raw.info['projs'])
n_proj = len(raw.info['projs'])
raw.del_proj(0)
assert_equal(len(raw.info['projs']), n_proj - 1)
raw.add_proj(projs, remove_existing=False)
assert_equal(len(raw.info['projs']), 2 * n_proj - 1)
raw.add_proj(projs, remove_existing=True)
assert_equal(len(raw.info['projs']), n_proj)
# test apply_proj() with and without preload
for preload in [True, False]:
raw = Raw(fif_fname, preload=preload, proj=False)
data, times = raw[:, 0:2]
raw.apply_proj()
data_proj_1 = np.dot(raw._projector, data)
# load the file again without proj
raw = Raw(fif_fname, preload=preload, proj=False)
# write the file with proj. activated, make sure proj has been applied
raw.save(op.join(tempdir, 'raw.fif'), proj=True, overwrite=True)
raw2 = Raw(op.join(tempdir, 'raw.fif'), proj=False)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# read orig file with proj. active
raw2 = Raw(fif_fname, preload=preload, proj=True)
data_proj_2, _ = raw2[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_true(all(p['active'] for p in raw2.info['projs']))
# test that apply_proj works
raw.apply_proj()
data_proj_2, _ = raw[:, 0:2]
assert_allclose(data_proj_1, data_proj_2)
assert_allclose(data_proj_2, np.dot(raw._projector, data_proj_2))
tempdir = _TempDir()
out_fname = op.join(tempdir, 'test_raw.fif')
raw = read_raw_fif(test_fif_fname, preload=True).crop(0, 0.002, copy=False)
raw.pick_types(meg=False, eeg=True)
raw.info['projs'] = [raw.info['projs'][-1]]
raw._data.fill(0)
raw._data[-1] = 1.
raw.save(out_fname)
raw = read_raw_fif(out_fname, proj=True, preload=False)
assert_allclose(raw[:, :][0][:1], raw[0, :][0])
def test_movement_compensation():
"""Test movement compensation"""
lims = (0, 8)
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, allow_maxshield=True, preload=True).crop(*lims)
head_pos = read_head_pos(pos_fname)
#
# Movement compensation, no regularization, no tSSS
#
raw_sss = maxwell_filter(raw,
head_pos=head_pos,
origin=mf_head_origin,
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss,
Raw(sss_movecomp_fname).crop(*lims),
4.6,
12.4,
chpi_med_tol=58)
#
# Movement compensation, regularization, no tSSS
#
raw_sss = maxwell_filter(raw, head_pos=head_pos, origin=mf_head_origin)
assert_meg_snr(raw_sss,
Raw(sss_movecomp_reg_in_fname).crop(*lims),
0.7,
1.9,
chpi_med_tol=121)
#
# Movement compensation, regularization, tSSS at the end
#
raw_nohpi = filter_chpi(raw.copy())
with warnings.catch_warnings(record=True) as w: # untested feature
raw_sss_mv = maxwell_filter(raw_nohpi,
head_pos=head_pos,
st_duration=4.,
origin=mf_head_origin,
st_fixed=False)
assert_equal(len(w), 1)
assert_true('is untested' in str(w[0].message))
# Neither match is particularly good because our algorithm actually differs
assert_meg_snr(raw_sss_mv,
Raw(sss_movecomp_reg_in_st4s_fname).crop(*lims), 0.6, 1.3)
tSSS_fname = op.join(sss_path, 'test_move_anon_st4s_raw_sss.fif')
assert_meg_snr(raw_sss_mv,
Raw(tSSS_fname).crop(*lims),
0.6,
1.0,
chpi_med_tol=None)
assert_meg_snr(Raw(sss_movecomp_reg_in_st4s_fname),
Raw(tSSS_fname),
0.8,
1.0,
chpi_med_tol=None)
#
# Movement compensation, regularization, tSSS at the beginning
#
raw_sss_mc = maxwell_filter(raw_nohpi,
head_pos=head_pos,
st_duration=4.,
origin=mf_head_origin)
assert_meg_snr(raw_sss_mc,
Raw(tSSS_fname).crop(*lims),
0.6,
1.0,
chpi_med_tol=None)
assert_meg_snr(raw_sss_mc, raw_sss_mv, 0.6, 1.4)
# some degenerate cases
with warnings.catch_warnings(record=True): # maxshield
raw_erm = Raw(erm_fname, allow_maxshield=True)
assert_raises(ValueError,
maxwell_filter,
raw_erm,
coord_frame='meg',
head_pos=head_pos) # can't do ERM file
head_pos_bad = head_pos[:, :9]
assert_raises(ValueError, maxwell_filter, raw,
head_pos=head_pos_bad) # bad shape
head_pos_bad = 'foo'
assert_raises(TypeError, maxwell_filter, raw,
head_pos=head_pos_bad) # bad type
head_pos_bad = head_pos[::-1]
assert_raises(ValueError, maxwell_filter, raw, head_pos=head_pos_bad)
head_pos_bad = head_pos.copy()
head_pos_bad[0, 0] = 1. # bad time given the first_samp...
assert_raises(ValueError, maxwell_filter, raw, head_pos=head_pos_bad)
def test_has_eeg_average_ref_proj():
"""Test checking whether an EEG average reference exists"""
assert_true(not _has_eeg_average_ref_proj([]))
raw = Raw(raw_fname, add_eeg_ref=True, preload=False)
assert_true(_has_eeg_average_ref_proj(raw.info['projs']))
def test_compute_proj_raw():
"""Test SSP computation on raw"""
tempdir = _TempDir()
# Test that the raw projectors work
raw_time = 2.5 # Do shorter amount for speed
raw = Raw(raw_fname, preload=True).crop(0, raw_time, False)
for ii in (0.25, 0.5, 1, 2):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs = compute_proj_raw(raw, duration=ii - 0.1, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(w) == 1)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_%d_raw.fif' % ii), overwrite=True)
# Test that purely continuous (no duration) raw projection works
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs = compute_proj_raw(raw, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
assert_equal(len(w), 1)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, raw.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
raw.info['projs'] += projs
raw.save(op.join(tempdir, 'foo_rawproj_continuous_raw.fif'))
# test resampled-data projector, upsampling instead of downsampling
# here to save an extra filtering (raw would have to be LP'ed to be equiv)
raw_resamp = cp.deepcopy(raw)
raw_resamp.resample(raw.info['sfreq'] * 2, n_jobs=2)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs = compute_proj_raw(raw_resamp, duration=None, stop=raw_time,
n_grad=1, n_mag=1, n_eeg=0)
projs = activate_proj(projs)
proj_new, _, _ = make_projector(projs, raw.ch_names, bads=[])
assert_array_almost_equal(proj_new, proj, 4)
# test with bads
raw.load_bad_channels(bads_fname) # adds 2 bad mag channels
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
projs = compute_proj_raw(raw, n_grad=0, n_mag=0, n_eeg=1)
# test that bad channels can be excluded
proj, nproj, U = make_projector(projs, raw.ch_names,
bads=raw.ch_names)
assert_array_almost_equal(proj, np.eye(len(raw.ch_names)))
fname_event = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
event_id, tmin, tmax = 1, -0.2, 0.5
# Using the same inverse operator when inspecting single trials Vs. evoked
snr = 3.0 # Standard assumption for average data but using it for single trial
lambda2 = 1.0 / snr**2
method = "dSPM" # use dSPM method (could also be MNE or sLORETA)
# Load data
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
raw = Raw(fname_raw)
events = mne.read_events(fname_event)
# Set up pick list
include = []
# Add a bad channel
raw.info['bads'] += ['EEG 053'] # bads + 1 more
# pick MEG channels
picks = mne.pick_types(raw.info,
meg=True,
eeg=False,
stim=False,
eog=True,
include=include,
def test_shielding_factor():
"""Test Maxwell filter shielding factor using empty room"""
with warnings.catch_warnings(record=True): # maxshield
raw_erm = Raw(erm_fname, allow_maxshield=True, preload=True)
picks = pick_types(raw_erm.info, meg='mag')
erm_power = raw_erm[picks][0].ravel()
erm_power = np.sqrt(np.sum(erm_power * erm_power))
# Vanilla SSS (second value would be for meg=True instead of meg='mag')
_assert_shielding(Raw(sss_erm_std_fname), erm_power, 10) # 1.5)
raw_sss = maxwell_filter(raw_erm, coord_frame='meg', regularize=None)
_assert_shielding(raw_sss, erm_power, 12) # 1.5)
# Fine cal
_assert_shielding(Raw(sss_erm_fine_cal_fname), erm_power, 12) # 2.0)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
origin=mf_meg_origin,
calibration=fine_cal_fname)
_assert_shielding(raw_sss, erm_power, 12) # 2.0)
# Crosstalk
_assert_shielding(Raw(sss_erm_ctc_fname), erm_power, 12) # 2.1)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
origin=mf_meg_origin,
cross_talk=ctc_fname)
_assert_shielding(raw_sss, erm_power, 12) # 2.1)
# Fine cal + Crosstalk
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
calibration=fine_cal_fname,
origin=mf_meg_origin,
cross_talk=ctc_fname)
_assert_shielding(raw_sss, erm_power, 13) # 2.2)
# tSSS
_assert_shielding(Raw(sss_erm_st_fname), erm_power, 37) # 5.8)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
origin=mf_meg_origin,
st_duration=1.)
