fix(render_points): resolve continuous norm for the matplotlib backend

Matplotlib points passed an un-scaled fresh_norm() to ax.scatter and let it
autoscale, diverging from shapes/labels: a constant-valued continuous points
layer mapped to the cmap floor instead of the [0, 1] reset, and a LogNorm was
autoscaled over the raw data (crashing on a 0/negative). Route continuous points
through _resolve_continuous_norm so fill and colorbar match the other element
types and LogNorm/PowerNorm survive.

Kept separate from the core fix as it is the only change that can shift point
visual baselines.

Adds a regression test for render_points(norm=LogNorm()) over data with a 0.

Author: timtreis

src/spatialdata_plot/pl/render.py

@@ -698,8 +698,7 @@ def _render_shapes(
         nan_count = int(pd.isna(cv).sum())
         if nan_count:
             logger.warning(
-                f"Found {nan_count} NaN values in color data. "
-                "These observations will be colored with the 'na_color'."
+                f"Found {nan_count} NaN values in color data. These observations will be colored with the 'na_color'."
             )
         color_spec = color_spec.evolve(color_vector=cv)
 
@@ -1416,7 +1415,14 @@ def _render_points(
 
     trans, trans_data = _prepare_transformation(sdata.points[element], coordinate_system, ax)
 
-    norm = render_params.cmap_params.fresh_norm()
+    # Continuous points resolve their norm through the shared resolver so the fill and colorbar match
+    # the shapes/labels treatment (degenerate [0, 1] reset, LogNorm/PowerNorm preserved) instead of
+    # letting ax.scatter autoscale a fresh linear norm. Non-continuous keeps the un-scaled fresh norm.
+    norm = (
+        _resolve_continuous_norm(color_spec.color_vector, render_params.cmap_params)
+        if color_spec.is_continuous
+        else render_params.cmap_params.fresh_norm()
+    )
 
     method = render_params.method
 

tests/pl/test_render_points.py

@@ -666,6 +666,18 @@ def test_groups_na_color_none_no_match_points(sdata_blobs: SpatialData):
     ).pl.show()
 
 
+def test_render_points_lognorm_with_zeros_does_not_crash(sdata_blobs: SpatialData):
+    # Regression: matplotlib points resolve their continuous norm through the shared resolver, so a
+    # LogNorm column containing 0 must derive a positive vmin instead of a LogNorm(vmin=0) that raises.
+    from matplotlib.colors import LogNorm
+
+    n = len(sdata_blobs["blobs_points"])
+    sdata_blobs["blobs_points"]["counts"] = pd.Series(np.linspace(0.0, 10.0, n))  # includes 0
+    fig, ax = plt.subplots()
+    sdata_blobs.pl.render_points("blobs_points", color="counts", norm=LogNorm(), method="matplotlib").pl.show(ax=ax)
+    plt.close(fig)
+
+
 @pytest.mark.parametrize("na_color", [None, "red"])
 def test_groups_warns_when_no_groups_match_points(sdata_blobs: SpatialData, caplog, na_color):
     """Warning fires regardless of na_color when no groups match."""