def test_stochastic(self):
# Tests the following system simulating jumps
# A + A -> C
# A + B -> D
# \emptyset -> A
# \emptyset -> B
stateList = ['A', 'B', 'C', 'D']
paramList = ['k1', 'k2', 'k3', 'k4']
transitionList = [
Transition(origState=('A','A'), destState='C', equation='A * (A - 1) * k1',
transitionType=TransitionType.T),
Transition(origState=('A','B'), destState='D', equation='A * B * k2',
transitionType=TransitionType.T)
]
# our birth and deaths
birthDeathList = [
Transition(origState='A', equation='k3', transitionType=TransitionType.B),
Transition(origState='B', equation='k4', transitionType=TransitionType.B)
]
ode = SimulateOdeModel(stateList,
paramList,
birthDeathList=birthDeathList,
transitionList=transitionList)
x0 = [0,0,0,0]
t = numpy.linspace(0, 100, 100)
ode.setParameters(paramEval).setInitialValue(x0, t[0])
simX, simT = ode.simulateJump(t, 5, full_output=True)
def test_hard(self):
# the SLIARD model is considered to be hard because a state can
# go to multiple state. This is not as hard as the SEIHFR model
# below.
stateList = ['S', 'L', 'I', 'A', 'R', 'D']
paramList = [
'beta', 'p', 'kappa', 'alpha', 'f', 'delta', 'epsilon', 'N'
]
odeList = [
Transition('S', '- beta * S/N * ( I + delta * A)', 'ODE'),
Transition('L', 'beta * S/N * (I + delta * A) - kappa * L', 'ODE'),
Transition('I', 'p * kappa * L - alpha * I', 'ODE'),
Transition('A', '(1-p) * kappa * L - epsilon * A', 'ODE'),
Transition('R', 'f * alpha * I + epsilon * A', 'ODE'),
Transition('D', '(1-f) * alpha * I', 'ODE')
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Hard: SLIARD Decomposition failed")
def test_simple(self):
ode1 = Transition('S','-beta*S*I', 'ode')
ode2 = Transition('I','beta*S*I - gamma * I', 'ode')
ode3 = Transition('R','gamma*I', 'ode')
stateList = ['S','I','R']
paramList = ['beta','gamma']
ode = SimulateOdeModel(stateList, paramList, odeList=[ode1,ode2,ode3])
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Simple: SIR Decomposition failed")
def test_simulateParam2(self):
'''
Stochastic ode under the interpretation that the parameters follow
some sort of distribution. In this case, a function handle which
has the same name as R
'''
t0 = 0
# the initial state, normalized to zero one
x0 = [1, 1.27e-6, 0]
# set the time sequence that we would like to observe
t = numpy.linspace(0, 150, 100)
# Standard. Find the solution.
ode = common_models.SIR()
ode.setParameters([0.5, 1.0 / 3.0])
ode.setInitialValue(x0, t0)
solutionReference = ode.integrate(t[1::], full_output=False)
# now we need to define our ode explicitly
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma']
transitionList = [
Transition(origState='S',
destState='I',
equation='beta*S*I',
transitionType=TransitionType.T),
Transition(origState='I',
destState='R',
equation='gamma*I',
transitionType=TransitionType.T)
]
# our stochastic version
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
# define our parameters in terms of two gamma distributions
# where the expected values are the same as before [0.5,1.0/3.0]
d = dict()
d['beta'] = (rgamma, {'shape': 100.0, 'rate': 200.0})
d['gamma'] = (rgamma, (100.0, 300.0))
odeS.setParameters(d).setInitialValue(x0, t0)
# now we generate the solutions
solutionDiff = odeS.simulateParam(t[1::], 1000) - solutionReference
# test :)
if numpy.any(abs(solutionDiff) >= 0.2):
raise Exception("Possible problem with simulating the parameters")
def test_simple(self):
ode1 = Transition('S', '-beta*S*I', 'ode')
ode2 = Transition('I', 'beta*S*I - gamma * I', 'ode')
ode3 = Transition('R', 'gamma*I', 'ode')
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma']
ode = SimulateOdeModel(stateList,
paramList,
odeList=[ode1, ode2, ode3])
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Simple: SIR Decomposition failed")
def test_derived_param(self):
# the derived parameters are treated separately when compared to the
# normal parameters and the odes
ode = common_models.Legrand_Ebola_SEIHFR()
odeList = [
Transition(
'S',
'-(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)'
),
Transition(
'E',
'(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)-alpha * E'
),
Transition(
'I',
'-gamma_I * (1 - theta_1) * (1 - delta_1) * I - gamma_D * (1 - theta_1) * delta_1 * I - gamma_H * theta_1 * I + alpha * E'
),
Transition(
'H',
'gamma_H * theta_1 * I - gamma_DH * delta_2 * H - gamma_IH * (1 - delta_2) * H'
),
Transition(
'F',
'- gamma_F * F + gamma_DH * delta_2 * H + gamma_D * (1 - theta_1) * delta_1 * I'
),
Transition(
'R',
'gamma_I * (1 - theta_1) * (1 - delta_1) * I + gamma_F * F + gamma_IH * (1 - delta_2) * H'
),
Transition('tau', '1')
]
ode1 = SimulateOdeModel(ode._stateList,
ode._paramList,
ode._derivedParamEqn,
odeList=odeList)
ode2 = ode1.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("FAILED!")
