def test_fieldtrip_rtepochs(free_tcp_port, tmpdir):
"""Test FieldTrip RtEpochs."""
raw_tmax = 7
raw = read_raw_fif(raw_fname, preload=True)
raw.crop(tmin=0, tmax=raw_tmax)
events_offline = find_events(raw, stim_channel='STI 014')
event_id = list(np.unique(events_offline[:, 2]))
tmin, tmax = -0.2, 0.5
epochs_offline = Epochs(raw, events_offline, event_id=event_id,
tmin=tmin, tmax=tmax)
epochs_offline.drop_bad()
isi_max = (np.max(np.diff(epochs_offline.events[:, 0])) /
raw.info['sfreq']) + 1.0
neuromag2ft_fname = op.realpath(op.join(os.environ['NEUROMAG2FT_ROOT'],
'neuromag2ft'))
# Works with neuromag2ft-3.0.2
cmd = (neuromag2ft_fname, '--file', raw_fname, '--speed', '8.0',
'--bufport', str(free_tcp_port))
with running_subprocess(cmd, after='terminate', verbose=False):
data_rt = None
events_ids_rt = None
with pytest.warns(RuntimeWarning, match='Trying to guess it'):
with FieldTripClient(host='localhost', port=free_tcp_port,
tmax=raw_tmax, wait_max=2) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
assert ([ch['ch_name'] for ch in raw_info['chs']] ==
[ch['ch_name'] for ch in raw.info['chs']])
# create the real-time epochs object
epochs_rt = RtEpochs(rt_client, event_id, tmin, tmax,
stim_channel='STI 014', isi_max=isi_max)
epochs_rt.start()
time.sleep(0.5)
for ev_num, ev in enumerate(epochs_rt.iter_evoked()):
if ev_num == 0:
data_rt = ev.data[None, :, :]
events_ids_rt = int(
ev.comment) # comment attribute contains event_id
else:
data_rt = np.concatenate(
(data_rt, ev.data[None, :, :]), axis=0)
events_ids_rt = np.append(events_ids_rt,
int(ev.comment))
_call_base_epochs_public_api(epochs_rt, tmpdir)
epochs_rt.stop(stop_receive_thread=True)
assert_array_equal(events_ids_rt, epochs_rt.events[:, 2])
assert_array_equal(data_rt, epochs_rt.get_data())
assert len(epochs_rt) == len(epochs_offline)
assert_array_equal(events_ids_rt, epochs_offline.events[:, 2])
assert_allclose(epochs_rt.get_data(), epochs_offline.get_data(),
rtol=1.e-5, atol=1.e-8) # defaults of np.isclose
def test_fieldtrip_rtepochs(free_tcp_port, tmpdir):
"""Test FieldTrip RtEpochs."""
raw_tmax = 7
raw = read_raw_fif(raw_fname, preload=True)
raw.crop(tmin=0, tmax=raw_tmax)
events_offline = find_events(raw, stim_channel='STI 014')
event_id = list(np.unique(events_offline[:, 2]))
tmin, tmax = -0.2, 0.5
epochs_offline = Epochs(raw, events_offline, event_id=event_id,
tmin=tmin, tmax=tmax)
epochs_offline.drop_bad()
isi_max = (np.max(np.diff(epochs_offline.events[:, 0])) /
raw.info['sfreq']) + 1.0
kill_signal = _start_buffer_thread(free_tcp_port)
try:
data_rt = None
events_ids_rt = None
with pytest.warns(RuntimeWarning, match='Trying to guess it'):
with FieldTripClient(host='localhost', port=free_tcp_port,
tmax=raw_tmax, wait_max=2) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
assert ([ch['ch_name'] for ch in raw_info['chs']] ==
[ch['ch_name'] for ch in raw.info['chs']])
# create the real-time epochs object
epochs_rt = RtEpochs(rt_client, event_id, tmin, tmax,
stim_channel='STI 014', isi_max=isi_max)
epochs_rt.start()
time.sleep(0.5)
for ev_num, ev in enumerate(epochs_rt.iter_evoked()):
if ev_num == 0:
data_rt = ev.data[None, :, :]
events_ids_rt = int(
ev.comment) # comment attribute contains event_id
else:
data_rt = np.concatenate(
(data_rt, ev.data[None, :, :]), axis=0)
events_ids_rt = np.append(events_ids_rt,
int(ev.comment))
_call_base_epochs_public_api(epochs_rt, tmpdir)
epochs_rt.stop(stop_receive_thread=True)
assert_array_equal(events_ids_rt, epochs_rt.events[:, 2])
assert_array_equal(data_rt, epochs_rt.get_data())
assert len(epochs_rt) == len(epochs_offline)
assert_array_equal(events_ids_rt, epochs_offline.events[:, 2])
assert_allclose(epochs_rt.get_data(), epochs_offline.get_data(),
rtol=1.e-5, atol=1.e-8) # defaults of np.isclose
finally:
kill_signal.put(False) # stop the buffer
def test_fieldtrip_rtepochs(free_tcp_port, tmpdir):
"""Test FieldTrip RtEpochs."""
