def __init__(self, no_people, total_area, no_agents, all_x, all_y,
infection_rate, first_infected, mobility, work_store,
home_store, no_mask, mask_effect, phase, infected_position):
self.num_agents = no_agents
grid_size = round(
math.sqrt((self.num_agents / no_people) * total_area) * 100)
self.grid = MultiGrid(grid_size, grid_size, False)
self.schedule = RandomActivation(self)
self.running = True
self.stepper = 0
for i in range(self.num_agents):
a = Agent(i, self, infection_rate, work_store, home_store,
mobility, mask_effect)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
if random.uniform(0, 1) < no_mask:
a.masked = 1
if phase:
if i == first_infected:
a.infected = 1
else:
if infected_position[i] == 1:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot infections": compute_informed},
agent_reporters={
"Infected": "infected",
"R-Number": "rnumber"
})
def __init__(self, home_store):
self.num_agents = 1000
self.grid = MultiGrid(200, 200, True)
self.schedule = RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = Agent(i, self)
self.schedule.add(a)
while True:
#x = round(int(np.random.normal(self.grid.width/2, 10, 1)))
#y = round(int(np.random.normal(self.grid.height/2, 10, 1)))
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
if len(
self.grid.get_neighbors(
(x, y), moore=True, include_center=True,
radius=10)) <= 7:
self.grid.place_agent(a, (x, y))
home_store[i, :] = x, y
break
if i < 1:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot informed": compute_informed},
agent_reporters={"Infected": "infected"})
def __init__(self, city_to_country, no_people, total_area, city_to_country_area, countryside):
self.num_agents = 2000
grid_size = round(math.sqrt((self.num_agents/no_people)*total_area)*100)
self.grid = MultiGrid(grid_size, grid_size, True)
self.schedule = RandomActivation(self)
self.running = True
centers = np.zeros((1, 2))
centers[0, :] = random.randrange(10, self.grid.width - 10), random.randrange(10, self.grid.height - 10)
x = np.zeros((1, round(int(city_to_country * self.num_agents))))
y = np.zeros((1, round(int(city_to_country * self.num_agents))))
x[0, :] = np.around(np.random.normal(centers[0, 0], 3, round(int(city_to_country * self.num_agents))))
y[0, :] = np.around(np.random.normal(centers[0, 1], 3, round(int(city_to_country * self.num_agents))))
count = 0
countryside_count = 0
while countryside_count < (countryside * self.num_agents):
countryside_count += counter(x)
runner = True
while runner:
new_center = (random.randrange(10, self.grid.width - 10), random.randrange(10, self.grid.height - 10))
if dist_check(new_center, centers):
centers = np.vstack((centers, new_center))
runner = False
new_x = np.around(np.random.normal(centers[count, 0], (1/(6*city_to_country_area*(math.sqrt(count+1))))
* self.grid.width, round(int(city_to_country * self.num_agents)
/ (count + 2))))
new_y = np.around(np.random.normal(centers[count, 1], (1/(6*city_to_country_area*(math.sqrt(count+1))))
* self.grid.height, round(int(city_to_country * self.num_agents)
/ (count + 2))))
while len(new_x) < round(int(city_to_country * self.num_agents)):
new_x = np.append(new_x, -1)
new_y = np.append(new_y, -1)
x = np.vstack((x, new_x))
y = np.vstack((y, new_y))
count += 1
new_x = np.delete(x.flatten(), np.where(x.flatten() == -1))
new_y = np.delete(y.flatten(), np.where(y.flatten() == -1))
x_countryside = np.around(np.random.uniform(0, self.grid.width-1, int(self.num_agents - len(new_x))))
y_countryside = np.around(np.random.uniform(0, self.grid.height-1, int(self.num_agents - len(new_y))))
