Fix unused cmap in render_points

src/spatialdata_plot/pl/render.py

@@ -523,7 +523,9 @@ def _render_points(
         palette=palette,
         na_color=default_color,
         cmap_params=render_params.cmap_params,
+        alpha=render_params.alpha,
         table_name=table_name,
+        render_type="points",
     )
 
     # color_source_vector is None when the values aren't categorical

src/spatialdata_plot/pl/utils.py

@@ -711,8 +711,10 @@ def _set_color_source_vec(
     groups: list[str] | str | None = None,
     palette: list[str] | str | None = None,
     cmap_params: CmapParams | None = None,
+    alpha: float = 1.0,
     table_name: str | None = None,
     table_layer: str | None = None,
+    render_type: Literal["points"] | None = None,
 ) -> tuple[ArrayLike | pd.Series | None, ArrayLike, bool]:
     if value_to_plot is None and element is not None:
         color = np.full(len(element), na_color)
@@ -757,9 +759,12 @@ def _set_color_source_vec(
             adata=sdata.table,
             cluster_key=value_to_plot,
             color_source_vector=color_source_vector,
+            cmap_params=cmap_params,
+            alpha=alpha,
             groups=groups,
             palette=palette,
             na_color=na_color,
+            render_type=render_type,
         )
 
         color_source_vector = color_source_vector.set_categories(color_mapping.keys())
@@ -912,15 +917,28 @@ def _get_categorical_color_mapping(
     na_color: ColorLike,
     cluster_key: str | None = None,
     color_source_vector: ArrayLike | pd.Series[CategoricalDtype] | None = None,
+    cmap_params: CmapParams | None = None,
+    alpha: float = 1,
     groups: list[str] | str | None = None,
     palette: list[str] | str | None = None,
+    render_type: Literal["points"] | None = None,
 ) -> Mapping[str, str]:
     if not isinstance(color_source_vector, Categorical):
         raise TypeError(f"Expected `categories` to be a `Categorical`, but got {type(color_source_vector).__name__}")
 
     if isinstance(groups, str):
         groups = [groups]
 
+    if not palette and render_type == "points" and cmap_params is not None and not cmap_params.cmap_is_default:
+        palette = cmap_params.cmap
+
+        color_idx = color_idx = np.linspace(0, 1, len(color_source_vector.categories))
+        if isinstance(palette, ListedColormap):
+            palette = [to_hex(x) for x in palette(color_idx, alpha=alpha)]
+        elif isinstance(palette, LinearSegmentedColormap):
+            palette = [to_hex(palette(x, alpha=alpha)) for x in color_idx]  # type: ignore[attr-defined]
+        return dict(zip(color_source_vector.categories, palette, strict=True))
+
     if isinstance(palette, str):
         palette = [palette]
 
@@ -2011,7 +2029,7 @@ def _is_coercable_to_float(series: pd.Series) -> bool:
 
 
 def _ax_show_and_transform(
-    array: MaskedArray[tuple[int, ...], Any],
+    array: MaskedArray[tuple[int, ...], Any] | npt.NDArray[Any],
     trans_data: CompositeGenericTransform,
     ax: Axes,
     alpha: float | None = None,

tests/pl/test_render_labels.py

@@ -84,9 +84,9 @@ def test_plot_can_color_labels_by_continuous_variable(self, sdata_blobs: Spatial
         sdata_blobs.pl.render_labels("blobs_labels", color="channel_0_sum").pl.show()
 
     def test_plot_can_color_labels_by_categorical_variable(self, sdata_blobs: SpatialData):
-        max_col = sdata_blobs.table.to_df().idxmax(axis=1)
-        max_col = pd.Categorical(max_col, categories=sdata_blobs.table.to_df().columns, ordered=True)
-        sdata_blobs.table.obs["which_max"] = max_col
+        max_col = sdata_blobs["table"].to_df().idxmax(axis=1)
+        max_col = pd.Categorical(max_col, categories=sdata_blobs["table"].to_df().columns, ordered=True)
+        sdata_blobs["table"].obs["which_max"] = max_col
 
         sdata_blobs.pl.render_labels("blobs_labels", color="which_max").pl.show()