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 Kvecinos(Model):
def __init__(self, height, width, initial_population, n_clases, k):
super().__init__()
self.height = height
self.width = width
self.initial_population = initial_population
# N_class será el número de colores
self.n_clases = n_clases
self.k = k
self.unique_id = 0
# Creación del planificador y del grid
self.schedule = RandomActivation(self)
self.grid = MultiGrid(self.width, self.height, torus=False)
self.colors = {
0: "red",
1: "blue",
2: "green0",
3: "violet",
4: "cyan",
5: "orange"
}
self.clases = []
self.setup()
self.running = True
def setup(self):
for agent, x, y in self.grid.coord_iter():
patch = Cell(self.unique_id, self, (x, y), "black")
self.unique_id += 1
self.grid.place_agent(patch, (x, y))
self.schedule.add(patch)
for i in range(self.initial_population):
x = random.randint(0, self.width - 1)
y = random.randint(0, self.height - 1)
pos = (x, y)
color = random.randint(0, self.n_clases - 1)
cell = self.grid.get_cell_list_contents(pos)[0]
self.clases.append(cell)
self.clases[i].color = self.colors[color]
individual = Individual(self.unique_id, self, pos,
self.colors[color])
self.unique_id += 1
self.grid.place_agent(individual, pos)
def step(self):
self.schedule.step()
class ConveyModel(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 = BaseScheduler(self)
# Create agents
for i in range(self.num_agents):
a = ConwayAgent(i, self)
# Add the agent to a random grid cell
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
while(len(self.grid.get_cell_list_contents((x,y)))):
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
self.schedule.add(a)
self.i = i
self.datacollector = DataCollector(
agent_reporters={"State": lambda a: a.die})
def step(self):
self.datacollector.collect(self)
new_agents = []
for (x, y) in product(range(self.grid.width), range(self.grid.height)):
ns = self.grid.iter_neighbors((x,y), True)
neighbors = 0
for n in ns:
if(n):
neighbors += 1
if(self.grid[x][y]): # live cell
if(neighbors < 2): # underpopulation
list(self.grid[x][y])[0].die = 1
elif(neighbors > 3): # overpopulation
list(self.grid[x][y])[0].die = 1
else: # dead cell
if(neighbors == 3):
new_agents.append((x, y))
for (x, y) in product(range(self.grid.width), range(self.grid.height)):
if self.grid[x][y]:
a = list(self.grid[x][y])[0]
if a.die:
self.grid.remove_agent(a)
self.schedule.remove(a)
for na in new_agents:
self.i += 1
a = ConwayAgent(self.i, self)
self.grid.place_agent(a, na)
self.schedule.add(a)
class Neighborhood(Model):
"""A model of a neighborhood with some number of agents."""
def __init__(self, N=10, width=None, height=None):
""" Neighborhood: a neighborhood containing people
Parameters
----------
N:
number of people in the neighborhood
width:
width of the (rectangular) neighborhood area
height:
height of the (rectangular) neighborhood area
"""
super().__init__()
self.num_agents = N
self.width = width or min(N, 100)
self.height = height or min(N, 100)
self.grid = MultiGrid(self.width, self.height, True)
self.schedule = RandomActivation(self)
# Create agents
for i in range(self.num_agents):
rand = random.random()
infection = rand >= (N-N**.5)/N
print(i, rand, (N-N**.5)/N)
a = Person(i, self, level_of_infection=int(infection))
print(a, a.level_of_infection)
self.schedule.add(a)
# adding the agent to a random position in the neighborhood
(x, y) = random.random() * self.width, random.random() * self.height
self.grid.place_agent(a, (int(x), int(y)))
def step(self):
"""Advance the model by one step."""
self.schedule.step()
def get_neighbors(self, person, radius=1):
""" get neighbors of person """
neighbor_objects = self.grid.get_cell_list_contents([person.pos])
return [*filter(lambda x: type(x) is Person and x is not person,
neighbor_objects)]
def move_agent(self, *args, **kwargs):
return self.grid.move_agent(*args, **kwargs)
class ExplorationArea(Model):
def __init__(
self,
nrobots,
wifi_range,
radar_radius=6,
alpha=8.175,
gamma=0.65,
inj_pri=0,
ninjured=None,
ncells=None,
obstacles_dist=None,
load_file=None,
dump_datas=True, # enable data collection
alpha_variation=False, # record datas for alpha variation studies
alpha_csv=alpha_csv, # aggregate datas
alpha_step_csv=alpha_step_csv, # single step datas
gamma_variation=False, # record datas for gamma variation studies
gamma_csv=gamma_csv,
optimization_task=False, # enable a small part of data collection for optimization task
time_csv=number_of_steps_csv,
robot_status_csv=robot_status_csv):
# checking params consistency
if not load_file and (not ncells or not obstacles_dist
or not ninjured):
print("Invalid params")
sys.exit(-1)
# used in server start
self.running = True
self.nrobots = nrobots
self.radar_radius = radar_radius
self.ncells = ncells
self.obstacles_dist = obstacles_dist
self.wifi_range = wifi_range
self.alpha = alpha
self.gamma = gamma
self.ninjured = ninjured
self.inj_pri = inj_pri
self.dump_datas = dump_datas
self.optimization_task = optimization_task
self.frontier = set()
self.broken_beans = 0
# Data collection tools
if self.dump_datas:
# it represents the sum of the difficulties of every cell
self.total_difficulty = 0
self.dc_robot_status = DataCollector({
"idling":
lambda m: self.get_number_robots_status(m, "idling"),
"travelling":
lambda m: self.get_number_robots_status(m, "travelling"),
"exploring":
lambda m: self.get_number_robots_status(m, "exploring"),
"deploying_bean":
lambda m: self.get_number_robots_status(m, "deploying_bean"),
"step":
lambda m: self.get_step(m)
})
self.time_csv = time_csv
self.robot_status_csv = robot_status_csv
if self.optimization_task:
self.total_idling_time = 0
self.alpha_variation = alpha_variation
self.gamma_variation = gamma_variation
if self.alpha_variation:
self.costs_each_path = list()
self.alpha_csv = alpha_csv
self.alpha_step = dict()
self.alpha_step_csv = alpha_step_csv
if self.gamma_variation:
self.gamma_df = pd.DataFrame(columns=["step", "mean", "std"])
self.gamma_csv = gamma_csv
self.schedule = RandomActivation(self)
# unique counter for agents
self.agent_counter = 1
self.nobstacle = 0
# graph of seen cells
self.seen_graph = nx.DiGraph()
rnd.seed()
# place a cell agent for store data and visualization on each cell of the grid
# if map is not taken from file, create it
if load_file == None:
self.grid = MultiGrid(ncells + 2, ncells + 2, torus=False)
for i in self.grid.coord_iter():
if i[1] != 0 and i[2] != 0 and i[1] != self.ncells + 1 and i[
2] != self.ncells + 1:
rand = np.random.random_sample()
obstacle = True if rand < self.obstacles_dist else False
# if obstacle
if obstacle:
self.nobstacle += 1
difficulty = math.inf
explored = -1
priority = 0
utility = -math.inf
# if free
else:
difficulty = np.random.randint(low=1, high=13)
if self.dump_datas:
self.total_difficulty += difficulty
explored = 0
priority = 0
utility = 1.0
# if contour cell
else:
difficulty = np.random.randint(low=1, high=13)
explored = -2
priority = -math.inf
utility = -math.inf
# place the agent in the grid
a = Cell(self.agent_counter, self, i[1:], difficulty, explored,
priority, utility)
self.grid.place_agent(a, i[1:])
self.agent_counter += 1
# create injured agents
valid_coord = []
for i in self.grid.coord_iter():
cell = [
e for e in self.grid.get_cell_list_contents(i[1:])
if isinstance(e, Cell)
][0]
if cell.explored == 0:
valid_coord.append(cell.pos)
for i in range(0, ninjured):
inj_index = rnd.choice(valid_coord)
a = Injured(self.agent_counter, self, inj_index)
self.schedule.add(a)
self.grid.place_agent(a, inj_index)
self.agent_counter += 1
else:
# load map from file
try:
with open(load_file, 'r') as f:
file = f.read()
except:
print("file not found")
sys.exit(-1)
exported_map = literal_eval(file)
self.ncells = int(math.sqrt(len(exported_map["Cell"].keys()))) - 2
self.grid = MultiGrid(self.ncells + 2,
self.ncells + 2,
torus=False)
for index in exported_map["Cell"].keys():
cell = exported_map["Cell"][index]
difficulty = cell[2]
explored = cell[3]
priority = cell[4]
utility = cell[5]
if difficulty == "inf":
difficulty = math.inf
if priority == "-inf":
priority = -math.inf
if utility == "-inf":
utility = -math.inf
if explored == -1:
self.nobstacle += 1
if self.dump_datas and utility == 1:
self.total_difficulty += difficulty
a = Cell(self.agent_counter, self, index, difficulty, explored,
priority, utility)
self.grid.place_agent(a, index)
self.agent_counter += 1
for index in exported_map["Injured"].keys():
a = Injured(self.agent_counter, self, index)
self.schedule.add(a)
self.grid.place_agent(a, index)
self.agent_counter += 1
# create robotic agents
row = 0
starting_coord = []
# data collection number of beans requested
if self.dump_datas:
self.deployed_beans_at_start = 0
# generating the list for the starting position of robots
for c in range(self.grid.width):
# take the agent cell
cell = [
e for e in self.grid.get_cell_list_contents(tuple([c, row]))
if isinstance(e, Cell)
][0]
if cell.explored != -1:
starting_coord.append(c)
for i in range(0, self.nrobots):
column = rnd.choice(starting_coord)
a = Robot(self.agent_counter, self, tuple([column, row]),
self.radar_radius)
self.schedule.add(a)
self.grid.place_agent(a, (column, row))
self.agent_counter += 1
# create initial frontier: add cell in front of the robot if valid and not obstacles
cell = [
e for e in self.grid.get_cell_list_contents(
tuple([column, row + 1])) if isinstance(e, Cell)
][0]
if cell.explored == 0:
self.frontier.add(tuple([column, row + 1]))
try:
cell = [
e for e in self.grid.get_cell_list_contents(
tuple([column + 1, row + 1])) if isinstance(e, Cell)
][0]
if cell.explored == 0:
self.frontier.add(tuple([column + 1, row + 1]))
except:
pass
try:
cell = [
e for e in self.grid.get_cell_list_contents(
tuple([column - 1, row + 1])) if isinstance(e, Cell)
][0]
if cell.explored == 0:
self.frontier.add(tuple([column - 1, row + 1]))
except:
pass
cell = [
e
for e in self.grid.get_cell_list_contents(tuple([column, row]))
if isinstance(e, Cell)
][0]
# in the cell where some robots are deployed, only one bean is deployed
if not cell.wifi_bean:
cell.wifi_bean = True
for index in self.grid.get_neighborhood(
cell.pos,
"moore",
include_center=False,
radius=self.wifi_range):
cell = [
e for e in self.grid.get_cell_list_contents(index)
if isinstance(e, Cell)
][0]
cell.wifi_covered = True
if self.dump_datas:
self.deployed_beans_at_start += 1
# what the model does at each time step
def step(self):
# data collection for alpha variation
if self.alpha_variation:
sim_step = self.get_step(self)
self.alpha_step[sim_step] = list()
# call step function for all of the robots in random order
self.schedule.step()
if self.dump_datas:
self.dc_robot_status.collect(self)
if self.optimization_task:
self.total_idling_time += self.get_number_robots_status(
self, "idling")
if self.gamma_variation:
distances = self.compute_robot_distances(self)
self.gamma_df = self.gamma_df.append(
{
"step": self.get_step(self),
"mean": distances[0],
"std": distances[1]
},
ignore_index=True,
sort=False)
# if all seen cells have benn explored, stop the simulation
# we do this so if there are unreachable cells, the cannot be seen, so the simulation stops anyway
stop_exploration_done = True
for node in self.seen_graph.nodes():
cell = [
obj for obj in self.grid.get_cell_list_contents(node)
if isinstance(obj, Cell)
][0]
if cell.explored == 0 or cell.explored == 1:
stop_exploration_done = False
stop_no_robots = False
if len([x for x in self.schedule.agents if isinstance(x, Robot)]) == 0:
stop_no_robots = True
stop = stop_exploration_done or stop_no_robots
if stop:
print("Simultation ended")
# Data collection
if self.dump_datas:
df = pd.read_csv(self.time_csv)
df = df.append(
{
"nrobots": self.nrobots,
"ncells": self.ncells,
"steps": self.schedule.steps,
"total_difficulty": self.total_difficulty,
"beans_deployed": self.get_number_bean_deployed(self)
},
ignore_index=True)
df.to_csv(self.time_csv, index=False)
df_robots_status = self.dc_robot_status.get_model_vars_dataframe(
)
df = pd.read_csv(self.robot_status_csv)
if len(df["sim_id"]) == 0:
df_robots_status["sim_id"] = 0
else:
df_robots_status["sim_id"] = df["sim_id"][df.index[-1]] + 1
df = df.append(df_robots_status, ignore_index=True, sort=False)
df.to_csv(self.robot_status_csv, index=False)
if self.alpha_variation:
mean = round(np.mean(self.costs_each_path), 3)
std = round(np.std(self.costs_each_path), 3)
df = pd.read_csv(self.alpha_csv)
df = df.append(
{
"nrobots": self.nrobots,
"radar_radius": self.radar_radius,
"alpha": self.alpha,
"gamma": self.gamma,
"mean": mean,
"std": std
},
ignore_index=True)
df.to_csv(self.alpha_csv, index=False)
tmp_df = pd.DataFrame(columns=["step", "cost"])
for s, costs in zip(self.alpha_step.keys(),
self.alpha_step.values()):
if not costs:
tmp_df = tmp_df.append({
"step": s,
"cost": -1
},
ignore_index=True,
sort=False)
continue
for c in costs:
tmp_df = tmp_df.append({
"step": s,
"cost": c
},
ignore_index=True,
sort=False)
df = pd.read_csv(self.alpha_step_csv)
if len(df["sim_id"]) == 0:
tmp_df["sim_id"] = 0
else:
tmp_df["sim_id"] = df["sim_id"][df.index[-1]] + 1
tmp_df["nrobots"] = self.nrobots
tmp_df["radar_radius"] = self.radar_radius
tmp_df["alpha"] = self.alpha
tmp_df["gamma"] = self.gamma
df = df.append(tmp_df, ignore_index=True, sort=False)
df.to_csv(self.alpha_step_csv, index=False)
if self.gamma_variation:
df = pd.read_csv(self.gamma_csv)
if len(df["sim_id"]) == 0:
self.gamma_df["sim_id"] = 0
else:
self.gamma_df["sim_id"] = df["sim_id"][df.index[-1]] + 1
self.gamma_df["nrobots"] = self.nrobots
self.gamma_df["radar_radius"] = self.radar_radius
self.gamma_df["alpha"] = self.alpha
self.gamma_df["gamma"] = self.gamma
df = df.append(self.gamma_df, ignore_index=True, sort=False)
df.to_csv(self.gamma_csv, index=False)
self.running = False
def run_model(self):
while (True):
# search for unexplored cells
stop = True
for node in self.seen_graph.nodes():
cell = [
obj for obj in self.grid.get_cell_list_contents(node)
if isinstance(obj, Cell)
][0]
if cell.explored == 0 or cell.explored == 1:
stop = False
# if all seen cells have benn explored, stop the simulation
# we do this so if there are unreachable cells, the cannot be seen, so the simulation stops anyway
if stop:
self.running = False
break
else:
self.step()
# Data collection utilities
@staticmethod
def get_step(m):
return m.schedule.steps
# these two should go faster since cells are not in the scheduler anymore
@staticmethod
def get_number_robots_status(m, status):
status_value = {
"idling": 0,
"travelling": 1,
"exploring": 2,
"deploying_bean": 3
}
return len([
x for x in m.schedule.agents
if isinstance(x, Robot) and x.status == status_value[status]
])
@staticmethod
def get_number_bean_deployed(m):
return sum([
x.number_bean_deployed
for x in m.schedule.agents if isinstance(x, Robot)
]) + m.deployed_beans_at_start
# function for gamma variation
@staticmethod
def compute_robot_distances(m):
nrobots = m.nrobots
T_up = np.full(
(nrobots, nrobots),
0.0) # if it's only zero, numpy represents only integers
# didn't dig deep in numpy doc but it looks like it handles triangualr matrices as "normal" matrices, so
# i just initilize a full matrix and then i'll use it as a triangular.
robots = [x for x in m.schedule.agents if isinstance(x, Robot)]
# the order of the robots in robots can change from step to step (due to the random scheduler),
# This shouldn't create any type of problem, but to avoid a lot of problems with indexes later on
# we sort them basing on the unique_id
robots.sort(key=lambda x: x.unique_id)
# I need the lowest id to shift back the ids to fit the matrices coordinations
lowest_id = robots[0].unique_id
for r in robots:
matrix_id_row = r.unique_id - lowest_id
# the distance of a robot to itself is zero by definition
y1, x1 = r.pos
for i in range(matrix_id_row + 1, nrobots):
r2 = robots[i] # i can do this because they are sorted
y2, x2 = r2.pos
T_up[matrix_id_row][i] = distance.euclidean([x1, y1], [x2, y2])
mean_dist_robots = list()
# the robot 0 has only the rows
mean_robot_zero = sum(T_up[0, 1:nrobots])
mean_dist_robots.append(mean_robot_zero)
for i in range(1, nrobots -
1): # the last row has no values, i iters the rows
mean_robot = (sum(T_up[0:i, i]) +
sum(T_up[i, i + 1:nrobots])) / (nrobots - 1)
mean_dist_robots.append(mean_robot)
# last robot has only the columns
mean_last_robot = sum(T_up[0:nrobots - 1, nrobots - 1])
mean_dist_robots.append(mean_last_robot)
return tuple([
round(np.mean(mean_dist_robots), 3),
round(np.std(mean_dist_robots), 3)
])
class PandemicsModel(Model):
def __init__(self, config=default_config, disease=dis.covid_disease):
self.agents_count = config.citizens_count + config.policemen_count
self.disease = disease
self.deceased = []
self.buried = []
self.deceased_counter = 0
self.infected_counter = 0
self.grid = MultiGrid(config.width, config.height, True)
self.safety_per_cell = np.ones((config.height, config.width))
self.buildings_map = np.zeros((config.height, config.width))
self.buildings_id_map = np.zeros((config.height, config.width))
self.schedule = SimultaneousActivation(self)
self.datacollector = DataCollector(
model_reporters={
"deceased": "deceased_counter",
"infected": "infected_counter"},
agent_reporters={
"hp": lambda a: a.profile["hp"],
"mask_protection": "mask_protection",
"infection_day": lambda a: a.profile["infection_day"],
"obedience": lambda a: a.profile["obedience"],
"fear": lambda a: a.profile["fear"]}
)
self.config = config
self.buildings = {b["id"] : b for b in self.config.buildings}
self.houses = [x for x in self.buildings.values() if x['type'] == 'house']
self.workplaces = [x for x in self.buildings.values() if x['type'] == 'workplace']
self.shops = [x for x in self.buildings.values() if x['type'] == 'shop']
self.add_buildings_to_map(self.buildings)
self.street_positions = []
for x in range(self.config.width):
for y in range(self.config.height):
if self.buildings_map[y][x] == 0:
self.street_positions.append((x, y))
self.house_to_agents = defaultdict(list)
self.workplace_to_agents = defaultdict(list)
self.current_location_type = None
# Create agents
for i in range(self.agents_count):
if i < config.policemen_count:
a = agent.create_distribution_policeman_agent(
i, self, config.policemen_mental_features_distribution)
a.assign_house(self, self.houses)
elif i < config.policemen_count + config.citizens_count:
a = agent.create_distribution_citizen_agent(
i, self, config.citizens_mental_features_distribution)
a.assign_house(self, self.houses)
a.assign_workplace(self, self.workplaces)
self.add_agent(a)
for i in self.random.choices(self.schedule.agents, k=config.infected_count):
i.start_infection()
self.running = True
self.steps_count = 0
self.datacollector.collect(self)
# Returns (type, id) of the building where agent a is currently located
def where_is_agent(self, a):
(x, y) = a.pos
return (self.buildings_map[y][x], self.buildings_id_map[y][x])
def compute_time_of_day(self):
return self.steps_count % self.config.steps_per_day / (self.config.steps_per_day / HOURS_PER_DAY)
def compute_current_location_type(self):
t = self.compute_time_of_day()
return self.config.day_plan[t] if t in self.config.day_plan else \
self.config.day_plan[min(self.config.day_plan.keys(), key=lambda k: k-t)]
# Updates current location type based on time of day and the config schedule
# Returns true if there is a change in current_location_type
def update_current_location_type(self):
t = self.compute_time_of_day()
if t in self.config.day_plan:
self.current_location_type = self.config.day_plan[t]
return True
return False
def add_buildings_to_map(self, buildings):
for b in buildings.values():
(x, y) = b["bottom-left"]
for i in range(x, x+b["width"]):
for j in range(y, y+b["height"]):
self.buildings_map[j][i] = self.config.building_tags[b['type']]
self.buildings_id_map[j][i] = b['id']
def add_agent(self, a):
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))
def bury_agent(self, a):
self.schedule.remove(a)
self.grid.remove_agent(a)
self.deceased_counter += 1
self.buried.append(a)
def risk_from_agents(self, agents, weight):
risk = 0
for a in agents:
risk += 1 - a.mask_protection
return risk*weight
def evaluate_safety_per_cell(self):
""" 1.0 is a perfectly safe cell - empty, with all neighbours
and neighbours-of-neighbours empty as well """
for content, x, y in model.grid.coord_iter():
self.safety_per_cell[y][x] = 1 # initial value
# Compute risk from (x,y) cell
ring0_risk = self.risk_from_agents(content, weight=0.5)
considered_cells = {(x,y)}
# Compute risk coming from the neighbours of (x,y) cell
neighbours = self.grid.get_neighborhood(
(x,y), moore=True, include_center=False)
neighbours_content = self.grid.get_cell_list_contents(neighbours)
ring1_risk = self.risk_from_agents(neighbours_content, 0.25)
considered_cells | set(neighbours)
# Compute risk coming from
# the neighbours of the neighbours of (x,y) cell
neighbours_of_neighbours = set()
for c in neighbours:
neighbours_of_neighbours | set(
self.grid.get_neighborhood(
(x,y),moore=True, include_center=False))
neighbours_of_neighbours -= considered_cells
ring2_risk = self.risk_from_agents(
self.grid.get_cell_list_contents(neighbours_of_neighbours), 0.125)
self.safety_per_cell[y][x] -= ring0_risk + ring1_risk + ring2_risk
def step(self):
if (self.update_current_location_type()):
for a in self.schedule.agents:
if (self.current_location_type is not None):
b = a.select_building(self.current_location_type)
a.teleport_to_building(b)
else:
a.teleport_to_street()
self.evaluate_safety_per_cell()
self.schedule.step()
self.steps_count += 1
# collect data
self.datacollector.collect(self)
for d in self.deceased:
self.bury_agent(d)
self.deceased = []
def run_model(self, n):
for i in range(n):
self.step()
class WolfSheep(Model):
"""
Wolf-Sheep Predation Model
"""
height = 20
width = 20
initial_sheep = 2
initial_wolves = 5
sheep_reproduce = 0.04
wolf_reproduce = 0.05
wolf_gain_from_food = 20
grass = False
grass_regrowth_time = 5
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 Shed
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
shed = Shed(self.next_id(), (x, y), self)
self.grid.place_agent(shed, (x, y))
self.schedule.add(shed)
# Create water source
while True:
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
this_cell = self.grid.get_cell_list_contents([(x, y)])
cell = [obj for obj in this_cell if isinstance(obj, Shed)]
if len(cell) == 0:
break
ws1 = WaterSource(self.next_id(), (x, y), self)
self.grid.place_agent(ws1, (x, y))
self.schedule.add(ws1)
while True:
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
this_cell = self.grid.get_cell_list_contents([(x, y)])
cell = [obj for obj in this_cell if isinstance(obj, Shed)]
if len(cell) == 0:
break
cell = [obj for obj in this_cell if isinstance(obj, WaterSource)]
if len(cell) == 0:
break
ws2 = WaterSource(self.next_id(), (x, y), self)
self.grid.place_agent(ws2, (x, y))
self.schedule.add(ws2)
# Create grass patches
if self.grass:
for agent, x, y in self.grid.coord_iter():
this_cell = self.grid.get_cell_list_contents([(x, y)])
is_water = [
obj for obj in this_cell if isinstance(obj, WaterSource)
]
is_shed = [obj for obj in this_cell if isinstance(obj, Shed)]
if len(is_water) > 0 or len(is_shed) > 0:
continue
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)
# 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)
sheep.target = shed
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)
if abs(ws1.pos[0] - x) + abs(ws1.pos[1] - y) < abs(
ws2.pos[0] - x) + abs(ws2.pos[1] - y):
wolf.target = ws1
else:
wolf.target = ws2
self.grid.place_agent(wolf, (x, y))
self.schedule.add(wolf)
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 HoominWorld(Model):
LEFT = 0
RIGHT = 1
STRAIGHT = 2
FRIENDNODELOGNAME = "friendnodes"
TOTALMESSAGELOGNAME = "totalmessages"
STEPSTOCOMPLETIONLOGNAME = "stepstocompletion"
verbose = False
description = "A model of foot traffic and radio communication in an urban environment"
def __init__(self,
height=50,
width=50,
initial_hoomins=10,
logtag="default"):
super().__init__()
print("initializing ", settings.width, settings.height)
#map height and width
self.height = settings.height
self.width = settings.width
#logging framework
self.logger = Logging("logs", logtag)
self.logtag = logtag
self.G = nx.Graph()
#graph visualization
if not settings.runheadless:
plt.ion()
plt.show()
#ignore this. it does nothing
self.hoomin_level = 0
#road generation tuning
self.straightweight = settings.straightweight
self.leftweight = settings.leftweight
self.rightweight = settings.rightweight
self.initial_roads = settings.initial_roads
self.initial_road_seeds = settings.initial_road_seeds
self.gridspacing = settings.gridspacing
self.roadcurrentcoord = np.array((0, 0))
self.roaddir = np.array((1, 0))
self.roadset = set()
#home tuning options
self.homes_per_hoomins = 1
self.initial_homes = self.homes_per_hoomins * initial_hoomins
self.homeset = set()
#scatterbrain metrics
self.total_scattermessages = 0
self.global_scattermessages = 0
self.hoominzero_nodecount = 0
#hoomin tuning values
self.initial_hoomins = settings.initial_hoomins
self.schedule = RandomHoominActivation(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
self.datacollector = DataCollector({
"Messages Exchanged":
lambda m: m.total_scattermessages,
"FriendGraph Node Count":
lambda m: m.hoominzero_nodecount
})
#initialize roads
for i in range(self.initial_road_seeds):
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
self.singleroad((x, y))
homelist = []
#initialize homes
for i in range(self.initial_homes):
road = self.random.sample(self.roadset, 1)
if len(road) > 0 and road[0] is not None:
neighbors = self.grid.get_neighborhood(road[0].pos, False,
True)
for neighbor in neighbors:
n = []
if len(self.grid.get_cell_list_contents(neighbor)) is 0:
n.append(neighbor)
if len(n) > 0:
homeblock = self.random.sample(n, 1)
home = Home(self.next_id(), homeblock[0], self)
homelist.append(home)
self.grid.place_agent(home, homeblock[0])
else:
print(
"systemic oppression under capitalism forclosed on one hoomin's home."
