def comparePolicies(k):
numVaccinated = k
TSHPolicy = vaccinateTopNTSH(numVaccinated)
AcqPolicy = vaccinateNAcquaintance(numVaccinated)
policies = [[TSHPolicy], [AcqPolicy]]
#Store statistics on proportion of population infected (Average,Var)
proportionInfected = np.zeros([numIterations, len(policies), 2])
#Store count of # nodes infected in each simulation
numInfected = np.zeros([numIterations, len(policies)])
for iteration in range(numIterations):
print("Beginning Simulation {0}".format(iteration))
G = generateRandomGraph(numNodes)
simulation = SeirSim(G,
exposureRate,
infectionRate,
recoveryRate,
logSim=True,
tallyFuncs=[
numNodesInState(0),
numNodesInState(1),
numNodesInState(2),
numNodesInState(3)
])
exper = Experiment(simulation)
logList, statsList = exper.compare(policies)
for policy in range(len(policies)):
propInfectedMean = np.mean(statsList[policy][:, 2] / numNodes)
propInfectedVar = np.var(statsList[policy][:, 2] / numNodes)
proportionInfected[iteration, policy, :] = np.array(
[propInfectedMean, propInfectedVar])
numRemoved = np.sum(
np.where(logList[policy][-1][0] == 3, np.ones(numNodes),
np.zeros(numNodes)))
numInfected[iteration, policy] = numRemoved - numVaccinated
#Compute mean, variance, 90% CI for each policy
numInfectedStatsL = np.zeros([2, 2])
propInfectedStatsL = np.zeros([2, 2])
for i in range(len(policies)):
mean = np.mean(proportionInfected[:, i, 0])
var = np.var(proportionInfected[:, i, 0])
mean2 = np.mean(numInfected[:, i])
var2 = np.var(numInfected[:, i])
propInfectedStatsL[i, :] = np.array([mean, var])
numInfectedStatsL[i, :] = np.array([mean2, var2])
return propInfectedStatsL, numInfectedStatsL
def Example4():
expt = Experiment()
expt.end_category = 3
expt.waveform = 'white'
expt.output = 'spectrum'
expt.compare = True
expt.parameter_sequence = ParameterSequence.SingleParameter(
14, 3, [40, 60, 80, 100], compare=True)
expt.notes = [60]
expt.run()
expt.save_results()
def Example3():
expt = Experiment()
expt.end_category = 12
expt.waveform = 'white'
expt.output = 'spectrum'
expt.compare = True
expt.parameter_sequence = ParameterSequence.SingleParameter(9,
12,
[40, 50, 60],
compare=True)
expt.run()
expt.save_results()
def Example1():
expt = Experiment()
expt.end_category = 12
expt.waveform = 'sine'
expt.notes = [60]
expt.input = 'velocity'
expt.output = 'ampl'
expt.compare = True
# Define the values to write to parameter 29 Category 12. In this example, only
# one value (90) is written.
expt.parameter_sequence = ParameterSequence.SingleParameter(29,
12, [90],
compare=True)
expt.run()
expt.analyse()
expt.save_results()
simulation = SeirSim(G,
exposureRate,
infectionRate,
recoveryRate,
logSim=True,
tallyFuncs=[
numNodesInState(0),
numNodesInState(1),
numNodesInState(2),
numNodesInState(3)
])
exper = Experiment(simulation)
logList, statsList = exper.compare(policies)
abort = False
for policy in range(len(policies)):
propInfectedMean = np.mean(statsList[policy][:, 2] / numNodes)
propInfectedVar = np.var(statsList[policy][:, 2] / numNodes)
proportionInfected[iteration, policy, :] = np.array(
[propInfectedMean, propInfectedVar])
numRemoved = np.sum(
np.where(logList[policy][-1][0] == 3, np.ones(numNodes),
np.zeros(numNodes)))
numInfected[iteration, policy] = numRemoved - numVaccinated
titles = [
'Random Immunization', 'Degree-Ranked Immunization',
'DIL-Ranked Immunization'
numVaccinated = 1000
DILPolicy = vaccinateTopNDIL(numVaccinated)
randomVaccPolicy = vaccinateNRandom(numVaccinated)
DegPolicy = vaccinateTopNDegree(numVaccinated)
tshPolicy = vaccinateTopNTSH(numVaccinated)
acquPolicy = vaccinateNAcquaintance(numVaccinated)
simulation = SeirSim(G,
exposureRate,
infectionRate,
recoveryRate,
logSim=True,
tallyFuncs=[
numNodesInState(0),
numNodesInState(1),
numNodesInState(2),
numNodesInState(3)
])
exper = Experiment(simulation)
logList, statsList = exper.compare([[None], [randomVaccPolicy], [DegPolicy],
[DILPolicy], [tshPolicy], [acquPolicy]])
titles = [
'No Vaccination', 'Random Vaccination', 'Degree-Ranked Vaccination',
'DIL-Ranked Vaccination', 'TSH-Ranked Vaccination',
'Acquaintance Vaccination'
]
plotExperiment(exper, titles)
