Python RtEpochs.get_data方法代码示例

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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)

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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)

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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)

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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)

# 需要导入模块: from mne.realtime import RtEpochs [as 别名]
# 或者: from mne.realtime.RtEpochs import get_data [as 别名]
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)