class ForestFire(Model):
'''
Simple Forest Fire model.
'''
def __init__(self, height, width, density):
'''
Create a new forest fire model.
Args:
height, width: The size of the grid to model
density: What fraction of grid cells have a tree in them.
'''
# Initialize model parameters
self.height = height
self.width = width
self.density = density
# Set up model objects
self.schedule = RandomActivation(self)
self.grid = Grid(height, width, torus=False)
self.datacollector = DataCollector(
{"Fine": lambda m: self.count_type(m, "Fine"),
"On Fire": lambda m: self.count_type(m, "On Fire"),
"Burned Out": lambda m: self.count_type(m, "Burned Out")})
# Place a tree in each cell with Prob = density
for (contents, x, y) in self.grid.coord_iter():
if random.random() < self.density:
# Create a tree
new_tree = TreeCell((x, y))
# Set all trees in the first column on fire.
if x == 0:
new_tree.condition = "On Fire"
self.grid._place_agent((x, y), new_tree)
self.schedule.add(new_tree)
self.running = True
def step(self):
'''
Advance the model by one step.
'''
self.schedule.step()
self.datacollector.collect(self)
# Halt if no more fire
if self.count_type(self, "On Fire") == 0:
self.running = False
@staticmethod
def count_type(model, tree_condition):
'''
Helper method to count trees in a given condition in a given model.
'''
count = 0
for tree in model.schedule.agents:
if tree.condition == tree_condition:
count += 1
return count
class ConwaysGameOfLife(Model):
'''
Represents the 2-dimensional array of cells in Conway's
Game of Life.
'''
def __init__(self, height=50, width=50, server=True):
'''
Create a new playing area of (height, width) cells.
'''
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
self.server = server
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
#Datacollector -- default for this model is no data collection, but one can use OABM to assign one.
#so this is an empty DataCollector instance from MESA
self.datacollector = DataCollector()
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self)
if self.random.random() < 0.1:
cell.state = cell.ALIVE
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.running = True
def step(self):
'''
Have the scheduler advance each cell by one step
'''
self.schedule.step()
def run_model(self, n=None):
if n:
self.num_steps = n
if self.server == False:
for _ in range(self.num_steps):
self.step()
return self
else:
from .server import server
server.launch()
class InspectionModel(Model):
'''
Simple Restaurant Inspection model.
'''
def __init__(self, height, width, density):
'''
Create a new restaurant inspection model.
Args:
height, width: The size of the grid to model
density: What fraction of grid cells have a restaurant in them.
'''
# Initialize model parameters
self.height = height
self.width = width
self.density = density
# Set up model objects
self.schedule = RandomActivation(self)
self.grid = Grid(height, width, torus=False)
self.datacollector = DataCollector(
{"Good": lambda m: self.count_type(m, "Good"),
"Bad": lambda m: self.count_type(m, "Bad")})
# Place a restaurant in each cell with Prob = density
for (contents, x, y) in self.grid.coord_iter():
if random.random() < self.density:
# Create a restaurant
new_restaurant = RestaurantCell((x, y))
self.grid._place_agent((x, y), new_restaurant)
self.schedule.add(new_restaurant)
self.running = True
def step(self):
'''
Advance the model by one step.
'''
self.schedule.step()
self.datacollector.collect(self)
@staticmethod
def count_type(model, restaurant_hygiene):
'''
Helper method to count restaurants in a given condition in a given model.
'''
count = 0
for restaurant in model.schedule.agents:
if restaurant.hygiene == restaurant_hygiene and restaurant.rating != 'Closed':
count += 1
return count
class MockModel(Model):
""" Test model for testing """
def __init__(self, width, height, key1=103, key2=104):
self.width = width
self.height = height
self.key1 = key1,
self.key2 = key2
self.schedule = SimultaneousActivation(self)
self.grid = Grid(width, height, torus=True)
for (c, x, y) in self.grid.coord_iter():
a = MockAgent(x + y * 100, self, x * y * 3)
self.grid.place_agent(a, (x, y))
self.schedule.add(a)
def step(self):
self.schedule.step()
class ConwaysGameOfLife(Model):
"""
Represents the 2-dimensional array of cells in Conway's
Game of Life.
"""
def __init__(self, size, **kwargs):
"""
Create a new playing area of (height, width) cells.
"""
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(size, size, torus=True)
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (_contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self)
if self.random.random() < 0.1:
cell.state = cell.ALIVE
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.running = True
def step(self):
"""
Have the scheduler advance each cell by one step
"""
self.schedule.step()
def on_click(self, x, y, **kwargs):
print(x, y)
cell = self.grid[x][y]
cell.state = cell.ALIVE
class CGoLModel(Model):
'''
Represents the 2-dimensional array of cells in Conway's
Game of Life.
'''
def __init__(self, height, width):
'''
Create a new playing area of (height, width) cells.
'''
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
pos = (x, y)
init_state = CGoLCell.DEAD
# Initially, make 10% of the cells ALIVE.
if random.random() < 0.1:
init_state = CGoLCell.ALIVE
cell = CGoLCell(pos, self, init_state)
# Put this cell in the grid at position (x, y)
self.grid.place_agent(cell, pos)
# Add this cell to the scheduler.
self.schedule.add(cell)
self.running = True
def step(self):
'''
Advance the model by one step.
