Apply suggestions from code review

trygvrad · timhoffm · web-flow · commit 23777b30515c · 2025-07-12T10:36:14.000+02:00
Co-authored-by: Tim Hoffmann &lt;2836374+timhoffm@users.noreply.github.com&gt;
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -2566,9 +2566,8 @@ def n_components(self):
         This is number of elements of the parameter to ``__call__`` and of
         *vmin*, *vmax*.
 
-        This class support only a single compoenent, as opposed to `MultiNorm`
+        This class support only a single component, as opposed to `MultiNorm`
         which supports multiple components.
-
         """
         return 1
 
@@ -3318,28 +3317,22 @@ def __init__(self, norms, vmin=None, vmax=None, clip=False):
             ``[vmin, vmax]`` for the constituent norms.
             If a list, each value is assigned to each of the constituent
             norms. Single values are repeated to form a list of appropriate size.
-
         """
-
         if cbook.is_scalar_or_string(norms):
             raise ValueError("A MultiNorm must be assigned multiple norms")
 
-        norms = [*norms]
-        for i, n in enumerate(norms):
-            if n is None:
-                norms[i] = Normalize()
-            elif isinstance(n, str):
-                scale_cls = _get_scale_cls_from_str(n)
+        def resolve(norm):
+             if isinstance(norm, str):
+                 scale_cls = _get_scale_cls_from_str(n)
                 norms[i] = mpl.colorizer._auto_norm_from_scale(scale_cls)()
-            elif not isinstance(n, Normalize):
+            elif isinstance(norm, Normalize):
+                return norm
+            else:  
                 raise ValueError(
                     "MultiNorm must be assigned multiple norms, where each norm "
-                    f"is of type `None` `str`, or `Normalize`, not {type(n)}")
+                    f"is of type `str`, or `Normalize`, not {type(n)}")
 
-        # Convert the list of norms to a tuple to make it immutable.
-        # If there is a use case for swapping a single norm, we can add support for
-        # that later
-        self._norms = tuple(norms)
+        self._norms = tuple(resolve(norm) for norm in norms)
 
         self.callbacks = cbook.CallbackRegistry(signals=["changed"])
 
