class ShapesModel(Model):
def __init__(self, N, width=20, height=10):
self.running = True
self.N = N # num of agents
self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1)) # tuples are fast
self.grid = SingleGrid(width, height, torus=False)
self.schedule = RandomActivation(self)
self.make_walker_agents()
def make_walker_agents(self):
unique_id = 0
while True:
if unique_id == self.N:
break
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
pos = (x, y)
heading = random.choice(self.headings)
# heading = (1, 0)
if self.grid.is_cell_empty(pos):
print("Creating agent {2} at ({0}, {1})"
.format(x, y, unique_id))
a = Walker(unique_id, self, pos, heading)
self.schedule.add(a)
self.grid.place_agent(a, pos)
unique_id += 1
def step(self):
self.schedule.step()
class ShapeExample(Model):
def __init__(self, N=2, width=20, height=10):
self.N = N # num of agents
self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1)) # tuples are fast
self.grid = SingleGrid(width, height, torus=False)
self.schedule = RandomActivation(self)
self.make_walker_agents()
self.running = True
def make_walker_agents(self):
unique_id = 0
while True:
if unique_id == self.N:
break
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
pos = (x, y)
heading = self.random.choice(self.headings)
# heading = (1, 0)
if self.grid.is_cell_empty(pos):
print("Creating agent {2} at ({0}, {1})".format(
x, y, unique_id))
a = Walker(unique_id, self, pos, heading)
self.schedule.add(a)
self.grid.place_agent(a, pos)
unique_id += 1
def step(self):
self.schedule.step()
def getGridStateAtStep(self, step=0):
plan_agent_keys = [uid for uid, a in self.planAgents.items()]
perception_agent_keys = [
uid for uid, a in self.perceptionAgents.items()
]
navGridAtStep = SingleGrid(self.navigationGrid.height,
self.navigationGrid.width, False)
for key in perception_agent_keys:
navGridAtStep.place_agent(self.perceptionAgents[key],
self.perceptionAgents[key].pos)
for key in plan_agent_keys:
for agent in self.planAgents[key]:
if agent.steps_left == step and navGridAtStep.is_cell_empty(
agent.pos):
navGridAtStep.place_agent(agent, agent.pos)
return navGridAtStep
class ShapesModel(Model):
def __init__(self, N, width=20, height=10):
self.running = True
self.N = N # num of agents
self.headings = ((1, 0), (0, 1), (-1, 0), (0, -1)) # tuples are fast
self.grid = SingleGrid(width, height, torus=False)
self.schedule = RandomActivation(self)
load_scene('shape_model/crossing.txt', self.grid, self)
"""
self.grid.place_agent(
Walker(1911, self, (4, 4), type="wall"),
(4, 4)
)
self.make_walls()
self.make_walker_agents()
"""
def make_walls(self):
for i in range(0, 50):
self.grid.place_agent(Walker(1911, self, (i, 5), type="wall"),
(i, 5))
def make_walker_agents(self):
unique_id = 0
while True:
if unique_id == self.N:
break
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
pos = (x, y)
heading = random.choice(self.headings)
# heading = (1, 0)
if self.grid.is_cell_empty(pos):
print("Creating agent {2} at ({0}, {1})".format(
x, y, unique_id))
a = Walker(unique_id, self, pos, heading)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
self.grid.place_agent(a, (x + 1, y))
self.grid.place_agent(a, (x, y + 1))
self.grid.place_agent(a, (x + 1, y + 1))
unique_id += 1
def step(self):
self.schedule.step()
class EvacuationModel(Model):
"""
This is a simulation of a crowd evacuation from a building.
Several variables are taken into account: the knowledge of the emergency exits, the age and weight of the agents
and the presence of stewards that can guide agents toward the emergency exits.
Agents have different strategies to escape the building such as taking the shortest path to an exit or a random one.
The goal is to study which combinations of agent types are more likely to escape the building and save themselves and
how the amount of casualties varies with respect to the different variables.
"""
def __init__(self,
N=10,
K=0,
width=50,
height=50,
fire_x=1,
fire_y=1,
civil_info_exchange=True):
self.num_civilians = N
self.num_stewards = K
self.civil_info_exchange = civil_info_exchange
self.fire_initial_pos = (fire_x, fire_y)
self.warning_UI = ""
self.agents_alive = N + K # Agents alive and inside the building
self.agents_saved = [] # Agents that managed to get out
self.agents_killed = [] # Agents that perished during the evacuation
self.grid = SingleGrid(height, width, False)
self.graph = None # General graph representing walkable terrain
self.schedule = RandomActivation(
self) # Every tick, agents move in a different random order
# Create exits
self.pos_exits = [(0, 5), (0, 25), (0, 45)]
for i in range(3):
self.pos_exits.append((self.grid.width - 1, 14 + i))
self.draw_environment(self.pos_exits)
self.graph = path_finding.create_graph(self)
# Define data collector
model_collector = {
"Agents killed": lambda killed: len(self.agents_killed),
"Agents saved": lambda saved: len(self.agents_saved)
}
for exit_pos in self.pos_exits:
title = "Exit {}".format(exit_pos)
model_collector[title] = partial(count_agents_saved, exit_pos)
self.datacollector = DataCollector(model_reporters=model_collector)
# Create fire
# for pos in self.fire_initial_pos: # Only 1 source of fire since we are setting it from UI
x, y = self.fire_initial_pos
if not self.is_inside_square((x, y), (0, 29),
(25, 39)) and not self.is_inside_square(
(x, y), (0, 10), (25, 20)):
pos = self.fire_initial_pos
else:
pos = (1, 1)
self.warning_UI = "<b>WARNING:</b> Sorry but the position of the fire is outside of the building, " \
"change the setting and click reset simulation."
fire_agent = FireAgent(pos, self)
self.schedule.add(fire_agent)
self.grid.place_agent(fire_agent, pos)
# Create civilian agents
for i in range(self.num_civilians):
# a civilian agent will know at least the main entrance to the building
known_exits = self.pos_exits[-3:]
a = CivilianAgent(i, self, known_exits)
self.schedule.add(a)
# Add the agent to a random grid cell
while True:
# pick the random coordinate
x = self.random.randrange(1, self.grid.width - 1)
y = self.random.randrange(1, self.grid.height - 1)
# check if the point is empty and inside of the building
if self.grid.is_cell_empty((x, y)) and not self.is_inside_square((x, y), (0, 29), (25, 39)) \
and not self.is_inside_square((x, y), (0, 10), (25, 20)):
break
self.grid.place_agent(a, (x, y))
# Create steward agents
for i in range(self.num_civilians,
self.num_civilians + self.num_stewards):
# a steward agent will know all exits.
known_exits = self.pos_exits
a = StewardAgent(i, self, known_exits)
self.schedule.add(a)
# Add the agent to a random grid cell
while True:
# pick the random coordinate
x = self.random.randrange(1, self.grid.width - 1)
y = self.random.randrange(1, self.grid.height - 1)
# check if the point is empty and inside of the building
if self.grid.is_cell_empty((x, y)) and not self.is_inside_square((x, y), (0, 29), (25, 39)) \
and not self.is_inside_square((x, y), (0, 10), (25, 20)):
break
self.grid.place_agent(a, (x, y))
self.running = True # Set this to false when we want to finish simulation (e.g. all agents are out of building)
self.datacollector.collect(self)
@staticmethod
def is_inside_square(point, bottom_left, top_right):
return bottom_left[0] <= point[0] <= top_right[0] and bottom_left[
1] <= point[1] <= top_right[1]
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
# Halt if no more agents in the building
if self.count_agents(self) == 0:
self.running = False
def remove_agent(self, agent, reason, **kwargs):
"""
Removes an agent from the simulation. Depending on the reason it can be
Args:
agent (Agent):
reason (Reasons):
Returns:
None
"""
if reason == Reasons.SAVED:
self.agents_saved.append(agent)
elif reason == Reasons.KILLED_BY_FIRE:
self.agents_killed.append(agent)
self.agents_alive -= 1
self.schedule.remove(agent)
self.grid.remove_agent(agent)
def draw_environment(self, exits=None):
length_E = int(self.grid.height /
5) # length of the vertical segments of the E
depth_E = int(self.grid.width /
2) # length of the horizontal segments of the E
for i in range(3):
start = max(0, 2 * i * length_E)
self.draw_wall((0, start), (0, start + length_E - 1))
for i in range(2):
start = 2 * i * length_E + length_E
self.draw_wall((depth_E, start), (depth_E, start + length_E - 1))
# Horizontal lines of the E (BB)
aux_y_coord = [
length_E, 2 * length_E, 3 * length_E - 1, 4 * length_E - 1
]
for y in aux_y_coord:
self.draw_wall((0, y), (depth_E, y))
top_left_corner = (0, self.grid.height - 1)
top_right_corner = (self.grid.width - 1, self.grid.height - 1)
bottom_right_corner = (self.grid.width - 1, 0)
# Draw long contour lines E
self.draw_wall((0, 0), bottom_right_corner)
self.draw_wall(top_left_corner, top_right_corner)
self.draw_wall(bottom_right_corner, top_right_corner)
# Draw exits
self.draw_exits(exits)
def draw_wall(self, start, end):
"""
Draws a line that goes from start point to end point.
Args:
start (List): Coordinates of line's starting point
end (List): Coordinates of line's end point
Returns:
None
"""
diff_x, diff_y = np.subtract(end, start)
wall_coordinates = np.asarray(start)
if self.grid.is_cell_empty(wall_coordinates.tolist()):
w = WallAgent(wall_coordinates.tolist(), self)
self.grid.place_agent(w, wall_coordinates.tolist())
while diff_x != 0 or diff_y != 0:
if abs(diff_x) == abs(diff_y):
# diagonal wall
wall_coordinates[0] += np.sign(diff_x)
wall_coordinates[1] += np.sign(diff_y)
diff_x -= 1
diff_y -= 1
elif abs(diff_x) < abs(diff_y):
# wall built in y dimension
wall_coordinates[1] += np.sign(diff_y)
diff_y -= 1
else:
# wall built in x dimension
wall_coordinates[0] += np.sign(diff_x)
diff_x -= 1
if self.grid.is_cell_empty(wall_coordinates.tolist()):
w = WallAgent(wall_coordinates.tolist(), self)
self.grid.place_agent(w, wall_coordinates.tolist())
def draw_exits(self, exits_list):
for ext in exits_list:
e = ExitAgent(ext, self)
if not self.grid.is_cell_empty(ext):
# Only walls should exist in the grid at this time, so no need to remove it from scheduler
agent = self.grid.get_cell_list_contents(ext)
self.grid.remove_agent(agent[0])
# Place exit
self.schedule.add(e)
self.grid.place_agent(e, ext)
def spread_fire(self, fire_agent):
fire_neighbors = self.grid.get_neighborhood(fire_agent.pos,
moore=True,
include_center=False)
for grid_space in fire_neighbors:
if self.grid.is_cell_empty(grid_space):
# Create new fire agent and add it to grid and scheduler
new_fire_agent = FireAgent(grid_space, self)
self.schedule.add(new_fire_agent)
self.grid.place_agent(new_fire_agent, grid_space)
else:
# If human agents, eliminate them and spread anyway
agent = self.grid.get_cell_list_contents(grid_space)[0]
if isinstance(agent, (CivilianAgent, StewardAgent)):
new_fire_agent = FireAgent(grid_space, self)
self.remove_agent(agent, Reasons.KILLED_BY_FIRE)
self.schedule.add(new_fire_agent)
self.grid.place_agent(new_fire_agent, grid_space)
@staticmethod
def count_agents(model):
"""
Helper method to count agents alive and still in the building.
"""
count = 0
for agent in model.schedule.agents:
agent_type = type(agent)
if (agent_type == CivilianAgent) or (agent_type == StewardAgent):
count += 1
return count
class DaisyModel(Model):
""" "Daisys" grow, when the temperature is right. But they influence temperature themselves via their ability to block a certain amount of sunlight (albedo, indicated by color). They spread and they mutate (changing albedo) and thus adapt to different conditions."""
def __init__(self,
N,
width,
height,
luminosity,
heat_radius,
mutation_range,
surface_albedo,
daisy_lifespan,
daisy_tmin,
daisy_tmax,
lum_model,
lum_increase):
# Setup parameter
self.dimensions = (width, height)
self.running = True # never stop!
self.num_agents = min([N, (width * height)]) # never more agents than cells
self.grid = SingleGrid(width, height, torus=True)
self.schedule = RandomActivation(self)
# Model parameter
self.mutation_range = mutation_range # default: 0.05
self.luminosity = luminosity # default 1.35
self.heat_radius = heat_radius
self.surface_albedo = surface_albedo # default: 0.4
self.lum_model = lum_model
self.lum_increase = lum_increase # tried 0.001
# Daisy parameter
self.daisy_lifespan = daisy_lifespan
self.daisy_tmin = daisy_tmin
self.daisy_tmax = daisy_tmax
# to inhibit using same postition twice: draw from urn
position_list = []
for i in range(width): # put positions in urn
for j in range(height):
position_list.append((i,j))
for i in range(self.num_agents): # draw from urn
a = DaisyAgent(i, self,
random.uniform(0.1, 0.9), # random starting albedo
self.daisy_lifespan, self.daisy_tmin, self.daisy_tmax)
self.schedule.add(a)
pos = random.choice(position_list)
self.grid.place_agent(a, pos)
position_list.remove(pos)
# Data collectors
self.datacollector = DataCollector(
model_reporters = {"Solar irradiance": get_irradiance,
"Population": get_population,
"Mean albedo": get_mean_albedo,
"Population: North - South": get_north_south_population
}
)
def step(self):
print(self.lum_model)
if self.lum_model == 'linear increase':
self.luminosity = linear_increase(self)
self.datacollector.collect(self)
self.schedule.step()
def get_lat(self, pos):
""" The grid is meant to be a sphere. This gets the latitude. Ranges from 0.0 (equator) to 1.0 (pole). """
return (pos[1] / self.dimensions[1])
def get_GNI(self, pos):
""" gives solar irradiance, depending on latitude"""
return self.luminosity * math.sin(self.get_lat(pos)*math.pi)
def expand_positionlist(self, pos_list):
""" expands a list of positions, adding neighboring positions """
expanded_list = []
for i in pos_list:
expanded_list += self.grid.get_neighborhood(i, moore=True, include_center=False)
return list(set(expanded_list))
def get_local_heat(self, pos):
""" Global Horizontal Irradiance (without diffusive irradiance) from pole (lower border) to pole (upper border). model is torus! """
neighborhood = self.grid.get_neighborhood(pos, moore=True, include_center=True)
if self.heat_radius > 1: # if radius of local temperature is >1, this expand the position list.
