def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format='matplotlib')
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(stc, 'sample', colormap=colormap)
# now do sparse version
vertices = sample_src[0]['vertno']
n_verts = len(vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
inds = np.where(np.any(stc_data, axis=1))[0]
stc_data = stc_data[inds]
vertices = vertices[inds]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=True)
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
# dense version
vertices = [s["vertno"] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format="matplotlib")
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(
stc, "sample", colormap=colormap, config_opts={"background": (1, 1, 0)}, subjects_dir=subjects_dir
)
assert_raises(RuntimeError, plot_source_estimates, stc, "sample", figure="foo", hemi="both")
# now do sparse version
vertices = sample_src[0]["vertno"]
n_verts = len(vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
inds = np.where(np.any(stc_data, axis=1))[0]
stc_data = stc_data[inds]
vertices = vertices[inds]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1), opacity=0.5, high_resolution=True)
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
sample_src = read_source_spaces(op.join(data_dir, 'subjects', 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format='matplotlib')
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(stc, 'sample', colormap=colormap,
config_opts={'background': (1, 1, 0)},
subjects_dir=subjects_dir, colorbar=True)
assert_raises(TypeError, plot_source_estimates, stc, 'sample',
figure='foo', hemi='both')
# now do sparse version
vertices = sample_src[0]['vertno']
n_verts = len(vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
inds = np.where(np.any(stc_data, axis=1))[0]
stc_data = stc_data[inds]
vertices = [vertices[inds], np.empty(0, dtype=np.int)]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=True)
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
sample_src = read_source_spaces(op.join(data_dir, 'subjects', 'sample',
'bem', 'sample-oct-6-src.fif'))
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format='matplotlib')
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(stc, 'sample', colormap=colormap,
config_opts={'background': (1, 1, 0)},
subjects_dir=subjects_dir, colorbar=True)
assert_raises(TypeError, plot_source_estimates, stc, 'sample',
figure='foo', hemi='both')
# now do sparse version
vertices = sample_src[0]['vertno']
inds = [111, 333]
stc_data = np.zeros((len(inds), n_time))
stc_data[0, 1] = 1.
stc_data[1, 4] = 2.
vertices = [vertices[inds], np.empty(0, dtype=np.int)]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src, stc, bgcolor=(1, 1, 1),
opacity=0.5, high_resolution=False)
def test_process_clim_round_trip():
"""Test basic input-output support."""
# With some negative data
out = _process_clim('auto', 'auto', True, -1.)
want = dict(
colormap=mne_analyze_colormap([0, 0.5, 1], 'matplotlib'),
clim=dict(kind='value', pos_lims=[1, 1, 1]),
transparent=True,
)
_assert_mapdata_equal(out, want)
out2 = _process_clim(**out)
_assert_mapdata_equal(out, out2)
_linearize_map(out) # smoke test
ticks = _get_map_ticks(out)
assert_allclose(ticks, [-1, 0, 1])
# With some positive data
out = _process_clim('auto', 'auto', True, 1.)
want = dict(
colormap=plt.get_cmap('hot'),
clim=dict(kind='value', lims=[1, 1, 1]),
transparent=True,
)
_assert_mapdata_equal(out, want)
out2 = _process_clim(**out)
_assert_mapdata_equal(out, out2)
_linearize_map(out)
ticks = _get_map_ticks(out)
assert_allclose(ticks, [1])
# With some actual inputs
clim = dict(kind='value', pos_lims=[0, 0.5, 1])
out = _process_clim(clim, 'auto', True)
want = dict(colormap=mne_analyze_colormap([0, 0.5, 1], 'matplotlib'),
clim=clim,
transparent=True)
_assert_mapdata_equal(out, want)
_linearize_map(out)
ticks = _get_map_ticks(out)
assert_allclose(ticks, [-1, -0.5, 0, 0.5, 1])
clim = dict(kind='value', pos_lims=[0.25, 0.5, 1])
out = _process_clim(clim, 'auto', True)
want = dict(colormap=mne_analyze_colormap([0, 0.5, 1], 'matplotlib'),
clim=clim,
transparent=True)
_assert_mapdata_equal(out, want)
_linearize_map(out)
ticks = _get_map_ticks(out)
assert_allclose(ticks, [-1, -0.5, -0.25, 0, 0.25, 0.5, 1])
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography."""
