def main(write_output=True):
from hedge.data import GivenFunction, ConstantGivenFunction
from hedge.backends import guess_run_context
rcon = guess_run_context()
dim = 2
def boundary_tagger(fvi, el, fn, points):
from math import atan2, pi
normal = el.face_normals[fn]
if -90/180*pi < atan2(normal[1], normal[0]) < 90/180*pi:
return ["neumann"]
else:
return ["dirichlet"]
if dim == 2:
if rcon.is_head_rank:
from hedge.mesh.generator import make_disk_mesh
mesh = make_disk_mesh(r=0.5, boundary_tagger=boundary_tagger,
max_area=1e-2)
elif dim == 3:
if rcon.is_head_rank:
from hedge.mesh.generator import make_ball_mesh
mesh = make_ball_mesh(max_volume=0.0001,
boundary_tagger=lambda fvi, el, fn, points:
["dirichlet"])
else:
raise RuntimeError, "bad number of dimensions"
if rcon.is_head_rank:
print "%d elements" % len(mesh.elements)
mesh_data = rcon.distribute_mesh(mesh)
else:
mesh_data = rcon.receive_mesh()
discr = rcon.make_discretization(mesh_data, order=5,
debug=[])
def dirichlet_bc(x, el):
from math import sin
return sin(10*x[0])
def rhs_c(x, el):
if la.norm(x) < 0.1:
return 1000
else:
return 0
def my_diff_tensor():
result = numpy.eye(dim)
result[0,0] = 0.1
return result
try:
from hedge.models.poisson import PoissonOperator
from hedge.second_order import \
IPDGSecondDerivative, LDGSecondDerivative, \
StabilizedCentralSecondDerivative
from hedge.mesh import TAG_NONE, TAG_ALL
op = PoissonOperator(discr.dimensions,
diffusion_tensor=my_diff_tensor(),
#dirichlet_tag="dirichlet",
#neumann_tag="neumann",
dirichlet_tag=TAG_ALL,
neumann_tag=TAG_NONE,
#dirichlet_tag=TAG_ALL,
#neumann_tag=TAG_NONE,
dirichlet_bc=GivenFunction(dirichlet_bc),
neumann_bc=ConstantGivenFunction(-10),
scheme=StabilizedCentralSecondDerivative(),
#scheme=LDGSecondDerivative(),
#scheme=IPDGSecondDerivative(),
)
bound_op = op.bind(discr)
from hedge.iterative import parallel_cg
u = -parallel_cg(rcon, -bound_op,
bound_op.prepare_rhs(discr.interpolate_volume_function(rhs_c)),
debug=20, tol=5e-4,
dot=discr.nodewise_dot_product,
x=discr.volume_zeros())
if write_output:
from hedge.visualization import SiloVisualizer, VtkVisualizer
vis = VtkVisualizer(discr, rcon)
visf = vis.make_file("fld")
vis.add_data(visf, [ ("sol", discr.convert_volume(u, kind="numpy")), ])
visf.close()
finally:
discr.close()
def compute_initial_condition(rcon,
discr,
method,
state,
maxwell_op,
potential_bc,
force_zero=False):
from hedge.models.poisson import PoissonOperator
from hedge.mesh import TAG_ALL, TAG_NONE
from hedge.data import ConstantGivenFunction, GivenVolumeInterpolant
def rel_l2_error(field, true):
from hedge.tools import relative_error
return relative_error(discr.norm(field - true), discr.norm(true))
mean_beta = method.mean_beta(state)
gamma = method.units.gamma_from_beta(mean_beta)
# see doc/notes.tm for derivation of IC
def make_scaling_matrix(beta_scale, other_scale):
if la.norm(mean_beta) < 1e-10:
return other_scale * numpy.eye(discr.dimensions)
else:
beta_unit = mean_beta / la.norm(mean_beta)
return (
other_scale * numpy.identity(discr.dimensions) +
(beta_scale - other_scale) * numpy.outer(beta_unit, beta_unit))
poisson_op = PoissonOperator(discr.dimensions,
diffusion_tensor=make_scaling_matrix(
1 / gamma**2, 1),
dirichlet_tag=TAG_ALL,
neumann_tag=TAG_NONE,
dirichlet_bc=potential_bc)
rho_prime = method.deposit_rho(state)
rho_tilde = rho_prime / gamma
bound_poisson = poisson_op.bind(discr)
if force_zero:
phi_tilde = discr.volume_zeros()
else:
from hedge.iterative import parallel_cg
phi_tilde = -parallel_cg(
rcon,
-bound_poisson,
bound_poisson.prepare_rhs(rho_tilde / maxwell_op.epsilon),
debug=40 if "poisson" in method.debug else False,
tol=1e-10)
from hedge.tools import ptwise_dot
from hedge.models.nd_calculus import GradientOperator
e_tilde = ptwise_dot(
2, 1, make_scaling_matrix(1 / gamma, 1),
GradientOperator(discr.dimensions).bind(discr)(phi_tilde))
e_prime = ptwise_dot(2, 1, make_scaling_matrix(1, gamma), e_tilde)
from pyrticle.tools import make_cross_product
v_e_to_h_cross = make_cross_product(method, maxwell_op, "v", "e", "h")
h_prime = (1 / maxwell_op.mu) * gamma / maxwell_op.c * v_e_to_h_cross(
mean_beta, e_tilde)
if "ic" in method.debug:
deposited_charge = discr.integral(rho_prime)
real_charge = numpy.sum(state.charges)
print "charge: supposed=%g deposited=%g error=%g %%" % (
real_charge, deposited_charge,
100 * abs(deposited_charge - real_charge) / abs(real_charge))
from hedge.models.nd_calculus import DivergenceOperator
bound_div_op = DivergenceOperator(discr.dimensions).bind(discr)
d_tilde = maxwell_op.epsilon * e_tilde
d_prime = maxwell_op.epsilon * e_prime
#divD_prime_ldg = bound_poisson.div(d_prime)
#divD_prime_ldg2 = bound_poisson.div(d_prime, maxwell_op.epsilon*gamma*phi_tilde)
from hedge.optemplate import InverseMassOperator
divD_prime_ldg3 = maxwell_op.epsilon*\
(InverseMassOperator().apply(discr, bound_poisson.op(gamma*phi_tilde)))
divD_prime_central = bound_div_op(d_prime)
print "l2 div D_prime error central: %g" % \
rel_l2_error(divD_prime_central, rho_prime)
#print "l2 div D_prime error ldg: %g" % \
#rel_l2_error(divD_prime_ldg, rho_prime)
#print "l2 div D_prime error ldg with phi: %g" % \
#rel_l2_error(divD_prime_ldg2, rho_prime)
print "l2 div D_prime error ldg with phi 3: %g" % \
rel_l2_error(divD_prime_ldg3, rho_prime)
if "vis_files" in method.debug:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("ic")
vis.add_data(
visf,
[
("phi_moving", phi_tilde),
("rho_moving", rho_tilde),
("e_moving", e_tilde),
("rho_lab", rho_prime),
#("divD_lab_ldg", divD_prime_ldg),
#("divD_lab_ldg2", divD_prime_ldg2),
#("divD_lab_ldg3", divD_prime_ldg3),
("divD_lab_central", divD_prime_central),
("e_lab", e_prime),
("h_lab", h_prime),
],
)
method.add_to_vis(vis, visf, state)
visf.close()
return maxwell_op.assemble_fields(e=e_prime, h=h_prime, discr=discr)
def test_elliptic():
"""Test various properties of elliptic operators."""