_assert_shielding(raw_sss, erm_power, 37) # 5.8)
# Crosstalk + tSSS
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
cross_talk=ctc_fname,
origin=mf_meg_origin,
st_duration=1.)
_assert_shielding(raw_sss, erm_power, 38) # 5.91)
# Fine cal + tSSS
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
calibration=fine_cal_fname,
origin=mf_meg_origin,
st_duration=1.)
_assert_shielding(raw_sss, erm_power, 38) # 5.98)
# Fine cal + Crosstalk + tSSS
_assert_shielding(Raw(sss_erm_st1FineCalCrossTalk_fname), erm_power,
39) # 6.07)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
regularize=None,
calibration=fine_cal_fname,
origin=mf_meg_origin,
cross_talk=ctc_fname,
st_duration=1.)
_assert_shielding(raw_sss, erm_power, 39) # 6.05)
# Fine cal + Crosstalk + tSSS + Reg-in
_assert_shielding(Raw(sss_erm_st1FineCalCrossTalkRegIn_fname), erm_power,
57) # 6.97)
raw_sss = maxwell_filter(raw_erm,
calibration=fine_cal_fname,
cross_talk=ctc_fname,
st_duration=1.,
origin=mf_meg_origin,
coord_frame='meg',
regularize='in')
_assert_shielding(raw_sss, erm_power, 53) # 6.64)
raw_sss = maxwell_filter(raw_erm,
calibration=fine_cal_fname,
cross_talk=ctc_fname,
st_duration=1.,
coord_frame='meg',
regularize='in')
_assert_shielding(raw_sss, erm_power, 58) # 7.0)
raw_sss = maxwell_filter(raw_erm,
calibration=fine_cal_fname_3d,
cross_talk=ctc_fname,
st_duration=1.,
coord_frame='meg',
regularize='in')
# Our 3D cal has worse defaults for this ERM than the 1D file
_assert_shielding(raw_sss, erm_power, 54)
# Show it by rewriting the 3D as 1D and testing it
temp_dir = _TempDir()
temp_fname = op.join(temp_dir, 'test_cal.dat')
with open(fine_cal_fname_3d, 'r') as fid:
with open(temp_fname, 'w') as fid_out:
for line in fid:
fid_out.write(' '.join(line.strip().split(' ')[:14]) + '\n')
raw_sss = maxwell_filter(raw_erm,
calibration=temp_fname,
cross_talk=ctc_fname,
st_duration=1.,
coord_frame='meg',
regularize='in')
# Our 3D cal has worse defaults for this ERM than the 1D file
_assert_shielding(raw_sss, erm_power, 44)
def test_basic():
"""Test Maxwell filter basic version"""
# Load testing data (raw, SSS std origin, SSS non-standard origin)
with warnings.catch_warnings(record=True): # maxshield
raw = Raw(raw_fname, allow_maxshield=True).crop(0., 1., False)
raw_err = Raw(raw_fname, proj=True, allow_maxshield=True)
raw_erm = Raw(erm_fname, allow_maxshield=True)
assert_raises(RuntimeError, maxwell_filter, raw_err)
assert_raises(TypeError, maxwell_filter, 1.) # not a raw
assert_raises(ValueError, maxwell_filter, raw, int_order=20) # too many
n_int_bases = int_order**2 + 2 * int_order
n_ext_bases = ext_order**2 + 2 * ext_order
nbases = n_int_bases + n_ext_bases
# Check number of bases computed correctly
assert_equal(_get_n_moments([int_order, ext_order]).sum(), nbases)
# Test SSS computation at the standard head origin
assert_equal(len(raw.info['projs']), 12) # 11 MEG projs + 1 AVG EEG
raw_sss = maxwell_filter(raw,
origin=mf_head_origin,
regularize=None,
bad_condition='ignore')
assert_equal(len(raw_sss.info['projs']), 1) # avg EEG
assert_equal(raw_sss.info['projs'][0]['desc'], 'Average EEG reference')
assert_meg_snr(raw_sss, Raw(sss_std_fname), 200., 1000.)
py_cal = raw_sss.info['proc_history'][0]['max_info']['sss_cal']
assert_equal(len(py_cal), 0)
py_ctc = raw_sss.info['proc_history'][0]['max_info']['sss_ctc']
assert_equal(len(py_ctc), 0)
py_st = raw_sss.info['proc_history'][0]['max_info']['max_st']
assert_equal(len(py_st), 0)
assert_raises(RuntimeError, maxwell_filter, raw_sss)
# Test SSS computation at non-standard head origin
raw_sss = maxwell_filter(raw,
origin=[0., 0.02, 0.02],
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, Raw(sss_nonstd_fname), 250., 700.)
# Test SSS computation at device origin
sss_erm_std = Raw(sss_erm_std_fname)
raw_sss = maxwell_filter(raw_erm,
coord_frame='meg',
origin=mf_meg_origin,
regularize=None,
bad_condition='ignore')
assert_meg_snr(raw_sss, sss_erm_std, 100., 900.)
