element=Vh.ufl_element())
true_initial_condition = dl.interpolate(ic_expr, Vh).vector()
orderPrior = 2
if orderPrior == 1:
gamma = 1
delta = 1e1
prior = LaplacianPrior(Vh, gamma, delta)
elif orderPrior == 2:
gamma = 1
delta = 8
prior = BiLaplacianPrior(Vh, gamma, delta)
# prior.mean = interpolate(Expression('min(0.6,exp(-50*(pow(x[0]-0.34,2) + pow(x[1]-0.71,2))))'), Vh).vector()
prior.mean = dl.interpolate(dl.Constant(0.5), Vh).vector()
if rank == 0:
print(
"Prior regularization: (delta - gamma*Laplacian)^order: delta={0}, gamma={1}, order={2}"
.format(delta, gamma, orderPrior))
wind_velocity = computeVelocityField(mesh)
problem = TimeDependentAD(mesh, [Vh, Vh, Vh], 0., 4., 1., .2,
wind_velocity, True, prior)
if rank == 0:
print(sep, "Generate synthetic observation", sep)
rel_noise = 0.001
utrue = problem.generate_vector(STATE)
x = [utrue, true_initial_condition, None]
problem.solveFwd(x[STATE], x, 1e-9)
def __init__(self, Vh):
test = dl.TestFunction(Vh[STATE])
f = dl.Constant(1.0)
self.f = dl.assemble(f * test * dl.dx)
def __init__(self, turbine, flow):
# add radius to model params
if model_parameters is None:
model_parameters = {"radius": turbine_radius}
else:
model_parameters.update({"radius": turbine_radius})
# check if gradient required
if "compute_gradient" in model_parameters:
compute_gradient = model_parameters["compute_gradient"]
else:
compute_gradient = True
# check if a wake and wake gradients are provided
if "wake" in model_parameters:
if "wake_gradients" in model_parameters:
gradients = model_parameters["wake_gradients"]
else:
gradients = None
self.wake = ADDolfinExpression(model_parameters["wake"],
compute_gradient=compute_gradient,
gradients=gradients)
# compute wake and gradients
else:
# mimic a SteadyConfiguration but change a few things along the way
config = otf.DefaultConfiguration()
config.params['steady_state'] = True
config.params[
'initial_condition'] = otf.ConstantFlowInitialCondition(config)
config.params['include_advection'] = True
config.params['include_diffusion'] = True
config.params['diffusion_coef'] = 3.0
config.params['quadratic_friction'] = True
config.params['newton_solver'] = True
config.params['friction'] = dolfin.Constant(0.0025)
config.params['theta'] = 1.0
config.params["dump_period"] = 0
xmin, ymin = -100, -200
xsize, ysize = 1000, 400
xcells, ycells = 400, 160
mesh = dolfin.RectangleMesh(xmin, ymin, xmin + xsize, ymin + ysize,
xcells, ycells)
V, H = config.finite_element(mesh)
config.function_space = dolfin.MixedFunctionSpace([V, H])
config.turbine_function_space = dolfin.FunctionSpace(mesh, 'CG', 2)
class Domain(object):
"""
Domain object used for setting boundary conditions etc later on
"""
def __init__(self, mesh):
class InFlow(dolfin.SubDomain):
def inside(self, x, on_boundary):
return dolfin.near(x[0], -100)
class OutFlow(dolfin.SubDomain):
def inside(self, x, on_boundary):
return dolfin.near(x[0], 900)
inflow = InFlow()
outflow = OutFlow()
self.mesh = mesh
self.boundaries = dolfin.FacetFunction("size_t", mesh)
self.boundaries.set_all(0)
inflow.mark(self.boundaries, 1)
outflow.mark(self.boundaries, 2)
self.ds = dolfin.Measure('ds')[self.boundaries]
config.domain = Domain(mesh)
config.set_domain(config.domain, warning=False)
# Boundary conditions
bc = otf.DirichletBCSet(config)
bc.add_constant_flow(1, 1.0 + 1e-10)
bc.add_zero_eta(2)
config.params['bctype'] = 'strong_dirichlet'
config.params['strong_bc'] = bc
config.params['free_slip_on_sides'] = True
# Optimisation settings
config.params['functional_final_time_only'] = True
config.params['automatic_scaling'] = False
# Turbine settings
config.params['turbine_pos'] = []
config.params['turbine_friction'] = []
config.params['turbine_x'] = turbine_radius * 2
config.params['turbine_y'] = turbine_radius * 2
config.params['controls'] = ['turbine_pos']
# Finally set some DOLFIN optimisation flags
dolfin.parameters['form_compiler']['cpp_optimize'] = True
dolfin.parameters['form_compiler']['cpp_optimize_flags'] = '-O3'
dolfin.parameters['form_compiler']['optimize'] = True
# place a turbine with default friction
turbine = [(0., 0.)]
config.set_turbine_pos(turbine)
# solve the shallow water equations
rf = otf.ReducedFunctional(config, plot=False)
dolfin.info_blue("Generating the wake model...")
