def test_storage(self):
a = np.eye(2, 2)
b = np.array([[0], [1]])
u = MonotonousInput()
ic = np.zeros((2, 1))
ss = sim.StateSpace("test", a, b, input_handle=u)
# run simulation to fill the internal storage
domain = sim.Domain((0, 10), step=.1)
res = sim.simulate_state_space(ss, ic, domain)
# don't return entries that are not there
self.assertRaises(KeyError, u.get_results, domain, "Unknown Entry")
# default key is "output"
ed = u.get_results(domain)
ed_explicit = u.get_results(domain, result_key="output")
self.assertTrue(np.array_equal(ed, ed_explicit))
# return np.ndarray as default
self.assertIsInstance(ed, np.ndarray)
# return EvalData if corresponding flag is set
self.assertIsInstance(u.get_results(domain, as_eval_data=True),
sim.EvalData)
def test_call_arguments(self):
a = np.eye(2, 2)
b = np.array([[0], [1]])
u = CorrectInput()
ic = np.zeros((2, 1))
ss = sim.StateSpace("test", {1: a}, {1: b}, input_handle=u)
# if caller provides correct kwargs no exception should be raised
res = sim.simulate_state_space(ss, ic, sim.Domain((0, 1), num=10))
def test_modal(self):
self.act_funcs = "eig_funcs"
a2, a1, a0, alpha, beta = self.param
controller = ut.get_parabolic_robin_backstepping_controller(
state=self.x_i_at_l,
approx_state=self.x_i_at_l,
d_approx_state=self.xd_i_at_l,
approx_target_state=self.x_ti_at_l,
d_approx_target_state=self.xd_ti_at_l,
integral_kernel_zz=self.int_kernel_zz(self.l),
original_beta=self.beta_i,
target_beta=self.beta_ti,
trajectory=self.traj,
scale=self.transform_i(-self.l))
# determine (A,B) with modal-transfomation
A = np.diag(np.real(self.eig_val))
B = a2 * np.array(
[self.adjoint_eig_funcs[i](self.l) for i in range(self.n)])
ss_modal = sim.StateSpace(self.act_funcs,
A,
B,
input_handle=controller)
# simulate
self.t, self.q = sim.simulate_state_space(
ss_modal, np.zeros((len(self.adjoint_eig_funcs))), self.dt)
eval_d = sim.evaluate_approximation(self.act_funcs, self.q, self.t,
self.dz)
x_0t = eval_d.output_data[:, 0]
yc, tc = tr.gevrey_tanh(self.T, 1)
x_0t_desired = np.interp(self.t, tc, yc[0, :])
self.assertLess(np.average((x_0t - x_0t_desired)**2), 1e-2)
# display results
if show_plots:
win1 = vis.PgAnimatedPlot([eval_d], title="Test")
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
controller = ct.Controller(ct.ControlLaw([ph.ScalarTerm(x_at_1, 1), ph.ScalarTerm(xt_at_1, -1)]))
# derive initial field variable x(z,0) and weights
start_state = cr.Function(lambda z: init_profile)
initial_weights = cr.project_on_base(start_state, eig_funcs)
# init trajectory
traj = tr.RadTrajectory(l, T, param_t, bound_cond_type, actuation_type)
# input with feedback
control_law = sim.SimulationInputSum([traj, controller])
# determine (A,B) with modal-transfomation
A = np.diag(eig_values)
B = -a2 * np.array([eig_funcs[i].derive()(l) for i in range(n)])
ss = sim.StateSpace("eig_funcs", A, B, input_handle=control_law)
# evaluate desired output data
z_d = np.linspace(0, l, len(spatial_domain))
y_d, t_d = tr.gevrey_tanh(T, 80)
C = tr.coefficient_recursion(np.zeros(y_d.shape), y_d, param)
x_l = tr.power_series(z_d, t_d, C)
evald_traj = vis.EvalData([t_d, z_d], x_l, name="x(z,t) desired")
# simulate
t, q = sim.simulate_state_space(ss, initial_weights, temporal_domain)
# pyqtgraph visualization
evald_x = sim.evaluate_approximation("eig_funcs", q, t, spatial_domain,
name="x(z,t) with x(z,0)=" + str(init_profile))
win1 = vis.PgAnimatedPlot([evald_x, evald_traj], title="animation", dt=temporal_domain.step)
def test_it(self):
# original system parameters
a2 = 1.5
a1 = 2.5
a0 = 28
alpha = -2
beta = -3
param = [a2, a1, a0, alpha, beta]
adjoint_param = ef.get_adjoint_rad_evp_param(param)
# target system parameters (controller parameters)
a1_t = -5
a0_t = -25
alpha_t = 3
beta_t = 2
# a1_t = a1; a0_t = a0; alpha_t = alpha; beta_t = beta
param_t = [a2, a1_t, a0_t, alpha_t, beta_t]
# original intermediate ("_i") and traget intermediate ("_ti") system parameters
_, _, a0_i, alpha_i, beta_i = ef.transform2intermediate(param)
_, _, a0_ti, alpha_ti, beta_ti = ef.transform2intermediate(param_t)
# system/simulation parameters
actuation_type = 'robin'
bound_cond_type = 'robin'
self.l = 1.