)
self.homeset = self.homeset.union(set(homelist))
#initialize hoomins
for i in range(self.initial_hoomins):
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
hoomin = SocialHoomin(self.next_id(), (x, y), self)
if i == 1:
for x in range(settings.initial_scattermessages):
hoomin.store_scattermessage("hoomin!")
hoomin.pos = (0, 0)
x = 0
y = 0
self.hoomin_zero_id = hoomin.unique_id
if i == self.initial_hoomins - 1:
self.final_hoomin_id = hoomin.unique_id
hoomin.pos = (self.width - 1, self.height - 1)
x = self.width - 1
y = self.height - 1
possiblehomes = self.homeset.difference(Home.claimedhomes)
if len(possiblehomes) > 0:
myhome = self.random.sample(possiblehomes, 1)
if len(myhome) > 0:
myhome[0].claim(hoomin)
self.grid.place_agent(hoomin, (x, y))
self.schedule.add(hoomin)
self.G.add_node(hoomin, agent=[hoomin])
#initialize hoomin friends
friendlist = set(self.schedule._agents)
for i in self.schedule._agents:
fren = self.random.sample(friendlist.difference(set([i])),
settings.friendsperhoomin)
for x in fren:
self.schedule._agents[i].addfriend(x)
self.G.add_edge(self.schedule._agents[i],
self.schedule._agents[x])
self.roadplace_grid()
self.running = True
self.datacollector.collect(self)
def roadplace_grid(self):
for h in range(self.height):
if h % self.gridspacing is 0:
for w in range(self.width):
road = Road(self.next_id(), (w, h), self)
self.grid.place_agent(road, (w, h))
for w in range(self.width):
if w % self.gridspacing is 0:
for h in range(self.height):
road = Road(self.next_id(), (w, h), self)
self.grid.place_agent(road, (w, h))
def roadplace_random(self, direction=0):
if direction is HoominWorld.STRAIGHT:
True
elif direction is HoominWorld.LEFT:
self.roaddir = np.array((-1 * self.roaddir[1], self.roaddir[0]))
elif direction is HoominWorld.RIGHT:
self.roaddir = np.array((self.roaddir[1], -1 * self.roaddir[0]))
else:
self.roaddir = np.array((0, 0))
print("bad bad bad")
# print("placing road, direction ", direction, " coord: ", self.roadcurrentcoord)
newcoord = self.roadcurrentcoord + self.roaddir
if newcoord[0] >= self.width or newcoord[0] < 0:
return None
if newcoord[1] >= self.height or newcoord[1] < 0:
return None
self.roadcurrentcoord += self.roaddir
road = Road(self.next_id(), tuple(self.roadcurrentcoord), self)
self.grid.place_agent(road, tuple(self.roadcurrentcoord))
return road
def singleroad(self, initialcoord=(0, 0)):
#initialize roads
#print("placing road seed: ", initialcoord)
roaddir = self.random.randrange(4)
roadseedx = self.random.randrange(self.width)
roadseedy = self.random.randrange(self.height)
road = Road(self.next_id(), (roadseedx, roadseedy), self)
self.roadcurrentcoord = (roadseedx, roadseedy)
self.grid.place_agent(road, (roadseedx, roadseedy))
#note: roads are not scheduled because they do nothing
road = None
counter = 0
roadlist = []
for i in range(self.initial_roads):
while road is None:
val = self.random.random()
#print("val: " , val)
if val <= self.straightweight:
road = self.roadplace_random(HoominWorld.STRAIGHT)
elif val > self.straightweight and val <= self.leftweight + self.straightweight:
road = self.roadplace_random(HoominWorld.LEFT)
elif val > self.leftweight + self.straightweight:
road = self.roadplace_random(HoominWorld.RIGHT)
if road is None:
#print("err: road is none")
True
roadlist.append(road)
road = None
counter += 1
#print("initialized ", counter, " road tiles")
self.roadset = self.roadset.union(set(roadlist))
del roadlist
def get_hoomin_level(self):
return self.hoomin_level
def logstep(self):
if not self.logger.isopen(HoominWorld.FRIENDNODELOGNAME):
self.logger.open(HoominWorld.FRIENDNODELOGNAME, overwrite=True)
if not self.logger.isopen(HoominWorld.TOTALMESSAGELOGNAME):
self.logger.open(HoominWorld.TOTALMESSAGELOGNAME, overwrite=True)
self.logger.write(
HoominWorld.TOTALMESSAGELOGNAME,
str(self.hoomin_level) + " " + str(self.global_scattermessages))
st = "STEP: " + str(self.hoomin_level) + " hoominzero: " + str(
self.schedule._agents[self.hoomin_zero_id].friendgraph.
number_of_nodes()) + " finalhoomin: " + str(self.schedule._agents[
self.final_hoomin_id].friendgraph.number_of_nodes())
self.logger.write(HoominWorld.FRIENDNODELOGNAME, st)
def step(self):
self.schedule.step()
self.datacollector.collect(self)
self.hoomin_level += 1
if self.hoomin_level % settings.graphrefreshfreq == 0 and settings.displayfriendgraph and not settings.runheadless:
plt.cla()
plt.clf()
nx.draw(self.schedule._agents[self.hoomin_zero_id].friendgraph)
plt.draw()
plt.pause(0.001)
if self.verbose:
print([self.schedule.time, "nothing yet"])
if len(self.schedule._agents[self.final_hoomin_id].scatterbuffer
) >= settings.initial_scattermessages:
print("model completed")
self.running = False
if not self.logger.isopen(HoominWorld.STEPSTOCOMPLETIONLOGNAME):
self.logger.open(HoominWorld.STEPSTOCOMPLETIONLOGNAME,
overwrite=True)
self.logger.write(HoominWorld.STEPSTOCOMPLETIONLOGNAME,
self.hoomin_level)
self.logger.close(HoominWorld.STEPSTOCOMPLETIONLOGNAME)
self.logstep()
def run_model(self, step_count=200):
if self.verbose:
print("Initializing hoomins",
self.schedule.get_hoomin_count(Hoomin))
while self.running:
self.step()
class CovidModel(Model):
def size(filename):
return Image.open(filename).size
def __init__(self,
filename,
num_infec_agents=20,
num_uninfec_agents=20,
num_rec_agents=20,
mask_efficacy=95,
passing=True,
steps_per_hour_slow=12,
steps_per_hour_fast=1,
hours_per_day=3,
days=1,
cleans_per_day=1):
im = Image.open(filename) # open image file
self.surfaces = []
self.surface_pos = []
self.entrances = []
self.entrance_pos = []
self.humans = []
self.filename = filename
self.width, self.height = im.size # Get the width and height of the image to iterate over
self.schedule = RandomActivation(self)
self.grid = MultiGrid(width=self.width,
height=self.height,
torus=False)
self.mask_efficacy = mask_efficacy / 100
self.passing = passing
self.steps_per_hour_slow = steps_per_hour_slow
self.steps_per_hour_fast = steps_per_hour_fast
self.steps_per_hour = self.steps_per_hour_fast
self.hours_per_day = hours_per_day
self.cleans_per_day = cleans_per_day
self.hours = 0
self.days = days
self.datacollector = DataCollector(
model_reporters={
"Uninfected": get_uninfected_agents,
"Recovered": get_recovered_agents,
"Infected": get_infected_agents,
"Quarantined of Infected": get_quarantined_agents,
"Average Distance": get_average_distance,
"Average Nearest Distance": get_avg_min_distance,
"Days": get_days,
"Hours": get_hours,
"Average R_0": get_average_r0
},
agent_reporters={
# "Uninfected": lambda x: x.infected == False and x.recovered == False,
# "Infected": lambda x: x.infected == True,
# "Recovered": (add later)
})
convert(filename, self, self.surfaces,
self.entrances) # convert environment, create position lists
for surface in self.surfaces:
self.surface_pos.append(surface.pos)
for entrance in self.entrances:
self.entrance_pos.append(entrance.pos)
# THIS SHOULD BE NOTED THAT THIS WILL ADD ONE TO THE TOTAL NUMBER OF HUMANS
# create professor
prof_seat = self.surfaces[0]
pos = prof_seat.pos
self.surfaces.remove(prof_seat)
prof = Faculty(new_id(),
self,
pos=pos,
next_pos=pos,
seat=prof_seat,
infected=False,
recovered=False,
masked=True,
arrived=True)
self.grid.place_agent(prof, pos)
self.schedule.add(prof)
self.humans.append(prof)
#
# setup_agnet(ag_type)
# Find random starting postion and seat for agent. Create new agent with these parameters. Update
# agent's infection status based off of ag_type. Give agent random caution level and update agent accordingly
# Place agent on grid and add to scheduler and list of humans.
def setup_agent(ag_type):
pos = random.choice(self.entrance_pos) # start agents at entrance
seat = random.choice(
self.surfaces) # get random goal postion for agent
self.surfaces.remove(seat) # make goal position unique
next_pos = seat.pos
new_human = Student(new_id(),
self,
pos=pos,
next_pos=next_pos,
seat=seat) # create new Student agent
if ag_type == "uninfec":
new_human.infected, new_human.recovered = False, False # set state of agent
elif ag_type == "infec":
new_human.init_infect() # needs deliberate setup
elif ag_type == "rec":
new_human.infected, new_human.recovered = False, True
new_human.caution_level = random.randint(
0,
max_caution_level) # create agents of different caution levels
if new_human.caution_level == 0:
new_human.masked = False
elif new_human.caution_level == 1:
new_human.masked = True
elif new_human.caution_level == 2:
new_human.masked = True
if random.random(
) < percent_immunocompromised: # create immunocomprimised agents
new_human.immunocompromised = True
else:
new_human.immunocompromised = False
self.grid.place_agent(new_human, pos) # place agent on grid
self.schedule.add(new_human) # add agent to schedule
self.humans.append(new_human)
for agents in range(num_uninfec_agents):
setup_agent("uninfec", )
for agents in range(num_infec_agents):
setup_agent("infec", )
for agents in range(num_rec_agents):
setup_agent("rec", )
self.running = True
#
# self.check_arrival(destination)
# Check arrival of nonquarantined agents at given destination.
def check_arrival(self, destination):
for human in self.humans:
if not human.quarantined: # only check agents at class
if destination == "seats":
if not human.arrived or human.pos not in self.surface_pos:
return False
elif destination == "exit":
if not human.arrived or human.pos not in self.entrance_pos:
return False
return True
#
# self.check_agents()
# Cautious agents check selves for symptoms and self quarantine if necessary.
def check_agents(self):
for human in self.humans:
if human.infected and human.symptomatic and human.caution_level > 0 and not human.quarantined: # if cautious person and symptomatic quarantine
human.quarantine()
#print("quarantined " + str(human.unique_id) + " on step" + str(self.schedule.steps))
#
# self.leave()
# Update agent's next position to be exit.
def leave(self):
for human in self.humans:
human.next_pos = random.choice(self.entrance_pos)
#
# self.clean_grid()
# Iterate over surface and door cells and clean cells of virus.
def clean_grid(self):
for pos in self.surface_pos:
for x in self.grid.get_cell_list_contents(pos):
if isinstance(x, BaseEnvironment):
x.clean()
for pos in self.entrance_pos:
for x in self.grid.get_cell_list_contents(pos):
if isinstance(x, BaseEnvironment):
x.clean()
#print("cleaned")
#
# self.reset
# Reset next postion of all agents to their seat.
def reset(self):
for human in self.humans:
human.next_pos = human.seat.pos
#
# self.step()
# Update steps_per_hour based off of agent arrival. Complete one class day. Move agents off grid,
# clean grid, and self-quarantine agents if necessary. Stop simulation if all agents are recovered or uninfected.
def step(self):
# can we stop?
if get_recovered_agents(self) + get_uninfected_agents(self) == len(
self.humans):
self.running = False
if self.check_arrival(
"seats"): # if all agents have arrived class has "started"
self.passing = False
if self.passing:
self.steps_per_hour = self.steps_per_hour_slow
else:
self.steps_per_hour = self.steps_per_hour_fast
if not self.passing:
self.hours += 1 / self.steps_per_hour # add time to class hours
if self.hours % self.hours_per_day < 0.001: # after a 3 hour class
self.leave() # move agents off grid
self.passing = True # passing period begins again
if self.hours % (self.hours_per_day / self.cleans_per_day) < 0.001:
self.clean_grid()
if self.check_arrival("exit"): # if all agents have left
self.check_agents() # agents check selves for symptoms
self.clean_grid() # clean grid
self.hours = 0
self.days += 1 # number of days of class increases
self.reset() # update scheduled position
#print("a(s): " + str(self.check_arrival("seats")) + " a(e): " + str(self.check_arrival("exit")) + ", s_p_h: " + str(self.steps_per_hour) + ", h: " + str(self.hours) + ", d: " + str(self.days) + ", s: " + str(self.schedule.steps))
self.schedule.step()
self.datacollector.collect(self)
def run_model(self):
#print("Rt: " + self.run_time)
for i in range(self.run_time):
self.step()
class Intersection(Model):
"""
Here the model is initialized. The Generatedmap.txt is read in order to locate traffic lights and car spawns.
Cars and traffic lights are spawned here.
"""
def __init__(self, spawnrate, tactic, offset, cycletime):
# Variable parameters
self.tactic = tactic # [ Offset, Proportional, Lookahead, GreenWave]
self.spawnrate = spawnrate
self.offset = offset
self.slowmotionrate = 0.2
self.cycletime = cycletime
# Value initializations
self.emission = [0, 0, 0] # emission per step
self.traveltime = []
self.averagetraveltime = 0
self.carID = 0
self.averageemission = []
# Reading roadmap and emission values
self.emissionvalues = reademissionvalues()
[
self.roadmap,
self.spawns,
self.lights,
self.height,
self.cellsperlane,
self.intersections,
self.streetlength,
self.gridsize,
] = readroadmap()
# Model and visualization parameters
self.schedule = RandomActivation(self)
self.width = self.height
self.grid = MultiGrid(self.width, self.height, True)
self.running = True
# Initializing matrix which shows what cars in front of traffic lights go to what over traffic lights.
self.tlightmatrix = np.empty((len(self.lights), len(self.lights)))
self.tlightmatrix[:] = np.nan
self.trafficlightlist = []
self.lightcombinations = [
["SR", "SD", "SL", "WR"],
["ER", "ED", "EL", "SR"],
["NR", "ND", "NL", "ER"],
["WR", "WD", "WL", "NR"],
]
# Data collection
self.datacollector = DataCollector(
model_reporters={
"tactic": "tactic",
"Spawnrate": "spawnrate",
"Offset": "offset",
"Cycletime": "cycletime",
"AverageTraveltime": "averagetraveltime",
"Totalcars": lambda m: self.numberofcars(),
"CO2": lambda m: self.getco2(),
"NOx": lambda m: self.getnox(),
"PM10": lambda m: self.getpm(),
"AverageCO2": lambda m: self.getaverageco2(),
"AverageNOx": lambda m: self.getaveragenox(),
"AveragePM": lambda m: self.getaveragepm(),
},
)
# Needed for green wave tactic
self.mostcars = []
self.goesto = []
self.firstgreenintersection = -1
self.secondgreenintersection = -1
self.firstcombination = None
self.secondcombination = None
self.firstcycledone = 0
# Needed for lookahead tactic
self.mostexpectedcars = [0, 0, 0] # cars,intersection,combination
# results in list of lists with intersectionnumbers in the right place, e.g.: [[0, 1],[2,3]]
# Needed for the offset tactic to know which lights to offset
self.intersectionmatrix = []
lastnumber = 0
for i in range(int(math.sqrt(self.intersections))):
tempmaptrix = []
for j in range(int(math.sqrt(self.intersections))):
tempmaptrix.append(j + lastnumber)
lastnumber = tempmaptrix[-1] + 1
self.intersectionmatrix.append(tempmaptrix)
self.intersectionmatrix = np.array(self.intersectionmatrix)
# Initialize information dictionary (which lights are suppesed to be green and how long they have been green for
self.trafficlightinfo = {}
for i in range(self.intersections):
self.trafficlightinfo.update(
{f"intersection{i}": {"Trafficlightinfo": {}, "Timeinfo": {}}}
)
# Initializes traffic lights
for i, light in enumerate(self.lights):
self.trafficlightinfo[f"intersection{light[1][3]}"]["Trafficlightinfo"][
f"{light[1][1:3]}"
] = i
self.trafficlightinfo[f"intersection{light[1][3]}"]["Timeinfo"].update(
{
"Currentgreen": -1,
"Currenttimegreen": 0,
"Maxtimegreen": 0,
"Allred": 1,
}
)
intersectionnumber = int(light[1][3])
intersectiony = np.where(self.intersectionmatrix == intersectionnumber)[0]
intersectionx = np.where(self.intersectionmatrix == intersectionnumber)[1]
direction = light[1][1]
lane = light[1][2]
location = light[0]
xlocation = int(location[0])
ylocation = self.height - 1 - int(location[1])
trafficlight = TrafficLightAgent(
f"{xlocation},{ylocation},{light[1][1:3]}",
self,
"red",
direction,
lane,
i,
intersectionnumber,
self.tactic,
self.offset,
[intersectionx, intersectiony],
self.cycletime,
)
self.trafficlightlist.append([light[1], i])
self.schedule.add(trafficlight)
self.grid.place_agent(trafficlight, (xlocation, ylocation))
self.tlightmatrix = lightconnection(
self.tlightmatrix, self.trafficlightlist, self.intersections
)
# Place legend
self.grid.place_agent(LegendCarIcon("Caricon", self), (65, 68))
self.grid.place_agent(LegendGreenTlightIcon("GreenTlighticon", self), (65, 69))
self.grid.place_agent(LegendRedTlightIcon("RedTlighticon", self), (65, 70))
# Get emission values
def getco2(self):
return self.emission[0]
def getnox(self):
return self.emission[1]
def getpm(self):
return self.emission[2]
def getaveragetraveltime(self):
return self.averagetraveltime
def getaverageco2(self):
totalco2 = 0
if len(self.averageemission) > 0:
for i, emissions in enumerate(self.averageemission):
totalco2 += emissions[0]
return totalco2 / len(self.averageemission)
else:
return 0
def getaveragenox(self):
totalnox = 0
if len(self.averageemission) > 0:
for i, emissions in enumerate(self.averageemission):
totalnox += emissions[1]
return totalnox / len(self.averageemission)
else:
return 0
def getaveragepm(self):
totalpm = 0
if len(self.averageemission) > 0:
for i, emissions in enumerate(self.averageemission):
totalpm += emissions[2]
return totalpm / len(self.averageemission)
else:
return 0
def numberofcars(self):
return len(self.schedule.agents) - 12 * self.intersections
def step(self):
"""
Step function that will randomly place cars based on the spawn chance
and will visit all the agents to perform their step function.
"""
# Spawn cars
for spawn in self.spawns:
location = spawn[0]
cell_contents = self.grid.get_cell_list_contents([location])
if (
not cell_contents
and random.randint(0, int(100 / self.slowmotionrate)) < self.spawnrate
):
location = spawn[0]
xlocation = int(location[0])
ylocation = self.height - 1 - int(location[1])
direction = spawn[1][1]
lane = spawn[1][2]
car = CarAgent(
f"car{self.carID}",
self,
direction,
lane,
[xlocation, ylocation]
)
self.carID += 1
self.schedule.add(car)
self.grid.place_agent(car, (xlocation, ylocation))
# Clear all previous step's emission and travel time
if len(self.traveltime) > 0:
self.averagetraveltime = sum(self.traveltime) / len(self.traveltime)
# For the greenwave tactic
if (
self.tactic == "GreenWave"
and len(self.schedule.agents) > 12 * self.intersections
and ((self.schedule.steps % (self.cycletime * 2)) == 0)
):
self.firstcycledone = 0
self.mostcars = np.argmax(np.nansum(self.tlightmatrix, axis=1))
if self.mostcars:
self.goesto = np.where(~np.isnan(self.tlightmatrix[self.mostcars]))
self.firstgreenintersection = self.lights[self.mostcars][1][3]
self.secondgreenintersection = self.lights[self.goesto[0][0]][1][3]
# Determines the direction of traffic lights
firstdirection = self.schedule.agents[int(self.mostcars)].direction
seconddirection = self.schedule.agents[self.goesto[0][0]].direction
# Determines combinations of traffic lights that can stay green
for i, combination in enumerate(self.lightcombinations):
if firstdirection + "D" in combination:
self.firstcombination = self.lightcombinations[i]
if seconddirection + "D" in combination:
self.secondcombination = self.lightcombinations[i]
# If 1st part of green wave is over (so 1 cycle has been done)
if not ((self.schedule.steps % (self.cycletime * 2)) == 0):
self.firstcycledone = 1
# For the proportional tactic
if (
self.tactic == "Proportional"
and len(self.schedule.agents) > 12 * self.intersections
):
for i in range(self.intersections):
currenttimegreen = self.trafficlightinfo[f"intersection{i}"][
"Timeinfo"
]["Currenttimegreen"]
maxgreentime = self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Maxtimegreen"
]
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currenttimegreen"
] = (currenttimegreen + 1)
if currenttimegreen > maxgreentime + 6:
carsinfront = np.nansum(self.tlightmatrix, axis=1)
totalcars = [0, 0, 0, 0]
for key in self.trafficlightinfo[f"intersection{i}"][
"Trafficlightinfo"
].keys():
trafficlight = int(
self.trafficlightinfo[f"intersection{i}"][
"Trafficlightinfo"
][key]
)
cars = carsinfront[trafficlight]
for j, combi in enumerate(self.lightcombinations):
if key in combi:
totalcars[j] = totalcars[j] + cars
# No cars? pick regular timeschedule
if sum(totalcars) == 0:
totalcars = [1, 1, 1, 1]
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currentgreen"
] = totalcars.index(max(totalcars))
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Maxtimegreen"
] = (max(totalcars) / (sum(totalcars) / 4) * self.cycletime)
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currenttimegreen"
] = 0
if currenttimegreen == maxgreentime:
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currentgreen"
] = -1
# For the lookahead tactic
if (
self.tactic == "Lookahead"
and len(self.schedule.agents) > 12 * self.intersections
):
self.mostexpectedcars = [0, 0, 0]
for i in range(self.intersections):
currenttimegreen = self.trafficlightinfo[f"intersection{i}"][
"Timeinfo"
]["Currenttimegreen"]
maxgreentime = self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Maxtimegreen"
]
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currenttimegreen"
] = (currenttimegreen + 1)
if currenttimegreen > maxgreentime + 6:
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"]["Allred"] = 0
carsexpected = np.nansum(self.tlightmatrix, axis=0)
carsinfront = np.nansum(self.tlightmatrix, axis=1)
totalcars = [0, 0, 0, 0]
# For every direction + lane in intersection
for key in self.trafficlightinfo[f"intersection{i}"][
"Trafficlightinfo"
].keys():
trafficlight = int(
self.trafficlightinfo[f"intersection{i}"][
"Trafficlightinfo"
][key]
)
cars = carsinfront[trafficlight]
# For every lightcombination
for j, combi in enumerate(self.lightcombinations):
if key in combi:
totalcars[j] = (
totalcars[j] + cars + carsexpected[trafficlight]
)
if totalcars[j] > self.mostexpectedcars[0]:
self.mostexpectedcars[0] = totalcars[j]
self.mostexpectedcars[1] = i
self.mostexpectedcars[2] = j
# Reset timers
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currentgreen"
] = (
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currentgreen"
]
+ 1
) % 4
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Maxtimegreen"
] = self.cycletime
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"][
"Currenttimegreen"
] = 0
if currenttimegreen == maxgreentime:
self.trafficlightinfo[f"intersection{i}"]["Timeinfo"]["Allred"] = 1
if self.mostexpectedcars[0] != 0:
# Change green light information of intersection with most expected cars
self.trafficlightinfo[f"intersection{self.mostexpectedcars[1]}"][
"Timeinfo"
]["Currentgreen"] = self.mostexpectedcars[2]
# Get trafficlightnumbers of ligths where most cars are expected
mostcars = 0
mostcarslight = None
comingfrom = np.nansum(self.tlightmatrix, axis=1)
for direction in self.lightcombinations[self.mostexpectedcars[2]]:
greenlight = int(
self.trafficlightinfo[
f"intersection{self.mostexpectedcars[1]}"
]["Trafficlightinfo"][direction]
)
# Where the cars to those lights come from.
lightscomingfrom = np.argwhere(
~np.isnan(self.tlightmatrix[:, greenlight])
)
for light in lightscomingfrom:
if light:
light = light[0]
carsinfront = np.sum(comingfrom[light])
if carsinfront > mostcars:
mostcars = int(carsinfront)
mostcarslight = light
# Find intersection + directon of this light and change this intersection's green light information
if mostcars != 0:
intersection = int(self.lights[mostcarslight][1][3])
direction = self.lights[mostcarslight][1][1:3]
for k, directs in enumerate(self.lightcombinations):
if direction in directs[0:3]:
self.trafficlightinfo[f"intersection{intersection}"][
"Timeinfo"
]["Currentgreen"] = k
pass
self.datacollector.collect(self)
self.emission = [0, 0, 0]
self.schedule.step()
class AntModel(Model):
def __init__(self, num_ln, num_fj, num_mk_col, num_ft_col, width, height):
"""
:param num_ln: Number of L. Niger agents
:param num_fj: Number of F. Japonica agents
:param num_mk_col: Number of M. Kuricola colonies
:param num_ft_col: Number of F. Tropicalis colonies
:param width: Width of the model grid
:param height: Height of the model grid
"""
super().__init__()
self.num_ln = num_ln
self.num_fj = num_fj
self.num_mk_col = num_mk_col
self.num_ft_col = num_ft_col
self.grid = MultiGrid(width, height, True)
self.schedule = RandomActivation(self)
self.running = True
for h in range(self.num_fj):
ant = FJaponica(uuid4(), self)
self.schedule.add(ant)
self.grid.place_agent(ant, self.grid.find_empty())
for j in range(self.num_mk_col):
colony = MKuricolaColony(uuid4(), self)
self.schedule.add(colony)
self.grid.place_agent(colony, self.grid.find_empty())
for k in range(self.num_ft_col):
colony = FTropicalisColony(uuid4(), self)
self.schedule.add(colony)
self.grid.place_agent(colony, self.grid.find_empty())
for i in range(self.num_ln):
ant = LNiger(uuid4(), self)
self.schedule.add(ant)
self.grid.place_agent(ant, self.grid.find_empty())
ant._init_post_place()
self.data_collector = DataCollector(
model_reporters={},
agent_reporters={"states": ant_state_collector})
self.weights_dict = json.load(open("newout.json", "r"))
def drop_pheromone(self, location):
"""
Drops a LNPheromone object at the given location if one does not already exist. If one does already exist,
1 is added to the existing object's 'tracks' field.
