Reject tuple norms with clear error, standardize na_is_transparent guards

- Reject tuple/sequence norms in _type_check_params with a clear
  TypeError pointing users to use a list instead
- Extract na_is_transparent local in all three groups-filtering guards
  (shapes, points, labels) for consistent style
- Add test_norm_tuple_raises

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -275,7 +275,8 @@ def _render_shapes(
     # When groups are specified, filter out non-matching elements by default.
     # Only show non-matching elements if the user explicitly sets na_color.
     _na = render_params.cmap_params.na_color
-    if groups is not None and values_are_categorical and (_na.default_color_set or _na.get_alpha_as_float() == 0.0):
+    na_is_transparent = _na.default_color_set or _na.get_alpha_as_float() == 0.0
+    if groups is not None and values_are_categorical and na_is_transparent:
         keep, color_source_vector, color_vector = _filter_groups_transparent_na(
             groups, color_source_vector, color_vector
         )
@@ -1522,12 +1523,8 @@ def _render_labels(
     # When groups are specified, zero out non-matching label IDs so they render as background.
     # Only show non-matching labels if the user explicitly sets na_color.
     _na = render_params.cmap_params.na_color
-    if (
-        groups is not None
-        and categorical
-        and color_source_vector is not None
-        and (_na.default_color_set or _na.get_alpha_as_float() == 0.0)
-    ):
+    na_is_transparent = _na.default_color_set or _na.get_alpha_as_float() == 0.0
+    if groups is not None and categorical and color_source_vector is not None and na_is_transparent:
         keep_vec = color_source_vector.isin(groups)
         matching_ids = instance_id[keep_vec]
         keep_mask = np.isin(label.values, matching_ids)

src/spatialdata_plot/pl/utils.py

@@ -2394,6 +2394,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
     norm = param_dict.get("norm")
     if norm is not None:
+        if element_type == "images" and isinstance(norm, Sequence) and not isinstance(norm, list):
+            raise TypeError("'norm' must be a list of Normalize objects, not a tuple or other sequence.")
         if element_type == "images" and isinstance(norm, list):
             if len(norm) == 0:
                 raise ValueError("'norm' list must not be empty.")

tests/pl/test_render_images.py

@@ -191,6 +191,15 @@ def test_norm_list_empty_raises(self, sdata_blobs: SpatialData):
                 norm=[],
             ).pl.show()
 
+    def test_norm_tuple_raises(self, sdata_blobs: SpatialData):
+        """Tuple of norms is rejected — only list is accepted."""
+        with pytest.raises(TypeError, match="not a tuple"):
+            sdata_blobs.pl.render_images(
+                element="blobs_image",
+                channel=[0, 1, 2],
+                norm=(Normalize(), Normalize(), Normalize()),
+            ).pl.show()
+
     def test_plot_correctly_normalizes_multichannel_images(self, sdata_raccoon: SpatialData):
         sdata_raccoon["raccoon_int16"] = Image2DModel.parse(
             sdata_raccoon["raccoon"].data.astype(np.uint16) * 257,  # 255 * 257 = 65535,