matplotlib · QuLogic · Mar 20, 2026 · Mar 14, 2026 · Mar 14, 2026 · Mar 14, 2026
diff --git a/lib/matplotlib/axes/_base.py b/lib/matplotlib/axes/_base.py
@@ -2479,8 +2479,7 @@ def _point_in_data_domain(self, x, y):
         (e.g. negative coordinates with a log scale).
         """
         for val, axis in zip([x, y], self._axis_map.values()):
-            vmin, vmax = axis.limit_range_for_scale(val, val)
-            if vmin != val or vmax != val:
+            if not axis._scale.val_in_range(val):
                 return False
         return True
 

diff --git a/lib/matplotlib/axis.py b/lib/matplotlib/axis.py
@@ -824,6 +824,13 @@
         current scale.
         """
         return self._scale.limit_range_for_scale(vmin, vmax, self.get_minpos())
+
+    def val_in_range(self, val):
+        """
+        Return `True` if the value(s) lie within the domain supported by the 
+        current scale.
+        """
+        return self._scale.val_in_range(val)
 
     def _get_autoscale_on(self):
         """Return whether this Axis is autoscaled."""

diff --git a/lib/matplotlib/axis.pyi b/lib/matplotlib/axis.pyi
@@ -144,6 +144,7 @@ class Axis(martist.Artist):
     def limit_range_for_scale(
         self, vmin: float, vmax: float
     ) -> tuple[float, float]: ...
+    def val_in_range(self, val: ArrayLike) -> bool | np.ndarray: ...
     def get_children(self) -> list[martist.Artist]: ...
     # TODO units
     converter: Any

diff --git a/lib/matplotlib/scale.py b/lib/matplotlib/scale.py
@@ -113,6 +113,17 @@
         This is used by log scales to determine a minimum value.
         """
         return vmin, vmax
+
+    def val_in_range(self, val):
+        """
+        Return whether the value(s) are within the valid range for this scale.
+        """
-        Return whether the value(s) are within the valid range for this scale.
-        """
+        Return whether the value(s) are within the valid range for this scale.
+        
+        This method is a generic implementation. Subclasses may implement
+        more efficent solutions for their domain.
+        """
-        Return whether the value(s) are within the valid range for this scale.
-        """
+        Return whether the value(s) are within the valid range for this scale.
+        
+        This method is a generic implementation. Subclasses may implement
+        more efficent solutions for their domain.
+        """
+        if np.isscalar(val):
+            vmin, vmax = self.limit_range_for_scale(val, val, minpos=1e-300)
+            return (vmax == val) and (vmin == val)
+
+        val = np.asanyarray(val)
+        return np.array([self.val_in_range(v) for v in val])
 
 
 def _make_axis_parameter_optional(init_func):
@@ -195,6 +206,13 @@
         `~matplotlib.transforms.IdentityTransform`.
         """
         return IdentityTransform()
+
+    def val_in_range(self, val):
+        """
+        Return `True` for all values.
+        """
-        """
-        Return `True` for all values.
-        """
+        """
+        Return whether the value(s) are within the valid range for this scale.
+        
+        This is True for all inputs.        
+        """
-        """
-        Return `True` for all values.
-        """
+        """
+        Return whether the value(s) are within the valid range for this scale.
+        
+        This is True for all inputs.        
+        """
+        val = np.asanyarray(val)
+        return np.ones(val.shape, dtype=bool)
 
 
 class FuncTransform(Transform):
@@ -399,6 +417,10 @@
 
         return (minpos if vmin <= 0 else vmin,
                 minpos if vmax <= 0 else vmax)
+
+    def val_in_range(self, val):
+        """Return `True` for positive values."""
+        return np.asanyarray(val) > 0
 
 
 class FuncScaleLog(LogScale):
@@ -819,6 +841,13 @@
             minpos = 1e-7  # Should rarely (if ever) have a visible effect.
         return (minpos if vmin <= 0 else vmin,
                 1 - minpos if vmax >= 1 else vmax)
+
+    def val_in_range(self, val):
+        """
+        Return `True` if value(s) lie between 0 and 1 (excluded)
+        """
+        val = np.asanyarray(val)
+        return (val > 0) & (val < 1)
 
 
 _scale_mapping = {

diff --git a/lib/matplotlib/scale.pyi b/lib/matplotlib/scale.pyi
@@ -3,6 +3,7 @@ from matplotlib.transforms import Transform
 
 from collections.abc import Callable, Iterable
 from typing import Literal
+import numpy as np
 from numpy.typing import ArrayLike
 
 class ScaleBase:
@@ -12,6 +13,7 @@ class ScaleBase:
     def limit_range_for_scale(
         self, vmin: float, vmax: float, minpos: float
     ) -> tuple[float, float]: ...
+    def val_in_range(self, val: ArrayLike) -> bool | np.ndarray: ...
 
 class LinearScale(ScaleBase):
     name: str