Cleanup

mesa/visualization/components/matplotlib.py

@@ -77,45 +77,35 @@ def draw_property_layers(ax, space, propertylayer_portrayal, model):
     """
     for layer_name, portrayal in propertylayer_portrayal.items():
         layer = getattr(model, layer_name, None)
-        if layer is None or not isinstance(layer, PropertyLayer):
+        if not isinstance(layer, PropertyLayer):
             continue
 
-        alpha = portrayal.get("alpha", 0.5)
-
-        # Convert boolean arrays to float
         data = layer.data.astype(float) if layer.data.dtype == bool else layer.data
+        width, height = data.shape if space is None else (space.width, space.height)
+
+        if space and data.shape != (width, height):
+            warnings.warn(
+                f"Layer {layer_name} dimensions ({data.shape}) do not match space dimensions ({width}, {height}).",
+                UserWarning,
+            )
 
+        # Get portrayal properties, or use defaults
+        alpha = portrayal.get("alpha", 1)
         vmin = portrayal.get("vmin", np.min(data))
         vmax = portrayal.get("vmax", np.max(data))
 
-        if space is not None:
-            # Check if the dimensions align
-            if data.shape != (space.width, space.height):
-                warnings.warn(
-                    f"Layer {layer_name} does not have the same dimensions as the space. "
-                    "Skipping visualization.",
-                    UserWarning,
-                )
-                continue
-            width, height = space.width, space.height
-        else:
-            width, height = data.shape
-
+        # Draw the layer
         if "color" in portrayal:
-            # Use color with alpha scaling
-            color = portrayal["color"]
-            rgba_color = to_rgba(color)  # Convert any color format to RGBA
+            rgba_color = to_rgba(portrayal["color"])
             normalized_data = (data - vmin) / (vmax - vmin)
-            rgba_data = np.zeros((*data.shape, 4))
-            rgba_data[..., :3] = rgba_color[:3]  # RGB channels
-            rgba_data[..., 3] = normalized_data * alpha * rgba_color[3]  # Alpha channel
+            rgba_data = np.full((*data.shape, 4), rgba_color)
+            rgba_data[..., 3] *= normalized_data * alpha
             im = ax.imshow(
-                rgba_data.transpose(1, 0, 2),  # Transpose to (height, width, 4)
+                rgba_data.transpose(1, 0, 2),
                 extent=(0, width, 0, height),
                 origin="lower",
             )
-        else:
-            # Use colormap
+        elif "colormap" in portrayal:
             cmap = portrayal.get("colormap", "viridis")
             if isinstance(cmap, list):
                 cmap = LinearSegmentedColormap.from_list(layer_name, cmap)
@@ -129,6 +119,10 @@ def draw_property_layers(ax, space, propertylayer_portrayal, model):
                 origin="lower",
             )
             plt.colorbar(im, ax=ax, label=layer_name)
+        else:
+            raise ValueError(
+                f"PropertyLayer {layer_name} portrayal must include 'color' or 'colormap'."
+            )
 
 
 def _draw_grid(space, space_ax, agent_portrayal, propertylayer_portrayal, model):