Python Epochs.get_data方法代码示例

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_reject_epochs():
    """Test of epochs rejection
    """
    epochs = Epochs(raw, events, event_id, tmin, tmax, baseline=(None, 0),
                    reject=reject, flat=flat)
    n_events = len(epochs.events)
    data = epochs.get_data()
    n_clean_epochs = len(data)
    # Should match
    # mne_process_raw --raw test_raw.fif --projoff \
    #   --saveavetag -ave --ave test.ave --filteroff
    assert_true(n_events > n_clean_epochs)
    assert_true(n_clean_epochs == 3)
    assert_true(epochs.drop_log == [[], [], [], ['MEG 2443'],
                                    ['MEG 2443'], ['MEG 2443'], ['MEG 2443']])

    # Ensure epochs are not dropped based on a bad channel
    raw_2 = raw.copy()
    raw_2.info['bads'] = ['MEG 2443']
    reject_crazy = dict(grad=1000e-15, mag=4e-15, eeg=80e-9, eog=150e-9)
    epochs = Epochs(raw_2, events, event_id, tmin, tmax, baseline=(None, 0),
                    reject=reject_crazy, flat=flat)
    epochs.drop_bad_epochs()
    assert_true(all(['MEG 2442' in e for e in epochs.drop_log]))
    assert_true(all(['MEG 2443' not in e for e in epochs.drop_log]))

    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), reject=reject, flat=flat,
                    reject_tmin=0., reject_tmax=.1)
    data = epochs.get_data()
    n_clean_epochs = len(data)
    assert_true(n_clean_epochs == 7)
    assert_true(epochs.times[epochs._reject_time][0] >= 0.)
    assert_true(epochs.times[epochs._reject_time][-1] <= 0.1)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_crop():
    """Test of crop of epochs
    """
    raw, events, picks = _get_data()
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=False,
                    reject=reject, flat=flat)
    assert_raises(RuntimeError, epochs.crop, None, 0.2)  # not preloaded
    data_normal = epochs.get_data()

    epochs2 = Epochs(raw, events[:5], event_id, tmin, tmax,
                     picks=picks, baseline=(None, 0), preload=True,
                     reject=reject, flat=flat)
    with warnings.catch_warnings(record=True) as w:
        epochs2.crop(-20, 200)
    assert_true(len(w) == 2)

    # indices for slicing
    tmin_window = tmin + 0.1
    tmax_window = tmax - 0.1
    tmask = (epochs.times >= tmin_window) & (epochs.times <= tmax_window)
    assert_true(tmin_window > tmin)
    assert_true(tmax_window < tmax)
    epochs3 = epochs2.crop(tmin_window, tmax_window, copy=True)
    data3 = epochs3.get_data()
    epochs2.crop(tmin_window, tmax_window)
    data2 = epochs2.get_data()
    assert_array_equal(data2, data_normal[:, :, tmask])
    assert_array_equal(data3, data_normal[:, :, tmask])

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_detrend():
    """Test detrending of epochs
    """
    # test first-order
    epochs_1 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=None, detrend=1)
    epochs_2 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                      baseline=None, detrend=None)
    data_picks = pick_types(epochs_1.info, meg=True, eeg=True,
                                 exclude='bads')
    evoked_1 = epochs_1.average()
    evoked_2 = epochs_2.average()
    evoked_2.detrend(1)
    # Due to roundoff these won't be exactly equal, but they should be close
    assert_true(np.allclose(evoked_1.data, evoked_2.data,
                            rtol=1e-8, atol=1e-20))

    # test zeroth-order case
    for preload in [True, False]:
        epochs_1 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                          baseline=(None, None), preload=preload)
        epochs_2 = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks,
                          baseline=None, preload=preload, detrend=0)
        a = epochs_1.get_data()
        b = epochs_2.get_data()
        # All data channels should be almost equal
        assert_true(np.allclose(a[:, data_picks, :], b[:, data_picks, :],
                                rtol=1e-16, atol=1e-20))
        # There are non-M/EEG channels that should not be equal:
        assert_true(not np.allclose(a, b))

