Improve schelling model documentation

examples/schelling/README.md

@@ -22,6 +22,14 @@ To run the model interactively, run ``mesa runserver`` in this directory. e.g.
     $ mesa runserver
 ```
 
+or
+
+Directly run the file ``run.py`` in the terminal. e.g.
+
+```
+    $ python run.py
+```
+
 Then open your browser to [http://127.0.0.1:8521/](http://127.0.0.1:8521/) and press Reset, then Run.
 
 To view and run some example model analyses, launch the IPython Notebook and open ``analysis.ipynb``. Visualizing the analysis also requires [matplotlib](http://matplotlib.org/).

examples/schelling/model.py

@@ -6,7 +6,7 @@ class SchellingAgent(mesa.Agent):
     Schelling segregation agent
     """
 
-    def __init__(self, pos, model, agent_type):
+    def __init__(self, unique_id, model, agent_type):
         """
         Create a new Schelling agent.
 
@@ -15,13 +15,14 @@ def __init__(self, pos, model, agent_type):
            x, y: Agent initial location.
            agent_type: Indicator for the agent's type (minority=1, majority=0)
         """
-        super().__init__(pos, model)
-        self.pos = pos
+        super().__init__(unique_id, model)
         self.type = agent_type
 
     def step(self):
         similar = 0
-        for neighbor in self.model.grid.iter_neighbors(self.pos, True):
+        for neighbor in self.model.grid.iter_neighbors(
+            self.pos, moore=True, radius=self.model.radius
+        ):
             if neighbor.type == self.type:
                 similar += 1
 
@@ -37,47 +38,64 @@ class Schelling(mesa.Model):
     Model class for the Schelling segregation model.
     """
 
-    def __init__(self, width=20, height=20, density=0.8, minority_pc=0.2, homophily=3):
-        super().__init__()
-        self.width = width
+    def __init__(
+        self,
+        height=20,
+        width=20,
+        homophily=3,
+        radius=1,
+        density=0.8,
+        minority_pc=0.2,
+        seed=None,
+    ):
+        """
+        Create a new Schelling model.
+
+        Args:
+            width, height: Size of the space.
+            density: Initial Chance for a cell to populated
+            minority_pc: Chances for an agent to be in minority class
+            homophily: Minimum number of agents of same class needed to be happy
+            radius: Search radius for checking similarity
+            seed: Seed for Reproducibility
+        """
+
+        super().__init__(seed=seed)
         self.height = height
+        self.width = width
         self.density = density
         self.minority_pc = minority_pc
         self.homophily = homophily
+        self.radius = radius
 
         self.schedule = mesa.time.RandomActivation(self)
         self.grid = mesa.space.SingleGrid(width, height, torus=True)
 
         self.happy = 0
         self.datacollector = mesa.DataCollector(
-            {"happy": "happy"},  # Model-level count of happy agents
-            # For testing purposes, agent's individual x and y
-            {"x": lambda a: a.pos[0], "y": lambda a: a.pos[1]},
+            model_reporters={"happy": "happy"},  # Model-level count of happy agents
         )
 
         # Set up agents
         # We use a grid iterator that returns
         # the coordinates of a cell as well as
         # its contents. (coord_iter)
-        for cell in self.grid.coord_iter():
-            x, y = cell[1]
+        for _, pos in self.grid.coord_iter():
             if self.random.random() < self.density:
                 agent_type = 1 if self.random.random() < self.minority_pc else 0
-
-                agent = SchellingAgent((x, y), self, agent_type)
-                self.grid.place_agent(agent, (x, y))
+                agent = SchellingAgent(self.next_id(), self, agent_type)
+                self.grid.place_agent(agent, pos)
                 self.schedule.add(agent)
 
-        self.running = True
         self.datacollector.collect(self)
 
     def step(self):
         """
-        Run one step of the model. If All agents are happy, halt the model.
+        Run one step of the model.
         """
         self.happy = 0  # Reset counter of happy agents
         self.schedule.step()
-        # collect data
+
         self.datacollector.collect(self)
 
         if self.happy == self.schedule.get_agent_count():

examples/schelling/server.py

@@ -26,20 +26,35 @@ def schelling_draw(agent):
     return portrayal
 
 
-canvas_element = mesa.visualization.CanvasGrid(schelling_draw, 20, 20, 500, 500)
+canvas_element = mesa.visualization.CanvasGrid(
+    portrayal_method=schelling_draw,
+    grid_width=20,
+    grid_height=20,
+    canvas_width=500,
+    canvas_height=500,
+)
 happy_chart = mesa.visualization.ChartModule([{"Label": "happy", "Color": "Black"}])
 
 model_params = {
     "height": 20,
     "width": 20,
-    "density": mesa.visualization.Slider("Agent density", 0.8, 0.1, 1.0, 0.1),
-    "minority_pc": mesa.visualization.Slider("Fraction minority", 0.2, 0.00, 1.0, 0.05),
-    "homophily": mesa.visualization.Slider("Homophily", 3, 0, 8, 1),
+    "density": mesa.visualization.Slider(
+        name="Agent density", value=0.8, min_value=0.1, max_value=1.0, step=0.1
+    ),
+    "minority_pc": mesa.visualization.Slider(
+        name="Fraction minority", value=0.2, min_value=0.00, max_value=1.0, step=0.05
+    ),
+    "homophily": mesa.visualization.Slider(
+        name="Homophily", value=3, min_value=0, max_value=8, step=1
+    ),
+    "radius": mesa.visualization.Slider(
+        name="Search Radius", value=1, min_value=1, max_value=5, step=1
+    ),
 }
 
 server = mesa.visualization.ModularServer(
-    Schelling,
-    [canvas_element, get_happy_agents, happy_chart],
-    "Schelling",
-    model_params,
+    model_cls=Schelling,
+    visualization_elements=[canvas_element, get_happy_agents, happy_chart],
+    name="Schelling Segregation Model",
+    model_params=model_params,
 )