raw_tmax = 7
raw = read_raw_fif(raw_fname, preload=True)
raw.crop(tmin=0, tmax=raw_tmax)
events_offline = find_events(raw, stim_channel='STI 014')
event_id = list(np.unique(events_offline[:, 2]))
tmin, tmax = -0.2, 0.5
epochs_offline = Epochs(raw, events_offline, event_id=event_id,
tmin=tmin, tmax=tmax)
epochs_offline.drop_bad()
isi_max = (np.max(np.diff(epochs_offline.events[:, 0])) /
raw.info['sfreq']) + 1.0
kill_signal = _start_buffer_thread(free_tcp_port)
try:
data_rt = None
events_ids_rt = None
with pytest.warns(RuntimeWarning, match='Trying to guess it'):
with FieldTripClient(host='localhost', port=free_tcp_port,
tmax=raw_tmax, wait_max=2) as rt_client:
# get measurement info guessed by MNE-Python
raw_info = rt_client.get_measurement_info()
assert ([ch['ch_name'] for ch in raw_info['chs']] ==
[ch['ch_name'] for ch in raw.info['chs']])
# create the real-time epochs object
epochs_rt = RtEpochs(rt_client, event_id, tmin, tmax,
stim_channel='STI 014', isi_max=isi_max)
epochs_rt.start()
time.sleep(0.5)
for ev_num, ev in enumerate(epochs_rt.iter_evoked()):
if ev_num == 0:
data_rt = ev.data[None, :, :]
events_ids_rt = int(
ev.comment) # comment attribute contains event_id
else:
data_rt = np.concatenate(
(data_rt, ev.data[None, :, :]), axis=0)
events_ids_rt = np.append(events_ids_rt,
int(ev.comment))
_call_base_epochs_public_api(epochs_rt, tmpdir)
epochs_rt.stop(stop_receive_thread=True)
assert_array_equal(events_ids_rt, epochs_rt.events[:, 2])
assert_array_equal(data_rt, epochs_rt.get_data())
assert len(epochs_rt) == len(epochs_offline)
assert_array_equal(events_ids_rt, epochs_offline.events[:, 2])
assert_allclose(epochs_rt.get_data(), epochs_offline.get_data(),
rtol=1.e-5, atol=1.e-8) # defaults of np.isclose
finally:
kill_signal.put(False) # stop the buffer
def test_mockclient():
"""Test the RtMockClient."""
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = Epochs(raw,
events[:7],
event_id=event_id,
tmin=tmin,
tmax=tmax,
picks=picks,
baseline=(None, 0),
preload=True)
data = epochs.get_data()
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=10, buffer_size=1000)
rt_data = rt_epochs.get_data()
assert_true(rt_data.shape == data.shape)
assert_array_equal(rt_data, data)
def test_mockclient():
"""Test the RtMockClient."""
raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose=False,
add_eeg_ref=False)
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=True, exclude=raw.info['bads'])
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = Epochs(raw, events[:7], event_id=event_id, tmin=tmin, tmax=tmax,
picks=picks, baseline=(None, 0), preload=True,
add_eeg_ref=False)
data = epochs.get_data()
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks,
isi_max=0.5, add_eeg_ref=False)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=10, buffer_size=1000)
rt_data = rt_epochs.get_data()
assert_true(rt_data.shape == data.shape)
assert_array_equal(rt_data, data)
def test_mockclient(tmpdir):
"""Test the RtMockClient."""
