def test_probabilistic_labels():
"""Test plotting of probabilistic labels
"""
mlab.options.backend = 'test'
brain = Brain("fsaverage",
"lh",
"inflated",
config_opts=dict(cortex="low_contrast"))
brain.add_label("BA1", color="darkblue")
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
label_file = pjoin(subj_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = io.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
brain.close()
def mask_vtx_data(overlay_fname, cortex_fname, thresh):
vtx_data = io.read_scalar_data(overlay_fname)
cortex_data = io.read_label(cortex_fname)
# Create a mask of 1s where there is cortex and 0s on the medial wall
mask = np.zeros_like(vtx_data)
mask[cortex_data] = 1
# Set all values that are not in cortex to thresh-1
vtx_data[mask == 0] = thresh-1
return vtx_data
def mask_vtx_data(overlay_fname, cortex_fname, thresh):
vtx_data = io.read_scalar_data(overlay_fname)
cortex_data = io.read_label(cortex_fname)
# Create a mask of 1s where there is cortex and 0s on the medial wall
mask = np.zeros_like(vtx_data)
mask[cortex_data] = 1
# Set all values that are not in cortex to thresh-1
vtx_data[mask == 0] = thresh-1
return vtx_data
def test_probabilistic_labels():
"""Test plotting of probabilistic labels
"""
mlab.options.backend = 'test'
brain = Brain("fsaverage", "lh", "inflated",
config_opts=dict(cortex="low_contrast"))
brain.add_label("BA1", color="darkblue")
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
label_file = pjoin(subj_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = io.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
You can also threshold based on the probability of that region being at each
vertex.
"""
brain.add_label("BA1", color="#2B8CBE", scalar_thresh=.5)
"""
It's also possible to plot just the label boundary, in case you wanted to
overlay the label on an activation plot to asses whether it falls within that
region.
"""
brain.add_label("BA45", color="#F0F8FF", borders=True, scalar_thresh=.5)
brain.add_label("BA45", color="#F0F8FF", alpha=.3, scalar_thresh=.5)
"""
Finally, with a few tricks, you can display the whole probabilistic map.
"""
subjects_dir = environ["SUBJECTS_DIR"]
label_file = join(subjects_dir, "fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = io.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5, colormap="RdPu")
"""
Adjust the colorbar to represent the coarseness of the probability estimates
more closely.
"""
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11
"""
You can also threshold based on the probability of that
region being at each vertex.
"""
brain.add_label("BA1", color="dodgerblue", scalar_thresh=.5)
"""
It's also possible to plot just the label boundary, in case
you wanted to overlay the label on an activation plot to
asses whether it falls within that region.
"""
brain.add_label("BA45", color="firebrick", borders=True)
brain.add_label("BA45", color="salmon", borders=True, scalar_thresh=.5)
"""
Finally, with a few tricks, you can display the whole probabilistic map.
"""
label_file = join(environ["SUBJECTS_DIR"],
"fsaverage", "label", "lh.BA6.label")
prob_field = np.zeros_like(brain._geo.x)
ids, probs = io.read_label(label_file, read_scalars=True)
prob_field[ids] = probs
brain.add_data(prob_field, thresh=1e-5)
"""
Adjust the colorbar to represent the coarseness of the probability
estimates more closely.
"""
brain.data["colorbar"].number_of_colors = 10
brain.data["colorbar"].number_of_labels = 11