Cleanup and restructure EpsteinCivilViolence and PdGrid examples

mesa/examples/__init__.py

@@ -1,3 +1,5 @@
+from mesa.examples.advanced.epstein_civil_violence.model import EpsteinCivilViolence
+from mesa.examples.advanced.pd_grid.model import PdGrid
 from mesa.examples.basic.boid_flockers.model import BoidFlockers
 from mesa.examples.basic.boltzmann_wealth_model.model import BoltzmannWealthModel
 from mesa.examples.basic.conways_game_of_life.model import ConwaysGameOfLife
@@ -10,4 +12,6 @@
     "ConwaysGameOfLife",
     "Schelling",
     "VirusOnNetwork",
+    "EpsteinCivilViolence",
+    "PdGrid",
 ]

mesa/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

mesa/examples/advanced/epstein_civil_violence/app.py

@@ -0,0 +1,72 @@
+from mesa.examples.advanced.epstein_civil_violence.agents import Citizen, Cop
+from mesa.examples.advanced.epstein_civil_violence.model import EpsteinCivilViolence
+from mesa.visualization import (
+    Slider,
+    SolaraViz,
+    make_plot_measure,
+    make_space_matplotlib,
+)
+
+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 = {
+        "size": 25,
+        "shape": "s",  # square marker
+    }
+
+    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 == "s":
+            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

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

@@ -1,6 +1,5 @@
 import mesa
-
-from .agent import Citizen, Cop
+from mesa.examples.advanced.epstein_civil_violence.agents import Citizen, Cop
 
 
 class EpsteinCivilViolence(mesa.Model):
@@ -43,8 +42,9 @@ 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
@@ -60,7 +60,7 @@ def __init__(
         self.iteration = 0
 
         self.grid = mesa.experimental.cell_space.OrthogonalMooreGrid(
-            (width, height), capacity=1, torus=True
+            (width, height), capacity=1, torus=True, random=self.random
         )
 
         model_reporters = {

mesa/examples/advanced/pd_grid/app.py

@@ -0,0 +1,51 @@
+"""
+Solara-based visualization for the Spatial Prisoner's Dilemma Model.
+"""
+
+from mesa.examples.advanced.pd_grid.model import PdGrid
+from mesa.visualization import SolaraViz, make_plot_measure, make_space_matplotlib
+from mesa.visualization.UserParam import Slider
+
+
+def pd_agent_portrayal(agent):
+    """
+    Portrayal function for rendering PD agents in the visualization.
+    """
+    return {
+        "color": "blue" if agent.move == "C" else "red",
+        "shape": "s",  # square marker
+        "size": 25,
+    }
+
+
+# Model parameters
+model_params = {
+    "width": Slider("Grid Width", value=50, min=10, max=100, step=1),
+    "height": Slider("Grid Height", value=50, min=10, max=100, step=1),
+    "activation_order": {
+        "type": "Select",
+        "value": "Random",
+        "values": PdGrid.activation_regimes,
+        "label": "Activation Regime",
+    },
+}
+
+
+# Create grid visualization component using Altair
+grid_viz = make_space_matplotlib(agent_portrayal=pd_agent_portrayal)
+
+# Create plot for tracking cooperating agents over time
+plot_component = make_plot_measure("Cooperating_Agents")
+
+# Initialize model
+initial_model = PdGrid()
+
+# Create visualization with all components
+page = SolaraViz(
+    model=initial_model,
+    components=[grid_viz, plot_component],
+    model_params=model_params,
+    name="Spatial Prisoner's Dilemma",
+)
+
+page

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

@@ -1,8 +1,7 @@
 import mesa
+from mesa.examples.advanced.pd_grid.agents import PDAgent
 from mesa.experimental.cell_space import OrthogonalMooreGrid
 
-from .agent import PDAgent
-
 
 class PdGrid(mesa.Model):
     """Model class for iterated, spatial prisoner's dilemma model."""

mesa/examples/advanced/pd_grid/readme.md

@@ -19,16 +19,17 @@ The Demographic Prisoner's Dilemma demonstrates how simple rules can lead to the
 
 ##### Web based model simulation
 
-To run the model interactively, run ``mesa runserver`` in this directory.
+To run the model interactively, run ``solara run app.py`` in this directory.
 
 ##### Jupyter Notebook
 
 Launch the ``Demographic Prisoner's Dilemma Activation Schedule.ipynb`` notebook and run the code.
 
 ## 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.
+* ``agents.py``: contains the agent class.
+* ``model.py``: contains the model class; the model takes a ``activation_order`` string as an argument, which determines in which order agents are activated: Sequential, Random or Simultaneous.
+* ``app.py``: contains the interactive visualization server.
 * ``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.
 
 ## Further Reading