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Fix alpha array ignored for RGB images in imshow. #31171

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Fix alpha array ignored for RGB images in imshow. #31171

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7 changes: 5 additions & 2 deletions lib/matplotlib/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -512,8 +512,11 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
if A.shape[2] == 3: # image has no alpha channel
A = np.dstack([A, np.ones(A.shape[:2])])
elif np.ndim(alpha) > 0: # Array alpha
# user-specified array alpha overrides the existing alpha channel
A = np.dstack([A[..., :3], alpha])
if A.shape[2] == 3: # image has no alpha channel, so add one
A = np.dstack([A, alpha])
else:
# Apply array alpha to existing alpha channel
A = np.dstack([A[..., :3], A[..., 3] * alpha])
else: # Scalar alpha
if A.shape[2] == 3: # broadcast scalar alpha
A = np.dstack([A, np.full(A.shape[:2], alpha, np.float32)])
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18 changes: 16 additions & 2 deletions lib/matplotlib/tests/test_image.py
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Expand Up @@ -36,6 +36,19 @@ def test_alpha_interp():
axr.imshow(img, interpolation="bilinear")


@check_figures_equal(extensions=["png"])
def test_imshow_alpha_array_with_rgb(fig_test, fig_ref):
"""Array alpha should work with RGB images (issue #26092)."""
np.random.seed(19680801)
arr_rgb = np.random.random((10, 10, 3))

alpha = np.ones((10, 10))
alpha[:5] = 0.2

fig_test.subplots().imshow(arr_rgb, alpha=alpha)
fig_ref.subplots().imshow(np.dstack([arr_rgb, alpha]))


@image_comparison(['interp_nearest_vs_none'], tol=3.7, # For Ghostscript 10.06+.
extensions=['pdf', 'svg'], remove_text=True)
def test_interp_nearest_vs_none():
Expand Down Expand Up @@ -1867,7 +1880,8 @@ def test_interpolation_stage_rgba_respects_alpha_param(fig_test, fig_ref, intp_s
new_array_alpha = np.random.rand(ny, nx)
axs_tst[1][2].imshow(im_rgba, interpolation_stage=intp_stage, alpha=new_array_alpha)
axs_ref[1][2].imshow(
np.concatenate( # combine rgb channels with new array alpha
(im_rgb, new_array_alpha.reshape((ny, nx, 1))), axis=-1
np.concatenate( # combine rgb channels with multiplied alpha
(im_rgb, (array_alpha * new_array_alpha).reshape((ny, nx, 1))),
axis=-1
), interpolation_stage=intp_stage
)
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