def animate(size, sweeps, pS, pI, pR):
'''
black= infected
white = rec
Animation for the sirs model for the given input.
'''
model = sirs(size, pS, pI, pR)
cMap = 'magma'
im = plt.imshow(model.lattice, cmap=cMap, animated=True, vmin=-1, vmax=1)
plt.draw()
plt.pause(0.0001)
for i in range(sweeps):
model.update()
plt.cla()
im = plt.imshow(model.lattice,
cmap=cMap,
animated=True,
vmin=-1,
vmax=1)
# plt.xlabel("X axis")
# plt.ylabel("Y axis")
# plt.title("Animation for SIRS model")
plt.pause(0.0001)
# if(model.infected==0):
# print(f"Infected = 0 at {i}")
plt.show()
turns = 100
crit = 1
initInfect = 3
size = 50
model = "SIRS"
series = []
grave = []
rule = np.arange(0, turns + 1, step=crit)
fig, ax = plt.subplots(2, 1)
for ind in range(size):
covid = sirs(p, i, r, graph)
print("Epoch: {}/{}".format(ind + 1, size))
infect, removed = covid.start(turns, initInfect, crit)
series.append(infect)
grave.append(removed)
ax[0].plot(rule, infect, linestyle='--', linewidth=0.5)
ax[1].plot(rule, removed, linestyle='--', linewidth=0.5)
series = np.array(series)
grave = np.array(grave)
ax[0].plot(rule, np.average(series, axis=0), linewidth=1.5, label='Ave')
ax[1].plot(rule, np.average(grave, axis=0), linewidth=1.5, label='Ave')
ax[0].legend()
ax[1].legend()
def task3(size, sweeps):
'''
Runs the simulation for p2=0.5 over p1-p3 plane and saves the average infected
and the scaled variance in a dat file.
Parameters:
-----------
size: Type Integer
Size of the system
sweeps: Type Integer
Number of sweeps for the simulation.
Returns:
--------
Saves a dat file of the average infected and scaled variance over p1-p3 plane.
'''
print("Starting Task3")
p2 = 0.5
p1s = np.linspace(0, 1, 21)
p3s = np.linspace(0, 1, 21)
#hard setting sweep to 1,000 for this task
sweeps = 1000
#Saving the data at each point in allArray
allArray = np.zeros((21 * 21, 4))
N = size * size
t1 = time.time()
#keeping track of the index
counter = 0
for i in range(21):
print(i)
start = time.time()
for j in range(21):
#create a sirs object which initialises a lattice with the given arguments
model = sirs(size, p1s[i], p2, p3s[j])
#empty array that will contain the number of infected over sweeps
infectedTotal = []
#iterate for sweeps
for n in range(sweeps):
#update the lattice for each sweep
model.update()
#wait 100 sweeps, equilibirum wait
if (n >= 100):
#then add the number of infected in a system to the infectedTotal array
infected = model.infected
infectedTotal.append(infected)
#stop sweeps if system reaches absorbing state
if (infected == 0):
#has reached absorbing state so break out of sweeps loop
break
if (infected == 0):
#is absorbing state, so set <I> and variance to 0
averageInfected = 0
variance = 0
else:
#not absorbing state so we calculate the average infected and scaled variance
infectedTotal = np.asarray(infectedTotal)
variance = np.var((infectedTotal)) / N
averageInfected = np.mean(infectedTotal) / N
#storing the data into a huge array, which we will save as a dat file
allArray[counter] = [p1s[i], p3s[j], averageInfected, variance]
#incrementing counter for the array index
counter += 1
print(f"Finished {i} in time {time.time()-start}s")
print(f"time taken = {time.time()-t1}")
np.savetxt("data/Task3ProcessedData.dat", allArray, fmt='%.7f')
def task5_Part2(size, sweeps):
'''
Runs the simulation for p1=0.8, p2=0.1,p3=0.02 for varying fraction of immunity for a 100x100 system.
-----------
size: Type Integer
Size of the system
sweeps: Type Integer
Number of sweeps for the simulation.
