Combine resistance and sensitivity/alpha
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66
agent.py
66
agent.py
@ -87,6 +87,24 @@ class RandomWalkerAnt(Agent):
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return weights
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def _choose_next_pos(self):
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def _combine_weights(res_weights, walk_weights):
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"""
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If we have a resistance -> Infinity we want to have a likelihood -> 0 for this direction
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Therefore we should multiply our two probabilities.
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For the case of no resistance field this will return the normal walk_weights
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res_weights : resistance weights: based on resistance field of neighbours
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see _get_resistance_weights for more info
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walk weights: In case of biased random walk (no positive pheromone gradient):
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forward: alpha,
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everywhere else: (1- alpaha)/5)
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In case of positive pheromone gradient present in front:
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max. positive gradient: self.sensitivity
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everyhwere else: (1-self.sensitivity)/5
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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 normalized
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def _pick_from_remaining_five(remaining_five):
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"""
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"""
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@ -96,7 +114,10 @@ class RandomWalkerAnt(Agent):
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self._prev_pos = self.pos
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if self._prev_pos is None:
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weights = self._get_resistance_weights()
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res_weights = self._get_resistance_weights()
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walk_weights = np.ones(6)
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weights = _combine_weights(res_weights, walk_weights)
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i = np.random.choice(range(6),p=weights)
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assert(self.pos is not self.neighbors()[i])
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self._next_pos = self.neighbors()[i]
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@ -155,28 +176,37 @@ class RandomWalkerAnt(Agent):
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index = np.argmax(gradient)
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if gradient[index] > 0:
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# follow positive gradient with likelihood self.sensitivity
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p = np.random.uniform()
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if p < self.sensitivity:
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self._next_pos = self.front_neighbors[index]
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# follow positive gradient with likelihood self.sensitivity * resistance_weight (re-normalized)
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all_neighbors_cells = self.neighbors()
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highest_gradient_cell = self.front_neighbors[index]
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highest_gradient_index_arr = np.where(all_neighbors_cells == highest_gradient_cell)
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assert(len(highest_gradient_index_arr) == 1)
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all_neighbors_index = highest_gradient_index_arr[0]
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sens_weights = np.ones(6) * (1-self.sensitivity)/5
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sens_weights[all_neighbors_index] = self.sensitivity
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res_weights = self._get_resistance_weights()
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weights = _combine_weights(res_weights, sens_weights)
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self._next_pos = np.random.choice(all_neighbors_cells, p=weights)
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self._prev_pos = self.pos
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else:
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other_neighbors = self.neighbors().copy()
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other_neighbors.remove(self.front_neighbors[index])
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_pick_from_remaining_five(other_neighbors)
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return
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# do biased random walk
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p = np.random.uniform()
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# TODO: This completely neglects resistance, relevant?
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if p < self.model.alpha:
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self._next_pos = self.front_neighbor
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self._prev_pos = self.pos
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else:
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other_neighbors = self.neighbors().copy()
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other_neighbors.remove(self.front_neighbor)
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_pick_from_remaining_five(other_neighbors)
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all_neighbors_cells = self.neighbors()
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front_index_arr = np.where(all_neighbors_cells == self.front_neighbor)
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assert(len(front_index_arr) == 1 )
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front_index = front_index_arr[0]
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res_weights = self._get_resistance_weights()
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walk_weights = np.ones(6) * (1-self.model.alpha) / 5
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walk_weights[front_index] = self.model.alpha
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weights = _combine_weights(res_weights, walk_weights)
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self._nex_pos = np.random.choice(all_neighbors_cells, p=weights)
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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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196
main.py
196
main.py
@ -1,4 +1,3 @@
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#!/bin/python
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"""
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main.py - Part of ants project
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@ -7,9 +6,7 @@ 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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import array
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from model import ActiveWalkerModel
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from agent import RandomWalkerAnt
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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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@ -17,13 +14,6 @@ from mesa.datacollection import DataCollector
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#from multihex import MultiHexGrid
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def main():
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pass
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# check_pheromone_exponential_decay()
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# check_ant_sensitivity_linear_decay()
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# check_ant_pheromone_exponential_decay()
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# check_ants_follow_gradient()
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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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@ -183,126 +173,88 @@ def check_ants_follow_gradient():
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model.step()
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# if __name__ == "__main__":
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# main()
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from model import kwargs_paper_setup1 as kwargs
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# kwargs["N_m"] = 10000
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model = ActiveWalkerModel(**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 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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# agent_densities = model.datacollector.get_model_vars_dataframe()["agent_dens"]
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# mean_dens = np.mean(agent_densities)
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# norm_dens = mean_dens/np.max(mean_dens)
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# plot_hexagon(norm_dens, title="Ant density overall")
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# plt.show()
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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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# agent_densities = model.datacollector.get_model_vars_dataframe()["agent_dens"]
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# mean_dens = np.mean(agent_densities)
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# norm_dens = mean_dens/np.max(mean_dens)
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# plot_hexagon(norm_dens, title="Ant density overall")
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# plt.show()
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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 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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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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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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# |%%--%%| <Z5Ra4Us5kN|y6CRYNrY9x>
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# Access the DataCollector
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datacollector = model.datacollector
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# |%%--%%| <y6CRYNrY9x|v2PfrSWbzG>
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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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# |%%--%%| <v2PfrSWbzG|74OaeOltqi>
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print(model_data.columns)
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# |%%--%%| <74OaeOltqi|WpQLCA0RuP>
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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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# |%%--%%| <WpQLCA0RuP|UufL3yaROS>
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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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# |%%--%%| <UufL3yaROS|mgJWQ0bqG1>
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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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# |%%--%%| <mgJWQ0bqG1|64kmoHYvCD>
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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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# |%%--%%| <64kmoHYvCD|JEzmDy4wuX>
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# |%%--%%| <JEzmDy4wuX|U9vmSFZUyD>
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# |%%--%%| <U9vmSFZUyD|r0xVXEqlAh>
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# |%%--%%| <r0xVXEqlAh|6K80EwwmVN>
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6
model.py
6
model.py
@ -107,16 +107,18 @@ class ActiveWalkerModel(Model):
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self.grid = MultiHexGridScalarFields(width=width, height=height, torus=True, fields=fields)
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if resistance_map_type is None:
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print("No resistance field")
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self.grid.fields["res"] = np.ones((width, height)).astype(float)
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elif resistance_map_type == "perlin":
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# perlin generates isotropic noise which may or may not be a good choice
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# perlin generates anisotropic noise which may or may not be a good choice
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# pip3 install git+https://github.com/pvigier/perlin-numpy
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from perlin_numpy import (
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generate_fractal_noise_2d,
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generate_perlin_noise_2d,
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)
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noise = generate_perlin_noise_2d(shape=(width,height), res=((10,10)))
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normalized_noise = (noise - np.min(noise))/(np.max(noise) - np.min(noise))
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# normalized to mean=1, min=0, and max=2
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normalized_noise = (noise - np.min(noise))/(np.max(noise) - np.min(noise)) * 2
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self.grid.fields["res"] = normalized_noise
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else:
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# possible other noise types: simplex or value
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