Clip multi-channel composited images to [0, 1] to prevent matplotlib warnings

The shared Normalize instance auto-ranges on the first channel, so
subsequent channels with different value ranges can exceed [0, 1].
This causes matplotlib's "Clipping input data to the valid range for
imshow with RGB data" warning.

Added np.clip(array, 0, 1) before _ax_show_and_transform in all
multi-channel compositing paths (2A, 2B, 2C, 2D).

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

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -1229,7 +1229,7 @@ def _render_images(
                 )
 
             _ax_show_and_transform(
-                stacked,
+                np.clip(stacked, 0, 1),
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1291,7 +1291,7 @@ def _render_images(
                 )  # TODO: update when pca is added as strategy
 
             _ax_show_and_transform(
-                colored,
+                np.clip(colored, 0, 1),
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1308,7 +1308,7 @@ def _render_images(
             colored = colored[:, :, :3]
 
             _ax_show_and_transform(
-                colored,
+                np.clip(colored, 0, 1),
                 trans_data,
                 ax,
                 render_params.alpha,
@@ -1327,7 +1327,7 @@ def _render_images(
             colored = colored[:, :, :3]
 
             _ax_show_and_transform(
-                colored,
+                np.clip(colored, 0, 1),
                 trans_data,
                 ax,
                 render_params.alpha,

tests/pl/test_render_images.py

@@ -270,3 +270,47 @@ def test_uint16_rgba_renders(self):
         fig, ax = plt.subplots()
         sdata.pl.render_images("img").pl.show(ax=ax)
         plt.close("all")
+
+
+class TestMultiChannelClipping:
+    """Regression tests: multi-channel compositing should not produce clipping warnings."""
+
+    def test_no_clipping_warning_3channel_different_ranges(self):
+        """3-channel image with different value ranges per channel should not clip."""
+        import warnings
+
+        rng = np.random.default_rng(42)
+        data = np.zeros((3, 50, 50), dtype=np.float32)
+        data[0] = rng.uniform(0.0, 0.5, (50, 50))
+        data[1] = rng.uniform(0.0, 1.0, (50, 50))
+        data[2] = rng.uniform(0.0, 1.5, (50, 50))
+        img = Image2DModel.parse(data, dims=("c", "y", "x"), c_coords=[0, 1, 2])
+        sdata = SpatialData(images={"img": img})
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            fig, ax = plt.subplots()
+            sdata.pl.render_images("img").pl.show(ax=ax)
+            plt.close("all")
+            clip_warns = [x for x in w if "Clipping input data" in str(x.message)]
+            assert len(clip_warns) == 0, f"Got unexpected clipping warning: {clip_warns[0].message}"
+
+    def test_no_clipping_warning_palette_compositing(self):
+        """Palette compositing should not produce clipping warnings."""
+        import warnings
+
+        rng = np.random.default_rng(42)
+        data = np.zeros((3, 50, 50), dtype=np.float32)
+        data[0] = rng.uniform(0.0, 0.5, (50, 50))
+        data[1] = rng.uniform(0.0, 1.0, (50, 50))
+        data[2] = rng.uniform(0.0, 1.5, (50, 50))
+        img = Image2DModel.parse(data, dims=("c", "y", "x"), c_coords=[0, 1, 2])
+        sdata = SpatialData(images={"img": img})
+
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            fig, ax = plt.subplots()
+            sdata.pl.render_images("img", palette=["red", "green", "blue"]).pl.show(ax=ax)
+            plt.close("all")
+            clip_warns = [x for x in w if "Clipping input data" in str(x.message)]
+            assert len(clip_warns) == 0, f"Got unexpected clipping warning: {clip_warns[0].message}"