def test_init(self):
self.assertRaises(TypeError, sim.WeakFormulation, ["a", "b"])
sim.WeakFormulation(ph.ScalarTerm(self.field_var_at1)) # scalar case
sim.WeakFormulation([
ph.ScalarTerm(self.field_var_at1),
ph.IntegralTerm(self.field_var, (0, 1))
]) # vector case
def setUp(self):
self.u = CorrectInput()
# setup temp and spat domain
spat_domain = sim.Domain((0, 1), num=3)
nodes, ini_funcs = sf.cure_interval(sf.LagrangeFirstOrder,
spat_domain.bounds,
node_count=3)
register_base("init_funcs", ini_funcs, overwrite=True)
# enter string with mass equations for testing
int1 = ph.IntegralTerm(
ph.Product(ph.TemporalDerivedFieldVariable("init_funcs", 2),
ph.TestFunction("init_funcs")), spat_domain.bounds)
s1 = ph.ScalarTerm(
ph.Product(
ph.TemporalDerivedFieldVariable("init_funcs", 2, location=0),
ph.TestFunction("init_funcs", location=0)))
int2 = ph.IntegralTerm(
ph.Product(ph.SpatialDerivedFieldVariable("init_funcs", 1),
ph.TestFunction("init_funcs", order=1)),
spat_domain.bounds)
s2 = ph.ScalarTerm(
ph.Product(ph.Input(self.u),
ph.TestFunction("init_funcs", location=1)), -1)
string_pde = sim.WeakFormulation([int1, s1, int2, s2])
self.cf = sim.parse_weak_formulation(string_pde)
self.ic = np.zeros((3, 2))
def test_rd():
# trajectory
bound_cond_type = 'robin'
actuation_type = 'dirichlet'
u = tr.RadTrajectory(l, T, param, bound_cond_type, actuation_type)
# integral terms
int1 = ph.IntegralTerm(
ph.Product(
ph.TemporalDerivedFieldVariable("init_funcs_2", order=1),
ph.TestFunction("init_funcs_2", order=0)), dz.bounds)
int2 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2", order=0),
ph.TestFunction("init_funcs_2", order=2)), dz.bounds, -a2)
int3 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2", order=1),
ph.TestFunction("init_funcs_2", order=0)), dz.bounds, -a1)
int4 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2", order=0),
ph.TestFunction("init_funcs_2", order=0)), dz.bounds, -a0)
# scalar terms from int 2
s1 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2",
order=1,
location=l),
ph.TestFunction("init_funcs_2", order=0, location=l)), -a2)
s2 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2",
order=0,
location=0),
ph.TestFunction("init_funcs_2", order=0, location=0)),
a2 * alpha)
s3 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_2",
order=0,
location=0),
ph.TestFunction("init_funcs_2", order=1, location=0)), -a2)
s4 = ph.ScalarTerm(
ph.Product(
ph.Input(u),
ph.TestFunction("init_funcs_2", order=1, location=l)), a2)
# derive state-space system
rad_pde = sim.WeakFormulation(
[int1, int2, int3, int4, s1, s2, s3, s4])
cf = sim.parse_weak_formulation(rad_pde)
ss = cf.convert_to_state_space()
# simulate system
t, q = sim.simulate_state_space(ss, np.zeros(ini_funcs_2.shape),
dt)
return t, q
def test_ScalarTerm(self):
self.assertRaises(TypeError, ph.ScalarTerm, 7) # factor is number
self.assertRaises(TypeError, ph.ScalarTerm,
cr.Function(np.sin)) # factor is Function
ph.ScalarTerm(self.input)
self.assertRaises(ValueError, ph.ScalarTerm,
self.test_func) # integration has to be done
t1 = ph.ScalarTerm(self.xdz_at1)
self.assertEqual(t1.scale, 1.0) # default scale
# check if automated evaluation works
self.assertTrue(np.allclose(t1.arg.args[0].data, np.array([-1, 1])))
def setUp(self):
interval = (0, 1)
nodes, funcs = sf.cure_interval(sf.LagrangeFirstOrder, interval, 3)
register_base("funcs", funcs, overwrite=True)
x_at1 = ph.FieldVariable("funcs", location=1)
x_dt_at1 = ph.TemporalDerivedFieldVariable("funcs", 1, location=1)
x_dz_at0 = ph.SpatialDerivedFieldVariable("funcs", 1, location=0)
exp_func = cr.Function(np.exp)
register_base("exp_func", exp_func, overwrite=True)
exp_at1 = ph.ScalarFunction("exp_func", location=1)
alpha = 2
self.term1 = ph.ScalarTerm(x_dt_at1, 1 + alpha)
self.term2 = ph.ScalarTerm(x_dz_at0, 2)
self.term3 = ph.ScalarTerm(ph.Product(x_at1, exp_at1))
self.weight_label = "funcs"
self.weights = np.array([1, 1, 1, 2, 2, 2])
def transform_i(z):
"""
intermediate (_i) transformation at z=l
"""
return np.exp(a1 / 2 / a2 * z) # x_i = x * transform_i
def transform_ti(z):
"""
target intermediate (_ti) transformation at z=l
"""
return np.exp(a1_t / 2 / a2 * z) # x_ti = x_t * transform_ti
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
x_fem_i_at_l = [ph.ScalarTerm(fem_field_variable, transform_i(l))]
x_i_at_l = [ph.ScalarTerm(field_variable, transform_i(l))]
xd_i_at_l = [ph.ScalarTerm(d_field_variable, transform_i(l)),
ph.ScalarTerm(field_variable, transform_i(l) * a1 / 2 / a2)]
x_ti_at_l = [ph.ScalarTerm(field_variable_t, transform_ti(l))]
xd_ti_at_l = [ph.ScalarTerm(d_field_variable_t, transform_ti(l)),
ph.ScalarTerm(field_variable_t, transform_ti(l) * a1_t / 2 / a2)]
# discontinuous operator (Kx)(t) = int_kernel_zz(l)*x(l,t)
def int_kernel_zz(z):
return alpha_ti - alpha_i + (a0_i - a0_ti) / 2 / a2 * z
# init trajectory
traj = tr.RadTrajectory(l, T, param_ti, bound_cond_type, actuation_type)
def test_fem(self):
# system/simulation parameters
actuation_type = 'robin'
bound_cond_type = 'robin'
self.l = 1.
