Merge branch 'viviane'

agent.py

@@ -17,6 +17,7 @@ import numpy.typing as npt
 from mesa.agent import Agent
 from mesa.space import Coordinate
 
+
 class RandomWalkerAnt(Agent):
     def __init__(self, unique_id, model,
                  look_for_pheromone=None,
@@ -112,8 +113,8 @@ class RandomWalkerAnt(Agent):
                     self.energy = self.model.e_0
 
                     #now look for other pheromone
-                    self.drop_pheromone = "B"
                     self.look_for_pheromone = "A"
+                    self.drop_pheromone = "B"
 
                     self._prev_pos = neighbor
                     self._next_pos = self.pos
@@ -127,11 +128,13 @@ class RandomWalkerAnt(Agent):
                     self.sensitivity = self.model.s_0
                     self.energy = self.model.e_0
 
-                    self.look_for_pheromone = "A" # Is this a correct interpretation?
+                    self.look_for_pheromone = "B"
                     self.drop_pheromone = "A"
 
                     self._prev_pos = neighbor
                     self._next_pos = self.pos
+                    self.model.successful_ants += 1
+
 
                     # recruit new ants
                     for agent_id in self.model.get_unique_ids(self.model.N_r):

main.py

@@ -1,3 +1,4 @@
+
 #!/bin/python
 """
 main.py - Part of ants project
@@ -14,7 +15,7 @@ import matplotlib.pyplot as plt
 from mesa.space import Coordinate
 from mesa.datacollection import DataCollector
 
-from multihex import MultiHexGrid
+#from multihex import MultiHexGrid
 
 def main():
     pass
@@ -186,6 +187,7 @@ def check_ants_follow_gradient():
 #     main()
 
 from model import kwargs_paper_setup1 as kwargs
+# kwargs["N_m"] = 10000
 model = ActiveWalkerModel(**kwargs)
 
 from hexplot import plot_hexagon
@@ -213,3 +215,94 @@ This program is distributed in the hope that it will be useful, but WITHOUT ANY
 
 You should have received a copy of the GNU Affero General Public License along with this program. If not, see <https://www.gnu.org/licenses/>
 """
+
+
+
+# |%%--%%| <Z5Ra4Us5kN|y6CRYNrY9x>
+
+# Access the DataCollector
+datacollector = model.datacollector
+
+
+# |%%--%%| <y6CRYNrY9x|v2PfrSWbzG>
+
+# Get the data from the DataCollector
+model_data = datacollector.get_model_vars_dataframe()
+
+# |%%--%%| <v2PfrSWbzG|74OaeOltqi>
+
+print(model_data.columns)
+
+# |%%--%%| <74OaeOltqi|WpQLCA0RuP>
+
+# Plot the number of alive ants over time
+plt.plot(model_data.index, model_data['alive_ants'])
+plt.xlabel('Time')
+plt.ylabel('Number of Alive Ants') #this should probably be "active" ants, since it is not considering those in the nest
+plt.title('Number of Alive Ants Over Time')
+plt.grid(True)
+plt.show()
+
+# |%%--%%| <WpQLCA0RuP|UufL3yaROS>
+
+# Plot the number of sucessful walkers over time
+plt.plot(model_data.index, model_data['sucessful_walkers'])
+plt.xlabel('Time')
+plt.ylabel('Number of Sucessful Walkers')
+plt.title('Number of Sucessful Walkers Over Time')
+plt.grid(True)
+plt.show()
+
+# |%%--%%| <UufL3yaROS|mgJWQ0bqG1>
+
+# Calculate the cumulative sum
+model_data['cumulative_sucessful_walkers'] = model_data['sucessful_walkers'].cumsum()
+
+# Plot the cumulative sum of sucessful walkers over time
+plt.plot(model_data.index, model_data['cumulative_sucessful_walkers'])
+plt.xlabel('Time')
+plt.ylabel('Cumulative Sucessful Walkers')
+plt.title('Cumulative Sucessful Walkers Over Time')
+plt.grid(True)
+plt.show()
+
+# Values over 100 are to be interpreted as walkers being sucessfull several times since the total max number of ants is 100
+
+# |%%--%%| <mgJWQ0bqG1|64kmoHYvCD>
+
+ # Connectivity measure
+def check_food_source_connectivity(food_sources, paths): #food_sources = nodes.is_nest, paths=result from BFS
+    connected_food_sources = set()
+
+    for source in food_sources:
+        if source in paths:
+            connected_food_sources.add(source)
+
+    connectivity = len(connected_food_sources)
+
+
+    return connectivity
+
+
+    # Calculate connectivity through BFS
+
+    current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
+
+
+# |%%--%%| <64kmoHYvCD|JEzmDy4wuX>
+
+
+
+# |%%--%%| <JEzmDy4wuX|U9vmSFZUyD>
+
+
+
+
+
+
+
+# |%%--%%| <U9vmSFZUyD|r0xVXEqlAh>
+
+
+
+# |%%--%%| <r0xVXEqlAh|6K80EwwmVN>

