Merge pull request #1704 from MartinSavc/example_subplots_share_axes

Example subplots share axes
2026-07-11 16:36:03 +08:00 · 2015-11-01 22:26:39 +01:00
parent 7284361ef6 4e364c6087
commit 856e199cc2
39 changed files with 240 additions and 139 deletions
@@ -35,13 +35,15 @@ background with the coins:

 """

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
 ax1.imshow(coins > 100, cmap=plt.cm.gray, interpolation='nearest')
 ax1.set_title('coins > 100')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')
 ax2.imshow(coins > 150, cmap=plt.cm.gray, interpolation='nearest')
 ax2.set_title('coins > 150')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')
 margins = dict(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0, right=1)
 fig.subplots_adjust(**margins)

@@ -162,12 +164,14 @@ segmentation = ndi.binary_fill_holes(segmentation - 1)
 labeled_coins, _ = ndi.label(segmentation)
 image_label_overlay = label2rgb(labeled_coins, image=coins)

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3), sharex=True, sharey=True)
 ax1.imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
 ax1.contour(segmentation, [0.5], linewidths=1.2, colors='y')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')
 ax2.imshow(image_label_overlay, interpolation='nearest')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 fig.subplots_adjust(**margins)

@@ -42,13 +42,15 @@ Let's also define a convenience function for plotting comparisons:

 def plot_comparison(original, filtered, filter_name):

-    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
+    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True)
    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
@@ -70,24 +70,31 @@ 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))
+fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex=True, sharey=True)
 ax1, ax2, ax3, ax4 = ax.ravel()

 ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
 ax1.set_title('Noisy image')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(median(noisy_image, disk(1)), vmin=0, vmax=255, cmap=plt.cm.gray)
 ax2.set_title('Median $r=1$')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')
+

 ax3.imshow(median(noisy_image, disk(5)), vmin=0, vmax=255, cmap=plt.cm.gray)
 ax3.set_title('Median $r=5$')
 ax3.axis('off')
+ax3.set_adjustable('box-forced')
+

 ax4.imshow(median(noisy_image, disk(20)), vmin=0, vmax=255, cmap=plt.cm.gray)
 ax4.set_title('Median $r=20$')
 ax4.axis('off')
+ax4.set_adjustable('box-forced')
+

 """

@@ -109,17 +116,19 @@ image.

 from skimage.filters.rank import mean

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7])
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)

 loc_mean = mean(noisy_image, disk(10))

 ax1.imshow(noisy_image, vmin=0, vmax=255, cmap=plt.cm.gray)
 ax1.set_title('Original')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(loc_mean, vmin=0, vmax=255, cmap=plt.cm.gray)
 ax2.set_title('Local mean $r=10$')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 """

@@ -143,22 +152,26 @@ 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))
+fig, ax = plt.subplots(2, 2, figsize=(10, 7), sharex='row', sharey='row')
 ax1, ax2, ax3, ax4 = ax.ravel()

 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(bilat, cmap=plt.cm.gray)
 ax2.set_title('Bilateral mean')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
 ax3.axis('off')
+ax3.set_adjustable('box-forced')

 ax4.imshow(bilat[200:350, 350:450], cmap=plt.cm.gray)
 ax4.axis('off')
+ax4.set_adjustable('box-forced')

 """

@@ -236,15 +249,17 @@ 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])
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=[10, 7], sharex=True, sharey=True)

 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(auto, cmap=plt.cm.gray)
 ax2.set_title('Local autolevel')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 """

@@ -271,22 +286,29 @@ 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, figsize=(7, 8))
+fig, axes = plt.subplots(nrows=3, ncols=2, figsize=(7, 8), sharex=True, sharey=True)
 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%',
+                'auto-level 1%',
+                'auto-level 5%',
+                'auto-level 10%']
+image_list = [image,
+                loc_autolevel,
+                loc_perc_autolevel0,
+                loc_perc_autolevel1,
+                loc_perc_autolevel2,
+                loc_perc_autolevel3]
+axes_list = axes.ravel().tolist()

