old_contents: """
  Cortical networks correlation matrix
  ====================================
  
  """
  import seaborn as sns
  import matplotlib.pyplot as plt
  sns.set(context="paper", font="monospace")
  
  df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
  corrmat = df.corr()
  
  f, ax = plt.subplots(figsize=(12, 9))
  sns.heatmap(corrmat, vmax=.8, linewidths=0, square=True)
  
  
  networks = corrmat.columns.get_level_values("network").astype(int).values
  
  start, end = ax.get_ylim()
  rect_kws = dict(facecolor="none", edgecolor=".2",
                  linewidth=1.5, capstyle="projecting")
  
  for n in range(1, 18):
      n_nodes = (networks == n).sum()
      rect = plt.Rectangle((start, end), n_nodes, -n_nodes, **rect_kws)                         
      start += n_nodes
      end -= n_nodes
      ax.add_artist(rect)
  
  f.tight_layout()
  
new_contents: """
  Cortical networks correlation matrix
  ====================================
  
  """
  import seaborn as sns
  import matplotlib.pyplot as plt
  sns.set(context="paper", font="monospace")
  
  df = sns.load_dataset("brain_networks", header=[0, 1, 2], index_col=0)
  corrmat = df.corr()
  
  f, ax = plt.subplots(figsize=(12, 9))
  sns.heatmap(corrmat, vmax=.8, linewidths=0, square=True)
  
  networks = corrmat.columns.get_level_values("network")
  for i, network in enumerate(networks):
      if i and network != networks[i - 1]:
          ax.axhline(len(networks) - i, c="w")
          ax.axvline(i, c="w")
  
  f.tight_layout()
  
subject: Improve networks heatmap gallery example