DOC: Limit figures to 8 inch widths.

doc/examples/plot_canny.py

@@ -10,9 +10,9 @@ edges are thinned down to 1-pixel curves by removing non-maximum pixels of the
 gradient magnitude. Finally, edge pixels are kept or removed using hysteresis
 thresholding on the gradient magnitude.
 
-The Canny has three adjustable parameters: the width of the Gaussian (the 
+The Canny has three adjustable parameters: the width of the Gaussian (the
 noisier the image, the greater the width), and the low and high threshold for
-the hysteresis thresholding. 
+the hysteresis thresholding.
 """
 import numpy as np
 import matplotlib.pyplot as plt
@@ -32,7 +32,7 @@ edges1 = filter.canny(im)
 edges2 = filter.canny(im, sigma=3)
 
 # display results
-plt.figure(figsize=(10, 4))
+plt.figure(figsize=(8, 3))
 
 plt.subplot(131)
 plt.imshow(im, cmap=plt.cm.jet)

doc/examples/plot_equalize.py

@@ -65,7 +65,7 @@ img_eq = exposure.equalize(img)
 
 
 # Display results
-f, axes = plt.subplots(2, 3, figsize=(11, 5))
+f, axes = plt.subplots(2, 3, figsize=(8, 4))
 
 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')

doc/examples/plot_gabors_from_lena.py

@@ -71,27 +71,23 @@ fb2 = fb2.reshape((-1,) + patch_shape)
 fb2_montage = montage2d(fb2, rescale_intensity=True)
 
 # --
-plt.figure(figsize=(9, 3))
+fig, axes = plt.subplots(2, 2, figsize=(7, 6))
+ax0, ax1, ax2, ax3 = axes.ravel()
 
+ax0.imshow(lena, cmap=plt.cm.gray)
+ax0.set_title("Lena (original)")
 
-plt.subplot(2, 2, 1)
-plt.imshow(lena, cmap=plt.cm.gray)
-plt.axis('off')
-plt.title("Lena (original)")
+ax1.imshow(fb1_montage, cmap=plt.cm.gray)
+ax1.set_title("K-means filterbank (codebook)\non Lena (original)")
 
-plt.subplot(2, 2, 2)
-plt.imshow(fb1_montage, cmap=plt.cm.gray)
-plt.axis('off')
-plt.title("K-means filterbank (codebook) on Lena (original)")
+ax2.imshow(lena_dog, cmap=plt.cm.gray)
+ax2.set_title("Lena (LGN-like DoG)")
 
-plt.subplot(2, 2, 3)
-plt.imshow(lena_dog, cmap=plt.cm.gray)
-plt.axis('off')
-plt.title("Lena (LGN-like DoG)")
+ax3.imshow(fb2_montage, cmap=plt.cm.gray)
+ax3.set_title("K-means filterbank (codebook)\non Lena (LGN-like DoG)")
 
-plt.subplot(2, 2, 4)
-plt.imshow(fb2_montage, cmap=plt.cm.gray)
-plt.axis('off')
-plt.title("K-means filterbank (codebook) on Lena (LGN-like DoG)")
+for ax in axes.ravel():
+    ax.axis('off')
 
+fig.subplots_adjust(hspace=0.3)
 plt.show()

doc/examples/plot_hog.py

@@ -90,7 +90,7 @@ image = color.rgb2gray(data.lena())
 fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
                     cells_per_block=(1, 1), visualise=True)
 
-plt.figure(figsize=(10, 5))
+plt.figure(figsize=(8, 4))
 
 plt.subplot(121).set_axis_off()
 plt.imshow(image, cmap=plt.cm.gray)

doc/examples/plot_hough_transform.py

@@ -52,7 +52,7 @@ References
 .. [1] C. Galamhos, J. Matas and J. Kittler,"Progressive probabilistic
        Hough transform for line detection", in IEEE Computer Society
        Conference on Computer Vision and Pattern Recognition, 1999.
-        
+
 .. [2] Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to
        Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15,
        pp. 11-15 (January, 1972)
@@ -79,7 +79,7 @@ image[idx, idx] = 255
 
 h, theta, d = hough(image)
 
-plt.figure(figsize=(12, 5))
+plt.figure(figsize=(8, 4))
 
 plt.subplot(121)
 plt.imshow(image, cmap=plt.cm.gray)
@@ -101,7 +101,7 @@ image = data.camera()
 edges = canny(image, 2, 1, 25)
 lines = probabilistic_hough(edges, threshold=10, line_length=5, line_gap=3)
 