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
epochs.load_data()
cmap = mne_analyze_colormap(format='matplotlib')
data_min = epochs._data.min()
plt.close('all')
fig = plot_topo_image_epochs(epochs,
sigma=0.5,
vmin=-200,
vmax=200,
colorbar=True,
title=title,
cmap=cmap)
assert epochs._data.min() == data_min
num_figures_before = len(plt.get_fignums())
_fake_click(fig, fig.axes[0], (0.08, 0.64))
assert num_figures_before + 1 == len(plt.get_fignums())
# test for auto-showing a colorbar when only 1 sensor type
ep = epochs.copy().pick_types(meg=False, eeg=True)
fig = plot_topo_image_epochs(ep,
vmin=None,
vmax=None,
colorbar=None,
cmap=cmap)
ax = [x for x in fig.get_children() if isinstance(x, matplotlib.axes.Axes)]
qm_cmap = [
y.cmap for x in ax for y in x.get_children()
if isinstance(y, matplotlib.collections.QuadMesh)
]
assert qm_cmap[0] is cmap
plt.close('all')
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography."""
import matplotlib.pyplot as plt
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
cmap = mne_analyze_colormap(format='matplotlib')
fig = plot_topo_image_epochs(epochs, sigma=0.5, vmin=-200, vmax=200,
colorbar=True, title=title, cmap=cmap)
_fake_click(fig, fig.axes[2], (0.08, 0.64))
plt.close('all')
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography
"""
import matplotlib.pyplot as plt
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
cmap = mne_analyze_colormap(format='matplotlib')
plot_topo_image_epochs(epochs, sigma=0.5, vmin=-200, vmax=200,
colorbar=True, title=title, cmap=cmap)
plt.close('all')
def plot_stc_time_point(stc, subject, limits=[5, 10, 15], time_index=0,
surf='inflated', measure='dSPM', subjects_dir=None):
"""Plot a time instant from a SourceEstimate using matplotlib
The same could be done with mayavi using proper 3D.
Parameters
----------
stc : instance of SourceEstimate
The SourceEstimate to plot.
subject : string
The subject name (only needed if surf is a string).
time_index : int
Time index to plot.
surf : str, or instance of surfaces
Surface to use (e.g., 'inflated' or 'white'), or pre-loaded surfaces.
measure : str
The label for the colorbar. None turns the colorbar off.
subjects_dir : str, or None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
"""
subjects_dir = get_subjects_dir(subjects_dir)
pl.figure(facecolor='k', figsize=(8, 5))
hemis = ['lh', 'rh']
if isinstance(surf, str):
surf = [read_surface(op.join(subjects_dir, subject, 'surf',
'%s.%s' % (h, surf))) for h in hemis]
my_cmap = mne_analyze_colormap(limits)
for hi, h in enumerate(hemis):
coords = surf[hi][0][stc.vertno[hi]]
if hi == 0:
vals = stc_all_cluster_vis.lh_data[:, time_index]
else:
vals = stc_all_cluster_vis.rh_data[:, time_index]
ax = pl.subplot(1, 2, 1 - hi, axis_bgcolor='none')
pl.tick_params(labelbottom='off', labelleft='off')
flipper = -1 if hi == 1 else 1
sc = ax.scatter(flipper * coords[:, 1], coords[:, 2], c=vals,
vmin=-limits[2], vmax=limits[2], cmap=my_cmap,
edgecolors='none', s=5)
ax.set_aspect('equal')
pl.axis('off')
try:
pl.tight_layout(0)
except:
pass
if measure is not None:
cax = pl.axes([0.85, 0.15, 0.025, 0.15], axisbg='k')
cb = pl.colorbar(sc, cax, ticks=[-limits[2], 0, limits[2]])
cb.set_label(measure, color='w')
pl.setp(pl.getp(cb.ax, 'yticklabels'), color='w')
pl.draw()
pl.show()
def test_plot_sparse_source_estimates():
"""Test plotting of (sparse) source estimates
"""
# dense version
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
colormap = mne_analyze_colormap(format='matplotlib')
# don't really need to test matplotlib method since it's not used now...
colormap = mne_analyze_colormap()
plot_source_estimates(stc,
'sample',
colormap=colormap,
config_opts={'background': (1, 1, 0)})
assert_raises(RuntimeError,
plot_source_estimates,
stc,
'sample',
figure='foo',
hemi='both')
# now do sparse version
vertices = sample_src[0]['vertno']
n_verts = len(vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
inds = np.where(np.any(stc_data, axis=1))[0]
stc_data = stc_data[inds]
vertices = vertices[inds]
stc = SourceEstimate(stc_data, vertices, 1, 1)
plot_sparse_source_estimates(sample_src,
stc,
bgcolor=(1, 1, 1),
opacity=0.5,
high_resolution=True)
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography."""
