adding axes sharing to displays of related images

for better interaction

sharing achieved by setting sharex and sharey, and
setting the axes 'adjustable' parameter to 'box-forced'

doc/examples/plot_adapt_rgb.py

@@ -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

doc/examples/plot_blob.py

@@ -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:

doc/examples/plot_circular_elliptical_hough_transform.py

@@ -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)

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

doc/examples/plot_entropy.py

@@ -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')

doc/examples/plot_gabor.py

@@ -100,7 +100,19 @@ for theta in (0, 1):
         # Save kernel and the power image for each image
         results.append((kernel, [power(img, kernel) for img in images]))
 
-fig, axes = plt.subplots(nrows=5, ncols=4, figsize=(5, 6))
+# Prepare exes for ploting
+fig = plt.figure(figsize=(5, 6))
+axes = np.zeros((5, 4), dtype=np.object)
+# first column
+for n in range(0, 5):
+    axes[n,0] = plt.subplot(5, 4, 1+n*4)
+# the other columns, each column axes are shared
+for m in range(1, 4):
+    axes[0,m] = plt.subplot(5, 4, 1+m, adjustable='box-forced')
+    for n in range(1, 5):
+        axes[n,m] = plt.subplot(5, 4, 1+n*4+m, sharex=axes[0,m], sharey=axes[0,m])
+        axes[n,m].set_adjustable('box-forced')
+
 plt.gray()
 
 fig.suptitle('Image responses for Gabor filter kernels', fontsize=12)

doc/examples/plot_holes_and_peaks.py

@@ -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()
 
 """

doc/examples/plot_ihc_color_separation.py

@@ -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')

doc/examples/plot_join_segmentations.py

@@ -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')
 

doc/examples/plot_local_equalize.py

@@ -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')

doc/examples/plot_log_gamma.py

@@ -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')

doc/examples/plot_marked_watershed.py

@@ -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()

doc/examples/plot_medial_transform.py

@@ -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')

doc/examples/plot_nonlocal_means.py

@@ -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')

doc/examples/plot_otsu.py

@@ -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')

doc/examples/plot_peak_local_max.py

@@ -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')

doc/examples/plot_phase_unwrap.py

@@ -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)

doc/examples/plot_rank_mean.py

@@ -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()

doc/examples/plot_regional_maxima.py

@@ -36,7 +36,10 @@ 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 = plt.figure(figsize=(8, 2.5))
+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')
 
 ax1.imshow(image)
 ax1.set_title('original image')
@@ -76,7 +79,10 @@ 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 = plt.figure(figsize=(8, 2.5))
+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')
 
 yslice = 197
 

doc/examples/plot_register_translation.py

@@ -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()

doc/examples/plot_restoration.py

@@ -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()
 

doc/examples/plot_skeleton.py

@@ -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')

doc/examples/plot_swirl.py

@@ -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')

doc/examples/plot_template.py

@@ -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()