309 lines
10 KiB
Python
Executable File
309 lines
10 KiB
Python
Executable File
#!/bin/python
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"""
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main.py - Part of ants project
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execute via `python main.py` in terminal or only UNIX: `./main.py`
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License: AGPL 3 (see end of file)
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(C) Alexander Bocken, Viviane Fahrni, Grace Kagho
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"""
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from model import ActiveWalkerModel
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import numpy as np
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import matplotlib.pyplot as plt
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from mesa.space import Coordinate
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from mesa.datacollection import DataCollector
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#from multihex import MultiHexGrid
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def check_pheromone_exponential_decay():
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"""
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Check whether wanted exponential decay of pheromones on grid is done correctly
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shows plot of pheromone placed on grid vs. equivalent exponential decay function
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"""
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width = 21
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height = width
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num_initial_roamers = 0
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num_max_agents = 100
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nest_position : Coordinate = (width //2, height //2)
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max_steps = 1000
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model = ActiveWalkerModel(width=width, height=height,
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num_initial_roamers=num_initial_roamers,
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nest_position=nest_position,
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num_max_agents=num_max_agents,
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max_steps=max_steps)
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model.grid.fields["A"][5,5] = 10
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model.datacollector = DataCollector(
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model_reporters={"pheromone_a": lambda m: m.grid.fields["A"][5,5] },
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agent_reporters={}
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)
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model.run_model()
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a_test = model.datacollector.get_model_vars_dataframe()["pheromone_a"]
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plt.figure()
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xx = np.linspace(0,1000, 10000)
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yy = a_test[0]*np.exp(-model.decay_rates["A"]*xx)
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plt.plot(xx, yy, label="correct exponential function")
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plt.scatter(range(len(a_test)), a_test, label="modeled decay", marker='o')
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plt.title("Exponential grid pheromone decay test")
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plt.legend(loc='best')
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plt.show()
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def check_ant_sensitivity_linear_decay():
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"""
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Check whether wanted linear decay of ant sensitivity is done correctly
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shows plot of ant sensitivity placed on grid vs. equivalent linear decay function
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not food sources are on the grid for this run to not reset sensitivities
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"""
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width = 50
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height = width
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num_initial_roamers = 1
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num_max_agents = 100
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nest_position : Coordinate = (width //2, height //2)
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max_steps = 1000
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num_food_sources = 0
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model = ActiveWalkerModel(width=width, height=height,
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num_initial_roamers=num_initial_roamers,
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nest_position=nest_position,
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num_max_agents=num_max_agents,
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num_food_sources=num_food_sources,
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max_steps=max_steps)
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model.datacollector = DataCollector(
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model_reporters={},
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agent_reporters={"sensitivity": lambda a: a.sensitivity}
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)
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start = model.schedule.agents[0].sensitivity_decay_rate
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model.run_model()
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a_test = model.datacollector.get_agent_vars_dataframe().reset_index()["sensitivity"]
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plt.figure()
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xx = np.linspace(0,1000, 10000)
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yy = a_test[0] - start*xx
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plt.title("Linear Ant Sensitivity decay test")
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plt.plot(xx, yy, label="correct linear function")
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plt.scatter(range(len(a_test)), a_test, label="modeled decay", marker='o')
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plt.legend(loc='best')
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plt.show()
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def check_ant_pheromone_exponential_decay():
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"""
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Check whether wanted exponential decay of pheromone drop rate for ants is correctly modeled
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shows plot of pheromone placed on grid vs. equivalent exponential decay function
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"""
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width = 50
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height = width
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num_initial_roamers = 1
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num_max_agents = 100
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nest_position : Coordinate = (width //2, height //2)
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num_food_sources = 0;
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max_steps = 1000
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model = ActiveWalkerModel(width=width, height=height,
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num_initial_roamers=num_initial_roamers,
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nest_position=nest_position,
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num_max_agents=num_max_agents,
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max_steps=max_steps)
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model.datacollector = DataCollector(
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model_reporters={},
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agent_reporters={"pheromone_drop_rate": lambda a: a.pheromone_drop_rate["A"]}
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)
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start = model.schedule.agents[0].pheromone_drop_rate["A"]
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model.run_model()
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a_test = model.datacollector.get_agent_vars_dataframe().reset_index()["pheromone_drop_rate"]
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plt.figure()
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xx = np.linspace(0,1000, 10000)
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yy = a_test[0]*np.exp(-model.schedule.agents[0].betas["A"]*xx)
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plt.plot(xx, yy, label="correct exponential function")
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plt.scatter(range(len(a_test)), a_test, label="modeled decay", marker='o')
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plt.title("Exponential pheromone drop rate decay test")
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plt.legend(loc='best')
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plt.show()
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def check_ants_follow_gradient():
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"""
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Create a path of neighbours with a static gradient.
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Observe whether ant correctly follows gradient once found. via matrix printouts
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8 = ant
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anything else: pheromone A density.
