matplotlib · QuLogic · Mar 2, 2026 · Jan 22, 2026 · Jan 29, 2026 · Jan 29, 2026
diff --git a/doc/api/next_api_changes/behavior/31021-AYS.rst b/doc/api/next_api_changes/behavior/31021-AYS.rst
@@ -0,0 +1,7 @@
+Rendering of images now more accurate
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+There have been several fixes to improve the accuracy of how images are
+resampled and placed during rendering.  Some inaccuracies were up to a pixel off
+in the output.  The most apparent improvement is that the alignment of data
+pixels with tick marks and grid lines is now reliable.  Nearly all image output
+has changed, but often only at a subtle level that is not obvious qualitatively.
diff --git a/lib/matplotlib/image.py b/lib/matplotlib/image.py
@@ -3,7 +3,6 @@
 operations.
 """
 
-import math
 import os
 import logging
 from pathlib import Path
@@ -207,12 +206,24 @@ def _resample(
     out = np.zeros(out_shape + data.shape[2:], data.dtype)  # 2D->2D, 3D->3D.
     if resample is None:
         resample = image_obj.get_resample()
+
+    # When an output pixel falls exactly on the edge between two input pixels, the Agg
+    # resampler will use the right input pixel as the nearest neighbor.  We want the
+    # left input pixel to be chosen instead, so we flip the supplied transform.
+    if interpolation == 'nearest':
+        transform += Affine2D().translate(-out.shape[1], -out.shape[0]).scale(-1, -1)
+
     _image.resample(data, out, transform,
                     _interpd_[interpolation],
                     resample,
                     alpha,
                     image_obj.get_filternorm(),
                     image_obj.get_filterrad())
+
+    # Because we flipped the supplied transform, we then flip the output image back.
+    if interpolation == 'nearest':
+        out = np.flip(out, axis=(0, 1))
+
     return out
 
 
@@ -393,10 +404,15 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
         if clipped_bbox is None:
             return None, 0, 0, None
 
-        out_width_base = clipped_bbox.width * magnification
-        out_height_base = clipped_bbox.height * magnification
+        # Define the magnified bbox after clipping
+        magnified_extents = clipped_bbox.extents * magnification
+        if ((not unsampled) and round_to_pixel_border):
+            # Round to the nearest output pixel
+            magnified_bbox = Bbox.from_extents((magnified_extents + 0.5).astype(int))
+        else:
+            magnified_bbox = Bbox.from_extents(magnified_extents)
 
-        if out_width_base == 0 or out_height_base == 0:
+        if magnified_bbox.width == 0 or magnified_bbox.height == 0:
             return None, 0, 0, None
 
         if self.origin == 'upper':
@@ -417,23 +433,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
 
         t = (t0
              + (Affine2D()
-                .translate(-clipped_bbox.x0, -clipped_bbox.y0)
-                .scale(magnification)))
-
-        # 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
-                (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)
-            extra_width = (out_width - out_width_base) / out_width_base
-            extra_height = (out_height - out_height_base) / out_height_base
-            t += Affine2D().scale(1.0 + extra_width, 1.0 + extra_height)
-        else:
-            out_width = int(out_width_base)
-            out_height = int(out_height_base)
-        out_shape = (out_height, out_width)
+                .scale(magnification)
+                .translate(-magnified_bbox.x0, -magnified_bbox.y0)))
+
+        out_shape = (int(magnified_bbox.height), int(magnified_bbox.width))
 
         if not unsampled:
             if not (A.ndim == 2 or A.ndim == 3 and A.shape[-1] in (3, 4)):
@@ -560,7 +563,10 @@ def _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification=1.0,
             t = Affine2D().translate(
                 int(max(subset.xmin, 0)), int(max(subset.ymin, 0))) + t
 
