Python ICA.method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 method [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.........

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import method [as 别名]

#.........这里部分代码省略.........
        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()
        ica_raw.filter(4, 20, l_trans_bandwidth='auto',
                       h_trans_bandwidth='auto', filter_length='auto',
                       phase='zero', fir_window='hamming')
        assert_equal(ica_raw.info['lowpass'], 20.)
        assert_equal(ica_raw.info['highpass'], 4.)
        assert_true((d1 != ica_raw._data[0]).any())
        d1 = ica_raw._data[0].copy()
        ica_raw.notch_filter([10], filter_length='auto', trans_bandwidth=10,
                             phase='zero', fir_window='hamming')
        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]