for i in range(self.heat_radius):
neighborhood = self.expand_positionlist(neighborhood)
heat = []
for i in neighborhood:
if self.grid.is_cell_empty(i): # empty cell: surface albedo
heat.append(self.get_GNI(pos) * (1 - self.surface_albedo) )
else:
inhabitant = self.grid.get_cell_list_contents(i)[0]
heat.append(self.get_GNI(pos) * (1 - inhabitant.albedo) ) # cell with daisy
return sum(heat)/ len(neighborhood)
class AgentKnowledgeMap():
'''
*** Constructor:
Inputs:
- height and width of the grid used by the AgSimulator
Actions:
- Construct navigationGrid
- Construct planGrid
- Create agent dictionaries
'''
def __init__(self, height, width, model):
self.navigationGrid = SingleGrid(height, width, False)
self.planGrid = MultiGrid(height, width, False)
self.planAgents = defaultdict(list)
self.perceptionAgents = {}
self.model = model
agent = FarmAgent(0, self.model.farmPos, self)
self.navigationGrid.place_agent(agent, self.model.farmPos)
self.attendancePoints = list()
'''
*** update function is used by each ActiveAgent to update ActiveAgentKnowledgeMap
Input:
- ActiveAgentPlanning objects are placed on planGrid
- PassiveAgentPerception objects are placed on navigationGrid
'''
def update(self, agent):
if (isinstance(agent, ActiveAgentPlanning)):
self.planGrid.place_agent(agent, agent.pos)
self.planAgents.setdefault(agent.unique_id, [])
self.planAgents[agent.unique_id].append(agent)
elif (isinstance(agent, PassiveAgentPerception)):
if self.navigationGrid.is_cell_empty(agent.pos):
self.navigationGrid.place_agent(agent, agent.pos)
self.perceptionAgents[agent.unique_id] = agent
else:
existing_agent = self.navigationGrid.get_cell_list_contents(
agent.pos)[0]
existing_agent.update(agent.state, agent.time_at_current_state)
# This function is used for removing a step from the KnowledgeMap
def removeOneStep(self, agentID):
if self.planAgents[agentID]:
self.planGrid.remove_agent(self.planAgents[agentID].pop(0))
# This function is used for canceling the entire plan in case a collision is detected
def cancelPlan(self, agentID):
while len(self.planAgents[agentID]) > 0:
self.planGrid.remove_agent(self.planAgents[agentID].pop(0))
'''
*** getGridStateAtStep returns a SingleGrid object with anticipated state of the grid at specified steps
Input:
- step for which the SingleGrid should be generated
Output:
- SingleGrid object with PassiveAgentPerception objects and ActiveAgentPlanning objects corresponding to chosen step
'''
def getGridStateAtStep(self, step=0):
plan_agent_keys = [uid for uid, a in self.planAgents.items()]
perception_agent_keys = [
uid for uid, a in self.perceptionAgents.items()
]
navGridAtStep = SingleGrid(self.navigationGrid.height,
self.navigationGrid.width, False)
for key in perception_agent_keys:
navGridAtStep.place_agent(self.perceptionAgents[key],
self.perceptionAgents[key].pos)
for key in plan_agent_keys:
for agent in self.planAgents[key]:
if agent.steps_left == step and navGridAtStep.is_cell_empty(
agent.pos):
navGridAtStep.place_agent(agent, agent.pos)
return navGridAtStep
# This function is used to get a numpy array containing 0 and 1;
# 0 for empty blocks at step X
# 1 for any kind of agent at step X
def getGridAtStepAsNumpyArray(self, step=0):
plan_agent_keys = [uid for uid, a in self.planAgents.items()]
perception_agent_keys = [
uid for uid, a in self.perceptionAgents.items()
]
return_numpy_array = numpy.zeros(
(self.navigationGrid.width, self.navigationGrid.height),
dtype='int8')
for key in perception_agent_keys:
return_numpy_array[self.perceptionAgents[key].pos[1],
self.perceptionAgents[key].pos[0]] = 1
for agent_key in self.planAgents:
agent_plans = self.planAgents[agent_key]
if len(agent_plans) > 0 and len(agent_plans) >= step:
for plan in agent_plans:
if plan.steps_left == step:
return_numpy_array[plan.pos[1], plan.pos[0]] = 1
elif len(agent_plans) == 0:
active_agent = self.model.schedule.getPassiveAgent(agent_key)
return_numpy_array[active_agent.pos[1],
active_agent.pos[0]] = 1
else:
return_numpy_array[agent_plans[-1].pos[1],
agent_plans[-1].pos[0]] = 1
return_numpy_array[self.model.farmPos[1], self.model.farmPos[0]] = 1
return return_numpy_array
class GTModel(Model):
def __init__(self, debug, size, i_n_agents, i_strategy, i_energy,
child_location, movement, k, T, M, p, d, strategies_to_count,
count_tolerance, mutation_type, death_threshold, n_groups):
self.grid = SingleGrid(size, size, torus=True)
self.schedule = RandomActivation(self)
self.running = True
self.debug = debug
self.size = size
self.agent_idx = 0
self.i_energy = i_energy
# Payoff matrix in the form (my_move, op_move) : my_reward
self.payoff = {
('C', 'C'): 2,
('C', 'D'): -3,
('D', 'C'): 3,
('D', 'D'): -1,
}
# Constant for max population control (cost of surviving)
self.k = k
# Constant for controlling dying of old age
self.M = M
# Minimum lifespan
self.T = T
# Minimum energy level to reproduce
self.p = p
# Mutation "amplitude"
self.d = d
# Whether to spawn children near parents or randomly
self.child_location = child_location
# Specify the type of movement allowed for the agents
self.movement = movement
# Specify how the agents mutate
self.mutation_type = mutation_type
# The minimum total_energy needed for an agent to survive
self.death_threshold = death_threshold
# Vars regarding which strategies to look for
self.strategies_to_count = strategies_to_count
self.count_tolerance = count_tolerance
# Add agents (one agent per cell)
all_coords = [(x, y) for x in range(size) for y in range(size)]
agent_coords = self.random.sample(all_coords, i_n_agents)
for _ in range(i_n_agents):
group_idx = (None if n_groups is None else self.random.choice(
range(n_groups)))
agent = GTAgent(self.agent_idx, group_idx, self, i_strategy.copy(),
i_energy)
self.agent_idx += 1
self.schedule.add(agent)
self.grid.place_agent(agent, agent_coords.pop())
# Collect data
self.datacollector = DataCollector(
model_reporters={
**{
'strategies': get_strategies,
'n_agents': total_n_agents,
'avg_agent_age': avg_agent_age,
'n_friendlier': n_friendlier,
'n_aggressive': n_aggressive,
'perc_cooperative_actions': perc_cooperative_actions,
'n_neighbors': n_neighbor_measure,
'avg_delta_energy': avg_delta_energy,
'perc_CC': perc_CC_interactions,
'lin_fit_NC': coop_per_neig,
'lin_fit_NC_intc': coop_per_neig_intc,
},
**{
label: strategy_counter_factory(strategy, count_tolerance)
for label, strategy in strategies_to_count.items()
}
})
def alpha(self):
# Return the cost of surviving, alpha
DC = self.payoff[('D', 'C')]
CC = self.payoff[('C', 'C')]
N = len(self.schedule.agents)
return self.k + 4 * (DC + CC) * N / (self.size * self.size)
def time_to_die(self, agent):
# There is a chance every iteration to die of old age: (A - T) / M
# There is a 100% to die if the agents total energy reaches 0
return (agent.total_energy < self.death_threshold
or self.random.random() < (agent.age - self.T) / self.M)
def get_child_location(self, agent):
if self.child_location == 'global':
return self.random.choice(sorted(self.grid.empties))
elif self.child_location == 'local':
# Iterate over the radius, starting at 1 to find empty cells
for rad in range(1, int(self.size / 2)):
possible_steps = [
cell for cell in self.grid.get_neighborhood(
agent.pos,
moore=False,
include_center=False,
radius=rad,
) if self.grid.is_cell_empty(cell)
]
if possible_steps:
return self.random.choice(possible_steps)
# If no free cells in radius size/2 pick a random empty cell
return self.random.choice(sorted(self.grid.empties))
def maybe_mutate(self, agent):
# Mutate by adding a random d to individual Pi's
if self.mutation_type == 'stochastic':
# Copy the damn list
new_strategy = agent.strategy.copy()
# There is a 20% chance of mutation
if self.random.random() < 0.2:
# Each Pi is mutated uniformly by [-d, d]
for i in range(4):
mutation = self.random.uniform(-self.d, self.d)
new_val = new_strategy[i] + mutation
# Keep probabilities in [0, 1]
new_val = (0 if new_val < 0 else
1 if new_val > 1 else new_val)
new_strategy[i] = new_val
# Mutate by choosing a random strategy from the list set
elif self.mutation_type == 'fixed':
new_strategy = random.choice(
list(self.strategies_to_count.values()))
elif self.mutation_type == 'gaussian_sentimental':
# Copy the damn list
new_strategy = agent.strategy.copy()
# There is a 20% chance of mutation
if self.random.random() < 0.2:
# Each Pi is mutated by a value drawn from a gaussian
# with mean=delta_energy
for i in range(4):
mutation = self.random.normalvariate(
(agent.delta_energy + self.alpha()) / 14, self.d)
new_val = new_strategy[i] + mutation
# Keep probabilities in [0, 1]
new_val = (0 if new_val < 0 else
1 if new_val > 1 else new_val)
new_strategy[i] = new_val
return new_strategy
def maybe_reproduce(self, agent):
# If we have the energy to reproduce, do so
if agent.total_energy >= self.p:
# Create the child
new_strategy = self.maybe_mutate(agent)
child = GTAgent(self.agent_idx, agent.group_id, self, new_strategy,
self.i_energy)
self.agent_idx += 1
# Set parent and child energy levels to p/2
child.total_energy = self.p / 2
agent.total_energy = self.p / 2
# Place child (Remove agent argument for global child placement)
self.schedule.add(child)
self.grid.place_agent(child, self.get_child_location(agent))
def step(self):
if self.debug:
print('\n\n==================================================')
print('==================================================')
print('==================================================')
pprint(vars(self))
# First collect data
self.datacollector.collect(self)
# Then check for dead agents and for new agents
for agent in self.schedule.agent_buffer(shuffled=True):
# First check if dead
if self.time_to_die(agent):
self.grid.remove_agent(agent)
self.schedule.remove(agent)
# Otherwise check if can reproduce
else:
self.maybe_reproduce(agent)
# Finally, step each agent
self.schedule.step()
def check_strategy(self, agent):
# Helper function to check which strategy an agent would count as
def is_same(strategy, a_strategy):
tol = self.count_tolerance
return all(strategy[i] - tol < a_strategy[i] < strategy[i] + tol
for i in range(4))
return [
name for name, strat in self.strategies_to_count.items()
if is_same(strat, agent.strategy)
]
class DiseaseModel(Model):
"""
A model with some number of agents.
highS: Number of agents with high sociability.
middleS: Number of agents with middle sociability.
lowS: Number of agents with low sociability.
width: Width of the grid.
height: Height of the grid.
edu_setting: If true, agents will follow a schedule and sit in classrooms,
else they will move freely through an open grid.
cureProb: Probability of agent getting better.
cureProbFac: Factor of cureProb getting higher.
mutateProb: Probability of a disease mutating.
diseaseRate: Rate at which the disease spreads.
"""
def __init__(self, highS, middleS, lowS, width, height, edu_setting=True,
cureProb=0.1, cureProbFac=2/1440, mutateProb=0.0050,
diseaseRate=0.38):
super().__init__()
self.num_agents = highS + middleS + lowS
self.lowS = lowS
self.middleS = middleS
self.highS = highS
self.initialCureProb = cureProb
self.cureProbFac = cureProbFac
self.mutateProb = mutateProb
self.diseaseRate = diseaseRate
self.edu_setting = edu_setting
self.maxDisease = 0 # amount of mutations
self.counter = 540 # keeps track of timesteps
self.removed = []
self.exit = (width - 1, floor(height / 2))
# Check if agents fit within grid
if self.num_agents > width * height:
raise ValueError("Number of agents exceeds grid capacity.")