'''
self.schedule.step()
class CGoLModel(Model):
'''
Represents the 2-dimensional array of cells in Conway's
Game of Life.
'''
def __init__(self, height, width):
'''
Create a new playing area of (height, width) cells.
'''
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
pos = (x, y)
init_state = CGoLCell.DEAD
# Initially, make 10% of the cells ALIVE.
if random.random() < 0.1:
init_state = CGoLCell.ALIVE
cell = CGoLCell(pos, self, init_state)
# Put this cell in the grid at position (x, y)
self.grid.place_agent(cell, pos)
# Add this cell to the scheduler.
self.schedule.add(cell)
self.running = True
def step(self):
'''
Advance the model by one step.
'''
self.schedule.step()
class ConwaysGameOfLife(Model):
'''
Represents the 2-dimensional array of cells in Conway's
Game of Life.
'''
def __init__(self, height=50, width=50):
'''
Create a new playing area of (height, width) cells.
'''
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self)
if self.random.random() < 0.1:
cell.state = cell.ALIVE
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.running = True
def step(self):
'''
Have the scheduler advance each cell by one step
'''
self.schedule.step()
class CivilViolenceModel(Model):
"""
Model 1 from "Modeling civil violence: An agent-based computational
approach," by Joshua Epstein.
http://www.pnas.org/content/99/suppl_3/7243.full
Attributes:
height: grid height
width: grid width
citizen_density: approximate % of cells occupied by citizens.
cop_density: approximate % of calles occupied by cops.
citizen_vision: number of cells in each direction (N, S, E and W) that
citizen can inspect
cop_vision: number of cells in each direction (N, S, E and W) that cop
can inspect
legitimacy: (L) citizens' perception of regime legitimacy, equal
across all citizens
max_jail_term: (J_max)
active_threshold: if (grievance - (risk_aversion * arrest_probability))
> threshold, citizen rebels
arrest_prob_constant: set to ensure agents make plausible arrest
probability estimates
movement: binary, whether agents try to move at step end
max_iters: model may not have a natural stopping point, so we set a
max.
"""
def __init__(self, height, width, citizen_density, cop_density,
citizen_vision, cop_vision, legitimacy,
max_jail_term, active_threshold=.1, arrest_prob_constant=2.3,
movement=True, max_iters=1000):
super(CivilViolenceModel, self).__init__()
self.height = height
self.width = width
self.citizen_density = citizen_density
self.cop_density = cop_density
self.citizen_vision = citizen_vision
self.cop_vision = cop_vision
self.legitimacy = legitimacy
self.max_jail_term = max_jail_term
self.active_threshold = active_threshold
self.arrest_prob_constant = arrest_prob_constant
self.movement = movement
self.running = True
self.max_iters = max_iters
self.iteration = 0
self.schedule = RandomActivation(self)
self.grid = Grid(height, width, torus=True)
model_reporters = {
"Quiescent": lambda m: self.count_type_citizens(m, "Quiescent"),
"Active": lambda m: self.count_type_citizens(m, "Active"),
"Jailed": lambda m: self.count_jailed(m)}
agent_reporters = {
"x": lambda a: a.pos[0],
"y": lambda a: a.pos[1],
'breed': lambda a: a.breed,
"jail_sentence": lambda a: getattr(a, 'jail_sentence', None),
"condition": lambda a: getattr(a, "condition", None),
"arrest_probability": lambda a: getattr(a, "arrest_probability",
None)
}
self.dc = DataCollector(model_reporters=model_reporters,
agent_reporters=agent_reporters)
unique_id = 0
if self.cop_density + self.citizen_density > 1:
raise ValueError(
'Cop density + citizen density must be less than 1')
for (contents, x, y) in self.grid.coord_iter():
if random.random() < self.cop_density:
cop = Cop(unique_id, (x, y), vision=self.cop_vision,
model=self)
unique_id += 1
self.grid[y][x] = cop
self.schedule.add(cop)
elif random.random() < (
self.cop_density + self.citizen_density):
citizen = Citizen(unique_id, (x, y),
hardship=random.random(),
regime_legitimacy=self.legitimacy,
risk_aversion=random.random(),
threshold=self.active_threshold,
vision=self.citizen_vision, model=self)
unique_id += 1
self.grid[y][x] = citizen
self.schedule.add(citizen)
def step(self):
"""
Advance the model by one step and collect data.
