DOC: Limit figures to 8 inch widths.

doc/examples/plot_skeleton.py

@@ -6,14 +6,14 @@ Skeletonize
 Skeletonization reduces binary objects to 1 pixel wide representations. This
 can be useful for feature extraction, and/or representing an object's topology.
 
-The algorithm works by making successive passes of the image. On each pass, 
+The algorithm works by making successive passes of the image. On each pass,
 border pixels are identified and removed on the condition that they do not
-break the connectivity of the corresponding object. 
+break the connectivity of the corresponding object.
 
 This module provides an example of calling the routine and displaying the
 results. The input is a 2D ndarray, with either boolean or integer elements.
 In the case of boolean, 'True' indicates foreground, and for integer arrays,
-the foreground is 1's.  
+the foreground is 1's.
 """
 from skimage.morphology import skeletonize
 from skimage.draw import draw
@@ -30,9 +30,9 @@ image[10:-10, -100:-10] = 1
 
 # foreground object 2
 rs, cs = draw.bresenham(250, 150, 10, 280)
-for i in range(10): image[rs+i, cs] = 1 
+for i in range(10): image[rs+i, cs] = 1
 rs, cs = draw.bresenham(10, 150, 250, 280)
-for i in range(20): image[rs+i, cs] = 1 
+for i in range(20): image[rs+i, cs] = 1
 
 # foreground object 3
 ir, ic = np.indices(image.shape)
@@ -45,7 +45,7 @@ image[circle2] = 0
 skeleton = skeletonize(image)
 
 # display results
-plt.figure(figsize=(10,6))
+plt.figure(figsize=(8, 4.5))
 
 plt.subplot(121)
 plt.imshow(image, cmap=plt.cm.gray)
@@ -57,7 +57,7 @@ plt.imshow(skeleton, cmap=plt.cm.gray)
 plt.axis('off')
 plt.title('skeleton', fontsize=20)
 
-plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.98, 
+plt.subplots_adjust(wspace=0.02, hspace=0.02, top=0.98,
                     bottom=0.02, left=0.02, right=0.98)
 
 plt.show()