spatial_disc = 10
dz = sim.Domain(bounds=(0, self.l), num=spatial_disc)
T = 1.
temporal_disc = 1e2
dt = sim.Domain(bounds=(0, T), num=temporal_disc)
n = 10
# create (not normalized) eigenfunctions
eig_freq, eig_val = ef.compute_rad_robin_eigenfrequencies(
param, self.l, n)
init_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, param, dz.bounds)
for om in eig_freq
])
init_adjoint_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, adjoint_param, dz.bounds)
for om in eig_freq
])
# normalize eigenfunctions and adjoint eigenfunctions
adjoint_and_eig_funcs = [
cr.normalize_function(init_eig_funcs[i], init_adjoint_eig_funcs[i])
for i in range(n)
]
eig_funcs = np.array([f_tuple[0] for f_tuple in adjoint_and_eig_funcs])
adjoint_eig_funcs = np.array(
[f_tuple[1] for f_tuple in adjoint_and_eig_funcs])
# eigenfunctions from target system ("_t")
eig_freq_t = np.sqrt(-a1_t**2 / 4 / a2**2 + (a0_t - eig_val) / a2)
eig_funcs_t = np.array([
ef.SecondOrderRobinEigenfunction(eig_freq_t[i], param_t,
dz.bounds).scale(eig_funcs[i](0))
for i in range(n)
])
# register eigenfunctions
register_base("eig_funcs", eig_funcs, overwrite=True)
register_base("adjoint_eig_funcs", adjoint_eig_funcs, overwrite=True)
register_base("eig_funcs_t", eig_funcs_t, overwrite=True)
# derive initial field variable x(z,0) and weights
start_state = cr.Function(lambda z: 0., domain=(0, self.l))
initial_weights = cr.project_on_base(start_state, adjoint_eig_funcs)
# controller initialization
x_at_l = ph.FieldVariable("eig_funcs", location=self.l)
xd_at_l = ph.SpatialDerivedFieldVariable("eig_funcs",
1,
location=self.l)
x_t_at_l = ph.FieldVariable("eig_funcs_t",
weight_label="eig_funcs",
location=self.l)
xd_t_at_l = ph.SpatialDerivedFieldVariable("eig_funcs_t",
1,
weight_label="eig_funcs",
location=self.l)
combined_transform = lambda z: np.exp((a1_t - a1) / 2 / a2 * z)
int_kernel_zz = lambda z: alpha_ti - alpha_i + (a0_i - a0_ti
) / 2 / a2 * z
controller = ct.Controller(
ct.ControlLaw([
ph.ScalarTerm(x_at_l,
(beta_i - beta_ti - int_kernel_zz(self.l))),
ph.ScalarTerm(x_t_at_l, -beta_ti * combined_transform(self.l)),
ph.ScalarTerm(x_at_l, beta_ti),
ph.ScalarTerm(xd_t_at_l, -combined_transform(self.l)),
ph.ScalarTerm(x_t_at_l,
-a1_t / 2 / a2 * combined_transform(self.l)),
ph.ScalarTerm(xd_at_l, 1),
ph.ScalarTerm(x_at_l, a1 / 2 / a2 + int_kernel_zz(self.l))
]))
# init trajectory
traj = tr.RadTrajectory(self.l, T, param_t, bound_cond_type,
actuation_type)
traj.scale = combined_transform(self.l)
# input with feedback
control_law = sim.SimulationInputSum([traj, controller])
# control_law = sim.simInputSum([traj])
# determine (A,B) with modal-transformation
A = np.diag(np.real(eig_val))
B = a2 * np.array(
[adjoint_eig_funcs[i](self.l) for i in range(len(eig_freq))])
ss_modal = sim.StateSpace("eig_funcs", A, B, input_handle=control_law)
# simulate
t, q = sim.simulate_state_space(ss_modal, initial_weights, dt)
eval_d = sim.evaluate_approximation("eig_funcs", q, t, dz)
x_0t = eval_d.output_data[:, 0]
yc, tc = tr.gevrey_tanh(T, 1)
x_0t_desired = np.interp(t, tc, yc[0, :])
self.assertLess(np.average((x_0t - x_0t_desired)**2), 1e-4)
# display results
if show_plots:
win1 = vis.PgAnimatedPlot([eval_d], title="Test")
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
def test_it(self):
# original system parameters
a2 = 1
a1 = 0 # attention: only a2 = 1., a1 =0 supported in this test case
a0 = 0
param = [a2, a1, a0, None, None]
# target system parameters (controller parameters)
a1_t = 0
a0_t = 0 # attention: only a2 = 1., a1 =0 and a0 =0 supported in this test case
param_t = [a2, a1_t, a0_t, None, None]
# system/simulation parameters
actuation_type = 'dirichlet'
bound_cond_type = 'dirichlet'
l = 1.