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_resample():
    """Test of resample of epochs
    """
    epochs = Epochs(
        raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, reject=reject, flat=flat
    )
    data_normal = cp.deepcopy(epochs.get_data())
    times_normal = cp.deepcopy(epochs.times)
    sfreq_normal = epochs.info["sfreq"]
    # upsample by 2
    epochs.resample(sfreq_normal * 2, npad=0)
    data_up = cp.deepcopy(epochs.get_data())
    times_up = cp.deepcopy(epochs.times)
    sfreq_up = epochs.info["sfreq"]
    # downsamply by 2, which should match
    epochs.resample(sfreq_normal, npad=0)
    data_new = cp.deepcopy(epochs.get_data())
    times_new = cp.deepcopy(epochs.times)
    sfreq_new = epochs.info["sfreq"]
    assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
    assert_true(sfreq_up == 2 * sfreq_normal)
    assert_true(sfreq_new == sfreq_normal)
    assert_true(len(times_up) == 2 * len(times_normal))
    assert_array_almost_equal(times_new, times_normal, 10)
    assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
    assert_array_almost_equal(data_new, data_normal, 5)

    # use parallel
    epochs = Epochs(
        raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, reject=reject, flat=flat
    )
    epochs.resample(sfreq_normal * 2, n_jobs=2, npad=0)
    assert_true(np.allclose(data_up, epochs._data, rtol=1e-8, atol=1e-16))

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_epochs_proj():
    """Test handling projection (apply proj in Raw or in Epochs)
    """
    exclude = raw.info["bads"] + ["MEG 2443", "EEG 053"]  # bads + 2 more
    this_picks = pick_types(raw.info, meg=True, eeg=False, stim=True, eog=True, exclude=exclude)
    epochs = Epochs(raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True)
    assert_true(all(p["active"] is True for p in epochs.info["projs"]))
    evoked = epochs.average()
    assert_true(all(p["active"] is True for p in evoked.info["projs"]))
    data = epochs.get_data()

    raw_proj = io.Raw(raw_fname, proj=True)
    epochs_no_proj = Epochs(
        raw_proj, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=False
    )

    data_no_proj = epochs_no_proj.get_data()
    assert_true(all(p["active"] is True for p in epochs_no_proj.info["projs"]))
    evoked_no_proj = epochs_no_proj.average()
    assert_true(all(p["active"] is True for p in evoked_no_proj.info["projs"]))
    assert_true(epochs_no_proj.proj is True)  # as projs are active from Raw

    assert_array_almost_equal(data, data_no_proj, decimal=8)

    # make sure we can exclude avg ref
    this_picks = pick_types(raw.info, meg=True, eeg=True, stim=True, eog=True, exclude=exclude)
    epochs = Epochs(
        raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True, add_eeg_ref=True
    )
    assert_true(_has_eeg_average_ref_proj(epochs.info["projs"]))
    epochs = Epochs(
        raw, events[:4], event_id, tmin, tmax, picks=this_picks, baseline=(None, 0), proj=True, add_eeg_ref=False
    )
    assert_true(not _has_eeg_average_ref_proj(epochs.info["projs"]))

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_resample():
    """Test of resample of epochs
    """
    epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True,
                    reject=reject, flat=flat)
    data_normal = cp.deepcopy(epochs.get_data())
    times_normal = cp.deepcopy(epochs.times)
    sfreq_normal = epochs.info['sfreq']
    # upsample by 2
    epochs.resample(sfreq_normal * 2)
    data_up = cp.deepcopy(epochs.get_data())
    times_up = cp.deepcopy(epochs.times)
    sfreq_up = epochs.info['sfreq']
    # downsamply by 2, which should match
    epochs.resample(sfreq_normal)
    data_new = cp.deepcopy(epochs.get_data())
    times_new = cp.deepcopy(epochs.times)
    sfreq_new = epochs.info['sfreq']

    assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
    assert_true(sfreq_up == 2 * sfreq_normal)
    assert_true(sfreq_new == sfreq_normal)
    assert_true(len(times_up) == 2 * len(times_normal))
    assert_array_almost_equal(times_new, times_normal, 10)
    assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
    assert_array_almost_equal(data_new, data_normal, 2)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_subtract_evoked():
    """Test subtraction of Evoked from Epochs
    """
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0))

    # make sure subraction fails if data channels are missing
    assert_raises(ValueError, epochs.subtract_evoked, epochs.average(picks[:5]))

    # do the subraction using the default argument
    epochs.subtract_evoked()

    # apply SSP now
    epochs.apply_proj()

    # use preloading and SSP from the start
    epochs2 = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, proj=True)

    evoked = epochs2.average()
    epochs2.subtract_evoked(evoked)

    # this gives the same result
    assert_allclose(epochs.get_data(), epochs2.get_data())

    # if we compute the evoked response after subtracting it we get zero
    zero_evoked = epochs.average()
    data = zero_evoked.data
    assert_array_almost_equal(data, np.zeros_like(data), decimal=20)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_scaler():
    """Test methods of Scaler."""
    raw = io.read_raw_fif(raw_fname)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    picks = picks[1:13:3]