:param location: An (x, y) tuple detailing the location to drop the pheromone.
:return: None
"""
if not self.is_pheromone_in_cell(location):
self.grid.place_agent(LNPheromone(uuid4(), self), location)
else:
self.get_pheromone_in_cell(location).tracks += 1
def is_pheromone_in_cell(self, location):
"""
Determines if a pheromone already exists in a given cell.
:param location: The location to check.
:return: boolean
"""
return True in [
type(x) == LNPheromone
for x in self.grid.get_cell_list_contents(location)
]
def is_ant_in_cell(self, location):
"""
Determines whether an ant exists in a given cell.
:param location: The location to check.
:return: boolean
"""
return True in [
isinstance(x, Ant)
for x in self.grid.get_cell_list_contents(location)
]
def is_colony_in_cell(self, location):
"""
Determines whether an aphid colony exists in a given cell.
:param location: The location to check.
:return: boolean
"""
return True in [
type(x) == MKuricolaColony or type(x) == FTropicalisColony
for x in self.grid.get_cell_list_contents(location)
]
def get_pheromone_in_cell(self, location):
"""
Returns a LNPheromone object from a cell. ASsumes the cell has already been proven to have a pheromone object
in it.
:param location: The cell location to check.
:return: The LNPheromone object within the cell.
"""
in_cell_pheromone = None
for i in self.grid.get_cell_list_contents(location):
if type(i) == LNPheromone:
in_cell_pheromone = i
return in_cell_pheromone
def get_closest_agent_of_type(self, agent, agent_type):
"""
Gets the closest agent (besides self) of type agent_type. Returns -1 if it cannot find one.
:param agent: The agent to find the closest agent_type to.
:param agent_type: The type of the agent we are looking for.
:return:
"""
for radius in range(1, self.grid.width):
for neighbor in self.grid.get_neighbors(pos=agent.pos,
moore=True,
include_center=False,
radius=radius):
if isinstance(neighbor, agent_type):
return neighbor
return -1
def get_closest_colony(self, agent):
"""
Gets the closest colony to an agent. If an agent is of type colony, it returns itself.
:param agent: The agent to find the closest colony to.
:return: The closest colony or -1 if not found.
"""
return self.get_closest_agent_of_type(agent, Colony)
@staticmethod
def distance_between_cells(location_a, location_b):
"""
Calculates the distance between two cells on the grid.
:param location_a: First cell location.
:param location_b: Second cell location.
:return:
"""
return np.sqrt((location_a[0] - location_b[0])**2 +
(location_a[1] - location_a[1])**2)
def get_nearest_cell_to_goal(self, goal_cell, possible_cells):
"""
Returns the cell from a list of possible cells which is closest to the end location.
:param goal_cell: The goal cell of the agent
:param possible_cells: Candidate cells.
:return: The location of the closest cell to the goal cell.
"""
closest_neighbor_index = -1
closest_neighbor_distance = np.inf
for i in range(0, len(possible_cells)):
dist = self.distance_between_cells(possible_cells[i], goal_cell)
if dist < closest_neighbor_distance:
closest_neighbor_index = i
closest_neighbor_distance = dist
return possible_cells[closest_neighbor_index]
def get_number_of_agents_in_radius(self, location, radius, agent_type):
"""
Returns the number of agents of type agent_type within a radius (not including center) of location.
:param location: Location to search around.
:param radius: Radius to search.
:param agent_type: Type of agent to search for.
:return: int
"""
total_agents = 0
for neighbor in self.grid.get_neighbors(pos=location,
moore=True,
include_center=False,
radius=radius):
if isinstance(neighbor, agent_type):
total_agents += 1
return total_agents
def get_all_of_agent_type(self, agent_type):
"""
Returns all instances of agents of type agent_type in the Grid.
:param agent_type: The type of agent to find.
:return: A list of agent objects.
"""
return [
x for x in self.grid.get_neighbors(pos=(0, 0),
moore=True,
include_center=True,
radius=self.grid.width)
if isinstance(x, agent_type)
]
def step(self):
"""
A method called every step that occurs
:return: None
"""
self.data_collector.collect(self)
self.schedule.step()
class SOMENModel(Model):
def __init__(self, width, height):
# Model attributes initialization
self.workers_number = 10
self.agents = []
self.workers = []
self.average_stress = 0
self.running = True
#SOBA
configuration.settings.init()
configuration.defineOccupancy.init()
configuration.defineMap.init()
self.clock = Time()
#Vars of control
self.num_occupants = 0
self.day = self.clock.day
self.NStep = 0
self.placeByStateByTypeAgent = {}
self.agentsWorkingByStep = []
self.agentsIn = 0
# Schedule
self.schedule = BaseScheduler(self)
self.grid = MultiGrid(width, height, False)
#Create the map
self.createRooms()
self.setMap(width, height)
self.createDoors()
self.createWalls()
#Create agents
self.setAgents()
# Create timer agent
self.timer = TimeAgent(len(self.agents), self)
self.schedule.add(self.timer)
self.agents.append(self.timer)
# Create sensor agent
self.sensor = SensorAgent(len(self.agents), self)
self.schedule.add(self.sensor)
self.agents.append(self.sensor)
'''
# Create workers agents
for i in range(self.workers_number):
worker = WorkerAgent(i+len(self.agents), self)
self.schedule.add(worker)
self.workers.append(worker)
'''
# Create data collectors
self.model_collector = DataCollector(
model_reporters={"Average Stress": lambda a: a.average_stress})
self.worker_collector = WorkerCollector(
agent_reporters={
"Stress": lambda a: a.stress,
"Event Stress": lambda a: a.event_stress,
"Time Pressure": lambda a: a.time_pressure,
"Effective Fatigue": lambda a: a.effective_fatigue,
"Productivity": lambda a: a.productivity,
'Emails read': lambda a: a.emails_read,
'Pending tasks': lambda a: len(a.tasks),
'Overtime hours': lambda a: a.overtime_hours,
'Rest at work hours': lambda a: a.rest_at_work_hours,
'Tasks completed': lambda a: a.tasks_completed
})
self.sensor_collector = SensorCollector(
agent_reporters={
"Temperature": lambda a: a.wbgt,
"Noise": lambda a: a.noise,
"Luminosity": lambda a: a.luminosity
})
self.time_collector = TimeCollector(agent_reporters={
"Day": lambda a: a.days,
"Time": lambda a: a.clock
})
#SOBA
def setAgents(self):
self.lights = []
id_light = 0
for room in self.rooms:
if room.typeRoom != 'out' and room.light == False:
light = Light(id_light, self, room)
self.lights.append(light)
id_light = id_light + 1
room.light = light
for room2 in self.rooms:
if room.name.split(r".")[0] == room2.name.split(r".")[0]:
room2.light = light
# Height and Width
height = self.grid.height
width = self.grid.width
# CREATE AGENTS
self.agents = []
# Create occupants
for n_type_occupants in configuration.defineOccupancy.occupancy_json:
self.placeByStateByTypeAgent[
n_type_occupants['type']] = n_type_occupants['states']
n_agents = n_type_occupants['N']
for i in range(0, n_agents):
a = WorkerAgent(i + len(self.agents) + 1000, self,
n_type_occupants)
self.workers.append(a)
self.schedule.add(a)
self.grid.place_agent(a, self.outBuilding.pos)
self.pushAgentRoom(a, self.outBuilding.pos)
self.num_occupants = self.num_occupants + 1
self.schedule.add(self.clock)
for light in self.lights:
self.schedule.add(light)
def isConected(self, pos):
nextRoom = False
for room in self.rooms:
if room.pos == pos:
nextRoom = room
if nextRoom == False:
return False
for x in range(0, width):
for y in range(0, height):
self.pos_out_of_map.append(x, y)
for room in self.rooms:
self.pos_out_of_map.remove(room.pos)
def createRooms(self):
rooms = configuration.defineMap.rooms_json
self.rooms = []
for room in rooms:
newRoom = 0
name = room['name']
typeRoom = room['type']
if typeRoom != 'out':
conectedTo = room.get('conectedTo')
entrance = room.get('entrance')
measures = room['measures']
dx = measures['dx']
dy = measures['dy']
newRoom = Room(name, typeRoom, conectedTo, dx, dy)
newRoom.entrance = entrance
else:
newRoom = Room(
name,
typeRoom,
None,
0,
0,
)
self.outBuilding = newRoom
self.rooms.append(newRoom)
for room1 in self.rooms:
if room1.conectedTo is not None:
for otherRooms in list(room1.conectedTo.values()):
for room2 in self.rooms:
if room2.name == otherRooms:
room1.roomsConected.append(room2)
room2.roomsConected.append(room1)
for room in self.rooms:
room.roomsConected = list(set(room.roomsConected))
sameRoom = {}
for room in self.rooms:
if sameRoom.get(room.name.split(r".")[0]) is None:
sameRoom[room.name.split(r".")[0]] = 1
else:
sameRoom[room.name.split(r".")
[0]] = sameRoom[room.name.split(r".")[0]] + 1
def setMap(self, width, height):
rooms_noPos = self.rooms
rooms_using = []
rooms_used = []
for room in self.rooms:
if room.entrance is not None:
room.pos = (int(1), 1)
rooms_using.append(room)
rooms_used.append(room)
rooms_noPos.remove(room)
break
while len(rooms_noPos) > 0:
for roomC in rooms_using:
xc, yc = roomC.pos
rooms_conected = roomC.conectedTo
rooms_using.remove(roomC)
if rooms_conected is not None:
orientations = list(rooms_conected.keys())
for orientation in orientations:
if orientation == 'R':
for room in rooms_noPos:
if room.name == rooms_conected['R']:
room.pos = (int(xc + 1), yc)
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'U':
for room in rooms_noPos:
if room.name == rooms_conected['U']:
room.pos = (xc, int(yc + 1))
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'D':
for room in rooms_noPos:
if room.name == rooms_conected['D']:
room.pos = (xc, int(yc - 1))
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'L':
for room in rooms_noPos:
if room.name == rooms_conected['L']:
room.pos = (int(xc - 1), yc)
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
else:
pass
self.rooms = rooms_used
def createDoors(self):
self.doors = []
for roomC in self.rooms:
roomsConected = roomC.roomsConected
for room in roomsConected:
door_created = False
same_corridor = False
if room.name != roomC.name:
for door in self.doors:
if (door.room1.name == roomC.name
and door.room2.name == room.name) or (
door.room2.name == roomC.name
and door.room1.name == room.name):
door_created = True
if room.name.split(r".")[0] == roomC.name.split(
r".")[0]:
same_corridor = True
if door_created == False and same_corridor == False:
d = Door(roomC, room)
self.doors.append(d)
room.doors.append(d)
roomC.doors.append(d)
def createWalls(self):
for room in self.rooms:
if room.typeRoom != 'out':
walls = []
xr, yr = room.pos
roomA = self.getRoom((xr, yr + 1))
if roomA != False:
if roomA.name.split(r".")[0] == room.name.split(r".")[0]:
pass
else:
wall = Wall(room, roomA)
walls.append(wall)
else:
wall = Wall(room)
walls.append(wall)
roomB = self.getRoom((xr, yr - 1))
if roomB != False:
if roomB.name.split(r".")[0] == room.name.split(r".")[0]:
pass
else:
wall = Wall(room, roomB)
walls.append(wall)
else:
wall = Wall(room)
walls.append(wall)
roomC = self.getRoom((xr + 1, yr))
if roomC != False:
if roomC.name.split(r".")[0] == room.name.split(r".")[0]:
pass
else:
wall = Wall(room, roomC)
walls.append(wall)
else:
wall = Wall(room)
walls.append(wall)
roomD = self.getRoom((xr - 1, yr))
if roomD != False:
if roomD.name.split(r".")[0] == room.name.split(r".")[0]:
pass
else:
wall = Wall(room, roomD)
walls.append(wall)
else:
wall = Wall(room)
walls.append(wall)
room.walls = walls
def getPosState(self, name, typeA):
placeByStateByTypeAgent = self.placeByStateByTypeAgent
n = 0
for state in self.placeByStateByTypeAgent[typeA]:
if state.get('name') == name:
pos1 = state.get('position')
if isinstance(pos1, dict):
for k, v in pos1.items():
if v > 0:
placeByStateByTypeAgent[typeA][n]['position'][
k] = v - 1
self.placeByStateByTypeAgent = placeByStateByTypeAgent
return k
return list(pos1.keys())[-1]
else:
return pos1
n = n + 1
def thereIsClosedDoor(self, beforePos, nextPos):
oldRoom = False
newRoom = False
for room in rooms:
if room.pos == beforePos:
oldRoom = room
if room.pos == nextPos:
newRoom = room
for door in self.doors:
if (door.room1.name == oldRoom.name and door.room2.name
== newRoom.name) or (door.room2.name == oldRoom.name
and door.room1.name == newRoom.name):
if door.state == False:
return True
return False
def thereIsOccupant(self, pos):
possible_occupant = self.grid.get_cell_list_contents([pos])
if (len(possible_occupant) > 0):
for occupant in possible_occupant:
if isinstance(occupant, WorkerAgent):
return True
return False
def ThereIsOtherOccupantInRoom(self, room, agent):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, WorkerAgent) and occupant != agent:
return True
return False
def ThereIsSomeOccupantInRoom(self, room):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, WorkerAgent):
return True
return False
def thereIsOccupantInRoom(self, room, agent):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, WorkerAgent) and occupant == agent:
return True
return False
def getRoom(self, pos):
for room in self.rooms:
if room.pos == pos:
return room
return False
def pushAgentRoom(self, agent, pos):
room = self.getRoom(pos)
room.agentsInRoom.append(agent)
def popAgentRoom(self, agent, pos):
room = self.getRoom(pos)
room.agentsInRoom.remove(agent)
def openDoor(self, agent, room1, room2):
for door in self.doors:
if ((door.room1 == room1 and door.room2 == room2)
or (door.room1 == room2 and door.room2 == room1)):
door.state = False
def closeDoor(self, agent, room1, room2):
numb = random.randint(0, 10)
for door in self.doors:
if ((door.room1 == room1 and door.room2 == room2)
or (door.room1 == room2 and door.room2 == room1)):
if 7 >= numb:
door.state = False
else:
door.state = True
def getMatrix(self, agent):
new_matrix = configuration.defineOccupancy.returnMatrix(
agent, self.clock.clock)
agent.markov_matrix = new_matrix
def getTimeInState(self, agent):
matrix_time_in_state = configuration.defineOccupancy.getTimeInState(
agent, self.clock.clock)
return matrix_time_in_state
def sobaStep(self):
aw = 0
for agent in self.agents:
if agent.state == 'working in my workplace':
aw = aw + 1
self.agentsWorkingByStep.append(aw)
self.schedule.step()
if (self.clock.day > self.day):
self.day = self.day + 1
self.NStep = self.NStep + 1
if self.clock.clock > 17:
model.ramenScript.generateJSON()
while (True):
pass
def step(self):
self.sobaStep()
if self.timer.new_day:
self.addTasks()
self.createEmailsDistribution()
self.average_stress = sum(
worker.stress for worker in self.workers) / len(self.workers)
if self.timer.new_hour:
self.worker_collector.collect(self)
self.sensor_collector.collect(self)
self.time_collector.collect(self)
self.model_collector.collect(self)
def addTasks(self):
''' Add tasks to workers '''
# Get task distribution params
mu, sigma = workload_settings.tasks_arriving_distribution_params
tasks_arriving_distribution = np.random.normal(
mu, sigma, self.workers_number * 10)
for worker in self.workers:
# Get number of tasks to add
tasks_number = math.floor(
abs(tasks_arriving_distribution[random.randint(
0, 10 * self.workers_number - 1)]))
worker.tasks_completed = 0
# Add tasks
for i in range(tasks_number):
worker.addTask(Task())
worker.calculateAverageDailyTasks(self.timer.days)
worker.calculateEventStress(tasks_number)
# worker.printTasksNumber()
# worker.printAverageDailyTasks()
# worker.printEventStress()
def createEmailsDistribution(self):
'''Create emails distribution'''
# Get emails distribution
mu, sigma = email_settings.emails_read_distribution_params
emails_read_distribution = np.random.normal(mu, sigma,
self.workers_number * 10)
for worker in self.workers:
emails_received = math.floor(
abs(emails_read_distribution[random.randint(
0, 10 * self.workers_number - 1)]))
emails_distribution_over_time = np.random.choice(
[0, 1],
size=(480, ),
p=[(480 - emails_received) / 480, emails_received / 480])
worker.emails_read = 0
worker.email_read_distribution_over_time = emails_distribution_over_time
class Map(Model):
def __init__(self, ncells, obstacles_dist, ninjured):
# used in server start
self.running = True
self.ncells = ncells
self.obstacles_dist = obstacles_dist
self.ninjured = ninjured
# grid and schedule representation
self.grid = MultiGrid(ncells + 2, ncells + 2, torus = False)
self.schedule = RandomActivation(self)
# unique counter for agents
self.agent_counter = 1
out_grid = {}
out_grid["Cell"] = {}
out_grid["Injured"] = {}
# place a cell agent for store data and visualization on each cell of the grid
for i in self.grid.coord_iter():
if i[1] != 0 and i[2] != 0 and i[1] != self.ncells + 1 and i[2] != self.ncells + 1:
rand = np.random.random_sample()
obstacle = True if rand < self.obstacles_dist else False
if obstacle:
difficulty = "inf"
explored = -1
priority = 0
utility = "-inf"
else:
difficulty = np.random.randint(low = 1, high = 13)
explored = 0
priority = 0
utility = 1.0
else:
difficulty = np.random.randint(low = 1, high = 13)
explored = -2
priority = "-inf"
utility = "-inf"
# generate big wall all across the map
'''
_, y, x = i
if x == 200 or x == 199:
difficulty = "inf"
explored = -1
priority = 0
utility = "-inf"
if (x == 200 or x ==199) and (y == 150 or y == 50 or y == 250):
difficulty = np.random.randint(low = 1, high = 13)
explored = 0
priority = 0
utility = 1.0
'''
# place the agent in the grid
out_grid["Cell"][i[1:]]= [self.agent_counter, i[1:], difficulty, explored, priority, utility]
a = Cell(self.agent_counter, self, i[1:], difficulty, explored, priority, utility)
self.schedule.add(a)
self.grid.place_agent(a, i[1:])
self.agent_counter += 1
# generate buildings structure
'''
for i in range(0, 50):
x = rnd.randint(20,300)
y = rnd.randint(20,300)
for j in range(0,rnd.randint(0,3)):
for k in range(0, rnd.randint(0,10)):
cell = [e for e in self.grid.get_cell_list_contents(tuple([x+j, y+k])) if isinstance(e, Cell)][0]
cell.difficulty = "inf"
cell.explored = -1
cell.priority = 0
cell.utility = "-inf"
ag_count = out_grid["Cell"][tuple([x+j,y+k])][0]
out_grid["Cell"][tuple([x+j,y+k])]= [ag_count, cell.pos, cell.difficulty, cell.explored, cell.priority, cell.utility]
for i in range(0, 50):
x = rnd.randint(20,300)
y = rnd.randint(20,300)
for j in range(0,rnd.randint(0,3)):
for k in range(0, rnd.randint(0,10)):
cell = [e for e in self.grid.get_cell_list_contents(tuple([x+k, y+j])) if isinstance(e, Cell)][0]
cell.difficulty = "inf"
cell.explored = -1
cell.priority = 0
cell.utility = "-inf"
ag_count = out_grid["Cell"][tuple([x+k,y+j])][0]
out_grid["Cell"][tuple([x+k,y+j])]= [ag_count, cell.pos, cell.difficulty, cell.explored, cell.priority, cell.utility]
'''
# create injured agents
valid_coord = []
for i in self.grid.coord_iter():
cell = [e for e in self.grid.get_cell_list_contents(i[1:]) if isinstance(e, Cell)][0]
if cell.explored == 0:
valid_coord.append(cell.pos)
for i in range(self.agent_counter, + self.agent_counter + self.ninjured):
inj_index = rnd.choice(valid_coord)
out_grid["Injured"][inj_index] = [i, inj_index]
a = Injured(i, self, inj_index)
self.schedule.add(a)
self.grid.place_agent(a, inj_index)
with open('robot_exploration/maps/mymap.py', 'w') as f:
f.writelines([str(out_grid), '\n'])
def step(self):
pass
class Forest (Model):
def __init__ (self,
endophytism = True, ## allow endophyte life style in model run
ws = 30, ## initial num of wood
endodisp=2.0, ## dispersal of endos
decompdisp=10.0, ## dispersal of decomps
leafdisp = 4.0, ## how well do leaves disperse
leaffall = 1, ## how frequently do leaves disperse
numdecomp=1, ## initial number of decomposers
numendo=1, ## initial number of endos
endoloss=0.05, ## rate of loss of endophyte infect per step
newwood = 15, ## total energy added in new logs each step
woodfreq = 1, ## how often to put new logs onto the landscape
width = 100, ## grid dimensions, only one (squares only)
kappa = 0.03, ## average rate of parent tree clusters per unit distance
sigma = 3.0, ## variance of child tree clusters, +/- spread of child clusters
mu = 2.2, ## average rate of child tree clusters per unit distance
nuke = False, ## make landscape, but no agents
):
self.endophytism = endophytism
self.nwood = ws
self.endodisp = endodisp
self.decompdisp = decompdisp
self.leafdisp = leafdisp
self.leaffall = leaffall
self.numdecomp = numdecomp
self.numendo = numendo
self.endoloss = endoloss
self.newwood = newwood
self.woodfreq = woodfreq
self.schedule = RandomActivation(self)
self.grid = MultiGrid(width, width, torus = True)
self.running = True
self.width = width
self.kappa = kappa
self.sigma = sigma
self.mu = mu
self.decompspor = 0 ## sporulation events this turn
self.endospor = 0 ## sporulation events this turn
self.datacollector = DataCollector(
model_reporters={
"Endophytes": sumendos,
"Endo_subs": Endo_subs,
"Decomposers": sumdecomps,
"Decomp_subs": Decomp_subs,
"Infected_trees": bluetrees,
"decompspor_count": decompspor_count,
"endospor_count": endospor_count,
"Trees": tracktrees,
})
## make initial agents:
if not nuke: ## if not a nuclear holocaust where life is devoid
self.make_trees()
for i in range(self.nwood): self.add_wood() ## no make_woods method
self.make_fungi()
def make_trees(self):
## let's use our thomas process module
tname = 1
positions = tp.makepos(tp.ThomasPP(kappa = self.kappa,
sigma=self.sigma,
mu=self.mu,
Dx=self.grid.width-1))
for i in positions:
try:
tree = Tree(tname, self, i,
disp = self.leafdisp,
leaffall = self.leaffall,
endoloss = self.endoloss,
infection = False)
self.schedule.add(tree)
self.grid.place_agent(tree, i)
tname += 1
except IndexError:
print ("Tree out-of-bounds, ipos=",i,"grid dim=", self.grid.width, self.grid.height)
## add initial wood to landscape
def add_wood(self):
wname = len(self.getall(Wood)) + 1
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
pos = (x, y)
## wood already present? then just add to the pile
if any([ type(i)==Wood for i in self.grid.get_cell_list_contents(pos) ]):
for i in self.grid.get_cell_list_contents(pos):
if type(i)==Wood:
i.energy += random.randrange(self.newwood) ##
else:
wood = Wood(wname, self, pos, energy = random.randrange(self.newwood)+1) ##
self.grid.place_agent(wood, (x,y))
self.schedule.add(wood)
wname += 1
## non-initial, step addition of wood
def cwd(self):
cwdlist = [round(random.randrange(self.newwood))+1] ## our first log of the step, at least 1
while sum(cwdlist) < round(self.newwood*.9)-1 : ## until we get at 90% of our assigned cwd...
