def propagateManyStateVectors(self, initialState, params, t0, dt, tf, rtol = 1e-12, atol = 1e-12):
"""
Propagates p state vectors in parallel.
:param initialState: [1-dimensional numpy array] Contains p initial state vectors in only 1 array.
:param params: Variable parameters for the dynamic model. If u = const, params[0] = u.
:param t0: Initial time.
:param tf: Final time.
:param dt: sample time.
:param rtol: Relative tolerance of the integrator.
:param atol: Absolute tolerance of the integrator.
:return:
"""
num = int((tf - t0)/dt) + 1
tf = (num - 1) * dt + t0 # includes the last value
time_vec = np.linspace(t0, tf, num)
modelFunc = self._dynModel.getPropagationFunction()
if self._integrator == Integrator.RK4:
statesVec = rk4Integrator(modelFunc, initialState, time_vec, args = (params,), event = self._integratorEventHandler)
else:
statesVec = odeint(modelFunc, initialState, time_vec, args = (params,), rtol = rtol, atol = atol)
timeVec = time_vec
final_state = statesVec[-1]
return (timeVec, statesVec, final_state)
def propagate(self, initialState, params, t0, tf, dt, rtol = 1e-12, atol = 1e-12):
"""
Simulate the model X_dot = F(X, t).
Cannot be used to propagate X_dot = F(X, u, t) if u = u(t).
:param initialState: Initial conditions.
:param params: Variable parameters for the dynamic model. If u = const, params[0] = u.
:param t0: Initial time.
:param tf: Final time.
:param dt: sample time.
:param rtol: Relative tolerance of the integrator.
:param atol: Absolute tolerance of the integrator.
:return: An array of vectors with the sampled evolution X(t).
"""
num = int((tf - t0)/dt) + 1
tf = (num - 1) * dt + t0 # includes the last value
time_vec = np.linspace(t0, tf, num)
modelFunc = self._dynModel.getPropagationFunction()
if self._integrator == Integrator.RK4:
self._statesVec = rk4Integrator(modelFunc, initialState, time_vec, args = (params,), event = self._integratorEventHandler)
else:
self._statesVec = odeint(modelFunc, initialState, time_vec, args = (params,), rtol = rtol, atol = atol)
self._timeVec = time_vec
return (self._timeVec, self._statesVec)
def propagateWithSTM(self, initial_state, initial_stm, params, t0, dt, tf, rtol = 1e-12, atol = 1e-12):
"""
Simulate the model X_dot = F(X,t), propagating X(t) and the State Transition Matrix (STM).
It can also simulate the model X_dot = F(X,u,t), propagating X(t), the STM=dX(t)/dX(t_0) and STM_input=dX(t)/du(t_0).
The STM equation is STM_dot = A(t)*STM, where A(t) is the Jacobian of F.
The STM input equation is STM_input_dot = A(t)*STM_input + B(t) where B(t)=dF/du.
:param initialState: Initial state conditions.
:param initial_stm: Initial STM + STM_input conditions. STM is a nxn matrix while STM_input is nxq (n: Nmbr of states, q: Nmbr of inputs).
initial_stm is a nx(n+q) matrix.
:param params: Variable parameters for the dynamic model.
:param t0: Initial time.
:param tf: Final time.
:param dt: sample time.
:param rtol: Relative tolerance of the integrator.
:param atol: Absolute tolerance of the integrator.
:return:
"""
num = int((tf - t0))/dt + 1
tf = (num - 1) * dt + t0 # includes the last value
time_vec = np.linspace(t0, tf, num)
state_length = initial_state.size
stm_shape = initial_stm.shape
stm_length = initial_stm.size
total_length = state_length + stm_length
modelPlusSTMFunc = self._dynModel.getPropagationFunction()
#modelPlusSTMFunc = self._dynModel.getModelPlusSTMFunction()
X0 = np.concatenate([initial_state, initial_stm.T.reshape(stm_length)]) # STM reshaped by columns
if self._integrator == Integrator.RK4:
X = rk4Integrator(modelPlusSTMFunc, X0, time_vec, args = (params,), event = self._integratorEventHandler)
else:
X = odeint(modelPlusSTMFunc, X0, time_vec, args = (params,), rtol = rtol, atol = atol)
Xf = X[-1] # last state
final_state = Xf[0:state_length:1]
final_stm = Xf[state_length:total_length:1]
final_stm = final_stm.reshape(stm_shape).T
# stms = np.zeros([X.shape[0], state_length, state_length])
# for i in range(0, X.shape[0]) :
# stms[i,:,:] = X[i,state_length:].reshape(stm_shape).T
#
# states = X[:,0:state_length]
stms = np.zeros([X.shape[0], stm_shape[0], stm_shape[1]])
for i in range(0, X.shape[0]) :
stms[i,:,:] = X[i,state_length:].reshape(stm_shape).T
states = X[:,0:state_length]
return (states, stms, time_vec, final_state, final_stm)
def propagateWithSTMtimeVec(self, initial_state, initial_stm, params, time_vec, rtol = 1e-12, atol = 1e-12):
"""
Simulate the model X_dot = F(X,t), propagating X(t) and the State Transition Matrix (STM).
The STM equation is STM_dot = A(t)*STM, where A(t) is the Jacobian of F.
This overload receives a time vector.
:param initialState: Initial state conditions.
:param initial_stm: Initial STM conditions.
:param params: Variable parameters for the dynamic model.
:param time_vec: Time vector.
:param rtol: Relative tolerance of the integrator.
:param atol: Absolute tolerance of the integrator.
:param event: Function to handle events within the integrator
:return:
"""
state_length = initial_state.size
stm_shape = initial_stm.shape
stm_length = initial_stm.size
total_length = state_length + stm_length
modelPlusSTMFunc = self._dynModel.getPropagationFunction()
X0 = np.concatenate([initial_state, initial_stm.T.reshape(stm_length)]) # STM reshaped by columns
if self._integrator == Integrator.RK4:
X = rk4Integrator(modelPlusSTMFunc, X0, time_vec, args = (params,), event = self._integratorEventHandler)
else:
X = odeint(modelPlusSTMFunc, X0, time_vec, args = (params,), rtol = rtol, atol = atol)
Xf = X[-1] # last state
final_state = Xf[0:state_length:1]
final_stm = Xf[state_length:total_length:1]
final_stm = final_stm.reshape(stm_shape).T
# stms = np.zeros([X.shape[0], state_length, state_length])
# for i in range(0, X.shape[0]) :
# stms[i,:,:] = X[i,state_length:].reshape(stm_shape).T
stms = np.zeros([X.shape[0], stm_shape[0], stm_shape[1]])
for i in range(0, X.shape[0]) :
aux_stm = X[i,state_length:]
aux = aux_stm.reshape((stm_shape[1], stm_shape[0])).T
stms[i,:,:] = aux
states = X[:,0:state_length]
return (states, stms, time_vec, final_state, final_stm)