spatial_disc = 30
self.dz = sim.Domain(bounds=(0, self.l), num=spatial_disc)
self.T = 1.
temporal_disc = 1e2
self.dt = sim.Domain(bounds=(0, self.T), num=temporal_disc)
self.n = 12
# original system parameters
a2 = 1.5
a1 = 2.5
a0 = 28
alpha = -2
beta = -3
self.param = [a2, a1, a0, alpha, beta]
adjoint_param = ef.get_adjoint_rad_evp_param(self.param)
# target system parameters (controller parameters)
a1_t = -5
a0_t = -25
alpha_t = 3
beta_t = 2
self.param_t = [a2, a1_t, a0_t, alpha_t, beta_t]
# actuation_type by b which is close to b_desired on a k times subdivided spatial domain
b_desired = self.l / 2
k = 51 # = k1 + k2
k1, k2, self.b = ut.split_domain(k, b_desired, self.l,
mode='coprime')[0:3]
M = np.linalg.inv(
ut.get_inn_domain_transformation_matrix(k1, k2, mode="2n"))
# original intermediate ("_i") and traget intermediate ("_ti") system parameters
_, _, a0_i, self.alpha_i, self.beta_i = ef.transform2intermediate(
self.param)
self.param_i = a2, 0, a0_i, self.alpha_i, self.beta_i
_, _, a0_ti, self.alpha_ti, self.beta_ti = ef.transform2intermediate(
self.param_t)
self.param_ti = a2, 0, a0_ti, self.alpha_ti, self.beta_ti
# create (not normalized) eigenfunctions
eig_freq, self.eig_val = ef.compute_rad_robin_eigenfrequencies(
self.param, self.l, self.n)
init_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, self.param, self.dz.bounds)
for om in eig_freq
])
init_adjoint_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, adjoint_param, self.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(self.n)
]
eig_funcs = np.array([f_tuple[0] for f_tuple in adjoint_and_eig_funcs])
self.adjoint_eig_funcs = np.array(
[f_tuple[1] for f_tuple in adjoint_and_eig_funcs])
# eigenfunctions of the in-domain intermediate (_id) and the intermediate (_i) system
eig_freq_i, eig_val_i = ef.compute_rad_robin_eigenfrequencies(
self.param_i, self.l, self.n)
self.assertTrue(all(np.isclose(eig_val_i, self.eig_val)))
eig_funcs_id = np.array([
ef.SecondOrderRobinEigenfunction(eig_freq_i[i], self.param_i,
self.dz.bounds, eig_funcs[i](0))
for i in range(self.n)
])
eig_funcs_i = np.array([
ef.SecondOrderRobinEigenfunction(
eig_freq_i[i], self.param_i, self.dz.bounds, eig_funcs[i](0) *
eig_funcs_id[i](self.l) / eig_funcs_id[i](self.b))
for i in range(self.n)
])
# eigenfunctions from target system ("_ti")
eig_freq_ti = np.sqrt((a0_ti - self.eig_val) / a2)
eig_funcs_ti = np.array([
ef.SecondOrderRobinEigenfunction(eig_freq_ti[i], self.param_ti,
self.dz.bounds, eig_funcs_i[i](0))
for i in range(self.n)
])
# create testfunctions
nodes, self.fem_funcs = sf.cure_interval(sf.LagrangeFirstOrder,
self.dz.bounds,
node_count=self.n)
# register eigenfunctions
# register_functions("eig_funcs", eig_funcs, overwrite=True)
register_base("adjoint_eig_funcs",
self.adjoint_eig_funcs,
overwrite=True)
register_base("eig_funcs", eig_funcs, overwrite=True)
register_base("eig_funcs_i", eig_funcs_i, overwrite=True)
register_base("eig_funcs_ti", eig_funcs_ti, overwrite=True)
register_base("fem_funcs", self.fem_funcs, overwrite=True)
# init trajectory
self.traj = tr.RadTrajectory(self.l, self.T, self.param_ti,
bound_cond_type, actuation_type)
# original () and target (_t) field variable
fem_field_variable = ph.FieldVariable("fem_funcs", location=self.l)
field_variable_i = ph.FieldVariable("eig_funcs_i",
weight_label="eig_funcs",
location=self.l)
d_field_variable_i = ph.SpatialDerivedFieldVariable(
"eig_funcs_i", 1, weight_label="eig_funcs", location=self.l)
field_variable_ti = ph.FieldVariable("eig_funcs_ti",
weight_label="eig_funcs",
location=self.l)
d_field_variable_ti = ph.SpatialDerivedFieldVariable(
"eig_funcs_ti", 1, weight_label="eig_funcs", location=self.l)
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
self.x_fem_i_at_l = [ph.ScalarTerm(fem_field_variable)]
self.x_i_at_l = [ph.ScalarTerm(field_variable_i)]
self.xd_i_at_l = [ph.ScalarTerm(d_field_variable_i)]
self.x_ti_at_l = [ph.ScalarTerm(field_variable_ti)]