newlog=round(random.randrange(self.newwood-sum(cwdlist)))+1 ## new log, at least 1 kg
cwdlist.append(newlog) ## put newlog on the list, until newwood reached)
wname = len(self.getall(Wood)) + 1
for i in cwdlist:
self.add_wood()
def make_fungi(self):
fname = len(self.getall(Fungus)) + 1
## decomposers first:
decomps=0
while decomps < self.numdecomp:
pos = self.findsubstrate(Wood)
if any([ type(i)==Fungus for i in self.grid.get_cell_list_contents(pos) ]):
pass
else:
fungus = Fungus(fname, self, pos, energy=10, endocomp=False, disp = self.decompdisp)
self.schedule.add(fungus)
self.grid.place_agent(fungus, pos)
fname += 1; decomps += 1
## then endophytes:
endos=0
while endos < self.numendo:
pos = self.findsubstrate(Wood)
if any([ type(i)==Fungus for i in self.grid.get_cell_list_contents(pos) ]):
pass
else:
fungus = Fungus(fname, self, pos, energy=10, endocomp=True, disp = self.endodisp)
self.schedule.add(fungus)
self.grid.place_agent(fungus, pos)
fname += 1; endos += 1
def findsubstrate (self, substrate):
Subs = self.getall(substrate)
try:
somestick = (random.choice(Subs).pos) ## pick from these, return position
return(somestick) ## pick from these, return position
except IndexError:
print("no substrates")
pass
def selthin(self, intensity):
## intensity should be in the form of a percentage
aa=self.getall(Tree)
if intensity > 1 or intensity < 0:
print("too intense! (or not intense enough)")
pass
else:
bb=random.sample(aa, int(len(aa)*intensity))
[ i.die() for i in bb ]
def getall(self, typeof):
if not any([ type(i)==typeof for i in self.schedule.agents ]):
return([])
else:
istype = np.array([ type(i)==typeof for i in self.schedule.agents ])
ags = np.array(self.schedule.agents)
return list(ags[istype])
############### deforestation functions ###########
## forest fragmentation:
def fragcenters(self,cens):
## cens=number of forest fragments
centers=[]
for i in range(cens):
x=int(random.random()*self.width) ## self.width instead
y=int(random.random()*self.width) ## self.width instead
z=(x,y)
centers.append(z)
return(centers)
def onefrag(self, center, ags, rad=10):
## center=center of fragment
## ags = list of trees
## rad = radius of fragment to be protected
survivors = []
for i in ags:
distf=((center[0]-i.pos[0])**2 + (center[1]-i.pos[1])**2)**(1/2)
if distf <= rad: survivors.append(i)
return(survivors)
def fragup(self, centers, rad):
## 1 - get trees
alltrees = self.getall(Tree)
## 2 - get centers
fcenters = self.fragcenters(centers)
## 3 - designate survivors
remnants = []
for i in fcenters:
surv=self.onefrag(i, alltrees, rad)
remnants.extend(surv)
## kill everything else
cuttrees = set(alltrees) - set(remnants)
for i in cuttrees: i.die()
## maybe useful for plotting, return the objects
return({"alltrees":alltrees,
"centers":fcenters,
"remnants":remnants,
"cuttrees":cuttrees,
})
## Selective thinning:
def selthin(self, intensity):
## intensity should be in the form of a percentage
aa=self.getall(Tree)
if intensity > 1:
print("too intense!")
pass
else:
bb=random.sample(aa, int(len(aa)*intensity))
for i in bb: i.die() ## kill em
## plot data:
return({"alltrees":aa,
"centers":None,
"remnants":self.getall(Tree),
"cuttrees":bb,
})
#############################################################
## step
def step(self):
if self.schedule.time % self.woodfreq == self.woodfreq - 1: ## = delay from start
self.cwd() ## add wood
self.schedule.step() ## agents do their thing
self.datacollector.collect(self) ## collect data
self.decompspor = 0 ## reset sporulation event tally
self.endospor = 0 ## reset sporulation event tally
class SpeedModel(Model):
"""
Model of the game "Spe_ed". This class controls the execution of the simulation.
"""
def __init__(self,
width,
height,
nb_agents,
agent_classes,
initial_agents_params=None,
cells=None,
data_collector=None,
save=False):
"""
Model-Initialization.
:param width: Width of the field
:param height: Height of the field
:param nb_agents: Number of Agents
:param agent_classes: List of classes of the agents that should be players in the game. The length has to be
equal or greater than nb_agents
:param initial_agents_params: A list of dictionaries containing initialization parameters for agents that should
be initialized at the start of the simulation
:param cells: A Spe_ed-cells like 2D-Array that initializes the field
:param data_collector: Mesa data collector function
:param save: whether or not track the games history
"""
super().__init__()
self.data_collector = data_collector
self.width = width
self.height = height
self.nb_agents = nb_agents
self.save = save
if self.save:
self.history = []
if initial_agents_params is None:
initial_agents_params = [{} for i in range(nb_agents)]
else:
initial_agents_params = copy.deepcopy(initial_agents_params)
self.schedule = SimultaneousActivation(self)
self.grid = MultiGrid(width, height, True)
# width and height are swapped since height is rows and width is columns
# an alternative to this representation would be to transpose cells everytime it is exposed
# but that could be inefficient
self.cells = np.zeros((height, width), dtype="int")
# Init initial agents
self.speed_agents = []
self.active_speed_agents = []
for i in range(nb_agents):
agent_params = initial_agents_params[i]
agent_params["model"] = self
# set to random position/direction if no position/direction is given
if "pos" not in agent_params:
agent_params["pos"] = self.random.choice(
list(self.grid.empties))
if "direction" not in agent_params:
agent_params["direction"] = self.random.choice(list(Direction))
agent = agent_classes[i](**agent_params)
# don't add agent to grid/cells if its out of bounds. But add it to the scheduler.
if self.grid.out_of_bounds(agent_params["pos"]):
self.schedule.add(agent)
self.speed_agents.append(agent)
else:
self.add_agent(agent)
self.speed_agents.append(agent)
self.active_speed_agents.append(agent)
if cells is not None:
self.init_cells_and_grid(cells)
def init_cells_and_grid(self, cells):
"""
Initializes Mesas Grid and the Model-cells the field with the information given in cells.
:param cells: A Spe_ed-cells like 2D-Array that initializes the field
:return: None
"""
self.cells = np.array(cells)
# add traces to grid
for y in range(self.cells.shape[0]):
for x in range(self.cells.shape[1]):
# cell is occupied by a collision
if self.cells[y, x] == -1:
agent = AgentTraceCollision(self, (x, y))
self.add_agent(agent)
# cell is occupied by head or trace
elif self.cells[y, x] != 0:
# head of the agent is not already a entry in self.grid
if len(self.grid.get_cell_list_contents((x, y))) == 0:
# add trace
agent = AgentTrace(
self, (x, y),
self.speed_agents[self.cells[y, x] -
1]) # get agent based on id
self.add_agent(agent)
def step(self):
"""
Computes one iteration of the model.
:return: None
"""
if self.data_collector:
self.data_collector.collect(self)
if self.save:
self.history.append(copy.deepcopy(model_to_json(self)))
self.schedule.step()
self.check_collisions()
self.check_game_finished()
def step_specific_agent(self, agent):
"""
Only steps one specific agent. This is only for specific applications (e.g. Multi-Minimax).
Don't use this method if not necessary since it doesn't increment all model parts (e.g. time).
:param cells: The agent to step
:return: None
"""
agent.step()
agent.advance()
self.check_collisions()
self.check_game_finished()
def check_collisions(self):
"""
Checks every active agent for collisions with traces or other agents. Colliding agents are eliminated.
:return: None
"""
agents_to_set_inactive = []
for agent in self.speed_agents:
for t in agent.trace:
cell_contents = self.grid.get_cell_list_contents(t)
if len(cell_contents) > 1:
if agent not in agents_to_set_inactive:
agents_to_set_inactive.append(agent)
self.add_agent(AgentTraceCollision(self, t))
for agent in agents_to_set_inactive:
agent.set_inactive()
def check_game_finished(self):
"""
Checks whether or not the game has finished (every agent is eliminated) and prints the result if finished.
:return: None
"""
if len(self.active_speed_agents) <= 1:
self.running = False
if self.save:
self.history.append(copy.deepcopy(model_to_json(self)))
path = os.path.abspath("") + "/res/simulatedGames/"
for entry in self.history:
entry["cells"] = entry["cells"].tolist()
with open(
path + datetime.datetime.now().strftime(
"%d-%m-%y__%H-%M-%S-%f") + ".json", "w") as f:
json.dump(self.history, f, indent=4)
def add_agent(self, agent):
"""
Adds an agent to the model.
:param agent: The agent to add to the model
:return: None
"""
self.schedule.add(agent)
self.grid.place_agent(agent, agent.pos)
# swapped position args since cells has the format (height, width)
pos = (agent.pos[1], agent.pos[0])
if isinstance(agent, SpeedAgent):
self.cells[pos] = agent.unique_id
elif type(agent) is AgentTraceCollision:
self.cells[pos] = -1
elif type(agent) is AgentTrace:
self.cells[pos] = agent.origin.unique_id
def remove_agent(self, agent):
"""
Removes an agent from the model.
:param agent: The agent to remove from the model
:return: None
"""
if agent in self.schedule.agents:
self.schedule.remove(agent)
self.grid.remove_agent(agent)
def get_agent_by_id(self, unique_id):
"""
Returns an agent-object by its unique_id.
:param unique_id: The agent id to search for
:return: Agent or None if no match
"""
for agent in self.speed_agents:
if agent.unique_id == unique_id:
return agent
return None
class SOBAModel(Model):
def __init__(self,
width,
height,
modelWay=None,
seed=int(time()),
nothing=1,
voting_method=False):
super().__init__(seed)
#Init configurations and defines
configuration.settings.init()
configuration.defineOccupancy.init()
configuration.defineMap.init()
#Way of working
if modelWay is None:
self.modelWay = configuration.settings.model
else:
self.modelWay = modelWay
#Mesa
self.schedule = BaseScheduler(self)
self.grid = MultiGrid(width, height, False)
self.running = True
#Control of time and energy
self.energy = Energy()
self.clock = Time()
self.voting_method = voting_method
self.sc = SocialChoice()
#Log
self.log = Log()
if self.voting_method:
self.log = Logsc()
self.roomsSchedule = []
self.agentSatisfationByStep = []
self.fangerSatisfationByStep = []
self.agentsActivityByTime = []
self.averageSatisfationByTime = []
self.totalSatisfationByTime = []
self.occupantsValues = False
if self.modelWay != 0 and os.path.isfile('../log/tmp/occupants.txt'):
self.occupantsValues = self.log.getOccupantsValues()
#Vars of control
self.complete = False
self.num_occupants = 0
self.day = self.clock.day
self.NStep = 0
self.timeToSampling = 'init' # Temperature ThermalLoads
self.placeByStateByTypeAgent = {}
self.lightsOn = []
#Create the map
self.createRooms()
self.createThermalzones()
self.setMap(width, height)
self.createDoors()
self.createWindows()
self.createWalls()
#Create agents
self.setAgents()
def consumeEnergy(self, appliance):
if isinstance(appliance, PC):
if appliance.state == 'on':
self.energy.consumeEnergyAppliance('PC', appliance.consumeOn)
elif appliance.state == 'standby':
self.energy.consumeEnergyAppliance('PC',
appliance.consumeStandby)
else:
self.energy.consumeEnergyAppliance('PC', 0)
elif isinstance(appliance, Light):
if appliance.state == 'on':
self.energy.consumeEnergyAppliance('Light', appliance.consume)
else:
self.energy.consumeEnergyAppliance('Light', 0)
elif isinstance(appliance, HVAC):
if appliance.state == 'on':
self.energy.consumeEnergyAppliance('HVAC', appliance.consumeOn)
else:
self.energy.consumeEnergyAppliance('HVAC', 0)
else:
pass
def isConected(self, pos):
nextRoom = False
for room in self.rooms:
if room.pos == pos:
nextRoom = room
if nextRoom == False:
return False
for x in range(0, width):
for y in range(0, height):
self.pos_out_of_map.append(x, y)
for room in self.rooms:
self.pos_out_of_map.remove(room.pos)
def createRooms(self):
rooms = configuration.defineMap.rooms_json
self.rooms = []
#occupantsByTypeRoom = configuration.defineMap.NumberOccupancyByTypeRoom
for room in rooms:
newRoom = 0
name = room['name']
typeRoom = room['type']
if typeRoom != 'out':
conectedTo = room.get('conectedTo')
nameThermalZone = room.get('thermalZone')
entrance = room.get('entrance')
measures = room['measures']
dx = measures['dx']
dy = measures['dy']
dh = measures['dh']
jsonWindows = room.get('windows')
newRoom = Room(name, typeRoom, conectedTo, nameThermalZone, dx,
dy, dh, jsonWindows)
newRoom.entrance = entrance
else:
newRoom = Room(name, typeRoom, None, False, 0, 0, 0, {})
self.outBuilding = newRoom
self.rooms.append(newRoom)
for room1 in self.rooms:
if room1.conectedTo is not None:
for otherRooms in list(room1.conectedTo.values()):
for room2 in self.rooms:
if room2.name == otherRooms:
room1.roomsConected.append(room2)
room2.roomsConected.append(room1)
for room in self.rooms:
room.roomsConected = list(set(room.roomsConected))
sameRoom = {}
for room in self.rooms:
if sameRoom.get(room.name.split(r".")[0]) is None:
sameRoom[room.name.split(r".")[0]] = 1
else:
sameRoom[room.name.split(r".")
[0]] = sameRoom[room.name.split(r".")[0]] + 1
def createThermalzones(self):
self.thermalZones = []
namesThermalZonesCreated = []
for room1 in self.rooms:
posibleThermalZone = room1.nameThermalZone
if posibleThermalZone not in namesThermalZonesCreated and posibleThermalZone != False and posibleThermalZone is not None:
namesThermalZonesCreated.append(posibleThermalZone)
rooms = []
for room2 in self.rooms:
if room2.nameThermalZone == posibleThermalZone:
rooms.append(room2)
TZ = ThermalZone(self, posibleThermalZone, rooms)
for room3 in TZ.rooms:
room3.thermalZone = TZ
self.thermalZones.append(TZ)
if self.modelWay == 2:
hoursRoomsOnOffByDay = {}
hoursRoomsOnStrings = self.log.getScheduleRooms()
hoursRoomsOn = []
for row in hoursRoomsOnStrings:
hoursRoomsOn.append([row[0], float(row[1]), float(row[2])])
for room in self.rooms:
count = 0
if room.typeRoom != 'out' and room.typeRoom != 'restroom':
hoursOneRoomOn = []
for row in hoursRoomsOn:
if row[0] == room.name:
hoursOneRoomOn.append([row[1], row[2]])
hoursOneRoomOnByDay = []
for i in range(0, 5):
hoursOneDay = []
for hour in hoursOneRoomOn:
if int(hour[0]) == i:
hoursOneDay.append(hour[1])
hoursOneRoomOnByDay.append(hoursOneDay)
hoursOnOffOneRoomByDay = []
for i in range(0, 5):
hoursOnOffOneRoomOneDay = []
hourOn = hoursOneRoomOnByDay[i]
if len(hourOn) > 0:
auxHour = hourOn[0]
hourOnAux = hourOn[0]
for hour in hourOn:
if (auxHour != hour):
if (hour >
(auxHour +
configuration.settings.setOffWorthIt)
):
hourOff = auxHour - 0.01
pairOnOff = [
int(hourOnAux * 100) / 100,
int(hourOff * 100) / 100
]
hoursOnOffOneRoomOneDay.append(
pairOnOff)
auxHour = hour + 0.01
hourOnAux = hour
else:
auxHour = hour + 0.01
else:
auxHour = auxHour + 0.01
pairOnOffObligatory = [hourOnAux, hourOn.pop()]
hoursOnOffOneRoomOneDay.append(pairOnOffObligatory)
hoursOnOffOneRoomByDay.append(
hoursOnOffOneRoomOneDay)
else:
hoursOnOffOneRoomByDay.append(False)
hoursRoomsOnOffByDay[room.name] = hoursOnOffOneRoomByDay
count = count + 1
for tz in self.thermalZones:
schedule = []
for i in range(0, 5):
scheduleByDay = []
for room in tz.rooms:
hoursByDay = hoursRoomsOnOffByDay.get(room.name)
if hoursByDay is not None and hoursByDay != False:
hoursOneDay = hoursByDay[i]
if hoursOneDay is not None and hoursOneDay != False:
for hours in hoursOneDay:
hourOn = hours[0]
hourOff = hours[1]
for pairHours in scheduleByDay:
if hourOn > pairHours[0] and pairHours[
1] > hourOn:
scheduleByDay.remove(
[pairHours[0], pairHours[1]])
hourOn = pairHours[0]
if (hourOff < pairHours[1]):
hourOff = pairHours[1]
else:
pass
elif hourOff > pairHours[
0] and pairHours[1] > hourOff:
scheduleByDay.remove(
[pairHours[0], pairHours[1]])
hourOff = pairHours[1]
if (hourOn > pairHours[0]):
hourOn = pairHours[0]
else:
pass
elif hourOff > pairHours[
1] and pairHours[0] > hourOn:
scheduleByDay.remove(
[pairHours[0], pairHours[1]])
scheduleByDay.append([hourOn, hourOff])
if len(scheduleByDay) == 0:
scheduleByDay.append([False])
schedule.append(scheduleByDay)
scheduleJoined = []
for day in schedule:
if day != False:
scheduleJoined.append(sorted(day))
for day in scheduleJoined:
i = 0
if day != False:
while (len(day) > (i + 1)):
if (day[i][1] +
configuration.settings.setOffWorthIt
) > day[i + 1][0]:
day[i][1] = day[i + 1][1]
day.pop(i + 1)
else:
i = i + 1
tz.schedule = scheduleJoined
def setMap(self, width, height):
rooms_noPos = self.rooms
rooms_using = []
rooms_used = []
for room in self.rooms:
if room.entrance is not None:
room.pos = (int(1), 2)
rooms_using.append(room)
rooms_used.append(room)
rooms_noPos.remove(room)
break
while len(rooms_noPos) > 0:
for roomC in rooms_using:
xc, yc = roomC.pos
rooms_conected = roomC.conectedTo
rooms_using.remove(roomC)
if rooms_conected is not None:
orientations = list(rooms_conected.keys())
for orientation in orientations:
if orientation == 'R':
for room in rooms_noPos:
if room.name == rooms_conected['R']:
room.pos = (int(xc + 1), yc)
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'U':
for room in rooms_noPos:
if room.name == rooms_conected['U']:
room.pos = (xc, int(yc + 1))
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'D':
for room in rooms_noPos:
if room.name == rooms_conected['D']:
room.pos = (xc, int(yc - 1))
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
elif orientation == 'L':
for room in rooms_noPos:
if room.name == rooms_conected['L']:
room.pos = (int(xc - 1), yc)
rooms_noPos.remove(room)
rooms_used.append(room)
rooms_using.append(room)
else:
pass
self.rooms = rooms_used
def createDoors(self):
self.doors = []
for roomC in self.rooms:
roomsConected = roomC.roomsConected
for room in roomsConected:
door_created = False
same_corridor = False
if room.name != roomC.name:
for door in self.doors:
if (door.room1.name == roomC.name
and door.room2.name == room.name) or (
door.room2.name == roomC.name
and door.room1.name == room.name):
door_created = True
if room.name.split(r".")[0] == roomC.name.split(
r".")[0]:
same_corridor = True
if door_created == False and same_corridor == False:
d = Door(roomC, room)
self.doors.append(d)
room.doors.append(d)
roomC.doors.append(d)
def createWindows(self):
for room in self.rooms:
windows = []
json = room.jsonWindows
if json is None:
pass
else:
for k in json:
window = Window(k, json[k]['l1'], json[k]['l2'])
windows.append(window)
room.windows = windows
def createWalls(self):
for room in self.rooms:
if room.typeRoom != 'out':
walls = []
innerWalls = []
adjRooms = []
xr, yr = room.pos
roomA = self.getRoom((xr, yr + 1))
if roomA != False:
if roomA.typeRoom != 'out':
if roomA.name.split(r".")[0] == room.name.split(
r".")[0]:
pass
else:
wall = Wall(room.dx, room.dh, room, roomA)
innerWalls.append(wall)
adjRooms.append(roomA)
else:
wall = Wall(room.dx, room.dh, orientation='N')
walls.append(wall)
else:
wall = Wall(room.dx, room.dh, orientation='N')
walls.append(wall)
roomB = self.getRoom((xr, yr - 1))
if roomB != False:
if roomB.typeRoom != 'out':
if roomB.name.split(r".")[0] == room.name.split(
r".")[0]:
pass
else:
wall = Wall(room.dx, room.dh, room, roomB)
innerWalls.append(wall)
adjRooms.append(roomB)
else:
wall = Wall(room.dx, room.dh, orientation='S')
walls.append(wall)
else:
wall = Wall(room.dx, room.dh, orientation='S')
walls.append(wall)
roomC = self.getRoom((xr + 1, yr))
if roomC != False:
if roomC.typeRoom != 'out':
if roomC.name.split(r".")[0] == room.name.split(
r".")[0]:
pass
else:
wall = Wall(room.dy, room.dh, room, roomC)
innerWalls.append(wall)
adjRooms.append(roomC)
else:
wall = Wall(room.dy, room.dh, orientation='E')
walls.append(wall)
else:
wall = Wall(room.dy, room.dh, orientation='E')
walls.append(wall)
roomD = self.getRoom((xr - 1, yr))
if roomD != False:
if roomD.typeRoom != 'out':
if roomD.name.split(r".")[0] == room.name.split(
r".")[0]:
pass
else:
wall = Wall(room.dy, room.dh, room, roomD)
innerWalls.append(wall)
adjRooms.append(roomD)
else:
wall = Wall(room.dy, room.dh, orientation='W')
walls.append(wall)
else:
wall = Wall(room.dy, room.dh, orientation='W')
walls.append(wall)
room.walls = walls
room.innerWalls = innerWalls
room.roomsAdj = adjRooms
def setAgents(self):
# Identifications
id_offset = 1000
# Height and Width
height = self.grid.height
width = self.grid.width
# CREATE AGENTS
#Create Lightlights
self.lights = []
id_light = 0
for room in self.rooms:
if room.typeRoom != 'out' and room.light == False:
light = Light(id_light, self, room)
self.lights.append(light)
id_light = id_light + 1
room.light = light
for room2 in self.rooms:
if room.name.split(r".")[0] == room2.name.split(r".")[0]:
room2.light = light
id_hvac = id_light + id_offset
#Create HVAC
self.HVACs = []
for thermalZone in self.thermalZones:
restroom = False
for room in thermalZone.rooms:
if room.typeRoom == 'restroom':
restroom = True
if restroom == False:
hvac = HVAC(id_hvac, self, thermalZone)
thermalZone.hvac = hvac
self.HVACs.append(hvac)
id_hvac = id_hvac + 1
else:
thermalZone.hvac = False
#Create PC
'''
self.workplaces = []
id_aux = 0
for room in self.rooms:
#for i in range(0, room.PCs):
pc = PC(id_pc + id_aux, self, room)
room.PCs.append(pc)
self.workplaces.append(pc)
id_aux = id_aux + 1
'''
id_occupant = id_hvac + id_offset
id_pc = id_occupant + id_offset
self.workplaces = []
self.agents = []
# Create occupants
if self.modelWay == 0:
countPC = 0
print('Número de ocupantes: ',
configuration.defineOccupancy.occupancy_json[0]['N'])
for n_type_occupants in configuration.defineOccupancy.occupancy_json:
self.placeByStateByTypeAgent[
n_type_occupants['type']] = n_type_occupants['states']
n_agents_perfect = int(
(n_type_occupants['N'] *
n_type_occupants['environment'][0]) / 100)
for i in range(0, n_agents_perfect):
rooms_with_already_pc = []
a = Occupant(id_occupant, self, n_type_occupants, 1)
self.agents.append(a)
id_occupant = 1 + id_occupant
for state_use_PCs in n_type_occupants['PCs']:
roomPC = False
name_room_with_pc = a.positionByState[state_use_PCs]
for room in self.rooms:
if room.name.split(r".")[0] == name_room_with_pc:
roomPC = room
if roomPC != False and roomPC.typeRoom != 'out':
if roomPC not in rooms_with_already_pc:
pc = PC(id_pc, self, roomPC)
id_pc = id_pc + 1
pc.owner = a
self.workplaces.append(pc)
a.PCs[state_use_PCs] = pc
pc.states_when_is_used.append(state_use_PCs)
roomPC.PCs.append(pc)
else:
for pcaux in roomPC.PCs:
if pcaux.owner == a:
a.PCs[state_use_PCs] = pcaux
pc.states_when_is_used.append(
state_use_PCs)
self.schedule.add(a)
self.grid.place_agent(a, self.outBuilding.pos)
self.pushAgentRoom(a, self.outBuilding.pos)
self.num_occupants = self.num_occupants + 1
n_agents_good = int((n_type_occupants['N'] *
n_type_occupants['environment'][1]) / 100)
for i in range(0, n_agents_good):
rooms_with_already_pc = []
a = Occupant(id_occupant, self, n_type_occupants, 2)
self.agents.append(a)
id_occupant = 1 + id_occupant
for state_use_PCs in n_type_occupants['PCs']:
roomPC = False
name_room_with_pc = a.positionByState[state_use_PCs]
for room in self.rooms:
if room.name.split(r".")[0] == name_room_with_pc:
roomPC = room
if roomPC != False and roomPC.typeRoom != 'out':
if roomPC not in rooms_with_already_pc:
pc = PC(id_pc, self, roomPC)
id_pc = id_pc + 1
pc.owner = a
self.workplaces.append(pc)
a.PCs[state_use_PCs] = pc
pc.states_when_is_used.append(state_use_PCs)
roomPC.PCs.append(pc)
else:
for pcaux in roomPC.PCs:
if pcaux.owner == a:
a.PCs[state_use_PCs] = pcaux
pc.states_when_is_used.append(
state_use_PCs)
self.schedule.add(a)
self.grid.place_agent(a, self.outBuilding.pos)
self.pushAgentRoom(a, self.outBuilding.pos)
self.num_occupants = self.num_occupants + 1
n_agents_bad = int(n_type_occupants['N'] *
n_type_occupants['environment'][2] / 100)
allAgents = n_agents_perfect + n_agents_good + n_agents_bad