"""
self.schedule.step()
self.dc.collect(self)
self.iteration += 1
if self.iteration > self.max_iters:
self.running = False
@staticmethod
def count_type_citizens(model, condition, exclude_jailed=True):
"""
Helper method to count agents by Quiescent/Active.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == 'cop':
continue
if exclude_jailed and agent.jail_sentence:
continue
if agent.condition == condition:
count += 1
return count
@staticmethod
def count_jailed(model):
"""
Helper method to count jailed agents.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == 'citizen' and agent.jail_sentence:
count += 1
return count
class ColorPatchModel(Model):
'''
represents a 2D lattice where agents live
'''
def __init__(self, width, height):
'''
Create a 2D lattice with strict borders where agents live
The agents next state is first determined before updating the grid
'''
self._grid = Grid(width, height, torus=False)
self._schedule = SimultaneousActivation(self)
# self._grid.coord_iter()
# --> should really not return content + col + row
# -->but only col & row
# for (contents, col, row) in self._grid.coord_iter():
# replaced content with _ to appease linter
for (_, row, col) in self._grid.coord_iter():
cell = ColorCell((row, col), self,
ColorCell.OPINIONS[random.randrange(0, 16)])
self._grid.place_agent(cell, (row, col))
self._schedule.add(cell)
self.running = True
def step(self):
'''
Advance the model one step.
'''
self._schedule.step()
# the following is a temporary fix for the framework classes accessing
# model attributes directly
# I don't think it should
# --> it imposes upon the model builder to use the attributes names that
# the framework expects.
#
# Traceback included in docstrings
@property
def grid(self):
"""
/mesa/visualization/modules/CanvasGridVisualization.py
is directly accessing Model.grid
76 def render(self, model):
77 grid_state = defaultdict(list)
---> 78 for y in range(model.grid.height):
79 for x in range(model.grid.width):
80 cell_objects = model.grid.get_cell_list_contents([(x, y)])
AttributeError: 'ColorPatchModel' object has no attribute 'grid'
"""
return self._grid
@property
def schedule(self):
"""
mesa_ABM/examples_ABM/color_patches/mesa/visualization/ModularVisualization.py",
line 278, in run_model
while self.model.schedule.steps < self.max_steps and self.model.running:
AttributeError: 'NoneType' object has no attribute 'steps'
"""
return self._schedule
class TestBaseGrid(unittest.TestCase):
'''
Testing a non-toroidal grid.
'''
torus = False
def setUp(self):
'''
Create a test non-toroidal grid and populate it with Mock Agents
'''
self.grid = Grid(3, 5, self.torus)
self.agents = []
counter = 0
for y in range(3):
for x in range(5):
if TEST_GRID[y][x] == 0:
continue
counter += 1
# Create and place the mock agent
a = MockAgent(counter, None)
self.agents.append(a)
self.grid.place_agent(a, (x, y))
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for agent in self.agents:
x, y = agent.pos
assert self.grid[y][x] == agent
def test_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, get_cell_list_contents accurately
reports that fact.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.get_cell_list_contents([(x, y)])
def test_listfree_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, get_cell_list_contents accurately
reports that fact, even when single position is not wrapped in a list.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.get_cell_list_contents((x, y))
def test_iter_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, iter_cell_list_contents
accurately reports that fact.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.iter_cell_list_contents([(x, y)])
def test_listfree_iter_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, iter_cell_list_contents
accurately reports that fact, even when single position is not
wrapped in a list.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.iter_cell_list_contents((x, y))
def test_neighbors(self):
'''
Test the base neighborhood methods on the non-toroid.
'''
neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((4, 1), moore=True)
assert len(neighborhood) == 5
neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
assert len(neighborhood) == 2
neighbors = self.grid.get_neighbors((4, 1), moore=False)
assert len(neighbors) == 0
neighbors = self.grid.get_neighbors((4, 1), moore=True)
assert len(neighbors) == 2
neighbors = self.grid.get_neighbors((1, 1), moore=False,
include_center=True)
assert len(neighbors) == 3
neighbors = self.grid.get_neighbors((3, 1), moore=False, radius=2)
assert len(neighbors) == 4
def test_coord_iter(self):
ci = self.grid.coord_iter()
# no agent in first space
first = next(ci)
assert first[0] is None
assert first[1] == 0
assert first[2] == 0
# first agent in the second space
second = next(ci)
assert second[0].unique_id == 1
assert second[0].pos == (1, 0)
assert second[1] == 1
assert second[2] == 0
class EpsteinCivilViolence(Model):
"""
Model 1 from "Modeling civil violence: An agent-based computational
approach," by Joshua Epstein
http://www.pnas.org/content/99/suppl_3/7243.full
Attributes:
height: grid height
width: grid width
citizen_density: approximate % of cells occupied by citizens.
cop_density: approximate % of calles occupied by cops.
citizen_vision: number of cells in each direction (N, S, E and W) that
citizen can inspect
cop_vision: number of cells in each direction (N, S, E and W) that cop
can inspect
legitimacy: (L) citizens' perception of regime legitimacy, equal
across all citizens
max_jail_term: (J_max)
active_threshold: if (grievance - (risk_aversion * arrest_probability))
> threshold, citizen rebels
arrest_prob_constant: set to ensure agents make plausible arrest
probability estimates
movement: binary, whether agents try to move at step end
max_iters: model may not have a natural stopping point, so we set a
max.
