Address review: deduplicate cmap(0) call, add error tests, trim warning

- Reuse pre-computed zero_colors array instead of calling cmap(0) twice
- Add tests for norm list length mismatch and empty norm list
- Remove unconditional multi-cmap warning (signal-based blend handles it)
- Clarify norms field naming with inline comment

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

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -1203,10 +1203,9 @@ def _additive_blend(
     zero_colors = np.array([cm(0.0)[:3] for cm in channel_cmaps])
     canvas = np.mean(zero_colors, axis=0)
     composite = np.full((height, width, 3), canvas, dtype=float)
-    for ch, cmap in zip(channels, channel_cmaps, strict=True):
-        zero_rgb = np.array(cmap(0.0)[:3])
+    for idx, (ch, cmap) in enumerate(zip(channels, channel_cmaps, strict=True)):
         rgba = cmap(np.asarray(layers[ch]))
-        composite += rgba[..., :3] - zero_rgb
+        composite += rgba[..., :3] - zero_colors[idx]
     return np.clip(composite, 0, 1, out=composite)
 
 
@@ -1260,10 +1259,6 @@ def _render_images(
 
     # True if user gave n cmaps for n channels
     got_multiple_cmaps = isinstance(render_params.cmap_params, list)
-    if got_multiple_cmaps:
-        logger.warning(
-            "You're blending multiple cmaps. Consider using 'palette' for black-to-color compositing instead."
-        )
 
     # not using got_multiple_cmaps here because of ruff :(
     if isinstance(render_params.cmap_params, list) and len(render_params.cmap_params) != n_channels:

src/spatialdata_plot/pl/render_params.py

@@ -264,7 +264,7 @@ class ImageRenderParams:
     element: str
     channel: list[str] | list[int] | int | str | None = None
     palette: ListedColormap | list[str] | None = None
-    norms: list[Normalize] | None = None
+    norms: list[Normalize] | None = None  # plural to distinguish from the scalar norm in CmapParams
     alpha: float = 1.0
     scale: str | None = None
     zorder: int = 0

tests/pl/test_render_images.py

@@ -2,6 +2,7 @@
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+import pytest
 import scanpy as sc
 from matplotlib.colors import Normalize
 from spatial_image import to_spatial_image
@@ -171,6 +172,25 @@ def test_plot_can_normalize_single_channel_via_list(self, sdata_blobs: SpatialDa
             cmap="Greys",
         ).pl.show()
 
+    # --- Validation errors ---
+
+    def test_norm_list_length_mismatch_raises(self, sdata_blobs: SpatialData):
+        """norm list length must match number of channels."""
+        with pytest.raises(ValueError, match="must match the number of channels"):
+            sdata_blobs.pl.render_images(
+                element="blobs_image",
+                channel=[0, 1, 2],
+                norm=[Normalize(), Normalize()],  # 2 norms for 3 channels
+            ).pl.show()
+
+    def test_norm_list_empty_raises(self, sdata_blobs: SpatialData):
+        """Empty norm list is rejected at validation time."""
+        with pytest.raises(ValueError, match="must not be empty"):
+            sdata_blobs.pl.render_images(
+                element="blobs_image",
+                norm=[],
+            ).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,