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')
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')
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')
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')
def test_plot_image_epochs():
"""Test plotting of epochs image
"""
epochs = _get_epochs()
plot_image_epochs(epochs, picks=[1, 2])
plt.close('all')
def test_plot_image_epochs():
"""Test plotting of epochs image
"""
plot_image_epochs(epochs, picks=[1, 2])
def test_plot_image_epochs():
"""Test plotting of epochs image
"""
epochs = _get_epochs()
plot_image_epochs(epochs, picks=[1, 2])
plt.close('all')
def test_plot_image_epochs():
"""Test plotting of epochs image
"""
plot_image_epochs(epochs, picks=[1, 2])
# 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)