further plotting experiments
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18
agent.py
18
agent.py
@ -9,7 +9,7 @@ License: AGPL 3 (see end of file)
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"""
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TO DISCUSS:
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Is the separation of energy and sensitivity useful?
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Is the separation of energy and sensitivity useful? -> only if we have the disconnect via resistance
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"""
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import numpy as np
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@ -22,7 +22,7 @@ class RandomWalkerAnt(Agent):
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def __init__(self, unique_id, model,
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look_for_pheromone=None,
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drop_pheromone=None,
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sensitivity_max = 30000,
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sensitivity_max = 10000,
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) -> None:
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super().__init__(unique_id=unique_id, model=model)
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@ -81,9 +81,11 @@ class RandomWalkerAnt(Agent):
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# bit round-about but self.model.grid.fields['res'][positions]
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# gets interpreted as slices, not multiple singular positions
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resistance = np.array([ self.model.grid.fields['res'][x,y] for x,y in positions ])
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easiness = np.max(self.model.grid.fields['res']) - resistance + 1e-15 # + epsilon to not divide by zero
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easiness = np.max(self.model.grid.fields['res']) - resistance + np.min(self.model.grid.fields['res']) + 1e-15 # + epsilon to not divide by zero
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weights = easiness/ np.sum(easiness)
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#inv_weights = resistance/ np.sum(resistance)
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#weights = 1 - inv_weights
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#weights /= np.sum(weights)
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return weights
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def _choose_next_pos(self):
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@ -103,7 +105,7 @@ class RandomWalkerAnt(Agent):
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"""
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combined = res_weights * walk_weights
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normalized = combined / np.sum(combined)
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return list(normalized)
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return normalized
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def _pick_from_remaining_five(remaining_five):
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"""
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@ -213,8 +215,6 @@ class RandomWalkerAnt(Agent):
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self._prev_pos = self.pos
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def step(self):
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self.sensitivity -= self.model.d_s
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self.energy -= self.model.grid.fields['res'][self.pos] * self.model.d_e
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# Die and get removed if no energy
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if self.energy < self.model.e_min:
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self.model.schedule.remove(self)
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@ -222,6 +222,10 @@ class RandomWalkerAnt(Agent):
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self._choose_next_pos()
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self._adjust_pheromone_drop_rate()
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self.sensitivity -= self.model.d_s
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self.energy -= self.model.grid.fields['res'][self.pos] * self.model.d_e
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def _adjust_pheromone_drop_rate(self):
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if(self.drop_pheromone is not None):
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self.pheromone_drop_rate -= self.pheromone_drop_rate * self.model.beta
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@ -2,7 +2,7 @@
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import numpy as np
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import matplotlib.pyplot as plt
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def plot_hexagon(A, title=None):
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def plot_hexagon(A, title=None, block=True):
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X, Y = np.meshgrid(range(A.shape[0]), range(A.shape[-1]))
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X, Y = X*2, Y*2
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@ -28,4 +28,4 @@ def plot_hexagon(A, title=None):
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if(title is not None):
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plt.title(title)
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plt.show(block=False)
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plt.show(block=block)
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145
main.py
145
main.py
@ -226,24 +226,145 @@ def viviane_bfs_example_run():
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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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def fixed_distance_tests():
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"""
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position a target food source a known distance away from nest
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check for no. successful ants for n runs
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"""
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from tqdm import tqdm
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runs = 10
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from model import kwargs_paper_setup1 as kwargs
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kwargs["N_f"] = 0
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kwargs["gamma"] /= 2 # field decays three times slower
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kwargs["beta"] /= 2 # drop rates decays three times slower
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kwargs["d_s"] /= 2 # drop rates decays three times slower
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kwargs["d_e"] /= 2 # drop rates decays three times slower
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successful_walkers = {}
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for distance in tqdm(range(5,30), position=0, desc="dis"):
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successful_walkers[distance] = []
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for _ in tqdm(range(runs), position=1, desc="run", leave=False):
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model = ActiveWalkerModel(**kwargs)
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nest_location = kwargs["nest_position"]
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food_location = (nest_location[0] - distance, nest_location[1])
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model.grid.add_food(size=100, pos=food_location)
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for _ in tqdm(range(model.max_steps), position=2, desc="step", leave=False):
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model.step()
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successful_walkers[distance].append(model.datacollector.get_model_vars_dataframe().reset_index()["successful_walkers"][kwargs["max_steps"]])