model.py

@@ -15,6 +15,7 @@ from multihex import MultiHexGridScalarFields
 from mesa.time import SimultaneousActivation
 from mesa.datacollection import DataCollector
 from agent import RandomWalkerAnt
+from collections import deque
 
 kwargs_paper_setup1 = {
         "width": 100,
@@ -62,6 +63,7 @@ kwargs_paper_setup2 = {
         "resistance_map_type" : None,
 }
 
+
 class ActiveWalkerModel(Model):
     def __init__(self, width : int, height : int,
                  N_0 : int, # number of initial roamers
@@ -98,6 +100,7 @@ class ActiveWalkerModel(Model):
         self.q_tr : float    = q_tr  # threshold under which ant cannot distinguish concentrations
         self.e_min : float   = e_min # energy at which walker dies
         self.N_f : int       = N_f #num food sources
+        self.successful_ants = 0    # for viviane's graph
 
         fields=["A", "B", "nests", "food", "res"]
         self.schedule = SimultaneousActivation(self)
@@ -120,8 +123,6 @@ class ActiveWalkerModel(Model):
             raise NotImplemented(f"{resistance_map_type=} is not implemented.")
 
 
-
-
         self._unique_id_counter = -1
 
         self.max_steps = max_steps
@@ -136,15 +137,60 @@ class ActiveWalkerModel(Model):
         for _ in range(N_f):
             self.grid.add_food(food_size)
 
+
+        # Breadth-first-search algorithm for connectivity
+        #def bfs(graph, start_node, threshold): #graph=grid, start_node=nest, threshold=TBD?
+         #   visited = set()
+         #   queue = deque([(start_node, [])])
+         #   paths = {}
+         #   connected_food_sources = set()
+
+         #   while queue:
+         #       current_node, path = queue.popleft()
+                #current_node = tuple(current_node)
+         #       visited.add(current_node)
+
+         #       if current_node in graph:
+         #           for neighbor, m.grid.fields["A"] in graph[current_node].items():
+         #               if neighbor not in visited and m.grid.fields["A"] >= threshold:
+         #                   new_path = path + [neighbor]
+         #                   queue.append((neighbor, new_path))
+
+                            # Check if the neighbor is a food source
+         #                   if neighbor in self.grid_food:
+         #                       if neighbor not in paths:
+         #                           paths[neighbor] = new_path
+         #                           connected_food_sources.add(neighbor)
+
+         #   connectivity = len(connected_food_sources)
+
+         #   return connectivity
+
+
+        # Calculate connectivity through BFS
+
+       # current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
+
+
+
+
         self.datacollector = DataCollector(
                 # model_reporters={"agent_dens": lambda m: m.agent_density()},
                 model_reporters = {"pheromone_a": lambda m: m.grid.fields["A"],
                                     "pheromone_b": lambda m: m.grid.fields["B"],
+                                    "alive_ants": lambda m: m.schedule.get_agent_count(),
+                                    "sucessful_walkers": lambda m: m.successful_ants,
+                                    #"connectivity": lambda m: check_food_source_connectivity(self.grid_food,current_paths),
                                    },
                 agent_reporters={}
                 )
         self.datacollector.collect(self) # keep at end of __init___
 
+    #def subset_agent_count(self):
+       # subset_agents = [agent for agent in self.schedule.agents if agent.sensitivity == self.s_0]
+       # count = float(len(subset_agents))
+       # return count
+
     def agent_density(self):
         a = np.zeros((self.grid.width, self.grid.height))
         for i in range(self.grid.width):