"""
def __init__(
self,
height=40,
width=40,
citizen_density=0.7,
cop_density=0.074,
citizen_vision=7,
cop_vision=7,
legitimacy=0.8,
max_jail_term=1000,
active_threshold=0.1,
arrest_prob_constant=2.3,
movement=True,
initial_unemployment_rate=0.1,
corruption_level=0.1,
susceptible_level=0.3,
honest_level=0.6,
max_iters=1000,
):
super().__init__()
self.height = height
self.width = width
self.citizen_density = citizen_density
self.cop_density = cop_density
self.citizen_vision = citizen_vision
self.cop_vision = cop_vision
self.legitimacy = legitimacy
self.max_jail_term = max_jail_term
self.active_threshold = active_threshold
self.arrest_prob_constant = arrest_prob_constant
self.movement = movement
self.initial_unemployment_rate = initial_unemployment_rate
self.corruption_level = corruption_level
self.susceptible_level = susceptible_level
self.max_iters = max_iters
self.iteration = 0
self.schedule = RandomActivation(self)
self.grid = Grid(height, width, torus=True)
model_reporters = {
"Quiescent":
lambda m: self.count_type_citizens(m, "Quiescent"),
"Active":
lambda m: self.count_type_citizens(m, "Active"),
"Jailed":
lambda m: self.count_jailed(m),
"Employed":
lambda m: self.count_employed(m),
"Corrupted":
lambda m: self.count_moral_type_citizens(m, "Corrupted"),
"Honest":
lambda m: self.count_moral_type_citizens(m, "Honest"),
"Susceptible":
lambda m: self.count_moral_type_citizens(m, "Susceptible")
}
agent_reporters = {
"x": lambda a: a.pos[0],
"y": lambda a: a.pos[1],
"breed": lambda a: a.breed,
"jail_sentence": lambda a: getattr(a, "jail_sentence", None),
"condition": lambda a: getattr(a, "condition", None),
"arrest_probability":
lambda a: getattr(a, "arrest_probability", None),
"is_employed": lambda a: getattr(a, "is_employed", None),
"moral_condition": lambda a: getattr(a, "moral_condition", None),
}
self.datacollector = DataCollector(model_reporters=model_reporters,
agent_reporters=agent_reporters)
unique_id = 0
if self.cop_density + self.citizen_density > 1:
raise ValueError(
"Cop density + citizen density must be less than 1")
if self.initial_unemployment_rate > 1:
raise ValueError(
"initial_unemployment_rate must be between [0,1] ")
if self.corruption_level + self.susceptible_level > 1:
raise ValueError("moral level must be less than 1 ")
for (contents, x, y) in self.grid.coord_iter():
if self.random.random() < self.cop_density:
cop = Cop(unique_id, self, (x, y), vision=self.cop_vision)
unique_id += 1
self.grid[y][x] = cop
self.schedule.add(cop)
elif self.random.random() < (self.cop_density +
self.citizen_density):
moral_state = "Honest"
is_employed = 1
if self.random.random() < self.initial_unemployment_rate:
is_employed = 0
p = self.random.random()
if p < self.corruption_level:
moral_state = "Corrupted"
elif p < self.corruption_level + self.susceptible_level:
moral_state = "Susceptible"
citizen = Citizen(
unique_id,
self,
(x, y),
#updated hardship formula: if agent is employed hardship is alleviated
hardship=self.random.random() -
(is_employed * self.random.uniform(0.05, 0.15)),
legitimacy=self.legitimacy,
#updated regime legitimacy, so inital corruption rate is taken into consideration
regime_legitimacy=self.legitimacy - self.corruption_level,
risk_aversion=self.random.random(),
active_threshold=self.active_threshold,
#updated threshold: if agent is employed threshold for rebelling is raised
threshold=self.active_threshold +
(is_employed * self.random.uniform(0.05, 0.15)),
vision=self.citizen_vision,
is_employed=is_employed,
moral_state=moral_state,
)
unique_id += 1
self.grid[y][x] = citizen
self.schedule.add(citizen)
self.running = True
self.datacollector.collect(self)
def step(self):
"""
Advance the model by one step and collect data.
"""
self.schedule.step()
# collect data
self.datacollector.collect(self)
self.iteration += 1
if self.iteration > self.max_iters:
self.running = False
@staticmethod
def count_type_citizens(model, condition, exclude_jailed=False):
"""
Helper method to count agents by Quiescent/Active.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == "cop":
continue
if exclude_jailed and agent.jail_sentence:
continue
if agent.condition == condition:
count += 1
return count
@staticmethod
def count_moral_type_citizens(model,
moral_condition,
exclude_jailed=False):
"""
Helper method to count agents by Quiescent/Active.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == "cop":
continue
if exclude_jailed and agent.jail_sentence:
continue
if agent.moral_state == moral_condition:
count += 1
return count
@staticmethod
def count_jailed(model):
"""
Helper method to count jailed agents.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == "citizen" and agent.jail_sentence:
count += 1
return count
@staticmethod
def count_employed(model):
"""
Helper method to count employed agents.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == "cop":
continue
if agent.is_employed == 1:
count += 1
return count
@staticmethod
def count_corrupted(model):
"""
Helper method to count corrupted agents.
