expand ax.scatter kwargs that can be used

docs/overview.md

@@ -177,20 +177,20 @@ def agent_portrayal(agent):
 
 model_params = {
     "N": {
-       "type": "SliderInt",
-       "value": 50,
-       "label": "Number of agents:",
-       "min": 10,
-       "max": 100,
-       "step": 1,
+        "type": "SliderInt",
+        "value": 50,
+        "label": "Number of agents:",
+        "min": 10,
+        "max": 100,
+        "step": 1,
    }
 }
 
 page = SolaraViz(
     MyModel,
     [
-       make_space_component(agent_portrayal),
-       make_plot_component("mean_age")
+        make_space_component(agent_portrayal),
+        make_plot_component("mean_age")
     ],
     model_params=model_params
 )

mesa/examples/basic/boltzmann_wealth_model/app.py

@@ -1,18 +1,10 @@
 from mesa.examples.basic.boltzmann_wealth_model.model import BoltzmannWealthModel
-from mesa.visualization import (
-    SolaraViz,
-    make_plot_component,
-    make_space_component,
-)
+from mesa.visualization import SolaraViz, make_plot_component, make_space_component
 
 
 def agent_portrayal(agent):
-    size = 10
-    color = "tab:red"
-    if agent.wealth > 0:
-        size = 50
-        color = "tab:blue"
-    return {"size": size, "color": color}
+    color = agent.wealth  # we are using a colormap to translate wealth to color
+    return {"color": color}
 
 
 model_params = {
@@ -28,6 +20,11 @@
     "height": 10,
 }
 
+
+def post_process(ax):
+    ax.get_figure().colorbar(ax.collections[0], label="wealth", ax=ax)
+
+
 # Create initial model instance
 model1 = BoltzmannWealthModel(50, 10, 10)
 
@@ -36,7 +33,10 @@
 # Under the hood these are just classes that receive the model instance.
 # You can also author your own visualization elements, which can also be functions
 # that receive the model instance and return a valid solara component.
-SpaceGraph = make_space_component(agent_portrayal)
+
+SpaceGraph = make_space_component(
+    agent_portrayal, cmap="viridis", vmin=0, vmax=10, post_process=post_process
+)
 GiniPlot = make_plot_component("Gini")
 
 # Create the SolaraViz page. This will automatically create a server and display the

mesa/visualization/components/matplotlib.py

@@ -309,6 +309,7 @@ def draw_orthogonal_grid(
     agent_portrayal: Callable,
     ax: Axes | None = None,
     draw_grid: bool = True,
+    **kwargs,
 ):
     """Visualize a orthogonal grid.
 
@@ -317,6 +318,7 @@ def draw_orthogonal_grid(
         agent_portrayal: a callable that is called with the agent and returns a dict
         ax: a Matplotlib Axes instance. If none is provided a new figure and ax will be created using plt.subplots
         draw_grid: whether to draw the grid
+        kwargs: additional keyword arguments passed to ax.scatter
 
     Returns:
         Returns the Axes object with the plot drawn onto it.
@@ -333,7 +335,7 @@ def draw_orthogonal_grid(
     arguments = collect_agent_data(space, agent_portrayal, size=s_default)
 
     # plot the agents
-    _scatter(ax, arguments)
+    _scatter(ax, arguments, **kwargs)
 
     # further styling
     ax.set_xlim(-0.5, space.width - 0.5)
@@ -354,6 +356,7 @@ def draw_hex_grid(
     agent_portrayal: Callable,
     ax: Axes | None = None,
     draw_grid: bool = True,
+    **kwargs,
 ):
     """Visualize a hex grid.
 