Returns:
--------
Saves a dat file of the average infected at a coressponding fraction of immunity.
'''
print("Starting Task5 part 2")
#initialising variables to fit the task
p1 = 0.8
p2 = 0.1
p3 = .02
immuneProbabilities = np.linspace(0, 1, 101)
precision = len(immuneProbabilities)
N = size * size
simulations = 5
#creating a 101x5 array to store data
infectionArray = np.zeros((precision, simulations))
#run the program for multiple simulations
for s in range(simulations):
print(s)
t1 = time.time()
#looping for immune probability 0->1 at an interval of 0.01
for i in range(len(immuneProbabilities)):
print(f"Starting immune fraction {immuneProbabilities[i]}")
#initialising a sirs object with new immune fraction.
model = sirs(size,
p1,
p2,
p3,
isImmune=True,
immuneProbability=immuneProbabilities[i])
infectedTotal = []
for n in range(sweeps):
#update system for each sweep
model.update()
#take measurements after the equilibirum wait
if (n >= 100):
infected = model.infected
infectedTotal.append(infected)
if (infected == 0):
#is absorbing state so break from sweeps loop
break
#sets average infection to 0 if system in absoribing state
if (infected == 0):
infectionArray[i, s] = 0
#else take the averag scaled infection
else:
infectionArray[i, s] = np.mean() / N
print(f"Time taken {s} == {time.time()-t1}s")
#calculating the standard error and mean scaled variance over 5 simulations
finalArray = []
errors = []
#using scipy.stats.sem to calculate standard error
for i in range(precision):
finalArray.append(np.mean(infectionArray[i]))
errors.append(sem(infectionArray[i]))
#saving arrays as a dat file for plotting
combined = np.array((immuneProbabilities, finalArray, errors))
np.savetxt('data/Task7_RawDataCombined.dat', (infectionArray), fmt='%.6ft')
np.savetxt('data/Task7_ProcessedData.dat',
np.transpose(combined),
fmt='%.6f')
def task4(size, sweeps):
'''
Runs the simulation for p2=p3=0.5 over varying p1 and saves the scaled variance in a dat file.
Parameters:
-----------
size: Type Integer
Size of the system
sweeps: Type Integer
Number of sweeps for the simulation.
Returns:
--------
Saves a dat file of the scaled variance over a cut of p2,p3.
'''
print("Starting Task4")
t1 = time.time()
p3 = 0.5
p2 = 0.5
p1s = np.linspace(0.2, 0.5, 31)
N = size * size
times = 5
allArray = np.zeros((len(p1s), times))
for s in range(times):
print(f"Starting {s}")
for i in range(len(p1s)):
start = time.time()
#create a sirs object which initialises a lattice with the given arguments
model = sirs(size, p1s[i], p2, p3)
#empty array that will contain the number of infected over sweeps
infectedTotal = []
for n in range(sweeps):
#update system for each sweep
model.update()
#wait 100 sweeps, equilibirum wait
if (n >= 100):
#then add the number of infected in a system to the infectedTotal array
infected = model.infected
infectedTotal.append(infected)
#stop sweeps if system reaches absorbing state
if (infected == 0):
#has reached absorbing state so break out of sweeps loop
break
if (infected == 0):
#is absorbing state so set variance to 0
variance = 0
else:
#is not absorbing state so caluclate the scaled variance
variance = np.var(infectedTotal) / N
#storing the variance in the array for index i,s
allArray[i, s] = variance
print(f"Time for {s} {i} at time = {time.time()-start}")
#saving the array as a dat file
np.savetxt(f"data/Task4_RawDataCombined.dat",
np.transpose(allArray),
fmt='%.6f')
#calculating the standard error and the average scaled varaince over 5 simulations
averageInfected = []
standardErrors = []
for i in range(len(p1s)):
averageInfected.append(np.mean(allArray[i]))
standardErrors.append(sem(allArray[i]))
combined = np.array((p1s, averageInfected, standardErrors))
np.savetxt("data/Task4_ProcessedData.dat",
np.transpose(combined),
fmt='%.6f')
print("DONEEE")