#
# Our electrodes are shown after being morphed to fsaverage brain so we'll use
# this fsaverage brain to plot the locations of our electrodes. We'll use
# :func:`~mne.viz.snapshot_brain_montage` to save the plot as image data
# (along with xy positions of each electrode in the image), so that later
# we can plot frequency band power on top of it.
fig = plot_alignment(raw.info,
subject='fsaverage',
subjects_dir=subjects_dir,
surfaces=['pial'],
coord_frame='mri')
az, el, focalpoint = 160, -70, [0.067, -0.040, 0.018]
mne.viz.set_3d_view(fig, azimuth=az, elevation=el, focalpoint=focalpoint)
xy, im = snapshot_brain_montage(fig, raw.info)
###############################################################################
# Compute frequency features of the data
# --------------------------------------
#
# Next, we'll compute the signal power in the gamma (30-90 Hz) band,
# downsampling the result to 10 Hz (to save time).
sfreq = 10
gamma_power_t = evoked.copy().filter(
30, 90).apply_hilbert(envelope=True).resample(sfreq)
gamma_info = gamma_power_t.info
###############################################################################
# Visualize the time-evolution of the gamma power on the brain
# subjects_dir = '/home/gauthv/PycharmProjects/ecogAnalysis/'
mlab_fig = get_mayavi_fig('../ecb43e/both_lowres.stl', trodes_mat)
# path_data = mne.datasets.misc.data_path() + '/ecog/sample_ecog.mat'
mat = loadmat(trodes_mat)
ch_names = [x for x in epoch_arr[0].ch_names if 'GRID' in x]
# elec = mat['elec'][:len(ch_names)]
elec = mat['Grid']
ch_names = [x for x in epoch_arr[0].ch_names if 'GRID' in x]
dig_ch_pos = dict(zip(ch_names, elec))
mon = mne.channels.DigMontage(dig_ch_pos=dig_ch_pos)
info = mne.create_info(ch_names, 1000., 'ecog', montage=mon)
# fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
# surfaces=['pial'], meg=False)
# info = epoch_arr[0].info
# fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir, surfaces=['pial'])
# fig = plot_alignment(info, subject='ecb43e', subjects_dir=subjects_dir, surfaces=['pial'])
mlab.view(210, 90)
xy, im = snapshot_brain_montage(mlab_fig, epoch_arr.info)
# Convert from a dictionary to array to plot
xy_pts = np.stack(xy[ch] for ch in epoch_arr.info['ch_names']
if ch in ch_names)
activity = np.zeros((xy_pts.shape[0], ))
create_animation(epoch_arr, xy_pts, im)
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces=['pial'])
mlab.view(200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces='pial')
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack(xy[ch] for ch in info['ch_names'])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
_, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
plt.show()
subjects_dir=subjects_dir,
surfaces=['pial'])
mlab.view(200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig = plot_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces='pial')
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack(xy[ch] for ch in info['ch_names'])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
_, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
plt.show()
subjects_dir=subjects_dir,
surfaces=['pial'])
mne.viz.set_3d_view(fig, 200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig_scatter = plot_alignment(info,
subject='sample',
subjects_dir=subjects_dir,
surfaces='pial')
mne.viz.set_3d_view(fig_scatter, 200, 70)
xy, im = snapshot_brain_montage(fig_scatter, montage)
# Convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in info['ch_names']])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
_, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
plt.show()
subjects_dir = mne.datasets.sample.data_path() + '/subjects'
fig = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces=['pial'])
mlab.view(200, 70)
###############################################################################
# Sometimes it is useful to make a scatterplot for the current figure view.
# This is best accomplished with matplotlib. We can capture an image of the
# current mayavi view, along with the xy position of each electrode, with the
# `snapshot_brain_montage` function.
# We'll once again plot the surface, then take a snapshot.
fig_scatter = plot_alignment(info, subject='sample', subjects_dir=subjects_dir,
surfaces='pial')
mlab.view(200, 70)
xy, im = snapshot_brain_montage(fig_scatter, mon)
# Convert from a dictionary to array to plot
xy_pts = np.vstack([xy[ch] for ch in info['ch_names']])
# Define an arbitrary "activity" pattern for viz
activity = np.linspace(100, 200, xy_pts.shape[0])
# This allows us to use matplotlib to create arbitrary 2d scatterplots
_, ax = plt.subplots(figsize=(10, 10))
ax.imshow(im)
ax.scatter(*xy_pts.T, c=activity, s=200, cmap='coolwarm')
ax.set_axis_off()
plt.show()