rf.j(rf.initial_control())
# get state
state = rf.last_state
V = dolfin.VectorFunctionSpace(config.function_space.mesh(),
"CG",
2,
dim=2)
u_out = dolfin.TrialFunction(V)
v_out = dolfin.TestFunction(V)
M_out = dolfin_adjoint.assemble(
dolfin.inner(v_out, u_out) * dolfin.dx)
out_state = dolfin.Function(V)
rhs = dolfin_adjoint.assemble(
dolfin.inner(v_out,
state.split()[0]) * dolfin.dx)
dolfin_adjoint.solve(M_out,
out_state.vector(),
rhs,
"cg",
"sor",
annotate=False)
dolfin.info_green("Wake model generated.")
self.wake = ADDolfinExpression(out_state.split()[0],
compute_gradient)
super(ApproximateShallowWater,
self).__init__("ApproximateShallowWater", flow_field,
model_parameters)
init_mode="random",
alpha=0.0,
)
cmd_kwargs = parse_command_line()
parameters.update(**cmd_kwargs)
if parameters["restart_folder"]:
load_parameters(
parameters, os.path.join(parameters["restart_folder"],
"parameters.dat"))
parameters.update(**cmd_kwargs)
R = parameters["R"]
Rz = parameters["Rz"]
res = parameters["res"]
dt = TimeStepSelector(parameters["dt"])
tau = df.Constant(parameters["tau"])
h = df.Constant(parameters["h"])
M = df.Constant(parameters["M"])
#geo_map = EllipsoidMap(R, R, Rz)
geo_map = CylinderMap(R, 2 * np.pi * R)
geo_map.initialize(res, restart_folder=parameters["restart_folder"])
W = geo_map.mixed_space(4)
# Define trial and test functions
du = df.TrialFunction(W)
chi, xi, eta, etahat = df.TestFunctions(W)
# Define functions
def eta_v(s):
return df.Constant(b)/2.*(1./Lmid**2*(u.dx(s,0) + u.ds(s)*dsdx(s))**2 \
+0.25*((u.ds(s)*dsdz(s))**2) \
+ eps_reg)**((1.-n)/(2*n))
from mpi4py import MPI
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
size = comm.Get_size()
print("Hello World from {}/{}".format(rank, size))
import dolfin as df
#df.parameters.reorder_dofs_serial = False
nx = ny = 10
mesh = df.RectangleMesh(df.Point(0, 0), df.Point(1, 1), nx, ny)
V = df.VectorFunctionSpace(mesh, 'CG', 1, dim=3)
vector_value_constant = df.Constant(list([0, 1, 2]))
our_function = df.interpolate(vector_value_constant, V)
print("{}: My vector is of shape {}.".format(
rank,
our_function.vector().array().shape))
print(our_function.vector().array())
quad_scheme="default")
fiber_space = dolfin.FunctionSpace(mesh, fiber_element)
fiber = dolfin.Function(fiber_space, "data/fiber.xml")
microstructure = pulse.Microstructure(f0=fiber)
# Create the geometry
geometry = pulse.HeartGeometry(
mesh=mesh,
markers=markers,
marker_functions=marker_functions,
microstructure=microstructure,
)
activation = dolfin.Function(dolfin.FunctionSpace(geometry.mesh, "R", 0))
activation.assign(dolfin.Constant(0.2))
matparams = pulse.HolzapfelOgden.default_parameters()
material = pulse.HolzapfelOgden(activation=activation,
parameters=matparams,
f0=geometry.f0)
# LV Pressure
lvp = dolfin.Constant(1.0)
lv_marker = markers_dict["ENDO"][0]
lv_pressure = pulse.NeumannBC(traction=lvp, marker=lv_marker, name="lv")
neumann_bc = [lv_pressure]
# Add spring term at the base with stiffness 1.0 kPa/cm^2
base_spring = 1.0
robin_bc = [
pulse.RobinBC(value=dolfin.Constant(base_spring),
def mass_lumps_rowsum_petsc(me):
if me._mass_lumps_rowsum_petsc is None:
me._mass_lumps_rowsum_petsc = dl.assemble(
dl.Constant(1.0) * me.test_function * dl.dx)
return me._mass_lumps_rowsum_petsc
def setup_scalar_equation(self):
sim = self.simulation
V = sim.data['Vphi']
mesh = V.mesh()
P = V.ufl_element().degree()
# Source term
source_cpp = sim.input.get_value('solver/source', '0', 'string')
f = dolfin.Expression(source_cpp, degree=P)
# Create the solution function
sim.data['phi'] = dolfin.Function(V)
# DG elliptic penalty
penalty = define_penalty(mesh, P, k_min=1.0, k_max=1.0)
penalty_dS = dolfin.Constant(penalty)
penalty_ds = dolfin.Constant(penalty * 2)
yh = dolfin.Constant(1 / (penalty * 2))
# Define weak form
u, v = dolfin.TrialFunction(V), dolfin.TestFunction(V)
a = dot(grad(u), grad(v)) * dx
L = f * v * dx
# Symmetric Interior Penalty method for -∇⋅∇φ
n = dolfin.FacetNormal(mesh)
a -= dot(n('+'), avg(grad(u))) * jump(v) * dS
a -= dot(n('+'), avg(grad(v))) * jump(u) * dS
# Symmetric Interior Penalty coercivity term
a += penalty_dS * jump(u) * jump(v) * dS
# Dirichlet boundary conditions
# Nitsche's (1971) method, see e.g. Epshteyn and Rivière (2007)
dirichlet_bcs = sim.data['dirichlet_bcs'].get('phi', [])
