new_contents: import numpy as np
  
  
  def _consecutive_index_generator(length, offset=0):
      """Generate pair of ids of consecutive images. Offset is the distance between the images
      """
      offset += 1
      for i in range(length - offset):
          yield (i, i + offset)
  
  def generate_learning_set(array, random_permutation=True, offset=0):
      """Generate learning set of consecutive scans
  
      Parameters
      ----------
      array: numpy array of shape n_scans x n_voxels
          Array of masked scans
  
      random_permutation: boolean
          If True, consecutive scans are switched with a probability of .5
  
      offset: int
          Distance between two consecutive scans
  
      Returns
      -------
      learning_set: list of (img_a, img_b, label)
          Two consecutive images, label is 1 if images are ordered, 0 otherwise
      """
      np.random.seed()
  
      learning_set = []
      for (ia, ib) in _consecutive_index_generator(array.shape[0], offset=offset):
          label = np.random.randint(0, 2)
          if label == 0:
              ia, ib = ib, ia
          learning_set.append((ia, ib, label))
  
      return learning_set
  
  if __name__ == '__main__':
      array = np.arange(5)
      res = generate_learning_set(array)
      assert(len(res) == 4)
      for ia, ib, label in res:
          if label == 0:
              ia, ib = ib, ia
      
      res = generate_learning_set(array, random_permutation=False, offset=1)
      assert(len(res) == 3)
      for ia, ib, label in res:
          assert(ia < ib)
          assert(label == 1)
      print('Basic testing is OK')
subject: Add small script to generate learning set.