-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')
+for i in range(0,len(image_list)):
+    axes_list[i].imshow(image_list[i], cmap=plt.cm.gray, vmin=0, vmax=255)
+    axes_list[i].set_title(title_list[i])
+    axes_list[i].axis('off')
+    axes_list[i].set_adjustable('box-forced')

 """

@@ -304,22 +326,26 @@ noisy_image = img_as_ubyte(data.camera())

 enh = enhance_contrast(noisy_image, disk(5))

-fig, ax = plt.subplots(2, 2, figsize=[10, 7])
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
 ax1, ax2, ax3, ax4 = ax.ravel()

 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(enh, cmap=plt.cm.gray)
 ax2.set_title('Local morphological contrast enhancement')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
 ax3.axis('off')
+ax3.set_adjustable('box-forced')

 ax4.imshow(enh[200:350, 350:450], cmap=plt.cm.gray)
 ax4.axis('off')
+ax4.set_adjustable('box-forced')

 """

@@ -336,22 +362,22 @@ 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])
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex='row', sharey='row')
 ax1, ax2, ax3, ax4 = ax.ravel()

 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
-ax1.axis('off')

 ax2.imshow(penh, cmap=plt.cm.gray)
 ax2.set_title('Local percentile morphological\n contrast enhancement')
-ax2.axis('off')

 ax3.imshow(noisy_image[200:350, 350:450], cmap=plt.cm.gray)
-ax3.axis('off')

 ax4.imshow(penh[200:350, 350:450], cmap=plt.cm.gray)
-ax4.axis('off')
+
+for ax in ax.ravel():
+    ax.axis('off')
+    ax.set_adjustable('box-forced')

 """

@@ -393,24 +419,24 @@ loc_otsu = p8 >= t_loc_otsu
 t_glob_otsu = threshold_otsu(p8)
 glob_otsu = p8 >= t_glob_otsu

-fig, ax = plt.subplots(2, 2)
+fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
 ax1, ax2, ax3, ax4 = ax.ravel()

 fig.colorbar(ax1.imshow(p8, cmap=plt.cm.gray), ax=ax1)
 ax1.set_title('Original')
-ax1.axis('off')

 fig.colorbar(ax2.imshow(t_loc_otsu, cmap=plt.cm.gray), ax=ax2)
 ax2.set_title('Local Otsu ($r=%d$)' % radius)
-ax2.axis('off')

 ax3.imshow(p8 >= t_loc_otsu, cmap=plt.cm.gray)
 ax3.set_title('Original >= local Otsu' % t_glob_otsu)
-ax3.axis('off')

 ax4.imshow(glob_otsu, cmap=plt.cm.gray)
 ax4.set_title('Global Otsu ($t=%d$)' % t_glob_otsu)
-ax4.axis('off')
+
+for ax in ax.ravel():
+    ax.axis('off')
+    ax.set_adjustable('box-forced')

 """

@@ -429,15 +455,17 @@ 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)
+fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)

 ax1.imshow(m)
 ax1.set_title('Original')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(m >= t, interpolation='nearest')
 ax2.set_title('Local Otsu ($r=%d$)' % radius)
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 """

@@ -468,25 +496,24 @@ 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])
+fig, ax = plt.subplots(2, 2, figsize=[10, 7], sharex=True, sharey=True)
 ax1, ax2, ax3, ax4 = ax.ravel()

 ax1.imshow(noisy_image, cmap=plt.cm.gray)
 ax1.set_title('Original')
-ax1.axis('off')

 ax2.imshow(closing, cmap=plt.cm.gray)
 ax2.set_title('Gray-level closing')
-ax2.axis('off')

 ax3.imshow(opening, cmap=plt.cm.gray)
 ax3.set_title('Gray-level opening')
-ax3.axis('off')

 ax4.imshow(grad, cmap=plt.cm.gray)
 ax4.set_title('Morphological gradient')
-ax4.axis('off')