for dbc in dirichlet_bcs:
bcval, dds = dbc.func(), dbc.ds()
# SIPG for -∇⋅∇φ
a -= dot(n, grad(u)) * v * dds
a -= dot(n, grad(v)) * u * dds
L -= dot(n, grad(v)) * bcval * dds
# Weak Dirichlet
a += penalty_ds * u * v * dds
L += penalty_ds * bcval * v * dds
# Neumann boundary conditions
neumann_bcs = sim.data['neumann_bcs'].get('phi', [])
for nbc in neumann_bcs:
L += nbc.func() * v * nbc.ds()
# Robin boundary conditions
# See Juntunen and Stenberg (2009)
# n⋅∇φ = (φ0 - φ)/b + g
robin_bcs = sim.data['robin_bcs'].get('phi', [])
for rbc in robin_bcs:
b, rds = rbc.blend(), rbc.ds()
dval, nval = rbc.dfunc(), rbc.nfunc()
# From IBP of the main equation
a -= dot(n, grad(u)) * v * rds
# Test functions for the Robin BC
z1 = 1 / (b + yh) * v
z2 = -yh / (b + yh) * dot(n, grad(v))
# Robin BC added twice with different test functions
for z in [z1, z2]:
a += b * dot(n, grad(u)) * z * rds
a += u * z * rds
L += dval * z * rds
L += b * nval * z * rds
# Does the system have a null-space?
self.has_null_space = len(dirichlet_bcs) + len(robin_bcs) == 0
self.form_lhs = a
self.form_rhs = L
'incompressible': args.incompressible,
'density': 10.0
}
if args.material in ['fung', 'guccione']:
if args.material == 'fung':
material_dict['d'] = [10.0] * 3 + [0.0] * 3 + [5.0] * 3
else:
material_dict['bt'] = 10.0
material_dict['bf'] = 1.0
material_dict['bfs'] = 5.0
from numpy import sqrt
material_dict['C'] = 20.0
material_dict['kappa'] = 1e4
if args.dim == 2:
e1 = dlf.Constant([1.0 / sqrt(2.0)] * 2)
e2 = dlf.Constant([1.0 / sqrt(2.0), -1.0 / sqrt(2.0)])
else:
e1 = dlf.Constant([1.0 / sqrt(2.0)] * 2 + [0.0])
e2 = dlf.Constant([1.0 / sqrt(2.0), -1.0 / sqrt(2.0), 0.0])
material_dict['fibers'] = {
'fiber_files': [e1, e2],
'fiber_names': ['e1', 'e2'],
'element': None
}
else:
material_dict.update({'inv_la': inv_la, 'mu': mu})
# Mesh subdictionary
mesh_dict = {'mesh_file': mesh_file, 'boundaries': boundaries}
import dolfin as df
import time
mesh = df.UnitSquareMesh(200, 200)
print mesh
S1 = df.FunctionSpace(mesh, 'CG', 1)
S3 = df.VectorFunctionSpace(mesh, 'CG', 1, 3)
u = df.TrialFunction(S3)
v = df.TestFunction(S3)
m = df.Function(S3) # magnetisation
Heff = df.Function(S3) # effective field
Ms = df.Function(S1) # saturation magnetisation
alpha = df.Function(S1) # damping
gamma = df.Constant(1) # gyromagnetic ratio
m.assign(df.Constant((1, 0, 0)))
Heff.assign(df.Constant((0, 0, 1)))
alpha.assign(df.Constant(1))
Ms.assign(df.Constant(1))
# just assembling it
LLG = -gamma / (1 + alpha * alpha) * df.cross(m, Heff) - alpha * gamma / (
1 + alpha * alpha) * df.cross(m, df.cross(m, Heff))
L = df.dot(LLG, df.TestFunction(S3)) * df.dP
dmdt = df.Function(S3)
start = time.time()
for i in xrange(1000):
df.assemble(L, tensor=dmdt.vector())
stop = time.time()
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import dolfin
import pulse
from problem import Problem
from force import ca_transient
# pulse.parameters['log_level'] = dolfin.WARNING
geometry = pulse.HeartGeometry.from_file(pulse.mesh_paths['simple_ellipsoid'])
# Scale mesh to fit Windkessel parameters
geometry.mesh.coordinates()[:] *= 2.4
activation = dolfin.Constant(0.0)
matparams = pulse.Guccione.default_parameters()
material = pulse.Guccione(activation=activation,
parameters=matparams)
# Add spring term at the base with stiffness 1.0 kPa/cm^2
base_spring = 1.0
robin_bc = [pulse.RobinBC(value=dolfin.Constant(base_spring),
marker=geometry.markers["BASE"][0])]
# Fix the basal plane in the longitudinal direction
# 0 in V.sub(0) refers to x-direction, which is the longitudinal direction
def fix_basal_plane(W):
V = W if W.sub(0).num_sub_spaces() == 0 else W.sub(0)
def apex_to_base(
mesh: df.Mesh,
base_marker: int,
ffun: df.MeshFunction,
use_krylov_solver: bool = False,
krylov_solver_atol: Optional[float] = None,
krylov_solver_rtol: Optional[float] = None,
krylov_solver_max_its: Optional[int] = None,
verbose: bool = False,
):
"""
Find the apex coordinate and compute the laplace
equation to find the apex to base solution
Arguments
---------
mesh : dolfin.Mesh
The mesh
base_marker : int
The marker value for the basal facets
ffun : dolfin.MeshFunctionSizet (optional)
A facet function containing markers for the boundaries.