# Create grid with random activation
self.grid = SingleGrid(width, height, True)
self.schedule = RandomActivation(self)
if edu_setting:
# Create walls
numberRooms = 3
self.add_walls(numberRooms, width, height)
self.midWidthRoom = floor(width / numberRooms / 2)
self.midHeightRoom = floor(height / numberRooms / 2)
self.widthRoom = floor(width / numberRooms)
self.heightRoom = floor(height / numberRooms)
numberRows = floor((self.heightRoom) / 2)
widthRows = self.widthRoom - 4
location = [[] for _ in range(numberRooms * 2)]
for i in range(numberRooms):
for j in range(0, numberRows, 2):
startWidth = 2 + (i % 3) * self.widthRoom
for currentWidth in range(widthRows):
location[i] += [(startWidth + currentWidth, j)]
for i in range(3, numberRooms * 2):
for j in range(0, numberRows, 2):
startWidth = 2 + (i % 3) * self.widthRoom
for currentWidth in range(widthRows):
location[i] += [(startWidth + currentWidth,
height - 1 - j)]
# Set 3 goals per roster
self.roster = [[location[0], location[3], location[1]],
[location[5], location[2], location[0]],
[location[4], location[1], location[5]]]
# Create agents
self.addAgents(lowS, 0, 0)
self.addAgents(middleS, lowS, 1)
self.addAgents(highS, lowS + highS, 2)
# set up data collecter
self.datacollector = DataCollector(
model_reporters={"diseasepercentage": disease_collector},
agent_reporters={"disease": "disease"})
def heuristic(self, start, goal):
"""
Returns manhattan distance.
start: current location (x,y)
goal: goal location (x,y)
"""
dx = abs(start[0] - goal[0])
dy = abs(start[1] - goal[1])
return dx + dy
def get_vertex_neighbors(self, pos):
"""
Returns all neighbors.
pos: current position
"""
n = self.grid.get_neighborhood(pos, moore=False)
neighbors = []
for item in n:
if not abs(item[0] - pos[0]) > 1 and not abs(item[1] - pos[1]) > 1:
neighbors += [item]
return neighbors
def move_cost(self, location):
"""
Return the cost of a location.
"""
if self.grid.is_cell_empty(location):
return 1 # Normal movement cost
else:
return 100 # Very difficult to go through walls
def add_walls(self, n, widthGrid, heightGrid):
"""
Add walls in grid.
n: number of rooms horizontally
widthGrid: width of the grid
heightGrid: height of the grid
"""
widthRooms = floor(widthGrid / n)
heightRooms = floor(heightGrid / n)
heightHall = heightGrid - 2 * heightRooms
# Add horizontal walls
for i in range(n - 1):
for y in range(heightRooms):
brick = wall(self.num_agents, self)
self.grid.place_agent(brick, ((i + 1) * widthRooms, y))
self.grid.place_agent(brick, ((i + 1) * widthRooms, y +
heightRooms + heightHall))
doorWidth = 2
# Add vertical walls
for x in range(widthGrid):
if (x % widthRooms) < (widthRooms - doorWidth):
brick = wall(self.num_agents, self)
self.grid.place_agent(brick, (x, heightRooms))
self.grid.place_agent(brick, (x, heightRooms + heightHall - 1))
def addAgents(self, n, startID, sociability):
"""
Add agents with a sociability.
n: number of agents
startID: ID of the first added agent
sociability: sociability of the agents
"""
disease_list = np.random.randint(0, 2, n)
for i in range(n):
# Set schedule for every agent if educational setting
if self.edu_setting:
a_roster = []
rosterNumber = self.random.randrange(len(self.roster))
rooms = self.roster[rosterNumber]
for roomNumber in range(len(rooms)):
loc = self.random.choice(rooms[roomNumber])
a_roster += [loc]
(self.roster[rosterNumber][roomNumber]).remove(loc)
else:
a_roster = []
a = DiseaseAgent(i + startID, sociability, self, disease_list[i],
a_roster)
self.schedule.add(a)
# Set agent outside grid, ready to enter, if edu setting
# else randomly place on empty spot on grid
if self.edu_setting:
self.removed += [a]
a.pos = None
else:
self.grid.place_agent(a, self.grid.find_empty())
def step(self):
"""
Continue one step in simulation.
"""
self.counter += 1
self.datacollector.collect(self)
self.schedule.step()
class Anthill(Model):
def __init__(self):
self.grid = SingleGrid(WIDTH, HEIGHT, False)
self.schedule = RandomActivation(self)
self.running = True
self.internalrate = 0.2
self.ant_id = 1
self.tau = np.zeros((WIDTH, HEIGHT))
self.datacollector = DataCollector({
"Total number of Ants":
lambda m: self.get_total_ants_number(),
"mean tau":
lambda m: self.evaluation1(),
"sigma":
lambda m: self.evaluation2(),
"sigma*":
lambda m: self.evaluation3(),
})
# List containing all coordinates of the boundary, initial ants location and brood location
self.bound_vals = []
self.neigh_bound = []
self.datacollector.collect(self)
for i in range(WIDTH):
for j in range(HEIGHT):
if i == 0 or j == 0 or i == WIDTH - 1 or j == HEIGHT - 1:
self.bound_vals.append((i, j))
if i == 1 or i == WIDTH - 2 or j == 1 or j == HEIGHT - 2:
self.neigh_bound.append((i, j))
# Make a Fence boundary
b = 0
for h in self.bound_vals:
br = Fence(b, self)
self.grid.place_agent(br, (h[0], h[1]))
b += 1
def step(self):
'''Advance the model by one step.'''
# Add new ants into the internal area ont he boundary
for xy in self.neigh_bound:
# Add with probability internal rate and if the cell is empty
if self.random.uniform(
0, 1) < self.internalrate and self.grid.is_cell_empty(
xy) == True:
a = Ant(self.ant_id, self)
self.schedule.add(a)
self.grid.place_agent(a, xy)
self.ant_id += 1
# Move the ants
self.schedule.step()
self.datacollector.collect(self)
# Remove all ants on bounary
for (agents, i, j) in self.grid.coord_iter():
if (i, j) in self.neigh_bound and type(agents) is Ant:
self.grid.remove_agent(agents)
self.schedule.remove(agents)
data_tau.append(self.mean_tau_ant)
data_sigma.append(np.sqrt(self.sigma))
data_sigmastar.append(self.sigmastar)
if len(data_sigmastar) > 20:
if abs(data_sigmastar[-2] - data_sigmastar[-1]) < 0.0000001 or len(
data_sigmastar) == 2000:
try:
# TAU
with open("results/m1_tau_5.pkl", 'rb') as f:
tau_old = pickle.load(f)
tau_old[int(len(tau_old) + 1)] = data_tau
f.close()
pickle.dump(tau_old, open("results/m1_tau_5.pkl", 'wb'))
except:
pickle.dump({1: data_tau},
open("results/m1_tau_5.pkl", 'wb'))
try:
# SIGMA
with open("results/m1_sigma_5.pkl", 'rb') as f:
sigma_old = pickle.load(f)
sigma_old[int(len(sigma_old) + 1)] = data_sigma
f.close()
pickle.dump(sigma_old, open("results/m1_sigma_5.pkl",
'wb'))
except:
pickle.dump({1: data_sigma},
open("results/m1_sigma_5.pkl", 'wb'))
try:
# SIGMASTAR
with open("results/m1_sigmastar_5.pkl", 'rb') as f:
sigmastar_old = pickle.load(f)
sigmastar_old[int(len(sigmastar_old) +
1)] = data_sigmastar
f.close()
pickle.dump(sigmastar_old,
open("results/m1_sigmastar_5.pkl", 'wb'))
except:
pickle.dump({1: data_sigmastar},
open("results/m1_sigmastar_5.pkl", 'wb'))
try:
# MATRIX
with open("results/m1_matrix_5.pkl", 'rb') as f:
matrix_old = pickle.load(f)
matrix_old[int(len(matrix_old) + 1)] = self.tau
f.close()
pickle.dump(matrix_old,
open("results/m1_matrix_5.pkl", 'wb'))
except:
pickle.dump({1: self.tau},
open("results/m1_matrix_5.pkl", 'wb'))
print(
"_______________________________________________________________________"
)
print("DONE")
self.running = False
# with open("tau2_new.txt", "a") as myfile:
# myfile.write(str(self.mean_tau_ant) + '\n')
# with open("sigma2_new.txt", "a") as myfile:
# myfile.write(str(np.sqrt(self.sigma)) + '\n')
# with open("datasigmastar2_new.txt","a") as myfile:
# myfile.write(str(self.sigmastar) + "\n")
def get_total_ants_number(self):
total_ants = 0
for (agents, _, _) in self.grid.coord_iter():
if type(agents) is Ant:
total_ants += 1
return total_ants
def evaluation1(self):
##creat a empty grid to store currently information
total_ants = np.zeros((WIDTH, HEIGHT))
## count the number of currently information
for (agents, i, j) in self.grid.coord_iter():
if type(agents) is Ant:
total_ants[i][j] = 1
else:
total_ants[i][j] = 0
##update the tau
self.tau = self.tau + total_ants
##calcualte the mean tau
self.mean_tau_ant = self.tau.sum() / ((WIDTH - 2)**2)
return self.mean_tau_ant
def evaluation2(self):
## we need to minus the mean tau so we need to ensure the result of boundary is zero
## so we let the bounday equal mean_tau_ant in this way the (tau-mean_tau_ant) is zero of boundary
for site in self.bound_vals:
self.tau[site[0]][site[1]] = self.mean_tau_ant
## calculate the sigmaa
self.sigma = ((self.tau - self.mean_tau_ant)**2).sum() / (
(WIDTH - 2)**2)
## rechange the boundaryy
for site in self.bound_vals:
self.tau[site[0]][site[1]] = 0
return np.sqrt(self.sigma)
def evaluation3(self):
## calculate the sigmastar
self.sigmastar = np.sqrt(self.sigma) / self.mean_tau_ant
return self.sigmastar
class modelSim(Model):
"""
details of the world
introduce time is when animal agents first get introduced into the wrold
disp_rate is the dispersal rate for experiment 3
dist is perceptual strength for animals if fixed
det is decision determinacy of animals if fixed
cog_fixed determines if cognition of animals is fixed to particular values or is allowed to evolve
if skip_300 is True, patchiness values are not calculated for the first 300 steps-- this makes the model run faster
collect_cog_dist creates a seperate dataframe for all cognition values for agents at every timestep
if evolve_disp is true, dispersion rate of plants is free to evolve
"""
def __init__(self, introduce_time, disp_rate, dist, det, cog_fixed = False, \
skip_300 = True, collect_cog_dist = False, evolve_disp = False):
self.skip_300 = skip_300
self.cog_fixed = cog_fixed
self.evolve_disp = evolve_disp
self.collect_cog_dist = collect_cog_dist
self.dist = dist
self.det = det
self.disp_rate = disp_rate
self.intro_time = introduce_time
(self.a1num, self.a2num) = (20, 20)
self.schedule = RandomActivation(
self) # agents take a step in random order
self.grid = SingleGrid(
200, 200,
True) # the world is a grid with specified height and width
self.initialize_perception()
disp = np.power(self.disp_rate, range(0, 100))
self.disp = disp / sum(disp)
self.grid_ind = np.indices((200, 200))
positions = np.maximum(abs(100 - self.grid_ind[0]),
abs(100 - self.grid_ind[1]))
self.positions = np.minimum(positions, 200 - positions)
self.agentgrid = np.zeros(
(self.grid.width, self.grid.height
)) # allows for calculation of patchiness of both agents
self.coggrid = np.full(
(self.nCogPar, self.grid.width, self.grid.height), 101.0)
self.dispgrid = np.full((2, self.grid.width, self.grid.height), 101.0)
self.age = []
(self.nstep, self.unique_id, self.reprod, self.food, self.death,
self.combat) = (0, 0, 0, 0, 0, 0)
self.cmap = colors.ListedColormap([
'midnightblue', 'mediumseagreen', 'white', 'white', 'white',
'white', 'white'