+for ax in ax.ravel():
+    ax.axis('off')
+    ax.set_adjustable('box-forced')
 """

 .. image:: PLOT2RST.current_figure
@@ -518,15 +545,17 @@ import matplotlib.pyplot as plt

 image = data.camera()

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True)

 fig.colorbar(ax1.imshow(image, cmap=plt.cm.gray), ax=ax1)
 ax1.set_title('Image')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 fig.colorbar(ax2.imshow(entropy(image, disk(5)), cmap=plt.cm.jet), ax=ax2)
 ax2.set_title('Entropy')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 """

@@ -680,10 +709,15 @@ 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)
+ax0.set_title('filters.rank.median')
+ax0.imshow(rc)
+ax0.axis('off')
+ax0.set_adjustable('box-forced')
+ax1.set_title('scipy.ndimage.percentile')
+ax1.imshow(rndi)
+ax1.axis('off')
+ax1.set_adjustable('box-forced')

 """
 .. image:: PLOT2RST.current_figure
@@ -48,8 +48,8 @@ import matplotlib.pyplot as plt
 image = data.astronaut()

 fig = plt.figure(figsize=(14, 7))
-ax_each = fig.add_subplot(121)
-ax_hsv = fig.add_subplot(122)
+ax_each = fig.add_subplot(121, adjustable='box-forced')
+ax_hsv = fig.add_subplot(122, sharex=ax_each, sharey=ax_each, adjustable='box-forced')

 # We use 1 - sobel_each(image)
 # but this will not work if image is not normalized
@@ -107,7 +107,7 @@ def sobel_gray(image):
    return filters.sobel(image)

 fig = plt.figure(figsize=(7, 7))
-ax = fig.add_subplot(111)
+ax = fig.add_subplot(111, sharex=ax_each, sharey=ax_each, adjustable='box-forced')

 # We use 1 - sobel_gray(image)
 # but this will not work if image is not normalized
@@ -61,8 +61,12 @@ titles = ['Laplacian of Gaussian', 'Difference of Gaussian',
          'Determinant of Hessian']
 sequence = zip(blobs_list, colors, titles)

+
+fig,axes = plt.subplots(1, 3, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+axes = axes.ravel()
 for blobs, color, title in sequence:
-    fig, ax = plt.subplots(1, 1)
+    ax = axes[0]
+    axes = axes[1:]
    ax.set_title(title)
    ax.imshow(image, interpolation='nearest')
    for blob in blobs:
@@ -35,7 +35,7 @@ edges1 = feature.canny(im)
 edges2 = feature.canny(im, sigma=3)

 # display results
-fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3))
+fig, (ax1, ax2, ax3) = plt.subplots(nrows=1, ncols=3, figsize=(8, 3), sharex=True, sharey=True)

 ax1.imshow(im, cmap=plt.cm.jet)
 ax1.axis('off')
@@ -138,7 +138,7 @@ image_rgb[cy, cx] = (0, 0, 255)
 edges = color.gray2rgb(edges)
 edges[cy, cx] = (250, 0, 0)

-fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4))
+fig2, (ax1, ax2) = plt.subplots(ncols=2, nrows=1, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 ax1.set_title('Original picture')
 ax1.imshow(image_rgb)
@@ -38,7 +38,7 @@ astro = astro[220:300, 220:320]
 noisy = astro + 0.6 * astro.std() * np.random.random(astro.shape)
 noisy = np.clip(noisy, 0, 1)

-fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
+fig, ax = plt.subplots(nrows=2, ncols=3, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 plt.gray()

@@ -19,7 +19,7 @@ image = camera()
 edge_roberts = roberts(image)
 edge_sobel = sobel(image)