self.xd_ti_at_l = [ph.ScalarTerm(d_field_variable_ti)]
# shift transformation
shifted_fem_funcs_i = np.array([
ef.FiniteTransformFunction(
func,
M,
self.b,
self.l,
scale_func=lambda z: np.exp(a1 / 2 / a2 * z))
for func in self.fem_funcs
])
shifted_eig_funcs_id = np.array([
ef.FiniteTransformFunction(func, M, self.b, self.l)
for func in eig_funcs_id
])
register_base("sh_fem_funcs_i", shifted_fem_funcs_i, overwrite=True)
register_base("sh_eig_funcs_id", shifted_eig_funcs_id, overwrite=True)
sh_fem_field_variable_i = ph.FieldVariable("sh_fem_funcs_i",
weight_label="fem_funcs",
location=self.l)
sh_field_variable_id = ph.FieldVariable("sh_eig_funcs_id",
weight_label="eig_funcs",
location=self.l)
self.sh_x_fem_i_at_l = [
ph.ScalarTerm(sh_fem_field_variable_i),
ph.ScalarTerm(field_variable_i),
ph.ScalarTerm(sh_field_variable_id, -1)
]
# discontinuous operator (Kx)(t) = int_kernel_zz(l)*x(l,t)
self.int_kernel_zz = lambda z: self.alpha_ti - self.alpha_i + (
a0_i - a0_ti) / 2 / a2 * z
a2, a1, _, _, _ = self.param
controller = ut.get_parabolic_robin_backstepping_controller(
state=self.sh_x_fem_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=np.exp(-a1 / 2 / a2 * self.b))
# determine (A,B) with modal-transfomation
rad_pde = ut.get_parabolic_robin_weak_form("fem_funcs", "fem_funcs",
controller, self.param,
self.dz.bounds, self.b)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# simulate
t, q = sim.simulate_state_space(ss_weak, np.zeros(
(len(self.fem_funcs))), self.dt)
# weights of the intermediate system
mat = cr.calculate_base_transformation_matrix(self.fem_funcs,
eig_funcs)
q_i = np.zeros((q.shape[0], len(eig_funcs_i)))
for i in range(q.shape[0]):
q_i[i, :] = np.dot(q[i, :], np.transpose(mat))
eval_i = sim.evaluate_approximation("eig_funcs_i", q_i, t, self.dz)
x_0t = eval_i.output_data[:, 0]
yc, tc = tr.gevrey_tanh(self.T, 1)
x_0t_desired = np.interp(t, tc, yc[0, :])
self.assertLess(np.average((x_0t - x_0t_desired)**2), 1e-2)
# display results
if show_plots:
eval_d = sim.evaluate_approximation("fem_funcs", q, t, self.dz)
win1 = vis.PgSurfacePlot(eval_i)
win2 = vis.PgSurfacePlot(eval_d)
app.exec_()
def test_it(self):
# system/simulation parameters
actuation_type = 'robin'
bound_cond_type = 'robin'
self.l = 1.
spatial_disc = 10
self.dz = sim.Domain(bounds=(0, self.l), num=spatial_disc)
self.T = 1.
temporal_disc = 1e2
self.dt = sim.Domain(bounds=(0, self.T), num=temporal_disc)
self.n = 10
# original system parameters
a2 = 1.5
a1_z = cr.Function(lambda z: 1, derivative_handles=[lambda z: 0])
a0_z = lambda z: 3
alpha = -2
beta = -3
self.param = [a2, a1_z, a0_z, alpha, beta]
# target system parameters (controller parameters)
a1_t = -5
a0_t = -25
alpha_t = 3
beta_t = 2
self.param_t = [a2, a1_t, a0_t, alpha_t, beta_t]
adjoint_param_t = ef.get_adjoint_rad_evp_param(self.param_t)
# original intermediate ("_i") and traget intermediate ("_ti") system parameters
_, _, a0_i, alpha_i, beta_i = ef.transform2intermediate(self.param,
d_end=self.l)
self.param_i = a2, 0, a0_i, alpha_i, beta_i
_, _, a0_ti, alpha_ti, beta_ti = ef.transform2intermediate(
self.param_t)
self.param_ti = a2, 0, a0_ti, alpha_ti, beta_ti
# create (not normalized) target (_t) eigenfunctions
eig_freq_t, self.eig_val_t = ef.compute_rad_robin_eigenfrequencies(
self.param_t, self.l, self.n)
init_eig_funcs_t = np.array([
ef.SecondOrderRobinEigenfunction(om, self.param_t, self.dz.bounds)
for om in eig_freq_t
])
init_adjoint_eig_funcs_t = np.array([
ef.SecondOrderRobinEigenfunction(om, adjoint_param_t,
self.dz.bounds)
for om in eig_freq_t
])
# normalize eigenfunctions and adjoint eigenfunctions
adjoint_and_eig_funcs_t = [
cr.normalize_function(init_eig_funcs_t[i],
init_adjoint_eig_funcs_t[i])
for i in range(self.n)
]
eig_funcs_t = np.array(
[f_tuple[0] for f_tuple in adjoint_and_eig_funcs_t])
self.adjoint_eig_funcs_t = np.array(
[f_tuple[1] for f_tuple in adjoint_and_eig_funcs_t])
