Add a figure test for alignment at half display pixels

Author: ayshih

lib/matplotlib/tests/baseline_images/test_image/alignment_half_display_pixels.png

@@ -1908,6 +1908,64 @@ def test_nn_pixel_alignment(nonaffine_identity):
             axs[i, j].hlines(seps, -1, N, lw=0.5, color='red', ls='dashed')
 
 
+@image_comparison(['alignment_half_display_pixels.png'], style='mpl20')
+def test_alignment_half_display_pixels(nonaffine_identity):
+    # All values in this test are chosen carefully so that many display pixels are
+    # aligned with an edge or a corner of an input pixel
+
+    # Layout:
+    # Top row is origin='upper', bottom row is origin='lower'
+    # Column 1: affine transform, anchored at whole pixel
+    # Column 2: affine transform, anchored at half pixel
+    # Column 3: nonaffine transform, anchored at whole pixel
+    # Column 4: nonaffine transform, anchored at half pixel
+    # Column 5: affine transform, anchored at half pixel, interpolation='hanning'
+
+    # Each axes patch is magenta, so seeing a magenta line at an edge of the image
+    # means that the image is not filling the axes
+
+    fig = plt.figure(figsize=(5, 2), dpi=100)
+    fig.set_facecolor('g')
+
+    corner_x = [0.01, 0.199, 0.41, 0.599, 0.809]
+    corner_y = [0.05, 0.53]
+
+    axs = []
+    for cy in corner_y:
+        for ix, cx in enumerate(corner_x):
+            my = cy + 0.0125 if ix in [1, 3, 4] else cy
+            axs.append(fig.add_axes([cx, my, 0.17, 0.425], xticks=[], yticks=[]))
+
+    # Verify that each axes has been created with the correct width/height and that all
+    # four corners are on whole pixels (columns 1 and 3) or half pixels (columns 2, 4,
+    # and 5)
+    for i, ax in enumerate(axs):
+        extents = ax.get_window_extent().extents
+        assert_allclose(extents[2:4] - extents[0:2], 85, rtol=0, atol=1e-13)
+        assert_allclose(extents % 1, 0.5 if i % 5 in [1, 3, 4] else 0,
+                        rtol=0, atol=1e-13)
+
+    N = 10
+
+    data = np.arange(N**2).reshape((N, N)) % 9
+    seps = np.arange(-0.5, N)
+
+    for i, ax in enumerate(axs):
+        ax.set_facecolor('m')
+
+        transform = nonaffine_identity + ax.transData if i % 4 >= 2 else ax.transData
+        ax.imshow(data, cmap='Blues',
+                  interpolation='hanning' if i % 5 == 4 else 'nearest',
+                  origin='upper' if i >= 5 else 'lower',
+                  transform=transform)
+
+        ax.vlines(seps, -0.5, N - 0.5, lw=0.5, color='red', ls=(0, (2, 4)))
+        ax.hlines(seps, -0.5, N - 0.5, lw=0.5, color='red', ls=(0, (2, 4)))
+
+        for spine in ax.spines:
+            ax.spines[spine].set_linestyle((0, (5, 10)))
+
+
 @image_comparison(['image_bounds_handling.png'], tol=0.006)
 def test_image_bounds_handling(nonaffine_identity):
     # TODO: The second and third panels in the bottom row show that the handling of