from hedge.tools import unit_vector
def matrix_rep(op):
h, w = op.shape
mat = numpy.zeros(op.shape)
for j in range(w):
mat[:, j] = op(unit_vector(w, j))
return mat
def check_grad_mat():
import pyublas
if not pyublas.has_sparse_wrappers():
return
grad_mat = op.grad_matrix()
#print len(discr), grad_mat.nnz, type(grad_mat)
for i in range(10):
u = numpy.random.randn(len(discr))
mat_result = grad_mat * u
op_result = numpy.hstack(op.grad(u))
err = la.norm(mat_result - op_result) * la.norm(op_result)
assert err < 1e-5
def check_matrix_tgt():
big = numpy.zeros((20, 20), flavor=numpy.SparseBuildMatrix)
small = numpy.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print small
from hedge._internal import MatrixTarget
tgt = MatrixTarget(big, 4, 4)
tgt.begin(small.shape[0], small.shape[1])
print "YO"
tgt.add_coefficients(4, 4, small)
print "DUDE"
tgt.finalize()
print big
import pymbolic
v_x = pymbolic.var("x")
truesol = pymbolic.parse("math.sin(x[0]**2*x[1]**2)")
truesol_c = pymbolic.compile(truesol, variables=["x"])
def laplace(expression, variables):
return sum(
pymbolic.diff(pymbolic.diff(expression, var), var)
for var in variables)
rhs = laplace(truesol, [v_x[0], v_x[1]])
rhs_c = pymbolic.compile(rhs, variables=["x", "el"])
from hedge.mesh import TAG_ALL, TAG_NONE
from hedge.mesh.generator import make_disk_mesh
mesh = make_disk_mesh(r=0.5, max_area=0.1, faces=20)
mesh = mesh.reordered_by("cuthill")
from hedge.backends import CPURunContext
rcon = CPURunContext()
from hedge.tools import EOCRecorder
eocrec = EOCRecorder()
for order in [1, 2, 3, 4, 5]:
for flux in ["ldg", "ip"]:
from hedge.discretization.local import TriangleDiscretization
discr = rcon.make_discretization(
mesh,
TriangleDiscretization(order),
debug=discr_class.noninteractive_debug_flags())
from hedge.data import GivenFunction
from hedge.models.poisson import PoissonOperator
op = PoissonOperator(
discr.dimensions,
dirichlet_tag=TAG_ALL,
dirichlet_bc=GivenFunction(lambda x, el: truesol_c(x)),
neumann_tag=TAG_NONE)
bound_op = op.bind(discr)
if order <= 3:
mat = matrix_rep(bound_op)
sym_err = la.norm(mat - mat.T)
#print sym_err
assert sym_err < 1e-12
#check_grad_mat()
from hedge.iterative import parallel_cg
truesol_v = discr.interpolate_volume_function(
lambda x, el: truesol_c(x))
sol_v = -parallel_cg(rcon,
-bound_op,
bound_op.prepare_rhs(
discr.interpolate_volume_function(rhs_c)),
tol=1e-10,
max_iterations=40000)
eocrec.add_data_point(order, discr.norm(sol_v - truesol_v))
#print eocrec.pretty_print()
assert eocrec.estimate_order_of_convergence()[0, 1] > 8
def test_elliptic():
"""Test various properties of elliptic operators."""
from hedge.tools import unit_vector
def matrix_rep(op):
h, w = op.shape
mat = numpy.zeros(op.shape)
for j in range(w):
mat[:, j] = op(unit_vector(w, j))
return mat
def check_grad_mat():
import pyublas
if not pyublas.has_sparse_wrappers():
return
grad_mat = op.grad_matrix()
# print len(discr), grad_mat.nnz, type(grad_mat)
for i in range(10):
u = numpy.random.randn(len(discr))
mat_result = grad_mat * u
op_result = numpy.hstack(op.grad(u))
err = la.norm(mat_result - op_result) * la.norm(op_result)
assert la.norm(mat_result - op_result) * la.norm(op_result) < 1e-5
def check_matrix_tgt():
big = num.zeros((20, 20), flavor=num.SparseBuildMatrix)
small = num.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print small
from hedge._internal import MatrixTarget
tgt = MatrixTarget(big, 4, 4)
tgt.begin(small.shape[0], small.shape[1])
print "YO"
tgt.add_coefficients(4, 4, small)
print "DUDE"
tgt.finalize()
print big
import pymbolic
v_x = pymbolic.var("x")
truesol = pymbolic.parse("math.sin(x[0]**2*x[1]**2)")
