class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, width, height):
self.num_agents = N
self.schedule = RandomActivation(self)
self.grid = MultiGrid(width, height, True)
self.running = True
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": lambda a: a.wealth})
def move(self):
possible_steps = self.model.grid.get_neighborhood(self.pos,
moore=True,
include_center=False)
new_position = random.choice(possible_steps)
self.model.grid.move_agent(self, new_position)
def step(self):
'''Advance the model by one step.'''
# print("New step.")
self.datacollector.collect(self)
self.schedule.step()
class MoneyModel(Model):
"""A simple model of an economy where agents exchange currency at random.
All the agents begin with one unit of currency, and each time step can give
a unit of currency to another agent. Note how, over time, this produces a
highly skewed distribution of wealth.
"""
def __init__(self, N, width, height):
self.num_agents = N
self.running = True
self.grid = MultiGrid(height, width, True)
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": lambda a: a.wealth}
)
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
def step(self):
self.datacollector.collect(self)
self.schedule.step()
def run_model(self, n):
for i in range(n):
self.step()
class miModelo(Model):
def __init__(self, N, seed=None):
self.current_id = 0
self.running = True
# Definimos el schedule para hacer la ejecucion en orden aleatorio
self.schedule = RandomActivation(self)
#Definimos el grid de tamanio 10x10 y sin fronteras flexibles
self.grid = MultiGrid(10, 10, False)
for i in range(N):
a = miAgente(self.next_id(), self, 5)
self.schedule.add(a)
pos_x = self.random.randint(0, 9)
pos_y = self.random.randint(0, 9)
self.grid.place_agent(a, [pos_x, pos_y])
self.datacollector = DataCollector(model_reporters={
"Nagentes": contarAgentes,
"NumberTicks": getCurrentTick
})
def step(self):
self.schedule.step()
self.datacollector.collect(self)
# Paramos la simulacion cuando hay menos de dos agentes
if self.schedule.get_agent_count() < 2:
self.running = False
class CollisionModel(Model):
def __init__(self, N, width, height, init_value):
self.num_agents = N
self.init_value = init_value
self.grid = MultiGrid(width, height, True)
self.schedule = BaseScheduler(self)
# Create Agents
for i in range(self.num_agents):
a = CollisionAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
#model_reporters={"AvgReward": compute_avgreward},
#model_reporters={"AvgCollision": compute_avgcollision},
model_reporters={"0": compute_avg_reward_angle_0, "90": compute_avg_reward_angle_1, "180": compute_avg_reward_angle_2, "270": compute_avg_reward_angle_3},
agent_reporters={"Reward": lambda a: a.reward})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class StigmergyPrey(Model):
def __init__(self, N, width, height):
self.stigmergies = []
self.preys = []
self.grid = MultiGrid(width, height, True)
for i in range(N):
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
agent = None
if i % 2 == 0:
agent = Stigmergy(i, self, 20 * (i + 1), i, 1)
self.stigmergies.append(agent)
else:
agent = Prey(i, self)
self.preys.append(agent)
self.grid.place_agent(agent, (x, y))
def step(self, directions):
observations = []
for stigmergy in self.stigmergies:
stig_value = stigmergy.step()
if stig_value == 0: self.stigmergies.remove(stigmergy)
observations.append(stig_value)
for prey in self.preys:
prey.step()
return observations
class DiseaseModel(Model):
def __init__(self, home_store):
self.num_agents = 1000
self.grid = MultiGrid(200, 200, True)
self.schedule = RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = Agent(i, self)
self.schedule.add(a)
while True:
#x = round(int(np.random.normal(self.grid.width/2, 10, 1)))
#y = round(int(np.random.normal(self.grid.height/2, 10, 1)))
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
if len(
self.grid.get_neighbors(
(x, y), moore=True, include_center=True,
radius=10)) <= 7:
self.grid.place_agent(a, (x, y))
home_store[i, :] = x, y
break
if i < 1:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot informed": compute_informed},
agent_reporters={"Infected": "infected"})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, width, height):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.cities = []
self.running = True
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini}, # `compute_gini` defined above
agent_reporters={"Wealth": "wealth"})
def establish_cities(self):
for contents, x, y in self.grid.coord_iter():
if len(contents) > 2:
for agent in contents:
agent.in_city = True
def step(self):
self.establish_cities()
self.datacollector.collect(self)
self.schedule.step()
class MoneyModel(Model):
"""Een model met N aantal agents, in een 30x30 grid.
Elke agent begint met een wealth van 0 of 1 (random).
Legenda:
Zwart: Geen geld
Grijs: 1-2
Groen: 3-5
Blauw: >5"""
def __init__(self, N, width, height, neg):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self, neg)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": "wealth"})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class MoneyModel(Model):
"""a model with some number of agents"""
def __init__(self, N, width, height):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
# create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": "wealth"})
def step(self):
"""advance the model by one step"""
self.datacollector.collect(self)
self.schedule.step()
class HungerModel(Model):
"""A model with some number of agents."""
def __init__(self,N,width,height,num_of_food=10):
self.num_agents = N
self.num_food = num_of_food
self.grid = MultiGrid(width,height,True)
self.schedule= RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = HungryAgent(i,self)
self.schedule.add(a)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a,(x,y))
for i in range(self.num_food):
kombinacija = sve_kombinacije[i]
id_offset = i+1000
f = FoodAgent(id_offset,self, kombinacija[0],kombinacija[1],kombinacija[2])
self.schedule.add(f)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(f,(x,y))
self.datacollector = DataCollector(
model_reporters = {"TotalKnowledge":compute_knowledge}) #prosledice automacki
# agent_reporters = {"Knowledge":"knowledge"})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class SIRModel(Model):
def __init__(self, N, width, height, infection_period, infection_prob,
initial_infected):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
self.infection_period = infection_period
self.infection_prob = infection_prob
for i in range(self.num_agents):
agent = SIRAgent(i, self)
if i < initial_infected:
agent.infection()
self.schedule.add(agent)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(agent, (x, y))
def step(self):
self.schedule.step()
class MoneyModel(Model):
"""
A model with N agents
"""
def __init__(self, N, width, height):
self.running = True
self.num_agents = N
self.grid = MultiGrid(width, height, True) # create a toroidal grid
self.schedule = RandomActivation(self)
# create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# add agent to random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini}, # A function to call
agent_reporters={"Wealth": "wealth"} # An agent attribute
)
def step(self):
"""
Advance the model by 1 step
"""
self.datacollector.collect(self)
self.schedule.step()
class bacModel(Model):
'''world model for Eden Growth Model Simulation'''
def __init__(self, beginRad, splitChance, x=-1, y=-1, mut_rate=MUTATION_RATE):
self.running = True
self.num_agents = 0
self.schedule = RandomActivation(self)
self.grid = MultiGrid(WIDTH, HEIGHT, IS_TOROIDAL) #True for toroidal
#self.datacollector = DataCollector(
# model_reporters = {"Identifier": function_name}, #note no parentheses, just function name
# agent_reporter = {"Identifier2": function_name2})
if x == -1:
x = self.grid.width // 2
if y == -1:
y = self.grid.height // 2
#create subsequent agents
positions = self.grid.get_neighborhood((x,y), moore=False, radius=beginRad, include_center=True)
for coord in positions:
roll = random.random()
a = bacAgent(self.num_agents, self, False, splitChance)
self.num_agents += 1
self.schedule.add(a)
self.grid.place_agent(a, coord)
def step(self):
#self.datacollector.collect(self)
self.schedule.step()
class TransModel(Model):
# constructor for grid of width x height with N agents
def __init__(self, N, width, height):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
# create agents
for i in range(self.num_agents):
a = TransAgent(i, self)
self.schedule.add(a)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
# create data collector
self.datacollector = DataCollector(
model_reporters = {'Transit': transitPercent,
'PfH': pfhPercent},
agent_reporters = {'Time': 'time',
'Money': 'money'})
# advance the model by one tick
def step(self):
self.datacollector.collect(self)
self.schedule.step()
def crear_nodo(self,nodo_id, tipo, ocupantes = [], tamano = None,
ind_pos_def = None):
assert tipo in ['casa','tienda', 'ciudad']
if tipo == 'casa':
assert len(ocupantes)>0, 'No hay ocupantes a asignar en la casa'
if not tamano:
tamano = 2#len(ocupantes)//2+1
habitantes = [ind.unique_id for ind in ocupantes]
elif tipo in ['tienda', 'ciudad']:
if not tamano:
tamano = 20
habitantes = []
espacio = MultiGrid(width = tamano,
height = tamano,
torus = False)
if not ind_pos_def:
disponibles = espacio.empties[::]
shuffle(disponibles)
for i in ocupantes:
i.casa_id = nodo_id if tipo=='casa' else None
i.n_familiares = len(habitantes) if tipo=='casa' else 0
i.nodo_actual = nodo_id
i_pos = disponibles.pop() if not ind_pos_def else [0,0]
espacio.place_agent(i, i_pos)
self.add_node(nodo_id, tipo = tipo,
habitantes = habitantes,
espacio = espacio)
class BoltzmannWealthModel(Model):
"""A simple model of an economy where agents exchange currency at random.
All the agents begin with one unit of currency, and each time step can give
a unit of currency to another agent. Note how, over time, this produces a
highly skewed distribution of wealth.