spatial_disc = 10
dz = sim.Domain(bounds=(0, l), num=spatial_disc)
T = 1.
temporal_disc = 1e2
dt = sim.Domain(bounds=(0, T), num=temporal_disc)
n = 10
# eigenvalues /-functions original system
eig_freq = np.array([(i + 1) * np.pi / l for i in range(n)])
eig_values = a0 - a2 * eig_freq**2 - a1**2 / 4. / a2
norm_fac = np.ones(eig_freq.shape) * np.sqrt(2)
eig_funcs = np.asarray([
ef.SecondOrderDirichletEigenfunction(eig_freq[i], param, dz.bounds,
norm_fac[i]) for i in range(n)
])
register_base("eig_funcs", eig_funcs, overwrite=True)
# eigenfunctions target system
eig_freq_t = np.sqrt(-eig_values.astype(complex))
norm_fac_t = norm_fac * eig_freq / eig_freq_t
eig_funcs_t = np.asarray([
ef.SecondOrderDirichletEigenfunction(eig_freq_t[i], param_t,
dz.bounds, norm_fac_t[i])
for i in range(n)
])
register_base("eig_funcs_t", eig_funcs_t, overwrite=True)
# derive initial field variable x(z,0) and weights
start_state = cr.Function(lambda z: 0., domain=(0, l))
initial_weights = cr.project_on_base(start_state, eig_funcs)
# init trajectory / input of target system
traj = tr.RadTrajectory(l, T, param_t, bound_cond_type, actuation_type)
# init controller
x_at_1 = ph.FieldVariable("eig_funcs", location=1)
xt_at_1 = ph.FieldVariable("eig_funcs_t",
weight_label="eig_funcs",
location=1)
controller = ct.Controller(
ct.ControlLaw(
[ph.ScalarTerm(x_at_1, 1),
ph.ScalarTerm(xt_at_1, -1)]))
# input with feedback
control_law = sim.SimulationInputSum([traj, controller])
# determine (A,B) with modal-transfomation
A = np.diag(eig_values)
B = -a2 * np.array([eig_funcs[i].derive()(l) for i in range(n)])
ss = sim.StateSpace("eig_funcs", A, B, input_handle=control_law)
# simulate
t, q = sim.simulate_state_space(ss, initial_weights, dt)
eval_d = sim.evaluate_approximation("eig_funcs", q, t, dz)
x_0t = eval_d.output_data[:, 0]
yc, tc = tr.gevrey_tanh(T, 1)
x_0t_desired = np.interp(t, tc, yc[0, :])
self.assertLess(np.average((x_0t - x_0t_desired)**2), 0.5)
# display results
if show_plots:
eval_d = sim.evaluate_approximation("eig_funcs", q, t, dz)
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
def test_it(self):
actuation_type = 'dirichlet'
bound_cond_type = 'dirichlet'
param = [1., -2., -1., None, None]
adjoint_param = ef.get_adjoint_rad_evp_param(param)
a2, a1, a0, _, _ = param
l = 1.
spatial_disc = 10
dz = sim.Domain(bounds=(0, l), num=spatial_disc)
T = 1.