]) #'yellow', 'orange', 'red', 'brown'])
bounds = [0, 1, 2, 3, 4, 5, 6, 7]
self.norm = colors.BoundaryNorm(bounds, self.cmap.N)
self.expect_NN = []
self.NN = [5, 10]
for i in self.NN:
self.expect_NN.append(
(math.factorial(2 * i) * i) / (2**i * math.factorial(i))**2)
grid_ind_food = np.indices((21, 21))
positions_food = np.maximum(abs(10 - grid_ind_food[0]),
abs(10 - grid_ind_food[1]))
self.positions_food = np.minimum(positions_food, 21 - positions_food)
if self.collect_cog_dist:
self.cog_dist_dist = pd.DataFrame(columns=[])
self.cog_dist_det = pd.DataFrame(columns=[])
for i in range(self.a1num): # initiate a1 agents at random locations
self.introduce_agents("A1")
self.nA1 = self.a1num
self.nA2 = 0
# self.agent_steps = {}
def initialize_perception(self):
self.history = pd.DataFrame(columns=[
"nA1", "nA2", "age", "LIP5", "LIP10", "LIPanim5", "LIPanim10",
"Morsita5", "Morsita10", "Morsitaanim5", "Morsitaanim10", "NN5",
"NN10", "NNanim5", "NNanim10", "reprod", "food", "death", "combat",
"dist", "det", "dist_lower", "det_lower", "dist_upper",
"det_upper", "dist_ci", "det_ci"
])
self.nCogPar = 2
(self.start_energy, self.eat_energy, self.tire_energy, self.reproduction_energy, self.cognition_energy) \
= (10, 5, 3, 20, 1)
def introduce_agents(self, which_agent):
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
if which_agent == "A1":
if self.grid.is_cell_empty((x, y)):
a = A1(self.unique_id, self, self.start_energy, disp_rate=0)
self.unique_id += 1
self.grid.position_agent(a, x, y)
self.schedule.add(a)
self.agentgrid[x][y] = 1
else:
self.introduce_agents(which_agent)
elif which_agent == "A2":
if self.cog_fixed:
c = (self.dist, self.det)
else:
c = tuple([0] * self.nCogPar)
a = A2(self.unique_id,
self,
self.start_energy,
cognition=c,
disp_rate=0)
self.unique_id += 1
if self.agentgrid[x][y] == 1:
die = self.grid.get_cell_list_contents([(x, y)])[0]
die.dead = True
self.grid.remove_agent(die)
self.schedule.remove(die)
self.grid.place_agent(a, (x, y))
self.schedule.add(a)
self.agentgrid[x][y] = 2
self.coggrid[:, x, y] = c
elif self.agentgrid[x][y] == 0:
self.grid.place_agent(a, (x, y))
self.schedule.add(a)
self.agentgrid[x][y] = 2
self.coggrid[:, x, y] = c
def flatten_(self, n, grid, full_grid=False, mean=True, range_=False):
if full_grid:
return (grid[n].flatten())
i = grid[n].flatten()
if mean:
i = np.delete(i, np.where(i == 101))
if len(i) == 0:
# if range_:
return ([0] * 4)
#else:
# return(0)
if range_:
if self.cog_fixed:
return ([np.mean(i)] * 4)
return (np.concatenate(
([np.mean(i)], np.percentile(i, [2.5, 97.5]),
self.calculate_ci(i))))
return ([np.mean(i), 0, 0, 0])
else:
return (i)
def calculate_ci(self, data):
if np.min(data) == np.max(data):
return ([0.0])
return ([
np.mean(data) - st.t.interval(
0.95, len(data) - 1, loc=np.mean(data), scale=st.sem(data))[0]
])
def return_zero(self, num, denom):
if self.nstep == 1:
# print("whaaat")
return (0)
if denom == "old_nA2":
denom = self.history["nA2"][self.nstep - 2]
if denom == 0.0:
return 0
return (num / denom)
def nearest_neighbor(self, agent): # fix this later
if agent == "a1":
x = np.argwhere(self.agentgrid == 1)
if len(x) <= 10:
return ([-1] * len(self.NN))
elif len(x) > 39990:
return ([0.97, 0.99])
# if self.nstep<300 and self.skip_300:
# return([-1,-1] )
else:
x = np.argwhere(self.agentgrid == 2)
if len(x) <= 10:
return ([-1] * len(self.NN))
density = len(x) / (self.grid.width)**2
expect_NN_ = self.expect_NN
expect_dist = np.array(expect_NN_) / (density**0.5)
distances = [0, 0]
for i in x:
distx = abs(x[:, 0] - i[0])
distx[distx > 100] = 200 - distx[distx > 100]
disty = abs(x[:, 1] - i[1])
disty[disty > 100] = 200 - disty[disty > 100]
dist = (distx**2 + disty**2)**0.5
distances[0] += (np.partition(dist, 5)[5])
distances[1] += (np.partition(dist, 10)[10])
mean_dist = np.array(distances) / len(x)
out = mean_dist / expect_dist
return (out)
def quadrant_patch(
self, agent
): # function to calculate the patchiness index of agents at every step
if agent == "a1":
x = self.agentgrid == 1
else:
x = self.agentgrid == 2
gsize = np.array([5, 10])
gnum = 200 / gsize
qcs = []
for i in range(2):
x_ = x.reshape(int(gnum[i]), gsize[i], int(gnum[i]),
gsize[i]).sum(1).sum(2)
mean = np.mean(x_)
var = np.var(x_)
if mean == 0.0:
return ([-1] * 4)
lip = 1 + (var - mean) / (mean**2)
morsita = np.sum(x) * ((np.sum(np.power(x_, 2)) - np.sum(x_)) /
(np.sum(x_)**2 - np.sum(x_)))
qcs += [lip, morsita]
return (qcs)
def l_function(self, agent):
if agent == "a1":
x = np.argwhere(self.agentgrid == 1)
else:
x = np.argwhere(self.agentgrid == 2)
if len(x) == 0:
return (-1)
distances = np.array([])
for i in x:
distx = abs(x[:, 0] - i[0])
distx[distx > 100] = 200 - distx[distx > 100]
disty = abs(x[:, 1] - i[1])
disty[disty > 100] = 200 - disty[disty > 100]
dist = (distx**2 + disty**2)**0.5
distances = np.concatenate((distances, dist[dist != 0]))
l = np.array([])
for i in np.arange(5, 51, 5):
l = np.append(l, sum(distances < i))
k = (l * 200**2) / (len(x)**2)
l = (k / math.pi)**0.5
return (abs(l - np.arange(5, 51, 5)))
def collect_hist(self):
if self.nstep < 300 and self.skip_300:
NNcalc = [-1, -1] #self.nearest_neighbor("a1")
NNanimcalc = [-1, -1] #self.nearest_neighbor("a2")
else:
NNcalc = self.nearest_neighbor("a1")
NNanimcalc = self.nearest_neighbor("a2")
quadrantcalc = self.quadrant_patch("a1")
quadrantanimcalc = self.quadrant_patch("a2")
dist_values = self.flatten_(0,
grid=self.coggrid,
mean=True,
range_=False)
det_values = self.flatten_(1,
grid=self.coggrid,
mean=True,
range_=False)
# l_f = 0#self.l_function("a1")
dat = {
"nA1": self.nA1,
"nA2": self.nA2,
"age": self.return_zero(sum(self.age), self.nA2),
"LIP5": quadrantcalc[0],
"LIP10": quadrantcalc[2],
"LIPanim5": quadrantanimcalc[0],
"LIPanim10": quadrantanimcalc[2],
"Morsita5": quadrantcalc[1],
"Morsita10": quadrantcalc[3],
"Morsitaanim5": quadrantanimcalc[1],
"Morsitaanim10": quadrantanimcalc[3],
"NN5": NNcalc[0],
"NN10": NNcalc[1],
"NNanim5": NNanimcalc[0],
"NNanim10":
NNanimcalc[1], #"l_ripley" : l_f,# self.nearest_neighbor("a2"),
"reprod": self.return_zero(self.reprod, "old_nA2"),
"food": self.return_zero(self.food, self.nA2),
"death": self.return_zero(self.death, "old_nA2"),
"combat": self.return_zero(self.combat, "old_nA2"),
"dist": dist_values[0],
"det": det_values[0],
"dist_lower": dist_values[1],
"det_lower": det_values[1],
"dist_upper": dist_values[2],
"det_upper": det_values[2],
"dist_ci": dist_values[3],
"det_ci": det_values[3],
"disp_a1": self.flatten_(0, grid=self.dispgrid)[0],
"disp_a2": self.flatten_(1, grid=self.dispgrid)[0]
}
self.history = self.history.append(dat, ignore_index=True)
self.age = []
(self.reprod, self.food, self.death, self.combat) = (0, 0, 0, 0)
if self.collect_cog_dist:
if (self.nstep % 10) == 0:
self.cog_dist_dist[str(self.nstep - 1)] = self.flatten_(
0, grid=self.coggrid, full_grid=True, mean=False)
self.cog_dist_det[str(self.nstep - 1)] = self.flatten_(
1, grid=self.coggrid, full_grid=True, mean=False)
def step(self):
self.nstep += 1 # step counter
if self.nstep == self.intro_time:
for i in range(self.a2num):
self.introduce_agents("A2")
self.schedule.step()
self.nA1 = np.sum(self.agentgrid == 1)
self.nA2 = np.sum(self.agentgrid == 2)
self.collect_hist()
if self.nstep % 10 == 0:
sys.stdout.write((str(self.nstep) + " " + str(self.nA1) + " " +
str(self.nA2) + "\n"))
def visualize(self):
f, ax = plt.subplots(1)
self.agentgrid = self.agentgrid.astype(int)
ax.imshow(self.agentgrid,
interpolation='nearest',
cmap=self.cmap,
norm=self.norm)
# plt.axis("off")
return (f)
class DiseaseSimModel(Model):
"""
The model class holds the model-level attributes, manages the agents,
and generally handles
the global level of our model.
There is only one model-level parameter: how many agents
the model contains. When a new model
is started, we want it to populate itself with the given number of agents.
The scheduler is a special model component which controls the order
in which agents are activated.
"""
def __init__(
self,
width=50,
height=50,
population_density=0.75,
vaccine_density=0,
initial_infection_fraction=0.1,
initial_vaccination_fraction=0.00,
prob_infection=0.2,
prob_agent_movement=0.0,
disease_planner_config={
"latent_period_mu": 2 * 4,
"latent_period_sigma": 0,
"incubation_period_mu": 5 * 4,
"incubation_period_sigma": 0,
"recovery_period_mu": 14 * 4,
"recovery_period_sigma": 0,
},
max_timesteps=200,
early_stopping_patience=14,
toric=True,
seed=None):
super().__init__()
self.width = width
self.height = height
# fraction of the whole grid that is initiailized with agents
self.population_density = population_density
self.vaccine_density = vaccine_density
self.n_agents = False
self.n_vaccines = False
self.initial_infection_fraction = initial_infection_fraction
self.initial_vaccination_fraction = initial_vaccination_fraction
self.prob_infection = prob_infection
self.prob_agent_movement = prob_agent_movement
self.disease_planner_config = disease_planner_config
self.max_timesteps = max_timesteps
self.early_stopping_patience = early_stopping_patience
self.toric = toric
self.seed = seed
self.initialize_observation()
self.initialize_disease_planner()
self.initialize_scheduler()
self.initialize_grid()
self.initialize_contact_network()
self.initialize_agents(
infection_fraction=self.initial_infection_fraction,
vaccination_fraction=self.initial_vaccination_fraction)
self.initialize_datacollector()
self.running = True
self.datacollector.collect(self)
###########################################################################
###########################################################################
# Setup Initialization Helper Functions
###########################################################################
def initialize_observation(self):
"""
Observation is a nd-array of shape (width, height, num_states)
where each AgentState will be marked in a separate challenge
for each of the cells
"""
self.observation = np.zeros((self.width, self.height, len(AgentState)))
def initialize_disease_planner(self):
"""
Initializes a disease planner that the Agents can use to "schedule"
infection progressions
"""
self.disease_planner = SEIRDiseasePlanner(
latent_period_mu=self.disease_planner_config["latent_period_mu"],
latent_period_sigma=self.
disease_planner_config["latent_period_sigma"], # noqa
incubation_period_mu=self.
disease_planner_config["incubation_period_mu"], # noqa
incubation_period_sigma=self.
disease_planner_config["incubation_period_sigma"], # noqa
recovery_period_mu=self.
