Python ICA.pick_sources_epochs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 pick_sources_epochs [as 别名]
def test_ica_full_data_recovery():
    """Test recovery of full data when no source is rejected"""
    # Most basic recovery
    raw = io.Raw(raw_fname, preload=True).crop(0, stop, False).crop(1.5)
    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)
    n_channels = 5
    data = raw._data[:n_channels].copy()
    data_epochs = epochs.get_data()
    for n_components, n_pca_components, ok in [(2, n_channels, True),
                                               (2, n_channels // 2, False)]:
        ica = ICA(n_components=n_components,
                  max_pca_components=n_pca_components,
                  n_pca_components=n_pca_components)
        ica.decompose_raw(raw, picks=list(range(n_channels)))
        raw2 = ica.pick_sources_raw(raw, exclude=[])
        if ok:
            assert_allclose(data[:n_channels], raw2._data[:n_channels],
                            rtol=1e-10, atol=1e-15)
        else:
            diff = np.abs(data[:n_channels] - raw2._data[:n_channels])
            assert_true(np.max(diff) > 1e-14)

        ica = ICA(n_components=n_components,
                  max_pca_components=n_pca_components,
                  n_pca_components=n_pca_components)
        ica.decompose_epochs(epochs, picks=list(range(n_channels)))
        epochs2 = ica.pick_sources_epochs(epochs, exclude=[])
        data2 = epochs2.get_data()[:, :n_channels]
        if ok:
            assert_allclose(data_epochs[:, :n_channels], data2,
                            rtol=1e-10, atol=1e-15)
        else:
            diff = np.abs(data_epochs[:, :n_channels] - data2)
            assert_true(np.max(diff) > 1e-14)

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import pick_sources_epochs [as 别名]
                                     score_func='pearsonr')

# get the source most correlated with the ECG.
ecg_source_idx = np.argsort(np.abs(ecg_scores))[-1]
ica.exclude += [ecg_source_idx]



# Restore sensor space data
# plot spatial sensitivities of a few ICA components
# title = 'Spatial patterns of ICA components (Magnetometers)'
# source_idx = range(0, ica.n_components_)
# ica.plot_topomap(source_idx, ch_type='mag')
# plt.suptitle(title, fontsize=12)

epochs_ica = ica.pick_sources_epochs(epochs)
# epochs_ica.save("sef_bilat-tsss-mc-autobad-epochs.fif")

print epochs_ica
epochs_ica.info


#### Evoked data

evoked_left = epochs_ica["Left"].average()
evoked_right = epochs_ica["Right"].average()

print "plot evoked for Left"
evoked_left.plot()

print "plot evoked for Right"

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import pick_sources_epochs [as 别名]
epochs = mne.Epochs(raw, events, event_ids, tmin, tmax, picks=picks, baseline=baseline, preload=True, reject=reject)

# Fit ICA, find and remove major artifacts

ica = ICA(None, 50).decompose_epochs(epochs, decim=2)

for ch_name in ["MRT51-2908", "MLF14-2908"]:  # ECG, EOG contaminated chs
    scores = ica.find_sources_epochs(epochs, ch_name, "pearsonr")
    ica.exclude += list(np.argsort(np.abs(scores))[-2:])

ica.plot_topomap(np.unique(ica.exclude))  # plot components found


# select ICA sources and reconstruct MEG signals, compute clean ERFs

epochs = ica.pick_sources_epochs(epochs)

evoked = [epochs[k].average() for k in event_ids]

contrast = evoked[1] - evoked[0]

evoked.append(contrast)

for e in evoked:
    e.plot(ylim=dict(mag=[-400, 400]))

plt.show()

# estimate noise covarariance
noise_cov = mne.compute_covariance(epochs.crop(None, 0, copy=True))

# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import pick_sources_epochs [as 别名]
def test_ica_core():
    """Test ICA on raw and epochs
    """
    # setup parameter
    # XXX. The None cases helped revealing bugs but are time consuming.
    noise_cov = [None, test_cov]
    # removed None cases to speed up...
    n_components = [3, 1.0]  # for future dbg add cases
    max_pca_components = [4]
    picks_ = [picks]
    iter_ica_params = product(noise_cov, n_components, max_pca_components,
                              picks_)

    # # test init catchers
    assert_raises(ValueError, ICA, n_components=3, max_pca_components=2)
    assert_raises(ValueError, ICA, n_components=1.3, max_pca_components=2)

    # test essential core functionality
    for n_cov, n_comp, max_n, pcks in iter_ica_params:
      # Test ICA raw
        ica = ICA(noise_cov=n_cov, n_components=n_comp,
                  max_pca_components=max_n, n_pca_components=max_n,
                  random_state=0)

        print ica  # to test repr

        # test fit checker
        assert_raises(RuntimeError, ica.get_sources_raw, raw)
        assert_raises(RuntimeError, ica.get_sources_epochs, epochs)

        # test decomposition
        ica.decompose_raw(raw, picks=pcks, start=start, stop=stop)
        print ica  # to test repr
        # test re-init exception
        assert_raises(RuntimeError, ica.decompose_raw, raw, picks=picks)

        sources = ica.get_sources_raw(raw)
        assert_true(sources.shape[0] == ica.n_components_)

        # test preload filter
        raw3 = raw.copy()
        raw3._preloaded = False
        assert_raises(ValueError, ica.pick_sources_raw, raw3,
                      include=[1, 2])

        for excl, incl in (([], []), ([], [1, 2]), ([1, 2], [])):
            raw2 = ica.pick_sources_raw(raw, exclude=excl, include=incl,
                                        copy=True)

            assert_array_almost_equal(raw2[:, :][1], raw[:, :][1])

        #######################################################################
        # test epochs decomposition

        # test re-init exception
        assert_raises(RuntimeError, ica.decompose_epochs, epochs, picks=picks)
        ica = ICA(noise_cov=n_cov, n_components=n_comp,
                  max_pca_components=max_n, n_pca_components=max_n,
                  random_state=0)

        ica.decompose_epochs(epochs, picks=picks)
        print ica  # to test repr
        # test pick block after epochs fit
        assert_raises(ValueError, ica.pick_sources_raw, raw)

        sources = ica.get_sources_epochs(epochs)
        assert_true(sources.shape[1] == ica.n_components_)

        assert_raises(ValueError, ica.find_sources_epochs, epochs,
                      target=np.arange(1))

        # test preload filter
        epochs3 = epochs.copy()
        epochs3.preload = False
        assert_raises(ValueError, ica.pick_sources_epochs, epochs3,
                      include=[1, 2])

        # test source picking
        for excl, incl in (([], []), ([], [1, 2]), ([1, 2], [])):
            epochs2 = ica.pick_sources_epochs(epochs, exclude=excl,
                                      include=incl, copy=True)

            assert_array_almost_equal(epochs2.get_data(),
                                      epochs.get_data())