"""
def __init__(self, N=100, width=10, height=10):
self.num_agents = N
self.grid = MultiGrid(height, width, True)
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": "wealth"})
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.running = True
self.datacollector.collect(self)
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self, n):
for i in range(n):
self.step()
class BoltzmannWealthModel(Model):
def __init__(self, T, N, lamda, width=10, height=10):
self.num_agents = N
self.T = T
self.grid = MultiGrid(height, width, True)
self.lamda = lamda
self.count = 0
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(agent_reporters={'mi': 'm'})
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.running = True
self.datacollector.collect(self)
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self, n):
for i in tqdm(range(1, n)):
self.count += 1
#print("step:{}".format(i))
self.step()
class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, width, height):
self.num_agents = N
self.running = True
self.grid = MultiGrid(height, width, True)
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": lambda a: a.wealth})
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i)
self.schedule.add(a)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
def step(self):
self.datacollector.collect(self)
self.schedule.step()
def run_model(self, n):
for i in range(n):
self.step()
class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, width, height):
super().__init__()
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
model_reporters={"Gini":
compute_gini}, # `compute_gini` defined above
agent_reporters={"Wealth": "wealth"})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class EpidemicModel(Model):
def __init__(self, num_agent, width, height):
super().__init__()
self.num_agent = num_agent
self.grid = MultiGrid(width=width, height=height, torus=True)
self.schedule = RandomActivation(self)
# Create agents
for u_id in range(0, num_agent):
a = EpidemicAgent(unique_id=u_id, model=self)
self.schedule.add(a)
# Place the agents in the grid
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(agent=a, pos=(x, y))
# Pick one agent and infect him/her
agent = self.random.choice(self.schedule.agents)
agent.state = 1
self.data_collector = DataCollector(model_reporters={
"Susceptibles": susc,
"Infecteds": inf,
"Recovereds": rec
})
def step(self):
self.data_collector.collect(self)
self.schedule.step()
class WalkerWorld(Model):
'''
Random walker world.
'''
height = 10
width = 10
def __init__(self, height, width, agent_count):
'''
Create a new WalkerWorld.
Args:
height, width: World size.
agent_count: How many agents to create.
'''
self.height = height
self.width = width
self.grid = MultiGrid(self.height, self.width, torus=True)
self.agent_count = agent_count
self.schedule = RandomActivation(self)
# Create agents
for i in range(self.agent_count):
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
a = WalkerAgent((x, y), self, True)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
def step(self):
self.schedule.step()
class WalkerWorld(Model):
'''
Random walker world.
'''
height = 10
width = 10
def __init__(self, height, width, agent_count):
'''
Create a new WalkerWorld.
Args:
height, width: World size.
agent_count: How many agents to create.
'''
self.height = height
self.width = width
self.grid = MultiGrid(self.height, self.width, torus=True)
self.agent_count = agent_count
self.schedule = RandomActivation(self)
# Create agents
for i in range(self.agent_count):
x = random.randrange(self.width)
y = random.randrange(self.height)
a = WalkerAgent((x, y), self, True)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
def step(self):
self.schedule.step()
class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, width, height):
self.num_agents = N
self.running = True
self.grid = MultiGrid(height, width, True)
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(
model_reporters={"Gini": compute_gini},
agent_reporters={"Wealth": lambda a: a.wealth})
# Create agents
for i in range(self.num_agents):
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
def step(self):
self.datacollector.collect(self)
self.schedule.step()
def run_model(self, n):
for i in range(n):
self.step()
class TransModel(Model):
def __init__(self, N, width, height, tax_val):
self.num_agents = N
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
self.steps = 0
self.tax_value = float(tax_val)
# create agents
for i in range(self.num_agents):
agent = TransAgent(i, self)
self.schedule.add(agent)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(agent, (x, y))
# create data collector
self.data_collector = DataCollector(model_reporters={
'Transit': transit_percent,
'PfH': pfh_percent
},
agent_reporters={
'Time': 'time',
'Money': 'money'
})
def step(self):
"""Adance the model by one tick."""
self.steps += 1
self.schedule.step()
self.data_collector.collect(self)
class DiseaseModel(Model):
def __init__(self, no_people, total_area, no_agents, all_x, all_y,
infection_rate, first_infected, mobility, work_store,
home_store):
self.num_agents = no_agents
grid_size = round(
math.sqrt((self.num_agents / no_people) * total_area) * 100)
self.grid = MultiGrid(grid_size, grid_size, False)
self.schedule = RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = Agent(i, self, infection_rate, work_store, home_store,
mobility)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
if i == first_infected:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot infections": compute_informed},
agent_reporters={
"Infected": "infected",
"R-Number": "rnumber"
})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class TestMultiGrid(unittest.TestCase):
'''
Testing a toroidal MultiGrid
'''
torus = True
def setUp(self):
'''
Create a test non-toroidal grid and populate it with Mock Agents
'''
width = 3
height = 5
self.grid = MultiGrid(width, height, self.torus)
self.agents = []
counter = 0
for x in range(width):
for y in range(height):
for i in range(TEST_MULTIGRID[x][y]):
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 on the MultiGrid.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid[x][y]
def test_neighbors(self):
'''
Test the toroidal MultiGrid neighborhood methods.
'''
neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
assert len(neighborhood) == 4
neighbors = self.grid.get_neighbors((1, 4), moore=False)
assert len(neighbors) == 0
neighbors = self.grid.get_neighbors((1, 4), moore=True)
assert len(neighbors) == 5
neighbors = self.grid.get_neighbors((1, 1),
moore=False,
include_center=True)
assert len(neighbors) == 7
neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
assert len(neighbors) == 11
class Money_Model(Model):
def __init__(self, N, width=50, height=50, torus=True):
self.num_agents = N
self.schedule = RandomActivation(self)
self.grid = MultiGrid(height, width, torus)
self.create_agents()
self.dc = DataCollector({"Gini": lambda m: m.compute_gini()},
{"Wealth": lambda a: a.wealth})
self.running = True
def create_agents(self):
for i in range(self.num_agents):
a = Money_Agent(i)
self.schedule.add(a)
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
def step(self):
self.dc.collect(self)
self.schedule.step()
def run_model(self, steps):
for i in range(steps):
self.step()
def compute_gini(self):
agent_wealths = [agent.wealth for agent in self.schedule.agents]
x = sorted(agent_wealths)
N = self.num_agents
B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
return (1 + (1/N) - 2*B)
class MoneyModel(Model):
"""A model with some number of agents."""
def __init__(self, N, elevation):
# num_agents is a parameter and stays constant throughout the simulation
self.num_agents = N
self.width = elevation.shape[0]
self.height = elevation.shape[1]
self.z = elevation
# Adds the grid to the model object
self.grid = MultiGrid(self.width, self.height, True)
# Adds the scheduler to the model object
self.schedule = RandomActivation(self)
# create agents (for loop hence multiple agents) and add them to the schedular (one at a time)
for i in range(self.num_agents):
# The money agent class (object) is being computed for every value of i
a = MoneyAgent(i, self)
self.schedule.add(a)
# Add the agents to a random cell
# x = self.random.randrange(self.width)
# y = self.random.randrange(self.height)
self.grid.place_agent(a, (50, 14))
def step(self):
'''Advance the model by one step.'''
self.schedule.step()
class ForageModel(Model):
"""A model with some number of agents."""
def __init__(self, n_agents, width, height):
self.running = True # this was important for visualization
self.num_agents = n_agents
self.grid = MultiGrid(
width, height, torus=True) # True means toroidal space (for now)
self.schedule = RandomActivation(self)
# Create agents
for i in range(self.num_agents):
a = OysterCatcher(i, self)
self.schedule.add(a)
# Add the agent to a random grid cell
coords = (random.randrange(self.grid.width),
random.randrange(self.grid.height))
self.grid.place_agent(a, coords)
self.dc = DataCollector(model_reporters={
"agent_count":
lambda m: m.schedule.get_agent_count(),
"gini":
compute_gini
},
agent_reporters={
"name": lambda a: a.unique_id,
"reserve": lambda a: a.reserve
})
def step(self):
'''Advance the model by one step.'''
self.dc.collect(self)
self.schedule.step()
class bacServerModel(Model):
################
###Deprecated###
################
def __init__(self, width, height, beginRad):
self.running = True
self.num_agents = width * height
self.schedule = RandomActivation(self)
self.grid = MultiGrid(width, height, IS_TOROIDAL) #True for toroidal
#self.datacollector = DataCollector(
# model_reporters = {"Identifier": function_name}, #note no parentheses, just function name
# agent_reporter = {"Identifier2": function_name2})
for x in range(self.grid.width):
for y in range(self.grid.height):
a = bacServerAgent(self.num_agents, self)
self.num_agents += 1
self.schedule.add(a)
self.grid.place_agent(a, (x,y))
x = self.grid.width // 2
y = self.grid.height // 2
#create subsequent agents
positions = self.grid.get_neighborhood((x,y), moore=False, radius=beginRad, include_center=True)
for coord in positions:
ag = self.grid.get_cell_list_contents([coord])[0]
ag.activate()
def step(self):
#self.datacollector.collect(self)
self.schedule.step()
class HungerModel(Model):
"""A model with some number of agents."""
def __init__(self,N,width,height,num_of_food=64):
self.num_agents = N
self.num_food = num_of_food
self.grid = MultiGrid(width,height,True)
self.schedule= RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = HungryAgent(i,self)
self.schedule.add(a)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a,(x,y))
for i in range(self.num_food):
kombinacija = sve_kombinacije[i]
id_offset = i+1000
f = FoodAgent(id_offset,self, kombinacija[0],kombinacija[1],kombinacija[2])
self.schedule.add(f)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(f,(x,y))
self.datacollector = DataCollector(
model_reporters = {"TotalKnowledge":compute_knowledge,"TotalEnergy":total_energy,"TotalExperience":measure_experience,"TotalFood":total_pojedena_hrana,"TotalPoison":total_pojedeni_otrovi})
# agent_reporters = {"Knowledge":"knowledge"})
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class HumanCapital(Model):
##Inicialización del modelo.
def __init__(self, N_buenos_empleo, seed=None):
self.current_id = 0
self.running = True
self.width = 7
self.height = 7
# Definimos el schedule para hacer la ejecucion en orden aleatorio
self.schedule = RandomActivation(self)
#Definimos el grid de tamanio 7x7 y sin fronteras flexibles
self.grid = MultiGrid(self.width, self.height, False)
##Declaración del grid.
for y in range(0, self.height):
for x in range(0, self.width):
a = Agente(self.next_id(), self)
self.schedule.add(a)
self.grid.place_agent(a, [x, y])
self.datacollector = DataCollector(model_reporters={
"Nagentes": contarAgentes,
"NumberTicks": getCurrentTick
})
##Itinerario.
def step(self):
#Ejecutar el step de los agentes.
self.schedule.step()
#Ejecutar el datacollector
self.datacollector.collect(self)
class TestMultiGrid(unittest.TestCase):
'''
Testing a toroidal MultiGrid
'''
torus = True
def setUp(self):
'''
Create a test non-toroidal grid and populate it with Mock Agents
'''
width = 3
height = 5
self.grid = MultiGrid(width, height, self.torus)
self.agents = []
counter = 0
for x in range(width):
for y in range(height):
for i in range(TEST_MULTIGRID[x][y]):
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 on the MultiGrid.
