old_contents: import numpy as np
  
  from skimage import data
  from skimage import img_as_float
  
  from skimage.filter import harris
  
  
  def test_square_image():
      im = np.zeros((50, 50)).astype(float)
      im[:25, :25] = 1.
      results = harris(im)
      assert results.any()
      assert len(results) == 1
  
  def test_noisy_square_image():
      im = np.zeros((50, 50)).astype(float)
      im[:25, :25] = 1.
      im = im + np.random.uniform(size=im.shape) * .5
      results = harris(im)
      assert results.any()
      assert len(results) == 1
  
  def test_squared_dot():
      im = np.zeros((50, 50))
      im[4:8, 4:8] = 1
      im = img_as_float(im)
      results = harris(im, min_distance=3)
      print results
      assert (results == np.array([[6, 6]])).all()
  
  def test_rotated_lena():
      """
      The harris filter should yield the same results with an image and it's
      rotation.
      """
      im = img_as_float(data.lena().mean(axis=2))
      results = harris(im)
      im_rotated = im.T
      results_rotated = harris(im_rotated)
      assert (results[:, 0] == results_rotated[:, 1]).all()
  
  
  if __name__ == '__main__':
      from numpy import testing
      testing.run_module_suite()
  
  
new_contents: import numpy as np
  
  from skimage import data
  from skimage import img_as_float
  
  from skimage.filter import harris
  
  
  def test_square_image():
      im = np.zeros((50, 50)).astype(float)
      im[:25, :25] = 1.
      results = harris(im)
      assert results.any()
      assert len(results) == 1
  
  def test_noisy_square_image():
      im = np.zeros((50, 50)).astype(float)
      im[:25, :25] = 1.
      im = im + np.random.uniform(size=im.shape) * .5
      results = harris(im)
      assert results.any()
      assert len(results) == 1
  
  def test_squared_dot():
      im = np.zeros((50, 50))
      im[4:8, 4:8] = 1
      im = img_as_float(im)
      results = harris(im, min_distance=3)
      print results
      assert (results == np.array([[6, 6]])).all()
  
  def test_rotated_lena():
      """
      The harris filter should yield the same results with an image and it's
      rotation.
      """
      im = img_as_float(data.lena().mean(axis=2))
      results = harris(im)
      im_rotated = im.T
      results_rotated = harris(im_rotated)
      assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
      assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()
  
  
  if __name__ == '__main__':
      from numpy import testing
      testing.run_module_suite()
  
  
subject: Make test more robust.