Apply suggestions from code review

trygvrad · QuLogic · web-flow · commit a752b6765703 · 2025-09-04T23:45:27.000+02:00
Co-authored-by: Elliott Sales de Andrade &lt;quantum.analyst@gmail.com&gt;
diff --git a/lib/matplotlib/cbook.py b/lib/matplotlib/cbook.py
@@ -701,7 +701,7 @@ def safe_masked_invalid(x, copy=False):
             try:
                 mask = np.empty(x.shape, dtype=np.dtype('bool, '*len(x.dtype.descr)))
                 for dd, dm in zip(x.dtype.descr, mask.dtype.descr):
-                    mask[dm[0]] = ~(np.isfinite(x[dd[0]]))
+                    mask[dm[0]] = ~np.isfinite(x[dd[0]])
                 xm = np.ma.array(x, mask=mask, copy=False)
             except TypeError:
                 return x
diff --git a/lib/matplotlib/colorizer.py b/lib/matplotlib/colorizer.py
@@ -222,8 +222,7 @@ def _set_cmap(self, cmap):
         # bury import to avoid circular imports
         in_init = self._cmap is None
         cmap_obj = _ensure_cmap(cmap, accept_multivariate=True)
-        if not in_init:
-            if self.norm.n_components != cmap_obj.n_variates:
+        if not in_init and self.norm.n_components != cmap_obj.n_variates:
                 raise ValueError(f"The colormap {cmap} does not support "
                                  f"{self.norm.n_components} variates as required by "
                                  f"the {type(self.norm)} on this Colorizer")
@@ -784,9 +783,7 @@ def _ensure_norm(norm, n_components=1):
         if isinstance(norm, colors.MultiNorm) and norm.n_components == n_components:
             return norm
         raise ValueError(
-                "Invalid norm for multivariate colormap with "
-                f"{n_components} inputs"
-            )
+            f"Invalid norm for multivariate colormap with {n_components} inputs")
 
 
 def _ensure_cmap(cmap, accept_multivariate=False):
@@ -870,12 +867,12 @@ def _ensure_multivariate_data(data, n_components):
     Parameters
     ----------
     n_components : int
-        -  number of variates in the data
+        Number of variates in the data.
     data : np.ndarray, PIL.Image or None
 
     Returns
     -------
-        np.ndarray, PIL.Image or None
+    np.ndarray, PIL.Image or None
     """
 
     if isinstance(data, np.ndarray):
diff --git a/lib/matplotlib/tests/test_colors.py b/lib/matplotlib/tests/test_colors.py
@@ -2184,7 +2184,7 @@ def test_colorizer_multinorm_explicit():
 
     with pytest.raises(ValueError,
                        match=("Invalid norm for multivariate colormap with 2 inputs")):
-        ca = mcolorizer.Colorizer('BiOrangeBlue', ['linear', 'linear', "log"])
+        ca = mcolorizer.Colorizer('BiOrangeBlue', ['linear', 'linear', 'log'])
 
     # valid explicit construction
     ca = mcolorizer.Colorizer('BiOrangeBlue', [mcolors.Normalize(), 'log'])
diff --git a/lib/matplotlib/tests/test_image.py b/lib/matplotlib/tests/test_image.py
@@ -467,7 +467,7 @@ def test_format_cursor_data_multinorm(data, text):
 
     # This is a test for ColorizingArtist._format_cursor_data_override()
     # with data with multiple channels.
-    # It includes is a workaround so that we can test this functionality
+    # It includes a workaround so that we can test this functionality
     # before the MultiVar/BiVariate colormaps and MultiNorm are exposed
     # via the top-level methods (ax.imshow())
     # i.e. we here set the hidden variables _cmap and _norm