If not provided, the markers stored within the mesh will
be used.
use_krylov_solver: bool
If True use Krylov solver, by default False
krylov_solver_atol: float (optional)
If a Krylov solver is used, this option specifies a
convergence criterion in terms of the absolute
residual. Default: 1e-15.
krylov_solver_rtol: float (optional)
If a Krylov solver is used, this option specifies a
convergence criterion in terms of the relative
residual. Default: 1e-10.
krylov_solver_max_its: int (optional)
If a Krylov solver is used, this option specifies the
maximum number of iterations to perform. Default: 10000.
verbose: bool
If true, print more info, by default False
"""
# Find apex by solving a laplacian with base solution = 0
# Create Base variational problem
V = df.FunctionSpace(mesh, "CG", 1)
u = df.TrialFunction(V)
v = df.TestFunction(V)
a = df.dot(df.grad(u), df.grad(v)) * df.dx
L = v * df.Constant(1) * df.dx
apex = df.Function(V)
base_bc = df.DirichletBC(V, 1, ffun, base_marker, "topological")
# Solver options
solver = solve_system(
a,
L,
base_bc,
apex,
solver_parameters={
"linear_solver": "cg",
"preconditioner": "amg"
},
use_krylov_solver=use_krylov_solver,
krylov_solver_atol=krylov_solver_atol,
krylov_solver_rtol=krylov_solver_rtol,
krylov_solver_max_its=krylov_solver_max_its,
verbose=verbose,
)
if utils.DOLFIN_VERSION_MAJOR < 2018:
dof_x = utils.gather_broadcast(V.tabulate_dof_coordinates()).reshape(
(-1, 3))
apex_values = utils.gather_broadcast(apex.vector().get_local())
ind = apex_values.argmax()
apex_coord = dof_x[ind]
else:
dof_x = V.tabulate_dof_coordinates()
apex_values = apex.vector().get_local()
local_max_val = apex_values.max()
local_apex_coord = dof_x[apex_values.argmax()]
comm = utils.mpi_comm_world()
from mpi4py import MPI
global_max, apex_coord = comm.allreduce(
sendobj=(local_max_val, local_apex_coord),
op=MPI.MAXLOC,
)
df.info(" Apex coord: ({0:.2f}, {1:.2f}, {2:.2f})".format(*apex_coord))
# Update rhs
L = v * df.Constant(0) * df.dx
apex_domain = df.CompiledSubDomain(
"near(x[0], {0}) && near(x[1], {1}) && near(x[2], {2})".format(
*apex_coord), )
apex_bc = df.DirichletBC(V, 0, apex_domain, "pointwise")
# Solve the poisson equation
bcs = [base_bc, apex_bc]
if solver is not None:
# Reuse existing solver
A, b = df.assemble_system(a, L, bcs)
solver.set_operator(A)
solver.solve(apex.vector(), b)
else:
solve_system(
a,
L,
bcs,
apex,
use_krylov_solver=use_krylov_solver,
krylov_solver_atol=krylov_solver_atol,
krylov_solver_rtol=krylov_solver_rtol,
krylov_solver_max_its=krylov_solver_max_its,
solver_parameters={"linear_solver": "gmres"},
verbose=verbose,
)
return apex
def __init__(self, mesh, Vh, t_init, t_final, t_1, dt, wind_velocity,
gls_stab, Prior):
self.mesh = mesh
self.Vh = Vh
self.t_init = t_init
self.t_final = t_final
self.t_1 = t_1
self.dt = dt
self.sim_times = np.arange(self.t_init, self.t_final + .5 * self.dt,
self.dt)
u = dl.TrialFunction(Vh[STATE])
v = dl.TestFunction(Vh[STATE])
kappa = dl.Constant(.001)
dt_expr = dl.Constant(self.dt)
r_trial = u + dt_expr * (-dl.div(kappa * dl.nabla_grad(u)) +
dl.inner(wind_velocity, dl.nabla_grad(u)))
r_test = v + dt_expr * (-dl.div(kappa * dl.nabla_grad(v)) +
dl.inner(wind_velocity, dl.nabla_grad(v)))
h = dl.CellSize(mesh)
vnorm = dl.sqrt(dl.inner(wind_velocity, wind_velocity))
if gls_stab:
tau = dl.Min((h * h) / (dl.Constant(2.) * kappa), h / vnorm)
else:
tau = dl.Constant(0.)