raw = read_raw_fif(raw_fname, preload=True, verbose=False)
picks = pick_types(raw.info,
meg='grad',
eeg=False,
eog=True,
stim=True,
exclude=raw.info['bads'])
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = Epochs(raw,
events[:7],
event_id=event_id,
tmin=tmin,
tmax=tmax,
picks=picks,
baseline=(None, 0),
preload=True)
data = epochs.get_data()
rt_client = MockRtClient(raw)
# choose "large" value, should always be longer than execution time of
# get_data()
isi_max = 0.5
rt_epochs = RtEpochs(rt_client,
event_id,
tmin,
tmax,
picks=picks,
isi_max=isi_max)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=10, buffer_size=1000)
# get_data() should return immediately and not wait for the timeout
start_time = time.time()
rt_data = rt_epochs.get_data()
retrieval_time = time.time() - start_time
assert retrieval_time < isi_max
assert rt_data.shape == data.shape
assert_array_equal(rt_data, data)
assert len(rt_epochs) == len(epochs)
# iteration over epochs should block until timeout
rt_iter_data = list()
start_time = time.time()
for cur_epoch in rt_epochs:
rt_iter_data.append(cur_epoch)
retrieval_time = time.time() - start_time
assert retrieval_time >= isi_max
rt_iter_data = np.array(rt_iter_data)
assert rt_iter_data.shape == data.shape
assert_array_equal(rt_iter_data, data)
assert len(rt_epochs) == len(epochs)
_call_base_epochs_public_api(rt_epochs, tmpdir)
def test_rejection(buffer_size):
"""Test rejection."""
event_id, tmin, tmax = 1, 0.0, 0.5
sfreq = 1000
ch_names = ['Fz', 'Cz', 'Pz', 'STI 014']
raw_tmax = 5
info = create_info(ch_names=ch_names, sfreq=sfreq,
ch_types=['eeg', 'eeg', 'eeg', 'stim'])
raw_array = np.random.randn(len(ch_names), raw_tmax * sfreq)
raw_array[-1, :] = 0
epoch_start_samples = np.arange(raw_tmax) * sfreq
raw_array[-1, epoch_start_samples] = event_id
reject_threshold = np.max(raw_array) - np.min(raw_array) + 1
reject = {'eeg': reject_threshold}
epochs_to_reject = [1, 3]
epochs_to_keep = np.setdiff1d(np.arange(len(epoch_start_samples)),
epochs_to_reject)
expected_drop_log = [list() for _ in range(len(epoch_start_samples))]
for cur_epoch in epochs_to_reject:
raw_array[1, epoch_start_samples[cur_epoch]] = reject_threshold + 1
expected_drop_log[cur_epoch] = [ch_names[1]]
raw = RawArray(raw_array, info)
events = find_events(raw, shortest_event=1, initial_event=True)
picks = pick_types(raw.info, eeg=True)
epochs = Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
baseline=None, picks=picks, preload=True,
reject=reject)
epochs_data = epochs.get_data()
assert len(epochs) == len(epoch_start_samples) - len(epochs_to_reject)
assert_array_equal(epochs_data[:, 1, 0],
raw_array[1, epoch_start_samples[epochs_to_keep]])
assert_array_equal(epochs.drop_log, expected_drop_log)
assert_array_equal(epochs.selection, epochs_to_keep)
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks,
baseline=None, isi_max=0.5,
find_events=dict(initial_event=True),
reject=reject)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=raw_tmax,
buffer_size=buffer_size)
assert len(rt_epochs) == len(epochs_to_keep)
assert_array_equal(rt_epochs.drop_log, expected_drop_log)
assert_array_equal(rt_epochs.selection, epochs_to_keep)
rt_data = rt_epochs.get_data()
assert rt_data.shape == epochs_data.shape
assert_array_equal(rt_data, epochs_data)
def test_rejection(buffer_size):
event_id, tmin, tmax = 1, 0.0, 0.5
sfreq = 1000
ch_names = ['Fz', 'Cz', 'Pz', 'STI 014']
raw_tmax = 5
info = create_info(ch_names=ch_names, sfreq=sfreq,
ch_types=['eeg', 'eeg', 'eeg', 'stim'])
raw_array = np.random.randn(len(ch_names), raw_tmax * sfreq)
raw_array[-1, :] = 0
epoch_start_samples = np.arange(raw_tmax) * sfreq
raw_array[-1, epoch_start_samples] = event_id
reject_threshold = np.max(raw_array) - np.min(raw_array) + 1
reject = {'eeg': reject_threshold}
epochs_to_reject = [1, 3]
epochs_to_keep = np.setdiff1d(np.arange(len(epoch_start_samples)),
epochs_to_reject)
expected_drop_log = [list() for _ in range(len(epoch_start_samples))]
for cur_epoch in epochs_to_reject:
raw_array[1, epoch_start_samples[cur_epoch]] = reject_threshold + 1
expected_drop_log[cur_epoch] = [ch_names[1]]
raw = RawArray(raw_array, info)
events = find_events(raw, shortest_event=1, initial_event=True)
picks = pick_types(raw.info, eeg=True)
epochs = Epochs(raw, events, event_id=event_id, tmin=tmin, tmax=tmax,
baseline=None, picks=picks, preload=True,
reject=reject)
epochs_data = epochs.get_data()
assert len(epochs) == len(epoch_start_samples) - len(epochs_to_reject)
assert_array_equal(epochs_data[:, 1, 0],
raw_array[1, epoch_start_samples[epochs_to_keep]])
assert_array_equal(epochs.drop_log, expected_drop_log)
assert_array_equal(epochs.selection, epochs_to_keep)
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks,
baseline=None, isi_max=0.5,
find_events=dict(initial_event=True),
reject=reject)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=raw_tmax,
buffer_size=buffer_size)
assert len(rt_epochs) == len(epochs_to_keep)
assert_array_equal(rt_epochs.drop_log, expected_drop_log)
assert_array_equal(rt_epochs.selection, epochs_to_keep)
rt_data = rt_epochs.get_data()
assert rt_data.shape == epochs_data.shape
assert_array_equal(rt_data, epochs_data)
def test_mockclient(tmpdir):
"""Test the RtMockClient."""
