add num_max_agents, cleanup

agent.py

@@ -88,6 +88,7 @@ class RandomWalkerAnt(Agent):
             for neighbor in self.front_neighbors:
                 if self.model.grid.is_food(neighbor):
                     self.drop_pheromone = "B"
+                    self.look_for_pheromone = "A"
                     self.sensitivity = self.sensitivity_0
 
                     self.prev_pos = neighbor
@@ -100,17 +101,16 @@ class RandomWalkerAnt(Agent):
                     self.drop_pheromone = "A"
                     self.sensitivity = self.sensitivity_0
 
-                    #TODO: Do we flip the ant here or reset prev pos?
-                    # For now, flip ant just like at food
                     self.prev_pos = neighbor
                     self._next_pos = self.pos
 
                     # recruit new ants
                     for agent_id in self.model.get_unique_ids(self.model.num_new_recruits):
-                        agent = RandomWalkerAnt(unique_id=agent_id, model=self.model, look_for_pheromone="B", drop_pheromone="A")
-                        agent._next_pos = self.pos
-                        self.model.schedule.add(agent)
-                        self.model.grid.place_agent(agent, pos=neighbor)
+                        if self.model.schedule.get_agent_count() <  self.model.num_max_agents:
+                            agent = RandomWalkerAnt(unique_id=agent_id, model=self.model, look_for_pheromone="B", drop_pheromone="A")
+                            agent._next_pos = self.pos
+                            self.model.schedule.add(agent)
+                            self.model.grid.place_agent(agent, pos=neighbor)
 
         # follow positive gradient
         if self.look_for_pheromone is not None:
@@ -176,7 +176,9 @@ class RandomWalkerAnt(Agent):
         assert(self.prev_pos is not None)
         all_neighbors = self.neighbors()
         neighbors_at_the_back = self.neighbors(pos=self.prev_pos, include_center=True)
-        return list(filter(lambda i: i not in neighbors_at_the_back, all_neighbors))
+        front_neighbors = list(filter(lambda i: i not in neighbors_at_the_back, all_neighbors))
+        assert(len(front_neighbors) == 3) # not sure whether always the case, used for debugging
+        return front_neighbors
 
     @property
     def front_neighbor(self):

main.py

@@ -11,6 +11,7 @@ from agent import RandomWalkerAnt
 import numpy as np
 import matplotlib.pyplot as plt
 from mesa.space import Coordinate
+from mesa.datacollection import DataCollector
 
 def main():
     check_pheromone_exponential_decay()
@@ -22,9 +23,6 @@ def check_pheromone_exponential_decay():
     Check whether wanted exponential decay of pheromones on grid is done correctly
     shows plot of pheromone placed on grid vs. equivalent exponential decay function
     """
-
-    from mesa.datacollection import DataCollector
-
     width = 21
     height = width
     num_initial_roamers = 0
@@ -46,9 +44,6 @@ def check_pheromone_exponential_decay():
     model.run_model()
     a_test = model.datacollector.get_model_vars_dataframe()["pheromone_a"]
 
-    import matplotlib.pyplot as plt
-    import numpy as np
-
     plt.figure()
     xx = np.linspace(0,1000, 10000)
     yy = a_test[0]*np.exp(-model.decay_rates["A"]*xx)
@@ -66,8 +61,6 @@ def check_ant_sensitivity_linear_decay():
     shows plot of ant sensitivity placed on grid vs. equivalent linear decay function
     not food sources are on the grid for this run to not reset sensitivities
     """
-    from mesa.datacollection import DataCollector
-
     width = 50
     height = width
     num_initial_roamers = 1
@@ -91,9 +84,6 @@ def check_ant_sensitivity_linear_decay():
     model.run_model()
     a_test = model.datacollector.get_agent_vars_dataframe().reset_index()["sensitivity"]
 
-    import matplotlib.pyplot as plt
-    import numpy as np
-
     plt.figure()
     xx = np.linspace(0,1000, 10000)
     yy = a_test[0] - start*xx
@@ -109,9 +99,6 @@ def check_ant_pheromone_exponential_decay():
     Check whether wanted exponential decay of pheromone drop rate for ants is correctly modeled
     shows plot of pheromone placed on grid vs. equivalent exponential decay function
     """
-
-    from mesa.datacollection import DataCollector
-
     width = 50
     height = width
     num_initial_roamers = 1
@@ -134,10 +121,6 @@ def check_ant_pheromone_exponential_decay():
     model.run_model()
     a_test = model.datacollector.get_agent_vars_dataframe().reset_index()["pheromone_drop_rate"]
 
-    import matplotlib.pyplot as plt
-    import numpy as np
-
-
     plt.figure()
     xx = np.linspace(0,1000, 10000)
     yy = a_test[0]*np.exp(-model.schedule.agents[0].betas["A"]*xx)
@@ -148,8 +131,6 @@ def check_ant_pheromone_exponential_decay():
 
     plt.show()
 
-
-
 if __name__ == "__main__":
     main()
 

model.py

@@ -39,9 +39,10 @@ class ActiveWalkerModel(Model):
                                                }
 
         for agent_id in self.get_unique_ids(num_initial_roamers):
-            agent = RandomWalkerAnt(unique_id=agent_id, model=self, look_for_pheromone="A", drop_pheromone="A")
-            self.schedule.add(agent)
-            self.grid.place_agent(agent, pos=nest_position)
+            if self.schedule.get_agent_count() < self.num_max_agents:
+                agent = RandomWalkerAnt(unique_id=agent_id, model=self, look_for_pheromone="A", drop_pheromone="A")
+                self.schedule.add(agent)
+                self.grid.place_agent(agent, pos=nest_position)
 
         for _ in range(num_food_sources):
             self.grid.add_food(food_size)

server.py

@@ -23,9 +23,9 @@ def setup(params=None):
     if params is None:
         params = {
                   "width": 50, "height": 50,
-                  "num_max_agents" : 100,
+                  "num_max_agents" : 1000,
                   "nest_position" : (25,25),
-                  "num_initial_roamers" : 5,
+                  "num_initial_roamers" : 20,
                   }