"""
count = 0
for agent in model.schedule.agents:
if agent.breed == "cop":
continue
if agent.is_corrupted == 1:
count += 1
return count
class CivilViolenceModel(Model):
"""
Model 1 from "Modeling civil violence: An agent-based computational
approach," by Joshua Epstein.
http://www.pnas.org/content/99/suppl_3/7243.full
Attributes:
height: grid height
width: grid width
citizen_density: approximate % of cells occupied by citizens.
cop_density: approximate % of calles occupied by cops.
citizen_vision: number of cells in each direction (N, S, E and W) that
citizen can inspect
cop_vision: number of cells in each direction (N, S, E and W) that cop
can inspect
legitimacy: (L) citizens' perception of regime legitimacy, equal
across all citizens
max_jail_term: (J_max)
active_threshold: if (grievance - (risk_aversion * arrest_probability))
> threshold, citizen rebels
arrest_prob_constant: set to ensure agents make plausible arrest
probability estimates
movement: binary, whether agents try to move at step end
max_iters: model may not have a natural stopping point, so we set a
max.
propaganda_agent_density: % of propaganda agent
propaganda_allowed: True if we are allowing propaganda
"""
def __init__(
self,
height=40,
width=40,
citizen_density=70,
cop_density=4,
citizen_vision=7,
cop_vision=7,
legitimacy=82,
max_jail_term=30,
active_threshold=4,
arrest_prob_constant=2.3,
movement=True,
max_iters=1000,
propaganda_agent_density=2,
propaganda_factor=1,
exposure_threshold=10,
):
super().__init__()
self.height = height
self.width = width
self.citizen_density = citizen_density / 100
self.cop_density = cop_density / 100
self.propaganda_agent_density = propaganda_agent_density / 100
self.citizen_vision = citizen_vision
self.cop_vision = cop_vision
self.legitimacy = legitimacy / 100
self.max_jail_term = max_jail_term
self.active_threshold = active_threshold / 100
self.arrest_prob_constant = arrest_prob_constant
self.movement = movement
self.max_iters = max_iters
# initiate the model's grid and schedule
self.iteration = 0
self.schedule = RandomActivation(self)
self.grid = Grid(height, width, torus=True)
self.propaganda_factor = propaganda_factor / 1000
self.exposure_threshold = exposure_threshold
# initiate data collectors for agent state feedback
model_reporters = {
"Quiescent":
lambda m: self.count_type_citizens(m, False),
"Active":
lambda m: self.count_type_citizens(m, True),
"Jailed":
lambda m: self.count_jailed(m),
"Active Propaganda Agents":
lambda m: self.count_propaganda_agents(m),
"Total Inactive Grievance":
lambda m: self.report_total_inactive_grievance(m),
"Total Inactive Net Risk":
lambda m: self.report_total_inactive_net_risk(m),
"Total Influence":
lambda m: self.report_total_influence(m),
"Ripeness Index":
lambda m: self.report_ripeness_index(m)
}
agent_reporters = {
"x": lambda a: a.pos[0],
"y": lambda a: a.pos[1],
'breed': lambda a: a.agent_class,
"jail_sentence": lambda a: getattr(a, 'jail_time', None),
"active": lambda a: getattr(a, "active", None),
"arrest_probability":
lambda a: getattr(a, "arrest_probability", None)
}
self.datacollector = DataCollector(model_reporters=model_reporters,
agent_reporters=agent_reporters)
unique_id = 0
if self.cop_density + self.citizen_density + self.propaganda_agent_density > 1:
raise ValueError(
'Cop density + citizen density + propaganda agent density must be less than 1'
)
# initialize agents in the grid with respect to the given densities
for (contents, x, y) in self.grid.coord_iter():
if self.random.random() < self.propaganda_agent_density:
agent = PropagandaAgent(
unique_id,
self,
influence=self.random.random(),
exposure_threshold=self.exposure_threshold,
vision=self.citizen_vision,
pos=(x, y))
unique_id += 1
self.grid[y][x] = agent
self.schedule.add(agent)
elif self.random.random(
) < self.cop_density + self.propaganda_agent_density:
cop = CopAgent(unique_id,
self,
vision=self.cop_vision,
pos=(x, y))
unique_id += 1
self.grid[y][x] = cop
self.schedule.add(cop)
elif (self.random.random() < self.cop_density +
self.citizen_density + self.propaganda_agent_density):
citizen = PopulationAgent(
unique_id,
self,
hardship=self.random.random(),
legitimacy=self.legitimacy,
risk_aversion=self.random.random(),
threshold=self.active_threshold,
susceptibility=self.random.random(),
propaganda_factor=self.propaganda_factor,
vision=self.citizen_vision,
pos=(x, y))
unique_id += 1
self.grid[y][x] = citizen
self.schedule.add(citizen)
self.running = True
self.datacollector.collect(self)
def step(self):
# Advance the model by one step and collect data.
self.schedule.step()
self.datacollector.collect(self)
self.iteration += 1
if self.iteration > self.max_iters:
self.running = False
@staticmethod
def count_type_citizens(model, count_actives, exclude_jailed=True):
"""
Helper method to count agents by Quiescent/Active depending on ther active flag.