truesol_c = pymbolic.compile(truesol, variables=["x"])
rhs = pymbolic.simplify(pymbolic.laplace(truesol, [v_x[0], v_x[1]]))
rhs_c = pymbolic.compile(rhs, variables=["x", "el"])
from hedge.mesh import TAG_ALL, TAG_NONE
from hedge.mesh.generator import make_disk_mesh
mesh = make_disk_mesh(r=0.5, max_area=0.1, faces=20)
mesh = mesh.reordered_by("cuthill")
from hedge.backends import CPURunContext
rcon = CPURunContext()
from hedge.tools import EOCRecorder
eocrec = EOCRecorder()
for order in [1, 2, 3, 4, 5]:
for flux in ["ldg", "ip"]:
from hedge.discretization.local import TriangleDiscretization
discr = rcon.make_discretization(
mesh, TriangleDiscretization(order), debug=discr_class.noninteractive_debug_flags()
)
from hedge.data import GivenFunction
from hedge.models.poisson import PoissonOperator
op = PoissonOperator(
discr.dimensions,
dirichlet_tag=TAG_ALL,
dirichlet_bc=GivenFunction(lambda x, el: truesol_c(x)),
neumann_tag=TAG_NONE,
)
bound_op = op.bind(discr)
if order <= 3:
mat = matrix_rep(bound_op)
sym_err = la.norm(mat - mat.T)
# print sym_err
assert sym_err < 1e-12
# check_grad_mat()
from hedge.iterative import parallel_cg
truesol_v = discr.interpolate_volume_function(lambda x, el: truesol_c(x))
sol_v = -parallel_cg(
rcon,
-bound_op,
bound_op.prepare_rhs(discr.interpolate_volume_function(rhs_c)),
tol=1e-10,
max_iterations=40000,
)
eocrec.add_data_point(order, discr.norm(sol_v - truesol_v))
# print eocrec.pretty_print()
assert eocrec.estimate_order_of_convergence()[0, 1] > 8
def main(write_output=True):
from hedge.data import GivenFunction, ConstantGivenFunction
from hedge.backends import guess_run_context
rcon = guess_run_context()
dim = 2
def boundary_tagger(fvi, el, fn, points):
from math import atan2, pi
normal = el.face_normals[fn]
if -90 / 180 * pi < atan2(normal[1], normal[0]) < 90 / 180 * pi:
return ["neumann"]
else:
return ["dirichlet"]
if dim == 2:
if rcon.is_head_rank:
from hedge.mesh.generator import make_disk_mesh
mesh = make_disk_mesh(r=0.5,
boundary_tagger=boundary_tagger,
max_area=1e-2)
elif dim == 3:
if rcon.is_head_rank:
from hedge.mesh.generator import make_ball_mesh
mesh = make_ball_mesh(
max_volume=0.0001,
boundary_tagger=lambda fvi, el, fn, points: ["dirichlet"])
else:
raise RuntimeError, "bad number of dimensions"
if rcon.is_head_rank:
print "%d elements" % len(mesh.elements)
mesh_data = rcon.distribute_mesh(mesh)
else:
mesh_data = rcon.receive_mesh()
discr = rcon.make_discretization(mesh_data, order=5, debug=[])
def dirichlet_bc(x, el):
from math import sin
return sin(10 * x[0])
def rhs_c(x, el):
if la.norm(x) < 0.1:
return 1000
else:
return 0
def my_diff_tensor():
result = numpy.eye(dim)
result[0, 0] = 0.1
return result
try:
from hedge.models.poisson import PoissonOperator
from hedge.second_order import \
IPDGSecondDerivative, LDGSecondDerivative, \
StabilizedCentralSecondDerivative
from hedge.mesh import TAG_NONE, TAG_ALL
op = PoissonOperator(
discr.dimensions,
diffusion_tensor=my_diff_tensor(),
#dirichlet_tag="dirichlet",
#neumann_tag="neumann",
dirichlet_tag=TAG_ALL,
neumann_tag=TAG_NONE,
#dirichlet_tag=TAG_ALL,
#neumann_tag=TAG_NONE,
dirichlet_bc=GivenFunction(dirichlet_bc),
neumann_bc=ConstantGivenFunction(-10),
scheme=StabilizedCentralSecondDerivative(),
#scheme=LDGSecondDerivative(),
#scheme=IPDGSecondDerivative(),
)
bound_op = op.bind(discr)
from hedge.iterative import parallel_cg
u = -parallel_cg(rcon,
-bound_op,
bound_op.prepare_rhs(
discr.interpolate_volume_function(rhs_c)),
debug=20,
tol=5e-4,
dot=discr.nodewise_dot_product,
x=discr.volume_zeros())
if write_output:
from hedge.visualization import SiloVisualizer, VtkVisualizer
vis = VtkVisualizer(discr, rcon)
