def test_SimulateCTMC(self):
'''
Stochastic ode under the interpretation that we have a continuous
time Markov chain as the underlying process
'''
#x0 = [1,1.27e-6,0] # original
x0 = [2362206.0, 3.0, 0.0]
t = numpy.linspace(0, 250, 50)
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma', 'N']
transitionList = [
Transition(origState='S',
destState='I',
equation='beta*S*I/N',
transitionType=TransitionType.T),
Transition(origState='I',
destState='R',
equation='gamma*I',
transitionType=TransitionType.T)
]
# initialize the model
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
odeS.setParameters([0.5, 1.0 / 3.0, x0[0]]).setInitialValue(x0, t[0])
solution = odeS.integrate(t[1::])
odeS.transitionMean(x0, t[0])
odeS.transitionVar(x0, t[0])
odeS.transitionMean(solution[10, :], t[10])
odeS.transitionVar(solution[10, :], t[10])
simX, simT = odeS.simulateJump(250, 3, full_output=True)
def test_SimulateCTMC(self):
'''
Stochastic ode under the interpretation that we have a continuous
time Markov chain as the underlying process
'''
#x0 = [1,1.27e-6,0] # original
x0 = [2362206.0, 3.0, 0.0]
t = numpy.linspace(0, 250, 50)
stateList = ['S','I','R']
paramList = ['beta','gamma','N']
transitionList = [
Transition(origState='S',destState='I',equation='beta * S * I/N',transitionType=TransitionType.T),
Transition(origState='I',destState='R',equation='gamma * I',transitionType=TransitionType.T)
]
# initialize the model
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
odeS.setParameters([0.5, 1.0/3.0, x0[0]]).setInitialValue(x0, t[0])
solution = odeS.integrate(t[1::])
odeS.transitionMean(x0,t[0])
odeS.transitionVar(x0,t[0])
odeS.transitionMean(solution[10,:],t[10])
odeS.transitionVar(solution[10,:],t[10])
simX,simT = odeS.simulateJump(250, 3, full_output=True)
