Fix NaN handling and transparency in shape rendering

src/spatialdata_plot/pl/render.py

@@ -9,6 +9,7 @@
 import geopandas as gpd
 import matplotlib
 import matplotlib.pyplot as plt
+import matplotlib.ticker
 import numpy as np
 import pandas as pd
 import scanpy as sc
@@ -141,6 +142,15 @@ def _render_shapes(
         color_source_vector = color_source_vector[mask]
         color_vector = color_vector[mask]
 
+    # continuous case: leave NaNs as NaNs; utils maps them to na_color during draw
+    if color_source_vector is None and not values_are_categorical:
+        color_vector = np.asarray(color_vector, dtype=float)
+        if np.isnan(color_vector).any():
+            nan_count = int(np.isnan(color_vector).sum())
+            logger.warning(
+                f"Found {nan_count} NaN values in color data. These observations will be colored with the 'na_color'."
+            )
+
     # Using dict.fromkeys here since set returns in arbitrary order
     # remove the color of NaN values, else it might be assigned to a category
     # order of color in the palette should agree to order of occurence
@@ -195,7 +205,10 @@ def _render_shapes(
 
         # Handle circles encoded as points with radius
         if is_point.any():
-            scale = shapes[is_point]["radius"] * render_params.scale
+            radius_values = shapes[is_point]["radius"]
+            # Convert to numeric, replacing non-numeric values with NaN
+            radius_numeric = pd.to_numeric(radius_values, errors="coerce")
+            scale = radius_numeric * render_params.scale
             shapes.loc[is_point, "geometry"] = _geometry[is_point].buffer(scale.to_numpy())
 
         # apply transformations to the individual points
@@ -218,6 +231,20 @@ def _render_shapes(
 
         # in case we are coloring by a column in table
         if col_for_color is not None and col_for_color not in transformed_element.columns:
+            # Ensure color vector length matches the number of shapes
+            if len(color_vector) != len(transformed_element):
+                if len(color_vector) == 1:
+                    # If single color, broadcast to all shapes
+                    color_vector = [color_vector[0]] * len(transformed_element)
+                else:
+                    # If lengths don't match, pad or truncate to match
+                    if len(color_vector) > len(transformed_element):
+                        color_vector = color_vector[: len(transformed_element)]
+                    else:
+                        # Pad with the last color or na_color
+                        na_color = render_params.cmap_params.na_color.get_hex_with_alpha()
+                        color_vector = list(color_vector) + [na_color] * (len(transformed_element) - len(color_vector))
+
             transformed_element[col_for_color] = color_vector if color_source_vector is None else color_source_vector
         # Render shapes with datashader
         color_by_categorical = col_for_color is not None and color_source_vector is not None
@@ -447,12 +474,13 @@ def _render_shapes(
             path.vertices = trans.transform(path.vertices)
 
     if not values_are_categorical:
-        # If the user passed a Normalize object with vmin/vmax we'll use those,
-        # if not we'll use the min/max of the color_vector
-        _cax.set_clim(
-            vmin=render_params.cmap_params.norm.vmin or min(color_vector),
-            vmax=render_params.cmap_params.norm.vmax or max(color_vector),
-        )
+        vmin = render_params.cmap_params.norm.vmin
+        vmax = render_params.cmap_params.norm.vmax
+        if vmin is None:
+            vmin = float(np.nanmin(color_vector))
+        if vmax is None:
+            vmax = float(np.nanmax(color_vector))
+        _cax.set_clim(vmin=vmin, vmax=vmax)
 
     if (
         len(set(color_vector)) != 1