    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    y = epochs.events[:, -1]

    methods = (None, dict(mag=5, grad=10, eeg=20), 'mean', 'median')
    infos = (epochs.info, epochs.info, None, None)
    epochs_data_t = epochs_data.transpose([1, 0, 2])
    for method, info in zip(methods, infos):
        if method == 'median' and not check_version('sklearn', '0.17'):
            assert_raises(ValueError, Scaler, info, method)
            continue
        if method == 'mean' and not check_version('sklearn', ''):
            assert_raises(ImportError, Scaler, info, method)
            continue
        scaler = Scaler(info, method)
        X = scaler.fit_transform(epochs_data, y)
        assert_equal(X.shape, epochs_data.shape)
        if method is None or isinstance(method, dict):
            sd = DEFAULTS['scalings'] if method is None else method
            stds = np.zeros(len(picks))
            for key in ('mag', 'grad'):
                stds[pick_types(epochs.info, meg=key)] = 1. / sd[key]
            stds[pick_types(epochs.info, meg=False, eeg=True)] = 1. / sd['eeg']
            means = np.zeros(len(epochs.ch_names))
        elif method == 'mean':
            stds = np.array([np.std(ch_data) for ch_data in epochs_data_t])
            means = np.array([np.mean(ch_data) for ch_data in epochs_data_t])
        else:  # median
            percs = np.array([np.percentile(ch_data, [25, 50, 75])
                              for ch_data in epochs_data_t])
            stds = percs[:, 2] - percs[:, 0]
            means = percs[:, 1]
        assert_allclose(X * stds[:, np.newaxis] + means[:, np.newaxis],
                        epochs_data, rtol=1e-12, atol=1e-20, err_msg=method)

        X2 = scaler.fit(epochs_data, y).transform(epochs_data)
        assert_array_equal(X, X2)

        # inverse_transform
        Xi = scaler.inverse_transform(X)
        assert_array_almost_equal(epochs_data, Xi)

    # Test init exception
    assert_raises(ValueError, Scaler, None, None)
    assert_raises(ValueError, scaler.fit, epochs, y)
    assert_raises(ValueError, scaler.transform, epochs)
    epochs_bad = Epochs(raw, events, event_id, 0, 0.01,
                        picks=np.arange(len(raw.ch_names)))  # non-data chs
    scaler = Scaler(epochs_bad.info, None)
    assert_raises(ValueError, scaler.fit, epochs_bad.get_data(), y)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_preload_epochs():
    """Test preload of epochs
    """
    epochs_preload = Epochs(
        raw, events[:16], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, reject=reject, flat=flat
    )
    data_preload = epochs_preload.get_data()

    epochs = Epochs(
        raw, events[:16], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=False, reject=reject, flat=flat
    )
    data = epochs.get_data()
    assert_array_equal(data_preload, data)
    assert_array_almost_equal(epochs_preload.average().data, epochs.average().data, 18)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_pick_channels_mixin():
    """Test channel-picking functionality
    """
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks, baseline=(None, 0))
    ch_names = epochs.ch_names[:3]

    ch_names_orig = epochs.ch_names
    dummy = epochs.pick_channels(ch_names, copy=True)
    assert_equal(ch_names, dummy.ch_names)
    assert_equal(ch_names_orig, epochs.ch_names)
    assert_equal(len(ch_names_orig), epochs.get_data().shape[1])

    epochs.pick_channels(ch_names)
    assert_equal(ch_names, epochs.ch_names)
    assert_equal(len(ch_names), epochs.get_data().shape[1])

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_concatenatechannels():
    """Test methods of ConcatenateChannels
    """
    raw = fiff.Raw(raw_fname, preload=False)
    events = read_events(event_name)
    picks = fiff.pick_types(raw.info, meg=True, stim=False, ecg=False,
                            eog=False, exclude='bads')
    picks = picks[1:13:3]
    with warnings.catch_warnings(record=True) as w:
        epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                        baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    concat = ConcatenateChannels(epochs.info)
    y = epochs.events[:, -1]
    X = concat.fit_transform(epochs_data, y)

    # Check data dimensions
    assert_true(X.shape[0] == epochs_data.shape[0])
    assert_true(X.shape[1] == epochs_data.shape[1] * epochs_data.shape[2])

    assert_array_equal(concat.fit(epochs_data, y).transform(epochs_data), X)

    # Check if data is preserved
    n_times = epochs_data.shape[2]
    assert_array_equal(epochs_data[0, 0, 0:n_times], X[0, 0:n_times])