@@ -362,6 +365,7 @@ def draw_hex_grid(
         agent_portrayal: a callable that is called with the agent and returns a dict
         ax: a Matplotlib Axes instance. If none is provided a new figure and ax will be created using plt.subplots
         draw_grid: whether to draw the grid
+        kwargs: additional keyword arguments passed to ax.scatter
 
     Returns:
         Returns the Axes object with the plot drawn onto it.
@@ -394,7 +398,7 @@ def draw_hex_grid(
     arguments["loc"] = loc
 
     # plot the agents
-    _scatter(ax, arguments)
+    _scatter(ax, arguments, **kwargs)
 
     # further styling and adding of grid
     ax.set_xlim(-1, space.width + 0.5)
@@ -443,6 +447,7 @@ def draw_network(
     draw_grid: bool = True,
     layout_alg=nx.spring_layout,
     layout_kwargs=None,
+    **kwargs,
 ):
     """Visualize a network space.
 
@@ -453,6 +458,7 @@ def draw_network(
         draw_grid: whether to draw the grid
         layout_alg: a networkx layout algorithm or other callable with the same behavior
         layout_kwargs: a dictionary of keyword arguments for the layout algorithm
+        kwargs: additional keyword arguments passed to ax.scatter
 
     Returns:
         Returns the Axes object with the plot drawn onto it.
@@ -488,7 +494,7 @@ def draw_network(
     arguments["loc"] = pos[arguments["loc"]]
 
     # plot the agents
-    _scatter(ax, arguments)
+    _scatter(ax, arguments, **kwargs)
 
     # further styling
     ax.set_axis_off()
@@ -506,14 +512,15 @@ def draw_network(
 
 
 def draw_continuous_space(
-    space: ContinuousSpace, agent_portrayal: Callable, ax: Axes | None = None
+    space: ContinuousSpace, agent_portrayal: Callable, ax: Axes | None = None, **kwargs
 ):
     """Visualize a continuous space.
 
     Args:
         space: the space to visualize
         agent_portrayal: a callable that is called with the agent and returns a dict
         ax: a Matplotlib Axes instance. If none is provided a new figure and ax will be created using plt.subplots
+        kwargs: additional keyword arguments passed to ax.scatter
 
     Returns:
         Returns the Axes object with the plot drawn onto it.
@@ -536,7 +543,7 @@ def draw_continuous_space(
     arguments = collect_agent_data(space, agent_portrayal, size=s_default)
 
     # plot the agents
-    _scatter(ax, arguments)
+    _scatter(ax, arguments, **kwargs)
 
     # further visual styling
     border_style = "solid" if not space.torus else (0, (5, 10))
@@ -552,14 +559,15 @@ def draw_continuous_space(
 
 
 def draw_voroinoi_grid(
-    space: VoronoiGrid, agent_portrayal: Callable, ax: Axes | None = None
+    space: VoronoiGrid, agent_portrayal: Callable, ax: Axes | None = None, **kwargs
 ):
     """Visualize a voronoi grid.
 
     Args:
         space: the space to visualize
         agent_portrayal: a callable that is called with the agent and returns a dict
         ax: a Matplotlib Axes instance. If none is provided a new figure and ax will be created using plt.subplots
+        kwargs: additional keyword arguments passed to ax.scatter
 
     Returns:
         Returns the Axes object with the plot drawn onto it.
@@ -589,7 +597,7 @@ def draw_voroinoi_grid(
     ax.set_xlim(x_min - x_padding, x_max + x_padding)
     ax.set_ylim(y_min - y_padding, y_max + y_padding)
 
-    _scatter(ax, arguments)
+    _scatter(ax, arguments, **kwargs)
 
     for cell in space.all_cells:
         polygon = cell.properties["polygon"]
@@ -604,8 +612,15 @@ def draw_voroinoi_grid(
     return ax
 
 
-def _scatter(ax: Axes, arguments):
-    """Helper function for plotting the agents."""
+def _scatter(ax: Axes, arguments, **kwargs):
+    """Helper function for plotting the agents.
+
+    Args:
+        ax: a Matplotlib Axes instance
+        arguments: the agents specific arguments for platting
+        kwargs: additional keyword arguments for ax.scatter
+
+    """
     loc = arguments.pop("loc")
 
     x = loc[:, 0]
@@ -624,6 +639,7 @@ def _scatter(ax: Axes, arguments):
                 marker=mark,
                 zorder=z_order,
                 **{k: v[logical] for k, v in arguments.items()},
+                **kwargs,
             )