def test_simulateParam2(self):
'''
Stochastic ode under the interpretation that the parameters follow
some sort of distribution. In this case, a function handle which
has the same name as R
'''
t0 = 0
# the initial state, normalized to zero one
x0 = [1,1.27e-6,0]
# set the time sequence that we would like to observe
t = numpy.linspace(0, 150, 100)
# Standard. Find the solution.
ode = common_models.SIR()
ode.setParameters([0.5,1.0/3.0])
ode.setInitialValue(x0,t0)
solutionReference = ode.integrate(t[1::],full_output=False)
# now we need to define our ode explicitly
stateList = ['S','I','R']
paramList = ['beta','gamma']
transitionList = [
Transition(origState='S',destState='I',equation='beta * S * I',transitionType=TransitionType.T),
Transition(origState='I',destState='R',equation='gamma * I',transitionType=TransitionType.T)
]
# our stochastic version
odeS = SimulateOdeModel(stateList,
paramList,
transitionList=transitionList)
# define our parameters in terms of two gamma distributions
# where the expected values are the same as before [0.5,1.0/3.0]
d = dict()
d['beta'] = (rgamma,{'shape':100.0,'rate':200.0})
d['gamma'] = (rgamma,(100.0,300.0))
odeS.setParameters(d).setInitialValue(x0,t0)
# now we generate the solutions
solutionDiff = odeS.simulateParam(t[1::],1000) - solutionReference
# test :)
if numpy.any(abs(solutionDiff)>=0.2):
raise Exception("Possible problem with simulating the parameters")
def test_derived_param(self):
# the derived parameters are treated separately when compared to the
# normal parametes and the odes
ode = common_models.Legrand_Ebola_SEIHFR()
odeList = [
Transition('S', '-(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)'),
Transition('E', '(beta_I * S * I + beta_H_Time * S * H + beta_F_Time * S * F)-alpha * E'),
Transition('I','-gamma_I * (1 - theta_1) * (1 - delta_1) * I - gamma_D * (1 - theta_1) * delta_1 * I - gamma_H * theta_1 * I + alpha * E'),
Transition('H', 'gamma_H * theta_1 * I - gamma_DH * delta_2 * H - gamma_IH * (1 - delta_2) * H'),
Transition('F','- gamma_F * F + gamma_DH * delta_2 * H + gamma_D * (1 - theta_1) * delta_1 * I'),
Transition('R', 'gamma_I * (1 - theta_1) * (1 - delta_1) * I + gamma_F * F + gamma_IH * (1 - delta_2) * H'),
Transition('tau', '1')
]
ode1 = SimulateOdeModel(ode._stateList, ode._paramList, ode._derivedParamEqn, odeList=odeList)
ode2 = ode1.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("FAILED!")
def test_hard(self):
# the SLIARD model is considered to be hard because a state can
# go to multiple state. This is not as hard as the SEIHFR model
# below.
stateList = ['S', 'L','I','A','R','D']
paramList = ['beta','p','kappa','alpha','f','delta','epsilon', 'N']
odeList = [
Transition('S', '- beta * S/N * ( I + delta * A)', 'ODE'),
Transition('L', 'beta * S/N * (I + delta * A) - kappa * L', 'ODE'),
Transition('I', 'p * kappa * L - alpha * I', 'ODE'),
Transition('A', '(1-p) * kappa * L - epsilon * A', 'ODE'),
Transition('R', 'f * alpha * I + epsilon * A', 'ODE'),
Transition('D', '(1-f) * alpha * I', 'ODE')
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Hard: SLIARD Decomposition failed")
def test_stochastic(self):
# Tests the following system simulating jumps
# A + A -> C
# A + B -> D
# \emptyset -> A
# \emptyset -> B
stateList = ['A', 'B', 'C', 'D']
paramList = ['k1', 'k2', 'k3', 'k4']
transitionList = [
Transition(origState=('A', 'A'),
destState='C',
equation='A * (A - 1) * k1',
transitionType=TransitionType.T),
Transition(origState=('A', 'B'),
destState='D',
equation='A * B * k2',
transitionType=TransitionType.T)
]
# our birth and deaths
birthDeathList = [
Transition(origState='A',
equation='k3',
transitionType=TransitionType.B),
Transition(origState='B',
equation='k4',
transitionType=TransitionType.B)
]
ode = SimulateOdeModel(stateList,
paramList,
birthDeathList=birthDeathList,
transitionList=transitionList)
x0 = [0, 0, 0, 0]
t = numpy.linspace(0, 100, 100)
ode.setParameters(paramEval).setInitialValue(x0, t[0])
simX, simT = ode.simulateJump(t, 5, full_output=True)
def test_bd(self):
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma', 'B', 'mu']
odeList = [
Transition(origState='S',
equation='-beta * S * I + B - mu * S',
transitionType=TransitionType.ODE),
Transition(origState='I',
equation='beta * S * I - gamma * I - mu * I',
transitionType=TransitionType.ODE),
Transition(origState='R',
destState='R',
equation='gamma * I',
transitionType=TransitionType.ODE)
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x==0, sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(diffEqZero) == False):
raise Exception("Birth Death: SIR+BD Decomposition failed")
def test_bd(self):
stateList = ['S', 'I', 'R']
paramList = ['beta', 'gamma', 'B', 'mu']
odeList = [
Transition(origState='S',
equation='-beta * S * I + B - mu * S',
transitionType=TransitionType.ODE),
Transition(origState='I',
equation='beta * S * I - gamma * I - mu * I',
transitionType=TransitionType.ODE),
Transition(origState='R',
destState='R',
equation='gamma * I',
transitionType=TransitionType.ODE)
]
ode = SimulateOdeModel(stateList, paramList, odeList=odeList)
ode2 = ode.returnObjWithTransitionsAndBD()
diffEqZero = map(lambda x: x == 0,
sympy.simplify(ode.getOde() - ode2.getOde()))
if numpy.any(numpy.array(list(diffEqZero)) == False):
raise Exception("Birth Death: SIR+BD Decomposition failed")
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)
