def inv_map(self, sol: Solution) -> Solution:
sol.dual = sol.y[:, self.costate_idxs]
sol.y = np.delete(sol.y, self.costate_idxs, axis=1)
sol.nondynamical_parameters = np.delete(sol.nondynamical_parameters,
self.constraint_adjoints_idxs)
return sol
def map(self, sol: Solution, lam=empty_array, nu=empty_array) -> Solution:
# sol.dual = lam
if len(sol.nondynamical_parameters) == 0:
sol.nondynamical_parameters = np.ones(self.nu_len)
return sol
def inv_map(self, sol: Solution, retain_dual=True) -> Solution:
if not retain_dual:
sol.dual = empty_array
sol.nondynamical_parameters = empty_array
sol.cost = self.compute_cost(sol.t, sol.y, sol.q, sol.u,
sol.dynamical_parameters, sol.const)
return sol
def scale_sol(sol: Trajectory, scale_factors, inv=False):
sol = copy.deepcopy(sol)
if inv:
op = np.multiply
else:
op = np.divide
sol.t = op(sol.t, scale_factors[0])
sol.y = op(sol.y, scale_factors[1])
sol.q = op(sol.q, scale_factors[2])
if sol.u.size > 0:
sol.u = op(sol.u, scale_factors[3])
sol.dynamical_parameters = op(sol.dynamical_parameters,
scale_factors[4])
sol.nondynamical_parameters = op(sol.nondynamical_parameters,
scale_factors[5])
sol.const = op(sol.const, scale_factors[6])
return sol
def solve(self, solinit, **kwargs):
solinit = copy.deepcopy(solinit)
nstates = solinit.y.shape[1]
nquads = 0
def return_nil(*_, **__):
return np.array([])
if solinit.q.size > 0:
nquads = solinit.q.shape[1]
else:
nquads = 0
self.quadrature_function = return_nil
ndyn = solinit.dynamical_parameters.size
nnondyn = solinit.nondynamical_parameters.size
empty_array = np.array([])
if nquads == 0:
# TODO: Try to vectorize
def _fun(t, y, params=empty_array, const=solinit.const):
return np.vstack([self.derivative_function(yi[:nstates], params[:ndyn], const) for yi in y.T]).T
def _bc(ya, yb, params=empty_array, const=solinit.const):
return self.boundarycondition_function(ya, yb, params[:ndyn], params[ndyn:ndyn + nnondyn], const)
else:
def _fun(t, y, params=empty_array, const=solinit.const):
y = y.T
o1 = np.vstack([self.derivative_function(yi[:nstates], params[:ndyn], const) for yi in y])
o2 = np.vstack([self.quadrature_function(yi[:nstates], params[:ndyn], const) for yi in y])
return np.hstack((o1, o2)).T
def _bc(ya, yb, params=np.array([]), const=solinit.const):
return self.boundarycondition_function(ya[:nstates], ya[nstates:nstates+nquads], yb[:nstates],
yb[nstates:nstates+nquads], params[:ndyn],
params[ndyn:ndyn+nnondyn], const)
if self.derivative_function_jac is not None:
def _fun_jac(t, y, params=np.array([]), const=solinit.const):
y = y.T
df_dy = np.zeros((y[0].size, y[0].size, t.size))
df_dp = np.zeros((y[0].size, ndyn+nnondyn, t.size))
for ii, yi in enumerate(y):
df_dy[:, :, ii], _df_dp = self.derivative_function_jac(yi, params[:ndyn], const)
if nstates > 1 and len(_df_dp.shape) == 1:
_df_dp = np.array([_df_dp]).T
df_dp[:, :, ii] = np.hstack((_df_dp, np.zeros((nstates, nnondyn))))
if ndyn + nnondyn == 0:
return df_dy
else:
return df_dy, df_dp
else:
_fun_jac = None
if self.boundarycondition_function_jac is not None:
if nquads > 0:
def _bc_jac(ya, yb, params=np.array([]), const=solinit.const):
dbc_dya, dbc_dyb, dbc_dp = \
self.boundarycondition_function_jac(ya[:nstates], ya[nstates:nstates+nquads], yb[:nstates],
yb[nstates:nstates+nquads], params[:ndyn],
params[ndyn:ndyn+nnondyn], const)
return dbc_dya, dbc_dyb, dbc_dp
else:
def _bc_jac(ya, yb, params=np.array([]), const=solinit.const):
dbc_dya, dbc_dyb, dbc_dp = \
self.boundarycondition_function_jac(ya, yb, params[:ndyn], params[ndyn:ndyn+nnondyn], const)
return dbc_dya, dbc_dyb, dbc_dp
else:
_bc_jac = None
if nquads > 0:
opt = solve_bvp(_fun, _bc, solinit.t, np.hstack((solinit.y, solinit.q)).T,
np.hstack((solinit.dynamical_parameters, solinit.nondynamical_parameters)),
max_nodes=self.max_nodes, fun_jac=_fun_jac, bc_jac=_bc_jac)
else:
opt = solve_bvp(_fun, _bc, solinit.t, solinit.y.T,
np.hstack((solinit.dynamical_parameters, solinit.nondynamical_parameters)),
max_nodes=self.max_nodes, fun_jac=_fun_jac, bc_jac=_bc_jac)
sol = Trajectory(solinit)
sol.t = opt['x']
sol.y = opt['y'].T[:, :nstates]
sol.q = opt['y'].T[:, nstates:nstates+nquads]
sol.dual = np.zeros_like(sol.y)
if opt['p'] is not None:
sol.dynamical_parameters = opt['p'][:ndyn]
sol.nondynamical_parameters = opt['p'][ndyn:ndyn+nnondyn]
else:
sol.dynamical_parameters = np.array([])
sol.nondynamical_parameters = np.array([])
sol.converged = opt['success']
out = BVPResult(sol=sol, success=opt['success'], message=opt['message'], rms_residuals=opt['rms_residuals'],
niter=opt['niter'])
return out