# # transformed original eigenfunctions
self.eig_funcs = np.array([
ef.TransformedSecondOrderEigenfunction(
self.eig_val_t[i],
[eig_funcs_t[i](0), alpha * eig_funcs_t[i](0), 0, 0],
[a2, a1_z, a0_z], np.linspace(0, self.l, 1e4))
for i in range(self.n)
])
# create testfunctions
nodes, self.fem_funcs = sf.cure_interval(sf.LagrangeFirstOrder,
self.dz.bounds,
node_count=self.n)
# register functions
register_base("eig_funcs_t", eig_funcs_t, overwrite=True)
register_base("adjoint_eig_funcs_t",
self.adjoint_eig_funcs_t,
overwrite=True)
register_base("eig_funcs", self.eig_funcs, overwrite=True)
register_base("fem_funcs", self.fem_funcs, overwrite=True)
# init trajectory
self.traj = tr.RadTrajectory(self.l, self.T, self.param_ti,
bound_cond_type, actuation_type)
# original () and target (_t) field variable
fem_field_variable = ph.FieldVariable("fem_funcs", location=self.l)
field_variable_t = ph.FieldVariable("eig_funcs_t",
weight_label="eig_funcs",
location=self.l)
d_field_variable_t = ph.SpatialDerivedFieldVariable(
"eig_funcs_t", 1, weight_label="eig_funcs", location=self.l)
field_variable = ph.FieldVariable("eig_funcs", location=self.l)
d_field_variable = ph.SpatialDerivedFieldVariable("eig_funcs",
1,
location=self.l)
# intermediate (_i) and target intermediate (_ti) transformations by z=l
# x_i = x * transform_i_at_l
self.transform_i_at_l = np.exp(
integrate.quad(lambda z: a1_z(z) / 2 / a2, 0, self.l)[0])
# x = x_i * inv_transform_i_at_l
self.inv_transform_i_at_l = np.exp(
-integrate.quad(lambda z: a1_z(z) / 2 / a2, 0, self.l)[0])
# x_ti = x_t * transform_ti_at_l
self.transform_ti_at_l = np.exp(a1_t / 2 / a2 * self.l)
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
self.x_fem_i_at_l = [
ph.ScalarTerm(fem_field_variable, self.transform_i_at_l)
]
self.x_i_at_l = [ph.ScalarTerm(field_variable, self.transform_i_at_l)]
self.xd_i_at_l = [
ph.ScalarTerm(d_field_variable, self.transform_i_at_l),
ph.ScalarTerm(field_variable,
self.transform_i_at_l * a1_z(self.l) / 2 / a2)
]
self.x_ti_at_l = [
ph.ScalarTerm(field_variable_t, self.transform_ti_at_l)
]
self.xd_ti_at_l = [
ph.ScalarTerm(d_field_variable_t, self.transform_ti_at_l),
ph.ScalarTerm(field_variable_t,
self.transform_ti_at_l * a1_t / 2 / a2)
]
# discontinuous operator (Kx)(t) = int_kernel_zz(l)*x(l,t)
self.int_kernel_zz = alpha_ti - alpha_i + integrate.quad(
lambda z: (a0_i(z) - a0_ti) / 2 / a2, 0, self.l)[0]
controller = ut.get_parabolic_robin_backstepping_controller(
state=self.x_fem_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,
original_beta=beta_i,
target_beta=beta_ti,
trajectory=self.traj,
scale=self.inv_transform_i_at_l)
rad_pde = ut.get_parabolic_robin_weak_form("fem_funcs", "fem_funcs",
controller, self.param,
self.dz.bounds)
cf = sim.parse_weak_formulation(rad_pde)
ss_weak = cf.convert_to_state_space()
# simulate
t, q = sim.simulate_state_space(ss_weak, np.zeros(
(len(self.fem_funcs))), self.dt)
eval_d = sim.evaluate_approximation("fem_funcs", q, t, self.dz)
x_0t = eval_d.output_data[:, 0]
yc, tc = tr.gevrey_tanh(self.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 setUp(self):
# original system parameters
a2 = 1.5
a1 = 2.5
a0 = 28
alpha = -2
beta = -3
self.param = [a2, a1, a0, alpha, beta]
adjoint_param = ef.get_adjoint_rad_evp_param(self.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
self.param_t = [a2, a1_t, a0_t, alpha_t, beta_t]
# original intermediate ("_i") and target intermediate ("_ti") system parameters
_, _, a0_i, self.alpha_i, self.beta_i = ef.transform2intermediate(
self.param)
self.param_i = a2, 0, a0_i, self.alpha_i, self.beta_i
_, _, a0_ti, self.alpha_ti, self.beta_ti = ef.transform2intermediate(
self.param_t)
self.param_ti = a2, 0, a0_ti, self.alpha_ti, self.beta_ti
# system/simulation parameters
actuation_type = 'robin'
bound_cond_type = 'robin'
self.l = 1.
spatial_disc = 10
self.dz = sim.Domain(bounds=(0, self.l), num=spatial_disc)
self.T = 1.