temporal_disc = 1e2
dt = sim.Domain(bounds=(0, T), num=temporal_disc)
omega = np.array([(i + 1) * np.pi / l for i in range(spatial_disc)])
eig_values = a0 - a2 * omega**2 - a1**2 / 4. / a2
norm_fak = np.ones(omega.shape) * np.sqrt(2)
eig_funcs = np.array([
ef.SecondOrderDirichletEigenfunction(omega[i], param, dz.bounds,
norm_fak[i])
for i in range(spatial_disc)
])
register_base("eig_funcs", eig_funcs, overwrite=True)
adjoint_eig_funcs = np.array([
ef.SecondOrderDirichletEigenfunction(omega[i], adjoint_param,
dz.bounds, norm_fak[i])
for i in range(spatial_disc)
])
register_base("adjoint_eig_funcs", adjoint_eig_funcs, overwrite=True)
# derive initial field variable x(z,0) and weights
start_state = cr.Function(lambda z: 0., domain=(0, l))
initial_weights = cr.project_on_base(start_state, adjoint_eig_funcs)
# init trajectory
u = tr.RadTrajectory(l, T, param, bound_cond_type, actuation_type)
# determine (A,B) with weak-formulation (pyinduct)
# derive sate-space system
rad_pde = ut.get_parabolic_dirichlet_weak_form("eig_funcs",
"adjoint_eig_funcs", u,
param, dz.bounds)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# determine (A,B) with modal-transfomation
A = np.diag(eig_values)
B = -a2 * np.array(
[adjoint_eig_funcs[i].derive()(l) for i in range(spatial_disc)])
ss_modal = sim.StateSpace("eig_funcs", A, B, input_handle=u)
# TODO: resolve the big tolerance (rtol=3e-01) between ss_modal.A and ss_weak.A
# check if ss_modal.(A,B) is close to ss_weak.(A,B)
self.assertTrue(
np.allclose(np.sort(np.linalg.eigvals(ss_weak.A[1])),
np.sort(np.linalg.eigvals(ss_modal.A[1])),
rtol=3e-1,
atol=0.))
self.assertTrue(
np.allclose(np.array([i[0] for i in ss_weak.B[1]]), ss_modal.B[1]))
# display results
if show_plots:
t, q = sim.simulate_state_space(ss_modal, initial_weights, dt)
eval_d = sim.evaluate_approximation("eig_funcs",
q,
t,
dz,
spat_order=1)
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
def test_it(self):
actuation_type = 'robin'
bound_cond_type = 'robin'
param = [2., 1.5, -3., -1., -.5]
adjoint_param = ef.get_adjoint_rad_evp_param(param)
a2, a1, a0, alpha, beta = param
l = 1.
spatial_disc = 10
dz = sim.Domain(bounds=(0, l), num=spatial_disc)
T = 1.
temporal_disc = 1e2
dt = sim.Domain(bounds=(0, T), num=temporal_disc)
n = 10
eig_freq, eig_val = ef.compute_rad_robin_eigenfrequencies(param, l, n)
init_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, param, dz.bounds)
for om in eig_freq
])
init_adjoint_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, adjoint_param, dz.bounds)
for om in eig_freq
])
# normalize eigenfunctions and adjoint eigenfunctions
adjoint_and_eig_funcs = [
cr.normalize_function(init_eig_funcs[i], init_adjoint_eig_funcs[i])
for i in range(n)
]
eig_funcs = np.array([f_tuple[0] for f_tuple in adjoint_and_eig_funcs])
adjoint_eig_funcs = np.array(
[f_tuple[1] for f_tuple in adjoint_and_eig_funcs])
# register eigenfunctions
register_base("eig_funcs", eig_funcs, overwrite=True)
register_base("adjoint_eig_funcs", adjoint_eig_funcs, overwrite=True)
# derive initial field variable x(z,0) and weights
start_state = cr.Function(lambda z: 0., domain=(0, l))
initial_weights = cr.project_on_base(start_state, adjoint_eig_funcs)
# init trajectory
u = tr.RadTrajectory(l, T, param, bound_cond_type, actuation_type)
# determine (A,B) with weak-formulation (pyinduct)
rad_pde = ut.get_parabolic_robin_weak_form("eig_funcs",
"adjoint_eig_funcs", u,
param, dz.bounds)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# determine (A,B) with modal-transfomation
A = np.diag(np.real_if_close(eig_val))
B = a2 * np.array(
[adjoint_eig_funcs[i](l) for i in range(len(eig_freq))])
ss_modal = sim.StateSpace("eig_funcs", A, B, input_handle=u)
# check if ss_modal.(A,B) is close to ss_weak.(A,B)
self.assertTrue(
np.allclose(np.sort(np.linalg.eigvals(ss_weak.A[1])),
np.sort(np.linalg.eigvals(ss_modal.A[1])),
rtol=1e-05,
atol=0.))
self.assertTrue(
np.allclose(np.array([i[0] for i in ss_weak.B[1]]), ss_modal.B[1]))
# display results
if show_plots:
t, q = sim.simulate_state_space(ss_modal, initial_weights, dt)
eval_d = sim.evaluate_approximation("eig_funcs",
q,
t,
dz,
spat_order=1)
win1 = vis.PgAnimatedPlot([eval_d], title="Test")
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
