Use Matplotlib OO style throughout doc/examples/applications (#7346)

Co-authored-by: Marianne Corvellec <marianne.corvellec@ens-lyon.org>
Co-authored-by: Lars Grüter <lagru+github@mailbox.org>

doc/examples/applications/plot_3d_image_processing.py

@@ -94,7 +94,7 @@
 
 def show_plane(ax, plane, cmap="gray", title=None):
     ax.imshow(plane, cmap=cmap)
-    ax.axis("off")
+    ax.set_axis_off()
 
     if title:
         ax.set_title(title)

doc/examples/applications/plot_coins_segmentation.py

@@ -19,7 +19,7 @@
 
 fig, axes = plt.subplots(1, 2, figsize=(8, 3))
 axes[0].imshow(coins, cmap=plt.cm.gray)
-axes[0].axis('off')
+axes[0].set_axis_off()
 axes[1].plot(hist_centers, hist, lw=2)
 axes[1].set_title('histogram of gray values')
 
@@ -42,9 +42,9 @@
 axes[1].set_title('coins > 150')
 
 for a in axes:
-    a.axis('off')
+    a.set_axis_off()
 
-plt.tight_layout()
+fig.tight_layout()
 
 ######################################################################
 # Edge-based segmentation
@@ -61,7 +61,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(edges, cmap=plt.cm.gray)
 ax.set_title('Canny detector')
-ax.axis('off')
+ax.set_axis_off()
 
 ######################################################################
 # These contours are then filled using mathematical morphology.
@@ -73,7 +73,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(fill_coins, cmap=plt.cm.gray)
 ax.set_title('filling the holes')
-ax.axis('off')
+ax.set_axis_off()
 
 
 ######################################################################
@@ -87,7 +87,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(coins_cleaned, cmap=plt.cm.gray)
 ax.set_title('removing small objects')
-ax.axis('off')
+ax.set_axis_off()
 
 ######################################################################
 # However, this method is not very robust, since contours that are not
@@ -107,7 +107,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(elevation_map, cmap=plt.cm.gray)
 ax.set_title('elevation map')
-ax.axis('off')
+ax.set_axis_off()
 
 ######################################################################
 # Next we find markers of the background and the coins based on the extreme
@@ -120,7 +120,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(markers, cmap=plt.cm.nipy_spectral)
 ax.set_title('markers')
-ax.axis('off')
+ax.set_axis_off()
 
 ######################################################################
 # Finally, we use the watershed transform to fill regions of the elevation
@@ -132,7 +132,7 @@
 fig, ax = plt.subplots(figsize=(4, 3))
 ax.imshow(segmentation_coins, cmap=plt.cm.gray)
 ax.set_title('segmentation')
-ax.axis('off')
+ax.set_axis_off()
 
 ######################################################################
 # This last method works even better, and the coins can be segmented and
@@ -150,8 +150,8 @@
 axes[1].imshow(image_label_overlay)
 
 for a in axes:
-    a.axis('off')
+    a.set_axis_off()
 
-plt.tight_layout()
+fig.tight_layout()
 
 plt.show()

doc/examples/applications/plot_colocalization_metrics.py

@@ -145,11 +145,11 @@
     a.set_axis_off()
 
 # plot pixel intensity scatter
-plt.figure()
-plt.scatter(proteinA, proteinB)
-plt.title('Pixel intensity')
-plt.xlabel('Protein A intensity')
-plt.ylabel('Protein B intensity')
+fig, ax = plt.subplots()
+ax.scatter(proteinA, proteinB)
+ax.set_title('Pixel intensity')
+ax.set_xlabel('Protein A intensity')
+ax.set_ylabel('Protein B intensity')
 
 #####################################################################
 # The intensities look linearly correlated so Pearson's Correlation Coefficient

doc/examples/applications/plot_cornea_spot_inpainting.py

@@ -147,7 +147,7 @@ def plot_comparison(plot1, plot2, title1, title2):
 
 mask_0 = image_med < thresh_0
 
-plot_comparison(mask_0, mask_2, "No offset", "offset = 15")
+plot_comparison(mask_0, mask_2, "No offset", "Offset = 15")
 