all_x = np.concatenate((new_x, x_countryside))
all_y = np.concatenate((new_y, y_countryside))
for i in range(self.num_agents):
a = Agent(i, self)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
if i == 1:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot informed": compute_informed},
agent_reporters={"Infected": "infected"})
def __init__(self, mobility):
self.num_agents = 1000
self.grid = MultiGrid(100, 100, True)
self.schedule = RandomActivation(self)
self.running = True
for i in range(self.num_agents):
a = Agent(i, mobility, self)
self.schedule.add(a)
x = self.random.randrange(self.grid.width)
y = self.random.randrange(self.grid.height)
self.grid.place_agent(a, (x, y))
if i < 1:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot informed": compute_informed},
agent_reporters={"Infected": "infected"})
def __init__(self, city_to_country, no_people, total_area, city_to_country_area, countryside, no_agents, Nc_N, n):
self.num_agents = no_agents
grid_size = round(math.sqrt((self.num_agents / no_people) * total_area) * 100)
self.grid = MultiGrid(grid_size, grid_size, False)
self.schedule = RandomActivation(self)
self.running = True
centers = np.zeros((1, 2))
centers[0, :] = random.randrange(10, self.grid.width - 10), random.randrange(10, self.grid.height - 10)
x = np.zeros((1, round(int(city_to_country * self.num_agents))))
y = np.zeros((1, round(int(city_to_country * self.num_agents))))
x[0, :] = np.around(np.random.normal(centers[0, 0], 3, round(int(city_to_country * self.num_agents))))
y[0, :] = np.around(np.random.normal(centers[0, 1], 3, round(int(city_to_country * self.num_agents))))
count = 0
countryside_count = 0
while countryside_count < (countryside * self.num_agents):
countryside_count += counter(x)
runner = True
while runner:
new_center = (random.randrange(10, self.grid.width - 10), random.randrange(10, self.grid.height - 10))
if dist_check(new_center, centers):
centers = np.vstack((centers, new_center))
runner = False
new_x = np.around(
np.random.normal(centers[count, 0], (1 / (6 * city_to_country_area * (math.sqrt(count + 1))))
* self.grid.width, round(int(city_to_country * self.num_agents)
/ (count + 2))))
new_y = np.around(
np.random.normal(centers[count, 1], (1 / (6 * city_to_country_area * (math.sqrt(count + 1))))
* self.grid.height, round(int(city_to_country * self.num_agents)
/ (count + 2))))
while len(new_x) < round(int(city_to_country * self.num_agents)):
new_x = np.append(new_x, -1)
new_y = np.append(new_y, -1)
x = np.vstack((x, new_x))
y = np.vstack((y, new_y))
count += 1
label = city_labeler(x)
for i in range(len(label)):
city_label[i] = label[i]
new_x = np.delete(x.flatten(), np.where(x.flatten() == -1))
new_y = np.delete(y.flatten(), np.where(y.flatten() == -1))
x_countryside = np.around(np.random.uniform(0, self.grid.width - 1, int(self.num_agents - len(new_x))))
y_countryside = np.around(np.random.uniform(0, self.grid.height - 1, int(self.num_agents - len(new_y))))
all_x = np.concatenate((new_x, x_countryside))
all_y = np.concatenate((new_y, y_countryside))
for i in range(self.num_agents):
a = Agent(i, self)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
home_store1[i, :] = int(all_x[i]), int(all_y[i])
if i == 1:
a.infected = 1
#a.working = 1
flux_store = np.zeros((1, 3))
for i in range(round(len(centers) / 2)):
print(i)
#print(round(len(centers))/2)
n_cities = random.sample(range(1, round(len(centers) / 2)), n)
for j in range(len(n_cities)):
mi = np.count_nonzero(city_label == i+1)
nj = np.count_nonzero(city_label == n_cities[j])
