examples: Clean-up Epstein Civil Violence and PD grid

examples/advanced/epstein_civil_violence/Readme.md

@@ -18,8 +18,9 @@ Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and p
 
 ## Files
 
-* ``EpsteinCivilViolence.py``: Core model and agent code.
-* ``EpsteinCivilViolenceServer.py``: Sets up the interactive visualization.
+* ``model.py``: Core model code.
+* ``agent.py``: Agent classes.
+* ``app.py``: Sets up the interactive visualization.
 * ``Epstein Civil Violence.ipynb``: Jupyter notebook conducting some preliminary analysis of the model.
 
 ## Further Reading

examples/advanced/epstein_civil_violence/app.py

@@ -0,0 +1,70 @@
+from mesa.visualization import SolaraViz, Slider, make_space_matplotlib, make_plot_measure
+
+from agents import Citizen, Cop
+from model import EpsteinCivilViolence
+
+COP_COLOR = "#000000"
+AGENT_QUIET_COLOR = "#648FFF"
+AGENT_REBEL_COLOR = "#FE6100"
+JAIL_COLOR = "#808080"
+JAIL_SHAPE = "rect"
+
+
+def citizen_cop_portrayal(agent):
+    if agent is None:
+        return
+
+    portrayal = {
+        "shape": "circle",
+        "x": agent.pos[0],
+        "y": agent.pos[1],
+        "filled": True,
+    }
+
+    if isinstance(agent, Citizen):
+        color = AGENT_QUIET_COLOR if agent.condition == "Quiescent" else AGENT_REBEL_COLOR
+        color = JAIL_COLOR if agent.jail_sentence else color
+        shape = JAIL_SHAPE if agent.jail_sentence else "circle"
+        portrayal["color"] = color
+        portrayal["shape"] = shape
+        if shape == "rect":
+            portrayal["w"] = 0.9
+            portrayal["h"] = 0.9
+        else:
+            portrayal["r"] = 0.5
+            portrayal["filled"] = False
+        portrayal["layer"] = 0
+
+    elif isinstance(agent, Cop):
+        portrayal["color"] = COP_COLOR
+        portrayal["r"] = 0.9
+        portrayal["layer"] = 1
+
+    return portrayal
+
+
+model_params = {
+    "height": 40,
+    "width": 40,
+    "citizen_density": Slider("Initial Agent Density", 0.7, 0.0, 0.9, 0.1),
+    "cop_density": Slider("Initial Cop Density", 0.04, 0.0, 0.1, 0.01),
+    "citizen_vision": Slider("Citizen Vision", 7, 1, 10, 1),
+    "cop_vision": Slider("Cop Vision", 7, 1, 10, 1),
+    "legitimacy": Slider("Government Legitimacy", 0.82, 0.0, 1, 0.01),
+    "max_jail_term": Slider("Max Jail Term", 30, 0, 50, 1),
+}
+
+# space_component = make_space_matplotlib(citizen_cop_portrayal)
+chart_component = make_plot_measure(["Quiescent", "Active", "Jailed"])
+
+epstein_model = EpsteinCivilViolence()
+
+page = SolaraViz(
+    epstein_model,
+    components=[
+        # space_component,
+        chart_component],
+    model_params=model_params,
+    name="Epstein Civil Violence",
+)
+page  # noqa

examples/advanced/epstein_civil_violence/epstein_civil_violence/model.py → examples/advanced/epstein_civil_violence/model.py

@@ -1,6 +1,6 @@
 import mesa
 
-from .agent import Citizen, Cop
+from agents import Citizen, Cop
 
 
 class EpsteinCivilViolence(mesa.Model):
@@ -43,8 +43,9 @@ def __init__(
         arrest_prob_constant=2.3,
         movement=True,
         max_iters=1000,
+        seed=None
     ):
-        super().__init__()
+        super().__init__(seed=seed)
         self.width = width
         self.height = height
         self.citizen_density = citizen_density
@@ -59,9 +60,7 @@ def __init__(
         self.max_iters = max_iters
         self.iteration = 0
 
