Visualize PropertyLayers (#2336)

This PR adds support for visualizing PropertyLayers in the Matplotlib-based space visualization component. It allows users to overlay PropertyLayer data on top of the existing grid and agent visualizations, or on its own.

It introduces a new `propertylayer_portrayal` parameter to customize the appearance of PropertyLayers and refactors the existing space visualization code for better modularity and flexibility.

Author: EwoutH

mesa/visualization/components/matplotlib.py

@@ -1,116 +1,190 @@
 """Matplotlib based solara components for visualization MESA spaces and plots."""
 
-from collections import defaultdict
+import warnings
 
+import matplotlib.pyplot as plt
 import networkx as nx
+import numpy as np
 import solara
+from matplotlib.cm import ScalarMappable
+from matplotlib.colors import LinearSegmentedColormap, Normalize, to_rgba
 from matplotlib.figure import Figure
-from matplotlib.ticker import MaxNLocator
 
 import mesa
 from mesa.experimental.cell_space import VoronoiGrid
+from mesa.space import PropertyLayer
 from mesa.visualization.utils import update_counter
 
 
-def make_space_matplotlib(agent_portrayal=None):  # noqa: D103
+def make_space_matplotlib(agent_portrayal=None, propertylayer_portrayal=None):
+    """Create a Matplotlib-based space visualization component.
+
+    Args:
+        agent_portrayal (function): Function to portray agents
+        propertylayer_portrayal (dict): Dictionary of PropertyLayer portrayal specifications
+
+    Returns:
+        function: A function that creates a SpaceMatplotlib component
+    """
     if agent_portrayal is None:
 
         def agent_portrayal(a):
             return {"id": a.unique_id}
 
     def MakeSpaceMatplotlib(model):
-        return SpaceMatplotlib(model, agent_portrayal)
+        return SpaceMatplotlib(model, agent_portrayal, propertylayer_portrayal)
 
     return MakeSpaceMatplotlib
 
 
 @solara.component
-def SpaceMatplotlib(model, agent_portrayal, dependencies: list[any] | None = None):  # noqa: D103
+def SpaceMatplotlib(
+    model,
+    agent_portrayal,
+    propertylayer_portrayal,
+    dependencies: list[any] | None = None,
+):
+    """Create a Matplotlib-based space visualization component."""
     update_counter.get()
     space_fig = Figure()
     space_ax = space_fig.subplots()
     space = getattr(model, "grid", None)
     if space is None:
-        # Sometimes the space is defined as model.space instead of model.grid
-        space = model.space
-    if isinstance(space, mesa.space.NetworkGrid):
-        _draw_network_grid(space, space_ax, agent_portrayal)
+        space = getattr(model, "space", None)
+
+    if isinstance(space, mesa.space._Grid):
+        _draw_grid(space, space_ax, agent_portrayal, propertylayer_portrayal, model)
     elif isinstance(space, mesa.space.ContinuousSpace):
-        _draw_continuous_space(space, space_ax, agent_portrayal)
+        _draw_continuous_space(space, space_ax, agent_portrayal, model)
+    elif isinstance(space, mesa.space.NetworkGrid):
+        _draw_network_grid(space, space_ax, agent_portrayal)
     elif isinstance(space, VoronoiGrid):
         _draw_voronoi(space, space_ax, agent_portrayal)
-    else:
-        _draw_grid(space, space_ax, agent_portrayal)
-    solara.FigureMatplotlib(space_fig, format="png", dependencies=dependencies)
-
+    elif space is None and propertylayer_portrayal:
+        draw_property_layers(space_ax, space, propertylayer_portrayal, model)
 
-# matplotlib scatter does not allow for multiple shapes in one call
-def _split_and_scatter(portray_data, space_ax):
-    grouped_data = defaultdict(lambda: {"x": [], "y": [], "s": [], "c": []})
-
-    # Extract data from the dictionary
-    x = portray_data["x"]
-    y = portray_data["y"]
-    s = portray_data["s"]
-    c = portray_data["c"]
-    m = portray_data["m"]
-
-    if not (len(x) == len(y) == len(s) == len(c) == len(m)):
-        raise ValueError(
-            "Length mismatch in portrayal data lists: "
-            f"x: {len(x)}, y: {len(y)}, size: {len(s)}, "
-            f"color: {len(c)}, marker: {len(m)}"
-        )
-
-    # Group the data by marker
-    for i in range(len(x)):
-        marker = m[i]
-        grouped_data[marker]["x"].append(x[i])
-        grouped_data[marker]["y"].append(y[i])
-        grouped_data[marker]["s"].append(s[i])
-        grouped_data[marker]["c"].append(c[i])
+    solara.FigureMatplotlib(space_fig, format="png", dependencies=dependencies)
 