for key in ('job', 'frame'):
vals = [
x.info['proc_history'][0]['max_info']['sss_info'][key]
for x in [raw_sss, sss_erm_std]
]
assert_equal(vals[0], vals[1])
# Check against SSS functions from proc_history
sss_info = raw_sss.info['proc_history'][0]['max_info']
assert_equal(_get_n_moments(int_order),
proc_history._get_sss_rank(sss_info))
# Degenerate cases
raw_bad = raw.copy()
raw_bad.comp = True
assert_raises(RuntimeError, maxwell_filter, raw_bad)
del raw_bad
assert_raises(ValueError, maxwell_filter, raw, coord_frame='foo')
assert_raises(ValueError, maxwell_filter, raw, origin='foo')
assert_raises(ValueError, maxwell_filter, raw, origin=[0] * 4)
def test_other_systems():
"""Test Maxwell filtering on KIT, BTI, and CTF files
"""
io_dir = op.join(op.dirname(__file__), '..', '..', 'io')
# KIT
kit_dir = op.join(io_dir, 'kit', 'tests', 'data')
sqd_path = op.join(kit_dir, 'test.sqd')
mrk_path = op.join(kit_dir, 'test_mrk.sqd')
elp_path = op.join(kit_dir, 'test_elp.txt')
hsp_path = op.join(kit_dir, 'test_hsp.txt')
raw_kit = read_raw_kit(sqd_path, mrk_path, elp_path, hsp_path)
with warnings.catch_warnings(record=True): # head fit
assert_raises(RuntimeError, maxwell_filter, raw_kit)
raw_sss = maxwell_filter(raw_kit, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 65, 65)
# XXX this KIT origin fit is terrible! Eventually we should get a
# corrected HSP file with proper coverage
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
assert_raises(RuntimeError,
maxwell_filter,
raw_kit,
ignore_ref=True,
regularize=None) # bad condition
raw_sss = maxwell_filter(raw_kit,
origin='auto',
ignore_ref=True,
bad_condition='warning',
verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
assert_true('more than 20 mm from' in log_file)
# fits can differ slightly based on scipy version, so be lenient here
_assert_n_free(raw_sss, 28, 34) # bad origin == brutal reg
# Let's set the origin
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit,
origin=(0., 0., 0.04),
ignore_ref=True,
bad_condition='warning',
regularize=None,
verbose='warning')
log_file = log_file.getvalue()
assert_true('badly conditioned' in log_file)
_assert_n_free(raw_sss, 80)
# Now with reg
with warnings.catch_warnings(record=True):
with catch_logging() as log_file:
raw_sss = maxwell_filter(raw_kit,
origin=(0., 0., 0.04),
ignore_ref=True,
verbose=True)
log_file = log_file.getvalue()
assert_true('badly conditioned' not in log_file)
_assert_n_free(raw_sss, 65)
# BTi
bti_dir = op.join(io_dir, 'bti', 'tests', 'data')
bti_pdf = op.join(bti_dir, 'test_pdf_linux')
bti_config = op.join(bti_dir, 'test_config_linux')
bti_hs = op.join(bti_dir, 'test_hs_linux')
with warnings.catch_warnings(record=True): # weght table
raw_bti = read_raw_bti(bti_pdf, bti_config, bti_hs, preload=False)
raw_sss = maxwell_filter(raw_bti)
_assert_n_free(raw_sss, 70)
# CTF
fname_ctf_raw = op.join(io_dir, 'tests', 'data', 'test_ctf_comp_raw.fif')
raw_ctf = Raw(fname_ctf_raw, compensation=2)
assert_raises(RuntimeError, maxwell_filter, raw_ctf) # compensated
raw_ctf = Raw(fname_ctf_raw)
assert_raises(ValueError, maxwell_filter, raw_ctf) # cannot fit headshape
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04))
_assert_n_free(raw_sss, 68)
raw_sss = maxwell_filter(raw_ctf, origin=(0., 0., 0.04), ignore_ref=True)
_assert_n_free(raw_sss, 70)
def test_ica_full_data_recovery():
"""Test recovery of full data when no source is rejected"""
# Most basic recovery
raw = Raw(raw_fname).crop(0.5, stop, copy=False)
raw.load_data()
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')[:10]
with warnings.catch_warnings(record=True): # bad proj
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
evoked = epochs.average()
n_channels = 5
data = raw._data[:n_channels].copy()
data_epochs = epochs.get_data()
data_evoked = evoked.data
for method in ['fastica']:
stuff = [(2, n_channels, True), (2, n_channels // 2, False)]
for n_components, n_pca_components, ok in stuff:
ica = ICA(n_components=n_components,
max_pca_components=n_pca_components,
n_pca_components=n_pca_components,
method=method,
max_iter=1)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=list(range(n_channels)))
raw2 = ica.apply(raw.copy(), exclude=[])
if ok:
assert_allclose(data[:n_channels],
raw2._data[:n_channels],
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(data[:n_channels] - raw2._data[:n_channels])
assert_true(np.max(diff) > 1e-14)
ica = ICA(n_components=n_components,
max_pca_components=n_pca_components,
n_pca_components=n_pca_components)
with warnings.catch_warnings(record=True):
ica.fit(epochs, picks=list(range(n_channels)))
epochs2 = ica.apply(epochs.copy(), exclude=[])
data2 = epochs2.get_data()[:, :n_channels]
if ok:
assert_allclose(data_epochs[:, :n_channels],
data2,
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(data_epochs[:, :n_channels] - data2)
assert_true(np.max(diff) > 1e-14)
evoked2 = ica.apply(evoked.copy(), exclude=[])
data2 = evoked2.data[:n_channels]
if ok:
assert_allclose(data_evoked[:n_channels],
data2,
rtol=1e-10,
atol=1e-15)
else:
diff = np.abs(evoked.data[:n_channels] - data2)
assert_true(np.max(diff) > 1e-14)
assert_raises(ValueError, ICA, method='pizza-decomposision')
from mne.datasets import sample
from mne.io import Raw
from mne.minimum_norm import apply_inverse_raw, read_inverse_operator
data_path = sample.data_path()
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
snr = 1.0 # use smaller SNR for raw data
lambda2 = 1.0 / snr**2
method = "sLORETA" # use sLORETA method (could also be MNE or dSPM)
# Load data
raw = Raw(fname_raw)
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
start, stop = raw.time_as_index([0, 15]) # read the first 15s of data
# Compute inverse solution
stc = apply_inverse_raw(raw,
inverse_operator,
lambda2,
method,
label,
start,
stop,
pick_ori=None)
def test_ica_core():
"""Test ICA on raw and epochs"""
raw = Raw(raw_fname).crop(1.5, stop, copy=False)
raw.load_data()
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
# XXX. The None cases helped revealing bugs but are time consuming.
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
noise_cov = [None, test_cov]
