plots for linear sensitivity decay and grid pheromone decay

main.py

@@ -13,12 +13,23 @@ import matplotlib.pyplot as plt
 from mesa.space import Coordinate
 
 def main():
+    check_pheromone_exponential_decay()
+    check_ant_sensitivity_linear_decay()
+
+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 = 5
+    num_initial_roamers = 0
     num_max_agents = 100
     nest_position : Coordinate = (width //2, height //2)
-    max_steps = 100
+    max_steps = 1000
 
     model = ActiveWalkerModel(width=width, height=height,
                               num_initial_roamers=num_initial_roamers,
@@ -26,28 +37,71 @@ def main():
                               num_max_agents=num_max_agents,
                               max_steps=max_steps)
 
-    # just initial testing of MultiHexGrid
+    model.grid.fields["A"][5,5] = 10
-    a = model.agent_density()
+    model.datacollector = DataCollector(
-    for loc in model.grid.iter_neighborhood(nest_position):
+            model_reporters={"pheromone_a": lambda m: m.grid.fields["A"][5,5] },
-        a[loc] = 3
+                agent_reporters={}
-    for agent in model.grid.get_neighbors(pos=nest_position, include_center=True):
+                )
-        if agent.unique_id == 2:
+    model.run_model()
-            agent.look_for_chemical = "A"
+    a_test = model.datacollector.get_model_vars_dataframe()["pheromone_a"]
-            agent.prev_pos = (9,10)
-            a[agent.prev_pos] = 1
-            for pos in agent.front_neighbors:
-                a[pos] = 6
-            agent.step()
-            print(f"{agent._next_pos=}")
-            agent.advance()
-            print(agent.front_neighbor)
-            a[agent.front_neighbor] = 5
 
-        print(agent.pos, agent.unique_id, agent.look_for_chemical)
+    import matplotlib.pyplot as plt
-    neighbors = model.grid.get_neighborhood(nest_position)
+    import numpy as np
-    print(neighbors)
 
-    print(a)
+    plt.figure()
+    xx = np.linspace(0,1000, 10000)
+    yy = a_test[0]*np.exp(-model.decay_rates["A"]*xx)
+    plt.plot(xx, yy, label="correct exponential function")
+    plt.scatter(range(len(a_test)), a_test, label="modeled decay", marker='o')
+    plt.title("Exponential grid pheromone decay test")
+    plt.legend(loc='best')
+
+    plt.show()
+
+
+def check_ant_sensitivity_linear_decay():
+    """
+    Check whether wanted linear decay of ant sensitivity is done correctly
+    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
+    num_max_agents = 100
+    nest_position : Coordinate = (width //2, height //2)
+    max_steps = 1000
+    num_food_sources = 0
+
+    model = ActiveWalkerModel(width=width, height=height,
+                              num_initial_roamers=num_initial_roamers,
+                              nest_position=nest_position,
+                              num_max_agents=num_max_agents,
+                              num_food_sources=num_food_sources,
+                              max_steps=max_steps)
+
+    model.datacollector = DataCollector(
+            model_reporters={},
+            agent_reporters={"sensitivity": lambda a: a.sensitivity}
+                )
+    start = model.schedule.agents[0].sensitivity_decay_rate
+    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
+    plt.title("Linear Ant Sensitivity decay test")
+    plt.plot(xx, yy, label="correct linear function")
+    plt.scatter(range(len(a_test)), a_test, label="modeled decay", marker='o')
+    plt.legend(loc='best')
+
+    plt.show()
 
 
 if __name__ == "__main__":

model.py

@@ -20,6 +20,8 @@ class ActiveWalkerModel(Model):
     def __init__(self, width : int, height : int , num_max_agents : int,
                  num_initial_roamers : int,
                  nest_position : Coordinate,
+                 num_food_sources=5,
+                 food_size=10,
                  max_steps:int=1000) -> None:
         super().__init__()
         fields=["A", "B", "nests", "food"]
@@ -41,8 +43,8 @@ class ActiveWalkerModel(Model):
             self.schedule.add(agent)
             self.grid.place_agent(agent, pos=nest_position)
 
-        for _ in range(5):
+        for _ in range(num_food_sources):
-            self.grid.add_food(5)
+            self.grid.add_food(food_size)
 
         self.datacollector = DataCollector(
                 model_reporters={},