ar.fit(epochs['Auditory/Left'])
epochs_clean = ar.transform(epochs['Auditory/Left'])
evoked_clean = epochs_clean.average()
evoked = epochs['Auditory/Left'].average()
###############################################################################
# Now, we will manually mark the bad channels just for plotting.
evoked.info['bads'] = ['MEG 2443']
evoked_clean.info['bads'] = ['MEG 2443']
###############################################################################
# Let us plot the results.
import matplotlib.pyplot as plt # noqa
set_matplotlib_defaults(plt)
fig, axes = plt.subplots(2, 1, figsize=(6, 6))
for ax in axes:
ax.tick_params(axis='x', which='both', bottom='off', top='off')
ax.tick_params(axis='y', which='both', left='off', right='off')
ylim = dict(grad=(-170, 200))
evoked.pick_types(meg='grad', exclude=[])
evoked.plot(exclude=[], axes=axes[0], ylim=ylim, show=False)
axes[0].set_title('Before autoreject')
evoked_clean.pick_types(meg='grad', exclude=[])
evoked_clean.plot(exclude=[], axes=axes[1], ylim=ylim)
axes[1].set_title('After autoreject')
plt.tight_layout()
# may be saved by fitting :class:`autoreject.AutoReject` on a
# representative subsample of the data.
ar = AutoReject(picks=picks, random_state=42, n_jobs=1, verbose=True)
epochs_ar, reject_log = ar.fit_transform(this_epoch, return_log=True)
# %%
# We can visualize the cross validation curve over two variables
import numpy as np # noqa
import matplotlib.pyplot as plt # noqa
import matplotlib.patches as patches # noqa
from autoreject import set_matplotlib_defaults # noqa
set_matplotlib_defaults(plt, style='seaborn-white')
loss = ar.loss_['eeg'].mean(axis=-1) # losses are stored by channel type.
plt.matshow(loss.T * 1e6, cmap=plt.get_cmap('viridis'))
plt.xticks(range(len(ar.consensus)), ['%.1f' % c for c in ar.consensus])
plt.yticks(range(len(ar.n_interpolate)), ar.n_interpolate)
# Draw rectangle at location of best parameters
ax = plt.gca()
idx, jdx = np.unravel_index(loss.argmin(), loss.shape)
rect = patches.Rectangle((idx - 0.5, jdx - 0.5), 1, 1, linewidth=2,
edgecolor='r', facecolor='none')
ax.add_patch(rect)
ax.xaxis.set_ticks_position('bottom')
plt.xlabel(r'Consensus percentage $\kappa$')
plt.ylabel(r'Max sensors interpolated $\rho$')
ar = LocalAutoRejectCV(thresh_func=thresh_func, verbose='tqdm', picks=picks)
ar.fit(this_epoch)
epochs_ar = ar.transform(this_epoch)
###############################################################################
# We can visualize the cross validation curve over two variables
import numpy as np # noqa
import matplotlib.pyplot as plt # noqa
import matplotlib.patches as patches # noqa
from autoreject import set_matplotlib_defaults # noqa
set_matplotlib_defaults(plt, style='seaborn-white')
loss = ar.loss_['eeg'].mean(axis=-1) # losses are stored by channel type.
plt.matshow(loss.T * 1e6, cmap=plt.get_cmap('viridis'))
plt.xticks(range(len(ar.consensus_percs)), ar.consensus_percs)
plt.yticks(range(len(ar.n_interpolates)), ar.n_interpolates)
# Draw rectangle at location of best parameters
ax = plt.gca()
idx, jdx = np.unravel_index(loss.argmin(), loss.shape)
rect = patches.Rectangle((idx - 0.5, jdx - 0.5), 1, 1, linewidth=2,
edgecolor='r', facecolor='none')
ax.add_patch(rect)
ax.xaxis.set_ticks_position('bottom')
plt.xlabel(r'Consensus percentage $\kappa$')
plt.ylabel(r'Max sensors interpolated $\rho$')
#
#for idx, evoked in enumerate(evokeds):
# for cond in [1,3]:
# comm = evoked[cond].comment
# evoked[cond].plot_joint(title='Subject %s %s' % (idx + 1, comm))
event_id = {'stim/face':101, 'stim/house':102, 'imag/face':201, 'imag/house':202 }
exclude = [7, 11]
all_epochs=list()
chan_range= ['PO8','PO7']
# def figure params
set_matplotlib_defaults(plt)
#plt.tight_layout()
fig1, ax1 = plt.subplots(nrows=2, ncols=2)
fig2, ax2 = plt.subplots(nrows=2, ncols=2)
fig3, ax3 = plt.subplots(nrows=2, ncols=2)
fig4, ax4 = plt.subplots(nrows=2, ncols=2)
fig5, ax5 = plt.subplots(nrows=2, ncols=2)
fig6, ax6 = plt.subplots(nrows=2, ncols=2)
nplot_x=0
nplot_y =0
count=0
nsuj=0
coord=[(0,0), (0,1), (1,0), (1,1)]
