old_contents: """
  Display Probabilistic Labels
  ============================
  
  Freesurfer ships with some probabilistic labels of cytoarchitectonic
  and visual areas. Here we show several ways to visualize these labels
  to help characterize the location of your data.
  
  """
  from os import environ
  from os.path import join
  import numpy as np
  from surfer import Brain, io
  
  brain = Brain("fsaverage", "lh", "inflated",
                config_opts=dict(cortex="low_contrast"))
  
  """
  The simplest way is to use add_label.
  """
  brain.add_label("BA1", color="darkblue")
  
  """
  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
  
new_contents: """
  ============================
  Display Probabilistic Labels
  ============================
  
  Freesurfer ships with some probabilistic labels of cytoarchitectonic
  and visual areas. Here we show several ways to visualize these labels
  to help characterize the location of your data.
  
  """
  from os import environ
  from os.path import join
  import numpy as np
  from surfer import Brain, io
  
  brain = Brain("fsaverage", "lh", "inflated",
                config_opts=dict(cortex="low_contrast"))
  
  """
  The easiest way to label any vertex that could be in the region is with
  add_label.
  """
  brain.add_label("BA1", color="#A6BDDB")
  
  """
  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
  
subject: Tweak probabilistic labels example