self.M = dl.assemble(dl.inner(u, v) * dl.dx)
self.M_stab = dl.assemble(dl.inner(u, v + tau * r_test) * dl.dx)
self.Mt_stab = dl.assemble(dl.inner(u + tau * r_trial, v) * dl.dx)
Nvarf = (dl.inner(kappa * dl.nabla_grad(u), dl.nabla_grad(v)) +
dl.inner(wind_velocity, dl.nabla_grad(u)) * v) * dl.dx
Ntvarf = (dl.inner(kappa * dl.nabla_grad(v), dl.nabla_grad(u)) +
dl.inner(wind_velocity, dl.nabla_grad(v)) * u) * dl.dx
self.N = dl.assemble(Nvarf)
self.Nt = dl.assemble(Ntvarf)
stab = dl.assemble(tau * dl.inner(r_trial, r_test) * dl.dx)
self.L = self.M + dt * self.N + stab
self.Lt = self.M + dt * self.Nt + stab
boundaries = dl.FacetFunction("size_t", mesh)
boundaries.set_all(0)
class InsideBoundary(dl.SubDomain):
def inside(self, x, on_boundary):
x_in = x[0] > dl.DOLFIN_EPS and x[0] < 1 - dl.DOLFIN_EPS
y_in = x[1] > dl.DOLFIN_EPS and x[1] < 1 - dl.DOLFIN_EPS
return on_boundary and x_in and y_in
Gamma_M = InsideBoundary()
Gamma_M.mark(boundaries, 1)
ds_marked = dl.Measure("ds", subdomain_data=boundaries)
self.Q = dl.assemble(self.dt * dl.inner(u, v) * ds_marked(1))
self.Prior = Prior
if dlversion() <= (1, 6, 0):
self.solver = dl.PETScLUSolver()
else:
self.solver = dl.PETScLUSolver(self.mesh.mpi_comm())
self.solver.set_operator(self.L)
if dlversion() <= (1, 6, 0):
self.solvert = dl.PETScLUSolver()
else:
self.solvert = dl.PETScLUSolver(self.mesh.mpi_comm())
self.solvert.set_operator(self.Lt)
self.ud = self.generate_vector(STATE)
self.noise_variance = 0
# Part of model public API
self.gauss_newton_approx = False
def set_potential(self, potential):
if self.floating and potential != 0:
potential=0
print("Potential cannot be set when using floating")
self._potential = potential
self.set_value(df.Constant(self._potential))
class Bottom(df.SubDomain):
def inside(self, x, on_boundary):
return df.near(x[1], 0.0) and on_boundary
boundaries = df.MeshFunction("size_t", mesh, mesh.topology().dim()-1)
boundaries.set_all(0)
right = Right()
top = Top()
bottom = Bottom()
right.mark(boundaries, 1)
top.mark(boundaries, 2)
bottom.mark(boundaries, 3)
# Define Dirichlet boundary conditions
zero = df.Constant(0.0)
vzero = df.Constant((0.0, 0.0, 0.0))
dt = params.params["dt"]
squeeze = df.Expression("-1e-2*t", t=0.0, degree=1)
pprob.add_solid_dirichlet_condition(vzero, boundaries, 1)
# pprob.add_solid_dirichlet_condition(squeeze, boundaries, 2, n=1, time=True)
pprob.add_solid_dirichlet_condition(vzero, boundaries, 3)
def set_xdmf_parameters(f):
f.parameters['flush_output'] = True
f.parameters['functions_share_mesh'] = True
f.parameters['rewrite_function_mesh'] = False
# Files for output
f1 = df.XDMFFile(comm, '../data/demo_unitcube/uf.xdmf')
def _discretize_fenics():
# assemble system matrices - FEniCS code
########################################
import dolfin as df
mesh = df.UnitSquareMesh(GRID_INTERVALS, GRID_INTERVALS, 'crossed')
V = df.FunctionSpace(mesh, 'Lagrange', FENICS_ORDER)
u = df.TrialFunction(V)
v = df.TestFunction(V)
diffusion = df.Expression('(lower0 <= x[0]) * (open0 ? (x[0] < upper0) : (x[0] <= upper0)) *'
'(lower1 <= x[1]) * (open1 ? (x[1] < upper1) : (x[1] <= upper1))',
lower0=0., upper0=0., open0=0,
lower1=0., upper1=0., open1=0,
element=df.FunctionSpace(mesh, 'DG', 0).ufl_element())
def assemble_matrix(x, y, nx, ny):
diffusion.user_parameters['lower0'] = x/nx
diffusion.user_parameters['lower1'] = y/ny
diffusion.user_parameters['upper0'] = (x + 1)/nx
diffusion.user_parameters['upper1'] = (y + 1)/ny
diffusion.user_parameters['open0'] = (x + 1 == nx)
diffusion.user_parameters['open1'] = (y + 1 == ny)
return df.assemble(df.inner(diffusion * df.nabla_grad(u), df.nabla_grad(v)) * df.dx)
mats = [assemble_matrix(x, y, XBLOCKS, YBLOCKS)
for x in range(XBLOCKS) for y in range(YBLOCKS)]