-fig, (ax0, ax1) = plt.subplots(ncols=2)
+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)
+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')
@@ -17,7 +17,7 @@ from skimage.util import img_as_ubyte

 image = img_as_ubyte(data.camera())

-fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4))
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(10, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 img0 = ax0.imshow(image, cmap=plt.cm.gray)
 ax0.set_title('Image')
@@ -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')
@@ -70,7 +71,13 @@ img_eq = exposure.equalize_hist(img)
 img_adapteq = exposure.equalize_adapthist(img, clip_limit=0.03)

 # Display results
-fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(8, 5))
+fig = plt.figure(figsize=(8, 5))
+axes = np.zeros((2,4), dtype=np.object)
+axes[0,0] = fig.add_subplot(2, 4, 1)
+for i in range(1,4):
+    axes[0,i] = fig.add_subplot(2, 4, 1+i, sharex=axes[0,0], sharey=axes[0,0])
+for i in range(0,4):
+    axes[1,i] = fig.add_subplot(2, 4, 5+i)

 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')
@@ -90,11 +90,12 @@ image = color.rgb2gray(data.astronaut())
 fd, hog_image = hog(image, orientations=8, pixels_per_cell=(16, 16),
                    cells_per_block=(1, 1), visualise=True)

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
+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()
@@ -21,16 +21,13 @@ image = data.moon()
 # Rescale image intensity so that we can see dim features.
 image = rescale_intensity(image, in_range=(50, 200))

-
-# convenience function for plotting images
-def imshow(image, title, **kwargs):
-    fig, ax = plt.subplots(figsize=(5, 4))
-    ax.imshow(image, **kwargs)
-    ax.axis('off')
-    ax.set_title(title)
+fig,ax = plt.subplots(2, 2, figsize=(5, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+ax = ax.ravel()


-imshow(image, 'Original image')
+ax[0].imshow(image)
+ax[0].set_title('Original image')
+ax[0].axis('off')

 """
 .. image:: PLOT2RST.current_figure
@@ -52,8 +49,9 @@ mask = image

 filled = reconstruction(seed, mask, method='erosion')

-imshow(filled, 'after filling holes', vmin=image.min(), vmax=image.max())
-
+ax[1].imshow(filled)
+ax[1].set_title('after filling holes')
+ax[1].axis('off')
 """
 .. image:: PLOT2RST.current_figure

@@ -63,8 +61,9 @@ isolate the dark regions by subtracting the reconstructed image from the
 original image.
 """

-imshow(image - filled, 'holes')
-# plt.title('holes')
+ax[2].imshow(image-filled)
+ax[2].set_title('holes')
+ax[2].axis('off')

 """
 .. image:: PLOT2RST.current_figure
@@ -79,7 +78,10 @@ intensity instead of the maximum. The remainder of the process is the same.
 seed = np.copy(image)
 seed[1:-1, 1:-1] = image.min()
 rec = reconstruction(seed, mask, method='dilation')
-imshow(image - rec, 'peaks')
+
+ax[3].imshow(image-rec)
+ax[3].set_title('peaks')
+ax[3].axis('off')
 plt.show()

 """
@@ -26,7 +26,7 @@ from skimage.color import rgb2hed
 ihc_rgb = data.immunohistochemistry()
 ihc_hed = rgb2hed(ihc_rgb)

-fig, axes = plt.subplots(2, 2, figsize=(7, 6))
+fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax0, ax1, ax2, ax3 = axes.ravel()

 ax0.imshow(ihc_rgb)
@@ -61,7 +61,9 @@ h = rescale_intensity(ihc_hed[:, :, 0], out_range=(0, 1))
 d = rescale_intensity(ihc_hed[:, :, 2], out_range=(0, 1))
 zdh = np.dstack((np.zeros_like(h), d, h))