-        return output, clipped_bbox.x0, clipped_bbox.y0, t
+        return (output,
+                magnified_bbox.x0 / magnification,
+                magnified_bbox.y0 / magnification,
+                t)
 
     def make_image(self, renderer, magnification=1.0, unsampled=False):
         """
@@ -1061,21 +1067,24 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
                 B[:, :, 0:3] = A
                 B[:, :, 3] = 255
                 A = B
-        l, b, r, t = self.axes.bbox.extents
-        width = int(((round(r) + 0.5) - (round(l) - 0.5)) * magnification)
-        height = int(((round(t) + 0.5) - (round(b) - 0.5)) * magnification)
+        magnified_extents = (self.axes.bbox.extents * magnification + 0.5).astype(int)
+        l, b, r, t = magnified_extents / magnification
+        width = int((r - l) * magnification)
+        height = int((t - b) * magnification)
 
         invertedTransform = self.axes.transData.inverted()
-        x_pix = invertedTransform.transform(
-            [(x, b) for x in np.linspace(l, r, width)])[:, 0]
-        y_pix = invertedTransform.transform(
-            [(l, y) for y in np.linspace(b, t, height)])[:, 1]
+        x_pix_edges = invertedTransform.transform(
+            [(x, b) for x in np.linspace(l, r, width + 1)])[:, 0]
+        y_pix_edges = invertedTransform.transform(
+            [(l, y) for y in np.linspace(b, t, height + 1)])[:, 1]
+        x_pix_centers = (x_pix_edges[:-1] + x_pix_edges[1:]) / 2
+        y_pix_centers = (y_pix_edges[:-1] + y_pix_edges[1:]) / 2
 
         if self._interpolation == "nearest":
             x_mid = (self._Ax[:-1] + self._Ax[1:]) / 2
             y_mid = (self._Ay[:-1] + self._Ay[1:]) / 2
-            x_int = x_mid.searchsorted(x_pix)
-            y_int = y_mid.searchsorted(y_pix)
+            x_int = x_mid.searchsorted(x_pix_centers)
+            y_int = y_mid.searchsorted(y_pix_centers)
             # The following is equal to `A[y_int[:, None], x_int[None, :]]`,
             # but many times faster.  Both casting to uint32 (to have an
             # effectively 1D array) and manual index flattening matter.
@@ -1086,16 +1095,16 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
         else:  # self._interpolation == "bilinear"
             # Use np.interp to compute x_int/x_float has similar speed.
             x_int = np.clip(
-                self._Ax.searchsorted(x_pix) - 1, 0, len(self._Ax) - 2)
+                self._Ax.searchsorted(x_pix_centers) - 1, 0, len(self._Ax) - 2)
             y_int = np.clip(
-                self._Ay.searchsorted(y_pix) - 1, 0, len(self._Ay) - 2)
+                self._Ay.searchsorted(y_pix_centers) - 1, 0, len(self._Ay) - 2)
             idx_int = np.add.outer(y_int * A.shape[1], x_int)
             x_frac = np.clip(
-                np.divide(x_pix - self._Ax[x_int], np.diff(self._Ax)[x_int],
+                np.divide(x_pix_centers - self._Ax[x_int], np.diff(self._Ax)[x_int],
                           dtype=np.float32),  # Downcasting helps with speed.
                 0, 1)
             y_frac = np.clip(
-                np.divide(y_pix - self._Ay[y_int], np.diff(self._Ay)[y_int],
+                np.divide(y_pix_centers - self._Ay[y_int], np.diff(self._Ay)[y_int],
                           dtype=np.float32),
                 0, 1)
             f00 = np.outer(1 - y_frac, 1 - x_frac)
@@ -1248,22 +1257,24 @@ def make_image(self, renderer, magnification=1.0, unsampled=False):
         if (padded_A[0, 0] != bg).all():
             padded_A[[0, -1], :] = padded_A[:, [0, -1]] = bg
 
-        l, b, r, t = self.axes.bbox.extents
-        width = (round(r) + 0.5) - (round(l) - 0.5)
-        height = (round(t) + 0.5) - (round(b) - 0.5)
-        width = round(width * magnification)
-        height = round(height * magnification)
+        # Round to the nearest output pixels after magnification
+        l, b, r, t = (self.axes.bbox.extents * magnification + 0.5).astype(int)
+        width = r - l
+        height = t - b
+
         vl = self.axes.viewLim
 
