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abm_radiation_plotter.py
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abm_radiation_plotter.py
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from mesa import Agent, Model
from mesa.time import RandomActivation
from mesa.datacollection import DataCollector
from mesa.space import MultiGrid
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import random
import math
import datetime
from import_apple_data import average
begin_time = datetime.datetime.now()
def find_dist(pos1, pos2):
distance = math.sqrt(abs(pos1[0]-pos2[0])**2 + abs(pos1[1]-pos2[1])**2)
return distance
def dist_check(pos1, pos2):
distance = []
for i in range(np.shape(pos2)[0]):
distance.append(math.sqrt(abs(pos1[0]-pos2[i, 0])**2 + abs(pos1[1]-pos2[i, 1])**2))
if all(x > 10 for x in distance):
return True
else:
return False
def counter(array):
count = 0
if np.shape(array)[0] < 2:
for j in range(np.shape(array)[1]):
if j != -1:
count += 1
else:
for j in range(np.shape(array)[1]):
if array[-1, j] != -1:
count += 1
return count
def city_labeler(x):
home_label = np.zeros(num)
count = 0
for i in range(np.shape(x)[1]):
for j in range(np.shape(x)[0]):
if x[j, i] != -1:
home_label[count] = i+1
count += 1
return home_label
def colour_plotter(model):
c = mcolors.ColorConverter().to_rgb
rvb = make_colormap([c('black'), c('red'), 0.05, c('red'), c('yellow'), 0.5, c('yellow'), c('white')
, 0.9, c('white')])
agent_counts = np.zeros((model.grid.width, model.grid.height))
for cell in model.grid.coord_iter():
cell_content, x, y = cell
agent_count = len(cell_content)
agent_counts[x][y] = agent_count
plt.figure(figsize=(6, 6))
plt.imshow(agent_counts.T, interpolation='nearest', cmap=rvb)
plt.colorbar()
plt.show()
def infected_plotter(model, day):
no_infected = sum([1 for a in model.schedule.agents if a.infected == 1])
infected_index_x = np.zeros(no_infected)
index_x = np.zeros(model.num_agents)
infected_index_y = np.zeros(no_infected)
index_y = np.zeros(model.num_agents)
count = 0
count1 = 0
for cell in model.grid.coord_iter():
cell_content, x, y = cell
for a in cell_content:
index_x[count1] = x
index_y[count1] = model.grid.height - y
count1 += 1
if a.infected == 1:
infected_index_x[count] = x
infected_index_y[count] = model.grid.height - y
count += 1
plt.rcParams['axes.facecolor'] = 'black'
plt.figure(figsize=(6, 6))
plt.scatter(index_x, index_y, marker='s', c='blue', s=0.5)
plt.scatter(infected_index_x, infected_index_y, marker='s', c='red', s=0.5)
plt.title('Step = ' + str(day) + ' No. Infected = ' + str(no_infected) + '/' + str(model.num_agents))
plt.show()
def infected_calc(model):
no_infected = sum([1 for a in model.schedule.agents if a.infected == 1])
infected_index_x = np.zeros(no_infected)
index_x = np.zeros(model.num_agents)
infected_index_y = np.zeros(no_infected)
index_y = np.zeros(model.num_agents)
count = 0
count1 = 0
for cell in model.grid.coord_iter():
cell_content, x, y = cell
for a in cell_content:
index_x[count1] = x
index_y[count1] = model.grid.height - y
count1 += 1
if a.infected == 1:
infected_index_x[count] = x
infected_index_y[count] = model.grid.height - y
count += 1
return index_x, index_y, infected_index_x, infected_index_y, no_infected
class Agent(Agent):
def __init__(self, unique_id, model):
super().__init__(unique_id, model)
self.infected = 0
self.working = 0
# self.mobile = mobility
def spread_disease(self):
if self.infected == 0:
return
#print(self.mobile[day]/3000)
else:
cellmates = self.model.grid.get_cell_list_contents([self.pos])
for a in cellmates:
if a.infected != 1:
a.infected = 1
def move(self):
if (day % 4) - 2 == 0:
if work_store[self.unique_id, 0] != 0:
new_position = (tuple(work_store[self.unique_id, :]))
self.model.grid.move_agent(self, new_position)
self.working = 1
if (day % 4) == 0:
if work_store[self.unique_id, 0] != 0:
new_position = (tuple(home_store[self.unique_id, :]))
self.model.grid.move_agent(self, new_position)
self.working = 0
else:
"""possible_steps = self.model.grid.get_neighborhood(self.pos, moore=True, include_center=False)
new_position = self.random.choice(possible_steps)
self.model.grid.move_agent(self, new_position)
#else:"""
possible_steps = self.model.grid.get_neighborhood(self.pos, moore=True, include_center=False)
while True:
if self.working == 0:
new_position = self.random.choice(possible_steps)
if find_dist(new_position, home_store[self.unique_id, :]) <= 5:
self.model.grid.move_agent(self, new_position)
break
elif self.working == 1:
