old_contents: """
  ===============
  Contour finding
  ===============
  
  A marching squares method can be used to find constant valued contours in an
  image. Array values are linearly interpolated to provide better precision of
  the output contours. Contours which intersect the image edge are open; all
  others are closed.
  
  The `marching squares algorithm
  <http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special case of
  the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching
  Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics
  (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
  
  """
  import numpy as np
  import matplotlib.pyplot as plt
  
  from skimage import measure
  
  
  # Construct some test data
  x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
  r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
  
  # Find contours at a constant value of 0.8
  contours = measure.find_contours(r, 0.8)
  
  # Display the image and plot all contours found
  fig, ax = plt.subplots()
  ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)
  
  for n, contour in enumerate(contours):
      ax.plot(contour[:, 1], contour[:, 0], linewidth=2)
  
  ax.axis('image')
  ax.set_xticks([])
  ax.set_yticks([])
  plt.show()
  
new_contents: """
  ===============
  Contour finding
  ===============
  
  We use a marching squares method to find constant valued contours in an image.
  In ``skimage.measure.find_contours``, array values are linearly interpolated
  to provide better precision of the output contours. Contours which intersect
  the image edge are open; all others are closed.
  
  The `marching squares algorithm
  <http://www.essi.fr/~lingrand/MarchingCubes/algo.html>`__ is a special case of
  the marching cubes algorithm (Lorensen, William and Harvey E. Cline. Marching
  Cubes: A High Resolution 3D Surface Construction Algorithm. Computer Graphics
  (SIGGRAPH 87 Proceedings) 21(4) July 1987, p. 163-170).
  
  """
  import numpy as np
  import matplotlib.pyplot as plt
  
  from skimage import measure
  
  
  # Construct some test data
  x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
  r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
  
  # Find contours at a constant value of 0.8
  contours = measure.find_contours(r, 0.8)
  
  # Display the image and plot all contours found
  fig, ax = plt.subplots()
  ax.imshow(r, interpolation='nearest', cmap=plt.cm.gray)
  
  for n, contour in enumerate(contours):
      ax.plot(contour[:, 1], contour[:, 0], linewidth=2)
  
  ax.axis('image')
  ax.set_xticks([])
  ax.set_yticks([])
  plt.show()
  
subject: Change to active form + reintroduce function name