-        x_pix = np.linspace(vl.x0, vl.x1, width)
-        y_pix = np.linspace(vl.y0, vl.y1, height)
-        x_int = self._Ax.searchsorted(x_pix)
-        y_int = self._Ay.searchsorted(y_pix)
+        x_pix_edges = np.linspace(vl.x0, vl.x1, width + 1)
+        y_pix_edges = np.linspace(vl.y0, vl.y1, height + 1)
+        x_pix_centers = (x_pix_edges[:-1] + x_pix_edges[1:]) / 2
+        y_pix_centers = (y_pix_edges[:-1] + y_pix_edges[1:]) / 2
+        x_int = self._Ax.searchsorted(x_pix_centers)
+        y_int = self._Ay.searchsorted(y_pix_centers)
         im = (  # See comment in NonUniformImage.make_image re: performance.
             padded_A.view(np.uint32).ravel()[
                 np.add.outer(y_int * padded_A.shape[1], x_int)]
             .view(np.uint8).reshape((height, width, 4)))
-        return im, l, b, IdentityTransform()
+        return im, l / magnification, b / magnification, IdentityTransform()
 
     def _check_unsampled_image(self):
         return False

diff --git a/lib/matplotlib/tests/baseline_images/test_axes/extent_units.png b/lib/matplotlib/tests/baseline_images/test_axes/extent_units.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow.pdf b/lib/matplotlib/tests/baseline_images/test_axes/imshow.pdf
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow.png b/lib/matplotlib/tests/baseline_images/test_axes/imshow.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow.svg b/lib/matplotlib/tests/baseline_images/test_axes/imshow.svg
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.pdf b/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.pdf
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.png b/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.svg b/lib/matplotlib/tests/baseline_images/test_axes/imshow_clip.svg
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_angle_freqs.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_angle_freqs.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_angle_noise.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_angle_noise.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_freqs.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_freqs.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_freqs_linear.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_freqs_linear.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_freqs.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_freqs.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_freqs_linear.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_freqs_linear.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_noise.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_noise.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_noise_linear.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_magnitude_noise_linear.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_noise.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_noise.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_noise_linear.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_noise_linear.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_phase_freqs.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_phase_freqs.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/specgram_phase_noise.png b/lib/matplotlib/tests/baseline_images/test_axes/specgram_phase_noise.png
diff --git a/lib/matplotlib/tests/baseline_images/test_axes/use_colorizer_keyword.png b/lib/matplotlib/tests/baseline_images/test_axes/use_colorizer_keyword.png
diff --git a/lib/matplotlib/tests/baseline_images/test_bbox_tight/bbox_inches_inset_rasterized.pdf b/lib/matplotlib/tests/baseline_images/test_bbox_tight/bbox_inches_inset_rasterized.pdf
diff --git a/lib/matplotlib/tests/baseline_images/test_colorbar/colorbar_extend_alpha.png b/lib/matplotlib/tests/baseline_images/test_colorbar/colorbar_extend_alpha.png
diff --git a/lib/matplotlib/tests/baseline_images/test_colorbar/colorbar_keeping_xlabel.png b/lib/matplotlib/tests/baseline_images/test_colorbar/colorbar_keeping_xlabel.png
diff --git a/lib/matplotlib/tests/baseline_images/test_colors/light_source_shading_topo.png b/lib/matplotlib/tests/baseline_images/test_colors/light_source_shading_topo.png
diff --git a/lib/matplotlib/tests/baseline_images/test_colors/test_norm_abc.png b/lib/matplotlib/tests/baseline_images/test_colors/test_norm_abc.png
diff --git a/...lib/tests/baseline_images/test_constrainedlayout/test_colorbars_no_overlapH.png b/...lib/tests/baseline_images/test_constrainedlayout/test_colorbars_no_overlapH.png
diff --git a/...lib/tests/baseline_images/test_constrainedlayout/test_colorbars_no_overlapV.png b/...lib/tests/baseline_images/test_constrainedlayout/test_colorbars_no_overlapV.png
diff --git a/...ts/baseline_images/test_constrainedlayout/test_compressed_suptitle_colorbar.png b/...ts/baseline_images/test_constrainedlayout/test_compressed_suptitle_colorbar.png
diff --git a/...s/baseline_images/test_constrainedlayout/test_compressed_supylabel_colorbar.png b/...s/baseline_images/test_constrainedlayout/test_compressed_supylabel_colorbar.png
diff --git a/lib/matplotlib/tests/baseline_images/test_image/affine_fill_to_edges.png b/lib/matplotlib/tests/baseline_images/test_image/affine_fill_to_edges.png
diff --git a/lib/matplotlib/tests/baseline_images/test_image/bbox_image_inverted.pdf b/lib/matplotlib/tests/baseline_images/test_image/bbox_image_inverted.pdf
diff --git a/lib/matplotlib/tests/baseline_images/test_image/bbox_image_inverted.png b/lib/matplotlib/tests/baseline_images/test_image/bbox_image_inverted.png