"""
count = 0
for agent in model.schedule.agents:
if agent.agent_class in [COP_AGENT_CLASS, PROPAGANDA_AGENT_CLASS]:
continue
if exclude_jailed and agent.jail_time:
continue
if count_actives and agent.active:
count += 1
elif not count_actives and not agent.active:
count += 1
return count
@staticmethod
def count_jailed(model):
"""
Helper method to count jailed agents. (Both propaganda and population)
"""
count = 0
for agent in model.schedule.agents:
if agent.agent_class in [
POPULATION_AGENT_CLASS, PROPAGANDA_AGENT_CLASS
] and agent.jail_time:
count += 1
return count
@staticmethod
def count_propaganda_agents(model):
"""
Helper method to count non jailed propaganda agents.
"""
count = 0
for agent in model.schedule.agents:
if agent.agent_class in [PROPAGANDA_AGENT_CLASS
] and not agent.jail_time:
count += 1
return count
@staticmethod
def report_total_influence(model):
"""
Helper method to count total influence of propaganda agents.
"""
total = 0.
for agent in model.schedule.agents:
if agent.agent_class in [PROPAGANDA_AGENT_CLASS
] and not agent.jail_time:
total += agent.total_influence
return total
@staticmethod
def report_total_inactive_grievance(model):
"""
Helper method to count total grievance of non-jailed, inactive population agents.
"""
total = 0.
average = 0.
for agent in model.schedule.agents:
if agent.agent_class in [
POPULATION_AGENT_CLASS
] and not agent.active and not agent.jail_time:
total += agent.grievance
return total
@staticmethod
def report_total_inactive_net_risk(model):
"""
Helper method to count total net risk of non jailed, inactive population agents.
"""
total = 0.
for agent in model.schedule.agents:
if agent.agent_class in [
POPULATION_AGENT_CLASS
] and not agent.active and not agent.jail_time:
total += agent.net_risk
return total
@staticmethod
def report_ripeness_index(model):
"""
Helper method to count ripeness index, as defined in Epsteins paper:
RI = E[G] * Q / E[R], where:
E[G]: Expected value for total grievance of all non-jailed agents
E[R]: Expected value for total risk aversion of all non-jailed agents
"""
count, E_R, E_G = 0., 0., 0.
for agent in model.schedule.agents:
if agent.agent_class in [POPULATION_AGENT_CLASS
] and not agent.jail_time:
count += 1
E_R += agent.risk_aversion
E_G += agent.grievance
E_R /= count
E_G /= count
# number of inactive population agents
Q = model.count_type_citizens(model, count_actives=False)
return float(E_G) * Q / E_R
class TestBaseGrid(unittest.TestCase):
'''
Testing a non-toroidal grid.
'''
torus = False
def setUp(self):
'''
Create a test non-toroidal grid and populate it with Mock Agents
'''
width = 3 # width of grid
height = 5 # height of grid
self.grid = Grid(width, height, self.torus)
self.agents = []
counter = 0
for x in range(width):
for y in range(height):
if TEST_GRID[x][y] == 0:
continue
counter += 1
# Create and place the mock agent
a = MockAgent(counter, None)
self.agents.append(a)
self.grid.place_agent(a, (x, y))
def test_agent_positions(self):
'''
Ensure that the agents are all placed properly.
'''
for agent in self.agents:
x, y = agent.pos
assert self.grid[x][y] == agent
def test_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, get_cell_list_contents accurately
reports that fact.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.get_cell_list_contents([(x, y)])
def test_listfree_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, get_cell_list_contents accurately
reports that fact, even when single position is not wrapped in a list.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.get_cell_list_contents((x, y))
def test_iter_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, iter_cell_list_contents
accurately reports that fact.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.iter_cell_list_contents([(x, y)])
def test_listfree_iter_cell_agent_reporting(self):
'''
Ensure that if an agent is in a cell, iter_cell_list_contents
accurately reports that fact, even when single position is not
wrapped in a list.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid.iter_cell_list_contents((x, y))
def test_neighbors(self):
'''
Test the base neighborhood methods on the non-toroid.
'''
neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((1, 4), moore=False)
assert len(neighborhood) == 3
neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
assert len(neighborhood) == 5
neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
assert len(neighborhood) == 2
neighbors = self.grid.get_neighbors((4, 1), moore=False)
assert len(neighbors) == 0
neighbors = self.grid.get_neighbors((4, 1), moore=True)
assert len(neighbors) == 0
neighbors = self.grid.get_neighbors((1, 1), moore=False,
include_center=True)
assert len(neighbors) == 3
neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
assert len(neighbors) == 2
def test_coord_iter(self):
ci = self.grid.coord_iter()
# no agent in first space
first = next(ci)
assert first[0] is None
assert first[1] == 0
assert first[2] == 0
# first agent in the second space
second = next(ci)
assert second[0].unique_id == 1
assert second[0].pos == (0, 1)
assert second[1] == 0
assert second[2] == 1
def test_agent_move(self):
# get the agent at [0, 1]
agent = self.agents[0]
self.grid.move_agent(agent, (1, 1))
assert agent.pos == (1, 1)
# move it off the torus and check for the exception
if not self.torus:
with self.assertRaises(Exception):
self.grid.move_agent(agent, [-1, 1])
with self.assertRaises(Exception):
self.grid.move_agent(agent, [1, self.grid.height + 1])
else:
self.grid.move_agent(agent, [-1, 1])
assert agent.pos == (self.grid.width - 1, 1)
self.grid.move_agent(agent, [1, self.grid.height + 1])
assert agent.pos == (1, 1)
def test_agent_remove(self):
agent = self.agents[0]
x, y = agent.pos
self.grid.remove_agent(agent)
assert agent.pos is None
assert self.grid.grid[x][y] is None
class Epidemic(Model):
'''
Represents the 2-dimensional array of cells in Conway's
Game of Life.
