pep8 and other small changes

doc/examples/plot_active_contours.py

@@ -11,7 +11,8 @@ image energy.
 In the following two examples the active contour model is used (1) to segment
 the face of a person from the rest of an image by fitting a closed curve
 to the edges of the face and (2) to find the darkest curve between two fixed
-points while obeying smoothness considerations.
+points while obeying smoothness considerations. Typically it is a good idea to
+smooth images a bit before analyzing, as done in the following examples.
 
 .. [1] *Snakes: Active contour models*. Kass, M.; Witkin, A.; Terzopoulos, D.
        International Journal of Computer Vision 1 (4): 321 (1988).

skimage/segmentation/active_contour_model.py

@@ -8,7 +8,7 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
                          w_line=0, w_edge=1, gamma=0.01,
                          bc='periodic', max_px_move=1.0,
                          max_iterations=2500, convergence=0.1):
-    """Active contour model
+    """Active contour model.
 
     Active contours by fitting snakes to features of images. Supports single
     and multichannel 2D images. Snakes can be periodic (for segmentation) or
@@ -52,21 +52,24 @@ def active_contour_model(image, snake, alpha=0.01, beta=0.1,
 
     References
     ----------
-    .. [1]  Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour models". International Journal of Computer Vision 1 (4): 321 (1988).
+    .. [1]  Kass, M.; Witkin, A.; Terzopoulos, D. "Snakes: Active contour
+    models". International Journal of Computer Vision 1 (4): 321 (1988).
 
     Examples
     --------
-    >>> #from skimage.segmentation import active_contour_model
     >>> from skimage.draw import circle_perimeter
     >>> from skimage.filters import gaussian_filter
+    Create and smooth image:
     >>> img = np.zeros((100, 100))
     >>> rr, cc = circle_perimeter(35, 45, 25)
     >>> img[rr, cc] = 1
-    >>> img = gaussian_filter(img,2)
-    >>> s = np.linspace(0,2*np.pi,100)
-    >>> init = 50*np.array([np.cos(s),np.sin(s)]).T+50
+    >>> img = gaussian_filter(img, 2)
+    Initiliaze spline:
+    >>> s = np.linspace(0, 2*np.pi,100)
+    >>> init = 50*np.array([np.cos(s), np.sin(s)]).T+50
+    Fit spline to image:
     >>> snake = active_contour_model(img, init, w_edge=0, w_line=1)
-    >>> int(np.mean(np.sqrt((45-snake[:,0])**2 + (35-snake[:,1])**2)))
+    >>> int(np.mean(np.sqrt((45-snake[:, 0])**2 + (35-snake[:, 1])**2)))
     25
 
     """

skimage/segmentation/tests/test_active_contour_model.py

@@ -5,7 +5,7 @@ from skimage.filters import gaussian_filter
 from skimage.segmentation import active_contour_model
 from numpy.testing import assert_equal, assert_allclose, assert_raises
 
-def periodic_reference_test():
+def test_periodic_reference():
     img = data.astronaut()
     img = rgb2gray(img)
     s = np.linspace(0, 2*np.pi, 400)
@@ -20,7 +20,7 @@ def periodic_reference_test():
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
 
-def fixed_reference_test():
+def test_fixed_reference():
     img = data.text()
     x = np.linspace(5, 424, 100)
     y = np.linspace(136, 50, 100)
@@ -33,7 +33,7 @@ def fixed_reference_test():
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
 
-def free_reference_test():
+def test_free_reference():
     img = data.text()
     x = np.linspace(5, 424, 100)
     y = np.linspace(70, 40, 100)
@@ -46,7 +46,7 @@ def free_reference_test():
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
 
-def RGB_test():
+def test_RGB():
     img = gaussian_filter(data.text(), 1)
     imgR = np.zeros((img.shape[0], img.shape[1], 3))
     imgG = np.zeros((img.shape[0], img.shape[1], 3))
@@ -73,7 +73,7 @@ def RGB_test():
     assert_equal(np.array(snake[:10, 1], dtype=np.int32), refy)
 
 
-def end_points_tests():
+def test_end_points_tests():
     img = data.astronaut()
     img = rgb2gray(img)
     s = np.linspace(0, 2*np.pi, 400)
@@ -94,7 +94,7 @@ def end_points_tests():
     assert_allclose(snake[0, :], [x[0], y[0]], atol=1e-5)
 
 
-def bad_input_tests():
+def test_bad_input_tests():
     img = np.zeros((10, 10))
     x = np.linspace(5, 424, 100)
     y = np.linspace(136, 50, 100)