 #####################################################################
 # Remove fine-grained features
@@ -184,7 +184,7 @@ def plot_comparison(plot1, plot2, title1, title2):
 # Next, we can make the detected areas wider by applying a dilation filter:
 
 mask_dilate = ski.morphology.dilation(mask_open, footprint)
-plot_comparison(mask_open, mask_dilate, "before", "after dilation")
+plot_comparison(mask_open, mask_dilate, "Before", "After dilation")
 
 #####################################################################
 # Dilation enlarges bright regions and shrinks dark regions.

doc/examples/applications/plot_face_detection.py

@@ -45,14 +45,14 @@
 
 ``min_neighbor_number`` and ``intersection_score_threshold`` parameters are
 made to cluster the excessive detections of the same face and to filter out
-false detections.  True faces usually has a lot of detections around them and
+false detections.  True faces usually have a lot of detections around them and
 false ones usually have single detection. First algorithm searches for
 clusters: two rectangle detections are placed in the same cluster if the
 intersection score between them is larger then
 ``intersection_score_threshold``. The intersection score is computed using the
 equation (intersection area) / (small rectangle ratio). The described
 intersection criteria was chosen over intersection over union to avoid a corner
-case when small rectangle inside of a big one have small intersection score.
+case when small rectangle inside of a big one has small intersection score.
 Then each cluster is thresholded using ``min_neighbor_number`` parameter which
 leaves the clusters that have a same or bigger number of detections in them.
 
@@ -80,12 +80,11 @@
     img=img, scale_factor=1.2, step_ratio=1, min_size=(60, 60), max_size=(123, 123)
 )
 
-plt.imshow(img)
-img_desc = plt.gca()
-plt.set_cmap('gray')
+fig, ax = plt.subplots()
+ax.imshow(img, cmap='gray')
 
 for patch in detected:
-    img_desc.add_patch(
+    ax.axes.add_patch(
         patches.Rectangle(
             (patch['c'], patch['r']),
             patch['width'],

doc/examples/applications/plot_human_mitosis.py

@@ -76,10 +76,10 @@
 fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
 ax[0].imshow(image)
 ax[0].set_title('Original')
-ax[0].axis('off')
+ax[0].set_axis_off()
 ax[1].imshow(regions)
 ax[1].set_title('Multi-Otsu thresholding')
-ax[1].axis('off')
+ax[1].set_axis_off()
 plt.show()
 
 #####################################################################
@@ -111,13 +111,13 @@
 fig, ax = plt.subplots(ncols=3, figsize=(15, 5))
 ax[0].imshow(image)
 ax[0].set_title('Original')
-ax[0].axis('off')
+ax[0].set_axis_off()
 ax[2].imshow(cells)
 ax[2].set_title('All nuclei?')
-ax[2].axis('off')
+ax[2].set_axis_off()
 ax[1].imshow(dividing)
 ax[1].set_title('Dividing nuclei?')
-ax[1].axis('off')
+ax[1].set_axis_off()
 plt.show()
 
 #####################################################################
@@ -147,7 +147,7 @@
 fig, ax = plt.subplots(figsize=(5, 5))
 ax.imshow(binary_smoother_dividing)
 ax.set_title('Dividing nuclei')
-ax.axis('off')
+ax.set_axis_off()
 plt.show()
 
 #####################################################################
@@ -179,10 +179,10 @@
 fig, ax = plt.subplots(ncols=2, figsize=(10, 5))
 ax[0].imshow(cells, cmap='gray')
 ax[0].set_title('Overlapping nuclei')
-ax[0].axis('off')
+ax[0].set_axis_off()
 ax[1].imshow(color.label2rgb(segmented_cells, bg_label=0))
 ax[1].set_title('Segmented nuclei')
-ax[1].axis('off')
+ax[1].set_axis_off()
 plt.show()
 