disease_planner_config["recovery_period_mu"], # noqa
recovery_period_sigma=self.disease_planner_config[
"recovery_period_sigma"] # noqa
)
def initialize_scheduler(self):
"""
Initializes the scheduler
"""
self.schedule = CustomScheduler(self)
def initialize_grid(self):
"""
Initializes the initial Grid
"""
self.grid = SingleGrid(width=self.width,
height=self.height,
torus=self.toric)
def initialize_contact_network(self):
"""
Initializes the contact network
"""
self.contact_network = ContactNetwork()
def initialize_agents(self, infection_fraction, vaccination_fraction):
"""
Intializes the intial agents on the grid
"""
assert 0 < self.population_density <= 1, \
"population_density should be between (0, 1]"
# Assess the actual population
self.n_agents = int(self.width * self.height * self.population_density)
# Assess the available number of vaccines
self.n_vaccines = int(self.n_agents * self.vaccine_density)
# Assess the number of agents that
# have to be infected (the seed infection)
number_of_agents_to_infect = int(infection_fraction * self.n_agents)
number_of_agents_to_vaccinate = int(vaccination_fraction *
self.n_agents)
# Assess the maximum number of vaccines
# available in the whole simulation
self.max_vaccines = self.n_vaccines + number_of_agents_to_vaccinate
for i in range(self.n_agents):
agent = DiseaseSimAgent(
unique_id=i,
model=self,
prob_agent_movement=self.prob_agent_movement)
self.schedule.add(agent)
self.grid.position_agent(agent, x="random", y="random")
# Update model observation
# TODO- This has to be refactored to avoid repitition
agent_x, agent_y = agent.pos
self.observation[agent_x, agent_y, agent.state.value] = 1
# Seed the infection in a fraction of the agents
infection_condition = i < number_of_agents_to_infect
if infection_condition:
agent.trigger_infection(prob_infection=1.0)
# Seed the vaccination in a fraction of the agents
vaccination_condition = (
i >= number_of_agents_to_infect and i <
(number_of_agents_to_infect + number_of_agents_to_vaccinate)
) # noqa
if vaccination_condition:
agent.set_state(AgentState.VACCINATED)
def initialize_datacollector(self):
"""
Setup the initial datacollector
"""
self.datacollector = DataCollector(
model_reporters={
"Susceptible":
lambda m: m.get_population_fraction_by_state(
AgentState.SUSCEPTIBLE), # noqa
"Exposed":
lambda m: m.get_population_fraction_by_state(AgentState.EXPOSED
), # noqa
"Infectious":
lambda m: m.get_population_fraction_by_state(
AgentState.INFECTIOUS), # noqa
"Symptomatic":
lambda m: m.get_population_fraction_by_state(
AgentState.SYMPTOMATIC), # noqa
"Recovered":
lambda m: m.get_population_fraction_by_state(
AgentState.RECOVERED), # noqa
"Vaccinated":
lambda m: m.get_population_fraction_by_state(
AgentState.VACCINATED), # noqa
"R0/10":
lambda m: m.contact_network.compute_R0() / 10.0
})
###########################################################################
###########################################################################
# State Aggregation
# - Functions for easy access/aggregation of simulation wide state
###########################################################################
def get_observation(self):
# assert self.observation.sum(axis=-1).max() <= 1.0
# Assertion disabled for perf reasons
return self.observation
###########################################################################
###########################################################################
# Scheduler
# - Functions for easy access to scheduler
###########################################################################
def get_scheduler(self):
return self.schedule
def get_population_fraction_by_state(self, state: AgentState):
return self.schedule.get_agent_fraction_by_state(state)
def is_running(self):
return self.running
###########################################################################
###########################################################################
# Actions
# - Functions for actions that can be performed on the model
###########################################################################
def step(self):
"""
A model step. Used for collecting data and advancing the schedule
"""
self.propagate_infections()
self.datacollector.collect(self)
self.schedule.step()
self.simulation_completion_checks()
def vaccinate_cell(self, cell_x, cell_y):
"""
Vaccinates an agent at cell_x, cell_y, if present
Response with :
(is_vaccination_successful, vaccination_response)
of types
(boolean, VaccinationResponse)
"""
# Case 0 : No vaccines left
if self.n_vaccines <= 0:
return False, VaccinationResponse.AGENT_VACCINES_EXHAUSTED
self.n_vaccines -= 1
# Case 1 : Cell is empty
if self.grid.is_cell_empty((cell_x, cell_y)):
return False, VaccinationResponse.CELL_EMPTY
agent = self.grid[cell_x][cell_y]
if agent.state == AgentState.SUSCEPTIBLE:
# Case 2 : Agent is susceptible, and can be vaccinated
agent.set_state(AgentState.VACCINATED)
return True, VaccinationResponse.VACCINATION_SUCCESS
elif agent.state == AgentState.EXPOSED:
# Case 3 : Agent is already exposed, and its a waste of vaccination
return False, VaccinationResponse.AGENT_EXPOSED
elif agent.state == AgentState.INFECTIOUS:
# Case 4 : Agent is already infectious,
# and its a waste of vaccination
return False, VaccinationResponse.AGENT_INFECTIOUS
elif agent.state == AgentState.SYMPTOMATIC:
# Case 5 : Agent is already Symptomatic,
# and its a waste of vaccination
return False, VaccinationResponse.AGENT_SYMPTOMATIC
elif agent.state == AgentState.RECOVERED:
# Case 6 : Agent is already Recovered,
# and its a waste of vaccination
return False, VaccinationResponse.AGENT_RECOVERED
elif agent.state == AgentState.VACCINATED:
# Case 7 : Agent is already Vaccination,
# and its a waste of vaccination
return False, VaccinationResponse.AGENT_VACCINATED
raise NotImplementedError()
###########################################################################
###########################################################################
# Misc
###########################################################################
def simulation_completion_checks(self):
"""
Simulation is complete if :
- if the timesteps have exceeded the number of max_timesteps
or
- the fraction of susceptible population is <= 0
or
- the fraction of susceptible population has not changed since the
last N timesteps
"""
if self.schedule.steps > self.max_timesteps - 1:
self.running = False
return
susceptible_population = self.get_population_fraction_by_state(
AgentState.SUSCEPTIBLE)
if susceptible_population <= 0:
self.running = False
return
if self.schedule.steps > self.early_stopping_patience:
last_N_susceptible_population = \
self.datacollector.model_vars["Susceptible"][-1 *
self.early_stopping_patience:] # noqa
if len(set(last_N_susceptible_population)) == 1:
self.running = False
return
def tick(self):
"""
a mirror function for the internal step function
to help avoid confusion in the RL codebases (with the RL step)
"""
self.step()
def propagate_infections(self):
"""
Propagates infection during a single simulation step
"""
valid_infectious_agents = []
valid_infectious_agents += self.schedule.get_agents_by_state(
AgentState.INFECTIOUS)
valid_infectious_agents += self.schedule.get_agents_by_state(
AgentState.SYMPTOMATIC)
for _infectious_agent in valid_infectious_agents:
target_candidates = self.grid.get_neighbors(
pos=_infectious_agent.pos,
moore=True,
include_center=False,
radius=1)
for _target_candidate in target_candidates:
if _target_candidate.state == AgentState.SUSCEPTIBLE:
was_infection_successful =\
_target_candidate.trigger_infection(
prob_infection=self.prob_infection)
if was_infection_successful:
# Register infection in the contact network
self.contact_network.register_infection_spread(
_infectious_agent, _target_candidate)
class DiseaseModel(Model):
"""
A model with some number of agents.
highS: Number of agents with high sociability.
middleS: Number of agents with middle sociability.
lowS: Number of agents with low sociability.
width: Width of the grid.
height: Height of the grid.
edu_setting: Classrooms and set schedule if true, else random free movement.
cureProb: Probability of agent getting better.
cureProbFac: Factor of cureProb getting higher.
mutateProb: Probability of a disease mutating.
diseaseRate: Rate at which the disease spreads.
"""
def __init__(self, highS, middleS, lowS, width, height, edu_setting=True, cureProb=0.1, cureProbFac=2/1440, mutateProb=0.0050, diseaseRate=0.38):
super().__init__()
self.num_agents = highS + middleS + lowS
self.lowS = lowS
self.middleS = middleS
self.highS = highS
self.initialCureProb = cureProb
self.cureProbFac = cureProbFac
self.mutateProb = mutateProb
self.diseaseRate = diseaseRate
self.edu_setting = edu_setting
self.maxDisease = 0# amount of mutations
self.counter = 540 # keeps track of timesteps
self.removed = []
self.exit = (width-1,floor(height/2))
# Check if agents fit within grid
if self.num_agents > width * height:
raise ValueError("Number of agents exceeds grid capacity.")
# Create grid with random activation
self.grid = SingleGrid(width, height, True)
self.schedule = RandomActivation(self)
if edu_setting:
# Create walls
numberRooms = 3
self.add_walls(numberRooms, width, height)
self.midWidthRoom = floor(width / numberRooms / 2)
self.midHeightRoom = floor(height / numberRooms / 2)
# Calculate the centers of the 6 rooms
roomLeftDown = (5 * self.midWidthRoom, self.midHeightRoom)
roomLeftMid = (3 * self.midWidthRoom, self.midHeightRoom)
roomLeftUp = (self.midWidthRoom, self.midHeightRoom)
roomRightDown = (5 * self.midWidthRoom, 5 * self.midHeightRoom, )
roomRightMid = (3 * self.midWidthRoom, 5 * self.midHeightRoom)
roomRightUp = (self.midWidthRoom, 5 * self.midHeightRoom)
# Set 3 goals per roster
self.roster = [[roomLeftDown, roomLeftUp, roomRightMid], [roomRightMid, roomLeftDown, roomRightDown],
[roomRightUp, roomRightDown, roomLeftUp]]
# Create agents
self.addAgents(lowS, 0, 0)
self.addAgents(middleS, lowS, 1)
self.addAgents(highS, lowS + highS, 2)
self.datacollector = DataCollector(
model_reporters={"diseasepercentage": disease_collector},
agent_reporters={"disease": "disease"})
def heuristic(self, start, goal):
"""
Returns manhattan distance.
start: current location (x,y)
goal: goal location (x,y)
"""
dx = abs(start[0] - goal[0])
dy = abs(start[1] - goal[1])
return dx + dy
def get_vertex_neighbors(self, pos):
"""
Returns all neighbors.
pos: current position
"""
n = self.grid.get_neighborhood(pos, moore=False)
neighbors = []
for item in n:
if not abs(item[0]-pos[0]) > 1 and not abs(item[1]-pos[1]) > 1:
neighbors += [item]
return neighbors
def move_cost(self, location):
"""
Return the cost of a location.
"""
if self.grid.is_cell_empty(location):
return 1 # Normal movement cost
else:
return 100
def add_walls(self, n, widthGrid, heightGrid):
"""
Add walls in grid.
n: number of rooms horizontally
widthGrid: width of the grid
heightGrid: height of the grid
"""
widthRooms = floor(widthGrid/n)
heightRooms = floor(heightGrid/n)
widthHall = widthGrid - 2 * widthRooms
heightHall = heightGrid - 2 * heightRooms
# Add horizontal walls
for i in range(n - 1):
for y in range(heightRooms):
brick = wall(self.num_agents, self)
self.grid.place_agent(brick, ((i + 1) * widthRooms, y))
self.grid.place_agent(brick, ((i + 1) * widthRooms, y + heightRooms + heightHall))
doorWidth = 2
# Add vertical walls
for x in range(widthGrid):
if (x % widthRooms) < (widthRooms - doorWidth):
brick = wall(self.num_agents, self)
self.grid.place_agent(brick, (x, heightRooms))
self.grid.place_agent(brick, (x, heightRooms + heightHall - 1))
def addAgents(self, n, startID, sociability):
"""
Add agents with a sociability.
n: number of agents
startID: ID of the first added agent
sociability: sociability of the agents
"""
disease_list = np.random.randint(0,2,n)
for i in range(n):
a = DiseaseAgent(i + startID, sociability,self,disease_list[i])
self.schedule.add(a)
# Add the agent to a random grid cell
location = self.grid.find_empty()
self.grid.place_agent(a, location)
def step(self):
"""
Continue one step in simulation.
"""
self.counter += 1
self.datacollector.collect(self)
self.schedule.step()
class SchoolModel(Model):
"""
Model class for the Schelling segregation model.
...
Attributes
----------
height: int
grid height
width: int
grid width
num_schools: int
number of schools
f : float
fraction preference of agents for like
M : float
utility penalty for homogeneous neighbourhood
residential_steps :
number of steps for the residential model
minority_pc :
minority fraction
bounded : boolean
If True use bounded (predefined neighbourhood) for agents residential choice
cap_max : float
school capacity TODO: explain
radius : int
neighbourhood radius for agents calculation of residential choice (only used if not bounded)
household_types :
labels for different ethnic types of households
symmetric_positions :
use symmetric positions for the schools along the grid, or random
schelling :
if True use schelling utility function otherwise use assymetric
school_pos :
if supplied place schools in the supplied positions - also update school_num
extended_data :
if True collect extra data for agents (utility distribution and satisfaction)
takes up a lot of space
sample : int
subsample the empty residential sites to be evaluated to speed up computation
variable_f : variable_f
draw values of the ethnic preference, f from a normal distribution
sigma : float
The standard deviation of the normal distribution used for f
alpha : float
ratio of ethnic to distance to school preference for school utility
temp : float
temperature for the behavioural logit rule for agents moving
households : list
all household objects
schools : list
all school objects
residential_moves_per_step : int
number of agents to move residence at every step
school_moves_per_step : int
number of agents to move school at every step
num_households : int
total number of household agents
pm : list [ , ]
number of majority households, number of minority households
schedule : mesa schedule type
grid : mesa grid type
total_moves :
number of school moves made in particular step
res_moves :
number of residential site moves made in particular step
move :
type of move recipe - 'random' 'boltzmann' or 'deterministic'
school_locations : list
list of locations of all schools (x,y)
household_locations :
list of locations of all households (x,y)
closer_school_from_position : numpy array shape : (width x height)
map of every grid position to the closest school
"""
def __init__(self, height=100, width=100, density=0.9, num_neighbourhoods=16, schools_per_neighbourhood=2,minority_pc=0.5, homophily=3, f0=0.6,f1=0.6,\
M0=0.8,M1=0.8,T=0.75,
alpha=0.5, temp=1, cap_max=1.01, move="boltzmann", symmetric_positions=True,
residential_steps=70,schelling=False,bounded=True,
residential_moves_per_step=2000, school_moves_per_step =2000,radius=6,proportional = False,
torus=False,fs="eq", extended_data = False, school_pos=None, agents=None, sample=4, variable_f=True, sigma=0.35, displacement=8 ):
# Options for the model
self.height = height
self.width = width
print("h x w", height, width)
self.density = density
#self.num_schools= num_schools
self.f = [f0, f1]
self.M = [M0, M1]
self.residential_steps = residential_steps
self.minority_pc = minority_pc
self.bounded = bounded
self.cap_max = cap_max
self.T = T
self.radius = radius
self.household_types = [0, 1] # majority, minority !!
