changed adjustable parameter of axes to 'box-forced'

this fixes the size of axes around an image for shared axes

doc/examples/applications/plot_coins_segmentation.py

@@ -35,7 +35,9 @@ background with the coins:
 
 """
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
+fig = plt.figure(figsize=(6,3))
+ax1 = fig.add_subplot(1, 2, 1, adjustable='box-forced')
+ax2 = fig.add_subplot(1, 2, 2, sharex = ax1, sharey = ax1, adjustable='box-forced')
 ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
 ax1.set_title('coins > 100')
 ax1.axis('off')
@@ -162,8 +164,9 @@ segmentation = ndi.binary_fill_holes(segmentation - 1)
 labeled_coins, _ = ndi.label(segmentation)
 image_label_overlay = label2rgb(labeled_coins, image=coins)
 
-# TODO: this example would benefit from sharing axes over multiple figures
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
+fig = plt.figure(figsize=(6,3))
+ax1 = fig.add_subplot(1, 2, 1, adjustable='box-forced')
+ax2 = fig.add_subplot(1, 2, 2, sharex = ax1, sharey = ax1, adjustable='box-forced')
 ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
 ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
 ax1.axis('off')

doc/examples/applications/plot_morphology.py

@@ -46,9 +46,11 @@ def plot_comparison(original, filtered, filter_name):
     ax1.imshow(original, cmap=plt.cm.gray)
     ax1.set_title('original')
     ax1.axis('off')
+    ax1.set_adjustable('box-forced')
     ax2.imshow(filtered, cmap=plt.cm.gray)
     ax2.set_title(filter_name)
     ax2.axis('off')
+    ax2.set_adjustable('box-forced')
 
 """
 Erosion

doc/examples/applications/plot_rank_filters.py

@@ -70,7 +70,7 @@ noisy_image = img_as_ubyte(data.camera())
 noisy_image[noise > 0.99] = 255
 noisy_image[noise < 0.01] = 0
 
-fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex=True, sharey=True)
+fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
@@ -109,7 +109,7 @@ image.
 
 from skimage.filters.rank import mean
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 loc_mean = mean(noisy_image, disk(10))
 
@@ -143,7 +143,7 @@ noisy_image = img_as_ubyte(data.camera())
 
 bilat = mean_bilateral(noisy_image.astype(np.uint16), disk(20), s0=10, s1=10)
 
-fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex='row', sharey='row')
+fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -196,9 +196,13 @@ hist = np.histogram(noisy_image, bins=np.arange(0, 256))
 glob_hist = np.histogram(glob, bins=np.arange(0, 256))
 loc_hist = np.histogram(loc, bins=np.arange(0, 256))
 
-# this way histograms also share the axes
-fig, ax = plt.subplots(3, 2, figsize=(10, 10), sharex='col', sharey='col')
-ax1, ax2, ax3, ax4, ax5, ax6 = ax.ravel()
+fig = plt.figure()
+ax1 = plt.subplot(3, 2, 1, adjustable='box-forced')
+ax2 = plt.subplot(3, 2, 2)
+ax3 = plt.subplot(3, 2, 3, adjustable='box-forced', sharex=ax1, sharey=ax1)
+ax4 = plt.subplot(3, 2, 4)
+ax5 = plt.subplot(3, 2, 5, adjustable='box-forced', sharex=ax1, sharey=ax1)
+ax6 = plt.subplot(3, 2, 6)
 
 ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
 ax1.axis('off')
@@ -237,7 +241,7 @@ noisy_image = img_as_ubyte(data.camera())
 
 auto = autolevel(noisy_image.astype(np.uint16), disk(20))
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
@@ -272,13 +276,10 @@ loc_perc_autolevel1 = autolevel_percentile(image, selem=selem, p0=.01, p1=.99)
 loc_perc_autolevel2 = autolevel_percentile(image, selem=selem, p0=.05, p1=.95)
 loc_perc_autolevel3 = autolevel_percentile(image, selem=selem, p0=.1, p1=.9)
 
-fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 8), sharex=True, sharey=True)
+fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 8), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax0, ax1, ax2 = axes
 plt.gray()
 
-#ax0.imshow(np.hstack((image, loc_autolevel)), cmap=plt.cm.gray)
-#ax0.set_title('Original / auto-level')
-
 title_list = ['Original',
                 'auto_level',
                 'auto-level 0%',
@@ -298,18 +299,6 @@ for i in range(0,len(image_list)):
     axes_list[i].set_title(title_list[i])
     axes_list[i].axis('off')
 
-'''
-ax1.imshow(
-    np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
-ax1.set_title('Percentile auto-level 0%,1%')
-ax2.imshow(
-    np.hstack((loc_perc_autolevel2, loc_perc_autolevel3)), vmin=0, vmax=255)
-ax2.set_title('Percentile auto-level 5% and 10%')
-
-for ax in axes:
-    ax.axis('off')
-'''
-
 """
 
 .. image:: PLOT2RST.current_figure
@@ -326,7 +315,7 @@ noisy_image = img_as_ubyte(data.camera())
 
 enh = enhance_contrast(noisy_image, disk(5))
 
-fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -358,7 +347,7 @@ noisy_image = img_as_ubyte(data.camera())
 
 penh = enhance_contrast_percentile(noisy_image, disk(5), p0=.1, p1=.9)
 
-fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row', subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -415,7 +404,7 @@ loc_otsu = p8 >= t_loc_otsu
 t_glob_otsu = threshold_otsu(p8)
 glob_otsu = p8 >= t_glob_otsu
 
-fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
+fig, ax = plt.subplots(2, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 fig.colorbar(ax1.imshow(p8, cmap=plt.cm.gray), ax=ax1)
@@ -451,7 +440,7 @@ m = (np.tile(x, (n, 1)) * np.linspace(0.1, 1, n) * 128 + 128).astype(np.uint8)
 radius = 10
 t = rank.otsu(m, disk(radius))
 
-fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
+fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 ax1.imshow(m)
 ax1.set_title('Original')
@@ -490,7 +479,7 @@ opening = minimum(maximum(noisy_image, disk(5)), disk(5))
 grad = gradient(noisy_image, disk(5))
 
 # display results
-fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True)
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 ax1.imshow(noisy_image, cmap=plt.cm.gray)
@@ -540,7 +529,7 @@ import matplotlib.pyplot as plt
 
 image = data.camera()
 
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 fig.colorbar(ax1.imshow(image, cmap=plt.cm.gray), ax=ax1)
 ax1.set_title('Image')
@@ -702,13 +691,7 @@ Comparison of outcome of the three methods:
 
 """
 
-'''
-fig, ax = plt.subplots()
-ax.imshow(np.hstack((rc, rndi)))
-ax.set_title('filters.rank.median vs. scipy.ndimage.percentile')
-ax.axis('off')
-'''
-fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True)
+fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax0.set_title('filters.rank.median')
 ax0.imshow(rc)
 ax0.axis('off')

doc/examples/plot_edge_filter.py

@@ -19,7 +19,7 @@ image = camera()
 edge_roberts = roberts(image)
 edge_sobel = sobel(image)
 
-fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True)
+fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 ax0.imshow(edge_roberts, cmap=plt.cm.gray)
 ax0.set_title('Roberts Edge Detection')
@@ -66,7 +66,7 @@ diff_scharr_prewitt = edge_scharr - edge_prewitt
 diff_scharr_sobel = edge_scharr - edge_sobel
 max_diff = np.max(np.maximum(diff_scharr_prewitt, diff_scharr_sobel))
 
-fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
+fig, ((ax0, ax1), (ax2, ax3)) = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 
 ax0.imshow(img, cmap=plt.cm.gray)
 ax0.set_title('Original image')

doc/examples/plot_equalize.py

@@ -40,6 +40,7 @@ def plot_img_and_hist(img, axes, bins=256):
     # Display image
     ax_img.imshow(img, cmap=plt.cm.gray)
     ax_img.set_axis_off()
+    ax_img.set_adjustable('box-forced')
 
     # Display histogram
     ax_hist.hist(img.ravel(), bins=bins, histtype='step', color='black')

doc/examples/plot_hog.py

@@ -95,6 +95,7 @@ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
 ax1.axis('off')
 ax1.imshow(image, cmap=plt.cm.gray)
 ax1.set_title('Input image')
+ax1.set_adjustable('box-forced')
 
 # Rescale histogram for better display
 hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
@@ -102,4 +103,5 @@ hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 0.02))
 ax2.axis('off')
 ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
 ax2.set_title('Histogram of Oriented Gradients')
+ax1.set_adjustable('box-forced')
 plt.show()

doc/examples/plot_line_hough_transform.py

@@ -77,32 +77,33 @@ image[idx, idx] = 255
 
 h, theta, d = hough_line(image)
 
-fig, ax = plt.subplots(1, 3, figsize=(8, 4), sharex=True, sharey=True)
-fig.delaxes(ax[1])
-ax[1] = fig.add_subplot(1, 3, 2)
+fig = plt.figure(figsize=(8, 4))
+ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
+ax2 = plt.subplot(1, 3, 2)
+ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
 
-ax[0].imshow(image, cmap=plt.cm.gray)
-ax[0].set_title('Input image')
-ax[0].set_axis_off()
+ax1.imshow(image, cmap=plt.cm.gray)
+ax1.set_title('Input image')
+ax1.set_axis_off()
 
-ax[1].imshow(np.log(1 + h),
+ax2.imshow(np.log(1 + h),
              extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
                      d[-1], d[0]],
              cmap=plt.cm.gray, aspect=1/1.5)
-ax[1].set_title('Hough transform')
-ax[1].set_xlabel('Angles (degrees)')
-ax[1].set_ylabel('Distance (pixels)')
-ax[1].axis('image')
+ax2.set_title('Hough transform')
+ax2.set_xlabel('Angles (degrees)')
+ax2.set_ylabel('Distance (pixels)')
+ax2.axis('image')
 