    # Test init exception
    assert_raises(ValueError, concat.fit, epochs, y)
    assert_raises(ValueError, concat.transform, epochs, y)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_scaler():
    """Test methods of Scaler
    """
    raw = fiff.Raw(raw_fname, preload=False)
    events = read_events(event_name)
    picks = fiff.pick_types(raw.info, meg=True, stim=False, ecg=False,
                            eog=False, exclude='bads')
    picks = picks[1:13:3]

    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    scaler = Scaler(epochs.info)
    y = epochs.events[:, -1]

    # np invalid divide value warnings
    with warnings.catch_warnings(record=True):
        X = scaler.fit_transform(epochs_data, y)
        assert_true(X.shape == epochs_data.shape)
        X2 = scaler.fit(epochs_data, y).transform(epochs_data)

    assert_array_equal(X2, X)

    # Test init exception
    assert_raises(ValueError, scaler.fit, epochs, y)
    assert_raises(ValueError, scaler.transform, epochs, y)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_scaler():
    """Test methods of Scaler."""
    raw = io.read_raw_fif(raw_fname)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    picks = picks[1:13:3]

    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    scaler = Scaler(epochs.info)
    y = epochs.events[:, -1]

    # np invalid divide value warnings
    with warnings.catch_warnings(record=True):
        X = scaler.fit_transform(epochs_data, y)
        assert_true(X.shape == epochs_data.shape)
        X2 = scaler.fit(epochs_data, y).transform(epochs_data)

    assert_array_equal(X2, X)

    # Test inverse_transform
    with warnings.catch_warnings(record=True):  # invalid value in mult
        Xi = scaler.inverse_transform(X, y)
    assert_array_almost_equal(epochs_data, Xi)

    for kwargs in [{'with_mean': False}, {'with_std': False}]:
        scaler = Scaler(epochs.info, **kwargs)
        scaler.fit(epochs_data, y)
        assert_array_almost_equal(
            X, scaler.inverse_transform(scaler.transform(X)))
    # Test init exception
    assert_raises(ValueError, scaler.fit, epochs, y)
    assert_raises(ValueError, scaler.transform, epochs, y)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_regularized_csp():
    """Test Common Spatial Patterns algorithm using regularized covariance."""
    raw = io.read_raw_fif(raw_fname)
    events = read_events(event_name)
    picks = pick_types(raw.info, meg=True, stim=False, ecg=False,
                       eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    n_channels = epochs_data.shape[1]

    n_components = 3
    reg_cov = [None, 0.05, 'ledoit_wolf', 'oas']
    for reg in reg_cov:
        csp = CSP(n_components=n_components, reg=reg, norm_trace=False)
        csp.fit(epochs_data, epochs.events[:, -1])
        y = epochs.events[:, -1]
        X = csp.fit_transform(epochs_data, y)
        assert_true(csp.filters_.shape == (n_channels, n_channels))
        assert_true(csp.patterns_.shape == (n_channels, n_channels))
        assert_array_almost_equal(csp.fit(epochs_data, y).
                                  transform(epochs_data), X)

        # test init exception
        assert_raises(ValueError, csp.fit, epochs_data,
                      np.zeros_like(epochs.events))
        assert_raises(ValueError, csp.fit, epochs, y)
        assert_raises(ValueError, csp.transform, epochs)

        csp.n_components = n_components
        sources = csp.transform(epochs_data)
        assert_true(sources.shape[1] == n_components)

# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import get_data [as 别名]
def test_csp():
    """Test Common Spatial Patterns algorithm on epochs
    """
    raw = fiff.Raw(raw_fname, preload=False)
    events = read_events(event_name)
    picks = fiff.pick_types(raw.info, meg=True, stim=False, ecg=False,
                            eog=False, exclude='bads')
    picks = picks[1:13:3]
    epochs = Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True)
    epochs_data = epochs.get_data()
    n_channels = epochs_data.shape[1]

    n_components = 3
    csp = CSP(n_components=n_components)

    csp.fit(epochs_data, epochs.events[:, -1])
    y = epochs.events[:, -1]
    X = csp.fit_transform(epochs_data, y)
    assert_true(csp.filters_.shape == (n_channels, n_channels))
    assert_true(csp.patterns_.shape == (n_channels, n_channels))
    assert_array_almost_equal(csp.fit(epochs_data, y).transform(epochs_data),
                              X)

    # test init exception
    assert_raises(ValueError, csp.fit, epochs_data,
                  np.zeros_like(epochs.events))
    assert_raises(ValueError, csp.fit, epochs, y)
    assert_raises(ValueError, csp.transform, epochs, y)

    csp.n_components = n_components
    sources = csp.transform(epochs_data)
    assert_true(sources.shape[1] == n_components)