Python ICA.labels_方法代码示例

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_plot_ica_sources():
    """Test plotting of ICA panel."""
    raw = read_raw_fif(raw_fname).crop(0, 1).load_data()
    picks = _get_picks(raw)
    epochs = _get_epochs()
    raw.pick_channels([raw.ch_names[k] for k in picks])
    ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
                           ecg=False, eog=False, exclude='bads')
    ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=ica_picks)
    ica.exclude = [1]
    fig = ica.plot_sources(raw)
    fig.canvas.key_press_event('escape')
    # Sadly close_event isn't called on Agg backend and the test always passes.
    assert_array_equal(ica.exclude, [1])
    plt.close('all')

    # dtype can change int->np.int after load, test it explicitly
    ica.n_components_ = np.int64(ica.n_components_)
    fig = ica.plot_sources(raw, [1])
    # also test mouse clicks
    data_ax = fig.axes[0]
    _fake_click(fig, data_ax, [-0.1, 0.9])  # click on y-label

    raw.info['bads'] = ['MEG 0113']
    pytest.raises(RuntimeError, ica.plot_sources, inst=raw)
    ica.plot_sources(epochs)
    epochs.info['bads'] = ['MEG 0113']
    pytest.raises(RuntimeError, ica.plot_sources, inst=epochs)
    epochs.info['bads'] = []
    ica.plot_sources(epochs.average())
    evoked = epochs.average()
    fig = ica.plot_sources(evoked)
    # Test a click
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax,
                [line.get_xdata()[0], line.get_ydata()[0]], 'data')
    _fake_click(fig, ax,
                [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
    # plot with bad channels excluded
    ica.plot_sources(evoked, exclude=[0])
    ica.exclude = [0]
    ica.plot_sources(evoked)  # does the same thing
    ica.labels_ = dict(eog=[0])
    ica.labels_['eog/0/crazy-channel'] = [0]
    ica.plot_sources(evoked)  # now with labels
    pytest.raises(ValueError, ica.plot_sources, 'meeow')
    plt.close('all')

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_plot_ica_sources():
    """Test plotting of ICA panel
    """
    import matplotlib.pyplot as plt
    raw = io.read_raw_fif(raw_fname,
                          preload=False).crop(0, 1, copy=False).load_data()
    picks = _get_picks(raw)
    epochs = _get_epochs()
    raw.pick_channels([raw.ch_names[k] for k in picks])
    ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
                           ecg=False, eog=False, exclude='bads')
    ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=ica_picks)
    ica.exclude = [1]
    fig = ica.plot_sources(raw)
    fig.canvas.key_press_event('escape')
    # Sadly close_event isn't called on Agg backend and the test always passes.
    assert_array_equal(ica.exclude, [1])

    raw.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=raw)
    ica.plot_sources(epochs)
    epochs.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
    epochs.info['bads'] = []
    with warnings.catch_warnings(record=True):  # no labeled objects mpl
        ica.plot_sources(epochs.average())
        evoked = epochs.average()
        fig = ica.plot_sources(evoked)
        # Test a click
        ax = fig.get_axes()[0]
        line = ax.lines[0]
        _fake_click(fig, ax,
                    [line.get_xdata()[0], line.get_ydata()[0]], 'data')
        _fake_click(fig, ax,
                    [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
        # plot with bad channels excluded
        ica.plot_sources(evoked, exclude=[0])
        ica.exclude = [0]
        ica.plot_sources(evoked)  # does the same thing
        ica.labels_ = dict(eog=[0])
        ica.labels_['eog/0/crazy-channel'] = [0]
        ica.plot_sources(evoked)  # now with labels
    assert_raises(ValueError, ica.plot_sources, 'meeow')
    plt.close('all')

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_plot_ica_scores():
    """Test plotting of ICA scores."""
    import matplotlib.pyplot as plt

    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2, max_pca_components=3, n_pca_components=3)
    with warnings.catch_warnings(record=True):  # bad proj
        ica.fit(raw, picks=picks)
    ica.labels_ = dict()
    ica.labels_["eog/0/foo"] = 0
    ica.labels_["eog"] = 0
    ica.labels_["ecg"] = 1
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="foo")
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="eog")
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels="ecg")
    assert_raises(ValueError, ica.plot_scores, [0.3, 0.2], axhline=[0.1, -0.1], labels=["one", "one-too-many"])
    assert_raises(ValueError, ica.plot_scores, [0.2])
    plt.close("all")

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_plot_ica_scores():
    """Test plotting of ICA scores."""
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    with pytest.warns(RuntimeWarning, match='projection'):
        ica.fit(raw, picks=picks)
    ica.labels_ = dict()
    ica.labels_['eog/0/foo'] = 0
    ica.labels_['eog'] = 0
    ica.labels_['ecg'] = 1
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
    pytest.raises(
        ValueError,
        ica.plot_scores,
        [0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
    pytest.raises(ValueError, ica.plot_scores, [0.2])
    plt.close('all')