# removed None cases to speed up...
n_components = [2, 1.0] # for future dbg add cases
max_pca_components = [3]
picks_ = [picks]
methods = ['fastica']
iter_ica_params = product(noise_cov, n_components, max_pca_components,
picks_, methods)
# # test init catchers
assert_raises(ValueError, ICA, n_components=3, max_pca_components=2)
assert_raises(ValueError, ICA, n_components=2.3, max_pca_components=2)
# test essential core functionality
for n_cov, n_comp, max_n, pcks, method in iter_ica_params:
# Test ICA raw
ica = ICA(noise_cov=n_cov,
n_components=n_comp,
max_pca_components=max_n,
n_pca_components=max_n,
random_state=0,
method=method,
max_iter=1)
assert_raises(ValueError, ica.__contains__, 'mag')
print(ica) # to test repr
# test fit checker
assert_raises(RuntimeError, ica.get_sources, raw)
assert_raises(RuntimeError, ica.get_sources, epochs)
# test decomposition
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=pcks, start=start, stop=stop)
repr(ica) # to test repr
assert_true('mag' in ica) # should now work without error
# test re-fit
unmixing1 = ica.unmixing_matrix_
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=pcks, start=start, stop=stop)
assert_array_almost_equal(unmixing1, ica.unmixing_matrix_)
sources = ica.get_sources(raw)[:, :][0]
assert_true(sources.shape[0] == ica.n_components_)
# test preload filter
raw3 = raw.copy()
raw3.preload = False
assert_raises(ValueError, ica.apply, raw3, include=[1, 2])
#######################################################################
# test epochs decomposition
ica = ICA(noise_cov=n_cov,
n_components=n_comp,
max_pca_components=max_n,
n_pca_components=max_n,
random_state=0)
with warnings.catch_warnings(record=True):
ica.fit(epochs, picks=picks)
data = epochs.get_data()[:, 0, :]
n_samples = np.prod(data.shape)
assert_equal(ica.n_samples_, n_samples)
print(ica) # to test repr
sources = ica.get_sources(epochs).get_data()
assert_true(sources.shape[1] == ica.n_components_)
assert_raises(ValueError,
ica.score_sources,
epochs,
target=np.arange(1))
# test preload filter
epochs3 = epochs.copy()
epochs3.preload = False
assert_raises(ValueError, ica.apply, epochs3, include=[1, 2])
# test for bug with whitener updating
_pre_whitener = ica._pre_whitener.copy()
epochs._data[:, 0, 10:15] *= 1e12
ica.apply(epochs.copy())
assert_array_equal(_pre_whitener, ica._pre_whitener)
# test expl. var threshold leading to empty sel
ica.n_components = 0.1
assert_raises(RuntimeError, ica.fit, epochs)
offender = 1, 2, 3,
assert_raises(ValueError, ica.get_sources, offender)
assert_raises(ValueError, ica.fit, offender)
assert_raises(ValueError, ica.apply, offender)
from my_settings import *
subject = sys.argv[1]
import matplotlib
matplotlib.use('Agg')
# SETTINGS
n_jobs = 1
l_freq, h_freq = 1, 98 # High and low frequency setting for the band pass
n_freq = 50 # notch filter frequency
decim = 7 # decim value
raw = Raw(maxfiltered_folder + "%s_data_mc_raw_tsss.fif" % subject,
preload=True)
raw.del_proj(0)
raw.drop_channels(raw.info["bads"])
raw.add_eeg_average_proj()
raw.notch_filter(n_freq, n_jobs=n_jobs)
raw.filter(l_freq, h_freq, n_jobs=n_jobs)
raw.save(save_folder + "%s_filtered_data_mc_raw_tsss.fif" % subject,
overwrite=True)
# ICA Part
ica = ICA(n_components=0.95, method='fastica', max_iter=256)
picks = mne.pick_types(raw.info, meg=True, eeg=True,
stim=False, exclude='bads')
def test_render_report():
"""Test rendering -*.fif files for mne report.
"""
tempdir = _TempDir()
raw_fname_new = op.join(tempdir, 'temp_raw.fif')
event_fname_new = op.join(tempdir, 'temp_raw-eve.fif')
cov_fname_new = op.join(tempdir, 'temp_raw-cov.fif')
fwd_fname_new = op.join(tempdir, 'temp_raw-fwd.fif')
inv_fname_new = op.join(tempdir, 'temp_raw-inv.fif')
for a, b in [[raw_fname, raw_fname_new], [event_fname, event_fname_new],
[cov_fname, cov_fname_new], [fwd_fname, fwd_fname_new],
[inv_fname, inv_fname_new]]:
shutil.copyfile(a, b)
# create and add -epo.fif and -ave.fif files
epochs_fname = op.join(tempdir, 'temp-epo.fif')
evoked_fname = op.join(tempdir, 'temp-ave.fif')
raw = Raw(raw_fname_new)
picks = pick_types(raw.info, meg='mag', eeg=False) # faster with one type
epochs = Epochs(raw, read_events(event_fname), 1, -0.2, 0.2, picks=picks)
epochs.save(epochs_fname)
epochs.average().save(evoked_fname)
report = Report(info_fname=raw_fname_new, subjects_dir=subjects_dir)
if sys.version.startswith('3.5'): # XXX Some strange MPL/3.5 error...
raise SkipTest('Python 3.5 and mpl have unresolved issues')
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, on_error='raise')
assert_true(len(w) >= 1)
# Check correct paths and filenames
fnames = glob.glob(op.join(tempdir, '*.fif'))
for fname in fnames:
assert_true(
op.basename(fname) in [op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
assert_equal(len(report.fnames), len(fnames))
assert_equal(len(report.html), len(report.fnames))
# Check saving functionality
report.data_path = tempdir
report.save(fname=op.join(tempdir, 'report.html'), open_browser=False)
assert_true(op.isfile(op.join(tempdir, 'report.html')))
assert_equal(len(report.html), len(fnames))
assert_equal(len(report.html), len(report.fnames))
# Check saving same report to new filename
report.save(fname=op.join(tempdir, 'report2.html'), open_browser=False)
assert_true(op.isfile(op.join(tempdir, 'report2.html')))
# Check overwriting file
report.save(fname=op.join(tempdir, 'report.html'),
open_browser=False,
overwrite=True)
assert_true(op.isfile(op.join(tempdir, 'report.html')))
# Check pattern matching with multiple patterns
pattern = ['*raw.fif', '*eve.fif']
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
report.parse_folder(data_path=tempdir, pattern=pattern)
assert_true(len(w) >= 1)
fnames = glob.glob(op.join(tempdir, '*.raw')) + \
glob.glob(op.join(tempdir, '*.raw'))
for fname in fnames:
assert_true(
op.basename(fname) in [op.basename(x) for x in report.fnames])
assert_true(''.join(report.html).find(op.basename(fname)) != -1)
def test_ica_additional():
"""Test additional ICA functionality"""
tempdir = _TempDir()
stop2 = 500
raw = Raw(raw_fname).crop(1.5, stop, copy=False)
raw.load_data()
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
test_cov = read_cov(test_cov_name)
events = read_events(event_name)
picks = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=False,
exclude='bads')
epochs = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
preload=True)
# test if n_components=None works
with warnings.catch_warnings(record=True):
ica = ICA(n_components=None,
max_pca_components=None,
n_pca_components=None,
random_state=0)
ica.fit(epochs, picks=picks, decim=3)
# for testing eog functionality
picks2 = pick_types(raw.info,
meg=True,
stim=False,
ecg=False,
eog=True,
exclude='bads')
epochs_eog = Epochs(raw,
events[:4],
event_id,
tmin,
tmax,
picks=picks2,
baseline=(None, 0),
preload=True)
test_cov2 = test_cov.copy()
ica = ICA(noise_cov=test_cov2,
n_components=3,
max_pca_components=4,
n_pca_components=4)
assert_true(ica.info is None)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks[:5])
assert_true(isinstance(ica.info, Info))
assert_true(ica.n_components_ < 5)
ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4)
assert_raises(RuntimeError, ica.save, '')
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)