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return successful_walkers
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def fixed_distance_object_between():
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"""
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diameter of object: floor(50% of distance)
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"""
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from tqdm import tqdm
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runs = 10
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from model import kwargs_paper_setup1 as kwargs
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kwargs["N_f"] = 0
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kwargs["gamma"] /= 2 # field decays slower
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kwargs["beta"] /= 2 # drop rates decays slower
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kwargs["d_e"] /= 2 # live longer, search longer
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kwargs["d_s"] /= 2 # live longer, search longer
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successful_walkers = {}
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for distance in tqdm(range(5,30), position=0, desc="dis"):
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successful_walkers[distance] = []
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for _ in tqdm(range(runs), position=1, desc="run", leave=False):
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model = ActiveWalkerModel(**kwargs)
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nest_location = kwargs["nest_position"]
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food_location = (nest_location[0] - distance, nest_location[1])
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object_location = (nest_location[0] - distance//2, nest_location[1])
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place_blocking_object(object_location, radius=distance//4, model=model)
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model.grid.add_food(size=100, pos=food_location)
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for _ in tqdm(range(model.max_steps), position=2, desc="step", leave=False):
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model.step()
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successful_walkers[distance].append(model.datacollector.get_model_vars_dataframe().reset_index()["successful_walkers"][kwargs["max_steps"]])
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return successful_walkers
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def place_blocking_object(center, radius, model):
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positions = [center]
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next_outside = [center]
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# We grow from the center and add all neighbours of the outer edge of our blocking object
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# Add all neighbours of next_outside that aren't in positions to the object
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# by doing this radius times we should get an object of diameter 2 * radius + 1
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# positions: accumulator for all positions inside the object of radius radius
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# next_outside: keep track what we added in the last go-around. These will be used in the next step.
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for _ in range(radius):
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outside = next_outside
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next_oustide = []
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#otherwise interprets the tuple as something stupid
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for i in range(len(outside)):
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cell = outside[i]
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neighbours = model.grid.get_neighborhood(cell)
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for n in neighbours:
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if n not in positions:
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positions.append(n)
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next_outside.append(n)
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# some large number in comparison to the rest of the resistance field
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# such that the probability of stepping on these grid spots tend towards zero
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infinity = 1e20
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for pos in positions:
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model.grid.fields['res'][pos] = infinity
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def plot_heatmap():
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from hexplot import plot_hexagon
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from tqdm import tqdm
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# nests rather far away but also partially clumped.
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np.random.seed(6)
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from model import kwargs_paper_setup1 as kwargs
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kwargs["gamma"] /= 3 # field decays slower
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kwargs["beta"] /= 3 # drop rates decays slower
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kwargs["d_e"] /= 3 # live longer, search longer
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kwargs["d_s"] /= 3 # live longer, search longer
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model = ActiveWalkerModel(**kwargs)
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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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a[np.nonzero(model.grid.fields['nests'])] = -1
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plot_hexagon(a, title="food locations", block=False)
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for _ in tqdm(range(model.max_steps)):
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model.step()
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for time in np.arange(0, model.max_steps + 1, 1000):
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pheromone_concentration = model.datacollector.get_model_vars_dataframe()["pheromone_a"][time]
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a = pheromone_concentration
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#plot_hexagon(a)
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pheromone_concentration = model.datacollector.get_model_vars_dataframe()["pheromone_b"][time]
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b = pheromone_concentration
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#plot_hexagon(b)
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c = np.max([a,b], axis=0)
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c = a + b
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c = np.clip(c, 0, 200)
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plot_hexagon(c)
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#if __name__ == "__main__":
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plot_heatmap()
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#print("DISTANCE TEST VS SUCCESSFUL ANTS OBJECT INBETWEEN")
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#res = fixed_distance_tests()
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#res = fixed_distance_object_between()
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# print("Test")
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#from model import kwargs_paper_setup1 as kwargs
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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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#model.step()
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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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