mat0 = mats[0].copy()
mat0.zero()
h1_mat = df.assemble(df.inner(df.nabla_grad(u), df.nabla_grad(v)) * df.dx)
f = df.Constant(1.) * v * df.dx
F = df.assemble(f)
bc = df.DirichletBC(V, 0., df.DomainBoundary())
for m in mats:
bc.zero(m)
bc.apply(mat0)
bc.apply(h1_mat)
bc.apply(F)
# wrap everything as a pyMOR model
##################################
# FEniCS wrappers
from pymor.bindings.fenics import FenicsVectorSpace, FenicsMatrixOperator, FenicsVisualizer
# define parameter functionals (same as in pymor.analyticalproblems.thermalblock)
parameter_functionals = [ProjectionParameterFunctional('diffusion',
size=YBLOCKS*XBLOCKS,
index=YBLOCKS - y - 1 + x*YBLOCKS)
for x in range(XBLOCKS) for y in range(YBLOCKS)]
# wrap operators
ops = [FenicsMatrixOperator(mat0, V, V)] + [FenicsMatrixOperator(m, V, V) for m in mats]
op = LincombOperator(ops, [1.] + parameter_functionals)
rhs = VectorOperator(FenicsVectorSpace(V).make_array([F]))
h1_product = FenicsMatrixOperator(h1_mat, V, V, name='h1_0_semi')
# build model
visualizer = FenicsVisualizer(FenicsVectorSpace(V))
fom = StationaryModel(op, rhs, products={'h1_0_semi': h1_product},
visualizer=visualizer)
return fom
def test_forms(scheme, N, dim, th, plotter):
model, DS, bcs = prepare_model_and_bcs(scheme, N, dim, th)
prm = model.parameters["sigma"]
prm.add("12", 4.0)
if N == 3:
prm.add("13", 2.0)
prm.add("23", 1.0)
for key in ["rho", "nu"]:
prm = model.parameters[key]
prm.add("1", 42.)
prm.add("2", 4.0)
if N == 3:
prm.add("3", 1.0)
#dolfin.info(model.parameters, True)
# Check that bcs were recorded correctly
bcs_ret = model.bcs()
assert id(bcs_ret) == id(bcs)
# Create forms
with pytest.raises(RuntimeError):
model.update_TD_factors(1)
forms = model.create_forms()
# Check interpolated quantities
itype = {
"Monolithic": "lin",
#"SemiDecoupled": "sin",
"SemiDecoupled": "odd",
"FullyDecoupled": "log"
}
if N == 2:
pv = DS.primitive_vars_ctl(indexed=True)
model.parameters["rho"]["itype"] = itype[scheme]
rho_mat = model.collect_material_params("rho")
rho = model.density(rho_mat, pv["phi"])
phi_ic = prepare_initial_condition(DS)
r = dolfin.project(rho, phi_ic.function_space())
prm = model.parameters
tol = 1e-3
p1, p2 = dolfin.Point(0.5, 0.01), dolfin.Point(0.5, 0.99)
BBT = DS.mesh().bounding_box_tree()
if BBT.collides(p1):
assert dolfin.near(r(0.5, 0.01), prm["rho"]["1"], tol)
if BBT.collides(p2):
assert dolfin.near(r(0.5, 0.99), prm["rho"]["2"], tol)
del prm, tol
# Visual check (uncomment also 'pyplot.show()' in the finalizer below)
#pyplot.figure(); dolfin.plot(r, mode="warp", title=itype[scheme])
# Update order of time discretization
if scheme in ["Monolithic", "SemiDecoupled"]:
model.update_TD_factors(2)
flag = False
for c in forms["nln"].coefficients():
if c.name() == "TD_theta":
flag = True
assert float(c) == 0.5
assert flag
else:
model.update_TD_factors(2)
flag = False
for c in forms["lin"]["lhs"][0].coefficients():
if c.name() == "TD_gamma0":
flag = True
assert float(c) == 1.5
assert flag
# Check assembly of returned forms
if scheme == "Monolithic":
assert forms["lin"] is None
F = forms["nln"]
r = dolfin.assemble(F)
elif scheme == "FullyDecoupled":
assert forms["nln"] is None
bforms = forms["lin"]
for a, L in zip(bforms["lhs"], bforms["rhs"]):
A, b = dolfin.assemble_system(a, L)
# Test variable time step
dt = 42
model.update_time_step_value(dt)
assert dt == model.time_step_value()
if scheme in ["Monolithic", "FullyDecoupled"]:
F = forms["nln"] \
if scheme == "Monolithic" else forms["lin"]["lhs"][0]
flag = False
for c in F.coefficients():
if c.name() == "dt":
flag = True
a = dolfin.Constant(c) # for correct evaluation in parallel
assert dt == a(0) # Constant can be evaluated anywhere,
# independently of the mesh
assert flag