# test corrmap
ica2 = ica.copy()
ica3 = ica.copy()
corrmap([ica, ica2], (0, 0),
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
corrmap([ica, ica2], (0, 0), threshold=2, plot=False, show=False)
assert_true(ica.labels_["blinks"] == ica2.labels_["blinks"])
assert_true(0 in ica.labels_["blinks"])
template = _get_ica_map(ica)[0]
corrmap([ica, ica3],
template,
threshold='auto',
label='blinks',
plot=True,
ch_type="mag")
assert_true(ica2.labels_["blinks"] == ica3.labels_["blinks"])
plt.close('all')
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
ica.save(ica_badname)
read_ica(ica_badname)
assert_naming(w, 'test_ica.py', 2)
# test decim
ica = ICA(n_components=3, max_pca_components=4, n_pca_components=4)
raw_ = raw.copy()
for _ in range(3):
raw_.append(raw_)
n_samples = raw_._data.shape[1]
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(raw_._data.shape[1], n_samples)
# test expl var
ica = ICA(n_components=1.0, max_pca_components=4, n_pca_components=4)
with warnings.catch_warnings(record=True):
ica.fit(raw, picks=None, decim=3)
assert_true(ica.n_components_ == 4)
# epochs extraction from raw fit
assert_raises(RuntimeError, ica.get_sources, epochs)
# test reading and writing
test_ica_fname = op.join(op.dirname(tempdir), 'test-ica.fif')
for cov in (None, test_cov):
ica = ICA(noise_cov=cov,
n_components=2,
max_pca_components=4,
n_pca_components=4)
with warnings.catch_warnings(record=True): # ICA does not converge
ica.fit(raw, picks=picks, start=start, stop=stop2)
sources = ica.get_sources(epochs).get_data()
assert_true(ica.mixing_matrix_.shape == (2, 2))
assert_true(ica.unmixing_matrix_.shape == (2, 2))
assert_true(ica.pca_components_.shape == (4, len(picks)))
assert_true(sources.shape[1] == ica.n_components_)
for exclude in [[], [0]]:
ica.exclude = exclude
ica.labels_ = {'foo': [0]}
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.exclude == ica_read.exclude)
assert_equal(ica.labels_, ica_read.labels_)
ica.exclude = []
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [])
ica.exclude = [0, 1]
ica.apply(raw, exclude=[1])
assert_true(ica.exclude == [0, 1])
ica_raw = ica.get_sources(raw)
assert_true(
ica.exclude ==
[ica_raw.ch_names.index(e) for e in ica_raw.info['bads']])
# test filtering
d1 = ica_raw._data[0].copy()
with warnings.catch_warnings(record=True): # dB warning
ica_raw.filter(4, 20)
assert_true((d1 != ica_raw._data[0]).any())
d1 = ica_raw._data[0].copy()
with warnings.catch_warnings(record=True): # dB warning
ica_raw.notch_filter([10])
assert_true((d1 != ica_raw._data[0]).any())
ica.n_pca_components = 2
ica.method = 'fake'
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
assert_true(ica.n_pca_components == ica_read.n_pca_components)
assert_equal(ica.method, ica_read.method)
assert_equal(ica.labels_, ica_read.labels_)
# check type consistency
attrs = ('mixing_matrix_ unmixing_matrix_ pca_components_ '
'pca_explained_variance_ _pre_whitener')
def f(x, y):
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in [
'mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_', '_pre_whitener'
]:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert_true(ica.ch_names == ica_read.ch_names)
assert_true(isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check scrore funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw,
target='EOG 061',
score_func=func,
start=0,
stop=10)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, raw, target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw,
start_find=0,
stop_find=50,
ecg_ch=ch_name,
eog_ch=ch_name,
skew_criterion=idx,
var_criterion=idx,
kurt_criterion=idx)
with warnings.catch_warnings(record=True):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
ica.labels_ = None
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
assert_raises(ValueError,
ica.find_bads_ecg,
epochs.average(),
method='ctps')
assert_raises(ValueError,
ica.find_bads_ecg,
raw,
method='crazy-coupling')
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
idx, scores = ica.find_bads_eog(raw)
assert_true(isinstance(scores, list))
assert_equal(len(scores[0]), ica.n_components_)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(epochs_eog,
target='EOG 061',
score_func=func)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(epochs, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, epochs, target=np.arange(1))
# ecg functionality
ecg_scores = ica.score_sources(raw,
target='MEG 1531',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
ecg_events = ica_find_ecg_events(raw,
sources[np.abs(ecg_scores).argmax()])
assert_true(ecg_events.ndim == 2)
# eog functionality
eog_scores = ica.score_sources(raw,
target='EOG 061',
score_func='pearsonr')
with warnings.catch_warnings(record=True): # filter attenuation warning
eog_events = ica_find_eog_events(raw,
sources[np.abs(eog_scores).argmax()])
assert_true(eog_events.ndim == 2)
# Test ica fiff export
ica_raw = ica.get_sources(raw, start=0, stop=100)
assert_true(ica_raw.last_samp - ica_raw.first_samp == 100)
assert_true(len(ica_raw._filenames) == 0) # API consistency
ica_chans = [ch for ch in ica_raw.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
test_ica_fname = op.join(op.abspath(op.curdir), 'test-ica_raw.fif')
ica.n_components = np.int32(ica.n_components)
ica_raw.save(test_ica_fname, overwrite=True)
ica_raw2 = Raw(test_ica_fname, preload=True)
assert_allclose(ica_raw._data, ica_raw2._data, rtol=1e-5, atol=1e-4)
ica_raw2.close()
os.remove(test_ica_fname)
# Test ica epochs export
ica_epochs = ica.get_sources(epochs)
assert_true(ica_epochs.events.shape == epochs.events.shape)
ica_chans = [ch for ch in ica_epochs.ch_names if 'ICA' in ch]
assert_true(ica.n_components_ == len(ica_chans))
assert_true(ica.n_components_ == ica_epochs.get_data().shape[1])
assert_true(ica_epochs._raw is None)
assert_true(ica_epochs.preload is True)
# test float n pca components
ica.pca_explained_variance_ = np.array([0.2] * 5)
ica.n_components_ = 0
for ncomps, expected in [[0.3, 1], [0.9, 4], [1, 1]]:
ncomps_ = ica._check_n_pca_components(ncomps)
assert_true(ncomps_ == expected)
read_labels_from_annot(subject,
'aparc.a2009s',
hemi,
regexp='G_temp_sup-G_T_transv')[0]
for hi, hemi in enumerate(('lh', 'rh'))
]
stc = stc.in_label(labels[0] + labels[1])
stc.data.fill(0)
stc.data[:, (stc.times >= pulse_tmin) & (stc.times <= pulse_tmax)] = 10e-9
# ############################################################################
# Simulate data
# Simulate data with movement
with warnings.catch_warnings(record=True):
raw = Raw(fname_raw, allow_maxshield=True)
raw_movement = simulate_raw(raw,
stc,
trans,
src,
bem,
chpi=True,
head_pos=fname_pos_orig,
n_jobs=6,
verbose=True)
# Simulate data with no movement (use initial head position)
raw_stationary = simulate_raw(raw,
stc,
trans,
src,
def _test_raw_reader(reader, test_preloading=True, **kwargs):
"""Test reading, writing and slicing of raw classes.
Parameters
----------
reader : function
Function to test.
test_preloading : bool
Whether not preloading is implemented for the reader. If True, both
cases and memory mapping to file are tested.
**kwargs :
Arguments for the reader. Note: Do not use preload as kwarg.
Use ``test_preloading`` instead.
Returns
-------
raw : Instance of Raw
A preloaded Raw object.