'''
def __init__(self, height, width, map):
'''
Create a new playing area of (height, width) cells and map.
'''
# global time
self.globaltime = 0
# Greyscale image
self.map = map
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbours -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self)
# Creating a sea in the middle of the grid
if self.map[x, y] == 1.0:
if y > 37.5 and y < 55 and x > 37.5 and x < 62.5:
cell.state = cell.ALIVE
cell.starttime = 0
cell.endtime = 5000
cell.mobility = cell.IMMOBILE
cell.infection = random()
cell.infectious = True
cell.mutability = cell.IMMUTABLE
# Ardipithecus K
if x > 77 and x < 81 and y > 60 and y < 64:
cell.state = cell.DEAD
cell.mobility = cell.MOBILE
cell.infection = 0.
cell.starttime = 0
cell.endtime = 60
cell.infectious = True
if x > 50 and x < 54 and y > 67 and y < 71:
# if y > 60 and y < 65 and x > 65 and x < 70:
cell.state = cell.DEAD
cell.mobility = cell.MOBILE
cell.infection = 0.
cell.starttime = 1950
cell.endtime = 2050
cell.infectious = True
if x > 71 and x < 75 and y > 52 and y < 56:
# if y > 60 and y < 65 and x > 65 and x < 70:
cell.state = cell.DEAD
cell.starttime = 2050
cell.endtime = 2150
cell.mobility = cell.MOBILE
cell.infection = 0.
cell.infectious = True
else:
cell.mobility = cell.IMMOBILE
cell.activity = cell.INACTIVE
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.running = True
self.datacollector = DataCollector(
model_reporters={"Infection": compute_infection})
def step(self):
'''
Have the scheduler advance each cell by one step
'''
# global time
self.globaltime += 1
if self.globaltime == 88:
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self)
# Ardipithecus R
if x > 77 and x < 81 and y > 60 and y < 64:
cell.state = cell.DEAD
cell.mobility = cell.MOBILE
cell.infection = 0.
cell.starttime = 90 #1150
cell.endtime = 150 #1250
cell.infectious = True
cell.globaltime = self.globaltime
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.datacollector.collect(self)
self.schedule.step()
class infection_model(Model):
def __init__(self,
height=100,
width=100,
dummy="",
density=0.5,
p_inf=0.1,
p_rec=0.3,
p_reinf=0.05,
p_test=0.1,
p_death=0.2,
test_n=0,
hood="Moore",
datacollector={}):
'''
Create a new playing area of (height, width) cells.
'''
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.height = height
self.width = width
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
# Use the DataCollector method to store the relevant information of our
# model. This helps with plotting in the web socke, but also with running
#more models at the same time using BatchRunner (in batch.py)
self.datacollector = DataCollector(
model_reporters=self.compute_reporters())
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y), self, p_inf, p_rec, p_reinf, p_test, p_death,
test_n, hood)
if self.random.random() < density:
cell.state = cell.INFECTED
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.measure_CA = []
self.running = True
self.datacollector.collect(self)
def step(self):
'''
Have the scheduler advance each cell by one step
'''
self.measure_CA = [a for a in self.schedule.agents]
self.schedule.step()
# collect data
self.datacollector.collect(self)
def compute_reporters(self):
'''
Returns
A dictionary of the fractions of the population that are Infected, Quarantined,
Recovered or Dead
'''
mod_rep = {
"Fraction Infected":
lambda m: self.count_infected(m, self.height * self.width),
"Fraction Quarantined":
lambda m: self.count_quarantined(m, self.height * self.width),
"Fraction Recovered":
lambda m: self.count_recovered(m, self.height * self.width),
"Fraction Dead":
lambda m: self.count_dead(m, self.height * self.width),
}
return mod_rep
@staticmethod
def count_infected(model, grid_size):
"""
Helper method to count INFECTED cells in the model.
"""
list_state = [
a for a in model.schedule.agents
if (a.state == a.INFECTED or a.state == a.QUARANTINED)
]
return len(list_state) / grid_size
@staticmethod
def count_recovered(model, grid_size):
"""
Helper method to count RECOVERED cells in the model.
"""
list_state = [
a for a in model.schedule.agents
if (a.state == a.RECOVERED or a.state == a.DEAD)
]
return len(list_state) / grid_size
@staticmethod
def count_quarantined(model, grid_size):
"""
Helper method to count QUARANTINED cells in the model.