-fig, ax = plt.subplots()
+#fig, ax = plt.subplots()
+fig = plt.figure()
+ax = plt.subplot(1, 1, 1, sharex=ax0, sharey=ax0, adjustable='box-forced')
 ax.imshow(zdh)
 ax.set_title("Stain separated image (rescaled)")
 ax.axis('off')
@@ -40,7 +40,7 @@ seg2 = slic(coins, n_segments=117, max_iter=160, sigma=1, compactness=0.75,
 segj = join_segmentations(seg1, seg2)

 # show the segmentations
-fig, axes = plt.subplots(ncols=4, figsize=(9, 2.5))
+fig, axes = plt.subplots(ncols=4, figsize=(9, 2.5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 axes[0].imshow(coins, cmap=plt.cm.gray, interpolation='nearest')
 axes[0].set_title('Image')

@@ -77,30 +77,30 @@ image[idx, idx] = 255

 h, theta, d = hough_line(image)

-fig, ax = plt.subplots(1, 3, figsize=(8, 4))
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8,4))

-ax[0].imshow(image, cmap=plt.cm.gray)
-ax[0].set_title('Input image')
-ax[0].axis('image')
+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].axis('image')
+    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

@@ -109,22 +109,25 @@ 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))
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8,4), sharex=True, sharey=True)

-ax[0].imshow(image, cmap=plt.cm.gray)
-ax[0].set_title('Input image')
-ax[0].axis('image')
+ax1.imshow(image, cmap=plt.cm.gray)
+ax1.set_title('Input image')
+ax1.set_axis_off()
+ax1.set_adjustable('box-forced')

-ax[1].imshow(edges, cmap=plt.cm.gray)
-ax[1].set_title('Canny edges')
-ax[1].axis('image')
+ax2.imshow(edges, cmap=plt.cm.gray)
+ax2.set_title('Canny edges')
+ax2.set_axis_off()
+ax2.set_adjustable('box-forced')

-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].axis('image')
+ax3.set_title('Probabilistic Hough')
+ax3.set_axis_off()
+ax3.set_adjustable('box-forced')
 plt.show()
@@ -72,7 +72,14 @@ img_eq = rank.equalize(img, selem=selem)


 # Display results
-fig, axes = plt.subplots(2, 3, figsize=(8, 5))
+fig = plt.figure(figsize=(8, 5))
+axes = np.zeros((2, 3), dtype=np.object)
+axes[0,0] = plt.subplot(2, 3, 1, adjustable='box-forced')
+axes[0,1] = plt.subplot(2, 3, 2, sharex=axes[0,0], sharey=axes[0,0], adjustable='box-forced')
+axes[0,2] = plt.subplot(2, 3, 3, sharex=axes[0,0], sharey=axes[0,0], adjustable='box-forced')
+axes[1,0] = plt.subplot(2, 3, 4)
+axes[1,1] = plt.subplot(2, 3, 5)
+axes[1,2] = plt.subplot(2, 3, 6)

 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')
@@ -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))
+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),
@@ -54,7 +54,14 @@ gamma_corrected = exposure.adjust_gamma(img, 2)
 logarithmic_corrected = exposure.adjust_log(img, 1)

 # Display results
-fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(8, 5))
+fig = plt.figure(figsize=(8, 5))
+axes = np.zeros((2,3), dtype=np.object)
+axes[0, 0] = plt.subplot(2, 3, 1, adjustable='box-forced')
+axes[0, 1] = plt.subplot(2, 3, 2, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced')
+axes[0, 2] = plt.subplot(2, 3, 3, sharex=axes[0, 0], sharey=axes[0, 0], adjustable='box-forced')
+axes[1, 0] = plt.subplot(2, 3, 4)
+axes[1, 1] = plt.subplot(2, 3, 5)
+axes[1, 2] = plt.subplot(2, 3, 6)

 ax_img, ax_hist, ax_cdf = plot_img_and_hist(img, axes[:, 0])
 ax_img.set_title('Low contrast image')
@@ -45,7 +45,8 @@ gradient = rank.gradient(denoised, disk(2))
 labels = watershed(gradient, markers)