-    # Plot each group with the same marker
-    for marker, data in grouped_data.items():
-        space_ax.scatter(data["x"], data["y"], s=data["s"], c=data["c"], marker=marker)
 
+def draw_property_layers(ax, space, propertylayer_portrayal, model):
+    """Draw PropertyLayers on the given axes.
+
+    Args:
+        ax (matplotlib.axes.Axes): The axes to draw on.
+        space (mesa.space._Grid): The space containing the PropertyLayers.
+        propertylayer_portrayal (dict): Dictionary of PropertyLayer portrayal specifications.
+        model (mesa.Model): The model instance.
+    """
+    for layer_name, portrayal in propertylayer_portrayal.items():
+        layer = getattr(model, layer_name, None)
+        if not isinstance(layer, PropertyLayer):
+            continue
+
+        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,
+                stacklevel=2,
+            )
+
+        # 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))
+        colorbar = portrayal.get("colorbar", True)
+
+        # Draw the layer
+        if "color" in portrayal:
+            rgba_color = to_rgba(portrayal["color"])
+            normalized_data = (data - vmin) / (vmax - vmin)
+            rgba_data = np.full((*data.shape, 4), rgba_color)
+            rgba_data[..., 3] *= normalized_data * alpha
+            rgba_data = np.clip(rgba_data, 0, 1)
+            cmap = LinearSegmentedColormap.from_list(
+                layer_name, [(0, 0, 0, 0), (*rgba_color[:3], alpha)]
+            )
+            im = ax.imshow(
+                rgba_data.transpose(1, 0, 2),
+                extent=(0, width, 0, height),
+                origin="lower",
+            )
+            if colorbar:
+                norm = Normalize(vmin=vmin, vmax=vmax)
+                sm = ScalarMappable(norm=norm, cmap=cmap)
+                sm.set_array([])
+                ax.figure.colorbar(sm, ax=ax, orientation="vertical")
+
+        elif "colormap" in portrayal:
+            cmap = portrayal.get("colormap", "viridis")
+            if isinstance(cmap, list):
+                cmap = LinearSegmentedColormap.from_list(layer_name, cmap)
+            im = ax.imshow(
+                data.T,
+                cmap=cmap,
+                alpha=alpha,
+                vmin=vmin,
+                vmax=vmax,
+                extent=(0, width, 0, height),
+                origin="lower",
+            )
+            if colorbar:
+                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):
+    if propertylayer_portrayal:
+        draw_property_layers(space_ax, space, propertylayer_portrayal, model)
+
+    agent_data = _get_agent_data(space, agent_portrayal)
+
+    space_ax.set_xlim(0, space.width)
+    space_ax.set_ylim(0, space.height)
+    _split_and_scatter(agent_data, space_ax)
+
+    # Draw grid lines
+    for x in range(space.width + 1):
+        space_ax.axvline(x, color="gray", linestyle=":")
+    for y in range(space.height + 1):
+        space_ax.axhline(y, color="gray", linestyle=":")
+
+
+def _get_agent_data(space, agent_portrayal):
+    """Helper function to get agent data for visualization."""
+    x, y, s, c, m = [], [], [], [], []
+    for agents, pos in space.coord_iter():
+        if not agents:
+            continue
+        if not isinstance(agents, list):
+            agents = [agents]  # noqa PLW2901
+        for agent in agents:
+            data = agent_portrayal(agent)
+            x.append(pos[0] + 0.5)  # Center the agent in the cell
+            y.append(pos[1] + 0.5)  # Center the agent in the cell
+            default_size = (180 / max(space.width, space.height)) ** 2
+            s.append(data.get("size", default_size))
+            c.append(data.get("color", "tab:blue"))
+            m.append(data.get("shape", "o"))
+    return {"x": x, "y": y, "s": s, "c": c, "m": m}
 
-def _draw_grid(space, space_ax, agent_portrayal):
-    def portray(g):
-        x = []
-        y = []
-        s = []  # size
-        c = []  # color
-        m = []  # shape
-        for i in range(g.width):
-            for j in range(g.height):
-                content = g._grid[i][j]
-                if not content:
-                    continue
-                if not hasattr(content, "__iter__"):
-                    # Is a single grid
-                    content = [content]
-                for agent in content:
-                    data = agent_portrayal(agent)
-                    x.append(i)
-                    y.append(j)
-
-                    # This is the default value for the marker size, which auto-scales
-                    # according to the grid area.
-                    default_size = (180 / max(g.width, g.height)) ** 2
-                    # establishing a default prevents misalignment if some agents are not given size, color, etc.
-                    size = data.get("size", default_size)
-                    s.append(size)
-                    color = data.get("color", "tab:blue")
-                    c.append(color)
-                    mark = data.get("shape", "o")
-                    m.append(mark)
-        out = {"x": x, "y": y, "s": s, "c": c, "m": m}
-        return out
 