"""
list_state = [
a for a in model.schedule.agents if a.state == a.QUARANTINED
]
return len(list_state) / grid_size
@staticmethod
def count_dead(model, grid_size):
'''
Helper method to count DEAD cells in the model.
'''
list_state = [a for a in model.schedule.agents if a.state == a.DEAD]
return len(list_state) / grid_size
class EpiDyn(Model):
'''
Represents the 2-dimensional model for epidemic dynamics
'''
schedule_types = {
"Random": RandomActivation,
"Simultaneous": SimultaneousActivation
}
def __init__(self,
height=100,
width=100,
dummy="",
schedule_type="Simultaneous",
startblock=1,
density=0.1,
p_infect=0.25,
p_death=0.0,
spatial=1,
groupsize=4,
quarantine_delay=7,
neighbourdic={},
groupswitch=True,
switchperx=2):
'''
Create the CA field with (height, width) cells.
'''
#setting an explicit seed allows you to reproduce interesting runs
#self.random.seed(30)
# Set up the grid and schedule.
self.schedule_type = schedule_type
self.schedule = self.schedule_types[self.schedule_type](self)
self.neighbourdic = neighbourdic
self.quarantine_delay = quarantine_delay
self.groupswitch = groupswitch
self.switchperx = switchperx
self.counter = 0
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=True)
self.datacollector = DataCollector({
"Infectious":
lambda m: self.count_infectious(m, width * height),
"Removed":
lambda m: self.count_removed(m, width * height),
"Exposed":
lambda m: self.count_removed(m, width * height)
})
# Place a cell at each location, with default SENSTIVE,
# and some (a 2x2 block) initialized to INFECTIOUS
for (contents, x, y) in self.grid.coord_iter():
cell = Cell((x, y),
self,
spatial,
unique_id=int(0.5 * (x + y) * (x + y + 1) + y))
cell.state = cell.SENSITIVE
cell.p_infect = p_infect
cell.p_death = p_death
cell.groupsize = groupsize
if startblock:
if ((x == height / 2 or x == height / 2 + 1)
and (y == height / 2 or y == height / 2 + 1)):
cell.state = cell.INFECTIOUS
elif self.random.random() < density:
cell.state = cell.INFECTIOUS
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.measure_CA = []
self.running = True
self.datacollector.collect(self)
def step(self):
'''
Have the scheduler advance each cell by one step
'''
#Need this seperately so the Reset button works fo no group switches
if self.counter == 0:
self.neighbourdic.clear()
if (self.counter - self.quarantine_delay) % self.switchperx == 0:
if self.groupswitch == 1:
self.neighbourdic.clear()
self.measure_CA = [a for a in self.schedule.agents]
self.schedule.step()
# collect data
self.datacollector.collect(self)
self.counter = self.counter + 1
@staticmethod
def count_infectious(model, grid_size):
"""
Helper method to count cells in a given state in a given model.
"""
list_state = [
a for a in model.schedule.agents if a.state == a.INFECTIOUS
]
return len(list_state) / grid_size
@staticmethod
def count_removed(model, grid_size):
"""
Helper method to count cells in a given state in a given model.
"""
list_state = [a for a in model.schedule.agents if a.state == a.REMOVED]
return len(list_state) / grid_size
@staticmethod
def count_exposed(model, grid_size):
"""
Helper method to count cells in a given state in a given model.
"""
list_state = [
a for a in model.schedule.agents if a.state == a.NEIGHBOUR
]
return len(list_state) / grid_size
class ConwaysGameOfLife(Model):
"""
Represents the 2-dimensional array of cells in Conway's
Game of Life.
"""
def __init__(self, data, height=10, width=10, part="Part 1"):
"""
Create a new playing area of (height, width) cells.
"""
# Set up the grid and schedule.
# Use SimultaneousActivation which simulates all the cells
# computing their next state simultaneously. This needs to
# be done because each cell's next state depends on the current
# state of all its neighbors -- before they've changed.
self.schedule = SimultaneousActivation(self)
# Use a simple grid, where edges wrap around.
self.grid = Grid(height, width, torus=False)
self.dc = DataCollector({
"Empty":
lambda m: self.count_type(m, Cell.DEAD),
"Occupied":
lambda m: self.count_type(m, Cell.ALIVE),
})
self.numalive = 0
# Place a cell at each location, with some initialized to
# ALIVE and some to DEAD.
for (contents, x, y) in self.grid.coord_iter():
if data[(len(data) - 1) - y][x] == ".":
state = Cell.FLOOR
else:
state = Cell.DEAD
cell = Cell((x, y), self, state, part)
self.grid.place_agent(cell, (x, y))
self.schedule.add(cell)
self.running = True
self.dc.collect(self)
def step(self):
"""
Have the scheduler advance each cell by one step
"""
print(self.numalive)
prev = self.numalive
self.schedule.step()
self.schedule.step()
self.dc.collect(self)
self.numalive = self.count_type(self, Cell.ALIVE)
if prev == self.numalive:
self.running = False
@staticmethod
def count_type(model, condition):
"""
Helper method to count trees in a given condition in a given model.
"""
count = 0
for spot in model.schedule.agents:
if spot.state == condition:
count += 1
return count