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 setUp(self):
self.u = np.sin
self.input = ph.Input(self.u) # control input
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=3)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.phi = ph.TestFunction(
"ini_funcs") # eigenfunction or something else
self.dphi = ph.TestFunction("ini_funcs",
order=1) # eigenfunction or something else
self.dphi_at1 = ph.TestFunction(
"ini_funcs", order=1,
location=1) # eigenfunction or something else
self.field_var = ph.FieldVariable("ini_funcs")
self.field_var_at1 = ph.FieldVariable("ini_funcs", location=1)
def setUp(self):
self.input = ph.Input(np.sin)
self.phi = np.array([cr.Function(lambda x: 2 * x)])
register_base("phi", self.phi, overwrite=True)
self.test_func = ph.TestFunction("phi")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=2)
register_base("ini_funcs", self.ini_funcs, overwrite=True)
self.xdt = ph.TemporalDerivedFieldVariable("ini_funcs", order=1)
self.xdz_at1 = ph.SpatialDerivedFieldVariable("ini_funcs",
order=1,
location=1)
self.prod = ph.Product(self.input, self.xdt)
def setUp(self):
self.input = ph.Input(np.sin)
phi = cr.Function(np.sin)
psi = cr.Function(np.cos)
self.t_funcs = np.array([phi, psi])
register_base("funcs", self.t_funcs, overwrite=True)
self.test_funcs = ph.TestFunction("funcs")
self.s_funcs = np.array([cr.Function(self.scale)])[[0, 0]]
register_base("scale_funcs", self.s_funcs, overwrite=True)
self.scale_funcs = ph.ScalarFunction("scale_funcs")
nodes, self.ini_funcs = cure_interval(LagrangeFirstOrder, (0, 1),
node_count=2)
register_base("prod_ini_funcs", self.ini_funcs, overwrite=True)
self.field_var = ph.FieldVariable("prod_ini_funcs")
self.field_var_dz = ph.SpatialDerivedFieldVariable("prod_ini_funcs", 1)
def setUp(self):
self.psi = cr.Function(np.sin)
register_base("funcs", self.psi, overwrite=True)
self.funcs = ph.TestFunction("funcs")
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)
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))