 #####################################################################

doc/examples/applications/plot_image_comparison.py

@@ -46,10 +46,12 @@
 ax1.set_title('Equalized')
 ax2.imshow(comp_equalized, cmap='gray')
 ax2.set_title('Checkerboard comparison')
+
 for a in (ax0, ax1, ax2):
-    a.axis('off')
-plt.tight_layout()
-plt.plot()
+    a.set_axis_off()
+
+fig.tight_layout()
+
 
 ######################################################################
 # Diff
@@ -70,10 +72,12 @@
 ax1.set_title('Rotated')
 ax2.imshow(diff_rotated, cmap='gray')
 ax2.set_title('Diff comparison')
+
 for a in (ax0, ax1, ax2):
-    a.axis('off')
-plt.tight_layout()
-plt.plot()
+    a.set_axis_off()
+
+fig.tight_layout()
+
 
 ######################################################################
 # Blend
@@ -94,7 +98,10 @@
 ax1.set_title('Rotated')
 ax2.imshow(blend_rotated, cmap='gray')
 ax2.set_title('Blend comparison')
+
 for a in (ax0, ax1, ax2):
-    a.axis('off')
-plt.tight_layout()
-plt.plot()
+    a.set_axis_off()
+
+fig.tight_layout()
+
+plt.show()

doc/examples/applications/plot_morphology.py

@@ -41,10 +41,10 @@ def plot_comparison(original, filtered, filter_name):
     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_axis_off()
     ax2.imshow(filtered, cmap=plt.cm.gray)
     ax2.set_title(filter_name)
-    ax2.axis('off')
+    ax2.set_axis_off()
 
 
 ######################################################################
@@ -102,10 +102,10 @@ def plot_comparison(original, filtered, filter_name):
 # that are *smaller* than the structuring element are removed. The dilation
 # operation that follows ensures that light regions that are *larger* than
 # the structuring element retain their original size. Notice how the light
-# and dark shapes in the center their original thickness but the 3 lighter
+# and dark shapes in the center retain their original thickness but the 3 lighter
 # patches in the bottom get completely eroded. The size dependence is
 # highlighted by the outer white ring: The parts of the ring thinner than the
-# structuring element were completely erased, while the thicker region at the
+# structuring element are completely erased, while the thicker region at the
 # top retains its original thickness.
 #
 # Closing
@@ -125,12 +125,12 @@ def plot_comparison(original, filtered, filter_name):
 plot_comparison(phantom, closed, 'closing')
 
 ######################################################################
-# Since ``closing`` an image starts with an dilation operation, dark regions
+# Since ``closing`` an image starts with a dilation operation, dark regions
 # that are *smaller* than the structuring element are removed. The dilation
 # operation that follows ensures that dark regions that are *larger* than the
 # structuring element retain their original size. Notice how the white
 # ellipses at the bottom get connected because of dilation, but other dark
-# region retain their original sizes. Also notice how the crack we added is
+# regions retain their original sizes. Also notice how the crack we added is
 # mostly removed.
 #
 # White tophat

doc/examples/applications/plot_pixel_graphs.py

@@ -66,7 +66,7 @@
 _, ax = plt.subplots()
 ax.imshow(color.label2rgb(labeled, retina))
 ax.set_axis_off()
-_ = ax.set_title('thresholded vesselness')
+_ = ax.set_title('Thresholded vesselness')
 
 ###############################################################################
 # Finally, we can `skeletonize <skimage.morphology.skeletonize>` this label
@@ -90,7 +90,7 @@
 ax.scatter(centroid[1], centroid[0], label='centroid')
 ax.legend()
 ax.set_axis_off()
-ax.set_title('vessel graph center vs centroid')
+ax.set_title('Vessel graph center vs centroid')
 # sphinx_gallery_thumbnail_number = 4
 
 plt.show()