コード例 #1
0
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    import matplotlib.pyplot as plt
    epochs = _get_epochs()
    plot_image_epochs(epochs, picks=[1, 2])
    plt.close('all')
コード例 #2
0
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    import matplotlib.pyplot as plt
    epochs = _get_epochs()
    plot_image_epochs(epochs, picks=[1, 2])
    plt.close('all')
コード例 #3
0
def test_plot_epochs_image():
    """Test plotting of epochs image
    """
    import matplotlib.pyplot as plt
    epochs = _get_epochs()
    plot_epochs_image(epochs, picks=[1, 2])
    plt.close('all')
    with warnings.catch_warnings(record=True):
        plot_image_epochs(epochs, picks=[1, 2])
        plt.close('all')
コード例 #4
0
ファイル: test_epochs.py プロジェクト: leggitta/mne-python 
def test_plot_epochs_image():
    """Test plotting of epochs image
    """
    import matplotlib.pyplot as plt
    epochs = _get_epochs()
    plot_epochs_image(epochs, picks=[1, 2])
    plt.close('all')
    with warnings.catch_warnings(record=True):
        plot_image_epochs(epochs, picks=[1, 2])
        plt.close('all')
コード例 #5
0
ファイル: test_viz.py プロジェクト: anywave/aw-export-fif 
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    epochs = _get_epochs()
    plot_image_epochs(epochs, picks=[1, 2])
    plt.close('all')
コード例 #6
0
ファイル: test_viz.py プロジェクト: emanuele/mne-python 
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    plot_image_epochs(epochs, picks=[1, 2])
コード例 #7
0
ファイル: test_epochs.py プロジェクト: pelednoam/mne-python 
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    epochs = _get_epochs()
    plot_image_epochs(epochs, picks=[1, 2])
    plt.close('all')
コード例 #8
0
def test_plot_image_epochs():
    """Test plotting of epochs image
    """
    plot_image_epochs(epochs, picks=[1, 2])
コード例 #9
0
# Setup for reading the raw data
raw = io.Raw(raw_fname, preload=True)
raw.filter(1, 20, method='iir')  # replace baselining with high-pass
events = read_events(event_fname)

raw.info['bads'] = ['MEG 2443']  # set bad channels
picks = pick_types(raw.info, meg=True, eeg=False, stim=False, eog=False,
                   exclude='bads')
# Epoching
epochs = Epochs(raw, events, event_id, tmin, tmax, proj=False,
                picks=picks, baseline=None, preload=True,
                add_eeg_ref=False, verbose=False)

# Plot image epoch before xdawn
plot_image_epochs(epochs['vis_r'], picks=[230], vmin=-500, vmax=500)

# Estimates signal covariance
signal_cov = compute_raw_data_covariance(raw, picks=picks)

# Xdawn instance
xd = Xdawn(n_components=2, signal_cov=signal_cov)

# Fit xdawn
xd.fit(epochs)

# Denoise epochs
epochs_denoised = xd.apply(epochs)

# Plot image epoch after xdawn
plot_image_epochs(epochs_denoised['vis_r'], picks=[230], vmin=-500, vmax=500)