'''
for agent in self.agents:
x, y = agent.pos
assert agent in self.grid[x][y]
def test_neighbors(self):
'''
Test the toroidal MultiGrid neighborhood methods.
'''
neighborhood = self.grid.get_neighborhood((1, 1), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((1, 4), moore=True)
assert len(neighborhood) == 8
neighborhood = self.grid.get_neighborhood((0, 0), moore=False)
assert len(neighborhood) == 4
neighbors = self.grid.get_neighbors((1, 4), moore=False)
assert len(neighbors) == 0
neighbors = self.grid.get_neighbors((1, 4), moore=True)
assert len(neighbors) == 5
neighbors = self.grid.get_neighbors((1, 1), moore=False,
include_center=True)
assert len(neighbors) == 7
neighbors = self.grid.get_neighbors((1, 3), moore=False, radius=2)
assert len(neighbors) == 11
class BikeShare(Model):
"""A model with some number of potential riders."""
global hours_per_day
hours_per_day = 24
def __init__(self, N, M, width, height):
# self.running = True
self.num_agents = N
# self.grid = MultiGrid(width, height, True)
self.num_stations = M
self.radius = np.int(np.sqrt(width * height))
self.grid = MultiGrid(width, height, True)
self.grid_stations = SingleGrid(width, height, True)
self.schedule = RandomActivation(self)
self.timestamp = 0 # use to find days
self.datestamp = 0
threshold = 0.8
# create agents
for i in range(self.num_agents):
a = BikeRider(i, self, threshold)
self.schedule.add(a)
# add the agent to a random grid cell
x = np.random.randint(self.grid.width)
y = np.random.randint(self.grid.height)
self.grid.place_agent(a, (x, y))
for i in range(self.num_stations):
s = BikeStation(i, self)
self.schedule.add(s)
# add the station to a random grid cell
# x = np.random.randint(self.grid_stations.width)
# y = np.random.randint(self.grid_stations.height)
self.grid_stations.position_agent(s) # ensures one station max
# self.grid.place_agent(s, s.pos)
print ("Station " + str(s.unique_id) + "; " + str(s.pos))
# self.datacollector = DataCollector(
# model_reporters={"Gini": compute_gini},
# agent_reporters={"Wealth": lambda a: a.wealth}
# )
def step(self):
'''Advance the model by 1 step: arbitrary unit of time. '''
# self.datacollector.collect(self)
# print ("Step the schedule ...")
# print (str(self.timestamp))
self.timestamp += 1
if self.timestamp % hours_per_day == 0:
print ("\n**** new day " + str(self.datestamp))
self.datestamp += 1
self.timestamp = 0
self.schedule.step()
class MoniModel(Model):
"""
A model for monitoring agents
"""
def __init__(self, N, width, height):
#Monitoring space
self.width = width
self.height = height
self.grid = MultiGrid(height, width, False) #non toroidal grid
self.schedule = SimultaneousActivation(self)
self.abCount = 0 #initial abnormality count
self.detectedAb = 0
self.interactionCount = 0
# =============================================================================
# self.coveredArea = []
# self.interactionRateAverage = 0
# self.coveragePercentage = 0
# self.coveragePercentageAverage = 0
# =============================================================================
# Create agents
self.num_agents = N
for i in range(self.num_agents):
x = floor(self.width / N * i + self.width / N / 2)
# create and add agent with id number i to the scheduler
a = MoniAgent(i, self)
self.schedule.add(a)
#place agent at the center of its limit coor
self.grid.place_agent(a, (x, 0))
# this part is for visualization only
# self.running = True
def step(self):
# self.interactionCount = 0
self.schedule.step()
def run_model(self, n):
for i in range(n):
# self.initPos()
self.step()
# print(self.schedule.steps)
"""
Calculate fitness of a swarm (success ratio)
"""
def fitness(self):
for _ in range(100):
self.step()
if self.abCount == 0:
self.abCount = 1 #avoid division by 0 error
# return self.detectedAb/self.abCount
return (sum(self.schedule.agents[0].genome))
class Charts(Model):
# grid height
grid_h = 20
# grid width
grid_w = 20
"""init parameters "init_people", "rich_threshold", and "reserve_percent"
are all UserSettableParameters"""
def __init__(self, height=grid_h, width=grid_w, init_people=2, rich_threshold=10,
reserve_percent=50,):
self.height = height
self.width = width
self.init_people = init_people
self.schedule = RandomActivation(self)
self.grid = MultiGrid(self.width, self.height, torus=True)
# rich_threshold is the amount of savings a person needs to be considered "rich"
self.rich_threshold = rich_threshold
self.reserve_percent = reserve_percent
# see datacollector functions above
self.datacollector = DataCollector(model_reporters={
"Rich": get_num_rich_agents,
"Poor": get_num_poor_agents,
"Middle Class": get_num_mid_agents,
"Savings": get_total_savings,
"Wallets": get_total_wallets,
"Money": get_total_money,
"Loans": get_total_loans},
agent_reporters={
"Wealth": lambda x: x.wealth})
# create a single bank for the model
self.bank = Bank(1, self, self.reserve_percent)
# create people for the model according to number of people set by user
for i in range(self.init_people):
# set x, y coords randomly within the grid
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
p = Person(i, (x, y), self, True, self.bank, self.rich_threshold)
# place the Person object on the grid at coordinates (x, y)
self.grid.place_agent(p, (x, y))
# add the Person object to the model schedule
self.schedule.add(p)
self.running = True
self.datacollector.collect(self)
def step(self):
# tell all the agents in the model to run their step function
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self):
for i in range(self.run_time):
self.step()
class Sugarscape2ConstantGrowback(Model):
'''
Sugarscape 2 Constant Growback
'''
verbose = True # Print-monitoring
def __init__(self, height=50, width=50,
initial_population=100):
'''
Create a new Constant Growback model with the given parameters.
Args:
initial_population: Number of population to start with
'''
# Set parameters
self.height = height
self.width = width
self.initial_population = initial_population
self.schedule = RandomActivationByBreed(self)
self.grid = MultiGrid(self.height, self.width, torus=False)
self.datacollector = DataCollector({"SsAgent": lambda m: m.schedule.get_breed_count(SsAgent), })
# Create sugar
import numpy as np
sugar_distribution = np.genfromtxt("sugarscape/sugar-map.txt")
for _, x, y in self.grid.coord_iter():
max_sugar = sugar_distribution[x, y]
sugar = Sugar((x, y), self, max_sugar)
self.grid.place_agent(sugar, (x, y))
self.schedule.add(sugar)
# Create agent:
for i in range(self.initial_population):
x = random.randrange(self.width)
y = random.randrange(self.height)
sugar = random.randrange(6, 25)
metabolism = random.randrange(2, 4)
vision = random.randrange(1, 6)
ssa = SsAgent((x, y), self, False, sugar, metabolism, vision)
self.grid.place_agent(ssa, (x, y))
self.schedule.add(ssa)
self.running = True
def step(self):
self.schedule.step()
self.datacollector.collect(self)
if self.verbose:
print([self.schedule.time,
self.schedule.get_breed_count(SsAgent)])
def run_model(self, step_count=200):
if self.verbose:
print('Initial number Sugarscape Agent: ',
self.schedule.get_breed_count(SsAgent))
for i in range(step_count):
self.step()
if self.verbose:
print('')
print('Final number Sugarscape Agent: ',
self.schedule.get_breed_count(SsAgent))
class WolfSheepPredation(Model):
'''
Wolf-Sheep Predation Model
'''
initial_sheep = 100
initial_wolves = 50
sheep_gain_from_food = 4
grass = False
wolf_gain_from_food = 20
sheep_reproduce = 0.04
wolf_reproduce = 0.05
height = 20
width = 20
def __init__(self, height=20, width=20,
initial_sheep=100, initial_wolves=50, sheep_reproduce=0.04,
wolf_reproduce=0.05, wolf_gain_from_food=20,
grass=False, sheep_gain_from_food=4):
'''
Create a new Wolf-Sheep model with the given parameters.
Args:
initial_sheep: Number of sheep to start with
initial_wolves: Number of wolves to start with
sheep_reproduce: Probability of each sheep reproducing each step
wolf_reproduce: Probability of each wolf reproducing each step
wolf_gain_from_food: Energy a wolf gains from eating a sheep
grass: Whether to have the sheep eat grass for energy
sheep_gain_from_food: Energy sheep gain from grass, if enabled.
'''
# Set parameters
self.height = height
self.width = width
self.initial_sheep = initial_sheep
self.initial_wolves = initial_wolves
self.sheep_reproduce = sheep_reproduce
self.wolf_reproduce = wolf_reproduce
self.wolf_gain_from_food = wolf_gain_from_food
self.grass = grass
self.sheep_gain_from_food = sheep_gain_from_food
self.schedule = RandomActivation(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
# Create sheep:
for i in range(self.initial_sheep):
x = random.randrange(self.width)
y = random.randrange(self.height)
sheep = Sheep(self.grid, x, y, True)
self.grid.place_agent(sheep, (x, y))
self.schedule.add(sheep)
# Create wolves
for i in range(self.initial_wolves):
x = random.randrange(self.width)
y = random.randrange(self.height)
energy = random.randrange(2 * self.wolf_gain_from_food)
wolf = Wolf(self.grid, x, y, True, energy)
self.grid.place_agent(wolf, (x, y))
self.schedule.add(wolf)
self.running = True
def step(self):
self.schedule.step()
class BankReserves(Model):
"""
This model is a Mesa implementation of the Bank Reserves model from NetLogo.
