Skip to content

Scale an image to the axes edges regardless of whether it is being plotted with a nonaffine transform or an affine transform #30175

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
wants to merge 2 commits into
base: main
Choose a base branch
from
+36 −9
Draft

Scale an image to the axes edges regardless of whether it is being plotted with a nonaffine transform or an affine transform #30175

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
5fb67a4
Add test to check for consistency in scaling images to axes edges
ayshih Jun 14, 2025
43a1cf8
Remove different handling for nonaffine transforms
ayshih Jun 14, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion lib/matplotlib/image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -423,7 +423,7 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
# So that the image is aligned with the edge of the Axes, we want to
# round up the output width to the next integer. This also means
# scaling the transform slightly to account for the extra subpixel.
if ((not unsampled) and t.is_affine and round_to_pixel_border and
if ((not unsampled) and round_to_pixel_border and
(out_width_base % 1.0 != 0.0 or out_height_base % 1.0 != 0.0)):
out_width = math.ceil(out_width_base)
out_height = math.ceil(out_height_base)
Expand Down
43 changes: 35 additions & 8 deletions lib/matplotlib/tests/test_image.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1641,6 +1641,17 @@ def test__resample_valid_output():
resample(np.zeros((9, 9)), out)


@pytest.fixture
def nonaffine_identity():
class NonAffineIdentityTransform(Transform):
input_dims = 2
output_dims = 2

def inverted(self):
return self
return NonAffineIdentityTransform()


@pytest.mark.parametrize("data, interpolation, expected",
[(np.array([[0.1, 0.3, 0.2]]), mimage.NEAREST,
np.array([[0.1, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3, 0.2, 0.2, 0.2]])),
Expand All @@ -1649,7 +1660,7 @@ def test__resample_valid_output():
0.28476562, 0.2546875, 0.22460938, 0.20002441, 0.20002441]])),
]
)
def test_resample_nonaffine(data, interpolation, expected):
def test_resample_nonaffine(data, interpolation, expected, nonaffine_identity):
# Test that equivalent affine and nonaffine transforms resample the same

# Create a simple affine transform for scaling the input array
Expand All @@ -1661,20 +1672,36 @@ def test_resample_nonaffine(data, interpolation, expected):

# Create a nonaffine version of the same transform
# by compositing with a nonaffine identity transform
class NonAffineIdentityTransform(Transform):
input_dims = 2
output_dims = 2

def inverted(self):
return self
nonaffine_transform = NonAffineIdentityTransform() + affine_transform
nonaffine_transform = nonaffine_identity + affine_transform

nonaffine_result = np.empty_like(expected)
mimage.resample(data, nonaffine_result, nonaffine_transform,
interpolation=interpolation)
assert_allclose(nonaffine_result, expected, atol=5e-3)


@check_figures_equal()
def test_nonaffine_scaling_to_axes_edges(fig_test, fig_ref, nonaffine_identity):
# Test that plotting an image with equivalent affine and nonaffine
# transforms is scaled the same to the axes edges
data = np.arange(16).reshape((4, 4)) % 3

# Specifically choose an axes bbox that has a fractional pixel

fig_test.set_size_inches(5, 5)
fig_test.set_dpi(100)
ax = fig_test.subplots()
ax.set_position([0.2, 0.2, 300.5 / 500, 300.5 / 500])
ax.imshow(data, interpolation='nearest',
transform=nonaffine_identity + ax.transData)

fig_ref.set_size_inches(5, 5)
fig_ref.set_dpi(100)
ax = fig_ref.subplots()
ax.set_position([0.2, 0.2, 300.5 / 500, 300.5 / 500])
ax.imshow(data, interpolation='nearest')


def test_axesimage_get_shape():
# generate dummy image to test get_shape method
ax = plt.gca()
Expand Down
Loading