refactor(color): simplify the continuous-fill block and align idioms (#700)

/simplify pass (behaviour-preserving):
- continuous-reprocessing: drop the duplicated NaN-count warning across the
  try/except arms — coerce, then count once via pd.isna and warn once.
- has_valid_color: build the distinct-colors set once instead of twice.
- _add_legend_and_colorbar: branch on color_spec.is_categorical instead of
  duck-typing `source is not None and hasattr(remove_unused_categories)`.
- to_rgba: use the module-level colors.to_rgba_array (matching _map_color_seg)
  instead of instantiating ColorConverter(); drop the now-unused import.

Author: timtreis

src/spatialdata_plot/pl/_color.py

@@ -17,7 +17,6 @@
 from matplotlib import colors, rcParams
 from matplotlib.cm import ScalarMappable
 from matplotlib.colors import (
-    ColorConverter,
     Colormap,
     LinearSegmentedColormap,
     ListedColormap,
@@ -727,10 +726,10 @@ def to_rgba(self, cmap_params: CmapParams) -> np.ndarray:
         via norm+cmap with NaN/non-finite rows painted ``na_color``; an object vector mixes the two.
         """
         if self.source_vector is not None:  # categorical or none: color_vector holds per-row hex
-            return np.asarray(ColorConverter().to_rgba_array(list(self.color_vector)))
+            return np.asarray(colors.to_rgba_array(list(self.color_vector)))
         arr = np.asarray(self.color_vector)
         if arr.ndim == 2 and arr.shape[1] in (3, 4) and np.issubdtype(arr.dtype, np.number):
-            return np.asarray(ColorConverter().to_rgba_array(arr))
+            return np.asarray(colors.to_rgba_array(arr))
         rgba = np.empty((len(arr), 4), dtype=float)
         rgba[:] = colors.to_rgba(cmap_params.na_color.get_hex_with_alpha())
         if np.issubdtype(arr.dtype, np.number):
@@ -748,7 +747,7 @@ def to_rgba(self, cmap_params: CmapParams) -> np.ndarray:
             rgba[is_num] = cmap_params.cmap(norm(num[is_num]))
         color_mask = (~is_num) & series.notna().to_numpy()
         if color_mask.any():
-            rgba[color_mask] = ColorConverter().to_rgba_array(series[color_mask].tolist())
+            rgba[color_mask] = colors.to_rgba_array(series[color_mask].tolist())
         return rgba
 
     def align_to_length(self, n: int, na_color: Color) -> ColorSpec:

src/spatialdata_plot/pl/render.py

@@ -362,7 +362,7 @@ def _add_legend_and_colorbar(
     if palette is None and fill_has_decorations:
         palette = color_spec.make_palette()
 
-    if color_source_vector is not None and hasattr(color_source_vector, "remove_unused_categories"):
+    if color_spec.is_categorical:
         color_source_vector = color_source_vector.remove_unused_categories()
 
     wants_colorbar = _should_request_colorbar(
@@ -690,31 +690,24 @@ def _render_shapes(
     if color_spec.is_continuous:
         cv = color_spec.color_vector
         _series = cv if isinstance(cv, pd.Series) else pd.Series(cv)
-
         try:
             cv = np.asarray(_series, dtype=float)
         except (TypeError, ValueError):
-            nan_count = int(_series.isna().sum())
-            if nan_count:
-                logger.warning(
-                    f"Found {nan_count} NaN values in color data. "
-                    "These observations will be colored with the 'na_color'."
-                )
             cv = _series.to_numpy()
-        else:
-            if np.isnan(cv).any():
-                nan_count = int(np.isnan(cv).sum())
-                logger.warning(
-                    f"Found {nan_count} NaN values in color data. "
-                    "These observations will be colored with the 'na_color'."
-                )
+        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'."
+            )
         color_spec = color_spec.evolve(color_vector=cv)
 
     palette = color_spec.make_palette()
 
+    distinct_colors = set(color_spec.color_vector)
     has_valid_color = (
-        len(set(color_spec.color_vector)) != 1
-        or list(set(color_spec.color_vector))[0] != render_params.cmap_params.na_color.get_hex_with_alpha()
+        len(distinct_colors) != 1
+        or next(iter(distinct_colors)) != render_params.cmap_params.na_color.get_hex_with_alpha()
     )
     if has_valid_color and color_spec.is_categorical and col_for_color is not None:
         # necessary in case different shapes elements are annotated with one table