-    space_ax.set_xlim(-1, space.width)
-    space_ax.set_ylim(-1, space.height)
-    _split_and_scatter(portray(space), space_ax)
+def _split_and_scatter(portray_data, space_ax):
+    """Helper function to split and scatter agent data."""
+    for marker in set(portray_data["m"]):
+        mask = [m == marker for m in portray_data["m"]]
+        space_ax.scatter(
+            [x for x, show in zip(portray_data["x"], mask) if show],
+            [y for y, show in zip(portray_data["y"], mask) if show],
+            s=[s for s, show in zip(portray_data["s"], mask) if show],
+            c=[c for c, show in zip(portray_data["c"], mask) if show],
+            marker=marker,
+        )
 
 
 def _draw_network_grid(space, space_ax, agent_portrayal):
@@ -124,7 +198,7 @@ def _draw_network_grid(space, space_ax, agent_portrayal):
     )
 
 
-def _draw_continuous_space(space, space_ax, agent_portrayal):
+def _draw_continuous_space(space, space_ax, agent_portrayal, model):
     def portray(space):
         x = []
         y = []
@@ -139,15 +213,13 @@ def portray(space):
 
             # This is matplotlib's default marker size
             default_size = 20
-            # establishing a default prevents misalignment if some agents are not given size, color, etc.
             size = data.get("size", default_size)
             s.append(size)
             color = data.get("color", "tab:blue")
             c.append(color)
             mark = data.get("shape", "o")
             m.append(mark)
-        out = {"x": x, "y": y, "s": s, "c": c, "m": m}
-        return out
+        return {"x": x, "y": y, "s": s, "c": c, "m": m}
 
     # Determine border style based on space.torus
     border_style = "solid" if not space.torus else (0, (5, 10))
@@ -186,8 +258,6 @@ def portray(g):
                 if "color" in data:
                     c.append(data["color"])
         out = {"x": x, "y": y}
-        # This is the default value for the marker size, which auto-scales
-        # according to the grid area.
         out["s"] = s
         if len(c) > 0:
             out["c"] = c
@@ -216,18 +286,37 @@ def portray(g):
             alpha=min(1, cell.properties[space.cell_coloring_property]),
             c="red",
         )  # Plot filled polygon
-        space_ax.plot(*zip(*polygon), color="black")  # Plot polygon edges in red
+        space_ax.plot(*zip(*polygon), color="black")  # Plot polygon edges in black
+
+
+def make_plot_measure(measure: str | dict[str, str] | list[str] | tuple[str]):
+    """Create a plotting function for a specified measure.
 
+    Args:
+        measure (str | dict[str, str] | list[str] | tuple[str]): Measure(s) to plot.
+
+    Returns:
+        function: A function that creates a PlotMatplotlib component.
+    """
 
-def make_plot_measure(measure: str | dict[str, str] | list[str] | tuple[str]):  # noqa: D103
     def MakePlotMeasure(model):
         return PlotMatplotlib(model, measure)
 
     return MakePlotMeasure
 
 
 @solara.component
-def PlotMatplotlib(model, measure, dependencies: list[any] | None = None):  # noqa: D103
+def PlotMatplotlib(model, measure, dependencies: list[any] | None = None):
+    """Create a Matplotlib-based plot for a measure or measures.
+
+    Args:
+        model (mesa.Model): The model instance.
+        measure (str | dict[str, str] | list[str] | tuple[str]): Measure(s) to plot.
+        dependencies (list[any] | None): Optional dependencies for the plot.
+
+    Returns:
+        solara.FigureMatplotlib: A component for rendering the plot.
+    """
     update_counter.get()
     fig = Figure()
     ax = fig.subplots()
@@ -244,5 +333,5 @@ def PlotMatplotlib(model, measure, dependencies: list[any] | None = None):  # no
             ax.plot(df.loc[:, m], label=m)
         fig.legend()
     # Set integer x axis
-    ax.xaxis.set_major_locator(MaxNLocator(integer=True))
+    ax.xaxis.set_major_locator(plt.MaxNLocator(integer=True))
     solara.FigureMatplotlib(fig, dependencies=dependencies)

tests/test_solara_viz.py

@@ -100,11 +100,14 @@ def test_call_space_drawer(mocker):  # noqa: D103
         "Shape": "circle",
         "color": "gray",
     }
+    propertylayer_portrayal = None
     # initialize with space drawer unspecified (use default)
     # component must be rendered for code to run
     solara.render(SolaraViz(model, components=[make_space_matplotlib(agent_portrayal)]))
     # should call default method with class instance and agent portrayal
-    mock_space_matplotlib.assert_called_with(model, agent_portrayal)
+    mock_space_matplotlib.assert_called_with(
+        model, agent_portrayal, propertylayer_portrayal
+    )
 
     # specify no space should be drawn
     mock_space_matplotlib.reset_mock()