Let MultiNorm inherit from Norm ABC

trygvrad · trygvrad · commit d49737fd6fe7 · 2025-06-29T13:04:28.000+02:00
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -3277,7 +3277,7 @@ def inverse(self, value):
         return value
 
 
-class MultiNorm(Normalize):
+class MultiNorm(Norm):
     """
     A class which contains multiple scalar norms
     """
@@ -3442,6 +3442,12 @@ def autoscale(self, A):
         """
         For each constituent norm, Set *vmin*, *vmax* to min, max of the corresponding
         variate in *A*.
+
+        Parameters
+        ----------
+        A
+            Data, must be of length `n_variables` or be a structured array or scalar
+            with `n_variables` fields.
         """
         with self.callbacks.blocked():
             # Pause callbacks while we are updating so we only get
diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi
@@ -411,7 +411,7 @@ class BoundaryNorm(Normalize):
 
 class NoNorm(Normalize): ...
 
-class MultiNorm(Normalize):
+class MultiNorm(Norm):
     # Here "type: ignore[override]" is used for functions with a return type
     # that differs from the function in the base class.
     # i.e. where `MultiNorm` returns a tuple and Normalize returns a `float` etc.
@@ -438,6 +438,9 @@ class MultiNorm(Normalize):
     def clip(self, value: ArrayLike | bool) -> None: ...
     def __call__(self, value: ArrayLike, clip: ArrayLike | bool | None = ...) -> list: ...  # type: ignore[override]
     def inverse(self, value: ArrayLike) -> list: ...  # type: ignore[override]
+    def autoscale(self, A: ArrayLike) -> None: ...
+    def autoscale_None(self, A: ArrayLike) -> None: ...
+    def scaled(self) -> bool: ...
 
 def rgb_to_hsv(arr: ArrayLike) -> np.ndarray: ...
 def hsv_to_rgb(hsv: ArrayLike) -> np.ndarray: ...