 # display results
-fig, axes = plt.subplots(ncols=4, figsize=(8, 2.7))
+fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 8), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
+axes = axes.ravel()
 ax0, ax1, ax2, ax3 = axes

 ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')
@@ -61,6 +62,5 @@ ax3.set_title("Segmented")
 for ax in axes:
    ax.axis('off')

-fig.subplots_adjust(hspace=0.01, wspace=0.01, top=0.9, bottom=0,
-                    left=0, right=1)
+fig.tight_layout()
 plt.show()
@@ -54,7 +54,7 @@ skel, distance = medial_axis(data, return_distance=True)
 # Distance to the background for pixels of the skeleton
 dist_on_skel = distance * skel

-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1.imshow(data, cmap=plt.cm.gray, interpolation='nearest')
 ax1.axis('off')
 ax2.imshow(dist_on_skel, cmap=plt.cm.spectral, interpolation='nearest')
@@ -26,7 +26,7 @@ noisy = np.clip(noisy, 0, 1)

 denoise = denoise_nl_means(noisy, 7, 9, 0.08)

-fig, ax = plt.subplots(ncols=2, figsize=(8, 4))
+fig, ax = plt.subplots(ncols=2, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 ax[0].imshow(noisy)
 ax[0].axis('off')
@@ -28,7 +28,12 @@ image = camera()
 thresh = threshold_otsu(image)
 binary = image > thresh

-fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5))
+#fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5))
+fig = plt.figure(figsize=(8, 2.5))
+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')
+
 ax1.imshow(image, cmap=plt.cm.gray)
 ax1.set_title('Original')
 ax1.axis('off')
@@ -25,7 +25,7 @@ image_max = ndi.maximum_filter(im, size=20, mode='constant')
 coordinates = peak_local_max(im, min_distance=20)

 # display results
-fig, ax = plt.subplots(1, 3, figsize=(8, 3))
+fig, ax = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3 = ax.ravel()
 ax1.imshow(im, cmap=plt.cm.gray)
 ax1.axis('off')
@@ -26,7 +26,7 @@ image_wrapped = np.angle(np.exp(1j * image))
 # Perform phase unwrapping
 image_unwrapped = unwrap_phase(image_wrapped)

-fig, ax = plt.subplots(2, 2)
+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(image, cmap='gray', vmin=0, vmax=4 * np.pi), ax=ax1)
@@ -101,7 +101,7 @@ error = reconstruction_fbp - image
 print('FBP rms reconstruction error: %.3g' % np.sqrt(np.mean(error**2)))

 imkwargs = dict(vmin=-0.2, vmax=0.2)
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1.set_title("Reconstruction\nFiltered back projection")
 ax1.imshow(reconstruction_fbp, cmap=plt.cm.Greys_r)
 ax2.set_title("Reconstruction error\nFiltered back projection")
@@ -152,7 +152,7 @@ error = reconstruction_sart - image
 print('SART (1 iteration) rms reconstruction error: %.3g'
      % np.sqrt(np.mean(error**2)))

-fig, ax = plt.subplots(2, 2, figsize=(8, 8.5))
+fig, ax = plt.subplots(2, 2, figsize=(8, 8.5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 ax1, ax2, ax3, ax4 = ax.ravel()
 ax1.set_title("Reconstruction\nSART")
 ax1.imshow(reconstruction_sart, cmap=plt.cm.Greys_r)
@@ -38,15 +38,18 @@ 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))
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 3.2), sharex=True, sharey=True)
 ax1.imshow(data, cmap='gray', interpolation='nearest')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')
 ax1.set_title('Noisy data')
 ax2.imshow(markers, cmap='hot', interpolation='nearest')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')
 ax2.set_title('Markers')
 ax3.imshow(labels, cmap='gray', interpolation='nearest')
 ax3.axis('off')
+ax3.set_adjustable('box-forced')
 ax3.set_title('Segmentation')

 fig.subplots_adjust(hspace=0.01, wspace=0.01, top=1, bottom=0, left=0,
@@ -34,19 +34,16 @@ bilateral_result = rank.mean_bilateral(image, selem=selem, s0=500, s1=500)
 normal_result = rank.mean(image, selem=selem)


