Use dense Gaussian-cluster points for density visual tests

The shared blobs fixture only has ~200 points scattered across a
500x500 canvas, so density renders as nearly invisible single-pixel
dots. Add a dedicated sdata_dense_points fixture (~60k points across
three Gaussian clusters, with a categorical "gene" column) so the
test_plot_density_* cases produce a meaningful visual baseline.

Also fixes test_plot_density_categorical_with_groups, which previously
passed palette="tab20" alongside a single-element groups list -- our
palette parser treats single-string + single-group as a literal color
and rejected "tab20" with "Unknown color".

Author: timtreis

tests/conftest.py

@@ -80,6 +80,34 @@ def sdata_blobs() -> SpatialData:
     return blobs()
 
 
+@pytest.fixture()
+def sdata_dense_points() -> SpatialData:
+    """Dense (~20k) multi-cluster points dataset for density-rendering visual tests.
+
+    The blobs fixture is too sparse (~200 points across 500x500) for density to render
+    meaningfully without spreading; this fixture provides a Gaussian-cluster cloud with
+    a categorical ``gene`` column so the per-category density branch is exercised too.
+    """
+    rng = get_standard_RNG()
+    n_per_cluster = 20000
+    centers = [(120, 120), (380, 150), (250, 380)]
+    genes = ["gene_a", "gene_b", "gene_c"]
+    xs, ys, gs = [], [], []
+    for (cx, cy), gene in zip(centers, genes, strict=True):
+        xs.append(rng.normal(loc=cx, scale=18, size=n_per_cluster))
+        ys.append(rng.normal(loc=cy, scale=18, size=n_per_cluster))
+        gs.extend([gene] * n_per_cluster)
+    df = pd.DataFrame(
+        {
+            "x": np.concatenate(xs).clip(0, 500),
+            "y": np.concatenate(ys).clip(0, 500),
+            "gene": pd.Categorical(gs, categories=genes),
+        }
+    )
+    points = PointsModel.parse(df)
+    return SpatialData(points={"dense_points": points})
+
+
 @pytest.fixture()
 def sdata_blobs_str() -> SpatialData:
     return blobs(n_channels=5, c_coords=["c1", "c2", "c3", "c4", "c5"])

tests/pl/test_render_points.py

@@ -172,22 +172,22 @@ def test_plot_datashader_continuous_color(self, sdata_blobs: SpatialData):
             method="datashader",
         ).pl.show()
 
-    def test_plot_density_plain(self, sdata_blobs: SpatialData):
-        sdata_blobs.pl.render_points("blobs_points", density=True).pl.show()
+    def test_plot_density_plain(self, sdata_dense_points: SpatialData):
+        sdata_dense_points.pl.render_points("dense_points", density=True).pl.show()
 
-    def test_plot_density_categorical(self, sdata_blobs: SpatialData):
-        sdata_blobs.pl.render_points("blobs_points", color="genes", palette="tab20", density=True).pl.show()
+    def test_plot_density_categorical(self, sdata_dense_points: SpatialData):
+        sdata_dense_points.pl.render_points("dense_points", color="gene", density=True).pl.show()
 
-    def test_plot_density_categorical_with_groups(self, sdata_blobs: SpatialData):
-        sdata_blobs.pl.render_points(
-            "blobs_points", color="genes", groups=["gene_a"], palette="tab20", density=True
+    def test_plot_density_categorical_with_groups(self, sdata_dense_points: SpatialData):
+        sdata_dense_points.pl.render_points(
+            "dense_points", color="gene", groups=["gene_a", "gene_b"], density=True
         ).pl.show()
 
-    def test_plot_density_how_log(self, sdata_blobs: SpatialData):
-        sdata_blobs.pl.render_points("blobs_points", density=True, density_how="log").pl.show()
+    def test_plot_density_how_log(self, sdata_dense_points: SpatialData):
+        sdata_dense_points.pl.render_points("dense_points", density=True, density_how="log").pl.show()
 
-    def test_plot_density_how_eq_hist(self, sdata_blobs: SpatialData):
-        sdata_blobs.pl.render_points("blobs_points", density=True, density_how="eq_hist").pl.show()
+    def test_plot_density_how_eq_hist(self, sdata_dense_points: SpatialData):
+        sdata_dense_points.pl.render_points("dense_points", density=True, density_how="eq_hist").pl.show()
 
     def test_plot_points_categorical_color_column_matplotlib(self, sdata_blobs: SpatialData):
         sdata_blobs.pl.render_points("blobs_points", color="genes", method="matplotlib").pl.show()