Suppress colorbar when channels_as_legend=True; fix sequential test opacity; add colorbar suppression test

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -1388,7 +1388,12 @@ def _render_images(
             is_continuous=True,
             auto_condition=n_channels == 1,
         )
-        if wants_colorbar and legend_params.colorbar and colorbar_requests is not None:
+        if (
+            wants_colorbar
+            and legend_params.colorbar
+            and colorbar_requests is not None
+            and not render_params.channels_as_legend
+        ):
             sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
             colorbar_requests.append(
                 ColorbarSpec(

tests/_images/ChannelsAsCategories_channels_as_legend_sequential_single_channels.png

@@ -555,12 +555,14 @@ def test_plot_channels_as_legend_sequential_single_channels(self, sdata_blobs_st
                 element="blobs_image",
                 channel="c1",
                 palette=["cyan"],
+                alpha=0.5,
                 channels_as_legend=True,
             )
             .pl.render_images(
                 element="blobs_image",
                 channel="c2",
                 palette=["magenta"],
+                alpha=0.5,
                 channels_as_legend=True,
             )
             .pl.show()
@@ -578,6 +580,13 @@ def test_channels_as_legend_single_channel_shows_legend(self, sdata_blobs: Spati
         assert "0" in [t.get_text() for t in legend.get_texts()]
         plt.close("all")
 
+    def test_channels_as_legend_single_channel_suppresses_colorbar(self, sdata_blobs: SpatialData):
+        fig, ax = plt.subplots()
+        sdata_blobs.pl.render_images(element="blobs_image", channel=0, channels_as_legend=True).pl.show(ax=ax)
+        # channels_as_legend replaces the colorbar — only the legend axes should exist
+        assert len(fig.axes) == 1
+        plt.close("all")
+
     def test_channels_as_legend_rgb_warns_and_no_legend(self, caplog):
         data = np.zeros((3, 50, 50), dtype=np.float64)
         data[0], data[1], data[2] = 0.8, 0.2, 0.1