It is a highly abstracted, simplified model of an economy, with only one
type of agent and a single bank representing all banks in an economy. People
(represented by circles) move randomly within the grid. If two or more people
are on the same grid location, there is a 50% chance that they will trade with
each other. If they trade, there is an equal chance of giving the other agent
$5 or $2. A positive trade balance will be deposited in the bank as savings.
If trading results in a negative balance, the agent will try to withdraw from
its savings to cover the balance. If it does not have enough savings to cover
the negative balance, it will take out a loan from the bank to cover the
difference. The bank is required to keep a certain percentage of deposits as
reserves and the bank's ability to loan at any given time is a function of
the amount of deposits, its reserves, and its current total outstanding loan
amount.
"""
# grid height
grid_h = 20
# grid width
grid_w = 20
"""init parameters "init_people", "rich_threshold", and "reserve_percent"
are all UserSettableParameters"""
def __init__(self, height=grid_h, width=grid_w, init_people=2, rich_threshold=10,
reserve_percent=50,):
self.height = height
self.width = width
self.init_people = init_people
self.schedule = RandomActivation(self)
self.grid = MultiGrid(self.width, self.height, torus=True)
# rich_threshold is the amount of savings a person needs to be considered "rich"
self.rich_threshold = rich_threshold
self.reserve_percent = reserve_percent
# see datacollector functions above
self.datacollector = DataCollector(model_reporters={
"Rich": get_num_rich_agents,
"Poor": get_num_poor_agents,
"Middle Class": get_num_mid_agents,
"Savings": get_total_savings,
"Wallets": get_total_wallets,
"Money": get_total_money,
"Loans": get_total_loans},
agent_reporters={
"Wealth": lambda x: x.wealth})
# create a single bank for the model
self.bank = Bank(1, self, self.reserve_percent)
# create people for the model according to number of people set by user
for i in range(self.init_people):
# set x, y coords randomly within the grid
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
p = Person(i, (x, y), self, True, self.bank, self.rich_threshold)
# place the Person object on the grid at coordinates (x, y)
self.grid.place_agent(p, (x, y))
# add the Person object to the model schedule
self.schedule.add(p)
self.running = True
self.datacollector.collect(self)
def step(self):
# tell all the agents in the model to run their step function
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self):
for i in range(self.run_time):
self.step()
class WolfSheep(Model):
'''
Wolf-Sheep Predation Model
'''
height = 20
width = 20
initial_sheep = 100
initial_wolves = 50
sheep_reproduce = 0.04
wolf_reproduce = 0.05
wolf_gain_from_food = 20
grass = False
grass_regrowth_time = 30
sheep_gain_from_food = 4
verbose = False # Print-monitoring
description = 'A model for simulating wolf and sheep (predator-prey) ecosystem modelling.'
def __init__(self, height=20, width=20,
initial_sheep=100, initial_wolves=50,
sheep_reproduce=0.04, wolf_reproduce=0.05,
wolf_gain_from_food=20,
grass=False, grass_regrowth_time=30, sheep_gain_from_food=4):
'''
Create a new Wolf-Sheep model with the given parameters.
Args:
initial_sheep: Number of sheep to start with
initial_wolves: Number of wolves to start with
sheep_reproduce: Probability of each sheep reproducing each step
wolf_reproduce: Probability of each wolf reproducing each step
wolf_gain_from_food: Energy a wolf gains from eating a sheep
grass: Whether to have the sheep eat grass for energy
grass_regrowth_time: How long it takes for a grass patch to regrow
once it is eaten
sheep_gain_from_food: Energy sheep gain from grass, if enabled.
'''
super().__init__()
# Set parameters
self.height = height
self.width = width
self.initial_sheep = initial_sheep
self.initial_wolves = initial_wolves
self.sheep_reproduce = sheep_reproduce
self.wolf_reproduce = wolf_reproduce
self.wolf_gain_from_food = wolf_gain_from_food
self.grass = grass
self.grass_regrowth_time = grass_regrowth_time
self.sheep_gain_from_food = sheep_gain_from_food
self.schedule = RandomActivationByBreed(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
self.datacollector = DataCollector(
{"Wolves": lambda m: m.schedule.get_breed_count(Wolf),
"Sheep": lambda m: m.schedule.get_breed_count(Sheep)})
# Create sheep:
for i in range(self.initial_sheep):
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
energy = self.random.randrange(2 * self.sheep_gain_from_food)
sheep = Sheep(self.next_id(), (x, y), self, True, energy)
self.grid.place_agent(sheep, (x, y))
self.schedule.add(sheep)
# Create wolves
for i in range(self.initial_wolves):
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
energy = self.random.randrange(2 * self.wolf_gain_from_food)
wolf = Wolf(self.next_id(), (x, y), self, True, energy)
self.grid.place_agent(wolf, (x, y))
self.schedule.add(wolf)
# Create grass patches
if self.grass:
for agent, x, y in self.grid.coord_iter():
fully_grown = self.random.choice([True, False])
if fully_grown:
countdown = self.grass_regrowth_time
else:
countdown = self.random.randrange(self.grass_regrowth_time)
patch = GrassPatch(self.next_id(), (x, y), self,
fully_grown, countdown)
self.grid.place_agent(patch, (x, y))
self.schedule.add(patch)
self.running = True
self.datacollector.collect(self)
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
if self.verbose:
print([self.schedule.time,
self.schedule.get_breed_count(Wolf),
self.schedule.get_breed_count(Sheep)])
def run_model(self, step_count=200):
if self.verbose:
print('Initial number wolves: ',
self.schedule.get_breed_count(Wolf))
print('Initial number sheep: ',
self.schedule.get_breed_count(Sheep))
for i in range(step_count):
self.step()
if self.verbose:
print('')
print('Final number wolves: ',
self.schedule.get_breed_count(Wolf))
print('Final number sheep: ',
self.schedule.get_breed_count(Sheep))
class Trade(Model):
verbose = False # Print-monitoring
os.chdir(os.path.dirname(__file__))
fpath = os.getcwd() + '/parameters.csv'
reader = csv.reader(open(fpath, 'r'))
d = dict()
for key, value in reader:
d[key] = float(value)
height = int(d['height'])
width = int(d['width'])
ini_buyers = int(d['ini_buyers'])
ini_sellers = int(d['ini_sellers'])
def __init__(self, height=height, width=width, ini_buyers=ini_buyers, ini_sellers=ini_sellers):
'''Parameters'''
reader = csv.reader(open(self.fpath, 'r'))
d = dict()
for key, value in reader:
d[key] = float(value)
self.height = int(d['height'])
self.width = int(d['width'])
self.ini_buyers = int(d['ini_buyers'])
self.ini_sellers = int(d['ini_sellers'])
self.ini_cash = d['ini_cash']
self.num_w = int(d['num_w'])
self.trust_w = d['trust_w']
self.costs = d['costs'] * ini_buyers
self.mktresearch = d['mktresearch']
self.priceRange = d['priceRange']
self.csa = d['csa']
self.csa_length = int(d['csa_length'])
self.network = d['network']
self.lb = d['lb'] # Lower bound
self.ub = d['ub'] # Upper bound (in effect, unbounded)
self.up = d['up'] # Up rate
self.down = d['down'] # Down rate
'''
Entry mode
0: No entry
1: Full market research
2: Whenever Avg cash balance > entryThreshhold with a random position
3: Whenever Max cash balance > entryThreshhold enter nearby that position
'''
self.entry = int(d['entry'])
self.entryFrequency = int(d['entryFrequency'])
self.entryThreshhold = d['entryThreshhold'] * self.ini_cash
self.entryRadius = int(d['entryRadius']) # Area within high earner that a new seller will plop down
'''Debugging'''
self.sellerDebug = d['sellerDebug']
self.buyerDebug = d['buyerDebug']
self.networkDebug = d['networkDebug']
self.utilweightDebug = d['utilweightDebug']
self.entryDebug = d['entryDebug']
self.schedule = RandomActivationByType(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
self.datacollector = DataCollector(
{"Sellers": lambda m: m.schedule.get_type_count(Seller),
"Buyers": lambda m: m.schedule.get_type_count(Buyer)})
'''Initialization'''
self.cnt = 0 # Period counter
self.buyers = {} # Dictionary of buyer instances
self.sellers = {} # Dictionary of seller instances
self.sid_alive = []
self.pi = [0] * (height * width) # Profitability
prices = {}
for i in range(ini_sellers):
prices[i] = self.priceRange * np.random.rand() + 1
min_price = min(prices.values())
for i in range(self.num_w):
prices[i] = min_price * 0.9
self.prices = prices
e = {} # Embeddedness
for i in range(ini_sellers):
e[i] = 0.8*np.random.rand() + 0.2 # 0.2 - 1.0
for i in range(self.num_w):
e[i] = 0
self.e = e
'''Create buyers'''
for i in range(self.ini_buyers):
# It seems coincidence in the same cell is allowed
x = np.random.randint(self.width)
y = np.random.randint(self.height)
α = d['alpha']
trust = {}
β = d['beta']*np.random.rand()
for j in range(ini_sellers):
trust[j] = np.random.rand()
for j in range(self.num_w):
trust[j] = self.trust_w
γ = d['gamma']
'''
Network ties
ties[j]=0 means 'no connection with bid=j buyer'
ties[own bid] = 0 or 1 means nothing.