def top(x):
return np.abs(x[0] - width / 2) < dolfin.DOLFIN_EPS
def bottom(x):
return np.abs(x[0] + width / 2) < dolfin.DOLFIN_EPS
print('ugly stuff took {:.2f} s'.format(time.time() - t_ini))
nodal_space = dolfin.FunctionSpace(mesh, 'Lagrange', 1)
(L_i, ) = dolfin.TestFunctions(nodal_space)
(L_j, ) = dolfin.TrialFunctions(nodal_space)
bc_ground = dolfin.DirichletBC(nodal_space, dolfin.Constant(0.0), markers,
other)
bc_source = dolfin.DirichletBC(nodal_space, dolfin.Constant(1.0), markers,
metal)
rho = dolfin.Constant(0.0)
A_ij = dolfin.inner(dolfin.grad(L_i), dolfin.grad(L_j)) * dolfin.dx
b_ij = rho * L_j * dolfin.dx
A = dolfin.assemble(A_ij)
b = dolfin.assemble(b_ij)
bc_ground.apply(A, b)
bc_source.apply(A, b)
phi = dolfin.Function(nodal_space)
c = phi.vector()
print('before solve took {:.2f} s'.format(time.time() - t_ini))
t_ini = time.time()
def run_and_calculate_error(N, dt, tmax, polydeg_u, polydeg_p, modifier=None):
"""
Run Ocellaris and return L2 & H1 errors in the last time step
"""
say(N, dt, tmax, polydeg_u, polydeg_p)
# Setup and run simulation
sim = Simulation()
sim.input.read_yaml('kovasznay.inp')
sim.input.set_value('mesh/Nx', N)
sim.input.set_value('mesh/Ny', N)
sim.input.set_value('time/dt', dt)
sim.input.set_value('time/tmax', tmax)
sim.input.set_value('solver/polynomial_degree_velocity', polydeg_u)
sim.input.set_value('solver/polynomial_degree_pressure', polydeg_p)
sim.input.set_value('output/stdout_enabled', False)
if modifier:
modifier(sim) # Running regression tests, modify some input params
say('Running ...')
try:
t1 = time.time()
setup_simulation(sim)
run_simulation(sim)
duration = time.time() - t1
except KeyboardInterrupt:
raise
except BaseException as e:
raise
import traceback
traceback.print_exc()
return [1e10] * 6 + [1, dt, time.time() - t1]
say('DONE')
tmax_warning = ' <------ NON CONVERGENCE!!' if sim.time > tmax - dt / 2 else ''
# Interpolate the analytical solution to the same function space
Vu = sim.data['Vu']
Vp = sim.data['Vp']
lambda_ = sim.input.get_value('user_code/constants/LAMBDA',
required_type='float')
u0e = dolfin.Expression(
sim.input.get_value('boundary_conditions/0/u/cpp_code/0'),
LAMBDA=lambda_,
degree=polydeg_u)
u1e = dolfin.Expression(
sim.input.get_value('boundary_conditions/0/u/cpp_code/1'),
LAMBDA=lambda_,
degree=polydeg_u)
pe = dolfin.Expression(
'-0.5*exp(LAMBDA*2*x[0]) + 1/(4*LAMBDA)*(exp(2*LAMBDA) - 1.0)',
LAMBDA=lambda_,
degree=polydeg_p,
)
u0a = dolfin.project(u0e, Vu)
u1a = dolfin.project(u1e, Vu)
pa = dolfin.project(pe, Vp)
# Correct pa (we want to be spot on, not close)
int_pa = dolfin.assemble(pa * dolfin.dx)
vol = dolfin.assemble(dolfin.Constant(1.0) * dolfin.dx(domain=Vp.mesh()))
pa.vector()[:] -= int_pa / vol
# Calculate L2 errors
err_u0 = calc_err(sim.data['u0'], u0a)
err_u1 = calc_err(sim.data['u1'], u1a)
err_p = calc_err(sim.data['p'], pa)
# Calculate H1 errors
err_u0_H1 = calc_err(sim.data['u0'], u0a, 'H1')
err_u1_H1 = calc_err(sim.data['u1'], u1a, 'H1')
err_p_H1 = calc_err(sim.data['p'], pa, 'H1')
say('Number of time steps:', sim.timestep, tmax_warning)
loglines = sim.log.get_full_log().split('\n')
say('Num inner iterations:',
sum(1 if 'Inner iteration' in line else 0 for line in loglines))
say('max(ui_new-ui_prev)',
sim.reporting.get_report('max(ui_new-ui_prev)')[1][-1])
int_p = dolfin.assemble(sim.data['p'] * dolfin.dx)
say('p*dx', int_p)
say('pa*dx', dolfin.assemble(pa * dolfin.dx(domain=Vp.mesh())))
div_u_Vp = abs(
dolfin.project(dolfin.div(sim.data['u']),
Vp).vector().get_local()).max()
say('div(u)|Vp', div_u_Vp)
div_u_Vu = abs(
dolfin.project(dolfin.div(sim.data['u']),
Vu).vector().get_local()).max()
say('div(u)|Vu', div_u_Vu)
Vdg0 = dolfin.FunctionSpace(sim.data['mesh'], "DG", 0)
