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SIRS_test_Moore.py
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# -*- coding: utf-8 -*-
"""
Created on Fri May 8 15:04:38 2020
@author: hurva
"""
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 25 18:50:36 2020
@author: hurva
"""
import numpy as np
import matplotlib
import matplotlib.pylab as plt
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation
matplotlib.use("Agg")
from scipy import signal
import cv2
import copy
periodic = False
global verbal
verbal = 4
if periodic:
bType = cv2.BORDER_WRAP
else:
bType = cv2.BORDER_REFLECT
def custom(inf, h=0.01):
tempInf = np.zeros(((xSize + 2), (ySize + 2)))
tempInf[1:(xSize+1), 1:(ySize+1)] = np.copy(inf)
retInf = np.zeros((xSize, ySize))
if verbal == 1 or verbal == 1.1:
print("tempInf: \n", tempInf)
for i, row in enumerate(inf):
i += 1
for j, col in enumerate(row):
j += 1
neighb = np.copy(tempInf[(i-1):(i+2), (j-1):(j+2)])
neighb[1, 1] = 0
diff = (np.sum(neighb))
if verbal == 1:
print("diff: \n", diff)
retInf[(i-1), (j-1)] = diff
if verbal == 1 or verbal == 1.1:
print()
print("retInf: \n", retInf)
print("tempInf: \n", tempInf)
return retInf
def conv(inf, h=0.01):
retInf = np.copy(inf)
retInf = signal.convolve2d(retInf, np.ones((3, 3)), boundary='symm', mode='same')
return retInf
def susStep(sus, inf, rem, alpha, gamma, B, d, my, diff, h=0.01):
return sus + (B + gamma*rem - d*sus -alpha*inf*(1-my*inf)*sus - (diff * sus))*h
def infStep(sus, inf, alpha, beta, d, my, diff, h=0.01):
return inf + (alpha*inf*(1-my*inf)*sus - (beta + d)*inf + (diff * sus))*h
def remStep(rem, inf, beta, gamma, d, h=0.01):
return rem + (beta*inf - (gamma + d)*rem)*h
def timeStep(susMat, infMat, remMat, alpha, beta, gamma, B, d, my, Ds, Di, Dr, h=0.01):
tempSus = np.copy(susMat)
tempInf = np.copy(infMat)
tempRem = np.copy(remMat)
diff = Ds * conv(tempInf)
tempSus = susStep(susMat, infMat, remMat, alpha, gamma, B, d, my, diff, h)
tempInf = infStep(susMat, infMat, alpha, beta, d, my, diff, h)
tempRem = remStep(remMat, infMat, beta, gamma, d, h)
return tempSus, tempInf, tempRem
option = 4
xSize = 10
ySize = 10
iters = 40000
beta = 0.1 #1/21
alpha = 0.2 #2.5 / beta
gamma = 0.001
B = 0.00
d = 0.00
my = 0.5
Ds = 0.0005
Di = 0.0
Dr = 0.0
h = 0.05
susMat = np.ones((xSize, ySize, iters))
infMat = np.zeros((xSize, ySize, iters))
remMat = np.zeros((xSize, ySize, iters))
wanMat = np.zeros((xSize, ySize, iters))+0.0000001
infMat[0,0,0] = susMat[0,0,0]*0.001
infMat[3,3,0] = susMat[0,0,0]*0.002
susMat[0,0,0] -= susMat[0,0,0]*0.001
for i in range(iters-1):
tempSus, tempInf, tempRem = timeStep(susMat[:,:,i], infMat[:,:,i],
remMat[:,:,i], alpha, beta, gamma, B, d, my, Ds, Di, Dr, h)
susMat[:,:,(i+1)] = tempSus
infMat[:,:,(i+1)] = tempInf
remMat[:,:,(i+1)] = tempRem
if option == 0:
plt.plot(susMat[0,0,:],'g')
plt.plot(infMat[0,0,:],'r')
plt.plot(remMat[0,0,:],'b')