'''
ties = dict(zip(range(ini_buyers),[0]*ini_buyers))
buyer = Buyer(i, self.grid, (x, y), True, α, trust, β, γ, ties)
self.buyers[i] = buyer # Dictionary key is an integer
self.grid.place_agent(buyer, (x, y))
self.schedule.add(buyer)
'''Create sellers'''
for i in range(self.ini_sellers):
x = np.random.randint(self.width)
y = np.random.randint(self.height)
cash = self.ini_cash
costs = self.costs
price = self.prices[i]
w = False
if i < self.num_w:
w = True
e = self.e[i]
seller = Seller(i, self.grid, (x, y), True, cash, costs, price, w, e)
self.sellers[i] = seller
self.grid.place_agent(seller, (x, y))
self.schedule.add(seller)
self.running = True
def step(self):
'''Initialization'''
self.cnt += 1
self.sid_alive = [] # Excluding Wal-Mart
for sid, seller in self.sellers.items():
if seller.csa == False:
'''Adjacent sales'''
seller.sales = 0
'''Customer list'''
seller.customers[self.cnt] = []
else:
seller.customers[self.cnt] = seller.customers[self.cnt - 1]
'''A list of living sellers (excluding Wal-Mart)'''
if (seller.alive and seller.w == False):
self.sid_alive.append(sid)
# For entry
if self.entry == 1:
# Initialize the profitability vector
self.pi = [0] * (self.height * self.width)
elif self.entry == 2:
# Calculate the average cash balance (scalar)
total_cash = 0
cnt_seller = 0
total_cash = sum([self.sellers[sid].cash for sid in self.sid_alive])
self.avg_cash = total_cash / len(self.sid_alive)
elif self.entry == 3:
# Calculate the max cash balance (scalar)
temp_sids = self.sid_alive
cash_bals = [self.sellers[sid].cash for sid in temp_sids]
new_sellers = True
# Loops over maximal sellers until it finds one with no new firms nearby
while(new_sellers):
max_cash = max(cash_bals)
if(max_cash < self.entryThreshhold): break
max_cash_ind = cash_bals.index(max_cash)
max_sid = temp_sids[max_cash_ind]
max_x = self.sellers[max_sid].pos[0]
max_y = self.sellers[max_sid].pos[1]
if(self.entryDebug):
print("Max Cash, sid:", max_sid, ", Cell:(" + str(max_x) + ", " + str(max_y) + ")")
print("-Neighbor Ages:", end=" ")
new_sellers = False
# Check the age of all firms nearby the max cash balance firm
# (wants to avoid new firms)
for neighbor in self.grid.get_neighbors((max_x, max_y),True,True,self.entryRadius):
if(isinstance(neighbor, Seller) and self.entryDebug): print(str(neighbor.age), end=" ")
if(isinstance(neighbor, Seller) and neighbor.age < 52): new_sellers = True
if(new_sellers):
if(self.entryDebug):
print("\n-New Firm Exists Near sid: ", max_sid, ", Cell:(" + str(max_x) + ", " + str(max_y) + ")")
del temp_sids[max_cash_ind]
del cash_bals[max_cash_ind]
'''
Entry
Entry=1
Determine the most profitable position and whether to enter
Threshold: the fixed costs
Entry=2
Enter whenever Avg cash balance > entryThreshhold
Entry=3
Checks that no new firms are near the max balance seller
Enters within entryRadius units of the seller with max cash balance
'''
entry_on = False
if (self.entry == 1 and self.mktresearch):
opt = max(self.pi)
opt_pos = self.pi.index(opt)
if opt >= self.costs:
x = opt_pos // self.width
y = opt_pos % self.width
entry_on = True
elif (self.entry == 2 and self.avg_cash > self.entryThreshhold):
x = np.random.randint(self.width)
y = np.random.randint(self.height)
entry_on = True
elif (self.entry == 3 and max_cash > self.entryThreshhold and not new_sellers):
x = max_x + np.random.randint(-self.entryRadius,self.entryRadius)
y = max_y + np.random.randint(-self.entryRadius,self.entryRadius)
x = x % self.width
y = y % self.height
entry_on = True
if entry_on:
cash = self.ini_cash
costs = self.costs
w = False
price = np.mean([self.sellers[sid].price for sid in self.sid_alive])
# e = np.random.choice([self.sellers[sid].e for sid in self.sid_alive])
e = np.random.rand()
sid = max([seller.sid for seller in self.sellers.values()]) + 1
self.sid_alive.append(sid)
seller = Seller(sid, self.grid, (x, y), True, cash, costs, price, w, e)
self.sellers[sid] = seller
self.sellers[sid].customers[self.cnt] = []
for buyer in self.buyers.values():
buyer.trust[sid] = self.lb
self.grid.place_agent(seller, (x, y))
self.schedule.add(seller)
self.prices[sid] = price
if (self.entry >= 1 and self.entryDebug):
entry_NewFirm(sid, x, y)
self.mktresearch = False
'''Move'''
self.schedule.step()
self.datacollector.collect(self)
if self.verbose:
print([self.schedule.time,
self.schedule.get_type_count(Seller),
self.schedule.get_type_count(Buyer)])
'''Network'''
if self.network:
network.formation(self.cnt, self.buyers, self.sellers)
def run_model(self, step_count):
for _ in range(step_count):
self.step()
'''
Debugging
'''
'''Display trust levels'''
if self.buyerDebug:
debug.buyers(self.buyers)
'''Network'''
if self.networkDebug:
debug.network(self.buyers)
'''Display seller information'''
if self.sellerDebug:
debug.sellers(self.cnt, self.num_w, self.sellers, self.buyers)
'''End of the run'''
print("\n************\nPut a summary here.\n************")
class DDAModel(Model):
"""A simple DDA model"""
_width = _WIDTH # width and height of the world. These shouldn't be changed
_height = _HEIGHT
def __init__(self, N, iterations, bleedout_rate=np.random.normal(0.5, scale=0.1), mp=False):
"""
Create a new instance of the DDA model.
Parameters:
N - the number of agents
iterations - the number of iterations to run the model for
blr - the bleedout rate (the probability that agents leave at the midpoint) (default normal distribution
with mean=0.5 and sd=0.1)
mp - whether to use multiprocess (agents call step() method at same time) (doesn't work!) (default False)
"""
self.num_agents = N
self._bleedout_rate = bleedout_rate
self.iterations = iterations
self.mp = mp
# Locations of important parts of the environment. These shouldn't be changed
self.graveyard = (0, 0) # x,y locations of the graveyard
self.loc_a = (1, 0) # Location a (on left side of street)
self.loc_b = (23, 0) # Location b (on the right side)
self.loc_mid = (12, 0) # The midpoint
# 'Cameras' that store the number of agents who pass them over the course of an hour. The historical counts
# are saved by mesa using the DataCollector
self._camera_a = 0 # Camera A
self._camera_b = 0 # Camera B
self._camera_m = 0 # The midpoint
# Set up the scheduler. Note that this isn't actually used (see below re. agent's stepping)
self.schedule = RandomActivation(self) # Random order for calling agent's step methods
# For multiprocess step method
self.pool = Pool()
# Create the environment
self.grid = MultiGrid(DDAModel._width, DDAModel._height, False)
# Define a variable that can be used to indicate whether the model has finished
self.running = True
# Create a distribution that tells us the number of agents to be added to the world at each
self._agent_dist = DDAModel._make_agent_distribution(N)
# Create all the agents
for i in range(self.num_agents):
a = DDAAgent(i, self)
self.schedule.add(a) # Add the agents to the schedule
# All agents start as 'retired' in the graveyard
a.state = AgentStates.RETIRED
self.grid.place_agent(a, self.graveyard) # All agents start in the graveyard
print("Created {} agents".format(len(self.schedule.agents)))
# Define a collector for model data
self.datacollector = DataCollector(
model_reporters={"Bleedout rate": lambda m: m.bleedout_rate,
"Number of active agents": lambda m: len(m.active_agents()),
"Camera A counts": lambda m: m.camera_a,
"Camera B counts": lambda m: m.camera_b,
"Camera M counts": lambda m: m.camera_m
},
agent_reporters={"Location (x)": lambda agent: agent.pos[0],
"State": lambda agent: agent.state
}
)
def step(self):
"""Advance the model by one step."""
print("Iteration {}".format(self.schedule.steps))
self.datacollector.collect(self) # Collect data about the model
# See if the model has finished running.
if self.schedule.steps >= self.iterations:
self.running = False
return
# Things to do every hour.
# - 1 - reset the camera counters
# - 2 - activate some agents
num_to_activate = -1
s = self.schedule.steps # Number of steps (for convenience)
if s % 60 == 0: # On the hour
# Reset the cameras
self._reset_cameras()
# Calculate the number of agents to activate
num_to_activate = int(self._agent_dist[int((s / 60) % 24)])
print("\tActivating {} agents on hour {}".format(num_to_activate, s % 60))
else:
num_to_activate = 0
assert num_to_activate >= 0, \
"The number of agents to activate should be greater or equal to 0, not {}".format(num_to_activate)
if num_to_activate > 0:
# Choose some agents that are currently retired to activate.
retired_agents = [a for a in self.schedule.agents if a.state == AgentStates.RETIRED]
assert len(retired_agents) >= num_to_activate, \
"Too few agents to activate (have {}, need {})".format(len(retired_agents), num_to_activate)
to_activate = np.random.choice(retired_agents, size=num_to_activate, replace=False)
print("\t\tActivating agents: {}".format(to_activate))
for a in to_activate:
a.activate()
# XXXX HERE - see line 477 om wprlomgca,eras/py
# Call all agents' 'step' method.
if not self.mp: # Not using multiprocess. Do it the mesa way:
self.schedule.step()
else:
# Better to do it a different way to take advantage of multicore processors and to ignore agents who are not
# active (no need for them to step at all)
# NOTE: Doesn't work! The problem is that the DDAAgent needs the DDAModel class, which means
# that this class needs to be pickled and copied to the child processes. The first problem (which can be
# fixed by creating functions rather than using lambda, although this is messy) is that DDAModel uses
# lambda functions, that can't be pickled. Second and more difficult problem is that the Pool object itself
# cannot be shared. Possible solution here:
# https://stackoverflow.com/questions/25382455/python-notimplementederror-pool-objects-cannot-be-passed-between-processes
# but for the meantime I'm not going to try to fix this.
active_agents = self.active_agents() # Get all of the active agents
random.shuffle(active_agents)
if active_agents is None:
print("\tNo agents are active") # Nothing to do
else:
p = Pool()
p.map(DDAAgent._step_agent, active_agents) # Calls step() for all agents
# As not using the proper schedule method, need to update time manually.
self.schedule.steps += 1
self.schedule.time += 1
def increment_camera_a(self):
"""Used by agents to tell the model that they have just passed the camera at location A. It would be neater
to have the cameras detect the agents, but I think that this would be quite expensive."""