if allAgents < n_type_occupants['N']:
n_agents_bad = n_type_occupants['N'] - (n_agents_perfect +
n_agents_good)
for i in range(0, n_agents_bad):
rooms_with_already_pc = []
a = Occupant(id_occupant, self, n_type_occupants, 3)
self.agents.append(a)
id_occupant = 1 + id_occupant
for state_use_PCs in n_type_occupants['PCs']:
roomPC = False
name_room_with_pc = a.positionByState[state_use_PCs]
for room in self.rooms:
if room.name.split(r".")[0] == name_room_with_pc:
roomPC = room
if roomPC != False and roomPC.typeRoom != 'out':
if roomPC not in rooms_with_already_pc:
pc = PC(id_pc, self, roomPC)
id_pc = id_pc + 1
pc.owner = a
self.workplaces.append(pc)
a.PCs[state_use_PCs] = pc
pc.states_when_is_used.append(state_use_PCs)
roomPC.PCs.append(pc)
else:
for pcaux in roomPC.PCs:
if pcaux.owner == a:
a.PCs[state_use_PCs] = pcaux
pc.states_when_is_used.append(
state_use_PCs)
self.schedule.add(a)
self.grid.place_agent(a, self.outBuilding.pos)
self.pushAgentRoom(a, self.outBuilding.pos)
self.num_occupants = self.num_occupants + 1
else:
for n_type_occupants in configuration.defineOccupancy.occupancy_json:
self.placeByStateByTypeAgent[
n_type_occupants['type']] = n_type_occupants['states']
n_agents = n_type_occupants['N']
for i in range(0, n_agents):
rooms_with_already_pc = []
a = Occupant(id_occupant, self, n_type_occupants, '')
self.agents.append(a)
id_occupant = 1 + id_occupant
for state_use_PCs in n_type_occupants['PCs']:
roomPC = False
name_room_with_pc = a.positionByState[state_use_PCs]
for room in self.rooms:
if room.name.split(r".")[0] == name_room_with_pc:
roomPC = room
if roomPC != False and roomPC.typeRoom != 'out':
if roomPC not in rooms_with_already_pc:
pc = PC(id_pc, self, roomPC)
id_pc = id_pc + 1
pc.owner = a
self.workplaces.append(pc)
a.PCs[state_use_PCs] = pc
pc.states_when_is_used.append(state_use_PCs)
roomPC.PCs.append(pc)
else:
for pcaux in roomPC.PCs:
if pcaux.owner == a:
a.PCs[state_use_PCs] = pcaux
pc.states_when_is_used.append(
state_use_PCs)
self.schedule.add(a)
self.grid.place_agent(a, self.outBuilding.pos)
self.pushAgentRoom(a, self.outBuilding.pos)
self.num_occupants = self.num_occupants + 1
#Add to schedule
for pc in self.workplaces:
self.schedule.add(pc)
for light in self.lights:
self.schedule.add(light)
for hvac in self.HVACs:
self.schedule.add(hvac)
self.schedule.add(self.clock)
def getPosState(self, name, typeA):
placeByStateByTypeAgent = self.placeByStateByTypeAgent
n = 0
for state in self.placeByStateByTypeAgent[typeA]:
if state.get('name') == name:
pos1 = state.get('position')
if isinstance(pos1, dict):
for k, v in pos1.items():
if v > 0:
placeByStateByTypeAgent[typeA][n]['position'][
k] = v - 1
self.placeByStateByTypeAgent = placeByStateByTypeAgent
return k
return list(pos1.keys())[-1]
else:
return pos1
n = n + 1
def thereIsClosedDoor(self, beforePos, nextPos):
oldRoom = False
newRoom = False
for room in rooms:
if room.pos == beforePos:
oldRoom = room
if room.pos == nextPos:
newRoom = room
for door in self.doors:
if (door.room1.name == oldRoom.name and door.room2.name
== newRoom.name) or (door.room2.name == oldRoom.name
and door.room1.name == newRoom.name):
if door.state == False:
return True
return False
def thereIsPC(self, pos):
x, y = pos
for pc in self.workplaces:
if pc.x == x and pc.y == y:
return True
return False
def thereIsOccupant(self, pos):
possible_occupant = self.grid.get_cell_list_contents([pos])
if (len(possible_occupant) > 0):
for occupant in possible_occupant:
if isinstance(occupant, Occupant):
return True
return False
def ThereIsOtherOccupantInRoom(self, room, agent):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, Occupant) and occupant != agent:
return True
return False
def ThereIsSomeOccupantInRoom(self, room):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, Occupant):
return True
return False
def thereIsOccupantInRoom(self, room, agent):
for roomAux in self.rooms:
possible_occupant = []
if roomAux.name.split(r".")[0] == room.name.split(r".")[0]:
possible_occupant = self.grid.get_cell_list_contents(
roomAux.pos)
for occupant in possible_occupant:
if isinstance(occupant, Occupant) and occupant == agent:
return True
return False
def getRoom(self, pos):
for room in self.rooms:
if room.pos == pos:
return room
return False
def pushAgentRoom(self, agent, pos):
room = self.getRoom(pos)
room.agentsInRoom.append(agent)
def popAgentRoom(self, agent, pos):
room = self.getRoom(pos)
room.agentsInRoom.remove(agent)
def getLightWithRoom(self, room):
for light in self.lights:
if light.room == room:
return light
return False
def crossDoor(self, agent, room1, room2):
numb = random.randint(0, 10)
for door in self.doors:
if ((door.room1 == room1 and door.room2 == room2)
or (door.room1 == room2 and door.room2 == room1)):
if agent.leftClosedDoor >= numb:
door.state = False
else:
door.state = True
def getMatrix(self, agent):
new_matrix = configuration.defineOccupancy.returnMatrix(
agent, self.clock.clock)
agent.markov_matrix = new_matrix
def getTimeInState(self, agent):
matrix_time_in_state = configuration.defineOccupancy.getTimeInState(
agent, self.clock.clock)
return matrix_time_in_state
def end_work(self, agent, pc):
change = configuration.defineOccupancy.environmentBehaviour(
agent, self.clock.clock, 'pc')[int(agent.environment) - 1]
print(change)
if change == 'off':
pc.turn_off()
elif change == 'standby':
pc.turn_standby()
else:
pass
def switchLights(self, agent, currentRoom, nextRoom):
change = configuration.defineOccupancy.environmentBehaviour(
agent, self.clock.clock, 'light')[int(agent.environment) - 1]
light_switch_on = nextRoom.light
if light_switch_on != False and light_switch_on.state == 'off':
light_switch_on.switch_on()
if change == 'off':
light_switch_off = currentRoom.light
if self.ThereIsOtherOccupantInRoom(currentRoom, agent) == False:
if light_switch_off != False:
light_switch_off.switch_off()
else:
pass
def step(self):
if (self.running == False):
os.system("kill -9 %d" % (os.getppid()))
os.killpg(os.getpgid(os.getppid()), signal.SIGTERM)
if (self.clock.day == 5):
self.energy.finalDay(self.NStep)
self.energy.finalWeek()
self.running = False
if self.voting_method:
self.log.collectEnergyValues(
self, self.energy.energyByDayTotal,
self.energy.energyByDayHVAC, self.energy.energyByDayLPC,
configuration.settings.time_by_step,
self.energy.energyByStepTotal,
self.energy.energyByStepHVACsTotal,
self.energy.energyByStepLPCTotal)
self.log.collectComfortValues(
self, configuration.settings.time_by_step,
self.agentSatisfationByStep, self.fangerSatisfationByStep)
self.log.collectScheduleValues(
self, configuration.settings.time_by_step,
self.agentsActivityByTime)
self.log.collectSatisfactionValues(
self, configuration.settings.time_by_step,
self.totalSatisfationByTime, self.averageSatisfationByTime)
else:
self.log.collectEnergyValues(
self.modelWay, self.energy.energyByDayTotal,
self.energy.energyByDayHVAC, self.energy.energyByDayLPC,
configuration.settings.time_by_step,
self.energy.energyByStepTotal,
self.energy.energyByStepHVACsTotal,
self.energy.energyByStepLPCTotal)
self.log.collectComfortValues(
self.modelWay, configuration.settings.time_by_step,
self.agentSatisfationByStep, self.fangerSatisfationByStep)
self.log.collectScheduleValues(
self.modelWay, configuration.settings.time_by_step,
self.agentsActivityByTime)
self.log.collectSatisfactionValues(
self.modelWay, configuration.settings.time_by_step,
self.totalSatisfationByTime, self.averageSatisfationByTime)
if self.modelWay == 0:
self.log.saveScheduleRooms(self.roomsSchedule)
dictAgents = {}
for agent in self.agents:
agent.scheduleLog.append(
[self.day, agent.arrive, agent.leave])
scheduleByDay = {}
for e in agent.scheduleLog:
d = 'day' + str(e[0])
scheduleByDay[d] = {'arrive': e[1], 'leave': e[2]}
posByState = {}
for k, v in agent.positionByState.items():
posByState[k] = v
dictAgents[str(agent.unique_id)] = {
'TComfort': agent.TComfort,
'posByState': posByState,
'schedule': scheduleByDay
}
self.log.saveOccupantsValues(dictAgents)
return
if self.modelWay == 0 or self.modelWay == 1:
for hvac in self.HVACs:
if ((self.clock.clock >
(configuration.defineMap.ScheduleByTypeRoom.get(
hvac.thermalZone.rooms[0].typeRoom)[0]))
and (configuration.defineMap.ScheduleByTypeRoom.get(
hvac.thermalZone.rooms[0].typeRoom)[1] >
self.clock.clock)):
hvac.working = True
else:
hvac.working = False
elif self.modelWay == 2:
for hvac in self.HVACs:
for hours in hvac.thermalZone.schedule[self.clock.day]:
if hours != [False]:
if ((self.clock.getCorrectHour(
self.clock.clock +
configuration.settings.timeSetOnHVACBeforeGetT)
) > hours[0]) and ((self.clock.getDownCorrectHour(
hours[1] - configuration.settings.
timeSetOffHVACBeforeloseT)) >
self.clock.clock):
hvac.working = True
elif ((hvac.thermalZone.rooms[0].typeRoom == 'class')
and ((self.clock.getCorrectHour(
self.clock.clock + configuration.settings.
timeSetOnHVACBeforeGetTClass)) > hours[0])
and ((self.clock.getDownCorrectHour(
hours[1] - configuration.settings.
timeSetOffHVACBeforeloseT)) >
self.clock.clock)):
hvac.working = True
else:
usr = False
if self.clock.getDownCorrectHour(
hours[1]) > self.clock.clock:
for room in hvac.thermalZone.rooms:
if self.ThereIsSomeOccupantInRoom(
room) == True:
usr = True
if usr == True:
hvac.working = True
else:
hvac.working = False
else:
hvac.working = False
#Temperature in TZ
if (self.timeToSampling == 'init'):
for tz in self.thermalZones:
tz.getQ(self, configuration.settings.timeToSampling)
self.timeToSampling = configuration.settings.timeToSampling * (
1 / configuration.settings.time_by_step)
elif (self.timeToSampling > 1):
self.timeToSampling = self.timeToSampling - 1
else:
for tz in self.thermalZones:
tz.step()
tz.getQ(self, configuration.settings.timeToSampling)
self.timeToSampling = configuration.settings.timeToSampling * (
1 / configuration.settings.time_by_step)
self.schedule.step()
#Rooms occupancy
time = self.clock.clock
day = self.clock.day
for room in self.rooms:
if len(
room.agentsInRoom
) > 0 and room.typeRoom != 'out' and room.typeRoom != 'restRoom':
self.roomsSchedule.append([room.name, day, time])
#Satisfation collection
time = configuration.settings.time_by_step * self.NStep
sumat = 0
number = 0
for agent in self.agents:
if self.getRoom(agent.pos).typeRoom != 'out' and self.getRoom(
agent.pos).typeRoom != 'restroom' and self.getRoom(
agent.pos).typeRoom != 'hall' and self.getRoom(
agent.pos).typeRoom != 'corridor':
sumat = sumat + agent.comfort
number = number + 1
if number > 0:
self.agentSatisfationByStep.append(sumat / number)
else:
self.agentSatisfationByStep.append(0)
sumat = 0
number = 0
for hvac in self.HVACs:
varaux = False
for room in hvac.thermalZone.rooms:
if self.ThereIsSomeOccupantInRoom(
room
) and room.typeRoom != 'out' and room.typeRoom != 'restroom' and room.typeRoom != 'corridor' and room.typeRoom != 'hall':
varaux = True
if varaux == True:
sumat = sumat + hvac.fangerValue
number = number + 1
if number > 0:
self.fangerSatisfationByStep.append(sumat / number)
else:
self.fangerSatisfationByStep.append(0)
# Satisfaction SC
if self.voting_method:
time = configuration.settings.time_by_step * self.NStep
sumat = 0
number = 0
for agent in self.agents:
if self.getRoom(agent.pos).typeRoom != 'out' and self.getRoom(
agent.pos).typeRoom != 'restroom' and self.getRoom(
agent.pos).typeRoom != 'hall' and self.getRoom(
agent.pos).typeRoom != 'corridor':
sumat += agent.preference['{:.1f}'.format(
self.getRoom(
agent.pos).thermalZone.hvac.desiredTemperature)]
number += 1
self.totalSatisfationByTime.append(sumat)
if number > 0:
self.averageSatisfationByTime.append(sumat / number)
else:
self.averageSatisfationByTime.append(0)
#Ocupancy activity collection
time = configuration.settings.time_by_step * self.NStep
sumat = 0
number = 0
for agent in self.agents:
if (agent.state == 'working in my office') or (
agent.state == 'in a meeting') or (
agent.state
== 'working in my laboratory') or (agent.state
== 'giving class'):
sumat = sumat + 1
self.agentsActivityByTime.append(sumat)
if len(self.lightsOn) > 0 and (self.clock.clock >
configuration.settings.offLights):
for light in self.lightsOn:
light.switch_off()
self.energy.finalStep()
if (self.clock.day > self.day):
self.energy.finalDay(self.NStep)
if self.modelWay == 0:
for agent in self.agents:
timeA = self.clock.getDownCorrectHour(agent.arrive - 0.10)
timeB = self.clock.getDownCorrectHour(agent.leave - 0.10)
agent.scheduleLog.append([self.day, timeA, timeB])
agent.arrive = False
agent.leave = False
if self.occupantsValues != False:
for agent in self.agents:
day = 'day' + str(self.day + 1)
agent.behaviour['arriveTime'] = self.occupantsValues[str(
agent.unique_id)]['schedule'][day]['arrive']
agent.behaviour['leaveWorkTime'] = self.occupantsValues[
str(agent.unique_id)]['schedule'][day]['leave']
self.day = self.day + 1
self.NStep = self.NStep + 1
class MesaTraitsModel(Model):
'''
TODO: add new description here
'''
height = 20
width = 20
no_of_species = 4
no_of_seeds = 1
verbose = False # Print-monitoring
description = 'A model for creating patch expansion out of a few patches.'
def __init__(self,
height=60,
width=60,
no_of_species=4,
no_of_seeds=2,
no_of_agents=20):
'''
Create a new MesaTraitsModel.
Args:
'''
super().__init__()
# Set parameters
self.height = height
self.width = width
self.no_of_agents = no_of_agents
self.schedule = RandomActivationByBreed(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
self.no_of_species = no_of_species
self.no_of_seeds = no_of_seeds
self.patch_manager = PatchManager()
self.datacollector = DataCollector(
{"Organisms": lambda m: m.schedule.get_breed_count(Organism)})
# Create patches
for agent, x, y in self.grid.coord_iter():
#print("new patch added")
grown = False
patch = Patch(self.next_id(), (x, y), self, 0.04, None, grown)
self.grid.place_agent(patch, (x, y))
self.schedule.add(patch)
self.patch_manager.add_patch(patch)
list_of_cells = []
#select some random patches (without substitution)
total_seeds = no_of_species * no_of_seeds
for i in range(self.width):
for j in range(self.height):
list_of_cells.append((i, j))
cords = random.sample(list_of_cells, total_seeds)
#choose agents at those patches to be assigned the species
_ = 0
for i in range(no_of_species):
for j in range(no_of_seeds):
patch = self.grid.get_cell_list_contents(cords[_])
patch[0].grown = True
patch[0].species = i
self.patch_manager.remove_patch(patch[0])
_ += 1
#make the patch grow
self.patch_manager.grow_patches()
# make moving agents
for i in range(20): #20 agents
x = random.choice(range(self.width))
y = random.choice(range(self.height))
agent = Organism(self.next_id(), (x, y),
self,
energy_tank=100,
current_energy=50,
metabolic_cost=1,
energy_gain_per_patch=5,
age=5,
sexual=False,
maturity_age=20,
max_longevity=1000,
patch_affinity=[1, 1, 0, 0],
climatic_affinity=0.6,
climatic_affinity_sd=0.05,
line_of_sight=1,
dispersal_speed=2,
reproductive_delay=0,
offspring_number=1,
moore=True)
self.grid.place_agent(agent, (x, y))
self.schedule.add(agent)
self.running = True
self.datacollector.collect(self)
def step(self):
self.schedule.step()
# collect data
self.datacollector.collect(self)
def run_model(self, step_count=200):
if self.verbose:
pass
for i in range(step_count):
self.step()
if self.verbose:
pass
class TumorModel(Model):
def __init__(self, width, height,
initial_tumor_size=2,
first_cycle_offset=80,
treatment_cycles=30,
treatment_cycle_interval=4,
param_scale=1.0,
kde=0.02,
proliferative_growth_rate=0.121,
proliferative_to_quiescent_rate=0.03,
proliferative_elimination_rate=0.7,
quiescent_to_damaged_rate=0.7,
damaged_to_proliferative_rate=0.003,
damaged_elimination_rate=0.008):
self.initial_tumor_size = initial_tumor_size
self.first_cycle_offset = first_cycle_offset
self.treatment_cycles = treatment_cycles
self.treatment_cycle_interval = treatment_cycle_interval
self.dissolve_rate = (1 - kde)
self.proliferative_growth_rate = proliferative_growth_rate * param_scale
self.proliferative_elimination_rate = proliferative_elimination_rate * param_scale
self.proliferative_to_quiescent_rate = proliferative_to_quiescent_rate * param_scale
self.quiescent_to_damaged_rate = quiescent_to_damaged_rate * param_scale
self.damaged_to_proliferative_rate = damaged_to_proliferative_rate * param_scale
self.damaged_elimination_rate = damaged_elimination_rate * param_scale
super().__init__()
self.grid = MultiGrid(width, height, torus=False)
self.schedule = RandomActivation(self)
self.running = True
self.step_number = 0
self.tumor_cells_count = initial_tumor_size ** 2
# Calculate tumor area coordinates
x1 = self.grid.width // 2 - self.initial_tumor_size // 2
x2 = x1 + self.initial_tumor_size - 1
y1 = self.grid.height // 2 - self.initial_tumor_size // 2
y2 = y1 + self.initial_tumor_size - 1
# Create agents
for x in range(self.grid.width):
for y in range(self.grid.height):
# uncomment to place a proliferative cell on the center
if x in range(x1, x2 + 1) and y in range(y1, y2 + 1):
if x == x1 or x == x2 or y == y1 or y == y2:
a = ProliferativeCellAgent(y * width + x, self, 0)
else:
a = QuiescentCellAgent(y * width + x, self, 0)
else:
a = CellAgent(y * width + x, self, 0)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
self.datacollector = DataCollector(
agent_reporters={"C": "C"},
model_reporters={"MTD": compute_MTD})
def step(self):
if self.step_number >= self.first_cycle_offset and \
self.treatment_cycles > 0 and \
(self.step_number - self.first_cycle_offset) % \
self.treatment_cycle_interval == 0:
self.treatment_cycles -= 1
for x in range(self.grid.width):
for y in range(self.grid.height):
cell = self.grid.get_cell_list_contents([(x,y)])[0]
if cell.pos[0] == 0 or \
cell.pos[1] == 0 or \
cell.pos[0] == self.grid.width - 1 or \
cell.pos[1] == self.grid.height - 1:
cell.C = 1
self.tumor_cells_count = tumor_cells_count(self)
self.datacollector.collect(self)
self.schedule.step()
self.step_number += 1
class Model(Model):
"""A model with a number of agents.
It represents a space where you can experiment by varying parameters"""
def __init__(self, n_victims, n_offenders, n_criminal_generators, r_criminal_generators, max_cp,
pop_count, width, height):
self.grid = MultiGrid(width, height, torus=False)
self.schedule = RandomActivation(self)
self.datacollector = DataCollector(
model_reporters={"Average Perception of Safety": average_perception_of_safety,
"Crime-rate": crime_rate_single_run},
)
self.width = width
self.height = height
self.running = True
# Number of crimes committed per time step.
self.crime_number = 0.0
# User settable parameters
self.num_victims = n_victims
self.num_offenders = n_offenders
self.num_crime_areas = n_criminal_generators
self.crime_area_rad = r_criminal_generators
self.hotspot_rad = 1
self.max_criminal_preference = max_cp
self.pop_count = pop_count
# add light agents to the grid
self.light_layer()
# add possible victims to the grid
self.add_victims()
# add possible offenders to the grid
self.add_offenders()
# add crime areas
self.add_criminal_areas()
self.datacollector.collect(self)
def light_layer(self):
"""Add the light layer to the model. """
for i in range(self.width):
for j in range(self.height):
l = Light(uuid.uuid4(), i + j, self)
self.schedule.add(l)
# light is ordered from darkest to lightest from the
# bottom left corner to the top right corner.
self.grid.place_agent(l, (i, j))
# light is randomised, so long as the
# luminance difference of surrounding cells is within a limit
# self.grid.place_agent(l, self.r_pos(l))
def r_pos(self, new_l):
"""Randomises the positions of the light realistically by considering the difference
between the illuminance in one position and another"""
# Position precisely one light agent per position.
valid = False
while not valid:
x = self.random.randrange(self.width)
y = self.random.randrange(self.height)
# Check if there is already a light agent in this spot
if not (any(isinstance(agent, Light)
for agent in self.grid.get_cell_list_contents((x, y)))):
# Check whether given the difference in the influencing factors between the agent
# and surrounding agents, the new agent can be placed in this position.
# Returns True or False
if self.valid_pos((x, y), new_l, 1):
valid = True
return x, y
def valid_pos(self, n_pos, new_a, delta):
"""Check whether this position is a valid position given the difference in
the inlfueincing factors between the new agent and the surrounding agents"""
# Get the surrounding positions
neighborhood = self.grid.get_neighborhood(n_pos, moore=True,
include_center=False)
# Get the surrounding positions containing light agents.
cell_contents = []
for pos in neighborhood:
cell_contents.append(self.grid.get_cell_list_contents([pos]))
f_cell_contents = [val for sublist in cell_contents for val in sublist]
surr_light = [a for a in f_cell_contents if isinstance(a, Light)]
# If the surrounding light list is empty, return the position
if len(surr_light) == 0:
return True
else:
# Check whether the difference in illuminance is within the valid scope
for l in surr_light:
if abs(l.illuminance - new_a.illuminance) >= delta:
return False
return True
def get_random_pos(self):
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
return (x, y)
def add_victims(self):
for i in range(self.num_victims):
# Generate a safe location for this agent.
s = SafeLocation(uuid.uuid4(), self)
self.schedule.add(s)
self.grid.place_agent(s, self.get_random_pos())
# Generate the agent given its safe location.
p = PossibleVictim(uuid.uuid4(), self, s)
self.schedule.add(p)
self.grid.place_agent(p, self.get_random_pos())
def add_offenders(self):
for i in range(self.num_offenders):
c = PossibleOffender(uuid.uuid4(), self, self.max_criminal_preference)
self.schedule.add(c)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(c, (x, y))
def generate_criminal_areas(self):
""" Return a list of criminal areas with specified centroids and radius.
If criminal areas overlap, update the criminal areas.
DOES NOT ADD THE AREAS TO THE MODEL, AS IT IS ONLY THE POSITIONS
WITHIN THE AREAS THAT ARE ADDED TO THE MODEL. """
# ----- HELPER FUNCTION -----
def generate_centroid_list(n):
""" If no centroids have been passed,
generate random centroids for the criminal areas. """
centroids = []
for i in range(n):
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
centroids.append((x, y))
return centroids
def get_surr_pos(pos, r=3):
return self.grid.get_neighborhood(pos, moore=True, include_center=True, radius=r)
# ---------------------------
centroids = self.num_crime_areas
radius = self.crime_area_rad
# If the parameter passed as the centroid is an int, generate this number of centroids.
if isinstance(centroids, int):
centroids = generate_centroid_list(centroids)
criminal_area = []
# For every centroid in the list, create either a hotspot or a criminal area depending
# on the type.
for c in centroids:
criminal_area.append(CrimeGenerator(uuid.uuid4(), self, c, radius))
# Return a list of criminal areas.
return criminal_area
def add_criminal_areas(self, criminal_area=None):
"""Add crime areas to the model. """
# If no criminal area has been passed, generate random criminal area.
if criminal_area is None:
criminal_area = self.generate_criminal_areas()
def increment_crimes(self):
self.crime_number += 1
global crime_number
crime_number += 1
def check_victim_agents(self):
if not [agent for agent in self.schedule.agents if
isinstance(agent, PossibleVictim)]:
self.running = False
def step(self):
"""Advance the model by one step."""
self.schedule.step()
self.datacollector.collect(self)
self.check_victim_agents()
self.crime_number = 0
class Modelo(Model):
"""
A model with some number of ants and data.