self.symmetric_positions = symmetric_positions
self.schelling = schelling
self.school_pos = school_pos
self.extended_data = extended_data
self.sample = sample
self.variable_f = variable_f
self.sigma = sigma
self.fs = fs
# choice parameters
self.alpha = alpha
self.temp = temp
self.households = []
self.schools = []
self.neighbourhoods = []
self.residential_moves_per_step = residential_moves_per_step
self.school_moves_per_step = school_moves_per_step
self.num_households = int(width * height * density)
num_min_households = int(self.minority_pc * self.num_households)
self.num_neighbourhoods = num_neighbourhoods
self.schools_per_neigh = schools_per_neighbourhood
self.num_schools = int(num_neighbourhoods * self.schools_per_neigh)
self.pm = [
self.num_households - num_min_households, num_min_households
]
self.schedule = RandomActivation(self)
self.grid = SingleGrid(height, width, torus=torus)
self.total_moves = 0
self.res_moves = 0
self.move = move
self.school_locations = []
self.household_locations = []
self.neighbourhood_locations = []
self.closer_school_from_position = np.empty(
[self.grid.width, self.grid.height])
self.closer_neighbourhood_from_position = np.empty(
[self.grid.width, self.grid.height])
self.happy = 0
self.res_happy = 0
self.percent_happy = 0
self.seg_index = 0
self.res_seg_index = 0
self.residential_segregation = 0
self.collective_utility = 0
self.comp0,self.comp1,self.comp2,self.comp3,self.comp4,self.comp5,self.comp6,self.comp7, \
self.comp8, self.comp9, self.comp10, self.comp11, self.comp12, self.comp13, self.comp14, self.comp15 = 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
self.satisfaction = []
self.pi_jm = []
self.pi_jm_fixed = []
self.compositions = []
self.average_like_fixed = 0
self.average_like_variable = 0
self.my_collector = []
if torus:
self.max_dist = self.height / np.sqrt(2)
else:
self.max_dist = self.height * np.sqrt(2)
# Set up agents
# We use a grid iterator that returns
# the coordinates of a cell as well as
# its contents. (coord_iter)
# Set up schools in symmetric positions along the grid
# if schools already supplied place them where they should be
# TODO: fix
if self.school_pos:
school_positions = self.school_pos
self.school_locations = school_pos
self.num_schools = len(school_pos)
print("Option not working")
sys.exit()
# otherwise calculate the positions
else:
if self.num_neighbourhoods == 4:
neighbourhood_positions = [(width / 4, height / 4),
(width * 3 / 4, height / 4),
(width / 4, height * 3 / 4),
(width * 3 / 4, height * 3 / 4)]
elif self.num_neighbourhoods == 9:
n = 6
neighbourhood_positions = [(width/n,height/n),(width*3/n,height*1/n),(width*5/n,height*1/n),(width/n,height*3/n),\
(width*3/n,height*3/n),(width*5/n,height*3/n),(width*1/n,height*5/n),(width*3/n,height*5/n),\
(width*5/n,height*5/n)]
elif self.num_neighbourhoods in [25, 64, 16]:
neighbourhood_positions = []
n = int(np.sqrt(self.num_neighbourhoods) * 2)
print(n)
x1 = range(1, int(n + 1), 2)
xloc = np.repeat(x1, int(n / 2))
yloc = np.tile(x1, int(n / 2))
for i in range(self.num_neighbourhoods):
neighbourhood_positions.append(
(xloc[i] * height / n, yloc[i] * width / n))
print(neighbourhood_positions)
#for i in range(self.num_schools):i
i = 0
while len(self.neighbourhoods) < self.num_neighbourhoods:
if self.symmetric_positions or self.school_pos:
x = int(neighbourhood_positions[i][0])
y = int(neighbourhood_positions[i][1])
#print(x,y)
else:
x = random.randrange(start=2, stop=self.grid.width - 2)
y = random.randrange(start=2, stop=self.grid.height - 2)
pos = (x, y)
pos2 = (x + 1, y + 1)
if schools_per_neighbourhood == 2:
pos3 = (x - displacement, y - displacement)
pos2 = (x + displacement, y + displacement)
do_not_use = self.school_locations + self.neighbourhood_locations
#if (pos not in do_not_use) and (pos2 not in do_not_use ) and (pos3 not in do_not_use ):
if (pos not in do_not_use) and (pos2 not in do_not_use):
#print('pos',pos,pos2,pos3)
self.school_locations.append(pos2)
school = SchoolAgent(pos2, self)
self.grid.place_agent(school, school.unique_id)
self.schools.append(school)
self.schedule.add(school)
if self.schools_per_neigh == 2:
# Add another school
self.school_locations.append(pos3)
school = SchoolAgent(pos3, self)
self.grid.place_agent(school, school.unique_id)
self.schools.append(school)
self.schedule.add(school)
self.neighbourhood_locations.append(pos)
neighbourhood = NeighbourhoodAgent(pos, self)
self.grid.place_agent(neighbourhood, neighbourhood.unique_id)
self.neighbourhoods.append(neighbourhood)
self.schedule.add(neighbourhood)
else:
print(pos, pos2, pos3, "is found in", do_not_use)
i += 1
print("num_schools", len(self.school_locations))
print("schools completed")
#print(self.neighbourhood_locations)
#print("schools",self.school_locations, len(self.school_locations))
# Set up households
# If agents are supplied place them where they need to be
if agents:
for cell in agents:
[agent_type, x, y] = cell
if agent_type in [0, 1]:
pos = (x, y)
if self.grid.is_cell_empty(pos):
agent = HouseholdAgent(pos, self, agent_type)
self.grid.place_agent(agent, agent.unique_id)
self.household_locations.append(pos)
self.households.append(agent)
self.schedule.add(agent)
# otherwise produce them
else:
# create household locations but dont create agents yet
while len(self.household_locations) < self.num_households:
#Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
pos = (x, y)
if (pos not in (self.school_locations +
self.household_locations +
self.neighbourhood_locations)):
self.household_locations.append(pos)
#print(Dij)
for ind, pos in enumerate(self.household_locations):
# create a school or create a household
if ind < int(self.minority_pc * self.num_households):
agent_type = self.household_types[1]
else:
agent_type = self.household_types[0]
household_index = ind
agent = HouseholdAgent(pos, self, agent_type, household_index)
#decorator_agent = HouseholdAgent(pos, self, agent_type)
self.grid.place_agent(agent, agent.unique_id)
#self.grid.place_agent(decorator_agent, pos)
self.households.append(agent)
self.schedule.add(agent)
self.set_positions_to_school()
self.set_positions_to_neighbourhood()
self.calculate_all_distances()
self.calculate_all_distances_to_neighbourhoods()
for agent in self.households:
random_school_index = random.randint(0, len(self.schools) - 1)
#print("school_index", random_school_index, agent.Dj, len(agent.Dj))
candidate_school = self.schools[random_school_index]
agent.allocate(candidate_school, agent.Dj[random_school_index])
#closer_school = self.schools[p.argmin(Dj)]
#closer_school.students.append(agent)
# agent.allocate(closer_school, np.min(Dj))
#print(agent.school.unique_id)
self.pi_jm = np.zeros(shape=(len(self.school_locations),
len(self.household_types)))
self.local_compositions = np.zeros(shape=(len(self.school_locations),
len(self.household_types)))
self.avg_school_size = round(density * width * height /
(len(self.schools)))
if self.extended_data:
self.datacollector = DataCollector(
model_reporters={
"agent_count": lambda m: m.schedule.get_agent_count(),
"seg_index": "seg_index",
"residential_segregation": "residential_segregation",
"res_seg_index": "res_seg_index",
"fixed_res_seg_index": "fixed_res_seg_index",
"happy": "happy",
"percent_happy": "percent_happy",
"total_moves": "total_moves",
"compositions0": "compositions0",
"compositions1": "compositions1",
"comp0": "comp0",
"comp1": "comp1",
"comp2": "comp2",
"comp3": "comp3",
"comp4": "comp4",
"comp5": "comp5",
"comp6": "comp6",
"comp7": "comp7",
"compositions": "compositions",
"collective_utility": "collective_utility"
},
agent_reporters={
"local_composition": "local_composition",
"type": lambda a: a.type,
"id": lambda a: a.unique_id,
#"fixed_local_composition": "fixed_local_composition",
#"variable_local_composition": "variable_local_composition",
"school_utilities": "school_utilities",
"residential_utilities": "residential_utilities",
"pos": "pos"
})
else:
self.datacollector = DataCollector(
model_reporters={
"agent_count": lambda m: m.schedule.get_agent_count(),
"seg_index": "seg_index",
"residential_segregation": "residential_segregation",
"res_seg_index": "res_seg_index",
"fixed_res_seg_index": "fixed_res_seg_index",
"happy": "happy",
"percent_happy": "percent_happy",
"total_moves": "total_moves",
"compositions0": "compositions0",
"compositions1": "compositions1",
"comp0": "comp0",
"comp1": "comp1",
"comp2": "comp2",
"comp3": "comp3",
"comp4": "comp4",
"comp5": "comp5",
"comp6": "comp6",
"comp7": "comp7",
"compositions": "compositions",
"collective_utility": "collective_utility"
},
agent_reporters={
"local_composition": "local_composition",
"type": lambda a: a.type,
"id": lambda a: a.unique_id,
# "fixed_local_composition": "fixed_local_composition",
# "variable_local_composition": "variable_local_composition",
"pos": "pos"
})
# Calculate local composition
# set size
for school in self.schools:
#school.get_local_school_composition()
#cap = round(np.random.normal(loc=cap_max * self.avg_school_size, scale=self.avg_school_size * 0.05))
cap = self.avg_school_size * self.cap_max
school.capacity = cap
print("cap", self.avg_school_size, cap)
segregation_index(self)
#
print(
"height = %d; width = %d; density = %.2f; num_schools = %d; minority_pc = %.2f; "
"f0 = %.2f; f1 = %.2f; M0 = %.2f; M1 = %.2f;\
alpha = %.2f; temp = %.2f; cap_max = %.2f; move = %s; symmetric_positions = %s"
% (height, width, density, self.num_schools, minority_pc, f0, f1,
M0, M1, alpha, temp, cap_max, move, symmetric_positions))
self.total_considered = 0
self.running = True
self.datacollector.collect(self)
def calculate_all_distances(self):
"""
calculate distance between school and household
Euclidean or gis shortest road route
:return: dist
"""
Dij = distance.cdist(np.array(self.household_locations),
np.array(self.school_locations), 'euclidean')
for household_index, household in enumerate(self.households):
Dj = Dij[household_index, :]
household.Dj = Dj
# Calculate distances of the schools - define the school-neighbourhood and compare
# closer_school = household.schools[np.argmin(household.)]
closer_school_index = np.argmin(household.Dj)
household.closer_school = self.schools[closer_school_index]
household.closer_school.neighbourhood_students.append(household)
return (Dij)
def calculate_all_distances_to_neighbourhoods(self):
"""
calculate distance between school and household
Euclidean or gis shortest road route
:return: dist
"""
for household_index, household in enumerate(self.households):
# Calculate distances of the schools - define the school-neighbourhood and compare
# closer_school = household.schools[np.argmin(household.)]
household.closer_neighbourhood = self.get_closer_neighbourhood_from_position(
household.pos)
household.closer_neighbourhood.neighbourhood_students_indexes.append(
household_index)
# just sanity check
# for i, neighbourhood in enumerate(self.neighbourhoods):
# students = neighbourhood.neighbourhood_students_indexes
# print("students,",i, len(students))
def set_positions_to_school(self):
'''
calculate closer school from every position on the grid
Euclidean or gis shortest road route
:return: dist
'''
distance_dict = {}
# Add the agent to a random grid cell
all_grid_locations = []
for x in range(self.grid.width):
for y in range(self.grid.height):
all_grid_locations.append((x, y))
Dij = distance.cdist(np.array(all_grid_locations),
np.array(self.school_locations), 'euclidean')
for i, pos in enumerate(all_grid_locations):
Dj = Dij[i, :]
(x, y) = pos
# Calculate distances of the schools - define the school-neighbourhood and compare
# closer_school = household.schools[np.argmin(household.)]
closer_school_index = np.argmin(Dj)
self.closer_school_from_position[x][y] = closer_school_index
#print("closer_school_by_position",self.closer_school_from_position)
def set_positions_to_neighbourhood(self):
'''
calculate closer neighbourhood centre from every position on the grid
Euclidean or gis shortest road route
:return: dist
'''
distance_dict = {}
# Add the agent to a random grid cell
all_grid_locations = []
for x in range(self.grid.width):
for y in range(self.grid.height):
all_grid_locations.append((x, y))
Dij = distance.cdist(np.array(all_grid_locations),
np.array(self.neighbourhood_locations),
'euclidean')
for i, pos in enumerate(all_grid_locations):
Dj = Dij[i, :]
(x, y) = pos
# Calculate distances of the schools - define the school-neighbourhood and compare
# closer_school = household.schools[np.argmin(household.)]
closer_neighbourhood_index = np.argmin(Dj)
self.closer_neighbourhood_from_position[x][
y] = closer_neighbourhood_index
#print("closer_school_by_position", self.closer_school_from_position)
def get_closer_school_from_position(self, pos):
"""
:param pos: (x,y) position
:return school: school object closest to this position
"""
(x, y) = pos
school_index = self.closer_school_from_position[x][y]
school = self.get_school_from_index(school_index)
return (school)
def get_closer_neighbourhood_from_position(self, pos):
"""
:param pos: (x,y) position
:return school: school object closest to this position
"""
(x, y) = pos
neighbourhood_index = self.closer_neighbourhood_from_position[x][y]
neighbourhood = self.get_neighbourhood_from_index(neighbourhood_index)
return (neighbourhood)
def get_school_from_index(self, school_index):
"""
:param self: obtain the school object using the index
:param school_index:
:return: school object
"""
return (self.schools[int(school_index)])
def get_neighbourhood_from_index(self, neighbourhood_index):
"""
:param self: obtain the school object using the index
:param school_index:
:return: school object
"""
return (self.neighbourhoods[int(neighbourhood_index)])
def get_households_from_index(self, household_indexes):
"""
Retrieve household objects from their indexes
:param household_indexes: list of indexes to retrieve household objects
:return: households: household objects
"""
households = []
for household_index in household_indexes:
households.append(self.households[household_index])
return (households)
def step(self):
'''
Run one step of the model. If All agents are happy, halt the model.