temporal_disc = 1e2
self.dt = sim.Domain(bounds=(0, self.T), num=temporal_disc)
self.n = 10
# create (not normalized) eigenfunctions
eig_freq, self.eig_val = ef.compute_rad_robin_eigenfrequencies(
self.param, self.l, self.n)
init_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, self.param, self.dz.bounds)
for om in eig_freq
])
init_adjoint_eig_funcs = np.array([
ef.SecondOrderRobinEigenfunction(om, adjoint_param, self.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(self.n)
]
eig_funcs = np.array([f_tuple[0] for f_tuple in adjoint_and_eig_funcs])
self.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 - self.eig_val) / a2)
eig_funcs_t = np.array([
ef.SecondOrderRobinEigenfunction(eig_freq_t[i], self.param_t,
self.dz.bounds).scale(
eig_funcs[i](0))
for i in range(self.n)
])
# create testfunctions
nodes, self.fem_funcs = sf.cure_interval(sf.LagrangeFirstOrder,
self.dz.bounds,
node_count=self.n)
# register eigenfunctions
register_base("eig_funcs", eig_funcs, overwrite=True)
register_base("adjoint_eig_funcs",
self.adjoint_eig_funcs,
overwrite=True)
register_base("eig_funcs_t", eig_funcs_t, overwrite=True)
register_base("fem_funcs", self.fem_funcs, overwrite=True)
# init trajectory
self.traj = tr.RadTrajectory(self.l, self.T, self.param_ti,
bound_cond_type, actuation_type)
# original () and target (_t) field variable
fem_field_variable = ph.FieldVariable("fem_funcs", location=self.l)
field_variable = ph.FieldVariable("eig_funcs", location=self.l)
d_field_variable = ph.SpatialDerivedFieldVariable("eig_funcs",
1,
location=self.l)
field_variable_t = ph.FieldVariable("eig_funcs_t",
weight_label="eig_funcs",
location=self.l)
d_field_variable_t = ph.SpatialDerivedFieldVariable(
"eig_funcs_t", 1, weight_label="eig_funcs", location=self.l)
# intermediate (_i) and target intermediate (_ti) transformations by z=l
self.transform_i = lambda z: np.exp(a1 / 2 / a2 * z
) # x_i = x * transform_i
self.transform_ti = lambda z: np.exp(a1_t / 2 / a2 * z
) # x_ti = x_t * transform_ti
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
self.x_fem_i_at_l = [
ph.ScalarTerm(fem_field_variable, self.transform_i(self.l))
]
self.x_i_at_l = [
ph.ScalarTerm(field_variable, self.transform_i(self.l))
]
self.xd_i_at_l = [
ph.ScalarTerm(d_field_variable, self.transform_i(self.l)),
ph.ScalarTerm(field_variable,
self.transform_i(self.l) * a1 / 2 / a2)
]
self.x_ti_at_l = [
ph.ScalarTerm(field_variable_t, self.transform_ti(self.l))
]
self.xd_ti_at_l = [
ph.ScalarTerm(d_field_variable_t, self.transform_ti(self.l)),
ph.ScalarTerm(field_variable_t,
self.transform_ti(self.l) * a1_t / 2 / a2)
]
# discontinuous operator (Kx)(t) = int_kernel_zz(l)*x(l,t)
self.int_kernel_zz = lambda z: self.alpha_ti - self.alpha_i + (
a0_i - a0_ti) / 2 / a2 * z
def setUp(self):
# scalars
self.scalars = ph.Scalars(np.vstack(list(range(3))))
# inputs
self.u = np.sin
self.input = ph.Input(self.u)
self.input_squared = ph.Input(self.u, exponent=2)
nodes, self.ini_funcs = sf.cure_interval(sf.LagrangeFirstOrder, (0, 1),
node_count=3)
# TestFunctions
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.phi = ph.TestFunction("ini_funcs")
self.phi_at0 = ph.TestFunction("ini_funcs", location=0)
self.phi_at1 = ph.TestFunction("ini_funcs", location=1)
self.dphi = ph.TestFunction("ini_funcs", order=1)
self.dphi_at1 = ph.TestFunction("ini_funcs", order=1, location=1)
# FieldVars
self.field_var = ph.FieldVariable("ini_funcs")
self.field_var_squared = ph.FieldVariable("ini_funcs", exponent=2)
self.odd_weight_field_var = ph.FieldVariable(
"ini_funcs", weight_label="special_weights")
self.field_var_at1 = ph.FieldVariable("ini_funcs", location=1)
self.field_var_at1_squared = ph.FieldVariable("ini_funcs",
location=1,
exponent=2)
self.field_var_dz = ph.SpatialDerivedFieldVariable("ini_funcs", 1)
self.field_var_dz_at1 = ph.SpatialDerivedFieldVariable("ini_funcs",
1,
location=1)
self.field_var_ddt = ph.TemporalDerivedFieldVariable("ini_funcs", 2)
self.field_var_ddt_at0 = ph.TemporalDerivedFieldVariable("ini_funcs",
2,
location=0)
self.field_var_ddt_at1 = ph.TemporalDerivedFieldVariable("ini_funcs",
2,
location=1)
# create all possible kinds of input variables
self.input_term1 = ph.ScalarTerm(ph.Product(self.phi_at1, self.input))
