Jun 14, 2025 · Jun 14, 2025
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
        # 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) andt.is_affine andround_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)
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
        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]])),
                 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

    # 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()
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) andt.is_affine andround_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
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