self._camera_a += 1 # Increment the count of the current hour (most recent)
def increment_camera_b(self):
"""Used by agents to tell the model that they have just passed the camera at location B. It would be neater
to have the cameras detect the agents, but I think that this would be quite expensive."""
self._camera_b += 1 # Increment the count of the current hour (most recent)
def increment_camera_m(self):
"""Used by agents to tell the model that they have just passed the camera at the midpoint. This is only for
information really, in this scenario there is no camera at the midpoint"""
self._camera_m += 1 # Increment the count of the current hour (most recent)
@property
def camera_a(self) -> int:
"""Getter for the count of the camera at location A"""
return self._camera_a
@property
def camera_b(self) -> int:
"""Getter for the count of the camera at location B"""
return self._camera_b
@property
def camera_m(self) -> int:
"""Getter for the count of the camera at the midpoint"""
return self._camera_m
def _reset_cameras(self):
"""Reset the cameras to zero. Done on the hour"""
self._camera_a = 0
self._camera_b = 0
self._camera_m = 0
@staticmethod
def _step_agent(a):
"""Call the given agent's step method. Only required because Pool.map doesn't take lambda functions."""
a.step()
# bleedout rate is defined as a property: http://www.python-course.eu/python3_properties.php
@property
def bleedout_rate(self):
"""Get the current bleedout rate"""
return self._bleedout_rate
@bleedout_rate.setter
def bleedout_rate(self, blr: float) -> None:
"""Set the bleedout rate. It must be between 0 and 1 (inclusive). Failure
to do that raises a ValueError."""
if blr < 0 or blr > 1:
raise ValueError("The bleedout rate must be between 0 and 1, not '{}'".format(blr))
self._bleedout_rate = blr
def active_agents(self) -> List[DDAAgent]:
"""Return a list of the active agents (i.e. those who are not retired)"""
return [a for a in self.schedule.agents if a.state != AgentStates.RETIRED]
@classmethod
def _make_agent_distribution(cls, N):
"""Create a distribution that tells us the number of agents to be created at each hour"""
a = np.arange(0, 24, 1) # Create an array with one item for each hour
rv1 = norm(loc=12., scale=6.0) # A continuous, normal random variable with a peak at 12
dist = rv1.pdf(a) # Draw from the random variable pdf, taking values from a
return [round(item * N, ndigits=0) for item in dist] # Return a rounded list (the number of agents at each hour)
class SugarscapeModel(Model):
def __init__(self, height=50, width=50, init_agents=500, max_metabolism=3, max_vision=10, max_init_sugar=5, min_age=30, max_age=60, init_poll=3, ex_ratio=2, ex_mod=1, poll_growth_rule=True, inheritance_rule=True):
self.height = height
self.width = width
self.init_agents = init_agents
self.init_poll = init_poll
self.max_metabolism = max_metabolism
self.max_vision = max_vision
self.max_init_sugar = max_init_sugar
self.min_age = min_age
self.max_age = max_age
self.ex_ratio = ex_ratio
self.ex_mod = ex_mod
self.replacement_rule = True
self.pollution_rule = False
self.diffusion_rule = False
self.push_rule = False
self.poll_growth_rule = poll_growth_rule
self.expend_rule = True
self.inheritance_rule = inheritance_rule
self.map = self.import_map()
self.grid = MultiGrid(height, width, torus=True)
self.schedule = RandomActivationByType(self)
self.datacollector = DataCollector({'Pollution': (lambda m: m.total_pollution),
'Wealth': (lambda m: m.total_wealth/m.init_agents),
'Agents': (lambda m: len(m.schedule.agents_by_type[ScapeAgent]))},
{'Wealth': self.collect_wealth,
'Metabolism': self.collect_metabolism,
'Vision': self.collect_vision})
self.total_wealth = 0
self.total_pollution = 0
self.populate_sugar()
self.populate_agents()
def step(self):
''' Step method run by the visualization module'''
self.schedule.step([ScapeAgent, SugarPatch])
self.datacollector.collect(self)
# if self.schedule.time == 20:
# self.pollution_rule = True
if self.schedule.time == 30:
self.push_rule = True
self.total_wealth = 0
self.total_pollution = 0
for agent in self.schedule.agents_by_type[ScapeAgent]:
self.total_wealth += agent.wealth
for patch in self.schedule.agents_by_type[SugarPatch]:
self.total_pollution += patch.pollution
def import_map(self):
''' Imports a text file into an array to be used when generating and
placing the sugar Agents into the grid
'''
f = open('Maps/sugar_map.txt', 'r')
map_list = []
for line in f:
num_list = line.split(' ')
for num in num_list:
map_list.append(int(num[0]))
return map_list
def new_agent(self, uid, inheritance):
''' Place a new agent on the sugarscape in order to replace a death'''
free = False
while not free:
location = random.choice([cell for cell in self.grid.coord_iter()])
if len(location[0]) == 1:
free = True
pos = (location[1], location[2])
patch = self.grid.get_cell_list_contents([pos])[0]
if self.inheritance_rule:
if inheritance == 'rand':
wealth = random.randint(1, self.max_init_sugar)
else:
wealth = inheritance
else:
wealth = random.randint(1, self.max_init_sugar)
agent = ScapeAgent(uid, pos, wealth, random.randint(1,self.max_metabolism), random.randint(1,self.max_vision), random.randint(self.min_age, self.max_age), patch, self.ex_ratio, self.ex_mod)
self.grid.place_agent(agent, agent.pos)
self.schedule.add(agent)
def populate_agents(self):
''' Place ScapeAgent's in random unoccupied locations on the grid with randomomized
sets of parameters
'''
cells = [(cell[1], cell[2]) for cell in self.grid.coord_iter()]
for i in range(self.init_agents):
uid = 'a' + str(i)
location = random.choice(cells)
cells.remove(location)
patch = self.grid.get_cell_list_contents([location])[0]
agent = ScapeAgent(uid, location, random.randint(1,self.max_init_sugar), random.randint(1,self.max_metabolism), random.randint(1,self.max_vision), random.randint(self.min_age, self.max_age), patch, self.ex_ratio, self.ex_mod)
self.grid.place_agent(agent, location)
self.schedule.add(agent)
def populate_sugar(self):
''' Place SugarPatch's on every cell with maximum sugar content
according to the imported 'sugar_map.txt' file
'''
map_i = 0
for cell in self.grid.coord_iter():
x = cell[1]
y = cell[2]
uid = 's'+str(y)+str(x)
# patch = SugarPatch(uid, (x,y), 3)
patch = SugarPatch(uid, (x,y), self.map[map_i], self.init_poll)
self.grid.place_agent(patch, (x,y))
self.schedule.add(patch)
map_i += 1
def collect_wealth(self, agent):
'''Method for datacollector'''
if isinstance(agent, ScapeAgent):
return agent.wealth
def collect_metabolism(self, agent):
'''Method for datacollector'''
if isinstance(agent, ScapeAgent):
return agent.metabolism
def collect_vision(self, agent):
'''Method for datacollector'''
if isinstance(agent, ScapeAgent):
return agent.vision
def calc_gini(self, wealths):
'''Returns gini coefficient'''
sort_wealths = sorted(wealths)
num_agents = len(sort_wealths)
gini,count = 0,0
for wealth in sort_wealths:
gini += wealth * (num_agents - count)
count += 1
gini /= (num_agents*sum(sort_wealths))
return num_agents**(-1) - 2*gini + 1
class Movement(Model):
def __init__(self, width = 0, height = 0, torus = False,
time = 0, step_in_year = 0,
number_of_families = family_setting, number_of_monkeys = 0, monkey_birth_count = 0,
monkey_death_count = 0, monkey_id_count = 0,
number_of_humans = 0, grid_type = human_setting, run_type = run_setting, human_id_count = 0):
# change the # of families here for graph.py, but use server.py to change # of families in the movement model
# torus = False means monkey movement can't 'wrap around' edges
super().__init__()
self.width = width
self.height = height
self.time = time # time increases by 1/73 (decimal) each step
self.step_in_year = step_in_year # 1-73; each step is 5 days, and 5 * 73 = 365 days in a year
self.number_of_families = number_of_families
self.number_of_monkeys = number_of_monkeys # total, not in each family
self.monkey_birth_count = monkey_birth_count
self.monkey_death_count = monkey_death_count
self.monkey_id_count = monkey_id_count
self.number_of_humans = number_of_humans
self.grid_type = grid_type # string 'with_humans' or 'without_humans'
self.run_type = run_type # string with 'normal_run' or 'first_run'
self.human_id_count = human_id_count
# width = self._readASCII(vegetation_file)[1] # width as listed at the beginning of the ASCII file
# height = self._readASCII(vegetation_file)[2] # height as listed at the beginning of the ASCII file
width = 85
height = 100
self.grid = MultiGrid(width, height, torus) # creates environmental grid, sets schedule
# MultiGrid is a Mesa function that sets up the grid; options are between SingleGrid and MultiGrid
# MultiGrid allows you to put multiple layers on the grid
self.schedule = RandomActivation(self) # Mesa: Random vs. Staged Activation
# similar to NetLogo's Ask Agents - determines order (or lack of) in which each agents act
empty_masterdict = {'Outside_FNNR': [], 'Elevation_Out_of_Bound': [], 'Household': [], 'PES': [], 'Farm': [],
'Forest': [], 'Bamboo': [], 'Coniferous': [], 'Broadleaf': [], 'Mixed': [], 'Lichen': [],
'Deciduous': [], 'Shrublands': [], 'Clouds': [], 'Farmland': []}
# generate land
if self.run_type == 'first_run':
gridlist = self._readASCII(vegetation_file)[0] # list of all coordinate values; see readASCII function
gridlist2 = self._readASCII(elevation_file)[0] # list of all elevation values
gridlist3 = self._readASCII(household_file)[0] # list of all household coordinate values
gridlist4 = self._readASCII(pes_file)[0] # list of all PES coordinate values
gridlist5 = self._readASCII(farm_file)[0] # list of all farm coordinate values
gridlist6 = self._readASCII(forest_file)[0] # list of all managed forest coordinate values