"""
tempdir = _TempDir()
rng = np.random.RandomState(0)
if test_preloading:
raw = reader(preload=True, **kwargs)
# don't assume the first is preloaded
buffer_fname = op.join(tempdir, 'buffer')
picks = rng.permutation(np.arange(len(raw.ch_names)))[:10]
bnd = min(int(round(raw.info['buffer_size_sec'] * raw.info['sfreq'])),
raw.n_times)
slices = [
slice(0, bnd),
slice(bnd - 1, bnd),
slice(3, bnd),
slice(3, 300),
slice(None),
slice(1, bnd)
]
if raw.n_times >= 2 * bnd: # at least two complete blocks
slices += [
slice(bnd, 2 * bnd),
slice(bnd, bnd + 1),
slice(0, bnd + 100)
]
other_raws = [
reader(preload=buffer_fname, **kwargs),
reader(preload=False, **kwargs)
]
for sl_time in slices:
for other_raw in other_raws:
data1, times1 = raw[picks, sl_time]
data2, times2 = other_raw[picks, sl_time]
assert_allclose(data1, data2)
assert_allclose(times1, times2)
else:
raw = reader(**kwargs)
full_data = raw._data
assert_true(raw.__class__.__name__, repr(raw)) # to test repr
assert_true(raw.info.__class__.__name__, repr(raw.info))
# Test saving and reading
out_fname = op.join(tempdir, 'test_raw.fif')
raw.save(out_fname, tmax=raw.times[-1], overwrite=True, buffer_size_sec=1)
raw3 = Raw(out_fname)
assert_equal(set(raw.info.keys()), set(raw3.info.keys()))
assert_allclose(raw3[0:20][0], full_data[0:20], rtol=1e-6,
atol=1e-20) # atol is very small but > 0
assert_array_almost_equal(raw.times, raw3.times)
assert_true(not math.isnan(raw3.info['highpass']))
assert_true(not math.isnan(raw3.info['lowpass']))
assert_true(not math.isnan(raw.info['highpass']))
assert_true(not math.isnan(raw.info['lowpass']))
# Make sure concatenation works
first_samp = raw.first_samp
last_samp = raw.last_samp
concat_raw = concatenate_raws([raw.copy(), raw])
assert_equal(concat_raw.n_times, 2 * raw.n_times)
assert_equal(concat_raw.first_samp, first_samp)
assert_equal(concat_raw.last_samp - last_samp + first_samp, last_samp + 1)
return raw
def test_apply_mne_inverse_epochs():
"""Test MNE with precomputed inverse operator on Epochs
"""
inverse_operator = read_inverse_operator(fname_inv)
label_lh = read_label(fname_label % 'Aud-lh')
label_rh = read_label(fname_label % 'Aud-rh')
event_id, tmin, tmax = 1, -0.2, 0.5
raw = Raw(fname_raw)
picks = pick_types(raw.info,
meg=True,
eeg=False,
stim=True,
ecg=True,
eog=True,
include=['STI 014'],
exclude='bads')
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)
flat = dict(grad=1e-15, mag=1e-15)
events = read_events(fname_event)[:15]
epochs = Epochs(raw,
events,
event_id,
tmin,
tmax,
picks=picks,
baseline=(None, 0),
reject=reject,
flat=flat)
stcs = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
"dSPM",
label=label_lh,
pick_ori="normal")
assert_true(len(stcs) == 4)
assert_true(3 < stcs[0].data.max() < 10)
assert_true(stcs[0].subject == 'sample')
data = sum(stc.data for stc in stcs) / len(stcs)
flip = label_sign_flip(label_lh, inverse_operator['src'])
label_mean = np.mean(data, axis=0)
label_mean_flip = np.mean(flip[:, np.newaxis] * data, axis=0)
assert_true(label_mean.max() < label_mean_flip.max())
# test extracting a BiHemiLabel
stcs_rh = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
"dSPM",
label=label_rh,
pick_ori="normal")
stcs_bh = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
"dSPM",
label=label_lh + label_rh,
pick_ori="normal")
n_lh = len(stcs[0].data)
assert_array_almost_equal(stcs[0].data, stcs_bh[0].data[:n_lh])
assert_array_almost_equal(stcs_rh[0].data, stcs_bh[0].data[n_lh:])
# test without using a label (so delayed computation is used)
stcs = apply_inverse_epochs(epochs,
inverse_operator,
lambda2,
"dSPM",
pick_ori="normal")
assert_true(stcs[0].subject == 'sample')
label_stc = stcs[0].in_label(label_rh)
assert_true(label_stc.subject == 'sample')
assert_array_almost_equal(stcs_rh[0].data, label_stc.data)
def make_evoked(fname, comp):
raw = Raw(fname, compensation=comp)
picks = pick_types(raw.info, meg=True, ref_meg=True)
events = np.array([[0, 0, 1]], dtype=np.int)
evoked = Epochs(raw, events, 1, 0, 20e-3, picks=picks).average()
return evoked
def test_resample():
"""Test resample (with I/O and multiple files)
"""
tempdir = _TempDir()
raw = Raw(fif_fname).crop(0, 3, False)
raw.load_data()
raw_resamp = raw.copy()
sfreq = raw.info['sfreq']
# test parallel on upsample
raw_resamp.resample(sfreq * 2, n_jobs=2)
assert_equal(raw_resamp.n_times, len(raw_resamp.times))
raw_resamp.save(op.join(tempdir, 'raw_resamp-raw.fif'))
raw_resamp = Raw(op.join(tempdir, 'raw_resamp-raw.fif'), preload=True)
assert_equal(sfreq, raw_resamp.info['sfreq'] / 2)
assert_equal(raw.n_times, raw_resamp.n_times / 2)
assert_equal(raw_resamp._data.shape[1], raw_resamp.n_times)
assert_equal(raw._data.shape[0], raw_resamp._data.shape[0])
# test non-parallel on downsample
raw_resamp.resample(sfreq, n_jobs=1)
assert_equal(raw_resamp.info['sfreq'], sfreq)
assert_equal(raw._data.shape, raw_resamp._data.shape)
assert_equal(raw.first_samp, raw_resamp.first_samp)
assert_equal(raw.last_samp, raw.last_samp)
# upsampling then downsampling doubles resampling error, but this still
# works (hooray). Note that the stim channels had to be sub-sampled
# without filtering to be accurately preserved
# note we have to treat MEG and EEG+STIM channels differently (tols)
assert_allclose(raw._data[:306, 200:-200],
raw_resamp._data[:306, 200:-200],
rtol=1e-2, atol=1e-12)
assert_allclose(raw._data[306:, 200:-200],
raw_resamp._data[306:, 200:-200],
rtol=1e-2, atol=1e-7)
# now check multiple file support w/resampling, as order of operations
# (concat, resample) should not affect our data
raw1 = raw.copy()
raw2 = raw.copy()
raw3 = raw.copy()
raw4 = raw.copy()
raw1 = concatenate_raws([raw1, raw2])
raw1.resample(10.)
raw3.resample(10.)
raw4.resample(10.)
raw3 = concatenate_raws([raw3, raw4])
assert_array_equal(raw1._data, raw3._data)
assert_array_equal(raw1._first_samps, raw3._first_samps)
assert_array_equal(raw1._last_samps, raw3._last_samps)
assert_array_equal(raw1._raw_lengths, raw3._raw_lengths)
assert_equal(raw1.first_samp, raw3.first_samp)
assert_equal(raw1.last_samp, raw3.last_samp)
assert_equal(raw1.info['sfreq'], raw3.info['sfreq'])
# test resampling of stim channel
# basic decimation
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(8.)._data,
[[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation of multiple stim channels
raw = RawArray(2 * [stim], create_info(2, len(stim), 2 * ['stim']))
assert_allclose(raw.resample(8.)._data,
[[1, 1, 0, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 1, 0, 0]])
# decimation that could potentially drop events if the decimation is
# done naively
stim = [0, 0, 0, 1, 1, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
assert_allclose(raw.resample(4.)._data,
[[0, 1, 1, 0]])
# two events are merged in this case (warning)
stim = [0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(8.)
assert_true(len(w) == 1)
# events are dropped in this case (warning)
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.resample(4.)
