matplotlib
diff --git a/‎galleries/examples/misc/demo_ribbon_box.py
Copy file name to clipboardExpand all lines: galleries/examples/misc/demo_ribbon_box.py
+1-1Lines changed: 1 addition & 1 deletion b/‎galleries/examples/misc/demo_ribbon_box.py
Copy file name to clipboardExpand all lines: galleries/examples/misc/demo_ribbon_box.py
+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎lib/matplotlib/axes/_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/axes/_axes.py
+16-7Lines changed: 16 additions & 7 deletions b/‎lib/matplotlib/axes/_axes.py
Copy file name to clipboardExpand all lines: lib/matplotlib/axes/_axes.py
+16-7Lines changed: 16 additions & 7 deletions
diff --git a/‎lib/matplotlib/tests/test_image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_image.py
+11Lines changed: 11 additions & 0 deletions b/‎lib/matplotlib/tests/test_image.py
Copy file name to clipboardExpand all lines: lib/matplotlib/tests/test_image.py
+11Lines changed: 11 additions & 0 deletions
@@ -86,7 +86,7 @@ def main():
     background_gradient[:, :, :3] = [1, 1, 0]
     background_gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]]  # alpha channel
     ax.imshow(background_gradient, interpolation="bicubic", zorder=0.1,
-              extent=(0, 1, 0, 1), transform=ax.transAxes, aspect="auto")
+              extent=(0, 1, 0, 1), transform=ax.transAxes)
 
     plt.show()
 
 
@@ -5530,12 +5530,12 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
 
         The input may either be actual RGB(A) data, or 2D scalar data, which
         will be rendered as a pseudocolor image. For displaying a grayscale
-        image set up the colormapping using the parameters
+        image, set up the colormapping using the parameters
         ``cmap='gray', vmin=0, vmax=255``.
 
         The number of pixels used to render an image is set by the Axes size
-        and the *dpi* of the figure. This can lead to aliasing artifacts when
-        the image is resampled because the displayed image size will usually
+        and the figure *dpi*. This can lead to aliasing artifacts when
+        the image is resampled, because the displayed image size will usually
         not match the size of *X* (see
         :doc:`/gallery/images_contours_and_fields/image_antialiasing`).
         The resampling can be controlled via the *interpolation* parameter
@@ -5570,7 +5570,7 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
 
             This parameter is ignored if *X* is RGB(A).
 
-        aspect : {'equal', 'auto'} or float, default: :rc:`image.aspect`
+        aspect : {'equal', 'auto'} or float or None, default: None
             The aspect ratio of the Axes.  This parameter is particularly
             relevant for images since it determines whether data pixels are
             square.
@@ -5585,6 +5585,11 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
               that the data fit in the Axes. In general, this will result in
               non-square pixels.
 
+            Normally, None (the default) means to use :rc:`image.aspect`.  However, if
+            the image uses a transform that does not contain the axes data transform,
+            then None means to not modify the axes aspect at all (in that case, directly
+            call `.Axes.set_aspect` if desired).
+
         interpolation : str, default: :rc:`image.interpolation`
             The interpolation method used.
 
@@ -5718,16 +5723,20 @@ def imshow(self, X, cmap=None, norm=None, *, aspect=None,
         `~matplotlib.pyplot.imshow` expects RGB images adopting the straight
         (unassociated) alpha representation.
         """
-        if aspect is None:
-            aspect = mpl.rcParams['image.aspect']
-        self.set_aspect(aspect)
         im = mimage.AxesImage(self, cmap=cmap, norm=norm,
                               interpolation=interpolation, origin=origin,
                               extent=extent, filternorm=filternorm,
                               filterrad=filterrad, resample=resample,
                               interpolation_stage=interpolation_stage,
                               **kwargs)
 
+        if aspect is None and not (
+                im.is_transform_set()
+                and not im.get_transform().contains_branch(self.transData)):
+            aspect = mpl.rcParams['image.aspect']
+        if aspect is not None:
+            self.set_aspect(aspect)
+
         im.set_data(X)
         im.set_alpha(alpha)
         if im.get_clip_path() is None:
 
@@ -1491,3 +1491,14 @@ def test_axesimage_get_shape():
     im.set_data(z)
     assert im.get_shape() == (4, 3)
     assert im.get_size() == im.get_shape()
+
+
+def test_non_transdata_image_does_not_touch_aspect():
+    ax = plt.figure().add_subplot()
+    im = np.arange(4).reshape((2, 2))
+    ax.imshow(im, transform=ax.transAxes)
+    assert ax.get_aspect() == "auto"
+    ax.imshow(im, transform=Affine2D().scale(2) + ax.transData)
+    assert ax.get_aspect() == 1
+    ax.imshow(im, transform=ax.transAxes, aspect=2)
+    assert ax.get_aspect() == 2