"""
m = 5
ants = 1
data = pd.DataFrame()
grid_clusters = {}
lista_bordas = list()
def __init__(self, ants=1000, grid_size=5, data=pd.DataFrame()):
super().__init__()
#self.running = True
self.ants = ants
self.m = grid_size
self.v = False
self.grid = MultiGrid(int(self.m), int(self.m), True)
self.schedule = RandomActivation(self)
self.verbose = False # Print-monitoring
self.data = data
# Create ants
for i in range(self.ants):
# Add the agent ant to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
while len(self.grid.get_cell_list_contents((x, y))) != 0:
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
print("re-posicionando")
a = AntAgent("ant_" + str(i), (x, y), self)
self.schedule.add(a)
self.grid.place_agent(a, (x, y))
# Create data
for i in self.data.index:
# Add the agent ant to a random grid cell
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
while len(self.grid.get_cell_list_contents((x, y))) != 0:
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
print("re-posicionando")
d = DataAgent(i, (x, y), self)
self.schedule.add(d)
self.grid.place_agent(d, (x, y))
def step(self):
#print(self.data)
if (self.schedule.steps == 500 or self.schedule.steps == 1000
or self.schedule.steps == 1500 or self.schedule.steps == 2000
or self.schedule.steps == 2500 or self.schedule.steps == 3000
or self.schedule.steps == 3500 or self.schedule.steps == 4000
or self.schedule.steps == 4500 or self.schedule.steps == 5000):
self.validar_clusters()
if (self.schedule.steps == 510 or self.schedule.steps == 1010
or self.schedule.steps == 1510 or self.schedule.steps == 2010
or self.schedule.steps == 2510 or self.schedule.steps == 3010
or self.schedule.steps == 3510 or self.schedule.steps == 4010
or self.schedule.steps == 4510 or self.schedule.steps == 5010):
self.remover_bordas()
self.schedule.step()
def validar_clusters(self):
#print(self.grid.grid)
x = list()
y = list()
ids = list()
#classes= list()
for l in self.grid.grid:
for c in l:
for agent in c:
#print ("{} - {}".format(agent.unique_id, agent.pos))
if "data_" in agent.unique_id:
#coloca o dado no data frame
x_pos, y_pos = agent.pos
ids.append(agent)
x.append(x_pos)
y.append(y_pos)
positions = np.column_stack((x, y))
clustering = DBSCAN(eps=2, min_samples=3).fit(positions)
self.grid_clusters = {}
self.avaliar_clusters(clustering.labels_, ids)
self.apresentar_clusters()
self.definir_bordas()
def avaliar_clusters(self, indicacoes, agentes):
for i in range(len(indicacoes)):
#print("{} - {}".format(agentes[i], indicacoes[i]))
if indicacoes[i] == -1:
continue
if indicacoes[i] in self.grid_clusters.keys():
cluster = self.grid_clusters.get(indicacoes[i])
cluster.append(agentes[i])
self.grid_clusters.update({indicacoes[i]: cluster})
else:
cluster = list()
cluster.append(agentes[i])
self.grid_clusters.update({indicacoes[i]: cluster})
classes = list(self.data['class'].unique())
for numero_cluster in self.grid_clusters.keys():
cluster = self.grid_clusters.get(numero_cluster)
indices = list()
for a in self.grid_clusters.get(numero_cluster):
indices.append(a.index_df)
dados = self.data.loc[indices, :]
gini = self.gini_index(dados, classes)
self.grid_clusters.update(
{numero_cluster: {
'agentes': cluster,
'gini': gini
}})
def definir_bordas(self):
for k in self.grid_clusters.keys():
#print("Cluster {}:".format(k))
#indices = list()
for a in self.grid_clusters.get(k)['agentes']:
#print(self.grid.get_neighbors(a.pos, True))
x, y = a.pos
if (x + 1 >= self.grid.width):
xp1 = 0
else:
xp1 = x + 1
if (x - 1 < 0):
xm1 = self.grid.width - 1
else:
xm1 = x - 1
if (y + 1 >= self.grid.width):
yp1 = 0
else:
yp1 = y + 1
if (y - 1 < 0):
ym1 = self.grid.width - 1
else:
ym1 = y - 1
# x + 1
if (not self.tem_dado((xp1, y))):
self.add_borda((xp1, y))
# x - 1
if (not self.tem_dado((xm1, y))):
self.add_borda((xm1, y))
# y + 1
if (not self.tem_dado((x, yp1))):
self.add_borda((x, yp1))
# y - 1
if (not self.tem_dado((x, ym1))):
self.add_borda((x, ym1))
# x - 1 y - 1
if (not self.tem_dado((xm1, ym1))):
self.add_borda((xm1, ym1))
# x - 1 y + 1
if (not self.tem_dado((xm1, yp1))):
self.add_borda((xm1, yp1))
# x + 1 y - 1
if (not self.tem_dado((xp1, ym1))):
self.add_borda((xp1, ym1))
# x + 1 y + 1
if (not self.tem_dado((xp1, yp1))):
self.add_borda((xp1, yp1))
def gini_index(self, dados, classes):
# quantidade de elementos no cluster
n_instances = len(dados)
# calculo do indice de gini
gini = 0.0
score = 0.0
# indice do grupo, analisando o indice para cada classe
for classe in classes:
saida = dados['class'].value_counts()
if hasattr(saida, classe):
p = saida.loc[classe] / n_instances
else:
p = 0
score += p * p
# ponderando o indice pelo tamenho do grupo
gini += (1.0 - score)
return gini
def apresentar_clusters(self):
with open('log_aca.txt', 'a') as f:
print("Step: {}".format(self.schedule.steps), file=f)
for k in self.grid_clusters.keys():
print("Cluster {}:".format(k), file=f)
indices = list()
for a in self.grid_clusters.get(k)['agentes']:
indices.append(a.index_df)
dados = self.data.loc[indices, :]
print("Dados no cluster:", file=f)
print(dados['class'].value_counts(), file=f)
print("Gini index: {}".format(
self.grid_clusters.get(k)['gini']),
file=f)
print("", file=f)
def tem_dado(self, posicao):
ocupantes = self.grid.get_cell_list_contents(posicao)
for o in ocupantes:
if (isinstance(o, DataAgent) or isinstance(o, BordaAgent)):
return True
return False
def add_borda(self, posicao):
a = BordaAgent("borda", posicao, self)
self.schedule.add(a)
self.lista_bordas.append(a)
self.grid.place_agent(a, (posicao))
def remover_bordas(self):
print("removendo")
for o in self.lista_bordas:
if (isinstance(o, BordaAgent)):
print("borda")
self.grid.remove_agent(o)
self.lista_bordas = list()
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
grass_presence_probability = 1.0
moore = True
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,
initial_energy=10,
moore=True,
):
"""
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.initial_energy = initial_energy
self.schedule = RandomActivationByBreed(self)
self.grid = MultiGrid(self.height, self.width, torus=True)
self.counter = 0
self.moore = moore
self.datacollector = DataCollector(
{
"Wolves": lambda m: m.schedule.get_breed_count(Wolf),
"Sheep": lambda m: m.schedule.get_breed_count(Sheep),
}
)
# Create sheep:
for _ in range(initial_sheep):
initial_position = (random.randint(0, height - 1), random.randint(0, width - 1))
# Incrementing counter
self.counter += 1
# Creating sheep agent
sheep_agent = Sheep(self.counter, initial_position, self, moore, initial_energy)
# Adding sheep agent
self.schedule.add(sheep_agent)
# Placing agent
self.grid.place_agent(sheep_agent, initial_position)
# Create wolves
for _ in range(initial_wolves):
# Defining initial position
initial_position = (random.randint(0, height - 1), random.randint(0, width - 1))
# Incrementing counter
self.counter += 1
# Creating wolf agent
wolf_agent = Wolf(self.counter, initial_position, self, moore, initial_energy)
# Adding agent
self.schedule.add(wolf_agent)
# Placing agent
self.grid.place_agent(wolf_agent, initial_position)
# Create grass patches
for grid_height in range(height):
for grid_width in range(width):
# Incrementing counter
self.counter += 1
# Creating GrassPatch agent
grass_patch_agent = GrassPatch(
self.counter,
self,
True,
self.grass_regrowth_time
)
# Adding agent
self.schedule.add(grass_patch_agent)
# Placing agent
self.grid.place_agent(grass_patch_agent, (grid_height, grid_width))
def get_agents_at_position(self, position: tuple):
"""
Returns all the agents located at a specific location
"""
return self.grid.get_cell_list_contents([position])
def get_grass_patch_to_eat_at_position(self, position: tuple) -> Union[GrassPatch, None]:
"""
This function returns a GrassPatch agent if and only if this agent is eatable
"""
grass_patch = None
agents = self.get_agents_at_position(position)
for agent in agents:
if type(agent) == GrassPatch:
grass_patch = agent
break
return grass_patch if grass_patch.is_eatable() else None
def get_sheep_to_eat_at_position(self, position: tuple) -> Union[Sheep, None]:
"""
This function returns a sheep agent to be eaten by a wolf agent if the wolf agents calling this function shares
its tile with a sheep agent. If there are several sheep agents on the same tile, one of them will be chosen randomly.
"""
sheep_agent = None
agents = self.get_agents_at_position(position)
for agent in agents:
if type(agent) == Sheep:
sheep_agent = agent
break
return sheep_agent
def add_new_agent(self, agent_type: str, pos: tuple):
agent = None
# Increasing counter
self.counter += 1
if agent_type == Wolf:
agent = Wolf(self.counter, pos, self, self.moore, self.initial_energy)
elif agent_type == Sheep:
agent = Sheep(self.counter, pos, self, self.moore, self.initial_energy)
# Adding agent in schedule's agents array
self.schedule.add(agent)
# Placing agent in the grid
self.grid.place_agent(agent, pos)
def remove_agent(self, agent: Agent):
"""
This function is to be called when an agent dies
"""
# Removing agent from the grid
self.grid.remove_agent(agent)
# Removing agent from schedule
self.schedule.remove(agent)
def step(self):
self.schedule.step()
# Collect data
self.datacollector.collect(self)
# ... to be completed
def run_model(self, step_count=200):
# Iterating
for step in range(step_count):
self.schedule.step()
# Collect data
self.datacollector.collect()
class SlimeModel(Model):
def __init__(self, height, width, color, numAgents, gDense, kRate, dcDiffu,
dhRes, dtRes, secRate):
# number of agents per tile
self.n = numAgents
# grid density
self.gD = gDense
# rate of cAMP decay
self.k = kRate
# diffusion constant of cAMP
self.Dc = dcDiffu
# spatial resolution for cAMP simulation
self.Dh = dhRes
# time resolution for cAMP simulation
self.Dt = dtRes
# rate of cAMP secretion by an agent
self.f = secRate
# number of rows/columns in spatial array
self.w = masterHeight
# agent color
self.color = color
# height of grid
self.height = masterHeight
# width of grid
self.width = masterWidth
# Counter for generating sequential unique id's
self.j = 0
# Counter for DataVis agents' unique id's
self.dv = 0
# Counter for NumDataVis agents' unique id's
self.ndv = 0
# Create randomly ordered scheduler
self.schedule = SimultaneousActivation(self)
# Create grid (of type MultiGrid to support multiple agents per cell
self.grid = MultiGrid(self.width, self.height, torus=False)
# Initialize list of cAMP molecules
self.cAMPs = list()
# Initialize dict for datacollector with total datacollector
dc = {"Total Amount of cAMP": self.getAmts}
# Initialize for iterating through columns (x) and rows (y)
self.x = 0
self.y = 0
# Loop to fill datacollector dictionary with dict entries for each column and row
for x in range(masterWidth):
dc.update({("x: " + str(x)): self.getColAmts})
dc.update({("y: " + str(x)): self.getRowAmts})
# Create datacollector to retrieve total amounts of cAMP from dc dict created above
self.datacollector = DataCollector(dc)
# Variable for storing random numbers
r = 0
# Initial loop to create agents and fill agents list with them
for (contents, x, y) in self.grid.coord_iter():
# Create object of type cAMP
cell = cAMP([x, y], self, self.j, 0)
# Add random amoutn of cAMP to cell (<1)
cell.add(random.random())
# Place cAMP onto grid at coordinates x, y
self.grid._place_agent((x, y), cell)
# Add cAMP molecule to list
self.cAMPs.append(cell)
# print("x:", x, " y:", y)
if x == 50:
# Create DataVis agent
ag = DataVis([x, y], self, self.dv)
# Place DataVis agent
self.grid.place_agent(ag, tuple([x, y]))
# Increment unique id counter
self.dv += 1
elif x > 50:
# Create NumDataVis agent with appropriate slice num
ag = NumDataVis([x, y], self, self.ndv)
# Place NumDataVis agent
self.grid.place_agent(ag, tuple([x, y]))
# Increment unique id counter
self.ndv += 1
else:
# Loop to create SlimeAgents
if self.gD % 1 != 0:
r = random.random()
if r <= self.gD:
for i in range(self.n):
# Create object of type SlimeAgent
ag = SlimeAgent([x, y], self, self.j, self.color)
# Place agent onto grid at coordinates x, y
self.grid.place_agent(ag, tuple([x, y]))
# Add agent to schedule
self.schedule.add(ag)
# Increment j (unique_id variable)
self.j += 1
else:
for i in range(self.n):
# Create object of type SlimeAgent
ag = SlimeAgent([x, y], self, self.j, self.color)
# Place agent onto grid at coordinates x, y
self.grid.place_agent(ag, tuple([x, y]))
# Add agent to schedule
self.schedule.add(ag)
# Increment j (unique_id variable)
self.j += 1
# Print out number of agents
print("# of agents:", self.j)
self.running = True
# Method for getting total cAMP amount
def getAmts(self):
# Initialize empty total variable
total = 0
# Loop to get total amount of cAMP from cAMPs list
for molecule in self.cAMPs:
total += molecule.getAmt()
return total
def getRowAmts(self):
total = 0
for x in range(masterWidth):
try:
total += self.grid.get_cell_list_contents(
(x, self.y))[0].getAmt()
except IndexError:
continue
if self.y == 49:
self.y = 0
else:
self.y += 1
return total
def getRowAmt(self, y):
total = 0
for x in range(masterWidth - 1):
try:
total += self.grid.get_cell_list_contents((x, y))[0].getAmt()
except IndexError:
continue
if self.y == 49:
self.y = 0
else:
self.y += 1
return total
def getColAmts(self):
total = 0
for y in range(masterHeight):
try:
total += self.grid.get_cell_list_contents(
(self.x, y))[0].getAmt()
except IndexError:
continue
if self.x == 49:
self.x = 0
else:
self.x += 1
return total
def sweepForClusters():
blacklist = list()
neighbors = list()
for (contents, x, y) in self.grid.coord_iter():
for agent in contents[1::]:
if type(agent) == SlimeAgent and agent not in blacklist:
neighbors = agent.getNeighbors()
for neighbor in neighbors:
blacklist.append(neighbor)
'''
density = blacklist.length / density_coefficent_based_on_area
clusteredAgents =
'''
# Step method
def step(self):
cNeighbors = list()
neighbors = list()
lap = 0
amtSelf = 0
cAMPobj = cAMP
newDiag = 0
oldDiag = 0
nAgents = 0
layer = 1
secRate = 0
''' Perform cAMP decay and diffusion actions '''
for (contents, x, y) in self.grid.coord_iter():
# This block is a bit messy but it works for now
cont = True
for content in contents:
# Set row amounts if an object is DataVis
if type(content) is DataVis or type(content) is NumDataVis:
content.setRowAmt(self.getRowAmt(y))
cont = False
if cont:
# Initialize number of agents for layer coloring
nAgents = len(contents) - 1
# Reset lap to 0
lap = 0
# Set cAMPobj to current tile's cAMP agent
cAMPobj = contents[0]
# Set neighbors to cAMPobj's neighbors (Von Neumann)
neighbors = cAMPobj.getNeighbors()
# Add cAMP objects form neighbors to cNeighbors
for neighbor in neighbors:
if type(neighbor) is cAMP:
cNeighbors.append(neighbor)
# Add sum of neighbors to lap
for mol in cNeighbors:
lap += mol.getAmt()
amtSelf = cAMPobj.getAmt()
# Reassign lap to the laplacian (using previous neighbor sum value)
lap = (lap - 4 * amtSelf) / (self.Dh**2)
# Add decay to current cAMP object
cAMPobj.add(
(-cAMPobj.getDecayRate() * amtSelf + self.Dc * lap) *
self.Dt)
# Wipe cNeighbors
cNeighbors.clear()
# Iterate through all contents of a grid cell
for agent in contents[1::]:
# Get all neighbors (excuding self)
neighbors = agent.getNeighbors()
# Examine each neighbor
for neighbor in neighbors:
# Add cAMP neighbors to list
if type(neighbor) is cAMP:
cNeighbors.append(neighbor)
# Add cAMP secretion to the cell that the agent shares with a cAMP object
cAMPobj.add(agent.getSecRate() * self.Dt)
# Decide whether or not to move
newx = (x + random.randint(-1, 2)) % self.w
newy = (y + random.randint(-1, 2)) % self.w
# Calculate differences
newDiag = ((self.grid[newx - 1][newy - 1])[0]).getAmt()
diff = ((self.grid[x - 1][y - 1])[0]).getAmt()
# Fix if there are crazy values for diff
if diff > 10:
diff = 10
elif diff < 10:
diff = -10
# Decide to move
if random.random() < np.exp(diff) / (1 + np.exp(diff)):
agent.move(tuple([newx, newy]))
# Layers for coloring agents based on density
agent.addLayer()
layer = agent.getLayer()
# Only change color of agent that is on top of a stack
if layer >= nAgents:
self.pickColor(agent, nAgents)
# Wipe cNeighbors
cNeighbors.clear()
# Add step to schedule
self.schedule.step()
# Collect new data
self.datacollector.collect(self)
# Method to select a color based on the topmost agent
def pickColor(self, topAgent, nAgents):
shade = topAgent.getShades()
if nAgents <= 2:
topAgent.setShade(shade[0])
elif nAgents == 3:
topAgent.setShade(shade[1])
elif nAgents == 4:
topAgent.setShade(shade[2])
elif nAgents == 5:
topAgent.setShade(shade[3])
elif nAgents == 6:
topAgent.setShade(shade[4])
elif nAgents == 7:
topAgent.setShade(shade[5])
elif nAgents == 8:
topAgent.setShade(shade[6])
elif nAgents == 9:
topAgent.setShade(shade[7])
class LabModel(Model):
def __init__(self, width, height):
defineAgents.init()
self.num_Users = 0
self.schedule = BaseScheduler(self)
self.running = True
self.grid = MultiGrid(width, height, False)
self.agents_json = []
self.clock = Time()
self.day = self.clock.day
self.NStep = 0
self.createWalls(width, height)
self.createDoors(width, height)
self.savePosOutOfMap()
self.setRooms()
self.setAgents()
self.getRules()
def getRules(self):
listdata1 = post.getEvents(0)
self.actionsNear = listdata1[0]
print('Near actions: ', self.actionsNear)
listdata2 = post.getEvents(2)
self.actionsFar = listdata2[0]
print('Far actions: ', self.actionsFar)
def createWalls(self, width, height):
self.Walls = []
for y in range(int(height*0.2),height):
x = width
wall = Wall(x, y)
self.Walls.append(wall)
for x in range(0,int(width/2)):
y = 0
wall = Wall(x,y)
self.Walls.append(wall)
for x in range(0,width):
y = height*0.2
wall = Wall(x,y)
self.Walls.append(wall)
for y in range(0,height+1):
x = 0
wall = Wall(x,y)
self.Walls.append(wall)
for y in range(0, height+1):
x = width/2
wall = Wall(x,y)
self.Walls.append(wall)
for x in range(int(width/2), width):
y = height* 0.6
wall = Wall(x,y)
self.Walls.append(wall)
for x in range(0, width):
y = height
wall = Wall(x,y)
self.Walls.append(wall)
def createDoors(self, width, height):
door1 = Door(0,2,False, True)
door2 = Door(4,3, True)
door3 = Door(7,8)
door4 = Door(7,12)
self.doors = [door1, door2, door3, door4]
# Clean positions of doors
for wall in self.Walls:
for door in self.doors:
if ((wall.x == door.x) and (wall.y == door.y )):
self.Walls.remove(wall)
def savePosOutOfMap(self):
self.pos_out_of_map = []
for x in range(8,15):
for y in range(0,3):
pos = (x,y)
self.pos_out_of_map.append(pos)
def setRooms(self):
positions_room_out = []
positions_room_out.append((self.doors[0].x, self.doors[0].y)) #I understand that door 1 is out of the laboratory
self.roomOut = Room(positions_room_out)
positions_room1 = []
for x in range(1,7):
for y in range(1,3):
pos = (x, y)
positions_room1.append(pos)
room1 = Room(positions_room1)
positions_room2 = []
for x in range(1,7):
for y in range(4,17):
pos = (x, y)
positions_room2.append(pos)
positions_room2.append((self.doors[1].x, self.doors[1].y))
room2 = Room(positions_room2)
positions_room3 = []
for x in range(8,14):
for y in range(4,10):
pos = (x, y)
positions_room3.append(pos)
positions_room2.append((self.doors[2].x, self.doors[2].y))
room3 = Room(positions_room3)
positions_room4 = []
for x in range(8,14):
for y in range(11,17):
pos = (x, y)
positions_room4.append(pos)
positions_room2.append((self.doors[3].x, self.doors[3].y))
room4 = Room(positions_room4)
self.rooms = [room1, room2, room3, room4, self.roomOut]
def isInMap(self, possible_steps_out_of_map):
possible_step = possible_steps_out_of_map
for each_pos in self.pos_out_of_map:
xp, yp = each_pos
xu, yu = possible_step
if ((xp == xu) and (yp == yu)):
return False
return True
def thereIsPC(self, pos):
x,y = pos
for pc in self.workplaces:
if pc.x == x and pc.y == y:
return True
return False
def thereIsWall(self, possible_steps_before_walls):
possible_step = possible_steps_before_walls
for each_wall in self.Walls:
xw = each_wall.x
yw = each_wall.y
xu, yu = possible_step
if ((xw == xu) and (yw == yu)):
return True
return False
def thereIsClosedDoor(self, possible_steps_before_doors):
xu,yu = possible_steps_before_doors
for door in self.doors:
if ((xu == door.x) and (yu == door.y)):
if door.state == False:
return True
return False
def ThereIsUserInRoom(self, room):
for pos in room.pos_room:
possible_user = self.grid.get_cell_list_contents([pos])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent):
return True
return False
def setAgents(self):
# Identifications
id_offset = 100
id_sensorCoffe = self.num_Users + id_offset
id_sensorTV = id_sensorCoffe + id_offset
id_sensorAccess = id_sensorTV + id_offset
id_bulb = id_sensorAccess + id_offset
id_pc = id_sensorAccess + id_bulb
# Height and Width
height = self.grid.height
width = self.grid.width
# CREATE AGENT
# Create Coffe Maker
capacity_coffeMaker = 30
self.cm = CoffeMakerAgent(id_sensorCoffe, self, capacity_coffeMaker)
self.grid.place_agent(self.cm, (1, height-1))
# Create TV
self.tv = TVAgent(id_sensorTV, self)
self.grid.place_agent(self.tv, (4,height-1))
# Create Control Access
self.ca = AccessAgent(id_sensorAccess, self, self.doors[1])
self.grid.place_agent(self.ca, (2, 3))
# Create LightBulbs
self.bulbs = []
LB1 = Bulb(id_bulb, self, self.rooms[0])
self.bulbs.append(LB1)
LB2 = Bulb(id_bulb+1, self, self.rooms[1])
self.bulbs.append(LB2)
LB3 = Bulb(id_bulb+2, self, self.rooms[2])
self.bulbs.append(LB3)
LB4 = Bulb(id_bulb+3, self, self.rooms[3])
self.bulbs.append(LB4)
# Create PC
self.workplaces = []
PC1 = PC(id_pc, self, 9, 6, 'd')
self.grid.place_agent(PC1, (9, 6))
self.workplaces.append(PC1)
PC2 = PC(id_pc+1, self, 11, 6, 'd')
self.grid.place_agent(PC2, (11, 6))
self.workplaces.append(PC2)
PC3 = PC(id_pc+2, self, 12, 6, 'd')
self.grid.place_agent(PC3, (12, 6))
self.workplaces.append(PC3)
PC4 = PC(id_pc+3, self, 11, 7, 'u')
self.grid.place_agent(PC4, (11, 7))
self.workplaces.append(PC4)
PC5 = PC(id_pc+4, self, 12, 7, 'u')
self.grid.place_agent(PC5, (12, 7))
self.workplaces.append(PC5)
PC6 = PC(id_pc+5, self, 9, 14, 'd')
self.grid.place_agent(PC6, (9, 14))
self.workplaces.append(PC6)
PC7 = PC(id_pc+6, self, 11, 14, 'u')
self.grid.place_agent(PC7, (11, 14))
self.workplaces.append(PC7)
PC8 = PC(id_pc+7, self, 12, 14, 'u')
self.grid.place_agent(PC8, (12, 14))
self.workplaces.append(PC8)
PC9 = PC(id_pc+8, self, 11, 13, 'd')
self.grid.place_agent(PC9, (11, 13))
self.workplaces.append(PC9)
PC10 = PC(id_pc+9, self, 12, 13, 'd')
self.grid.place_agent(PC10, (12, 13))
self.workplaces.append(PC10)
def getFreePlace():
x = random.randrange(self.grid.width)
y = random.randrange(self.grid.height)
position = (x,y)
possible_free_place = self.grid.get_cell_list_contents([position])
if len(possible_free_place) == 0:
there_is_wall = self.thereIsWall(position)
if there_is_wall == False:
place_in_map = self.isInMap(position)
if place_in_map == True:
return position
return getFreePlace()
# Create users
countPC = 0
for n_type_users in defineAgents.agents_json:
n_agents = n_type_users['N']
for i in range(0, n_agents):
a = UserAgent(i, self, self.workplaces[countPC], n_type_users)
self.schedule.add(a)
self.grid.place_agent(a, (0, 2))
countPC = countPC + 1
#Add to schedule
for pc in self.workplaces:
self.schedule.add(pc)
for bulb in self.bulbs:
self.schedule.add(bulb)
self.schedule.add(self.cm)
self.schedule.add(self.tv)
self.schedule.add(self.ca)
self.schedule.add(self.clock)
def createAgent(self, pos):
if self.thereIsUser((0,2)) == False:
self.num_Users = self.num_Users + 1
a = UserAgent(self.num_Users - 1, self, self.workplaces[self.num_Users - 1])
self.schedule.add(a)
#x, y = getFreePlace()
self.grid.place_agent(a, (0, 2))
def ThereIsUserNear(self, pos):
xa, ya = pos
round_pos = []
round_pos.append((xa-1,ya-1))
round_pos.append((xa-1,ya))
round_pos.append((xa-1,ya+1))
round_pos.append((xa+1,ya+1))
round_pos.append((xa,ya+1))
round_pos.append((xa,ya-1))
round_pos.append((xa+1,ya))
round_pos.append((xa+1,ya-1))
for pos in round_pos:
xp, yp = pos
if (xp>=0 and yp>=0 and yp < self.grid.height and xp < self.grid.width):
possible_user = self.grid.get_cell_list_contents([pos])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent):
return True
else:
continue
else:
continue
return False
def ThereIsUserUp(self, pos, id_pc):
xa, ya = pos
possible_user = self.grid.get_cell_list_contents([(xa,ya+1)])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent) and user.pc.unique_id == id_pc:
return True
return False
def ThereIsUserDown(self, pos, id_pc):
xa, ya = pos
possible_user = self.grid.get_cell_list_contents([(xa,ya-1)])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent) and user.pc.unique_id == id_pc:
return True
return False
def ThereIsUserDownCM(self, pos):
xa, ya = pos
agents = []
possible_user = self.grid.get_cell_list_contents([(xa,ya-1)])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent):
agents.append(user)
return agents
def thereIsUser(self,pos):
possible_user = self.grid.get_cell_list_contents([pos])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent):
return True
return False
def ThereIsOtherUserInRoom(self, room, agent):
for pos in room.pos_room:
possible_user = self.grid.get_cell_list_contents([pos])
if (len(possible_user) > 0):
for user in possible_user:
if isinstance(user,UserAgent) and user != agent:
return True
return False
def getRoom(self, pos):
for room in self.rooms:
if pos in room.pos_room:
return room
return False
def getLightWithRoom(self, room):
for light in self.bulbs:
if light.room == room:
return light
return False
def openDoor(self,pos,agent = 0):
x_pos, y_pos = pos
doors = self.doors
for door in doors:
x = door.x
y = door.y
if ((x == x_pos) and (y == y_pos)):
if isinstance(agent, AccessAgent):
door.open(True)
else:
door.open()
def closeDoor(self,pos):
x_pos, y_pos = pos
doors = self.doors
for door in doors:
x = door.x
y = door.y
if ((x == x_pos) and (y == y_pos)):
door.close()
def getMatrix(self,agent):
new_matrix = defineAgents.returnMatrix(agent, self.clock.clock)
agent.markov_matrix = new_matrix
def getTimeInState(self, agent):
matrix_time_in_state = defineAgents.getTimeInState(agent, self.clock.clock)
return matrix_time_in_state
def step(self):
self.schedule.step()
self.NStep = self.NStep + 1
class PlaneModel(Model):
""" A model representing simple plane consisting of 16 rows of 6 seats (2 x 3) using a given boarding method """
method_types = {
'Random': methods.random,
'Front-to-back': methods.front_to_back,
'Front-to-back (4 groups)': methods.front_to_back_gr,
'Back-to-front': methods.back_to_front,
'Back-to-front (4 groups)': methods.back_to_front_gr,
'Window-Middle-Aisle': methods.win_mid_ais,
'Steffen Perfect': methods.steffen_perfect,
'Steffen Modified': methods.steffen_modified
}
def __init__(self, method, shuffle_enable=True, common_bags='normal'):
self.grid = MultiGrid(21, 7, False)
self.running = True
self.schedule = queue_method.QueueActivation(self)
self.method = self.method_types[method]
self.entry_free = True
self.shuffle_enable = shuffle_enable
self.common_bags = common_bags
# Create agents and splitting them into separate boarding groups accordingly to a given method
self.boarding_queue = []
self.method(self)
# Create patches representing corridor, seats and walls
id = 97
for row in (0, 1, 2, 4, 5, 6):
for col in (0, 1, 2):
patch = PatchAgent(id, self, 'WALL')
self.grid.place_agent(patch, (col, row))
id += 1
for col in range(3, 19):
patch = PatchAgent(id, self, 'SEAT')
self.grid.place_agent(patch, (col, row))
id += 1
for col in (19, 20):
patch = PatchAgent(id, self, 'WALL')
self.grid.place_agent(patch, (col, row))
id += 1
for col in range(21):
patch = PatchAgent(id, self, 'CORRIDOR', 'FREE')
self.grid.place_agent(patch, (col, 3))
id += 1
def step(self):
self.schedule.step()
if len(self.grid.get_cell_list_contents((0, 3))) == 1:
self.get_patch((0, 3)).state = 'FREE'
if self.get_patch((0, 3)).state == 'FREE' and len(self.boarding_queue) > 0:
a = self.boarding_queue.pop()
a.state = 'GOING'
self.schedule.add(a)
self.grid.place_agent(a, (0, 3))
self.get_patch((0, 3)).state = 'TAKEN'
if self.schedule.get_agent_count() == 0:
self.running = False
def get_patch(self, pos):
agents = self.grid.get_cell_list_contents(pos)
for agent in agents:
if isinstance(agent, PatchAgent):
return agent
return None
def get_passenger(self, pos):
agents = self.grid.get_cell_list_contents(pos)
for agent in agents:
if isinstance(agent, PassengerAgent):
return agent
return None
class EgyptSim(Model):
"""
Simulation Model for wealth distribution represented by grain in ancient Egypt
"""
# Variable declarations for non python programmer sanity
# Map variables
height = 30
width = 30
# Simulation Variables
timeSpan = 500
currentTime = 0
startingSettlements = 14
startingHouseholds = 7
startingHouseholdSize = 5
startingGrain = 3000
minAmbition = 0.1
minCompetency = 0.5
generationalVariation = 0.9
knowledgeRadius = 20
distanceCost = 10
fallowLimit = 4
popGrowthRate = 0.1
fission = False
fissionChance = 0.7
rental = True
rentalRate = 0.5
totalPopulation = startingSettlements * startingHouseholds * startingHouseholdSize
totalGrain = startingGrain * startingHouseholds
startingPopulation = totalPopulation
projectedHistoricalPopulation = totalPopulation
maxHouseholdGrain = startingGrain
# Step variables
mu = 0
sigma = 0
alpha = 0
beta = 0
# Visualisation
description = "A model simulating wealth growth and distribution in Ancient Egypt.\n\nThe model allows one to see how variables such as the flooding of the Nile, human character traits and random chance effect the acquisition and distribution of wealth."
