Fix categorical colors wrongly assigned to points with non-sequential index (#358)

When points have a shuffled or non-sequential index (e.g. from .sample()
or .subset()), _reparse_points sorts rows by index while adata.X retains
the original order. This causes get_values to return colors in sorted
order, misaligned with coordinates. Resetting the index to sequential
before adata construction and reparsing ensures both share the same
positional order.

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

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -743,6 +743,9 @@ def _render_points(
         )
         added_color_from_table = True
 
+    # Reset to sequential index so row order matches after _reparse_points round-trip (#358).
+    points = points.reset_index(drop=True)
+
     n_points = len(points)
     points_pd_with_color = points
     # When we pull colors from a table, keep the raw points (with color) for later,
@@ -758,7 +761,7 @@ def _render_points(
     if table_name is None:
         adata = AnnData(
             X=points[["x", "y"]].values,
-            obs=points[coords].reset_index(),
+            obs=points[coords],
             dtype=points[["x", "y"]].values.dtype,
         )
     else:

tests/_images/Points_sampled_points_categorical_color_datashader.png

@@ -606,6 +606,27 @@ def test_plot_groups_na_color_none_filters_points_datashader(self, sdata_blobs:
             "blobs_points", color="cat_color", groups=["a"], size=30, method="datashader"
         ).pl.show(ax=axs[1], title="default (filtered)")
 
+    @staticmethod
+    def _make_sampled_sdata() -> SpatialData:
+        """Points with two spatially separated clusters, shuffled via .sample() (#358)."""
+        rng = get_standard_RNG()
+        n = 100
+        x = np.concatenate([rng.uniform(0, 10, n // 2), rng.uniform(90, 100, n // 2)])
+        y = np.concatenate([rng.uniform(0, 10, n // 2), rng.uniform(90, 100, n // 2)])
+        df = pd.DataFrame({"x": x, "y": y, "cluster": pd.Categorical(["A"] * (n // 2) + ["B"] * (n // 2))})
+        sdata = SpatialData(points={"pts": PointsModel.parse(df)})
+        sampled = sdata.points["pts"].compute().sample(frac=0.8, random_state=42)
+        sdata.points["pts"] = PointsModel.parse(sampled)
+        return sdata
+
+    def test_plot_sampled_points_categorical_color_matplotlib(self):
+        """Regression test for #358: .sample() must not shuffle categorical colors."""
+        self._make_sampled_sdata().pl.render_points("pts", color="cluster", method="matplotlib").pl.show()
+
+    def test_plot_sampled_points_categorical_color_datashader(self):
+        """Regression test for #358: .sample() must not shuffle categorical colors."""
+        self._make_sampled_sdata().pl.render_points("pts", color="cluster", method="datashader").pl.show()
+
 
 def test_groups_na_color_none_no_match_points(sdata_blobs: SpatialData):
     """When no elements match the groups, the plot should render without error."""