# The '_populate' function below builds the environmental grid.
for x in [Elevation_Out_of_Bound]:
self._populate(empty_masterdict, gridlist2, x, width, height)
for x in [Household]:
self._populate(empty_masterdict, gridlist3, x, width, height)
for x in [PES]:
self._populate(empty_masterdict, gridlist4, x, width, height)
for x in [Farm]:
self._populate(empty_masterdict, gridlist5, x, width, height)
for x in [Forest]:
self._populate(empty_masterdict, gridlist6, x, width, height)
for x in [Bamboo, Coniferous, Broadleaf, Mixed, Lichen, Deciduous,
Shrublands, Clouds, Farmland, Outside_FNNR]:
self._populate(empty_masterdict, gridlist, x, width, height)
self.saveLoad(empty_masterdict, 'masterdict_veg', 'save')
self.saveLoad(self.grid, 'grid_veg', 'save')
self.saveLoad(self.schedule, 'schedule_veg', 'save')
# Pickling below
load_dict = {} # placeholder for model parameters, leave this here even though it does nothing
if self.grid_type == 'with_humans':
empty_masterdict = self.saveLoad(load_dict, 'masterdict_veg', 'load')
self.grid = self.saveLoad(self.grid, 'grid_veg', 'load')
if self.grid_type == 'without_humans':
empty_masterdict = self.saveLoad(load_dict, 'masterdict_without_humans', 'load')
self.grid = self.saveLoad(load_dict, 'grid_without_humans', 'load')
masterdict = empty_masterdict
startinglist = masterdict['Broadleaf'] + masterdict['Mixed'] + masterdict['Deciduous']
# Agents will start out in high-probability areas.
for coordinate in masterdict['Elevation_Out_of_Bound'] + masterdict['Household'] + masterdict['PES'] \
+ masterdict['Farm'] + masterdict['Forest']:
if coordinate in startinglist:
startinglist.remove(coordinate)
# Creation of resources (yellow dots in simulation)
# These include Fuelwood, Herbs, Bamboo, etc., but right now resource type and frequency are not used
if self.grid_type == 'with_humans':
for line in _readCSV('hh_survey.csv')[1:]: # see 'hh_survey.csv'
hh_id_match = int(line[0])
resource_name = line[1] # frequency is monthly; currently not-used
frequency = float(line[2]) / 6 # divided by 6 for 5-day frequency, as opposed to 30-day (1 month)
y = int(line[5])
x = int(line[6])
resource = Resource(_readCSV('hh_survey.csv')[1:].index(line),
self, (x, y), hh_id_match, resource_name, frequency)
self.grid.place_agent(resource, (int(x), int(y)))
resource_dict.setdefault(hh_id_match, []).append(resource)
if self.run_type == 'first_run':
self.saveLoad(resource_dict, 'resource_dict', 'save')
# Creation of land parcels
land_parcel_count = 0
# individual land parcels in each household (non-gtgp and gtgp)
for line in _readCSV('hh_land.csv')[2:]: # exclude headers; for each household:
age_1 = float(line[45])
gender_1 = float(line[46])
education_1 = float(line[47])
hh_id = int(line[0])
hh_size = 0 # calculate later
total_rice = float(line[41])
if total_rice in [-2, -3, -4]:
total_rice = 0
gtgp_rice = float(line[42])
if gtgp_rice in [-2, -3, -4]:
gtgp_rice = 0
total_dry = float(line[43])
if total_dry in [-2, -3, -4]:
total_dry = 0
gtgp_dry = float(line[44])
if gtgp_dry in [-2, -3, -4]:
gtgp_dry = 0
# non_gtgp_area = float(total_rice) + float(total_dry) - float(gtgp_dry) - float(gtgp_rice)
# gtgp_area = float(gtgp_dry) + float(gtgp_rice)
for i in range(1, 6): # for each household, which has up to 5 each of possible non-GTGP and GTGP parcels:
# non_gtgp_area = float(line[i + 47].replace("\"",""))
# gtgp_area = float(line[i + 52].replace("\"",""))
non_gtgp_area = float(total_rice) + float(total_dry) - float(gtgp_dry) - float(gtgp_rice)
gtgp_area = float(gtgp_dry) + float(gtgp_rice)
if gtgp_area in [-2, -3, -4]:
gtgp_area = 0
if non_gtgp_area in [-2, -3, -4]:
non_gtgp_area = 0
if non_gtgp_area > 0:
gtgp_enrolled = 0
non_gtgp_output = float(line[i].replace("\"",""))
pre_gtgp_output = 0
land_time = float(line[i + 25].replace("\"","")) # non-gtgp travel time
plant_type = float(line[i + 10].replace("\"","")) # non-gtgp plant type
land_type = float(line[i + 30].replace("\"","")) # non-gtgp land type
if land_type not in [-2, -3, -4]:
land_parcel_count += 1
if non_gtgp_output in [-3, '-3', -4, '-4']:
non_gtgp_output = 0
if pre_gtgp_output in [-3, '-3', -4, '-4']:
pre_gtgp_output = 0
lp = Land(land_parcel_count, self, hh_id, gtgp_enrolled,
age_1, gender_1, education_1,
gtgp_dry, gtgp_rice, total_dry, total_rice,
land_type, land_time, plant_type, non_gtgp_output,
pre_gtgp_output, hh_size, non_gtgp_area, gtgp_area)
self.schedule.add(lp)
if gtgp_area > 0:
gtgp_enrolled = 1
pre_gtgp_output = 0
non_gtgp_output = float(line[i].replace("\"",""))
land_time = float(line[i + 20].replace("\"","")) # gtgp travel time
plant_type = float(line[i + 15].replace("\"","")) # gtgp plant type
land_type = float(line[i + 35].replace("\"","")) # gtgp land type
if land_type not in [-3, '-3', -4, '-4']:
land_parcel_count += 1
if non_gtgp_output in [-3, '-3', -4, '-4']:
non_gtgp_output = 0
if pre_gtgp_output in [-3, '-3', -4, '-4']:
pre_gtgp_output = 0
lp = Land(land_parcel_count, self, hh_id, gtgp_enrolled,
age_1, gender_1, education_1,
gtgp_dry, gtgp_rice, total_dry, total_rice,
land_type, land_time, plant_type, non_gtgp_output,
pre_gtgp_output, hh_size, non_gtgp_area, gtgp_area)
self.schedule.add(lp)
# Creation of humans (brown dots in simulation)
self.number_of_humans = 0
self.human_id_count = 0
line_counter = 0
for line in _readCSV('hh_citizens.csv')[1:]: # exclude headers; for each household:
hh_id = int(line[0])
line_counter += 1
starting_position = (int(_readCSV('household.csv')[line_counter][4]),
int(_readCSV('household.csv')[line_counter][3]))
try:
resource = random.choice(resource_dict[str(hh_id)]) # random resource point for human
resource_position = resource.position
resource_frequency = resource.frequency
# to travel to, among the list of resource points reported by that household; may change later
# to another randomly-picked resource
except KeyError:
resource_position = starting_position # some households don't collect resources
resource_frequency = 0
hh_gender_list = line[1:10]
hh_age_list = line[10:19]
hh_education_list = line[19:28]
hh_marriage_list = line[28:37]
# creation of non-migrants
for list_item in hh_age_list:
if str(list_item) == '-3' or str(list_item) == '':
hh_age_list.remove(list_item)
for x in range(len(hh_age_list) - 1):
person = []
for item in [hh_age_list, hh_gender_list, hh_education_list, hh_marriage_list]:
person.append(item[x])
age = float(person[0])
gender = int(person[1])
education = int(person[2])
marriage = int(person[3])
if marriage != 1:
marriage = 6
if 15 < age < 59:
work_status = 1
elif 7 < age < 15:
work_status = 5
else:
work_status = 6
mig_years = 0
migration_network = int(line[37])
income_local_off_farm = int(line[57])
resource_check = 0
mig_remittances = int(line[48])
past_hh_id = hh_id
migration_status = 0
death_rate = 0
gtgp_part = 0
non_gtgp_area = 0
if str(gender) == '1':
if 0 < age <= 10:
age_category = 0
elif 10 < age <= 20:
age_category = 1
elif 20 < age <= 30:
age_category = 2
elif 30 < age <= 40:
age_category = 3
elif 40 < age <= 50:
age_category = 4
elif 50 < age <= 60:
age_category = 5
elif 60 < age <= 70:
age_category = 6
elif 70 < age <= 80:
age_category = 7
elif 80 < age <= 90:
age_category = 8
elif 90 < age:
age_category = 9
elif str(gender) != "1":
if 0 < age <= 10:
age_category = 10
elif 10 < age <= 20:
age_category = 11
elif 20 < age <= 30:
age_category = 12
elif 30 < age <= 40:
age_category = 13
elif 40 < age <= 50:
age_category = 14
elif 50 < age <= 60:
age_category = 15
elif 60 < age <= 70:
age_category = 16
elif 70 < age <= 80:
age_category = 17
elif 80 < age <= 90:
age_category = 18
elif 90 < age:
age_category = 19
children = 0
if gender == 2:
if marriage == 1 and age < 45:
children = random.randint(0, 4) # might already have kids
birth_plan_chance = random.random()
if birth_plan_chance < 0.03125:
birth_plan = 0
elif 0.03125 <= birth_plan_chance < 0.1875:
birth_plan = 1
elif 0.1875 <= birth_plan_chance < 0.5:
birth_plan = 2
elif 0.5 <= birth_plan_chance < 0.8125:
birth_plan = 3
elif 0.8125 <= birth_plan_chance < 0.96875:
birth_plan = 4
else:
birth_plan = 5
elif gender != 2:
birth_plan = 0
last_birth_time = random.uniform(0, 1)
human_demographic_structure_list[age_category] += 1
if str(person[0]) != '' and str(person[0]) != '-3' and str(person[1]) != '-3': # sorts out all blanks
self.number_of_humans += 1
self.human_id_count += 1
human = Human(self.human_id_count, self, starting_position, hh_id, age, # creates human
resource_check, starting_position, resource_position,
resource_frequency, gender, education, work_status,
marriage, past_hh_id, mig_years, migration_status, gtgp_part,
non_gtgp_area, migration_network, mig_remittances,
income_local_off_farm, last_birth_time, death_rate, age_category, children,
birth_plan)
if self.grid_type == 'with_humans':