# List of identifiers and colors for settlements
SETDICT = {"s1": "#FF0000",
"s2": "#FF4500",
"s3": "#BC8F8F",
"s4": "#00FF00",
"s5": "#00FFFF",
"s6": "#0000FF",
"s7": "#FF00FF",
"s8": "#FF1493",
"s9": "#708090",
"s10": "#DC143C",
"s11": "#FF8C00",
"s12": "#FF69B4",
"s13": "#800000",
"s14": "#7CFC00",
"s15": "#008B8B",
"s16": "#483D8B",
"s17": "#4B0082",
"s18": "#FF69B4",
"s19": "#000000",
"s20": "#8B4513"}
def __init__(self, height: int = 30, width: int = 30, timeSpan: int = 500,
startingSettlements: int = 14, startingHouseholds: int = 7,
startingHouseholdSize: int = 5, startingGrain: int = 3000,
minAmbition: float = 0.1, minCompetency: float = 0.5,
generationalVariation: float = 0.9, knowledgeRadius: int = 20,
distanceCost: int = 10, fallowLimit: int = 4, popGrowthRate: float = 0.1,
fission: bool = False, fissionChance: float = 0.7, rental: bool = True,
rentalRate: float = 0.5):
"""
Create a new EgyptSim model
Args:
height: The height of the simulation grid
width: The width of the simulation grid
timeSpan: The number of years over which the model is to run
startingSettlements: The starting number of Settlements
startingHouseholds: The starting number of Households per Settlement
startingHouseholdSize: The starting number of workers in a Household
startingGrain: The starting amount of grain for each Household
minAmbition: The minimum ambition value for a Household
minCompetency: The minimum competency value for a Household
generationalVariation: The difference between generations of a Household
knowledgeRadius: How far outside ther Settlement a Household can "see"
distanceCost: The cost to move grain per cell away from a settlemnt
fallowLimit: The number of years a field can lay fallow before it is harvested
popGrowthRate: The rate at which the population grows
fission: If Household fission (Moving between settlements) is allowed
fissionChance: The chance fission occuring
rental: If land rental is allowed
rentalRate: The rate at which households will rent land
"""
super().__init__()
# Set Parameters
# Map size
self.height = height
self.width = width
# If the number of starting settlements is greater than the maximum reasonable number of households considering territory and farming area
# Considers that each household needs at least two field to survive at a minimum number of members, a Settlment needs 9 (territory) + 2 * households
# Tiles to survive
if startingSettlements > ((width - 1) * height) // (9 + (startingHouseholds * 2)):
if startingSettlements > 20:
self.startingSettlements = 20
else:
self.startingSettlements = ((height - 1) * width) // (9 + (startingHouseholds * 2))
print("Too many starting settlements to support the settlements and household, truncating to: ", self.startingSettlements)
else:
self.startingSettlements = startingSettlements
# Simulation Variables
self.timeSpan = timeSpan
self.currentTime = 0
self.startingHouseholds = startingHouseholds
self.startingHouseholdSize = startingHouseholdSize
self.startingGrain = startingGrain
self.minAmbition = minAmbition
self.minCompetency = minCompetency
self.generationalVariation = generationalVariation
self.knowledgeRadius = knowledgeRadius
self.distanceCost = distanceCost
self.fallowLimit = fallowLimit
self.popGrowthRate = popGrowthRate
self.fission = fission
self.fissionChance = fissionChance
self.rental = rental
self.rentalRate = rentalRate
self.totalGrain = startingGrain * startingHouseholds * startingSettlements
self.totalPopulation = startingSettlements * startingHouseholds * startingHouseholdSize
self.startingPopulation = self.totalPopulation
self.projectedHistoricalPopulation = self.startingPopulation
self.maxHouseholdGrain = startingGrain
# Scheduler and Grid
self.schedule = EgyptSchedule(self)
self.grid = MultiGrid(height = self.height, width = self.width, torus=False)
# Define specific tables for data collection purposes
setlist = []
for i in range(self.startingSettlements):
setlist.append("s" + str(i + 1) + "_Population")
tables = {"Settlement Population": setlist}
# Data collection
self.datacollector = DataCollector(model_reporters =
{"Households": lambda m: m.schedule.get_breed_count(Household),
"Settlements": lambda m: m.schedule.get_breed_count(Settlement),
"Total Grain": lambda m: m.totalGrain,
"Total Population": lambda m: m.totalPopulation,
"Projected Hisorical Poulation (0.1% Growth)": lambda m: m.projectedHistoricalPopulation,
"Gini-Index": gini,
"Maximum Settlement Population": maxSetPop,
"Minimum Settlement Population": minSetPop,
"Mean Settlement Poulation" : meanSetPop,
"Maximum Household Wealth": maxHWealth,
"Minimum Household Wealth": minHWealth,
"Mean Household Wealth" : meanHWealth,
"Number of households with < 33% of wealthiest grain holding": lowerThirdGrainHoldings,
"Number of households with 33 - 66% of wealthiest grain holding": middleThirdGrainHoldings,
"Number of households with > 66% of wealthiest grain holding": upperThirdGrainHoldings
},
tables = tables)
self.setup()
self.running = True
self.collectTableData()
self.datacollector.collect(self)
def collectTableData(self):
setPops = {}
for s in self.schedule.get_breed(Settlement):
setPops[s.unique_id + "_Population"] = s.population
self.datacollector.add_table_row("Settlement Population", setPops, True)
def setupMapBase(self):
"""
Create the grid as field and river
"""
for agent, x, y in self.grid.coord_iter():
# If on left edge, make a river
if x == 0:
uid = "r" + str(x) + "|" + str(y)
river = River(uid, self, (x, y))
self.grid.place_agent(river, (x, y))
# Otherwise make a field
else:
uid = "f" + str(x) + "|" + str(y)
field = Field(uid, self, (x, y), 0.0)
self.grid.place_agent(field, (x, y))
self.schedule.add(field)
def setupSettlementsHouseholds(self):
"""
Add settlements and households to the simulation
"""
h = 1
for i in range(self.startingSettlements):
# Loop untill a suitable location is found
while True:
x = self.random.randrange(1, self.width)
y = self.random.randrange(self.height)
flag = False
cell = self.grid.get_cell_list_contents((x, y))
# Check that tile is available
for agent in cell:
if agent.settlementTerritory:
break
else:
flag = True
break
if flag:
break
# Add settlement to the grid
population = self.startingHouseholds * self.startingHouseholdSize
uid = "s" + str(i + 1) # Use a custom id for the datacollector
settlement = Settlement(uid, self, (x, y), population, self.startingHouseholds, uid, self.SETDICT[uid])
self.grid.place_agent(settlement, (x, y))
# Set the surrounding fields as territory
local = self.grid.get_neighbors((x, y), moore=True, include_center=True, radius=1)
for a in local:
a.settlementTerritory = True
# Add households for the settlement to the scheduler
for j in range(self.startingHouseholds):
huid = "h" + str(h) # Use a custom id for the datacollector
ambition = np.random.uniform(self.minAmbition, 1)
competency = np.random.uniform(self.minCompetency, 1)
genCount = self.random.randrange(5) + 10
household = Household(huid, self, settlement, (x, y), self.startingGrain,
self.startingHouseholdSize, ambition, competency, genCount)
# ! Dont add household to grid, is redundant
self.schedule.add(household)
h += 1
# Add settlement to the scheduler
self.schedule.add(settlement)
def setup(self):
"""
Setup model parameters
"""
self.setupMapBase()
self.setupSettlementsHouseholds()
def step(self):
self.currentTime += 1
self.maxHouseholdGrain = 0
self.setupFlood()
self.schedule.step()
self.projectedHistoricalPopulation = round(self.startingPopulation * ((1.001) ** self.currentTime))
self.datacollector.collect(self)
# Add settlement data to table
self.collectTableData()
# Cease running once time limit is reached or everyone is dead
if self.currentTime >= self.timeSpan or self.totalPopulation == 0:
self.running = False
def setupFlood(self):
"""
Sets up common variables used for the flood method in Fields
"""
self.mu = random.randint(0, 10) + 5
self.sigma = random.randint(0, 5) + 5
self.alpha = (2 * self.sigma ** 2)
self.beta = 1 / (self.sigma * math.sqrt(2 * math.pi))
class SamplePSO(PSO):
def __init__(self, population: int, dimension: int,
attraction_best_global: float,
attraction_best_personal: float, lim_vel_particles: float,
inertia_particle: float, max_iterations: int, width: int,
height: int, num_max_locales: int, suavizar_espacio: int):
super().__init__(population, dimension, attraction_best_global,
attraction_best_personal, lim_vel_particles,
inertia_particle, max_iterations)
self.width = width
self.height = height
self.grid = MultiGrid(self.width, self.height, torus=False)
# Variables para el espacio de busqueda
self.num_max_locales = num_max_locales
self.suavizar_espacio = suavizar_espacio
# Crear espacio de busqueda
self.setup_search_space()
# Crear particulas
# Lo hace la clase padre
# Colocar particulas
# Hay que adaptar pos interno al espacio de busqueda
self.place_particles(True)
# Captura de datos para grafica
self.datacollector = DataCollector({
"Best": lambda m: m.global_best_value,
"Average": lambda m: m.average()
})
def average(self):
sum = 0
for agent in self.schedule.agents:
if isinstance(agent, Particle):
sum += agent.personal_best_value
return sum / self.population
# Se modifica step del padre para añadir el collector
def step(self):
super().step()
# Collect data
self.datacollector.collect(self)
# Stop si llega al máximo
if self.global_best_value == 1:
self.running = False
def place_particles(self, initial=False):
for particle in self.particles:
pos = self.pos_particle_to_pos(particle)
if initial:
self.grid.place_agent(particle, pos)
else:
self.grid.move_agent(particle, pos)
def pos_particle_to_pos(self, particle: Particle):
# Convierte posiciones de particula [0 1] en posiciones de grid 2D
min_xcor = 0
max_xcor = self.width - 1
min_ycor = 0
max_ycor = self.height - 1
x_cor = self.convert(particle.pos_particle[0], min_xcor, max_xcor)
y_cor = self.convert(particle.pos_particle[1], min_ycor, max_ycor)
return (x_cor, y_cor)
@staticmethod
def convert(x: float, a: float, b: float) -> int:
# Bijection from [0, 1] to [a, b]
return int(a + x * (b - a))
def setup_search_space(self):
# Preparar un espacio de busqueda con colinas y valles
if self.num_max_locales == 0:
for agent, x, y in self.grid.coord_iter():
val = random.random()
patch = Patch(self.unique_id, self, (x, y), val)
self.unique_id += 1
self.grid.place_agent(patch, (x, y))
else:
n_elements = (self.width - 1) * (self.height - 1)
selected_elements = random.sample(range(n_elements),
self.num_max_locales)
element = 0
for (agentSet, x, y) in self.grid.coord_iter():
val = 10 * random.random(
) if element in selected_elements else 0
patch = Patch(self.unique_id, self, (x, y), val)
self.unique_id += 1
self.grid.place_agent(patch, (x, y))
element += 1
# Suavizado del espacio
for _ in range(self.suavizar_espacio):
for (agentSet, x, y) in self.grid.coord_iter():
for agent in agentSet:
if isinstance(agent, Patch):
agent.diffuse_val(1)
# Normalizacion del espacio 0 y 0.99999
min_val = 0
max_val = 0
for (agentSet, x, y) in self.grid.coord_iter():
for agent in agentSet:
if isinstance(agent, Patch):
if agent.val < min_val:
min_val = agent.val
if agent.val > max_val:
max_val = agent.val
for (agentSet, x, y) in self.grid.coord_iter():
for agent in agentSet:
if isinstance(agent, Patch):
agent.val = 0.99999 * (agent.val - min_val) / (max_val -
min_val)
# Marcar a 1 el máximo
max_val = 0
max_patch = None
for (agentSet, x, y) in self.grid.coord_iter():
for agent in agentSet:
if isinstance(agent, Patch):
if agent.val > max_val:
max_patch = agent
if isinstance(max_patch, Particle):
max_patch.val = 1
# Colorear patches
for (agentSet, x, y) in self.grid.coord_iter():
for agent in agentSet:
if isinstance(agent, Patch):
agent.set_color()
# Se han de definir los métodos evaluation y psoexternalUpdate
def evaluation(self, particle: Particle):
# Se podría usar particle.pos si se ejecutara primero pso_external_update
# Pero se ejecuta despues
# Hay que revisar la primera evaluación al crear particulas
if self.grid is not None:
pos = self.pos_particle_to_pos(particle)
for patch in self.grid.get_cell_list_contents(pos):
if isinstance(patch, Patch):
return patch.val
else:
return 0
def pso_external_update(self):
self.place_particles()
class GentrifiedNeighbourhood(Model):
def from_dict(self, d):
self.__dict__.update(d)
def __init__(self, people_inside, people_outside, outside_person_econ_dist_mean, share_threshold,
property_value_dist_mean, num_residential, num_commercial, num_streets, width, height):
super().__init__()
self.people_inside = int(people_inside)
self.people_outside = int(people_outside)
self.num_people = people_inside + people_outside
self.outside_person_econ_dist_mean = outside_person_econ_dist_mean
self.share_threshold = share_threshold
self.property_value_dist_mean = property_value_dist_mean
self.num_residential = int(num_residential)
self.num_commercial = int(num_commercial)
self.num_streets = int(num_streets)
self.schedule = RandomActivation(self)
self.grid = MultiGrid(width, height, True)
# Create agents
available = []
indices = []
count = 0
for i in range(height):
for j in range(width - 1):
available.append((i, j))
indices.append(count)
count += 1
occupied = []
res = []
res_ind = []
count = 0
sched = 0
for i in range(self.num_residential): # place residences randomly
a = r.Residential(sched, self)
placed = False
while not placed:
position = np.random.choice(indices)
indices.remove(position)
position = available[position]
# if (x,y) is valid and it doesn't already have a house on it, add a house
if position not in occupied:
self.grid.place_agent(a, position)
self.schedule.add(a)
occupied.append(position)
res.append(position)
res_ind.append(count)
count += 1
placed = True
sched += 1
else:
print('something wrong 1')
for i in range(self.num_commercial): # place commercial randomly
a = c.Commercial(sched, self)
placed = False
while not placed:
position = np.random.choice(indices)
indices.remove(position)
position = available[position]
# if (x,y) is valid and it doesn't already have a property on it, add a business
if position not in occupied:
self.grid.place_agent(a, position)
self.schedule.add(a)
occupied.append(position)
placed = True
sched += 1
for i in range(self.num_streets):
a = s.Street(sched, self)
placed = False
while not placed:
position = np.random.choice(indices)
indices.remove(position)
position = available[position]
# if (x,y) is valid and it doesn't already have a property on it, add a business
if position not in occupied:
self.grid.place_agent(a, position)
self.schedule.add(a)
occupied.append(position)
placed = True
sched += 1
homes = []
for i in range(self.people_inside):
a = p.Person(sched, self, True)
placed = False
while not placed:
position = np.random.choice(res_ind)
res_ind.remove(position)
position = res[position]
if position not in homes:
self.grid.place_agent(a, position)
self.schedule.add(a)
a.Home = position
a.resident_status = True
a.in_neighbourhood = True
homes.append(position)
a.home = position
Res = self.grid.get_cell_list_contents(position)
home = [obj for obj in Res if isinstance(obj, r.Residential)]
home = home[0]
home.vacancy = False
a.econ_status = home.property_value
placed = True
sched += 1
for i in range(self.people_outside):
a = p.Person(sched, self, False)
placed = False
while not placed:
y = random.randrange(self.grid.height)
if y != self.grid.height:
self.grid.place_agent(a, (y, self.grid.width - 1))
self.schedule.add(a)
a.resident_status = False
a.in_neighbourhood = False
placed = True
sched += 1
# DEBUG: Plot econ status distros
# econ_inside = [person.econ_status for person in self.schedule.agents if
# isinstance(person, p.Person) and person.in_neighbourhood]
# econ_outside = [person.econ_status for person in self.schedule.agents if
# isinstance(person, p.Person) and not person.in_neighbourhood]
#
# plt.hist(econ_inside)
# plt.figure()
# plt.hist(econ_outside)
# plt.show()
self.datacollector = DataCollector(model_reporters=model_reporters)
def step(self):
self.datacollector.collect(self)
self.schedule.step()
class CancerModel(Model):
def xprint(self, *args):
logger.info("CANCER MODEL: " + " ".join(map(str, args)))
def __init__(self, cure_agent_type, config):
self.xprint("STARTING SIMULATION !!!")
self.counter = 0
self.decay_number = 0
self.lifetime_counter = 0
self.metastasis_score = 0
eat_values = {CancerCell: 1, HealthyCell: -1, CancerStemCell: 5}
assert (issubclass(cure_agent_type, CureAgent))
agent_memory_range = config["Agent"]["memory_limit"]
agent_memory_type = config["Agent"]["memory_type"]
radoznalost = config["Agent"]["curiosity"]
turn_off_modifiers = config["Tumor"]["turn_off_modifiers"]
CC_mutation_probability = config["Tumor"]["mutation_probability"]
is_tumor_growing = config["Tumor"]["is_growing"]
tumor_movement_stopping_range = config["Tumor"][
"movement_stopping_range"]
steps_before_mutation = config["Model"]["steps_before_mutation"]
self.SAMPLE_i = config["Model"]["sample_i"]
self.cure_number = config["Model"]["NA_number"]
self.probabilites_ranges = config["Agent"]["probabilities_limits"]
self.modifier_fraction = config["Tumor"]["modifier_fraction"]
self.mode = config["Simulation"]["mode"]
fname = "xxx" if self.mode == "learning" else config["Simulation"][
"fname"]
self.MUTATION_PERCENTAGE = config["Model"]["mutation_percentage"]
tumor_growth_probability = config["Tumor"]["growth_probability"]
cancer_cell_number = config["Model"]["CC_number"]
#DATA COLLECTION
self.datacollector = DataCollector(
model_reporters={
"FitnessFunction": fitness_funkcija,
"AverageSpeed": speed_avg,
"AverageMemoryCapacity": memory_size_all_avg,
"PopulationHeterogenity": population_heterogenity,
"MutationAmount": mutation_amount,
"CancerStemCell Number": CSC_number,
"CSC Specialized Agents": CSC_specialized_agents,
"CancerHeterogenity1": cancer_heterogenity_1,
"CancerHeterogenity2": cancer_heterogenity_2,
"CC_Number": CC_number,
"HealthyCell_Number": HC_number,
"MetastasisScore": "metastasis_score",
"CancerSize": cancer_size,
"TotalTumorResiliance": overall_cancer_resiliance,
"TumorResiliance_Pi": cancer_resiliance_Pi,
"TumorResiliance_Pd": cancer_resiliance_Pd,
"TumorResiliance_Pa": cancer_resiliance_Pa,
"TumorResiliance_Pk": cancer_resiliance_Pk,
"TumorResiliance_Psd": cancer_resiliance_Psd,
"NumberOfMutatedCells": mutated_CCs_num,
"TumorCoverage": tumor_coverage,
"AveragePd": average_Pd,
"AveragePa": average_Pa,
"AveragePi": average_Pi,
"AveragePk": average_Pk,
"AveragePsd": average_Psd,
# "PopulationClusters":cluster_counts
},
agent_reporters={
"Pi": get_Pi,
"Pa": get_Pa,
"Pd": get_Pd,
"speed": get_speed,
"Psd": get_Psd,
"Pk": get_Pk,
"memory_size": get_memory_size,
"type": get_agent_type
})
grid_size = math.ceil(math.sqrt(cancer_cell_number * 4))
self.STEPS_BEFORE_MUTATION = steps_before_mutation
self.grid = MultiGrid(grid_size, grid_size, False)
self.NUM_OF_INJECTION_POINTS = config["Model"]["injection_points"]
# self.speeds = list(range(1,grid_size//2))
self.speeds = [
1
] #TODO ovo mozda bolje? ne znam da li treba u config fajlu?