raw = read_raw_fif(raw_fname, preload=True, verbose=False)
picks = pick_types(raw.info, meg='grad', eeg=False, eog=True,
stim=True, exclude=raw.info['bads'])
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = Epochs(raw, events[:7], event_id=event_id, tmin=tmin, tmax=tmax,
picks=picks, baseline=(None, 0), preload=True)
data = epochs.get_data()
rt_client = MockRtClient(raw)
# choose "large" value, should always be longer than execution time of
# get_data()
isi_max = 0.5
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks,
isi_max=isi_max)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=10, buffer_size=1000)
# get_data() should return immediately and not wait for the timeout
start_time = time.time()
rt_data = rt_epochs.get_data()
retrieval_time = time.time() - start_time
assert retrieval_time < isi_max
assert rt_data.shape == data.shape
assert_array_equal(rt_data, data)
assert len(rt_epochs) == len(epochs)
# iteration over epochs should block until timeout
rt_iter_data = list()
start_time = time.time()
for cur_epoch in rt_epochs:
rt_iter_data.append(cur_epoch)
retrieval_time = time.time() - start_time
assert retrieval_time >= isi_max
rt_iter_data = np.array(rt_iter_data)
assert rt_iter_data.shape == data.shape
assert_array_equal(rt_iter_data, data)
assert len(rt_epochs) == len(epochs)
_call_base_epochs_public_api(rt_epochs, tmpdir)
def test_mockclient():
"""Test the RtMockClient."""
event_id, tmin, tmax = 1, -0.2, 0.5
epochs = Epochs(raw, events[:7], event_id=event_id, tmin=tmin, tmax=tmax,
picks=picks, baseline=(None, 0), preload=True)
data = epochs.get_data()
rt_client = MockRtClient(raw)
rt_epochs = RtEpochs(rt_client, event_id, tmin, tmax, picks=picks)
rt_epochs.start()
rt_client.send_data(rt_epochs, picks, tmin=0, tmax=10, buffer_size=1000)
rt_data = rt_epochs.get_data()
assert_true(rt_data.shape == data.shape)
assert_array_equal(rt_data, data)
n_times = len(rt_epochs.times)
scores_x, scores, std_scores = [], [], []
# define a simple linear svm classifier
filt = FilterEstimator(rt_epochs.info, 1, 40)
scaler = preprocessing.StandardScaler()
vectorizer = Vectorizer()
clf = SVC(C=1, kernel='linear')
concat_classifier = Pipeline([('filter', filt), ('vector', vectorizer),
('scaler', scaler), ('svm', clf)])
data_picks = mne.pick_types(rt_epochs.info, meg='grad', eeg=False, eog=True,
stim=False, exclude=raw.info['bads'])
for ev_num, ev in enumerate(rt_epochs.iter_evoked()):
print("Just got epoch %d" % (ev_num + 1))
print rt_epochs.get_data().shape
print ev.data.shape
if ev_num == 0:
X = ev.data[None, data_picks, :]
y = int(ev.comment) # the comment attribute contains the event_id
else:
X = np.concatenate((X, ev.data[None, data_picks, :]), axis=0)
y = np.append(y, int(ev.comment))
if ev_num >= min_trials:
cv = ShuffleSplit(len(y), 5, test_size=0.2, random_state=42)
scores_t = cross_val_score(concat_classifier, X, y, cv=cv,
n_jobs=1) * 100
std_scores.append(scores_t.std())
scores.append(scores_t.mean())
scores_x.append(ev_num)