assert_true(len(w) == 1)
# test resampling events: this should no longer give a warning
stim = [0, 1, 1, 0, 0, 1, 1, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
events = find_events(raw)
raw, events = raw.resample(4., events=events)
assert_equal(events, np.array([[0, 0, 1], [2, 0, 1]]))
# test copy flag
stim = [1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0]
raw = RawArray([stim], create_info(1, len(stim), ['stim']))
raw_resampled = raw.resample(4., copy=True)
assert_true(raw_resampled is not raw)
raw_resampled = raw.resample(4., copy=False)
assert_true(raw_resampled is raw)
# resample should still work even when no stim channel is present
raw = RawArray(np.random.randn(1, 100), create_info(1, 100, ['eeg']))
raw.resample(10)
assert_true(len(raw) == 10)
'url': url,
'folder_name': folder_name,
'config_key': config_key
}
data_path = fetch_dataset(
dataset_params
)
return os.path.join(data_path, archive_name)
# Download data
destination = download_sample_data(dataset="ssvep", subject=12, session=1)
# Read data in MNE Raw and numpy format
raw = Raw(destination, preload=True, verbose='ERROR')
events = find_events(raw, shortest_event=0, verbose=False)
raw = raw.pick_types(eeg=True)
event_id = {'13 Hz': 2, '17 Hz': 4, '21 Hz': 3, 'resting-state': 1}
sfreq = int(raw.info['sfreq'])
eeg_data = raw.get_data()
###############################################################################
# Visualization of raw EEG data
# -----------------------------
#
# Plot few seconds of signal from the `Oz` electrode using matplotlib
n_seconds = 2
time = np.linspace(0, n_seconds, n_seconds * sfreq,
def test_filter():
"""Test filtering (FIR and IIR) and Raw.apply_function interface
"""
raw = Raw(fif_fname).crop(0, 7, False)
raw.load_data()
sig_dec = 11
sig_dec_notch = 12
sig_dec_notch_fit = 12
picks_meg = pick_types(raw.info, meg=True, exclude='bads')
picks = picks_meg[:4]
raw_lp = raw.copy()
raw_lp.filter(0., 4.0 - 0.25, picks=picks, n_jobs=2)
raw_hp = raw.copy()
raw_hp.filter(8.0 + 0.25, None, picks=picks, n_jobs=2)
raw_bp = raw.copy()
raw_bp.filter(4.0 + 0.25, 8.0 - 0.25, picks=picks)
raw_bs = raw.copy()
raw_bs.filter(8.0 + 0.25, 4.0 - 0.25, picks=picks, n_jobs=2)
data, _ = raw[picks, :]
lp_data, _ = raw_lp[picks, :]
hp_data, _ = raw_hp[picks, :]
bp_data, _ = raw_bp[picks, :]
bs_data, _ = raw_bs[picks, :]
assert_array_almost_equal(data, lp_data + bp_data + hp_data, sig_dec)
assert_array_almost_equal(data, bp_data + bs_data, sig_dec)
raw_lp_iir = raw.copy()
raw_lp_iir.filter(0., 4.0, picks=picks, n_jobs=2, method='iir')
raw_hp_iir = raw.copy()
raw_hp_iir.filter(8.0, None, picks=picks, n_jobs=2, method='iir')
raw_bp_iir = raw.copy()
raw_bp_iir.filter(4.0, 8.0, picks=picks, method='iir')
lp_data_iir, _ = raw_lp_iir[picks, :]
hp_data_iir, _ = raw_hp_iir[picks, :]
bp_data_iir, _ = raw_bp_iir[picks, :]
summation = lp_data_iir + hp_data_iir + bp_data_iir
assert_array_almost_equal(data[:, 100:-100], summation[:, 100:-100],
sig_dec)
# make sure we didn't touch other channels
data, _ = raw[picks_meg[4:], :]
bp_data, _ = raw_bp[picks_meg[4:], :]
assert_array_equal(data, bp_data)
bp_data_iir, _ = raw_bp_iir[picks_meg[4:], :]
assert_array_equal(data, bp_data_iir)
# do a very simple check on line filtering
raw_bs = raw.copy()
with warnings.catch_warnings(record=True):
warnings.simplefilter('always')
raw_bs.filter(60.0 + 0.5, 60.0 - 0.5, picks=picks, n_jobs=2)
data_bs, _ = raw_bs[picks, :]
raw_notch = raw.copy()
raw_notch.notch_filter(60.0, picks=picks, n_jobs=2, method='fft')
data_notch, _ = raw_notch[picks, :]
assert_array_almost_equal(data_bs, data_notch, sig_dec_notch)
# now use the sinusoidal fitting
raw_notch = raw.copy()
raw_notch.notch_filter(None, picks=picks, n_jobs=2, method='spectrum_fit')
data_notch, _ = raw_notch[picks, :]
data, _ = raw[picks, :]
assert_array_almost_equal(data, data_notch, sig_dec_notch_fit)
def test_raw_index_as_time():
""" Test index as time conversion"""
raw = Raw(fif_fname, preload=True)
t0 = raw.index_as_time([0], True)[0]
t1 = raw.index_as_time([100], False)[0]
t2 = raw.index_as_time([100], True)[0]
assert_equal(t2 - t1, t0)
# ensure we can go back and forth
t3 = raw.index_as_time(raw.time_as_index([0], True), True)
assert_array_almost_equal(t3, [0.0], 2)
t3 = raw.index_as_time(raw.time_as_index(raw.info['sfreq'], True), True)
assert_array_almost_equal(t3, [raw.info['sfreq']], 2)
t3 = raw.index_as_time(raw.time_as_index(raw.info['sfreq'], False), False)
assert_array_almost_equal(t3, [raw.info['sfreq']], 2)
i0 = raw.time_as_index(raw.index_as_time([0], True), True)
assert_equal(i0[0], 0)
i1 = raw.time_as_index(raw.index_as_time([100], True), True)
assert_equal(i1[0], 100)
# Have to add small amount of time because we truncate via int casting
i1 = raw.time_as_index(raw.index_as_time([100.0001], False), False)
assert_equal(i1[0], 100)
def test_load_bad_channels():
"""Test reading/writing of bad channels
"""
tempdir = _TempDir()
# Load correctly marked file (manually done in mne_process_raw)
raw_marked = Raw(fif_bad_marked_fname)
correct_bads = raw_marked.info['bads']
raw = Raw(test_fif_fname)
# Make sure it starts clean
assert_array_equal(raw.info['bads'], [])
# Test normal case
raw.load_bad_channels(bad_file_works)
# Write it out, read it in, and check
raw.save(op.join(tempdir, 'foo_raw.fif'))
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal(correct_bads, raw_new.info['bads'])
# Reset it
raw.info['bads'] = []
# Test bad case
assert_raises(ValueError, raw.load_bad_channels, bad_file_wrong)
# Test forcing the bad case
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
raw.load_bad_channels(bad_file_wrong, force=True)
n_found = sum(['1 bad channel' in str(ww.message) for ww in w])
assert_equal(n_found, 1) # there could be other irrelevant errors
# write it out, read it in, and check
raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal(correct_bads, raw_new.info['bads'])
# Check that bad channels are cleared
raw.load_bad_channels(None)
raw.save(op.join(tempdir, 'foo_raw.fif'), overwrite=True)
raw_new = Raw(op.join(tempdir, 'foo_raw.fif'))
assert_equal([], raw_new.info['bads'])
def test_compute_proj_epochs():
"""Test SSP computation on epochs"""
tempdir = _TempDir()
event_id, tmin, tmax = 1, -0.2, 0.3
raw = Raw(raw_fname, preload=True)
events = read_events(event_fname)
bad_ch = 'MEG 2443'
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
exclude=[])
epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=None, proj=False)
evoked = epochs.average()
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=1)
write_proj(op.join(tempdir, 'test-proj.fif.gz'), projs)
for p_fname in [proj_fname, proj_gz_fname,
op.join(tempdir, 'test-proj.fif.gz')]:
projs2 = read_proj(p_fname)
assert_true(len(projs) == len(projs2))
for p1, p2 in zip(projs, projs2):
assert_true(p1['desc'] == p2['desc'])
assert_true(p1['data']['col_names'] == p2['data']['col_names'])