self.input_term1_swapped = ph.ScalarTerm(
ph.Product(self.input, self.phi_at1))
self.input_term1_squared = ph.ScalarTerm(
ph.Product(self.input_squared, self.phi_at1))
self.input_term2 = ph.ScalarTerm(ph.Product(self.dphi_at1, self.input))
self.func_term = ph.ScalarTerm(self.phi_at1)
# same goes for field variables
self.field_term_at1 = ph.ScalarTerm(self.field_var_at1)
self.field_term_at1_squared = ph.ScalarTerm(self.field_var_at1_squared)
self.field_term_dz_at1 = ph.ScalarTerm(self.field_var_dz_at1)
self.field_term_ddt_at1 = ph.ScalarTerm(self.field_var_ddt_at1)
self.field_int = ph.IntegralTerm(self.field_var, (0, 1))
self.field_squared_int = ph.IntegralTerm(self.field_var_squared,
(0, 1))
self.field_dz_int = ph.IntegralTerm(self.field_var_dz, (0, 1))
self.field_ddt_int = ph.IntegralTerm(self.field_var_ddt, (0, 1))
self.prod_term_fs_at1 = ph.ScalarTerm(
ph.Product(self.field_var_at1, self.scalars))
self.prod_int_fs = ph.IntegralTerm(
ph.Product(self.field_var, self.scalars), (0, 1))
self.prod_int_f_f = ph.IntegralTerm(
ph.Product(self.field_var, self.phi), (0, 1))
self.prod_int_f_squared_f = ph.IntegralTerm(
ph.Product(self.field_var_squared, self.phi), (0, 1))
self.prod_int_f_f_swapped = ph.IntegralTerm(
ph.Product(self.phi, self.field_var), (0, 1))
self.prod_int_f_at1_f = ph.IntegralTerm(
ph.Product(self.field_var_at1, self.phi), (0, 1))
self.prod_int_f_at1_squared_f = ph.IntegralTerm(
ph.Product(self.field_var_at1_squared, self.phi), (0, 1))
self.prod_int_f_f_at1 = ph.IntegralTerm(
ph.Product(self.field_var, self.phi_at1), (0, 1))
self.prod_int_f_squared_f_at1 = ph.IntegralTerm(
ph.Product(self.field_var_squared, self.phi_at1), (0, 1))
self.prod_term_f_at1_f_at1 = ph.ScalarTerm(
ph.Product(self.field_var_at1, self.phi_at1))
self.prod_term_f_at1_squared_f_at1 = ph.ScalarTerm(
ph.Product(self.field_var_at1_squared, self.phi_at1))
self.prod_int_fddt_f = ph.IntegralTerm(
ph.Product(self.field_var_ddt, self.phi), (0, 1))
self.prod_term_fddt_at0_f_at0 = ph.ScalarTerm(
ph.Product(self.field_var_ddt_at0, self.phi_at0))
self.prod_term_f_at1_dphi_at1 = ph.ScalarTerm(
ph.Product(self.field_var_at1, self.dphi_at1))
self.temp_int = ph.IntegralTerm(
ph.Product(self.field_var_ddt, self.phi), (0, 1))
self.spat_int = ph.IntegralTerm(
ph.Product(self.field_var_dz, self.dphi), (0, 1))
self.spat_int_asymmetric = ph.IntegralTerm(
ph.Product(self.field_var_dz, self.phi), (0, 1))
self.alternating_weights_term = ph.IntegralTerm(
self.odd_weight_field_var, (0, 1))
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_dr():
# trajectory
bound_cond_type = 'dirichlet'
actuation_type = 'robin'
u = tr.RadTrajectory(l, T, param, bound_cond_type, actuation_type)
# integral terms
int1 = ph.IntegralTerm(
ph.Product(
ph.TemporalDerivedFieldVariable("init_funcs_1", order=1),
ph.TestFunction("init_funcs_1", order=0)), dz.bounds)
int2 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1", order=1),
ph.TestFunction("init_funcs_1", order=1)), dz.bounds, a2)
int3 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1", order=0),
ph.TestFunction("init_funcs_1", order=1)), dz.bounds, a1)
int4 = ph.IntegralTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1", order=0),
ph.TestFunction("init_funcs_1", order=0)), dz.bounds, -a0)
# scalar terms from int 2
s1 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1",
order=0,
location=l),
ph.TestFunction("init_funcs_1", order=0, location=l)), -a1)
s2 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1",
order=0,
location=l),
ph.TestFunction("init_funcs_1", order=0, location=l)),
a2 * beta)
s3 = ph.ScalarTerm(
ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs_1",
order=1,
location=0),
ph.TestFunction("init_funcs_1", order=0, location=0)), a2)
s4 = ph.ScalarTerm(
ph.Product(
ph.Input(u),
ph.TestFunction("init_funcs_1", order=0, location=l)), -a2)
# derive state-space system
rad_pde = sim.WeakFormulation(
[int1, int2, int3, int4, s1, s2, s3, s4])
cf = sim.parse_weak_formulation(rad_pde)
ss = cf.convert_to_state_space()
# simulate system
t, q = sim.simulate_state_space(ss, np.zeros(ini_funcs_1.shape),
dt)
# check if (x'(0,t_end) - 1.) < 0.1
self.assertLess(
np.abs(ini_funcs_1[0].derive(1)(sys.float_info.min) *
