def test_Shooting_3():
# This problem contains a parameter, but it is not explicit in the BCs.
# Since time is buried in the ODEs, this tests if the BVP solver calculates
# sensitivities with respect to parameters.
def odefun(X, u, p, const):
return 1 * p[0]
def bcfun(X0, q0, u0, Xf, qf, uf, p, ndp, const):
return X0[0] - 0, Xf[0] - 2
algo = Shooting(odefun, None, bcfun)
solinit = Trajectory()
solinit.t = np.linspace(0, 1, 2)
solinit.y = np.array([[0], [0]])
solinit.dynamical_parameters = np.array([1])
solinit.const = np.array([])
out = algo.solve(solinit)['sol']
assert abs(out.dynamical_parameters - 2) < tol
def solve(self, solinit, **kwargs):
solinit = copy.deepcopy(solinit)
nstates = solinit.y.shape[1]
def return_nil(*args, **kwargs):
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
def _fun(t, y, params=np.array([]), const=solinit.const):
y = y.T
o1 = np.vstack([
self.derivative_function(yi[:nstates], np.array([]),
params[:ndyn], const) for yi in y
])
o2 = np.vstack([
self.quadrature_function(yi[:nstates], np.array([]),
params[:ndyn], const) for yi in y
])
return np.hstack((o1, o2)).T
# TODO: The way parameters are used is inconsitent
def _bc(ya, yb, params=np.array([]), const=solinit.const):
return self.boundarycondition_function(
ya[:nstates], ya[nstates:nstates + nquads], np.array(
()), yb[:nstates], yb[nstates:nstates + nquads],
np.array(()), 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, np.array([]), 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:
def _bc_jac(ya, yb, params=np.array([]), const=solinit.const):
dbc_dya, dbc_dyb, dbc_dp = self.boundarycondition_function_jac(
ya, yb, np.array([]), params, 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