Add tests for 5-channel compositing and per-channel normalization

- 5-channel auto-color composite (path 2B, n>3): untested path
- 5-channel fluorescence palette (DAPI + 4 markers): path 2C with n>3
- Per-channel norm list: new feature, independent contrast per channel
- Single-element norm list: verifies list[Normalize] works for 1 channel

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

Author: timtreis

tests/pl/test_render_images.py

@@ -134,6 +134,43 @@ def test_plot_can_stick_to_zorder(self, sdata_blobs: SpatialData):
     def test_plot_can_render_multiscale_image_with_custom_cmap(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_images("blobs_multiscale_image", channel=0, scale="scale2", cmap="Greys").pl.show()
 
+    # --- Multichannel compositing (path 2B for 4+ channels) ---
+    # Uses sdata_blobs_str which has 5 named channels, simulating e.g. a
+    # multiplexed immunofluorescence panel (DAPI + 4 markers).
+
+    def test_plot_can_composite_five_channels(self, sdata_blobs_str: SpatialData):
+        """5-channel additive blend with auto-assigned colors (path 2B, n>3)."""
+        sdata_blobs_str.pl.render_images(element="blobs_image").pl.show()
+
+    def test_plot_can_composite_five_channels_with_palette(self, sdata_blobs_str: SpatialData):
+        """5-channel fluorescence-style overlay: DAPI + 4 markers (path 2C)."""
+        sdata_blobs_str.pl.render_images(
+            element="blobs_image",
+            palette=["blue", "green", "red", "magenta", "cyan"],
+        ).pl.show()
+
+    # --- Per-channel normalization ---
+    # Common in multiplexed imaging where markers have very different intensity
+    # ranges (e.g. bright DAPI vs dim cytokine stain).
+
+    def test_plot_can_normalize_channels_independently(self, sdata_blobs: SpatialData):
+        """Per-channel norm: each channel gets its own contrast window."""
+        sdata_blobs.pl.render_images(
+            element="blobs_image",
+            channel=[0, 1, 2],
+            palette=["red", "green", "blue"],
+            norm=[Normalize(vmin=0, vmax=0.5), Normalize(vmin=0.2, vmax=0.8), Normalize(vmin=0, vmax=1)],
+        ).pl.show()
+
+    def test_plot_can_normalize_single_channel_via_list(self, sdata_blobs: SpatialData):
+        """Single-element norm list works the same as a scalar norm."""
+        sdata_blobs.pl.render_images(
+            element="blobs_image",
+            channel=0,
+            norm=[Normalize(vmin=0.1, vmax=0.5)],
+            cmap="Greys",
+        ).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,