-ax[2].imshow(image, cmap=plt.cm.gray)
+ax3.imshow(image, cmap=plt.cm.gray)
 rows, cols = image.shape
 for _, angle, dist in zip(*hough_line_peaks(h, theta, d)):
     y0 = (dist - 0 * np.cos(angle)) / np.sin(angle)
     y1 = (dist - cols * np.cos(angle)) / np.sin(angle)
-    ax[2].plot((0, cols), (y0, y1), '-r')
-ax[2].axis((0, cols, rows, 0))
-ax[2].set_title('Detected lines')
-ax[2].set_axis_off()
+    ax3.plot((0, cols), (y0, y1), '-r')
+ax3.axis((0, cols, rows, 0))
+ax3.set_title('Detected lines')
+ax3.set_axis_off()
 
 # Line finding, using the Probabilistic Hough Transform
 
@@ -111,22 +112,25 @@ edges = canny(image, 2, 1, 25)
 lines = probabilistic_hough_line(edges, threshold=10, line_length=5,
                                  line_gap=3)
 
-fig2, ax = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True)
+fig2 = plt.figure(figsize=(8, 3))
+ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
+ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced')
+ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
 
-ax[0].imshow(image, cmap=plt.cm.gray)
-ax[0].set_title('Input image')
-ax[0].set_axis_off()
+ax1.imshow(image, cmap=plt.cm.gray)
+ax1.set_title('Input image')
+ax1.set_axis_off()
 
-ax[1].imshow(edges, cmap=plt.cm.gray)
-ax[1].set_title('Canny edges')
-ax[1].set_axis_off()
+ax2.imshow(edges, cmap=plt.cm.gray)
+ax2.set_title('Canny edges')
+ax2.set_axis_off()
 
-ax[2].imshow(edges * 0)
+ax3.imshow(edges * 0)
 
 for line in lines:
     p0, p1 = line
-    ax[2].plot((p0[0], p1[0]), (p0[1], p1[1]))
+    ax3.plot((p0[0], p1[0]), (p0[1], p1[1]))
 
-ax[2].set_title('Probabilistic Hough')
-ax[2].set_axis_off()
+ax3.set_title('Probabilistic Hough')
+ax3.set_axis_off()
 plt.show()

doc/examples/plot_local_otsu.py

@@ -37,7 +37,7 @@ threshold_global_otsu = threshold_otsu(img)
 global_otsu = img >= threshold_global_otsu
 
 
-fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True)
+fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 fig.colorbar(ax1.imshow(img, cmap=plt.cm.gray),

doc/examples/plot_random_walker_segmentation.py

@@ -38,13 +38,16 @@ markers[data > 1.3] = 2
 labels = random_walker(data, markers, beta=10, mode='bf')
 
 # Plot results
-fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2), sharex=True, sharey=True)
+fig = plt.figure(figsize=(8, 3.2))
+ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
 ax1.imshow(data, cmap='gray', interpolation='nearest')
 ax1.axis('off')
 ax1.set_title('Noisy data')
+ax2 = plt.subplot(1, 3, 2, sharex=ax1, sharey=ax1, adjustable='box-forced')
 ax2.imshow(markers, cmap='hot', interpolation='nearest')
 ax2.axis('off')
 ax2.set_title('Markers')
+ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
 ax3.imshow(labels, cmap='gray', interpolation='nearest')
 ax3.axis('off')
 ax3.set_title('Segmentation')

doc/examples/plot_tinting_grayscale_images.py

@@ -28,12 +28,11 @@ image = color.gray2rgb(grayscale_image)
 red_multiplier = [1, 0, 0]
 yellow_multiplier = [1, 1, 0]
 
-# sharing the axes makes the grid show beneath the image
-# this could be solved by calling set_axis_off() where this
-# behaviour is not wanted
 fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True)
 ax1.imshow(red_multiplier * image)
 ax2.imshow(yellow_multiplier * image)
+ax1.set_adjustable('box-forced')
+ax2.set_adjustable('box-forced')
 
 """
 .. image:: PLOT2RST.current_figure
@@ -114,6 +113,7 @@ for ax, hue in zip(axes.flat, hue_rotations):
     tinted_image = colorize(image, hue, saturation=0.3)
     ax.imshow(tinted_image, vmin=0, vmax=1)
     ax.set_axis_off()
+    ax.set_adjustable('box-forced')
 fig.tight_layout()
 
 """
@@ -148,6 +148,8 @@ masked_image[textured_regions, :] *= red_multiplier
 fig, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True, sharey=True)
 ax1.imshow(sliced_image)
 ax2.imshow(masked_image)
+ax1.set_adjustable('box-forced')
+ax2.set_adjustable('box-forced')
 
 plt.show()
 

doc/examples/plot_watershed.py

@@ -48,7 +48,7 @@ local_maxi = peak_local_max(distance, indices=False, footprint=np.ones((3, 3)),
 markers = ndi.label(local_maxi)[0]
 labels = watershed(-distance, markers, mask=image)
 
-fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7), sharex=True, sharey=True)
+fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax0, ax1, ax2 = axes
 
 ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')