self.grid.place_agent(human, starting_position)
self.schedule.add(human)
# creation of migrant
hh_migrants = line[38:43] # age, gender, marriage, education of migrants
if str(hh_migrants[0]) != '' and str(hh_migrants[0]) != '-3'\
and str(hh_migrants[1]) != '' and str(hh_migrants[1]) != '-3':
# if that household has any migrants, create migrant person
self.number_of_humans += 1
self.human_id_count += 1
age = float(hh_migrants[0])
gender = float(hh_migrants[1])
education = int(hh_migrants[2])
marriage = int(hh_migrants[3])
mig_years = int(hh_migrants[4])
if 15 < age < 59:
work_status = 1
elif 7 < age < 15:
work_status = 5
else:
work_status = 6
past_hh_id = hh_id
hh_id = 'Migrated'
migration_status = 1
migration_network = int(line[37])
last_birth_time = random.uniform(0, 1)
total_rice = float(line[43])
gtgp_rice = float(line[44])
total_dry = float(line[45])
gtgp_dry = float(line[46])
income_local_off_farm = float(line[57])
if total_rice in ['-3', '-4', -3, None]:
total_rice = 0
if total_dry in ['-3', '-4', -3, None]:
total_dry = 0
if gtgp_dry in ['-3', '-4', -3, None]:
gtgp_dry = 0
if gtgp_rice in ['-3', '-4', -3, None]:
gtgp_rice = 0
if (gtgp_dry + gtgp_rice) != 0:
gtgp_part = 1
else:
gtgp_part = 0
non_gtgp_area = ((total_rice) + (total_dry)) \
- ((gtgp_dry) + (gtgp_rice))
resource_check = 0
mig_remittances = int(line[48])
death_rate = 0
if gender == 1: # human male (monkeys are 0 and 1, humans are 1 and 2)
if 0 < age <= 10:
age_category = 0
elif 10 < age <= 20:
age_category = 1
elif 20 < age <= 30:
age_category = 2
elif 30 < age <= 40:
age_category = 3
elif 40 < age <= 50:
age_category = 4
elif 50 < age <= 60:
age_category = 5
elif 60 < age <= 70:
age_category = 6
elif 70 < age <= 80:
age_category = 7
elif 80 < age <= 90:
age_category = 8
elif 90 < age:
age_category = 9
elif gender != 1:
if 0 < age <= 10:
age_category = 10
elif 10 < age <= 20:
age_category = 11
elif 20 < age <= 30:
age_category = 12
elif 30 < age <= 40:
age_category = 13
elif 40 < age <= 50:
age_category = 14
elif 50 < age <= 60:
age_category = 15
elif 60 < age <= 70:
age_category = 16
elif 70 < age <= 80:
age_category = 17
elif 80 < age <= 90:
age_category = 18
elif 90 < age:
age_category = 19
children = 0
if gender == 2:
if marriage == 1 and age < 45:
children = random.randint(0, 4) # might already have kids
birth_plan_chance = random.random()
if birth_plan_chance < 0.03125:
birth_plan = 0
elif 0.03125 <= birth_plan_chance < 0.1875:
birth_plan = 1
elif 0.1875 <= birth_plan_chance < 0.5:
birth_plan = 2
elif 0.5 <= birth_plan_chance < 0.8125:
birth_plan = 3
elif 0.8125 <= birth_plan_chance < 0.96875:
birth_plan = 4
else:
birth_plan = 5
elif gender != 2:
birth_plan = 0
human_demographic_structure_list[age_category] += 1
human = Human(self.human_id_count, self, starting_position, hh_id, age, # creates human
resource_check, starting_position, resource_position,
resource_frequency, gender, education, work_status,
marriage, past_hh_id, mig_years, migration_status, gtgp_part, non_gtgp_area,
migration_network, mig_remittances, income_local_off_farm,
last_birth_time, death_rate, age_category, children, birth_plan)
if self.grid_type == 'with_humans':
self.grid.place_agent(human, starting_position)
self.schedule.add(human)
# Creation of monkey families (moving agents in the visualization)
for i in range(self.number_of_families): # the following code block creates families
starting_position = random.choice(startinglist)
saved_position = starting_position
from families import Family
family_size = random.randint(25, 45) # sets family size for each group--random integer
family_id = i
list_of_family_members = []
family_type = 'traditional' # as opposed to an all-male subgroup
split_flag = 0 # binary: 1 means its members start migrating out to a new family
family = Family(family_id, self, starting_position, family_size, list_of_family_members, family_type,
saved_position, split_flag)
self.grid.place_agent(family, starting_position)
self.schedule.add(family)
global_family_id_list.append(family_id)
# Creation of individual monkeys (not in the visualization submodel, but for the demographic submodel)
for monkey_family_member in range(family_size): # creates the amount of monkeys indicated earlier
id = self.monkey_id_count
gender = random.randint(0, 1)
if gender == 1: # gender = 1 is female, gender = 0 is male. this is different than with humans (1 or 2)
female_list.append(id)
last_birth_interval = random.uniform(0, 2)
else:
male_maingroup_list.append(id) # as opposed to the all-male subgroup
last_birth_interval = -9999 # males will never give birth
mother = 0 # no parent check for first generation
choice = random.random() # 0 - 1 float - age is determined randomly based on weights
if choice <= 0.11: # 11% of starting monkey population
age = random.uniform(0, 1) # are randomly aged befween
age_category = 0 # ages 0-1
demographic_structure_list[0] += 1
elif 0.11 < choice <= 0.27: # 16% of starting monkey population
age = random.uniform(1, 3) # are randomly aged befween
age_category = 1 # ages 1-3
demographic_structure_list[1] += 1
elif 0.27 < choice <= 0.42: # 15% of starting monkey population
age = random.uniform(3, 7) # are randomly aged between
age_category = 2 # ages 3-7
demographic_structure_list[2] += 1
elif 0.42 < choice <= 0.62: # 11% of starting monkey population
age = random.uniform(7, 10) # are randomly aged befween
age_category = 3 # ages 7-10
demographic_structure_list[3] += 1
elif 0.62 < choice <= 0.96: # 34% of starting monkey population
age = random.uniform(10, 25) # are randomly aged befween
age_category = 4 # ages 10-25
demographic_structure_list[4] += 1
if gender == 1:
if id not in reproductive_female_list:
reproductive_female_list.append(id)
# starting representation of male defection/gender ratio
structure_convert = random.random()
if gender == 0:
if structure_convert < 0.6:
gender = 1
last_birth_interval = random.uniform(0, 3)
if id not in reproductive_female_list:
reproductive_female_list.append(id)
elif 0.96 < choice: # 4% of starting monkey population
age = random.uniform(25, 30) # are randomly aged between
age_category = 5 # ages 25-30
demographic_structure_list[5] += 1
gender = 1
monkey = Monkey(id, self, gender, age, age_category, family, last_birth_interval, mother
)
self.number_of_monkeys += 1
self.monkey_id_count += 1
list_of_family_members.append(monkey.unique_id)
self.schedule.add(monkey)
def step(self):
# necessary; tells model to move forward
self.time += (1/73)
self.step_in_year += 1
if self.step_in_year > 73:
self.step_in_year = 1 # start new year
self.schedule.step()
def _readASCII(self, text):
# reads in a text file that determines the environmental grid setup
f = open(text, 'r')
body = f.readlines()
width = body[0][-4:] # last 4 characters of line that contains the 'width' value
height = body[1][-5:]
abody = body[6:] # ASCII file with a header
f.close()
abody = reversed(abody)
cells = []
for line in abody:
cells.append(line.split(" "))
return [cells, int(width), int(height)]
def _populate(self, masterdict, grid, land_type, width, height):
# places land tiles on the grid - connects color/land cover category with ASCII file values
counter = 0 # sets agent ID - not currently used
for y in range(height): # for each pixel,
for x in range(width):
value = float(grid[y][x]) # value from the ASCII file for that coordinate/pixel, e.g. 1550 elevation
land_grid_coordinate = x, y
land = land_type(counter, self)
if land_type.__name__ == 'Elevation_Out_of_Bound':
if (value < land_type.lower_bound or value > land_type.upper_bound) and value != -9999:
# if elevation is not 1000-2200, but is within the bounds of the FNNR, mark as 'elevation OOB'
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
elif land_type.__name__ == 'Forest':
if land_type.type == value:
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
elif land_type.__name__ == 'PES':
if land_type.type == value:
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
elif land_type.__name__ == 'Farm':
if land_type.type == value:
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
elif land_type.__name__ == 'Household':
if land_type.type == value:
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
else: # vegetation background
if land_type.type == value:
self.grid.place_agent(land, land_grid_coordinate)
masterdict[land.__class__.__name__].append(land_grid_coordinate)
counter += 1
def saveLoad(self, pickled_file, name, option):
""" This function pickles an object, which lets it be loaded easily later.
I haven't figured out how to utilize pickle to pickle class objects (if possible). """
if option == "save":
f = open(name, 'wb')
pickle.dump(pickled_file, f)
f.close()
elif option == "load":
f = open(name, 'rb')
new_pickled_file = pickle.load(f)
return new_pickled_file
else:
print('Invalid saveLoad option')