-plt.figure(figsize=(12, 4))
+plt.figure(figsize=(8, 3))
 
 plt.subplot(131)
 plt.imshow(image, cmap=plt.cm.gray)
@@ -121,3 +121,4 @@ for line in lines:
 plt.title('Lines found with PHT')
 plt.axis('image')
 plt.show()
+

doc/examples/plot_lena_tv_denoise.py

@@ -29,7 +29,7 @@ noisy = l + 0.4 * l.std() * np.random.random(l.shape)
 
 tv_denoised = tv_denoise(noisy, weight=10)
 
-plt.figure(figsize=(12,2.8))
+plt.figure(figsize=(8, 2))
 
 plt.subplot(131)
 plt.imshow(noisy, cmap=plt.cm.gray, vmin=40, vmax=220)

doc/examples/plot_otsu.py

@@ -25,7 +25,7 @@ image = camera()
 thresh = threshold_otsu(image)
 binary = image > thresh
 
-plt.figure(figsize=(10, 3.5))
+plt.figure(figsize=(8, 2.5))
 plt.subplot(1, 3, 1)
 plt.imshow(image, cmap=plt.cm.gray)
 plt.title('Original')

doc/examples/plot_radon_transform.py

@@ -28,7 +28,7 @@ from scipy.ndimage import zoom
 image = imread(data_dir + "/phantom.png", as_grey=True)
 image = zoom(image, 0.4)
 
-plt.figure(figsize=(9, 8.5), dpi=75)
+plt.figure(figsize=(8, 8.5))
 
 plt.subplot(221)
 plt.title("Original");

doc/examples/plot_random_walker_segmentation.py

@@ -58,7 +58,7 @@ markers[data > 1.3] = 2
 labels = random_walker(data, markers, beta=10, mode='bf')
 
 # Plot results
-plt.figure(figsize=(9, 3.5))
+plt.figure(figsize=(8, 3.2))
 plt.subplot(131)
 plt.imshow(data, cmap='gray', interpolation='nearest')
 plt.axis('off')

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()

doc/examples/plot_view_as_blocks.py

@@ -44,24 +44,21 @@ max_view = np.max(flatten_view, axis=2)
 median_view = np.median(flatten_view, axis=2)
 
 # -- display resampled images
-plt.figure(figsize=(10, 10))
+fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+ax0, ax1, ax2, ax3 = axes.ravel()
 
-plt.subplot(221)
-plt.title("Original rescaled with\n spline interpolation (order=3)")
+ax0.set_title("Original rescaled with\n spline interpolation (order=3)")
 l_resized = ndi.zoom(l, 2, order=3)
-plt.imshow(l_resized, cmap=cm.Greys_r)
+ax0.imshow(l_resized, cmap=cm.Greys_r)
 
-plt.subplot(222)
-plt.title("Block view with\n local mean pooling")
-plt.imshow(mean_view, cmap=cm.Greys_r)
+ax1.set_title("Block view with\n local mean pooling")
+ax1.imshow(mean_view, cmap=cm.Greys_r)
 
-plt.subplot(223)
-plt.title("Block view with\n local max pooling")
-plt.imshow(max_view, cmap=cm.Greys_r)
+ax2.set_title("Block view with\n local max pooling")
+ax2.imshow(max_view, cmap=cm.Greys_r)
 
-plt.subplot(224)
-plt.title("Block view with\n local median pooling")
-plt.imshow(median_view, cmap=cm.Greys_r)
+ax3.set_title("Block view with\n local median pooling")
+ax3.imshow(median_view, cmap=cm.Greys_r)
 
 plt.subplots_adjust(hspace=0.4, wspace=0.4)
 plt.show()

doc/examples/plot_watershed.py

@@ -45,16 +45,15 @@ local_maxi = is_local_maximum(distance, image, np.ones((3, 3)))
 markers = ndimage.label(local_maxi)[0]
 labels = watershed(-distance, markers, mask=image)
 
-plt.figure(figsize=(9, 3))
-plt.subplot(131)
-plt.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
-plt.axis('off')
-plt.subplot(132)
-plt.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
-plt.axis('off')
-plt.subplot(133)
-plt.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
-plt.axis('off')
+fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7))
+ax0, ax1, ax2 = axes
+
+ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
+ax1.imshow(-distance, cmap=plt.cm.jet, interpolation='nearest')
+ax2.imshow(labels, cmap=plt.cm.spectral, interpolation='nearest')
+
+for ax in axes:
+    ax.axis('off')
 
 plt.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
                     right=1)