matplotlib
diff --git a/‎lib/matplotlib/axes/_base.py
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+2-8Lines changed: 2 additions & 8 deletions b/‎lib/matplotlib/axes/_base.py
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+2-8Lines changed: 2 additions & 8 deletions
diff --git a/‎lib/matplotlib/tests/baseline_images/test_axes/sticky_tolerance_cf.png
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6.08 KB b/‎lib/matplotlib/tests/baseline_images/test_axes/sticky_tolerance_cf.png
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6.08 KB
diff --git a/‎lib/matplotlib/tests/test_axes.py
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+10Lines changed: 10 additions & 0 deletions b/‎lib/matplotlib/tests/test_axes.py
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+10Lines changed: 10 additions & 0 deletions
@@ -2962,22 +2962,16 @@ def handle_single_axis(
 
             # Prevent margin addition from crossing a sticky value.  A small
             # tolerance must be added due to floating point issues with
-            # streamplot; it is defined relative to x0, x1, x1-x0 but has
+            # streamplot; it is defined relative to x1-x0 but has
             # no absolute term (e.g. "+1e-8") to avoid issues when working with
             # datasets where all values are tiny (less than 1e-8).
-            tol = 1e-5 * max(abs(x0), abs(x1), abs(x1 - x0))
+            tol = 1e-5 * abs(x1 - x0)
             # Index of largest element < x0 + tol, if any.
             i0 = stickies.searchsorted(x0 + tol) - 1
             x0bound = stickies[i0] if i0 != -1 else None
-            # Ensure the boundary acts only if the sticky is the extreme value
-            if x0bound is not None and x0bound > x0:
-                x0bound = None
             # Index of smallest element > x1 - tol, if any.
             i1 = stickies.searchsorted(x1 - tol)
             x1bound = stickies[i1] if i1 != len(stickies) else None
-            # Ensure the boundary acts only if the sticky is the extreme value
-            if x1bound is not None and x1bound < x1:
-                x1bound = None
 
             # Add the margin in figure space and then transform back, to handle
             # non-linear scales.
 
@@ -701,6 +701,16 @@ def test_sticky_tolerance():
     axs.flat[3].barh(y=1, width=width, left=-20000.1)
 
 
+@image_comparison(['sticky_tolerance_cf.png'], remove_text=True, style="mpl20")
+def test_sticky_tolerance_contourf():
+    fig, ax = plt.subplots()
+
+    x = y = [14496.71, 14496.75]
+    data = [[0, 1], [2, 3]]
+
+    ax.contourf(x, y, data)
+
+
 def test_nargs_stem():
     with pytest.raises(TypeError, match='0 were given'):
         # stem() takes 1-3 arguments.