Let MultiNorm inherit from Norm ABC

trygvrad · trygvrad · commit 47b5116c8b71 · 2025-06-29T15:38:29.000+02:00
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -2337,6 +2337,12 @@ def _changed(self):
         """
         self.callbacks.process('changed')
 
+    @property
+    @abstractmethod
+    def n_variables(self):
+        # Returns the number of variables supported by this normalization
+        pass
+
 
 class Normalize(Norm):
     """
@@ -2394,11 +2400,6 @@ def __init__(self, vmin=None, vmax=None, clip=False):
         self._clip = clip
         self._scale = None
 
-    @property
-    def n_variables(self):
-        # To be overridden by subclasses with multiple inputs
-        return 1
-
     @property
     def vmin(self):
         # docstring inherited
@@ -2552,6 +2553,11 @@ def scaled(self):
         # docstring inherited
         return self.vmin is not None and self.vmax is not None
 
+    @property
+    def n_variables(self):
+        # docstring inherited
+        return 1
+
 
 class TwoSlopeNorm(Normalize):
     def __init__(self, vcenter, vmin=None, vmax=None):
@@ -3277,7 +3283,7 @@ def inverse(self, value):
         return value
 
 
-class MultiNorm(Normalize):
+class MultiNorm(Norm):
     """
     A class which contains multiple scalar norms
     """
@@ -3442,6 +3448,12 @@ 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
@@ -270,6 +270,9 @@ class Norm(ABC):
     def autoscale_None(self, A: ArrayLike) -> None: ...
     @abstractmethod
     def scaled(self) -> bool: ...
+    @abstractmethod
+    @property
+    def n_variables(self) -> int: ...
 
 
 class Normalize(Norm):
@@ -285,8 +288,6 @@ class Normalize(Norm):
     @vmax.setter
     def vmax(self, value: float | None) -> None: ...
     @property
-    def n_variables(self) -> int: ...
-    @property
     def clip(self) -> bool: ...
     @clip.setter
     def clip(self, value: bool) -> None: ...
@@ -307,6 +308,8 @@ class Normalize(Norm):
     def autoscale(self, A: ArrayLike) -> None: ...
     def autoscale_None(self, A: ArrayLike) -> None: ...
     def scaled(self) -> bool: ...
+    @property
+    def n_variables(self) -> Literal[1]: ...
 
 class TwoSlopeNorm(Normalize):
     def __init__(
@@ -411,7 +414,7 @@ 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 +441,11 @@ 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: ...
+    @property
+    def n_variables(self) -> int: ...
 
 def rgb_to_hsv(arr: ArrayLike) -> np.ndarray: ...
 def hsv_to_rgb(hsv: ArrayLike) -> np.ndarray: ...