visf = vis.make_file("fld")
vis.add_data(visf, [
("sol", discr.convert_volume(u, kind="numpy")),
])
visf.close()
finally:
discr.close()
def compute_initial_condition(rcon, discr, method, state,
maxwell_op, potential_bc,
force_zero=False):
from hedge.models.poisson import PoissonOperator
from hedge.mesh import TAG_ALL, TAG_NONE
from hedge.data import ConstantGivenFunction, GivenVolumeInterpolant
def rel_l2_error(field, true):
from hedge.tools import relative_error
return relative_error(
discr.norm(field-true),
discr.norm(true))
mean_beta = method.mean_beta(state)
gamma = method.units.gamma_from_beta(mean_beta)
# see doc/notes.tm for derivation of IC
def make_scaling_matrix(beta_scale, other_scale):
if la.norm(mean_beta) < 1e-10:
return other_scale*numpy.eye(discr.dimensions)
else:
beta_unit = mean_beta/la.norm(mean_beta)
return (other_scale*numpy.identity(discr.dimensions)
+ (beta_scale-other_scale)*numpy.outer(beta_unit, beta_unit))
poisson_op = PoissonOperator(
discr.dimensions,
diffusion_tensor=make_scaling_matrix(1/gamma**2, 1),
dirichlet_tag=TAG_ALL,
neumann_tag=TAG_NONE,
dirichlet_bc=potential_bc)
rho_prime = method.deposit_rho(state)
rho_tilde = rho_prime/gamma
bound_poisson = poisson_op.bind(discr)
if force_zero:
phi_tilde = discr.volume_zeros()
else:
from hedge.iterative import parallel_cg
phi_tilde = -parallel_cg(rcon, -bound_poisson,
bound_poisson.prepare_rhs(
rho_tilde/maxwell_op.epsilon),
debug=40 if "poisson" in method.debug else False, tol=1e-10)
from hedge.tools import ptwise_dot
from hedge.models.nd_calculus import GradientOperator
e_tilde = ptwise_dot(2, 1, make_scaling_matrix(1/gamma, 1),
GradientOperator(discr.dimensions).bind(discr)(phi_tilde))
e_prime = ptwise_dot(2, 1, make_scaling_matrix(1, gamma), e_tilde)
from pyrticle.tools import make_cross_product
v_e_to_h_cross = make_cross_product(method, maxwell_op, "v", "e", "h")
h_prime = (1/maxwell_op.mu)*gamma/maxwell_op.c * v_e_to_h_cross(mean_beta, e_tilde)
if "ic" in method.debug:
deposited_charge = discr.integral(rho_prime)
real_charge = numpy.sum(state.charges)
print "charge: supposed=%g deposited=%g error=%g %%" % (
real_charge,
deposited_charge,
100*abs(deposited_charge-real_charge)/abs(real_charge)
)
from hedge.models.nd_calculus import DivergenceOperator
bound_div_op = DivergenceOperator(discr.dimensions).bind(discr)
d_tilde = maxwell_op.epsilon*e_tilde
d_prime = maxwell_op.epsilon*e_prime
#divD_prime_ldg = bound_poisson.div(d_prime)
#divD_prime_ldg2 = bound_poisson.div(d_prime, maxwell_op.epsilon*gamma*phi_tilde)
from hedge.optemplate import InverseMassOperator
divD_prime_ldg3 = maxwell_op.epsilon*\
(InverseMassOperator().apply(discr, bound_poisson.op(gamma*phi_tilde)))
divD_prime_central = bound_div_op(d_prime)
print "l2 div D_prime error central: %g" % \
rel_l2_error(divD_prime_central, rho_prime)
#print "l2 div D_prime error ldg: %g" % \
#rel_l2_error(divD_prime_ldg, rho_prime)
#print "l2 div D_prime error ldg with phi: %g" % \
#rel_l2_error(divD_prime_ldg2, rho_prime)
print "l2 div D_prime error ldg with phi 3: %g" % \
rel_l2_error(divD_prime_ldg3, rho_prime)
if "vis_files" in method.debug:
from hedge.visualization import SiloVisualizer
vis = SiloVisualizer(discr)
visf = vis.make_file("ic")
vis.add_data(visf, [
("phi_moving", phi_tilde),
("rho_moving", rho_tilde),
("e_moving", e_tilde),
("rho_lab", rho_prime),
#("divD_lab_ldg", divD_prime_ldg),
#("divD_lab_ldg2", divD_prime_ldg2),
#("divD_lab_ldg3", divD_prime_ldg3),
("divD_lab_central", divD_prime_central),
("e_lab", e_prime),
("h_lab", h_prime),
],
)
method.add_to_vis(vis, visf, state)
visf.close()
return maxwell_op.assemble_fields(e=e_prime, h=h_prime, discr=discr)