-fig, axes = plt.subplots(nrows=3, figsize=(8, 10))
-ax0, ax1, ax2 = axes
+fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(8, 10), sharex=True, sharey=True)
+ax = axes.ravel()

-ax0.imshow(np.hstack((image, percentile_result)))
-ax0.set_title('Percentile mean')
-ax0.axis('off')
+titles = ['Original', 'Percentile mean', 'Bilateral mean', 'Local mean']
+imgs = [image, percentile_result, bilateral_result, normal_result]
+for n in range(0, len(imgs)):
+    ax[n].imshow(imgs[n])
+    ax[n].set_title(titles[n])
+    ax[n].set_adjustable('box-forced')
+    ax[n].axis('off')

-ax1.imshow(np.hstack((image, bilateral_result)))
-ax1.set_title('Bilateral mean')
-ax1.axis('off')
-
-ax2.imshow(np.hstack((image, normal_result)))
-ax2.set_title('Local mean')
-ax2.axis('off')

 plt.show()
@@ -36,19 +36,22 @@ Subtracting the dilated image leaves an image with just the coins and a flat,
 black background, as shown below.
 """

-fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 2.5))
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5), sharex=True, sharey=True)

 ax1.imshow(image)
 ax1.set_title('original image')
 ax1.axis('off')
+ax1.set_adjustable('box-forced')

 ax2.imshow(dilated, vmin=image.min(), vmax=image.max())
 ax2.set_title('dilated')
 ax2.axis('off')
+ax2.set_adjustable('box-forced')

 ax3.imshow(image - dilated)
 ax3.set_title('image - dilated')
 ax3.axis('off')
+ax3.set_adjustable('box-forced')

 fig.tight_layout()

@@ -76,7 +79,7 @@ mask, seed, and dilated images along a slice of the image (indicated by red
 line).
 """

-fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 2.5))
+fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(8, 2.5))

 yslice = 197

@@ -34,7 +34,10 @@ print(shift)
 # pixel precision first
 shift, error, diffphase = register_translation(image, offset_image)

-fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 3))
+fig = 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)

 ax1.imshow(image)
 ax1.set_axis_off()
@@ -60,7 +63,10 @@ print(shift)
 # subpixel precision
 shift, error, diffphase = register_translation(image, offset_image, 100)

-fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 3))
+fig = 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)

 ax1.imshow(image)
 ax1.set_axis_off()
@@ -42,7 +42,7 @@ astro += 0.1 * astro.std() * np.random.standard_normal(astro.shape)

 deconvolved, _ = restoration.unsupervised_wiener(astro, psf)

-fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5))
+fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(8, 5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 plt.gray()

@@ -79,7 +79,7 @@ print("Felzenszwalb's number of segments: %d" % len(np.unique(segments_fz)))
 print("Slic number of segments: %d" % len(np.unique(segments_slic)))
 print("Quickshift number of segments: %d" % len(np.unique(segments_quick)))

-fig, ax = plt.subplots(1, 3)
+fig, ax = plt.subplots(1, 3, sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})
 fig.set_size_inches(8, 3, forward=True)
 fig.subplots_adjust(0.05, 0.05, 0.95, 0.95, 0.05, 0.05)

@@ -47,7 +47,7 @@ image[circle2] = 0
 skeleton = skeletonize(image)

 # display results
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5))
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4.5), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 ax1.imshow(image, cmap=plt.cm.gray)
 ax1.axis('off')
@@ -45,7 +45,7 @@ def mse(x, y):
 img_noise = img + noise
 img_const = img + abs(noise)

-fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 4))
+fig, (ax0, ax1, ax2) = plt.subplots(nrows=1, ncols=3, figsize=(8, 4), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 mse_none = mse(img, img)
 ssim_none = ssim(img, img, dynamic_range=img.max() - img.min())
@@ -74,7 +74,7 @@ from skimage.transform import swirl
 image = data.checkerboard()
 swirled = swirl(image, rotation=0, strength=10, radius=120, order=2)

-fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 3))
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(8, 3), sharex=True, sharey=True, subplot_kw={'adjustable':'box-forced'})

 ax0.imshow(image, cmap=plt.cm.gray, interpolation='none')
 ax0.axis('off')
@@ -33,7 +33,10 @@ result = match_template(image, coin)
 ij = np.unravel_index(np.argmax(result), result.shape)
 x, y = ij[::-1]

-fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(8, 3))
+fig = plt.figure(figsize=(8, 3))
+ax1 = plt.subplot(1, 3, 1)
+ax2 = plt.subplot(1, 3, 2, adjustable='box-forced')
+ax3 = plt.subplot(1, 3, 3, sharex=ax2, sharey=ax2, adjustable='box-forced')

 ax1.imshow(coin)
 ax1.set_axis_off()
@@ -28,9 +28,11 @@ image = color.gray2rgb(grayscale_image)
 red_multiplier = [1, 0, 0]
 yellow_multiplier = [1, 1, 0]

-fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
+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
@@ -104,13 +106,14 @@ and a non-zero saturation:

 hue_rotations = np.linspace(0, 1, 6)

-fig, axes = plt.subplots(nrows=2, ncols=3)
+fig, axes = plt.subplots(nrows=2, ncols=3, sharex=True, sharey=True)

 for ax, hue in zip(axes.flat, hue_rotations):
    # Turn down the saturation to give it that vintage look.
    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()

 """
@@ -142,9 +145,11 @@ textured_regions = noisy > 4
 masked_image = image.copy()
 masked_image[textured_regions, :] *= red_multiplier

-fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(8, 4))
+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()

@@ -44,21 +44,26 @@ max_view = np.max(flatten_view, axis=2)
 median_view = np.median(flatten_view, axis=2)

 # -- display resampled images
-fig, axes = plt.subplots(2, 2, figsize=(8, 8))
+fig, axes = plt.subplots(2, 2, figsize=(8, 8), sharex=True, sharey=True)
 ax0, ax1, ax2, ax3 = axes.ravel()

 ax0.set_title("Original rescaled with\n spline interpolation (order=3)")
 l_resized = ndi.zoom(l, 2, order=3)
-ax0.imshow(l_resized, cmap=cm.Greys_r)
+#ax0.imshow(l_resized, cmap=cm.Greys_r)
+ax0.imshow(l_resized, extent=(0, 128, 128, 0), interpolation='nearest', cmap=cm.Greys_r)
+ax0.set_axis_off()

 ax1.set_title("Block view with\n local mean pooling")
-ax1.imshow(mean_view, cmap=cm.Greys_r)
+ax1.imshow(mean_view, interpolation='nearest', cmap=cm.Greys_r)
+ax1.set_axis_off()

 ax2.set_title("Block view with\n local max pooling")
-ax2.imshow(max_view, cmap=cm.Greys_r)
+ax2.imshow(max_view, interpolation='nearest', cmap=cm.Greys_r)
+ax2.set_axis_off()

 ax3.set_title("Block view with\n local median pooling")
-ax3.imshow(median_view, cmap=cm.Greys_r)
+ax3.imshow(median_view, interpolation='nearest', cmap=cm.Greys_r)
+ax3.set_axis_off()

 fig.subplots_adjust(hspace=0.4, wspace=0.4)
 plt.show()
@@ -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))
+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')