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The ant does not drop any new pheromones for this test
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"""
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width, height = 20,20
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params = {
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"width": width, "height": height,
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"num_max_agents": 1,
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"num_food_sources": 0,
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"nest_position": (10,10),
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"num_initial_roamers": 1,
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}
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model = ActiveWalkerModel(**params)
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def place_line(grid : MultiHexGrid, start_pos=None):
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strength = 5
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if start_pos is None:
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start_pos = (9,9)
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next_pos = start_pos
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for _ in range(width):
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grid.fields["A"][next_pos] = strength
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strength += 0.01
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next_pos = grid.get_neighborhood(next_pos)[0]
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place_line(model.grid)
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ant = model.schedule._agents[0]
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ant.looking_for_pheromone = "A"
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ant.drop_pheromone = None
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ant.threshold["A"] = 0
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ant.sensitivity_max = 100
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#model.grid.fields["A"] = np.diag(np.ones(width))
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model.decay_rates["A"] = 0
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while model.schedule.steps < 100:
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display_field = np.copy(model.grid.fields["A"])
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display_field[ant.pos] = 8
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print(display_field)
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print(20*"#")
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model.step()
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def viviane_bfs_example_run():
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# Breadth-first-search algorithm for connectivity
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def bfs(graph, start_node, threshold): #graph=grid, start_node=nest, threshold=TBD?
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from collections import deque
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visited = set()
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queue = deque([(start_node, [])])
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paths = {}
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connected_food_sources = set()
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while queue:
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current_node, path = queue.popleft()
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#current_node = tuple(current_node)
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visited.add(current_node)
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if current_node in graph:
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for neighbor, m.grid.fields["A"] in graph[current_node].items():
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if neighbor not in visited and m.grid.fields["A"] >= threshold:
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new_path = path + [neighbor]
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queue.append((neighbor, new_path))
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# Check if the neighbor is a food source
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if neighbor in self.grid_food:
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if neighbor not in paths:
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paths[neighbor] = new_path
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connected_food_sources.add(neighbor)
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connectivity = len(connected_food_sources)
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return connectivity
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# Calculate connectivity through BFS
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current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
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import numpy as np
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N = 121
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N_X = int(np.sqrt(N))
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N_Y = N // N_X
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# fancy way of saying absolutely nothing but 11
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xv, yv = np.meshgrid(np.arange(N_X), np.arange(N_Y), sparse=False, indexing='xy')
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print(f"{N_X=}")
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print(f"{N_Y=}")
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print(f"{(xv, yv)=}")
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print(f"{xv=}")
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from model import kwargs_paper_setup1 as kwargs
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if __name__ == "__main__":
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print("Test")
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kwargs["resistance_map_type"] = "perlin"
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print(kwargs)
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model = ActiveWalkerModel(**kwargs)
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from hexplot import plot_hexagon
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a = np.zeros_like(model.grid.fields['food'])
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a[np.nonzero(model.grid.fields['food'])] = 1
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plot_hexagon(a, title="Nest locations")
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plot_hexagon(model.grid.fields['res'], title="Resistance Map")
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from tqdm import tqdm as progress_bar
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for _ in progress_bar(range(model.max_steps)):
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model.step()
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# Access the DataCollector
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#datacollector = model.datacollector
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## Get the data from the DataCollector
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#model_data = datacollector.get_model_vars_dataframe()
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#print(model_data.columns)
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## Plot the number of alive ants over time
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#plt.plot(model_data.index, model_data['alive_ants'])
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#plt.xlabel('Time')
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#plt.ylabel('Number of Alive Ants') #this should probably be "active" ants, since it is not considering those in the nest
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#plt.title('Number of Alive Ants Over Time')
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#plt.grid(True)
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#plt.show()
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## Plot the number of sucessful walkers over time
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#plt.plot(model_data.index, model_data['sucessful_walkers'])
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#plt.xlabel('Time')
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#plt.ylabel('Number of Sucessful Walkers')
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#plt.title('Number of Sucessful Walkers Over Time')
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#plt.grid(True)
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#plt.show()
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## Calculate the cumulative sum
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#model_data['cumulative_sucessful_walkers'] = model_data['sucessful_walkers'].cumsum()
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## Plot the cumulative sum of sucessful walkers over time
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#plt.plot(model_data.index, model_data['cumulative_sucessful_walkers'])
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#plt.xlabel('Time')
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#plt.ylabel('Cumulative Sucessful Walkers')
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#plt.title('Cumulative Sucessful Walkers Over Time')
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#plt.grid(True)
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#plt.show()
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## Values over 100 are to be interpreted as walkers being sucessfull several times since the total max number of ants is 100
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# # Connectivity measure
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#def check_food_source_connectivity(food_sources, paths): #food_sources = nodes.is_nest, paths=result from BFS
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# connected_food_sources = set()
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# for source in food_sources:
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# if source in paths:
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# connected_food_sources.add(source)
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# connectivity = len(connected_food_sources)
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# return connectivity
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# # Calculate connectivity through BFS
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# current_paths = bfs(self.grid, self.grid.fields["nests"], 0.000001)
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"""
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This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, version 3.
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This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Affero General Public License for more details.
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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/>
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"""
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