'''
self.happy = 0 # Reset counter of happy agents
self.res_happy = 0
self.total_moves = 0
self.total_considered = 0
self.res_moves = 0
self.satisfaction = []
self.res_satisfaction = []
self.schedule.step()
satisfaction = 0
res_satisfaction = 0
print("happy", self.happy)
print("total_considered", self.total_considered)
# Once residential steps are done calculate school distances
if self.schedule.steps <= self.residential_steps or self.schedule.steps == 1:
# during the residential steps keep recalculating the school neighbourhood compositions
# this is required for the neighbourhoods metric
#print("recalculating neighbourhoods")
# TODO: check this, not sure if this and the recalculation below is needed
for school in self.schools:
school.neighbourhood_students = []
for neighbourhood in self.neighbourhoods:
neighbourhood.neighbourhood_students_indexes = []
# update the household locations after a move
self.household_locations = []
for i, household in enumerate(self.households):
self.household_locations.append(household.pos)
self.calculate_all_distances()
self.calculate_all_distances_to_neighbourhoods()
#print("all", self.calculate_all_distances()[i, :])
# for i, household in enumerate(self.households):
# print(household.calculate_distances())
# # Calculate distances of the schools - define the school-neighbourhood and compare
# # closer_school = household.schools[np.argmin(household.)]
# closer_school_index = np.argmin(household.Dj)
# household.closer_school = self.schools[closer_school_index]
# household.closer_school.neighbourhood_students.append(household)
#
# # Initialize house allocation to school
# #household.move_school(closer_school_index, self.schools[closer_school_index])
#
self.residential_segregation = segregation_index(
self, unit="neighbourhood")
self.res_seg_index = segregation_index(self,
unit="agents_neighbourhood")
self.fixed_res_seg_index = segregation_index(
self, unit="fixed_agents_neighbourhood", radius=1)
res_satisfaction = np.mean(self.res_satisfaction)
satisfaction = 0
# calculate these after residential_model
if self.schedule.steps > self.residential_steps:
self.collective_utility = calculate_collective_utility(self)
print(self.collective_utility)
self.seg_index = segregation_index(self)
satisfaction = np.mean(self.satisfaction)
print("seg_index", "%.2f"%(self.seg_index), "var_res_seg", "%.2f"%(self.res_seg_index), "neighbourhood",
"%.2f"%(self.residential_segregation), "fixed_res_seg_index","%.2f"%(self.fixed_res_seg_index), \
"res_satisfaction %.2f" %res_satisfaction,"satisfaction %.2f" %satisfaction,\
"average_like_fixed %.2f"%self.average_like_fixed,"average_like_var %.2f"%self.average_like_variable )
if self.happy == self.schedule.get_agent_count():
self.running = False
compositions = []
# remove this?
for school in self.schools:
self.my_collector.append([
self.schedule.steps, school.unique_id,
school.get_local_school_composition()
])
self.compositions = school.get_local_school_composition()
compositions.append(school.get_local_school_composition()[0])
compositions.append(school.get_local_school_composition()[1])
self.compositions1 = int(school.get_local_school_composition()[1])
self.compositions0 = int(school.get_local_school_composition()[0])
#print("school_students",school.neighbourhood_students)
#print("comps",compositions,np.sum(compositions) )
[
self.comp0, self.comp1, self.comp2, self.comp3, self.comp4,
self.comp5, self.comp6, self.comp7
] = compositions[0:8]
# collect data
#
self.datacollector.collect(self)
print("moves", self.total_moves, "res_moves", self.res_moves,
"percent_happy", self.percent_happy)
for i, household in enumerate(self.households):
household.school_utilities = []
household.residential_utilities = []
class SeparationBarrierModel(Model):
def __init__(self, height, width, palestinian_density, settlement_density,
settlers_violence_rate, settlers_growth_rate, suicide_rate, greed_level,
settler_vision=1, palestinian_vision=1,
movement=True, max_iters=1000):
super(SeparationBarrierModel, self).__init__()
self.height = height
self.width = width
self.palestinian_density = palestinian_density
self.settler_vision = settler_vision
self.palestinian_vision = palestinian_vision
self.settlement_density = settlement_density
self.movement = movement
self.running = True
self.max_iters = max_iters
self.iteration = 0
self.schedule = RandomActivation(self)
self.settlers_violence_rate = settlers_violence_rate
self.settlers_growth_rate = settlers_growth_rate
self.suicide_rate = suicide_rate
self.greed_level = greed_level
self.total_violence = 0
self.grid = SingleGrid(height, width, torus=False)
model_reporters = {
}
agent_reporters = {
# "x": lambda a: a.pos[0],
# "y": lambda a: a.pos[1],
}
self.dc = DataCollector(model_reporters=model_reporters,
agent_reporters=agent_reporters)
self.unique_id = 0
# Israelis and palestinans split the region in half
for (contents, x, y) in self.grid.coord_iter():
if random.random() < self.palestinian_density:
palestinian = Palestinian(self.unique_id, (x, y), vision=self.palestinian_vision, breed="Palestinian",
model=self)
self.unique_id += 1
self.grid.position_agent(palestinian, x,y)
self.schedule.add(palestinian)
elif ((y > (self.grid.height) * (1-self.settlement_density)) and random.random() < self.settlement_density):
settler = Settler(self.unique_id, (x, y),
vision=self.settler_vision, model=self, breed="Settler")
self.unique_id += 1
self.grid.position_agent(settler, x,y)
self.schedule.add(settler)
def add_settler(self, pos):
settler = Settler(self.unique_id, pos,
vision=self.settler_vision, model=self, breed="Settler")
self.unique_id += 1
self.grid.position_agent(settler, pos[0], pos[1])
self.schedule.add(settler)
def set_barrier(self,victim_pos, violent_pos):
#print("Set barrier - Greed level", self.greed_level)
visible_spots = self.grid.get_neighborhood(victim_pos,
moore=True, radius=self.greed_level + 1)
furthest_empty = self.find_furthest_empty_or_palestinian(victim_pos, visible_spots)
x,y = furthest_empty
current = self.grid[y][x]
#print ("Set barrier!!", pos, current)
free = True
if (current is not None and current.breed == "Palestinian"):
#print ("Relocating Palestinian")
free = self.relocate_palestinian(current, current.pos)
if (free):
barrier = Barrier(-1, furthest_empty, model=self)
self.grid.position_agent(barrier, x,y)
# Relocate the violent palestinian
#violent_x, violent_y = violent_pos
#if violent_pos != furthest_empty:
# violent_palestinian = self.grid[violent_y][violent_x]
# self.relocate_palestinian(violent_palestinian, furthest_empty)
def relocate_palestinian(self, palestinian, destination):
#print ("Relocating Palestinian in ", palestinian.pos, "To somehwhere near ", destination)
visible_spots = self.grid.get_neighborhood(destination,
moore=True, radius=palestinian.vision)
nearest_empty = self.find_nearest_empty(destination, visible_spots)
#print("First Nearest empty to ", palestinian.pos, " Is ", nearest_empty)
if (nearest_empty):
self.grid.move_agent(palestinian, nearest_empty)
else:
#print ("Moveing to random empty")
if (self.grid.exists_empty_cells()):
self.grid.move_to_empty(palestinian)
else:
return False
return True
def find_nearest_empty(self, pos, neighborhood):
nearest_empty = None
sorted_spots = self.sort_neighborhood_by_distance(pos, neighborhood)
index = 0
while (nearest_empty is None and index < len(sorted_spots)):
if self.grid.is_cell_empty(sorted_spots[index]):
nearest_empty = sorted_spots[index]
index += 1
return nearest_empty
def find_furthest_empty_or_palestinian(self, pos, neighborhood):
furthest_empty = None
sorted_spots = self.sort_neighborhood_by_distance(pos, neighborhood)
sorted_spots.reverse()
index = 0
while (furthest_empty is None and index < len(sorted_spots)):
spot = sorted_spots[index]
if self.grid.is_cell_empty(spot) or self.grid[spot[1]][spot[0]].breed == "Palestinian" :
furthest_empty = sorted_spots[index]
index += 1
return furthest_empty
def sort_neighborhood_by_distance(self, from_pos, neighbor_spots):
from_x, from_y = from_pos
return sorted(neighbor_spots, key = lambda spot: self.eucledean_distance(from_x, spot[0], from_y, spot[1], self.grid.width, self.grid.height))
def eucledean_distance(self, x1,x2,y1,y2,w,h):
# http://stackoverflow.com/questions/2123947/calculate-distance-between-two-x-y-coordinates
return math.sqrt(min(abs(x1 - x2), w - abs(x1 - x2)) ** 2 + min(abs(y1 - y2), h - abs(y1-y2)) ** 2)
def step(self):
"""
Advance the model by one step and collect data.
"""
self.violence_count = 0
# for i in range(100):
self.schedule.step()
self.total_violence += self.violence_count
# average = self.violence_count / 100
#print("Violence average %f " % average)
print("Total Violence: ", self.total_violence)
class SupermarketModel(Model):
def __init__(self, type=QueueType.CLASSIC, seed=None):
np.random.seed(seed)
# Mesa internals
self.running = True
self.steps_in_day = 7200
# World related
self.queue_type = QueueType[type]
self.terrain_map_name = 'map' if self.queue_type == QueueType.CLASSIC else 'map_snake'
with open(os.path.join(os.getcwd(), '..', 'resources', '{}.txt'.format(self.terrain_map_name))) as f:
self.width, self.height = map(int, f.readline().strip().split(' '))
self.capacity = int(f.readline().strip())
self.world = [list(c) for c in f.read().split('\n') if c]
self.grid = SingleGrid(self.width, self.height, True)
# Agent related
self.generated_customers_count = 0
self.schedule = BaseScheduler(self)
self.entry_points = []
self.queues = {}
self.queue_length_limit = 5
self.cashiers = {}
# TODO: Merge position (cash_registers) and open
# attribute (open_cashier) with cashiers dict
self.cash_registers = {}
self.open_cashier = set()
# Pathfinding
self.finder = IDAStarFinder()
self.snake_entry = None
self.snake_exit = None
# Populate grid from world
for col, line in enumerate(self.world):
for row, cell in enumerate(line):
if cell == 'X':
self.grid[row][col] = ObstacleAgent('{}:{}'.format(col, row), self)
elif cell == 'S':
self.snake_entry = (row, col)
elif cell == 'Z':
self.snake_exit = (row, col)
elif cell in ['1', '2', '3', '4', '5']:
cash_register = CashRegisterAgent(cell, self, (row, col))
self.cashier_row = col
self.cashiers[cell] = self.grid[row][col] = cash_register
self.cash_registers[cell] = (row, col)
self.queues[cell] = set()
# TODO: Add (remove) only upon cashier opening (closing)
self.schedule.add(cash_register)
cashier = CashierAgent('Y{}'.format(cell), self, (row + 1, col), cash_register)
self.grid[row + 1][col] = cashier
elif cell in ['A', 'B', 'C', 'D', 'E']:
self.entry_points.append((row, col, cell))
self.spawn_row = col
self.lane_switch_boundary = math.ceil((self.cashier_row - self.spawn_row) * 3 / 4)
self.heatmap = np.zeros((self.height, self.width))
world_matrix = np.matrix(self.world)
self.distance_matrix = np.zeros((self.height, self.width))
self.distance_matrix[world_matrix == 'X'] = np.inf
self.distance_matrix[world_matrix == '1'] = np.inf
self.distance_matrix[world_matrix == '2'] = np.inf
self.distance_matrix[world_matrix == '3'] = np.inf
self.distance_matrix[world_matrix == '4'] = np.inf
self.distance_matrix[world_matrix == '5'] = np.inf
self.distance_matrix[world_matrix == 'Y'] = np.inf
self.floor_fields = {}
for dest_label, (dest_col, dest_row) in self.cash_registers.items():
floor_field = self.calculate_floor_field((dest_row, dest_col - 1))
self.floor_fields[dest_label] = floor_field.copy()
# Save floor field heatmap into file
# floor_field[floor_field == np.inf] = -np.inf
# plt.figure(figsize=(14, 14))
# sns.heatmap(floor_field, vmin=0, fmt='.1f', vmax=np.max(floor_field), annot=True, cbar=False, square=True, cmap='mako', xticklabels=False, yticklabels=False)
# plt.tight_layout()
# plt.savefig(os.path.join('..', 'output', 'ff-heatmap{}.png'.format(dest_label)))
# plt.close()
self.datacollector = DataCollector(
model_reporters={"Total": get_total_agents,
"Shopping": get_shopping_agents,
"Queued": get_queued_agents,
"Queued (AVG)": get_avg_queued_agents,
"Queued Time (AVG)": get_avg_queued_steps,
"Total Time (AVG)": get_avg_total_steps,
"Paying": get_paying_agents})
if self.queue_type == QueueType.SNAKE:
self.distance_matrix[world_matrix == 'X'] = 1
self.distance_matrix[world_matrix == '1'] = 1
self.distance_matrix[world_matrix == '2'] = 1
self.distance_matrix[world_matrix == '3'] = 1
self.distance_matrix[world_matrix == '4'] = 1
self.distance_matrix[world_matrix == '5'] = 1
self.distance_matrix[world_matrix == 'Y'] = 1
self.movement_grid = Grid(matrix=self.distance_matrix, inverse=True)
coin = self.random.randint(1, len(self.cashiers))
self.cashiers[str(coin)].set_life()
coin = self.random.randint(1, len(self.cashiers))
while self.cashiers[str(coin)].open:
coin = self.random.randint(1, len(self.cashiers))
self.cashiers[str(coin)].set_life()
def step(self):
self.current_agents = len(self.schedule.agents) - len(self.cashiers.items())
if self.schedule.steps > self.steps_in_day and get_total_agents(self) == 0 and (self.schedule.steps - 3) % 250:
self.store_heatmap()
self.running = False
return
if self.schedule.steps < self.steps_in_day and self.steps_in_day and self.schedule.steps % 25 == 0 and self.current_agents < self.capacity and self.should_spawn_agent():
self.schedule.add(self.create_agent())
self.datacollector.collect(self)
self.schedule.step()
self.adjust_cashiers()
if self.queue_type == QueueType.SNAKE:
self.assign_cash_register_to_customer()
def adjust_cashiers(self):
# self.current_agents > (len(opened) + 1) * self.capacity / 7
# (len(opened) + 1) * self.queue_length_limit / self.current_agents
opened, closed = self.partition(self.cashiers.values(), lambda c: c.open)
if len(closed) > 0:
if len(opened) < self.ideal_number_of_cashier(self.schedule.steps) and self.current_agents > (len(opened) + 1) * self.queue_length_limit:
coin = self.random.randint(0, len(closed) - 1)
cashier = closed[coin]
cashier.set_life()
self.open_cashier.add(cashier.unique_id)
print(Back.WHITE + Fore.GREEN + 'OPENING NEW CASH_REGISTER: {}'.format(cashier.unique_id))
if len(opened) > 2:
np.random.shuffle(opened)
if self.queue_type == QueueType.CLASSIC:
for cashier in opened:
in_queue = len(self.queues[cashier.unique_id])
if (in_queue > 1 or in_queue == 0) and len(opened) > self.ideal_number_of_cashier(self.schedule.steps) and self.current_agents < (len(opened) + 1) * self.queue_length_limit:
self.close_cashier(cashier)
opened.remove(cashier)
break
elif self.queue_type == QueueType.SNAKE:
if len(opened) > self.ideal_number_of_cashier(self.schedule.steps) and self.current_agents < (len(opened) + 1) * self.queue_length_limit:
to_close = opened
if len(to_close) > 0:
coin = self.random.randint(0, len(to_close) - 1)
cashier = to_close[coin]
self.close_cashier(cashier)
opened.remove(cashier)
[cashier.set_life(cashier.remaining_life + 25) for cashier in opened]
def assign_cash_register_to_customer(self):
available, busy = self.partition(self.cashiers.values(), lambda c: c.open and not c.is_busy)
if (not self.grid.is_cell_empty(self.snake_exit)) and len(available) > 0:
customer = self.grid.get_cell_list_contents(self.snake_exit)[0]
coin = self.random.randint(0, len(available) - 1)
cashier = available[coin]
customer.objective = cashier.unique_id
dest_col, dest_row = cashier.pos
customer.destination = (dest_col - 1, dest_row)
cashier.is_busy = True
print(Back.WHITE + Fore.BLACK + 'ASSIGNING CASH_REGISTER {} TO CUSTOMER {}'.format(coin, customer.unique_id))
def store_heatmap(self):
self.heatmap /= np.max(self.heatmap)
sns.heatmap(self.heatmap, vmin=0, vmax=1)
plt.savefig(os.path.join('..', 'output', 'heatmap{}.png'.format('' if self.queue_type == QueueType.CLASSIC else '-snake')))
plt.close()
def close_cashier(self, cashier):
cashier.open = False
cashier.empty_since = 0
self.open_cashier.remove(cashier.unique_id)
print(Back.WHITE + Fore.RED + 'CLOSING CASH_REGISTER: {}'.format(cashier.unique_id))
def partition(self, elements, predicate):
left, right = [], []
for e in elements:
(left if predicate(e) else right).append(e)
return left, right
def create_agent(self):
agent = CustomerAgent(self.generated_customers_count, self, self.random_sprite())
self.generated_customers_count += 1
return agent
def should_spawn_agent(self):
relative_time = self.schedule.steps % self.steps_in_day
prob = (-math.cos(relative_time * np.pi / (self.steps_in_day / 2) + 1) + 1) / 2
return self.random.random() <= 0.85 if self.random.random() <= prob else False
def ideal_number_of_cashier(self, step):
prob = (step % self.steps_in_day) / self.steps_in_day
# if prob <= 0.125 or prob >= 0.875:
# return 2
# if prob <= 0.25 or prob >= 0.75:
# return 3
# if prob <= 0.375 or prob >= 0.625:
# return 4
# if prob <= 0.75 or prob >= 0.25:
# return 5
if prob <= 0.265 or prob >= 0.88:
return 2
if prob <= 0.36 or prob >= 0.765:
return 3
if prob <= 0.44 or prob >= 0.66:
return 4
return 5
def random_sprite(self):
sprites = [
'images/characters/grandpa3',
'images/characters/man5',
'images/characters/man8',
'images/characters/girl',
'images/characters/girl3',
'images/characters/girl9',
]
return sprites[self.random.randint(0, len(sprites) - 1)]
def calculate_floor_field(self, destination):
field = self.distance_matrix.copy()
for row in range(len(field)):
for col in range(len(field[row])):
if not np.isinf(field[row, col]):
field[row, col] = distance.euclidean([row, col], destination)
return field
class Factory(Model):
"""The Factory model that maintains the state of the whole factory."""
def __init__(self, grid_w, grid_h, n_robots):
"""Initialize factory."""
# Initialize.
self.orders = 0
self.n_robots = n_robots
self.scheduler = RandomActivation(self)
self.grid = SingleGrid(grid_w, grid_h, torus=False)
self.init_astar()
# Initialize departments.
self.machine = Machine("machine", self, self.grid.find_empty())
self.store = Store("store", self, self.grid.find_empty())
self.packaging = Packaging("packaging", self, self.grid.find_empty())
self.dept_positions = [self.machine.pos, self.store.pos, self.packaging.pos]
# Initialize robots.
for i in range(self.n_robots):
# Create robot.
r = Robot(i, self)
# Initialize random location.
pos = self.grid.find_empty()
self.grid.place_agent(r, pos)
# Register with scheduler.
self.scheduler.add(r)
# Initialize visualization.
plt.ion()
def add_order(self):
"""Increment the number of orders to the factory."""
self.orders += 1
def step(self):
"""Advance the factory by one step."""
# Step through factory. Check for orders.
if self.orders > 0:
self.store.orders += 1
self.orders -= 1
# Step through departments.
self.store.step()
self.machine.step()
self.packaging.step()
# Step through robots.
self.scheduler.step()
# Visualize.
self.visualize()
def init_astar(self):
"""Initialize a-star resources so that it doesn't have to calculated for each robot.
Initialized in such a way that:
* A diagonal paths are allowed.
* The path calculated takes into account all obstacles in the grid.
"""
def get_empty_neighborhood(pos):
"""A sub function to calculate empty neighbors of a point for a-star."""
neighbors = self.grid.get_neighborhood(pos=pos, moore=True)
return [n for n in neighbors if self.grid.is_cell_empty(n)]
# Initialize a path finder object once for the entire factory.
self.path_finder = astar.pathfinder(neighbors=get_empty_neighborhood,
distance=astar.absolute_distance,
cost=astar.fixed_cost(1))
def find_nearest_aimless_robot(self, pos):
"""Find the nearest aimless robot to a given position in the factory."""
def is_aimless(robot, pos):
"""Check if the robot satisfied aimless condition."""
if robot.destination is None:
return True
else:
return False
aimless_robots = [robot for robot in self.scheduler.agents if is_aimless(robot, pos)]
if len(aimless_robots) != 0:
robot_distances = [astar.absolute_distance(pos, robot.pos) for robot in aimless_robots]
nearest_index = np.argmin(robot_distances)
return aimless_robots[nearest_index]
else:
return None
def find_robot_at_position(self, pos):
"""Find robot that is at a given location in the factory that is not busy."""
for robot in self.scheduler.agents:
if robot.pos == pos:
return robot
return None
def find_next_position_towards_destination(self, curr_pos, dest_pos):
"""Find the next empty position to move in the direction of the destination."""
n_steps, path = self.path_finder(curr_pos, dest_pos) # Handles non-empty locations.
# NOTE: We cannot find a valid path to the destination when:
# 1) The destination has an another robot located inside it, which also occurs when curr_pos and
# dest_pos are the same.
# 2) The path is entirely blocked.
# In these cases we return the next position to be the curr_pos, in order to wait until things
# clear up.
if n_steps is None or n_steps <= 0: # No valid path to destination
next_pos = curr_pos
print("[MOVE] Warning: No path to destination from {} --> {}".format(curr_pos, dest_pos))
# This mean there's a valid path to destination.
else:
# index 0, is the curr_pos, index 1 is the next position.
next_pos = path[1]
return next_pos
def find_next_position_for_random_walk(self, curr_pos):
"""Find a valid location for a robot to just randomly walk into."""
def is_pos_empty(pos):
"""A sub function if a cell is empty for random walking."""
if self.grid.is_cell_empty(pos) and pos not in self.dept_positions:
return True
else:
return False
neighborhood = self.grid.get_neighborhood(curr_pos, moore=True)
empty_neighborhood = [n for n in neighborhood if is_pos_empty(n)]
if len(empty_neighborhood) > 0:
next_index = np.random.randint(len(empty_neighborhood))
next_pos = empty_neighborhood[next_index]
else:
next_pos = curr_pos
return next_pos
def visualize(self):
"""A chess board type visualization."""
def heatmap(a):
cMap = ListedColormap(['grey', 'black', 'green', 'orange', 'red', 'blue'])
sns.heatmap(a, vmin=0, vmax=6, cmap=cMap, linewidths=1)
plt.pause(0.15)
plt.clf()
g = np.zeros((self.grid.height, self.grid.width), dtype=int)
g[self.store.pos] = 3
g[self.machine.pos] = 4
g[self.packaging.pos] = 5
for robot in self.scheduler.agents:
if robot.destination is None:
g[robot.pos] = 1
else:
g[robot.pos] = 2
heatmap(g)
class Anthill(Model):
def __init__(self):
self.grid = SingleGrid(WIDTH, HEIGHT, False)
self.schedule = RandomActivation(self)
self.running = True
self.internalrate = 0.2
self.ant_id = 1
self.tau = np.zeros((WIDTH,HEIGHT))
self.datacollector = DataCollector({"Total number of Ants": lambda m: self.get_total_ants_number(),
"mean tau": lambda m: self.evaluation1(),
"sigma": lambda m: self.evaluation2(),
"sigma*" : lambda m: self.evaluation3(),
})
# List containing all coordinates of the boundary, initial ants location and brood location
self.bound_vals = []
self.neigh_bound = []
self.middle=[]
self.datacollector.collect(self)
self.passage_to_right = []
self.passage_to_left = []
# for i in range(WIDTH):
# for j in range(HEIGHT):
# if i == 0 or j == 0 or i == WIDTH-1 or j == HEIGHT-1:
# self.bound_vals.append((i,j))
# if i == 1 or i == WIDTH - 2 or j == 1 or j == HEIGHT-2:
# self.neigh_bound.append((i,j))
##
for i in range(WIDTH):
for j in range(HEIGHT):
##make boundary
if i == 0 or j == 0 or i == WIDTH - 1 or j == HEIGHT - 1:
self.bound_vals.append((i,j))
if i == MIDDLE and 0<j<WIDTH - 1:
self.bound_vals.append((i,j))
self.middle.append((i,j))
##save neighbor
if j ==1 and 1<= i <= MIDDLE-2:
self.neigh_bound.append((i,j))
if j ==1 and MIDDLE+2<=i<=WIDTH - 2:
self.neigh_bound.append((i,j))
if j ==HEIGHT - 1 and 1<= i <= MIDDLE-2:
self.neigh_bound.append((i,j))
if j ==HEIGHT - 1 and MIDDLE+2<=i<=WIDTH - 2:
self.neigh_bound.append((i,j))
if i == 1 and 2<= j<= MIDDLE-3:
self.neigh_bound.append((i, j))
if i == HEIGHT - 2 and 2<= j<= MIDDLE-3:
self.neigh_bound.append((i, j))
## we let the columns next to th middle become the entrance to next chamber
if i == MIDDLE-1 and 0<j<WIDTH-1:
self.passage_to_left.append((i, j))
if i == MIDDLE + 1 and 0 < j < WIDTH - 1:
self.passage_to_right.append((i, j))
# Make a Fence boundary
b = 0
for h in self.bound_vals:
br = Fence(b,self)
self.grid.place_agent(br,(h[0],h[1]))
b += 1
def step(self):
'''Advance the model by one step.'''
# Add new ants into the internal area ont he boundary
for xy in self.neigh_bound:
# Add with probability internal rate and if the cell is empty
if self.random.uniform(0, 1) < self.internalrate and self.grid.is_cell_empty(xy) == True:
a = Ant(self.ant_id, self)
self.schedule.add(a)
self.grid.place_agent(a,xy)
self.ant_id += 1
# Move the ants
self.schedule.step()
self.datacollector.collect(self)
# with open("data/p02_b0_tau.txt", "a") as myfile:
# myfile.write(str(self.mean_tau_ant) + '\n')
# with open("data/p02_b0_sigma.txt", "a") as myfile:
# myfile.write(str(self.sigma) + '\n')
# with open("data/p02_b0_sigmastar.txt","a") as myfile:
# myfile.write(str(self.sigmastar) + "\n")
def get_total_ants_number(self):
total_ants=0
for (agents, _, _) in self.grid.coord_iter():
if type(agents) is Ant:
total_ants += 1
return total_ants
def evaluation1(self):
##creat a empty grid to store currently information
total_ants = np.zeros((WIDTH,HEIGHT))
## count the number of currently information
for (agents, i, j) in self.grid.coord_iter():
if type(agents) is Ant:
total_ants[i][j] = 1
else:
total_ants[i][j] = 0
##update the tau
self.tau = self.tau + total_ants
##calcualte the mean tau
self.mean_tau_ant = self.tau.sum()/((WIDTH-2)**2)
return self.mean_tau_ant
def evaluation2(self):
## we need to minus the mean tau so we need to ensure the result of boundary is zero
## so we let the bounday equal mean_tau_ant in this way the (tau-mean_tau_ant) is zero of boundary
for site in self.bound_vals:
self.tau[site[0]][site[1]] = self.mean_tau_ant
##calculate the sigmaa
self.sigma = ((self.tau-self.mean_tau_ant)**2).sum()/((WIDTH-2)**2)
## rechange the boundaryy
for site in self.bound_vals:
self.tau[site[0]][site[1]] = 0
return np.sqrt(self.sigma)
def evaluation3(self):
##calculate the sigmastar
self.sigmastar = np.sqrt(self.sigma)/self.mean_tau_ant
return self.sigmastar
