adding shared axes to examples with multiple plots

doc/examples/applications/plot_coins_segmentation.py

@@ -35,7 +35,7 @@ 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')
@@ -162,7 +162,8 @@ 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))
+# 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)
 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

@@ -42,7 +42,7 @@ 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')

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))
+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)
@@ -109,7 +109,7 @@ 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))
 
@@ -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))
+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)
@@ -196,7 +196,8 @@ 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))
 
-fig, ax = plt.subplots(3, 2, figsize=(10, 10))
+# 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()
 
 ax1.imshow(noisy_image, interpolation='nearest', cmap=plt.cm.gray)
@@ -236,7 +237,7 @@ 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])
+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')
@@ -271,13 +272,33 @@ 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')
+#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()
+
+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')
+
+'''
 ax1.imshow(
     np.hstack((loc_perc_autolevel0, loc_perc_autolevel1)), vmin=0, vmax=255)
 ax1.set_title('Percentile auto-level 0%,1%')
@@ -287,6 +308,7 @@ ax2.set_title('Percentile auto-level 5% and 10%')
 
 for ax in axes:
     ax.axis('off')
+'''
 
 """
 
@@ -304,7 +326,7 @@ 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)
@@ -336,7 +358,7 @@ 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])
+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)
@@ -393,7 +415,7 @@ loc_otsu = p8 >= t_loc_otsu
 t_glob_otsu = threshold_otsu(p8)
 glob_otsu = p8 >= t_glob_otsu
 
-fig, ax = plt.subplots(2, 2)
+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)
@@ -429,7 +451,7 @@ 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)
+fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)
 
 ax1.imshow(m)
 ax1.set_title('Original')
@@ -468,7 +490,7 @@ 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])
+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)
@@ -518,7 +540,7 @@ 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')
@@ -680,10 +702,19 @@ 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')
+ax1.set_title('scipy.ndimage.percentile')
+ax1.imshow(rndi)
+ax1.axis('off')
 
 """
 .. image:: PLOT2RST.current_figure

doc/examples/plot_canny.py

@@ -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')

doc/examples/plot_denoise.py

@@ -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)
 
 plt.gray()
 

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)
+fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True)
 
 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)
 
 ax0.imshow(img, cmap=plt.cm.gray)
 ax0.set_title('Original image')

doc/examples/plot_equalize.py

@@ -70,7 +70,13 @@ 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')

doc/examples/plot_hog.py

@@ -90,7 +90,7 @@ 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)

doc/examples/plot_line_hough_transform.py

@@ -77,11 +77,13 @@ image[idx, idx] = 255
 
 h, theta, d = hough_line(image)
 
-fig, ax = plt.subplots(1, 3, figsize=(8, 4))
+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)
 
 ax[0].imshow(image, cmap=plt.cm.gray)
 ax[0].set_title('Input image')
-ax[0].axis('image')
+ax[0].set_axis_off()
 
 ax[1].imshow(np.log(1 + h),
              extent=[np.rad2deg(theta[-1]), np.rad2deg(theta[0]),
@@ -100,7 +102,7 @@ for _, angle, dist in zip(*hough_line_peaks(h, theta, d)):
     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')
+ax[2].set_axis_off()
 
 # Line finding, using the Probabilistic Hough Transform
 
@@ -109,15 +111,15 @@ 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))
+fig2, ax = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True)
 
 ax[0].imshow(image, cmap=plt.cm.gray)
 ax[0].set_title('Input image')
-ax[0].axis('image')
+ax[0].set_axis_off()
 
 ax[1].imshow(edges, cmap=plt.cm.gray)
 ax[1].set_title('Canny edges')
-ax[1].axis('image')
+ax[1].set_axis_off()
 
 ax[2].imshow(edges * 0)
 
@@ -126,5 +128,5 @@ for line in lines:
     ax[2].plot((p0[0], p1[0]), (p0[1], p1[1]))
 
 ax[2].set_title('Probabilistic Hough')
-ax[2].axis('image')
+ax[2].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))
+fig, ax = plt.subplots(2, 2, figsize=(8, 5), sharex=True, sharey=True)
 ax1, ax2, ax3, ax4 = ax.ravel()
 
 fig.colorbar(ax1.imshow(img, cmap=plt.cm.gray),

doc/examples/plot_random_walker_segmentation.py

@@ -38,7 +38,7 @@ 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_title('Noisy data')

doc/examples/plot_tinting_grayscale_images.py

@@ -28,7 +28,10 @@ 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))
+# 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)
 
@@ -104,7 +107,7 @@ 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.
@@ -142,7 +145,7 @@ 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)
 

doc/examples/plot_view_as_blocks.py

@@ -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()

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))
+fig, axes = plt.subplots(ncols=3, figsize=(8, 2.7), sharex=True, sharey=True)
 ax0, ax1, ax2 = axes
 
 ax0.imshow(image, cmap=plt.cm.gray, interpolation='nearest')