Fix review issues: reject norm list without cmap list, scope to images only

- Use resolved per-element cmap for list branching so norm list works
  correctly when cmap is auto-replicated
- Raise ValueError when norm is a list but cmap is not (no silent
  pass-through to _prepare_cmap_norm)
- Restrict list norm validation to element_type="images" only — labels
  has no list-norm support
- Add test for norm list without explicit cmap list

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: timtreis

src/spatialdata_plot/pl/basic.py

@@ -632,27 +632,34 @@ def render_images(
 
         for element, param_values in params_dict.items():
             cmap_params: list[CmapParams] | CmapParams
-            if isinstance(cmap, list):
+            resolved_cmap = param_values.get("cmap") or cmap
+            if isinstance(resolved_cmap, list):
                 if isinstance(norm, list):
-                    if len(norm) != len(cmap):
+                    if len(norm) != len(resolved_cmap):
                         raise ValueError(
-                            f"Length of 'norm' list ({len(norm)}) must match the number of colormaps ({len(cmap)})."
+                            f"Length of 'norm' list ({len(norm)}) must match "
+                            f"the number of colormaps ({len(resolved_cmap)})."
                         )
                     norms = norm
                 else:
-                    norms = [norm] * len(cmap)
+                    norms = [norm] * len(resolved_cmap)
                 cmap_params = [
                     _prepare_cmap_norm(
                         cmap=c,
                         norm=n,
                         na_color=param_values["na_color"],
                     )
-                    for c, n in zip(cmap, norms, strict=True)
+                    for c, n in zip(resolved_cmap, norms, strict=True)
                 ]
 
             else:
+                if isinstance(norm, list):
+                    raise ValueError(
+                        "Parameter 'norm' can only be a list when multiple colormaps are used. "
+                        "Pass a list of colormaps via 'cmap' or use a single Normalize."
+                    )
                 cmap_params = _prepare_cmap_norm(
-                    cmap=cmap,
+                    cmap=resolved_cmap,
                     norm=norm,
                     na_color=param_values["na_color"],
                     **kwargs,

src/spatialdata_plot/pl/utils.py

@@ -2409,14 +2409,16 @@ 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 in {"images", "labels"}:
+        if element_type == "images":
             if isinstance(norm, list):
                 if not norm:
                     raise ValueError("Parameter 'norm' list must not be empty.")
                 if not all(isinstance(n, Normalize) for n in norm):
                     raise TypeError("Every item in 'norm' list must be a Normalize instance.")
             elif not isinstance(norm, Normalize):
                 raise TypeError("Parameter 'norm' must be a Normalize or a list of Normalize instances.")
+        elif element_type == "labels" and not isinstance(norm, Normalize):
+            raise TypeError("Parameter 'norm' must be of type Normalize.")
         if element_type in {"shapes", "points"} and not isinstance(norm, bool | Normalize):
             raise TypeError("Parameter 'norm' must be a boolean or a mpl.Normalize.")
 

tests/pl/test_render_images.py

@@ -473,3 +473,10 @@ def test_norm_list_with_invalid_element_raises():
     sdata = _make_multichannel_sdata()
     with pytest.raises(TypeError, match="Normalize instance"):
         sdata.pl.render_images("img", norm=["not_a_norm"]).pl.show()
+
+
+def test_norm_list_without_cmap_list_raises():
+    """Norm list requires explicit cmap list."""
+    sdata = _make_multichannel_sdata()
+    with pytest.raises(ValueError, match="can only be a list when multiple colormaps"):
+        sdata.pl.render_images("img", norm=[Normalize(0, 1)] * 3).pl.show()