radius = math.sqrt((centers[i, 0] - centers[n_cities[j], 0]) ** 2 +
(centers[i, 1] - centers[n_cities[j], 1]) ** 2)
sij = 0
for k in range(len(all_x)):
if (all_x[k] - centers[i, 0]) ** 2 + (all_y[k] - centers[i, 1]) ** 2 < radius ** 2:
sij += 1
sij = sij - mi - nj
if sij < 0:
sij = 0
try:
Tij = (mi * Nc_N * mi * nj) / ((mi + sij) * (mi + nj + sij))*10
except ZeroDivisionError:
Tij = 0
if Tij > 75:
Tij = 75
if Tij > 1 and (i != n_cities[j]):
flux_store = np.vstack((flux_store, (Tij, i+1, n_cities[j])))
work_place = np.zeros(self.num_agents)
work_store1 = np.zeros((num, 2))
flux_store = np.delete(flux_store, 0, 0)
for i in np.unique(flux_store[:, 1]):
place = np.where(flux_store[:, 1] == i)[0]
place1 = np.where(city_label == i)[0]
for j in place1:
for k in place:
if random.uniform(0, 100) < flux_store[k, 0]:
work_place[j] = flux_store[k, 2]
for i in range(len(work_store1)):
if work_place[i] != 0:
n = int(work_place[i])
work_store1[i, :] = centers[n, 0], centers[n, 1]
global work_store, home_store
work_store = np.int64(work_store1)
home_store = np.int64(home_store1)
self.datacollector = DataCollector(
model_reporters={"Tot informed": compute_informed},
agent_reporters={"Infected": "infected"})
def __init__(self, no_people, total_area, no_agents, Nc_N, n, all_x, all_y,
centers, infection_rate, city_label, first_infected,
mobility_data):
self.num_agents = no_agents
grid_size = round(
math.sqrt((self.num_agents / no_people) * total_area) * 100)
self.grid = MultiGrid(grid_size, grid_size, False)
self.schedule = RandomActivation(self)
self.running = True
flux_store = np.zeros((1, 3))
home_store1 = np.zeros((self.num_agents, 2))
for i in range(round(len(centers) / 2)):
print(i, datetime.datetime.now() - begin_time)
n_cities = random.sample(range(1, round(len(centers) / 2)), n)
for j in range(len(n_cities)):
mi = np.count_nonzero(city_label == i + 1)
nj = np.count_nonzero(city_label == n_cities[j])
radius = math.sqrt(
(centers[i, 0] - centers[n_cities[j], 0])**2 +
(centers[i, 1] - centers[n_cities[j], 1])**2)
sij = 0
for k in range(len(all_x)):
if (all_x[k] - centers[i, 0])**2 + (
all_y[k] - centers[i, 1])**2 < radius**2:
sij += 1
sij = sij - mi - nj
if sij < 0:
sij = 0
try:
Tij = (mi * Nc_N * mi * nj) / ((mi + sij) *
(mi + nj + sij)) * 10
except ZeroDivisionError:
Tij = 0
if Tij > 75:
Tij = 75
if Tij > 1 and (i != n_cities[j]):
flux_store = np.vstack(
(flux_store, (Tij, i + 1, n_cities[j])))
work_place = np.zeros(self.num_agents)
work_store1 = np.zeros((num, 2))
flux_store = np.delete(flux_store, 0, 0)
for i in np.unique(flux_store[:, 1]):
place = np.where(flux_store[:, 1] == i)[0]
place1 = np.where(city_label == i)[0]
for j in place1:
for k in place:
if random.uniform(0, 100) < flux_store[k, 0]:
work_place[j] = flux_store[k, 2]
for i in range(len(work_store1)):
if work_place[i] != 0:
n = int(work_place[i])
work_store1[i, :] = centers[n, 0], centers[n, 1]
for i in range(self.num_agents):
home_store1[i, :] = int(all_x[i]), int(all_y[i])
work_store = np.int64(work_store1)
home_store = np.int64(home_store1)
for i in range(self.num_agents):
if mobility_data:
a = Agent(i, self, infection_rate, work_store, home_store)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
else:
a = Agent1(i, self, infection_rate, work_store, home_store)
self.schedule.add(a)
self.grid.place_agent(a, (int(all_x[i]), int(all_y[i])))
if i == first_infected:
a.infected = 1
self.datacollector = DataCollector(
model_reporters={"Tot infections": compute_informed},
agent_reporters={
"Infected": "infected",
"R-Number": "rnumber"
})