import matplotlib.pyplot as plt
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
epochs.load_data()
cmap = mne_analyze_colormap(format='matplotlib')
data_min = epochs._data.min()
fig = plot_topo_image_epochs(epochs, sigma=0.5, vmin=-200, vmax=200,
colorbar=True, title=title, cmap=cmap)
assert_equal(epochs._data.min(), data_min)
_fake_click(fig, fig.axes[2], (0.08, 0.64))
plt.close('all')
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography."""
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
epochs.load_data()
cmap = mne_analyze_colormap(format='matplotlib')
data_min = epochs._data.min()
plt.close('all')
fig = plot_topo_image_epochs(epochs, sigma=0.5, vmin=-200, vmax=200,
colorbar=True, title=title, cmap=cmap)
assert epochs._data.min() == data_min
num_figures_before = len(plt.get_fignums())
_fake_click(fig, fig.axes[0], (0.08, 0.64))
assert num_figures_before + 1 == len(plt.get_fignums())
plt.close('all')
def test_plot_topo_image_epochs():
"""Test plotting of epochs image topography."""
title = 'ERF images - MNE sample data'
epochs = _get_epochs()
epochs.load_data()
cmap = mne_analyze_colormap(format='matplotlib')
data_min = epochs._data.min()
plt.close('all')
fig = plot_topo_image_epochs(epochs, sigma=0.5, vmin=-200, vmax=200,
colorbar=True, title=title, cmap=cmap)
assert epochs._data.min() == data_min
num_figures_before = len(plt.get_fignums())
_fake_click(fig, fig.axes[0], (0.08, 0.64))
assert num_figures_before + 1 == len(plt.get_fignums())
# test for auto-showing a colorbar when only 1 sensor type
ep = epochs.copy().pick_types(meg=False, eeg=True)
fig = plot_topo_image_epochs(ep, vmin=None, vmax=None, colorbar=None,
cmap=cmap)
ax = [x for x in fig.get_children() if isinstance(x, matplotlib.axes.Axes)]
qm_cmap = [y.cmap for x in ax for y in x.get_children()
if isinstance(y, matplotlib.collections.QuadMesh)]
assert qm_cmap[0] is cmap
plt.close('all')
# Visualize the clusters
print('Visualizing clusters.')
import os
os.environ["SUBJECTS_DIR"] = "/mnt/file1/binder/KRNS/anatomies/surfaces/"
os.environ["subjects_dir"] = "/mnt/file1/binder/KRNS/anatomies/surfaces/"
# Now let's build a convenient representation of each cluster, where each
# cluster becomes a "time point" in the SourceEstimate
stc_all_cluster_vis = summarize_clusters_stc(clu, tstep=tstep,
vertno=fsave_vertices,
subject='fsaverage')
# Let's actually plot the first "time point" in the SourceEstimate, which
# shows all the clusters, weighted by duration
colormap = mne_analyze_colormap(limits=[0, 10, 50])
#subjects_dir = op.join(data_path, 'subjects')
# blue blobs are for condition A < condition B, red for A > B
brain = stc_all_cluster_vis.plot('fsaverage', 'inflated', 'lh', colormap,
time_label='Duration significant (ms)', time_viewer = True)
brain.set_data_time_index(0)
# The colormap requires brain data to be scaled -fmax -> fmax
brain.scale_data_colormap(fmin=-50, fmid=0, fmax=50, transparent=False)
brain.show_view('lateral')
brain.save_image('clusters.png')
##########################################################################################################################33
##########################################################################################################################33
##############################################################################################################################3
##############################################################################################################################3
## Transform to common cortical space
def plot_stc_time_point(stc,
subject,
limits=[5, 10, 15],
time_index=0,
surf='inflated',
measure='dSPM',
subjects_dir=None):
"""Plot a time instant from a SourceEstimate using matplotlib
The same could be done with mayavi using proper 3D.