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_plot_ica_scores():
    """Test plotting of ICA scores
    """
    import matplotlib.pyplot as plt
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    ica.labels_ = dict()
    ica.labels_['eog/0/foo'] = 0
    ica.labels_['eog'] = 0
    ica.labels_['ecg'] = 1
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
    assert_raises(
        ValueError,
        ica.plot_scores,
        [0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
    assert_raises(ValueError, ica.plot_scores, [0.2])
    plt.close('all')

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_ica_additional():
    """Test additional ICA functionality"""
    tempdir = _TempDir()
    stop2 = 500
    raw = Raw(raw_fname).crop(1.5, stop, 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()
    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"])
    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 = [0]
            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])
#.........这里部分代码省略.........

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_ [as 别名]
def test_ica_additional(method):
    """Test additional ICA functionality."""
    _skip_check_picard(method)

    tempdir = _TempDir()
    stop2 = 500
    raw = read_raw_fif(raw_fname).crop(1.5, stop).load_data()
    raw.del_proj()  # avoid warnings
    raw.set_annotations(Annotations([0.5], [0.5], ['BAD']))
    # XXX This breaks the tests :(
    # raw.info['bads'] = [raw.ch_names[1]]
    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')[1::2]
    epochs = Epochs(raw, events, None, tmin, tmax, picks=picks,
                    baseline=(None, 0), preload=True, proj=False)
    epochs.decimate(3, verbose='error')
    assert len(epochs) == 4

    # test if n_components=None works
    ica = ICA(n_components=None, max_pca_components=None,
              n_pca_components=None, random_state=0, method=method, max_iter=1)
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(epochs)
    # for testing eog functionality
    picks2 = np.concatenate([picks, pick_types(raw.info, False, eog=True)])
    epochs_eog = Epochs(raw, events[:4], event_id, tmin, tmax, picks=picks2,
                        baseline=(None, 0), preload=True)
    del picks2

    test_cov2 = test_cov.copy()
    ica = ICA(noise_cov=test_cov2, n_components=3, max_pca_components=4,
              n_pca_components=4, method=method)
    assert (ica.info is None)
    with pytest.warns(RuntimeWarning, match='normalize_proj'):
        ica.fit(raw, picks[:5])
    assert (isinstance(ica.info, Info))
    assert (ica.n_components_ < 5)

    ica = ICA(n_components=3, max_pca_components=4, method=method,
              n_pca_components=4, random_state=0)
    pytest.raises(RuntimeError, ica.save, '')

    ica.fit(raw, picks=[1, 2, 3, 4, 5], start=start, stop=stop2)

    # check passing a ch_name to find_bads_ecg
    with pytest.warns(RuntimeWarning, match='longer'):
        _, scores_1 = ica.find_bads_ecg(raw)
        _, scores_2 = ica.find_bads_ecg(raw, raw.ch_names[1])
    assert scores_1[0] != scores_2[0]

    # 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 (ica.labels_["blinks"] == ica2.labels_["blinks"])
    assert (0 in ica.labels_["blinks"])
    # test retrieval of component maps as arrays
    components = ica.get_components()
    template = components[:, 0]
    EvokedArray(components, ica.info, tmin=0.).plot_topomap([0], time_unit='s')

    corrmap([ica, ica3], template, threshold='auto', label='blinks', plot=True,
            ch_type="mag")
    assert (ica2.labels_["blinks"] == ica3.labels_["blinks"])

    plt.close('all')

    ica_different_channels = ICA(n_components=2, random_state=0).fit(
        raw, picks=[2, 3, 4, 5])
    pytest.raises(ValueError, corrmap, [ica_different_channels, ica], (0, 0))

    # test warnings on bad filenames
    ica_badname = op.join(op.dirname(tempdir), 'test-bad-name.fif.gz')
    with pytest.warns(RuntimeWarning, match='-ica.fif'):
        ica.save(ica_badname)
    with pytest.warns(RuntimeWarning, match='-ica.fif'):
        read_ica(ica_badname)

    # test decim
    ica = ICA(n_components=3, max_pca_components=4,
              n_pca_components=4, method=method, max_iter=1)
    raw_ = raw.copy()
    for _ in range(3):
        raw_.append(raw_)
    n_samples = raw_._data.shape[1]
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(raw, picks=picks[:5], decim=3)
    assert raw_._data.shape[1] == n_samples

    # test expl var
    ica = ICA(n_components=1.0, max_pca_components=4,
              n_pca_components=4, method=method, max_iter=1)
    with pytest.warns(UserWarning, match='did not converge'):
        ica.fit(raw, picks=None, decim=3)
    assert (ica.n_components_ == 4)
    ica_var = _ica_explained_variance(ica, raw, normalize=True)
#.........这里部分代码省略.........