new_position = self.random.choice(possible_steps)
if find_dist(new_position, work_store[self.unique_id, :]) <= 5:
self.model.grid.move_agent(self, new_position)
break
def step(self):
self.move()
self.spread_disease()
def compute_informed(model):
return sum([1 for a in model.schedule.agents if a.infected == 1])
class DiseaseModel(Model):
def __init__(self, city_to_country, no_people, total_area, city_to_country_area, countryside, no_agents, Nc_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
n = 50
flux_store = np.zeros((1, 3))
for i in range(round(len(centers) / 2)):
n_cities = random.sample(range(1, round(len(centers) / 3)), 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 step(self):
self.datacollector.collect(self)
self.schedule.step()
num = 2000
home_store1 = np.zeros((num, 2))
city_label = np.zeros(num)
#work_store = np.zeros((num, 2))
model = DiseaseModel(city_to_country=0.14,
no_people=67000000,
total_area=240000,
city_to_country_area=13,
countryside=0.8,
no_agents=num,
Nc_N=0.2)
#colour_plotter(model)
#recovery_count = np.zeros(1000)
"""steps = 500
for day in range(steps):
if day % 10 == 0:
infected_plotter(model, day)
elif day < 20:
infected_plotter(model, day)
model.step()"""
steps = 201
for day in range(steps):
if day == 0:
index_x, index_y, infected_index_x, infected_index_y, no_infected = infected_calc(model)
if day == 3:
index_x1, index_y1, infected_index_x1, infected_index_y1, no_infected1 = infected_calc(model)
if day == 10:
index_x5, index_y5, infected_index_x5, infected_index_y5, no_infected5 = infected_calc(model)
if day == 50:
index_x10, index_y10, infected_index_x10, infected_index_y10, no_infected10 = infected_calc(model)
if day == 100:
index_x50, index_y50, infected_index_x50, infected_index_y50, no_infected50 = infected_calc(model)
if day == 200:
index_x100, index_y100, infected_index_x100, infected_index_y100, no_infected100 = infected_calc(model)
model.step()
plt.rcParams['axes.facecolor'] = 'black'
fig, ax = plt.subplots(2, 3, figsize=(10, 7))
ax[0, 0].scatter(index_x, index_y, marker='s', c='blue', s=0.5)
ax[0, 0].scatter(infected_index_x, infected_index_y, marker='s', c='red', s=0.5)
ax[0, 0].title.set_text('Step=' + str(0) + ' No. Infected=' + str(no_infected) + '/' + str(2000))
ax[0, 0].axes.get_xaxis().set_visible(False)
ax[0, 0].axes.get_yaxis().set_visible(False)
ax[0, 1].scatter(index_x1, index_y1, marker='s', c='blue', s=0.5)
ax[0, 1].scatter(infected_index_x1, infected_index_y1, marker='s', c='red', s=0.5)
ax[0, 1].title.set_text('Step=' + str(3) + ' No. Infected=' + str(no_infected1) + '/' + str(2000))
ax[0, 1].axes.get_xaxis().set_visible(False)
ax[0, 1].axes.get_yaxis().set_visible(False)
ax[0, 2].scatter(index_x5, index_y5, marker='s', c='blue', s=0.5)
ax[0, 2].scatter(infected_index_x5, infected_index_y5, marker='s', c='red', s=0.5)
ax[0, 2].title.set_text('Step=' + str(10) + ' No. Infected=' + str(no_infected5) + '/' + str(2000))
ax[0, 2].axes.get_xaxis().set_visible(False)
ax[0, 2].axes.get_yaxis().set_visible(False)
ax[1, 0].scatter(index_x10, index_y10, marker='s', c='blue', s=0.5)
ax[1, 0].scatter(infected_index_x10, infected_index_y10, marker='s', c='red', s=0.5)
ax[1, 0].title.set_text('Step=' + str(50) + ' No. Infected=' + str(no_infected10) + '/' + str(2000))
ax[1, 0].axes.get_xaxis().set_visible(False)
ax[1, 0].axes.get_yaxis().set_visible(False)
ax[1, 1].scatter(index_x50, index_y50, marker='s', c='blue', s=0.5)
ax[1, 1].scatter(infected_index_x50, infected_index_y50, marker='s', c='red', s=0.5)
ax[1, 1].title.set_text('Step=' + str(100) + ' No. Infected=' + str(no_infected50) + '/' + str(2000))
ax[1, 1].axes.get_xaxis().set_visible(False)
ax[1, 1].axes.get_yaxis().set_visible(False)
ax[1, 2].scatter(index_x100, index_y100, marker='s', c='blue', s=0.5)
ax[1, 2].scatter(infected_index_x100, infected_index_y100, marker='s', c='red', s=0.5)
ax[1, 2].title.set_text('Step=' + str(200) + ' No. Infected=' + str(no_infected100) + '/' + str(2000))
ax[1, 2].axes.get_xaxis().set_visible(False)
ax[1, 2].axes.get_yaxis().set_visible(False)
fig.tight_layout()
plt.show()
"""
#colour_plotter(model)
out = model.datacollector.get_agent_vars_dataframe().groupby('Step').sum()
new_out = out.to_numpy()
plt.rcParams['axes.facecolor'] = 'white'
plt.figure(figsize=(12, 6))
plt.plot(np.arange(0, steps), new_out, color='blue', label='Real')
plt.xlabel('Days')
plt.ylabel('No. of People Infected')
plt.legend()
plt.grid()
plt.show() """
print(datetime.datetime.now() - begin_time)