Parameters
----------
stc : instance of SourceEstimate
The SourceEstimate to plot.
subject : string
The subject name (only needed if surf is a string).
time_index : int
Time index to plot.
surf : str, or instance of surfaces
Surface to use (e.g., 'inflated' or 'white'), or pre-loaded surfaces.
measure : str
The label for the colorbar. None turns the colorbar off.
subjects_dir : str, or None
Path to the SUBJECTS_DIR. If None, the path is obtained by using
the environment variable SUBJECTS_DIR.
"""
subjects_dir = get_subjects_dir(subjects_dir)
pl.figure(facecolor='k', figsize=(8, 5))
hemis = ['lh', 'rh']
if isinstance(surf, str):
surf = [
read_surface(
op.join(subjects_dir, subject, 'surf', '%s.%s' % (h, surf)))
for h in hemis
]
my_cmap = mne_analyze_colormap(limits)
for hi, h in enumerate(hemis):
coords = surf[hi][0][stc.vertno[hi]]
if hi == 0:
vals = stc_all_cluster_vis.lh_data[:, time_index]
else:
vals = stc_all_cluster_vis.rh_data[:, time_index]
ax = pl.subplot(1, 2, 1 - hi, axis_bgcolor='none')
pl.tick_params(labelbottom='off', labelleft='off')
flipper = -1 if hi == 1 else 1
sc = ax.scatter(flipper * coords[:, 1],
coords[:, 2],
c=vals,
vmin=-limits[2],
vmax=limits[2],
cmap=my_cmap,
edgecolors='none',
s=5)
ax.set_aspect('equal')
pl.axis('off')
try:
pl.tight_layout(0)
except:
pass
if measure is not None:
cax = pl.axes([0.85, 0.15, 0.025, 0.15], axisbg='k')
cb = pl.colorbar(sc, cax, ticks=[-limits[2], 0, limits[2]])
cb.set_label(measure, color='w')
pl.setp(pl.getp(cb.ax, 'yticklabels'), color='w')
pl.draw()
pl.show()
###############################################################################
# Visualize the clusters
print 'Visualizing clusters.'
# Now let's build a convenient representation of each cluster, where each
# cluster becomes a "time point" in the SourceEstimate
stc_all_cluster_vis = summarize_clusters_stc(clu,
tstep=tstep,
vertno=fsave_vertices,
subject='fsaverage')
# Let's actually plot the first "time point" in the SourceEstimate, which
# shows all the clusters, weighted by duration
colormap = mne_analyze_colormap(limits=[0, 10, 50])
subjects_dir = op.join(data_path, 'subjects')
# blue blobs are for condition A < condition B, red for A > B
brains = stc_all_cluster_vis.plot('fsaverage',
'inflated',
'both',
colormap,
subjects_dir=subjects_dir,
time_label='Duration significant (ms)')
for idx, brain in enumerate(brains):
brain.set_data_time_index(0)
# The colormap requires brain data to be scaled -fmax -> fmax
brain.scale_data_colormap(fmin=-50, fmid=0, fmax=50, transparent=False)
brain.show_view('lateral')
brain.save_image('clusters-%s.png' % ('lh' if idx == 0 else 'rh'))
n_signals_tot = 1 + len(vertices[0]) + len(vertices[1])
indices = seed_target_indices([0], np.arange(1, n_signals_tot))
# Compute the PSI in the frequency range 8Hz..30Hz. We exclude the baseline
# period from the connectivity estimation
fmin = 8.
fmax = 30.
tmin_con = 0.
sfreq = raw.info['sfreq'] # the sampling frequency
psi, freqs, times, n_epochs, _ = phase_slope_index(comb_ts, mode='multitaper',
indices=indices, sfreq=sfreq, fmin=fmin, fmax=fmax, tmin=tmin_con)
# Generate a SourceEstimate with the PSI. This is simple since we used a single
# seed (inspect the indices variable to see how the PSI scores are arranged in
# the output)
psi_stc = mne.SourceEstimate(psi, vertices=vertices, tmin=0, tstep=1,
subject='sample')
# Now we can visualize the PSI using the plot method. We use a custom colormap
# to show signed values
v_max = np.max(np.abs(psi))
colormap = mne_analyze_colormap(limits=[0, v_max / 3, v_max])
brain = psi_stc.plot(surface='inflated', hemi='lh',
time_label='Phase Slope Index (PSI)',
subjects_dir=subjects_dir, colormap=colormap)
brain.scale_data_colormap(fmin=-v_max, fmid=0., fmax=v_max, transparent=False)
brain.show_view('medial')
brain.add_label(fname_label, color='green', alpha=0.7)
