code: """
  Matplotlib colormaps in Nilearn
  ================================
  
  Visualize HCP connectome workbench color maps shipped with Nilearn
  which can be used for plotting brain images on surface.
  
  See :ref:`surface-plotting` for surface plotting details.
  """
  import numpy as np
  import matplotlib.pyplot as plt
  
  from nilearn.plotting.cm import _cmap_d as nilearn_cmaps
  from nilearn.plotting import show
  
  ###########################################################################
  # Plot color maps
  # ----------------
  
  nmaps = len(nilearn_cmaps)
  a = np.outer(np.arange(0, 1, 0.01), np.ones(10))
  
  # Initialize the figure
  plt.figure(figsize=(10, 4.2))
  plt.subplots_adjust(top=0.4, bottom=0.05, left=0.01, right=0.99)
  
  for index, cmap in enumerate(nilearn_cmaps):
      plt.subplot(1, nmaps + 1, index + 1)
      plt.imshow(a, cmap=nilearn_cmaps[cmap])
      plt.axis('off')
      plt.title(cmap, fontsize=10, va='bottom', rotation=90)
  
  ###########################################################################
  # Plot matplotlib color maps
  # --------------------------
  plt.figure(figsize=(10, 5))
  plt.subplots_adjust(top=0.8, bottom=0.05, left=0.01, right=0.99)
  deprecated_cmaps = ['Vega10', 'Vega20', 'Vega20b', 'Vega20c', 'spectral']
  m_cmaps = []
  for m in plt.cm.datad:
      if not m.endswith("_r") and m not in deprecated_cmaps:
          m_cmaps.append(m)
  m_cmaps.sort()
  
  for index, cmap in enumerate(m_cmaps):
      plt.subplot(1, len(m_cmaps) + 1, index + 1)
      plt.imshow(a, cmap=plt.get_cmap(cmap), aspect='auto')
      plt.axis('off')
      plt.title(cmap, fontsize=10, va='bottom', rotation=90)
  
  show()
  
apis: numpy.ones matplotlib.pyplot.figure matplotlib.pyplot.axis numpy.arange matplotlib.pyplot.title matplotlib.pyplot.subplots_adjust matplotlib.pyplot.subplot matplotlib.pyplot.imshow matplotlib.pyplot.get_cmap