poss = self.generate_cancer_cell_positions(grid_size,
cancer_cell_number)
num_CSC = math.ceil(percentage(1, cancer_cell_number))
pos_CSC = [self.random.choice(poss) for i in range(num_CSC)]
#ACTIVATE SIMULATION
self.schedule = RandomActivation(self)
self.running = True
#PLACE CANCER CELLS
for i in range(cancer_cell_number):
pos = poss[i]
has_modifiers = False if ((i < (
(1 - self.modifier_fraction) * cancer_cell_number))
or turn_off_modifiers is True
) else True #10 % will be with modifiers
c = CancerStemCell("CANCER_STEM_CELL-"+str(uuid.uuid4()),self,value = eat_values[CancerStemCell],has_modifiers=has_modifiers,mutation_probability=CC_mutation_probability,grows=is_tumor_growing,growth_probability=tumor_growth_probability) \
if pos in pos_CSC else CancerCell("CANCER_CELL-"+str(uuid.uuid4()),self,value=eat_values[CancerCell],has_modifiers=has_modifiers,mutation_probability=CC_mutation_probability,grows=is_tumor_growing,growth_probability=tumor_growth_probability)
self.grid.place_agent(c, pos)
self.schedule.add(c)
#PLACE HEALTHY CELLS
for (i, (contents, x, y)) in enumerate(self.grid.coord_iter()):
nbrs = self.grid.get_neighborhood([x, y], moore=True)
second_nbrs = []
for nbr in nbrs:
second_nbrs += self.grid.get_neighborhood(nbr, moore=True)
nbrs = nbrs + second_nbrs
nbr_contents = self.grid.get_cell_list_contents(nbrs)
nbr_CCs = [
nbr for nbr in nbr_contents if isinstance(nbr, CancerCell)
]
if not contents and len(nbr_CCs) == 0:
c = HealthyCell(uuid.uuid4(), self, eat_values[HealthyCell])
self.grid.place_agent(c, (x, y))
self.schedule.add(c)
if self.mode == "simulation":
self.duplicate_mutate_or_kill = self.simulation_mode_function
self.decay_probability = 0.04 #MAGIC NUMBER
self.read_nanoagents_from_file(
fname=fname,
cure_agent_type=cure_agent_type,
tumor_movement_stopping_range=tumor_movement_stopping_range,
agent_memory_range=agent_memory_range,
radoznalost=radoznalost)
elif self.mode == "learning":
self.decay_probability = 0
self.make_nanoagents_from_scratch(cure_agent_type, radoznalost,
agent_memory_type,
agent_memory_range,
tumor_movement_stopping_range,
grid_size)
else:
assert ("False")
def get_random_positions_on_empty_cells(self):
"""Gets the N random currently empty positions on the grid"""
#GETTING EMPTY POSITIONS (for placing nano agents)
empty_cells = [(x, y) for (i, (contents, x,
y)) in enumerate(self.grid.coord_iter())
if not contents]
positions = [
self.random.choice(empty_cells)
for i in range(self.NUM_OF_INJECTION_POINTS)
]
self.xprint("The 5 random empty positions are %s" % positions)
return positions
def inject_nanoagents(self):
"""Injects the nanoagents, they will be activated slowly after"""
from itertools import cycle
positions = cycle(self.get_random_positions_on_empty_cells())
for a in self.agents:
self.grid.place_agent(a, next(positions))
self.agents_iterator = iter(self.agents)
def activate_next_batch_of_agents(self):
agents_to_be_placed_at_each_steps = round(len(self.agents) /
14) #MAGIC NUMBER
for i in range(agents_to_be_placed_at_each_steps):
self.schedule.add(next(self.agents_iterator))
def read_nanoagents_from_file(self, fname, cure_agent_type, radoznalost,
agent_memory_range,
tumor_movement_stopping_range):
import pandas as pd
self.xprint("Reading nanoagents from file")
df = pd.read_csv(fname)
self.agents = []
for i, row in df.iterrows():
a = cure_agent_type(
uuid.uuid4(),
self,
speeds=self.speeds,
radoznalost=radoznalost,
memory_type=row.memory_size,
memory_range=agent_memory_range,
tumor_movement_stopping_range=tumor_movement_stopping_range,
probabilities_ranges=self.probabilites_ranges)
a.Pi = row.Pi
a.Pa = row.Pa
a.Pd = row.Pd
a.memory_size = row.memory_size
a.memorija = FixSizeOrderedDict(max=row.memory_size)
a.tumor_movement_stopping_rate = row.tumor_movement_stopping_rate
self.agents.append(a)
def make_nanoagents_from_scratch(self, cure_agent_type, radoznalost,
agent_memory_type, agent_memory_range,
tumor_movement_stopping_range, grid_size):
from itertools import cycle
self.xprint("Making nanoagents from scratch")
positions = cycle(self.get_random_positions_on_empty_cells())
for i in range(self.cure_number):
pos = next(positions)
self.xprint(pos)
a = cure_agent_type(
uuid.uuid4(),
self,
speeds=self.speeds,
radoznalost=radoznalost,
memory_type=agent_memory_type,
memory_range=agent_memory_range,
tumor_movement_stopping_range=tumor_movement_stopping_range,
probabilities_ranges=self.probabilites_ranges)
self.grid.place_agent(a, pos)
self.schedule.add(a)
def simulation_mode_function(self):
self.xprint("In simulation mode - not duplicating or mutating")
return
def generate_cancer_cell_positions(self, grid_size, cancer_cells_number):
center = grid_size // 2
poss = [(center, center)]
for pos in poss:
poss += [
n for n in self.grid.get_neighborhood(
pos, moore=True, include_center=False) if n not in poss
]
if len(set(poss)) >= cancer_cells_number:
break
poss = list(set(poss))
return poss
def duplicate_mutate_or_kill(self):
if self.mode == "simulation":
assert (False)
koliko = math.ceil(
percentage(self.MUTATION_PERCENTAGE, self.cure_number))
cureagents = [
c for c in self.schedule.agents if isinstance(c, CureAgent)
]
sortirani = sorted(cureagents, key=lambda x: x.points, reverse=True)
poslednji = sortirani[-koliko:]
prvi = sortirani[:koliko]
sredina = len(sortirani) // 2
pocetak_sredine = sredina - (koliko // 2)
kraj_sredine = sredina + (koliko // 2)
srednji = sortirani[pocetak_sredine:kraj_sredine]
self.mutate_agents(srednji)
assert (len(prvi) == len(poslednji))
self.remove_agents(poslednji)
self.duplicate_agents(prvi)
def mutate_agents(self, agents):
self.xprint("Mutating middle agents")
for a in agents:
a.mutate()
def remove_agents(self, agents):
for a in agents:
self.kill_cell(a)
def duplicate_agents(self, agents):
for a in agents:
a_new = a.copy()
self.grid.place_agent(a_new, (1, 1))
self.schedule.add(a_new)
def kill_cell(self, cell):
self.grid.remove_agent(cell)
self.schedule.remove(cell)
def detach_stem_cell(self, cell):
self.metastasis_score += 1
self.kill_cell(cell)
def kill_all_agents(self):
agents = [a for a in self.schedule.agents if isinstance(a, CureAgent)]
for a in agents:
a.kill_self()
def write_population_to_file(self, i):
df = record_model_population(self)
df.to_csv("./Populations/Population{}-step{}.csv".format(
self.SAMPLE_i, i))
def step(self):
self.datacollector.collect(self)
if self.mode == "simulation":
self.simulation_step()
self.write_population_to_file(self.counter)
self.schedule.step()
self.counter += 1
self.lifetime_counter += 1
if self.counter % self.STEPS_BEFORE_MUTATION == 0:
#ovde ga stavljamo da izbegnemo da na nultom koraku uradi to
self.duplicate_mutate_or_kill()
def simulation_step(self):
LIFETIME_OF_NANOAGENTS = 80
REINJECTION_PERIOD = 100
if self.lifetime_counter > LIFETIME_OF_NANOAGENTS:
self.kill_all_agents()
# if self.counter%(LIFETIME_OF_NANOAGENTS+REINJECTION_PERIOD)==0:
# #svakih 180 koraka se ubrizgavaju
# self.inject_nanoagents()
# self.lifetime_counter = 0
agents_at_each_step = round(self.cure_number / 14)
for i in range(agents_at_each_step):
try:
self.schedule.add(next(self.agents_iterator))
except StopIteration:
break
agents = [a for a in self.schedule.agents if isinstance(a, CureAgent)]
assert (len(agents) <= self.cure_number)
class Sugarscape2ConstantGrowback(Model):
'''
Sugarscape 2 Constant Growback
'''
verbose = True # Print-monitoring
def __init__(self,
N,
beta_c,
beta_d,
beta_w,
beer_consumption,
serving_speed,
height=34,
width=18,
bar1_y=1,
bar2_y=32,
stage=(9, 33)):
'''
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 = N
self.bar1 = (0, bar1_y)
self.bar1crowd = 0
self.bar2 = (17, bar1_y)
self.bar2crowd = 0
self.bar3 = (0, bar2_y)
self.bar3crowd = 0
self.bar4 = (17, bar2_y)
self.bar4crowd = 0
self.stage = stage
self.beta_c = beta_c
self.beta_w = beta_w
self.beta_d = beta_d
self.beer_consumption = beer_consumption
self.serving_speed = serving_speed
self.WaitingTimes = []
self.MaxAgents = 0
self.schedule = RandomActivationByBreed(self)
self.grid = MultiGrid(self.width, self.height, torus=False)
#self.datacollector = DataCollector({"SsAgent": lambda m: m.schedule.get_breed_count(SsAgent), })
self.datacollector = DataCollector({
"Waiting":
lambda m: m.schedule.AverageWaitingTime(True),
"MaxAgents":
lambda m: m.schedule.MaxAgents()
})
# Create sugar
import numpy as np
# Create agent:
for i in range(self.initial_population):
#x = random.randrange(self.width)
#y = random.randrange(self.height)
# Start position of agents at bottom of 'club'
x = 7 + int(5 * np.random.random())
y = 0
vision = 2
ssa = SsAgent((x, y), self, beta_c, beta_d, beta_w,
beer_consumption, serving_speed, True, vision)
self.grid.place_agent(ssa, (x, y))
self.schedule.add(ssa)
sugar_distribution = np.zeros([self.width, self.height])
for x in range(self.width):
for y in range(self.height):
sugar_distribution[x, y] = 0
for _, x, y in self.grid.coord_iter():
sugar = Sugar((x, y), self)
self.grid.place_agent(sugar, (x, y))
self.schedule.add(sugar)
self.running = True
def WaitingTime(self, bar):
# Calculate crowd (and thus waiting time) in front of a bar every model interation to decrease computational cost.
busy = 0
(x, y) = bar
for dx in range(-4, 5):
for dy in range(-4, 5):
x_cor = x + dx
y_cor = y + dy
if 0 <= x_cor < self.width and 0 <= y_cor < self.height:
busy += len(
self.grid.get_cell_list_contents([(x_cor, y_cor)]))
return busy
def step(self):
self.WaitingTimes = []
# Calculate bar crowds per bar.
self.bar1crowd = self.WaitingTime(self.bar1)
self.bar2crowd = self.WaitingTime(self.bar2)
self.bar3crowd = self.WaitingTime(self.bar3)
self.bar4crowd = self.WaitingTime(self.bar4)
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=10):
if self.verbose:
print('Initial number Sugarscape Agent: ',
self.schedule.get_breed_count(SsAgent))
for i in range(step_count):
print(step_count)
self.step()
if self.verbose:
print('')
print('Final number Sugarscape Agent: ',
self.schedule.get_breed_count(SsAgent))
class ciudad(Model):
"Inicialización del modelo"
"Se necesita incluir la base de datos, suponiendo que tendremos varias ciudades para que la inicialización sea genérica"
def __init__(self, n, m, width, height, porcentaje_infectados, densidad,
acumedad, sexo, edades, transporte, Casillas_zona,
acummovilidad, movilidad, acummovilidad2, movilidad2,
acummovilidad3, movilidad3, dias_cuarentena, long_paso,
porcentaje_en_cuarentena, pruebas_disponibles,
activacion_testing_aleatorio, activacion_contact_tracing,
activación_cuarentena_acordeon, dia_inicio_acordeon,
intervalo_acordeon, tiempo_zonificacion):
self.total_agentes = n #número de agentes a iniciar
self.schedule = RandomActivation(
self) #inicialización en paralelo de todos los agentes
self.grid = MultiGrid(width, height,
True) #creación de la grilla genérica
global PRUEBAS_DISPONIBLES
global ACTIVACION_CONTACT_TRACING
global ACTIVACION_TESTING_ALEATORIO
global OLAS
global ACTIVACION_CUARENTENA_ACORDEON
global DIA_INICIO_ACORDEON
global INTERVALO_ACORDEON
global ACTIVACION_CUARENTENA
global DURACION_CUARENTENA
global PORCENTAJE_EN_CUARENTENA
global ACTIVACION_CUARENTENA_ZONIFICADA
global DURACION_CUARENTENA_ZONIFICADA
global PORCENTAJE_EN_CUARENTENA_ZONIFICADA
global TIEMPO_ZONIFICACION
global MATRIZ_CASILLAS
global MATRIZ_MOVILIDAD2
global MATRIZ_MOVILIDAD3
global ACUMULADA_MOVILIDAD2
global ACUMULADA_MOVILIDAD3
#Inicialización de las variables globales
PRUEBAS_DISPONIBLES = pruebas_disponibles
ACTIVACION_CONTACT_TRACING = activacion_contact_tracing
ACTIVACION_TESTING_ALEATORIO = activacion_testing_aleatorio
OLAS = round(PRUEBAS_DISPONIBLES / 12)
ACTIVACION_CUARENTENA_ACORDEON = activación_cuarentena_acordeon
DIA_INICIO_ACORDEON = dia_inicio_acordeon
INTERVALO_ACORDEON = intervalo_acordeon
ACTIVACION_CUARENTENA = (True if dias_cuarentena > 0 else False)
DURACION_CUARENTENA = dias_cuarentena
PORCENTAJE_EN_CUARENTENA = porcentaje_en_cuarentena
TIEMPO_ZONIFICACION = tiempo_zonificacion
#Guardar los datos de las nuevas matrices de movilidad
MATRIZ_CASILLAS = Casillas_zona
MATRIZ_MOVILIDAD2 = movilidad2
MATRIZ_MOVILIDAD3 = movilidad3
ACUMULADA_MOVILIDAD2 = acummovilidad2
ACUMULADA_MOVILIDAD3 = acummovilidad3
"Creación de cada agente"
for i in range(self.total_agentes):
n_id = random.random()
estado = (2 if n_id > porcentaje_infectados else 1)
tcontagio = (random.randint(5, 9) if estado == 2 else 0)
nuevo = personas(i, self, self.total_agentes, m, estado, tcontagio,
densidad, acumedad, sexo, edades, transporte,
Casillas_zona, acummovilidad, movilidad,
dias_cuarentena, long_paso,
porcentaje_en_cuarentena) #asignación del id
self.schedule.add(nuevo) #creación del agente en el sistema
"Asignación del hogar"
for i in Casillas_zona:
values = Casillas_zona.get(i)
for j in values:
agentes = self.grid.get_cell_list_contents(j)
if len(agentes) > 0:
num_hogares = (1 if round(len(agentes) / 4) == 0 else
round(len(agentes) / 4))
lista = [e for e in range(0, num_hogares)]
for z in agentes:
z.hogar = random.choice(lista)
"Configurar el recolector de datos"
self.datacollector = DataCollector(
model_reporters={
"Susceptibles": susceptibles,
"Total infectados": total_infectados,
"Graves": infectados_graves,
"Críticos": infectados_criticos,
"Leves": infectados_leves,
"Recuperados": recuperados,
"Rt": rt,
"Recuento_zonas": recuento_zonas,
"0-4": recuento_ge1,
"5-19": recuento_ge2,
"20-39": recuento_ge3,
"40-59": recuento_ge4,
">60": recuento_ge5,
"En_cuarentena": en_cuarentena,
"Vivos": agentes_vivos,
"Día": dia,
"Contactos_prom_trabajo": prom_contactos_trabajo,
"Contactos_prom_transporte": prom_contactos_transporte,
"Contactos_prom_casa": prom_contactos_casa,
"Nuevos_infectados": nuevos_infectados,
"Detectados": detectados,
"En_testing": en_testing,
"En_cama": en_cama,
"En_UCI": en_uci,
"Detectados_por_intervencion": detectados_interv,
"#Intervenidos": intervenidos
})
self.running = True
"Avanzar el modelo"
def step(self):
global PRUEBAS_DISPONIBLES
global ACTIVACION_TESTING_ALEATORIO
global ACTIVACION_CONTACT_TRACING
global OLAS
global ACTIVACION_CUARENTENA_ACORDEON
global DIA_INICIO_ACORDEON
global INTERVALO_ACORDEON
global ACTIVACION_CUARENTENA
global DIA_INICIO_CUARENTENA
global DURACION_CUARENTENA
global PORCENTAJE_EN_CUARENTENA
self.datacollector.collect(self)
self.actualizar_movilidad()
self.cuarentena_regular(ACTIVACION_CUARENTENA, DIA_INICIO_CUARENTENA,
DURACION_CUARENTENA, PORCENTAJE_EN_CUARENTENA)
self.cuarentena_acordeon(ACTIVACION_CUARENTENA_ACORDEON,
DIA_INICIO_ACORDEON, INTERVALO_ACORDEON)
self.schedule.step()
self.dinamica_hospitales()
self.testing_aleatorio(PRUEBAS_DISPONIBLES,
ACTIVACION_TESTING_ALEATORIO)
self.contact_tracing(ACTIVACION_CONTACT_TRACING)
#self.cuarentena_zonificada(0.2)
def actualizar_movilidad(self):
global MATRIZ_CASILLAS
global MATRIZ_MOVILIDAD2
global MATRIZ_MOVILIDAD3
global ACUMULADA_MOVILIDAD2
global ACUMULADA_MOVILIDAD3
t = self.schedule.time
agentes = [agent for agent in self.schedule.agents]
if t == 35:
for i in agentes:
i.set_posf(ACUMULADA_MOVILIDAD2, MATRIZ_MOVILIDAD2,
MATRIZ_CASILLAS)
#print(i.unique_id," Estado:",i.estado, " zona_origen:",i.zona," zona_destino:",i.zona_destino)
if t == 49:
for i in agentes:
i.set_posf(ACUMULADA_MOVILIDAD3, MATRIZ_MOVILIDAD3,
MATRIZ_CASILLAS)
#print(i.unique_id," Estado:",i.estado, " zona_origen:",i.zona," zona_destino:",i.zona_destino)
def cuarentena_zonificada(self, porcentaje_cierre):
global TIEMPO_ZONIFICACION
agentes = [agent for agent in self.schedule.agents]
zonas = []
#Saca la lista de zonas
for i in agentes:
zonas.append(i.zona)
zonas = unique(zonas)
#Cálculo del porcentaje de detectados para cada zona
for i in range(len(zonas)):
suma_agentes_en_zona = 0
suma_detectados = 0
for j in agentes:
if j.zona == i:
suma_agentes_en_zona += 1
suma_detectados += j.detectado
porcentaje = suma_detectados / suma_agentes_en_zona
if porcentaje > porcentaje_cierre:
for j in agentes:
if j.cuarentena == 0:
j.activar_zonificacion(i, TIEMPO_ZONIFICACION)
def cuarentena_regular(self, activacion, inicio, duracion, porcentaje):
if activacion == True:
t = self.schedule.time
if t == inicio:
agentes = [agent for agent in self.schedule.agents]
print(activacion, inicio, duracion, porcentaje)
for i in agentes:
i.activar_cuarentena(activacion, porcentaje, duracion)
#print(i.cuarentena," t:",i.tcuarentena, " dt:",i.dias_cuarentena)
def cuarentena_acordeon(self, activacion, inicio, intervalo):
global DIA_INICIO_ACORDEON
global INTERVALO_ACORDEON
global PORCENTAJE_EN_CUARENTENA
if activacion == True:
t = self.schedule.time
if t == inicio:
agentes = [agent for agent in self.schedule.agents]
for i in agentes:
i.activar_cuarentena(activacion, PORCENTAJE_EN_CUARENTENA,
intervalo)
DIA_INICIO_ACORDEON += 2 * INTERVALO_ACORDEON
def contact_tracing(self, activacion):
global PRUEBAS_DISPONIBLES
if activacion == True:
if PRUEBAS_DISPONIBLES > 0:
lista_para_contact_tracing = []
lista_para_intervenir = []
agentes = [agent for agent in self.schedule.agents]
for i in agentes:
if i.para_contact == 1 and i.detectado == 1:
lista_para_contact_tracing.append(i)
if len(lista_para_contact_tracing) > 0:
for i in lista_para_contact_tracing:
i.recopilar_contactos(lista_para_intervenir)
if len(lista_para_intervenir) > 0:
lista_para_intervenir = unique(lista_para_intervenir)
for i in lista_para_intervenir:
if PRUEBAS_DISPONIBLES > 1:
i.contact_tracing()
PRUEBAS_DISPONIBLES -= 1
def testing_aleatorio(self, pruebas, activacion):
global TIEMPO
global PRUEBAS_DISPONIBLES
global OLAS
if activacion == True:
t = self.schedule.time
agentes = [agent for agent in self.schedule.agents]
if t == TIEMPO and pruebas > 0 and OLAS > 0:
disponibles = pruebas / OLAS
if OLAS == 1:
for i in range(pruebas):
n_agente = random.choice(agentes)
n_agente = self.agente_valido(n_agente)
n_agente.testeado = 1
n_agente.intervencion = 1
PRUEBAS_DISPONIBLES -= 1
else:
for i in range(round(disponibles)):
n_agente = random.choice(agentes)
n_agente = self.agente_valido(n_agente)
n_agente.testeado = 1
n_agente.intervencion = 1
PRUEBAS_DISPONIBLES -= 1
TIEMPO += 3
OLAS -= 1
def dinamica_hospitales(self):
global CAMAS_DISPONIBLES
global UCIS_DISPONIBLES
agentes = [agent for agent in self.schedule.agents
] #recopila los agentes del modelo
agentes_para_hospital = []
#0. trabajar solo con el arreglo de agentes graves o críticos
for i in agentes:
if i.estado == 3 or i.estado == 4:
agentes_para_hospital.append(i)
liberar_cama = []
liberar_uci = []
requerir_cama = []
if len(agentes_para_hospital) > 0:
for i in agentes_para_hospital:
#crear arreglos para priorizar salidas sobre ingresos en la orden de ejecución
if i.estado == 3 and i.en_cama == 0: #ingresa a los graves a hospitalización
requerir_cama.append(i)
if i.estado == 3 and i.en_cama == 1 and i.thospitalizado == 8: #sale de hospitalización
liberar_cama.append(i)
if i.estado == 4 and i.en_cama == 1 and i.thospitalizado >= 6: #sale de hospitalización a UCI
liberar_cama.append(i)
if i.estado == 4 and i.en_uci == 1 and i.tuci >= 10:
liberar_uci.append(i)
if i.estado == 4 and i.en_cama == 1 and i.thospitalizado == 6 and i.tcontagio >= 24:
liberar_cama.append(i)
#Se van a liberar las personas de la cama
if len(liberar_cama) > 0:
for i in liberar_cama:
if i.estado == 3 and i.en_cama == 1: #libero cama si son solo hospitalizados
i.en_cama = 0
CAMAS_DISPONIBLES += 1
if i.estado == 4:
if i.en_cama == 1 and i.thospitalizado == 6 and i.tcontagio >= 24: #libero la cama de segunda estancia
i.en_cama = 0
CAMAS_DISPONIBLES += 1
else:
if i.en_cama == 1:
suma = UCIS_DISPONIBLES - 1
if suma >= 0: #libero cama si lo puedo meter en UCI
i.procesado = 1
i.en_cama = 0
i.thospitalizado = 0
CAMAS_DISPONIBLES += 1
i.en_uci = 1
UCIS_DISPONIBLES -= 1
else:
i.procesado = 0
#Se van a liberar las UCIs y se meten en camas si hay disponibles
if len(liberar_uci) > 0:
#print("liberando UCIs")
for i in liberar_uci:
if i.en_uci == 1:
suma = CAMAS_DISPONIBLES - 1
if suma >= 0:
i.en_uci = 0
UCIS_DISPONIBLES += 1
i.en_cama = 1
CAMAS_DISPONIBLES -= 1
#Se van a asignar cama para los que las necesitan
if len(requerir_cama) > 0:
#print("asignado camas")
for i in requerir_cama:
if i.en_cama == 0:
suma = CAMAS_DISPONIBLES - 1
if suma >= 0:
i.en_cama = 1
i.procesado = 1
CAMAS_DISPONIBLES -= 1
def agente_valido(self, n_agente):
if n_agente.testeado == 0 and n_agente.detectado == 0 and n_agente.edad > 17 and n_agente.edad < 70:
return n_agente
else:
agentes = [agent for agent in self.schedule.agents]
return self.agente_valido(random.choice(agentes))
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