assert_true(p1['active'] == p2['active'])
# compare with sign invariance
p1_data = p1['data']['data'] * np.sign(p1['data']['data'][0, 0])
p2_data = p2['data']['data'] * np.sign(p2['data']['data'][0, 0])
if bad_ch in p1['data']['col_names']:
bad = p1['data']['col_names'].index('MEG 2443')
mask = np.ones(p1_data.size, dtype=np.bool)
mask[bad] = False
p1_data = p1_data[:, mask]
p2_data = p2_data[:, mask]
corr = np.corrcoef(p1_data, p2_data)[0, 1]
assert_array_almost_equal(corr, 1.0, 5)
# test that you can compute the projection matrix
projs = activate_proj(projs)
proj, nproj, U = make_projector(projs, epochs.ch_names, bads=[])
assert_true(nproj == 2)
assert_true(U.shape[1] == 2)
# test that you can save them
epochs.info['projs'] += projs
evoked = epochs.average()
evoked.save(op.join(tempdir, 'foo-ave.fif'))
projs = read_proj(proj_fname)
projs_evoked = compute_proj_evoked(evoked, n_grad=1, n_mag=1, n_eeg=0)
assert_true(len(projs_evoked) == 2)
# XXX : test something
# test parallelization
projs = compute_proj_epochs(epochs, n_grad=1, n_mag=1, n_eeg=0, n_jobs=2)
projs = activate_proj(projs)
proj_par, _, _ = make_projector(projs, epochs.ch_names, bads=[])
assert_allclose(proj, proj_par, rtol=1e-8, atol=1e-16)
# test warnings on bad filenames
clean_warning_registry()
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
proj_badname = op.join(tempdir, 'test-bad-name.fif.gz')
write_proj(proj_badname, projs)
read_proj(proj_badname)
print([ww.message for ww in w])
assert_equal(len(w), 2)
"""
# Author: Alexandre Gramfort <*****@*****.**>
#
# License: BSD (3-clause)
print(__doc__)
import mne
from mne.datasets import sample
from mne.io import Raw
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
# Reading events
raw = Raw(fname)
events = mne.find_events(raw, stim_channel='STI 014')
# Writing events
mne.write_events('events.fif', events)
for ind, before, after in events[:5]:
print("At sample %d stim channel went from %d to %d" %
(ind, before, after))
# Plot the events to get an idea of the paradigm
# Specify colors and an event_id dictionary for the legend.
event_id = {
'aud_l': 1,
'aud_r': 2,
# License: BSD (3-clause)
print(__doc__)
import numpy as np
import mne
from mne.io import Raw
from mne.datasets import sample
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
###############################################################################
# get raw data
raw = Raw(raw_fname)
# set picks
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
# pick times relative to the onset of the MEG measurement.
start, stop = raw.time_as_index([100, 115], use_first_samp=False)
# export to nitime using a copy of the data
raw_ts = raw.to_nitime(start=start, stop=stop, picks=picks, copy=True)
###############################################################################
# explore some nitime timeseries features
# get start
def test_needs_eeg_average_ref_proj():
"""Test checking whether a recording needs an EEG average reference"""
raw = Raw(raw_fname, add_eeg_ref=False, preload=False)
assert_true(_needs_eeg_average_ref_proj(raw.info))
raw = Raw(raw_fname, add_eeg_ref=True, preload=False)
assert_true(not _needs_eeg_average_ref_proj(raw.info))
# No EEG channels
raw = Raw(raw_fname, add_eeg_ref=False, preload=True)
eeg = [raw.ch_names[c] for c in pick_types(raw.info, meg=False, eeg=True)]
raw.drop_channels(eeg)
assert_true(not _needs_eeg_average_ref_proj(raw.info))
# Custom ref flag set
raw = Raw(raw_fname, add_eeg_ref=False, preload=False)
raw.info['custom_ref_applied'] = True
assert_true(not _needs_eeg_average_ref_proj(raw.info))
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF + gz)
"""
tempdir = _TempDir()
# test unicode io
for chars in [b'\xc3\xa4\xc3\xb6\xc3\xa9', b'a']:
with Raw(fif_fname) as r:
assert_true('Raw' in repr(r))
desc1 = r.info['description'] = chars.decode('utf-8')
temp_file = op.join(tempdir, 'raw.fif')
r.save(temp_file, overwrite=True)
with Raw(temp_file) as r2:
desc2 = r2.info['description']
assert_equal(desc1, desc2)
# Let's construct a simple test for IO first
raw = Raw(fif_fname).crop(0, 3.5, False)
raw.load_data()
# put in some data that we know the values of
data = np.random.randn(raw._data.shape[0], raw._data.shape[1])
raw._data[:, :] = data
# save it somewhere
fname = op.join(tempdir, 'test_copy_raw.fif')
raw.save(fname, buffer_size_sec=1.0)
# read it in, make sure the whole thing matches
raw = Raw(fname)
assert_allclose(data, raw[:, :][0], rtol=1e-6, atol=1e-20)
# let's read portions across the 1-sec tag boundary, too
inds = raw.time_as_index([1.75, 2.25])
sl = slice(inds[0], inds[1])
assert_allclose(data[:, sl], raw[:, sl][0], rtol=1e-6, atol=1e-20)
# now let's do some real I/O
fnames_in = [fif_fname, test_fif_gz_fname, ctf_fname]
fnames_out = ['raw.fif', 'raw.fif.gz', 'raw.fif']
for fname_in, fname_out in zip(fnames_in, fnames_out):
fname_out = op.join(tempdir, fname_out)
raw = Raw(fname_in)
nchan = raw.info['nchan']
ch_names = raw.info['ch_names']
meg_channels_idx = [k for k in range(nchan)
if ch_names[k][0] == 'M']
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_as_index([0, 5])
data, times = raw[meg_channels_idx, start:(stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ['STI 014']
include += meg_ch_names
picks = pick_types(raw.info, meg=True, eeg=False, stim=True,
misc=True, ref_meg=True, include=include,
exclude='bads')
# Writing with drop_small_buffer True
raw.save(fname_out, picks, tmin=0, tmax=4, buffer_size_sec=3,
drop_small_buffer=True, overwrite=True)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save(fname_out, picks, tmin=0, tmax=5, overwrite=True)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_equal(len(raw.info['dig']), 146)
raw2 = Raw(fname_out)
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_allclose(data, data2, rtol=1e-6, atol=1e-20)
assert_allclose(times, times2)
assert_allclose(raw.info['sfreq'], raw2.info['sfreq'], rtol=1e-5)
# check transformations
for trans in ['dev_head_t', 'dev_ctf_t', 'ctf_head_t']:
if raw.info[trans] is None:
assert_true(raw2.info[trans] is None)
else:
assert_array_equal(raw.info[trans]['trans'],
raw2.info[trans]['trans'])
# check transformation 'from' and 'to'
if trans.startswith('dev'):
from_id = FIFF.FIFFV_COORD_DEVICE
else:
from_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
if trans[4:8] == 'head':
to_id = FIFF.FIFFV_COORD_HEAD
else:
to_id = FIFF.FIFFV_MNE_COORD_CTF_HEAD
for raw_ in [raw, raw2]:
assert_equal(raw_.info[trans]['from'], from_id)
assert_equal(raw_.info[trans]['to'], to_id)
if fname_in == fif_fname or fname_in == fif_fname + '.gz':
assert_allclose(raw.info['dig'][0]['r'], raw2.info['dig'][0]['r'])
# test warnings on bad filenames
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
raw_badname = op.join(tempdir, 'test-bad-name.fif.gz')
raw.save(raw_badname)
Raw(raw_badname)
assert_true(len(w) > 0) # len(w) should be 2 but Travis sometimes has more