plt.show()
if option == 1:
for n in range(xSize):
for m in range (ySize):
c1 = [((n + m)/(np.max(xSize) + np.max(ySize))), 0, 0]
c2 = [0, ((n + m)/(np.max(xSize) + np.max(ySize))), 0]
c3 = [0, 0, ((n + m)/(np.max(xSize) + np.max(ySize)))]
plt.plot(susMat[n, m, :], color = c1)
plt.plot(infMat[n, m, :], color = c2)
plt.plot(remMat[n, m, :], color = c3)
plt.legend(["Susceptible", "Infected", "Removed"])
plt.show()
if verbal == 4:
print("Sus: \n", susMat[:, :, -1])
print("Inf: \n", infMat[:, :, -1])
print("Rem: \n", remMat[:, :, -1])
if option == 2:
plt.matshow(susMat[:, :, -1])
plt.show()
if option == 3:
fig = plt.figure()
def f1(time):
return susMat[:, :, time]
def f2(time):
return infMat[:, :, time]
time = -1;
plt.subplot(1, 2, 1)
im1 = plt.imshow(f1(time), animated=True, vmin=np.min(susMat), vmax=np.max(susMat))
plt.title('Susceptibale')
plt.subplot(1, 2, 2)
im2 = plt.imshow(f2(time), animated=True, vmin=np.min(infMat), vmax=np.max(infMat))
plt.title('Infected')
def updatefig(*args):
global time
time += 10
if time >= iters:
time = 0
print(time)
v = f1(time)
print(v)
im1.set_array(f1(time))
im2.set_array(f2(time))
return im1, im2,
ani = animation.FuncAnimation(fig, updatefig, frames=10, interval=0.1, blit=True)
plt.show()
if option == 4:
fig = plt.figure()
def f1(time):
return susMat[:, :, time]
def f2(time):
return infMat[:, :, time]
def f3(time):
return remMat[:, :, time]
def iniPlot():
print("init")
for n in range(xSize):
for m in range (ySize):
c1 = [((n + m)/(np.max(xSize) + np.max(ySize))), 0, 0]
c2 = [0, ((n + m)/(np.max(xSize) + np.max(ySize))), 0]
c3 = [0, 0, ((n + m)/(np.max(xSize) + np.max(ySize)))]
plt.plot(susMat[n, m, :], color = c1)
plt.plot(infMat[n, m, :], color = c2)
plt.plot(remMat[n, m, :], color = c3)
plt.legend(["Susceptible", "Infected", "Removed"])
x = np.zeros(iters)
x[100] = 1
y = np.linspace(0, 1, iters)
#line, = plt.plot(x, y)
#return line,
time = -1;
Writer = animation.writers['ffmpeg']
writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)
plt.subplot(2, 2, 1)
im1 = plt.imshow(f1(time), animated=True, vmin=np.min(susMat), vmax=np.max(susMat))
plt.title('Susceptibale')
plt.subplot(2, 2, 2)
im2 = plt.imshow(f2(time), animated=True, vmin=np.min(infMat), vmax=np.max(infMat))
plt.title('Infected')
plt.subplot(2, 2, 3)
im3 = plt.imshow(f2(time), animated=True, vmin=np.min(remMat), vmax=np.max(remMat))
plt.title('Removed')
plt.subplot(2, 2, 4)
iniPlot()
def updPlot(time):
global line
x = np.zeros(iters)
x[time] = 1
y = np.linspace(0, 1, iters)
line.set_data(x, y)
return line,
def updatefig(*args):
global time
time += 10
if time >= iters:
time = 0
if verbal == 5:
v = f1(time)
print(v)
print(time)
im1.set_array(f1(time))
im2.set_array(f2(time))
im3.set_array(f3(time))
return im1, im2, im3,
ani = animation.FuncAnimation(fig, updatefig, frames=10,
interval=0.1,blit=True, save_count=2000)
ani.save('lines.mp4')
plt.show()