-        self.grid = mesa.experimental.cell_space.OrthogonalMooreGrid(
-            (width, height), capacity=1, torus=True
-        )
+        self.grid = mesa.experimental.cell_space.OrthogonalMooreGrid((width, height), capacity=1, torus=True, random=self.random)
 
         model_reporters = {
             "Quiescent": lambda m: self.count_type_citizens(m, "Quiescent"),
@@ -144,3 +143,17 @@ def count_cops(model):
         Helper method to count jailed agents.
         """
         return len(model.agents_by_type[Cop])
+
+
+if __name__ == "__main__":
+
+    import mesa.experimental as experimental
+
+    import sys
+
+    print(sys.getrecursionlimit())
+
+    model = EpsteinCivilViolence()
+    import copy
+    # copy.deepcopy(model)
+    copy.deepcopy(experimental.cell_space.OrthogonalMooreGrid((100,100), torus=True, random=model.random))

examples/advanced/pd_grid/app.py

@@ -0,0 +1,45 @@
+import mesa
+from mesa.visualization import SolaraViz
+from mesa.visualization.UserParam import Slider
+# Import make_space_altair
+from mesa.visualization import make_space_altair, make_plot_measure
+
+from model import PdGrid
+
+def portray_pd_agent(agent):
+    """This function is called to portray each agent in the visualization."""
+    if agent is None:
+        return {}
+    return {
+        "shape": "rect",
+        "w": 1,
+        "h": 1,
+        "color": "blue" if agent.isCooroperating else "red",
+    }
+
+# Define model parameters
+model_params = {
+    "height": Slider("Grid Height", 50, 10, 100, 1),
+    "width": Slider("Grid Width", 50, 10, 100, 1),
+    # TODO: Implement Choice in UserParam. See https://github.com/projectmesa/mesa/issues/2376
+    # "activation_order": Choice(
+    #     "Activation regime",
+    #     value="Random",
+    #     choices=PdGrid.activation_regimes,
+    # ),
+}
+
+# Create the model instance
+pd_model = PdGrid()
+
+coop_plot = make_plot_measure("cooperating_agents")
+
+# Create the SolaraViz instance
+page = SolaraViz(
+    pd_model,
+    # TODO: Support the Cell Space in SolaraViz.
+    components=[coop_plot],  # [make_space_altair(portray_pd_agent)],
+    model_params=model_params,
+    name="Prisoner's Dilemma"
+)
+page  # noqa

examples/advanced/pd_grid/pd_grid/model.py → examples/advanced/pd_grid/model.py

@@ -1,7 +1,7 @@
 import mesa
 from mesa.experimental.cell_space import OrthogonalMooreGrid
 
-from .agent import PDAgent
+from agents import PDAgent
 
 
 class PdGrid(mesa.Model):
@@ -41,7 +41,7 @@ def __init__(
 
         self.datacollector = mesa.DataCollector(
             {
-                "Cooperating_Agents": lambda m: len(
+                "cooperating_agents": lambda m: len(
                     [a for a in m.agents if a.move == "C"]
                 )
             }

examples/advanced/pd_grid/readme.md

@@ -27,9 +27,10 @@ Launch the ``Demographic Prisoner's Dilemma Activation Schedule.ipynb`` notebook
 
 ## Files
 
-* ``run.py`` is the entry point for the font-end simulations.
-* ``pd_grid/``: contains the model and agent classes; the model takes a ``activation_order`` string as an argument, which determines in which order agents are activated: Sequential, Random or Simultaneous.
-* ``Demographic Prisoner's Dilemma Activation Schedule.ipynb``: Jupyter Notebook for running the scheduling experiment. This runs the model three times, one for each activation type, and demonstrates how the activation regime drives the model to different outcomes.
+* ``model.py``: contains the model, which takes a ``activation_order`` string as an argument, which determines in which order agents are activated: Sequential, Random or Simultaneous.
+* ``agents.py``: contains the agent class, which has a strategy and a score.
+* ``app.py``: contains the interactive visualization of the model.
+* ``analysis.ipynb``: Jupyter Notebook for running the scheduling experiment. This runs the model three times, one for each activation type, and demonstrates how the activation regime drives the model to different outcomes.
 
 ## Further Reading
 

mesa/experimental/cell_space/cell.py

@@ -40,15 +40,6 @@ class Cell:
         "__dict__",
     ]
 
-    # def __new__(cls,
-    #     coordinate: tuple[int, ...],
-    #     capacity: float | None = None,
-    #     random: Random | None = None,):
-    #     if capacity != 1:
-    #         return object.__new__(cls)
-    #     else:
-    #         return object.__new__(SingleAgentCell)
-
     def __init__(
         self,
         coordinate: Coordinate,