diff --git a/agent.py b/agent.py
index 48e6e7c..105fcae 100644
--- a/agent.py
+++ b/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):
diff --git a/main.py b/main.py
index 0d780ce..1d91e90 100755
--- a/main.py
+++ b/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
"""
+
+
+
+# |%%--%%|
+
+# Access the DataCollector
+datacollector = model.datacollector
+
+
+# |%%--%%|
+
+# Get the data from the DataCollector
+model_data = datacollector.get_model_vars_dataframe()
+
+# |%%--%%|
+
+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()
+
+# |%%--%%|
+
+# 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()
+
+# |%%--%%|
+
+# 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
+
+# |%%--%%|
+
+ # 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>
+
+
+
+# |%%--%%|
+
+
+
+
+
+
+
+# |%%--%%|
+
+
+
+# |%%--%%|
diff --git a/model.py b/model.py
index ae29610..357d95b 100644
--- a/model.py
+++ b/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):