(q[-1, 0] - q[-1, 1])) - 1, 0.1)
return t, q
def test_modal(self):
order = 8
def char_eq(w):
return w * (np.sin(w) + self.params.m * w * np.cos(w))
def phi_k_factory(freq, derivative_order=0):
def eig_func(z):
return np.cos(
freq * z) - self.params.m * freq * np.sin(freq * z)
def eig_func_dz(z):
return -freq * (np.sin(freq * z) +
self.params.m * freq * np.cos(freq * z))
def eig_func_ddz(z):
return freq**2 * (-np.cos(freq * z) +
self.params.m * freq * np.sin(freq * z))
if derivative_order == 0:
return eig_func
elif derivative_order == 1:
return eig_func_dz
elif derivative_order == 2:
return eig_func_ddz
else:
raise ValueError
# create eigenfunctions
eig_frequencies = ut.find_roots(char_eq,
n_roots=order,
grid=np.arange(0, 1e3, 2),
rtol=-2)
print("eigenfrequencies:")
print(eig_frequencies)
# create eigen function vectors
class SWMFunctionVector(cr.ComposedFunctionVector):
"""
String With Mass Function Vector, necessary due to manipulated scalar product
"""
@property
def func(self):
return self.members["funcs"][0]
@property
def scalar(self):
return self.members["scalars"][0]
eig_vectors = []
for n in range(order):
eig_vectors.append(
SWMFunctionVector(
cr.Function(phi_k_factory(eig_frequencies[n]),
derivative_handles=[
phi_k_factory(eig_frequencies[n],
der_order)
for der_order in range(1, 3)
],
domain=self.dz.bounds,
nonzero=self.dz.bounds),
phi_k_factory(eig_frequencies[n])(0)))
# normalize eigen vectors
norm_eig_vectors = [cr.normalize_function(vec) for vec in eig_vectors]
norm_eig_funcs = np.array([vec.func for vec in norm_eig_vectors])
register_base("norm_eig_funcs", norm_eig_funcs, overwrite=True)
norm_eig_funcs[0](1)
# debug print eigenfunctions
if 0:
func_vals = []
for vec in eig_vectors:
func_vals.append(np.vectorize(vec.func)(self.dz))
norm_func_vals = []
for func in norm_eig_funcs:
norm_func_vals.append(np.vectorize(func)(self.dz))
clrs = ["r", "g", "b", "c", "m", "y", "k", "w"]
for n in range(1, order + 1, len(clrs)):
pw_phin_k = pg.plot(title="phin_k for k in [{0}, {1}]".format(
n, min(n + len(clrs), order)))
for k in range(len(clrs)):
if k + n > order:
break
pw_phin_k.plot(x=np.array(self.dz),
y=norm_func_vals[n + k - 1],
pen=clrs[k])
app.exec_()
# create terms of weak formulation
terms = [
ph.IntegralTerm(ph.Product(
ph.FieldVariable("norm_eig_funcs", order=(2, 0)),
ph.TestFunction("norm_eig_funcs")),
self.dz.bounds,
scale=-1),
ph.ScalarTerm(ph.Product(
ph.FieldVariable("norm_eig_funcs", order=(2, 0), location=0),
ph.TestFunction("norm_eig_funcs", location=0)),
scale=-1),
ph.ScalarTerm(
ph.Product(ph.Input(self.u),
ph.TestFunction("norm_eig_funcs", location=1))),
ph.ScalarTerm(ph.Product(
ph.FieldVariable("norm_eig_funcs", location=1),
ph.TestFunction("norm_eig_funcs", order=1, location=1)),
scale=-1),
ph.ScalarTerm(
ph.Product(
ph.FieldVariable("norm_eig_funcs", location=0),
ph.TestFunction("norm_eig_funcs", order=1, location=0))),
ph.IntegralTerm(
ph.Product(ph.FieldVariable("norm_eig_funcs"),
ph.TestFunction("norm_eig_funcs", order=2)),
self.dz.bounds)
]
modal_pde = sim.WeakFormulation(terms, name="swm_lib-modal")
eval_data = sim.simulate_system(modal_pde,
self.ic,
self.dt,
self.dz,
der_orders=(2, 0))
# display results
if show_plots:
win = vis.PgAnimatedPlot(eval_data[0:2],
title="modal approx and derivative")
win2 = vis.PgSurfacePlot(eval_data[0])
app.exec_()
# test for correct transition
self.assertTrue(
np.isclose(eval_data[0].output_data[-1, 0], self.y_end, atol=1e-3))
def test_fem(self):
"""
use best documented fem case to test all steps in simulation process
"""
# enter string with mass equations
# nodes, ini_funcs = sf.cure_interval(sf.LagrangeFirstOrder,
nodes, ini_funcs = sf.cure_interval(sf.LagrangeSecondOrder,
self.dz.bounds,
node_count=11)
register_base("init_funcs", ini_funcs, overwrite=True)
int1 = ph.IntegralTerm(ph.Product(
ph.TemporalDerivedFieldVariable("init_funcs", 2),
ph.TestFunction("init_funcs")),
self.dz.bounds,
scale=self.params.sigma * self.params.tau**2)
s1 = ph.ScalarTerm(ph.Product(
ph.TemporalDerivedFieldVariable("init_funcs", 2, location=0),
ph.TestFunction("init_funcs", location=0)),
scale=self.params.m)
int2 = ph.IntegralTerm(ph.Product(
