added live-server visualization to project
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11
agent.py
11
agent.py
@ -14,14 +14,14 @@ from typing import overload
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class RandomWalkerAnt(Agent):
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class RandomWalkerAnt(Agent):
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def __init__(self, unique_id, model, look_for_chemical=None,
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def __init__(self, unique_id, model, look_for_chemical=None,
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energy_0=1, chemical_drop_rate_0=1, sensitvity_0=1, alpha=0.5) -> None:
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energy_0=1, chemical_drop_rate_0=1, sensitvity_0=1, alpha=0.5, drop_chemical=None) -> None:
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super().__init__(unique_id=unique_id, model=model)
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super().__init__(unique_id=unique_id, model=model)
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self._next_pos : None | Coordinate = None
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self._next_pos : None | Coordinate = None
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self.prev_pos : None | Coordinate = None
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self.prev_pos : None | Coordinate = None
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self.look_for_chemical = look_for_chemical
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self.look_for_chemical = look_for_chemical
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self.drop_chemical = None
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self.drop_chemical = drop_chemical
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self.energy : float = energy_0
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self.energy : float = energy_0
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self.sensitvity : float = sensitvity_0
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self.sensitvity : float = sensitvity_0
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self.chemical_drop_rate : float = chemical_drop_rate_0 #TODO: check whether needs to be separated into A and B
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self.chemical_drop_rate : float = chemical_drop_rate_0 #TODO: check whether needs to be separated into A and B
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@ -34,6 +34,10 @@ class RandomWalkerAnt(Agent):
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def step(self):
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def step(self):
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# follow positive gradient
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# follow positive gradient
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if self.prev_pos is None:
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i = np.random.choice(range(6))
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self._next_pos = self.neighbors()[i]
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return
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if self.look_for_chemical is not None:
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if self.look_for_chemical is not None:
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front_concentration = [self.model.grid.fields[self.look_for_chemical][cell] for cell in self.front_neighbors ]
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front_concentration = [self.model.grid.fields[self.look_for_chemical][cell] for cell in self.front_neighbors ]
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gradient = front_concentration - np.repeat(self.model.grid.fields[self.look_for_chemical][self.pos], 3)
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gradient = front_concentration - np.repeat(self.model.grid.fields[self.look_for_chemical][self.pos], 3)
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@ -61,8 +65,7 @@ class RandomWalkerAnt(Agent):
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def advance(self) -> None:
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def advance(self) -> None:
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self.drop_chemicals()
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self.drop_chemicals()
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self.prev_pos = self.pos
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self.prev_pos = self.pos
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self.pos = self._next_pos
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self.model.grid.move_agent(self, self._next_pos)
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# TODO: find out how to decorate with property properly
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# TODO: find out how to decorate with property properly
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def neighbors(self, pos=None, include_center=False):
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def neighbors(self, pos=None, include_center=False):
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10
model.py
10
model.py
@ -33,12 +33,12 @@ class ActiveWalkerModel(Model):
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self.nest_position : Coordinate = nest_position
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self.nest_position : Coordinate = nest_position
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self.num_max_agents = num_max_agents
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self.num_max_agents = num_max_agents
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self.decay_rates = {"A" :1,
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self.decay_rates : dict[str, float] = {"A" :0.1,
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"B": 1,
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"B": 0.1,
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}
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}
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for agent_id in self.get_unique_ids(num_initial_roamers):
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for agent_id in self.get_unique_ids(num_initial_roamers):
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agent = RandomWalkerAnt(unique_id=agent_id, model=self, look_for_chemical="A")
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agent = RandomWalkerAnt(unique_id=agent_id, model=self, look_for_chemical="A", drop_chemical="A")
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self.schedule.add(agent)
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self.schedule.add(agent)
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self.grid.place_agent(agent, pos=nest_position)
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self.grid.place_agent(agent, pos=nest_position)
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@ -64,8 +64,6 @@ class ActiveWalkerModel(Model):
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field = self.grid.fields[key]
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field = self.grid.fields[key]
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self.grid.fields[key] = field - self.decay_rates[key]*field
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self.grid.fields[key] = field - self.decay_rates[key]*field
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self.grid.step() # actually apply deposits on fields
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self.datacollector.collect(self)
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self.datacollector.collect(self)
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if self.schedule.steps >= self.max_steps:
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if self.schedule.steps >= self.max_steps:
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109
server.py
Executable file
109
server.py
Executable file
@ -0,0 +1,109 @@
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#!/bin/python
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"""
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server.py - Part of ants project
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This file sets up the mesa built-in visualization server
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and runs it on file execution.
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For now it displays ant locations as well as pheromone A
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concentrations on two seperate grids
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License: AGPL 3 (see end of file)
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(C) Alexander Bocken, Viviane Fahrni, Grace Kragho
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"""
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import numpy as np
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from mesa.visualization.modules import CanvasHexGrid, ChartModule, CanvasGrid
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from mesa.visualization.ModularVisualization import ModularServer
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from mesa.visualization.UserParam import UserSettableParameter
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from model import ActiveWalkerModel
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from collections import defaultdict
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def setup():
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# Set the model parameters
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params = {
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"width": 50, "height": 50,
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"num_max_agents" : 100,
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"nest_position" : (25,25),
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"num_initial_roamers" : 5,
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}
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class CanvasHexGridMultiAgents(CanvasHexGrid):
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"""
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A modification of CanvasHexGrid to not run visualization functions on all agents
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but on all grid positions instead
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"""
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package_includes = ["HexDraw.js", "CanvasHexModule.js", "InteractionHandler.js"]
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portrayal_method = None # Portrayal function
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canvas_width = 500
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canvas_height = 500
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def __init__(self, portrayal_method, grid_width, grid_height, canvas_width=500, canvas_height=500,):
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super().__init__(portrayal_method, grid_width, grid_height, canvas_width, canvas_height)
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def render(self, model):
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grid_state = defaultdict(list)
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for x in range(model.grid.width):
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for y in range(model.grid.height):
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portrayal = self.portrayal_method(model, (x, y))
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if portrayal:
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portrayal["x"] = x
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portrayal["y"] = y
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grid_state[portrayal["Layer"]].append(portrayal)
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return grid_state
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def get_color(level, normalization):
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"""
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level: level to calculate color between white and black (linearly)
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normalization: value for which we want full black color
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"""
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rgb = int(255 - level * 255 / normalization)
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mono = f"{rgb:0{2}x}" # hex value of rgb value with fixed length 2
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rgb = f"#{3*mono}"
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return rgb
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def portray_ant_density(model, pos):
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return {
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"Shape": "hex",
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"r": 1,
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"Filled": "true",
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"Layer": 0,
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"x": pos[0],
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"y": pos[1],
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"Color": get_color(level=len(model.grid[pos]), normalization=5)
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}
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def portray_pheromone_density(model, pos):
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return {
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"Shape": "hex",
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"r": 1,
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"Filled": "true",
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"Layer": 0,
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"x": pos[0],
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"y": pos[1],
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"Color": get_color(level=model.grid.fields["A"][pos], normalization=3)
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}
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width = params['width']
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height = params['height']
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pixel_ratio = 10
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grid_ants = CanvasHexGridMultiAgents(portray_ant_density, width, height, width*pixel_ratio, height*pixel_ratio)
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grid_pheromones = CanvasHexGridMultiAgents(portray_pheromone_density, width, height, width*pixel_ratio, height*pixel_ratio)
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return ModularServer(ActiveWalkerModel, [grid_ants, grid_pheromones],
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"Active Random Walker Ants", params)
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if __name__ == "__main__":
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server = setup()
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server.launch()
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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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