Apply suggestions from code review

trygvrad · QuLogic · web-flow · commit 55b85e3ef9e1 · 2025-04-17T13:17:58.000+02:00
Thank you @QuLogic Co-authored-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>
diff --git a/lib/matplotlib/colors.py b/lib/matplotlib/colors.py
@@ -3242,13 +3242,13 @@ def __init__(self, norms, vmin=None, vmax=None, clip=False):
             The constituent norms. The list must have a minimum length of 2.
         vmin, vmax : float, None, or list of float or None
             Limits of the constituent norms.
-            If a list, each each value is assigned to one of the constituent
+            If a list, each value is assigned to each of the constituent
             norms. Single values are repeated to form a list of appropriate size.
 
         clip : bool or list of bools, default: False
             Determines the behavior for mapping values outside the range
             ``[vmin, vmax]`` for the constituent norms.
-            If a list, each each value is assigned to one of the constituent
+            If a list, each value is assigned to each of the constituent
             norms. Single values are repeated to form a list of appropriate size.
 
         """
@@ -3348,7 +3348,8 @@ def _changed(self):
     def __call__(self, value, clip=None):
         """
         Normalize the data and return the normalized data.
-        Each variate in the input is assigned to the a constituent norm.
+
+        Each variate in the input is assigned to the constituent norm.
 
         Parameters
         ----------
@@ -3381,8 +3382,7 @@ def __call__(self, value, clip=None):
 
     def inverse(self, value):
         """
-        Maps the normalized value (i.e., index in the colormap) back to image
-        data value.
+        Map the normalized value (i.e., index in the colormap) back to image data value.
 
         Parameters
         ----------
@@ -3449,7 +3449,7 @@ def _iterable_variates_in_data(data, n_input):
         """
         if isinstance(data, np.ndarray) and data.dtype.fields is not None:
             data = [data[descriptor[0]] for descriptor in data.dtype.descr]
-        if not len(data) == n_input:
+        if len(data) != n_input:
             raise ValueError("The input to this `MultiNorm` must be of shape "
                              f"({n_input}, ...), or have a data type with {n_input} "
                              "fields.")
diff --git a/lib/matplotlib/colors.pyi b/lib/matplotlib/colors.pyi
@@ -403,17 +403,17 @@ class MultiNorm(Normalize):
         clip: ArrayLike | bool = ...
     ) -> None: ...
     @property
-    def norms(self) -> tuple: ...
+    def norms(self) -> tuple[Normalize, ...]: ...
     @property # type: ignore[override]
-    def vmin(self) -> tuple[float | None]: ...
+    def vmin(self) -> tuple[float | None, ...]: ...
     @vmin.setter
     def vmin(self, value: ArrayLike | float | None) -> None: ...
     @property # type: ignore[override]
-    def vmax(self) -> tuple[float | None]: ...
+    def vmax(self) -> tuple[float | None, ...]: ...
     @vmax.setter
     def vmax(self, value: ArrayLike | float | None) -> None: ...
     @property # type: ignore[override]
-    def clip(self) -> tuple[bool]: ...
+    def clip(self) -> tuple[bool, ...]: ...
     @clip.setter
     def clip(self, value: ArrayLike | bool) -> None: ...
     def __call__(self, value: ArrayLike, clip: ArrayLike | bool | None = ...) -> list: ... # type: ignore[override]