ph.SpatialDerivedFieldVariable("init_funcs", 1),
ph.TestFunction("init_funcs", order=1)),
self.dz.bounds,
scale=self.params.sigma)
s2 = ph.ScalarTerm(
ph.Product(ph.Input(self.u),
ph.TestFunction("init_funcs", location=1)),
-self.params.sigma)
# derive sate-space system
string_pde = sim.WeakFormulation([int1, s1, int2, s2], name="fem_test")
self.cf = sim.parse_weak_formulation(string_pde)
ss = self.cf.convert_to_state_space()
# generate initial conditions for weights
q0 = np.array([
cr.project_on_base(self.ic[idx], ini_funcs) for idx in range(2)
]).flatten()
# simulate
t, q = sim.simulate_state_space(ss, q0, self.dt)
# calculate result data
eval_data = []
for der_idx in range(2):
eval_data.append(
sim.evaluate_approximation(
"init_funcs",
q[:,
der_idx * ini_funcs.size:(der_idx + 1) * ini_funcs.size],
t, self.dz))
eval_data[-1].name = "{0}{1}".format(
self.cf.name, "_" + "".join(["d" for x in range(der_idx)]) +
"t" if der_idx > 0 else "")
# display results
if show_plots:
win = vis.PgAnimatedPlot(eval_data[:2],
title="fem approx and derivative")
win2 = vis.PgSurfacePlot(eval_data[0])
app.exec_()
# test for correct transition
self.assertTrue(
np.isclose(eval_data[0].output_data[-1, 0], self.y_end, atol=1e-3))
# save some test data for later use
root_dir = os.getcwd()
if root_dir.split(os.sep)[-1] == "tests":
res_dir = os.sep.join([os.getcwd(), "resources"])
else:
res_dir = os.sep.join([os.getcwd(), "tests", "resources"])
if not os.path.isdir(res_dir):
os.makedirs(res_dir)
file_path = os.sep.join([res_dir, "test_data.res"])
with open(file_path, "w+b") as f:
dump(eval_data, f)
eig_funcs = np.asarray(
[ef.SecondOrderDirichletEigenfunction(eig_freq[i], param, spatial_domain.bounds, norm_fac[i]) for i in range(n)])
re.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, spatial_domain.bounds, norm_fac_t[i]) for i in
range(n)])
re.register_base("eig_funcs_t", eig_funcs_t, overwrite=True)
# 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)]))
# 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)
weight_label="eig_funcs",
location=l)
d_field_variable_i = ph.SpatialDerivedFieldVariable("eig_funcs_i",
1,
weight_label="eig_funcs",
location=l)
field_variable_ti = ph.FieldVariable("eig_funcs_ti",
weight_label="eig_funcs",
location=l)
d_field_variable_ti = ph.SpatialDerivedFieldVariable("eig_funcs_ti",
1,
weight_label="eig_funcs",
location=l)
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
x_i_at_l = [ph.ScalarTerm(field_variable_i)]
xd_i_at_l = [ph.ScalarTerm(d_field_variable_i)]
x_ti_at_l = [ph.ScalarTerm(field_variable_ti)]
xd_ti_at_l = [ph.ScalarTerm(d_field_variable_ti)]
# shift transformation
shifted_fem_funcs_i = np.array([
ef.FiniteTransformFunction(func,
M,
b,
l,
scale_func=lambda z: np.exp(a1 / 2 / a2 * z))
for func in fem_funcs
])
shifted_eig_funcs_id = np.array(
[ef.FiniteTransformFunction(func, M, b, l) for func in eig_funcs_id])
d_field_variable_t = ph.SpatialDerivedFieldVariable("eig_funcs_t",
1,
weight_label="eig_funcs",
location=l)
field_variable = ph.FieldVariable("eig_funcs", location=l)
d_field_variable = ph.SpatialDerivedFieldVariable("eig_funcs", 1, location=l)
# intermediate (_i) and target intermediate (_ti) transformations by z=l
transform_i_at_l = np.exp(si.quad(lambda z: a1_z(z) / 2 / a2, 0,
l)[0]) # x_i = x * transform_i_at_l
inv_transform_i_at_l = np.exp(-si.quad(
lambda z: a1_z(z) / 2 / a2, 0, l)[0]) # x = x_i * inv_transform_i_at_l
transform_ti_at_l = np.exp(a1_t / 2 / a2 * l) # x_ti = x_t * transform_ti_at_l
# intermediate (_i) and target intermediate (_ti) field variable (list of scalar terms = sum of scalar terms)
x_fem_i_at_l = [ph.ScalarTerm(fem_field_variable, transform_i_at_l)]
x_i_at_l = [ph.ScalarTerm(field_variable, transform_i_at_l)]
xd_i_at_l = [
ph.ScalarTerm(d_field_variable, transform_i_at_l),
ph.ScalarTerm(field_variable,
transform_i_at_l * a1_z(l) / 2 / a2)
]
x_ti_at_l = [ph.ScalarTerm(field_variable_t, transform_ti_at_l)]
xd_ti_at_l = [
ph.ScalarTerm(d_field_variable_t, transform_ti_at_l),
ph.ScalarTerm(field_variable_t, transform_ti_at_l * a1_t / 2 / a2)
]
# discontinuous operator (Kx)(t) = int_kernel_zz(l)*x(l,t)
int_kernel_zz = alpha_ti - alpha_i + si.quad(
lambda z: (a0_i(z) - a0_ti) / 2 / a2, 0, l)[0]