div_u_DG0 = abs(
dolfin.project(dolfin.div(sim.data['u']),
Vdg0).vector().get_local()).max()
say('div(u)|DG0', div_u_DG0)
Vdg1 = dolfin.FunctionSpace(sim.data['mesh'], "DG", 1)
div_u_DG1 = abs(
dolfin.project(dolfin.div(sim.data['u']),
Vdg1).vector().get_local()).max()
say('div(u)|DG1', div_u_DG1)
if False:
# Plot the results
for fa, name in ((u0a, 'u0'), (u1a, 'u1'), (pa, 'p')):
p1 = dolfin.plot(sim.data[name] - fa,
title='%s_diff' % name,
key='%s_diff' % name)
p2 = dolfin.plot(fa, title=name + ' analytical', key=name)
p1.write_png('%g_%g_%s_diff' % (N, dt, name))
p2.write_png('%g_%g_%s' % (N, dt, name))
dolfin.interactive()
from numpy import argmax
for d in range(2):
up = sim.data['up%d' % d]
upp = sim.data['upp%d' % d]
V = up.function_space()
coords = V.tabulate_dof_coordinates().reshape((-1, 2))
up.vector()[:] -= upp.vector()
diff = abs(up.vector().get_local())
i = argmax(diff)
say('Max difference in %d direction is %.4e at %r' %
(d, diff[i], coords[i]))
if 'uppp%d' % d in sim.data:
uppp = sim.data['uppp%d' % d]
upp.vector()[:] -= uppp.vector()
diffp = abs(upp.vector().get_local())
ip = argmax(diffp)
say('Prev max diff. in %d direction is %.4e at %r' %
(d, diffp[ip], coords[ip]))
if False and N == 24:
# dolfin.plot(sim.data['u0'], title='u0')
# dolfin.plot(sim.data['u1'], title='u1')
# dolfin.plot(sim.data['p'], title='p')
# dolfin.plot(u0a, title='u0a')
# dolfin.plot(u1a, title='u1a')
# dolfin.plot(pa, title='pa')
plot_err(sim.data['u0'], u0a, title='u0a - u0')
plot_err(sim.data['u1'], u1a, title='u1a - u1')
plot_err(sim.data['p'], pa, 'pa - p')
# plot_err(sim.data['u0'], u0a, title='u0a - u0')
dolfin.plot(sim.data['up0'], title='up0 - upp0')
dolfin.plot(sim.data['upp0'], title='upp0 - uppp0')
# plot_err(sim.data['u1'], u1a, title='u1a - u1')
dolfin.plot(sim.data['up1'], title='up1 - upp1')
# dolfin.plot(sim.data['upp1'], title='upp1 - uppp1')
hmin = sim.data['mesh'].hmin()
return err_u0, err_u1, err_p, err_u0_H1, err_u1_H1, err_p_H1, hmin, dt, duration
def fix_basal_plane(W):
V = W if W.sub(0).num_sub_spaces() == 0 else W.sub(0)
bc = dolfin.DirichletBC(V.sub(0), dolfin.Constant(0.0), geometry.ffun,
markers_dict["BASE"][0])
return bc
# Initialize sub-domain instances.
#
left = Left()
right = Right()
top = Top()
bottom = Bottom()
#
# Next, we initialize the mesh function for boundary domains in sub-domains.
#
left.mark(boundaries, 1)
right.mark(boundaries, 2)
top.mark(boundaries, 3)
bottom.mark(boundaries, 4)
#
# Define Dirichlet boundary conditions for solid, allows for focusing on fluid problem comparable to darcy flow
zero = df.Constant((0, 0))
# Dirichlet BC were set according a simulation without boundary conditions.
# For time independency of boundary conditions set kwargs time=False
# no definition of paramtere 'Source' needed since one compartment (N=1)
zero = df.Constant(0.0)
start = df.Constant(2.6)
end = df.Constant(2.6)
fprob.add_fluid_dirichlet_condition(start, boundaries, 1, time=False)
fprob.add_fluid_dirichlet_condition(end, boundaries, 2, time=False)
#
def set_xdmf_parameters(f):
f.parameters['flush_output'] = True
f.parameters['functions_share_mesh'] = True
# Split vector functions into scalar components
ubar, udef, H_c, H, L = df.split(U)
phibar, phidef, xsi_c, xsi, chi = df.split(Phi)
# Values of model variables at previous time step
un = df.Function(Q)
u2n = df.Function(Q)
H0_c = df.Function(Q)
H0 = df.Function(Q_dg)
L0 = df.Function(Q_R)
Bhat = df.Function(Q) # The bed topography
beta2 = df.Function(Q) # The basal traction
adot = df.Function(Q) # The surface mass balance
l = softplus(df.Constant(0), Bhat) # Water surface, or the greater of
# bedrock topography or zero
B = softplus(Bhat, -rho / rho_w * H_c,
alpha=0.2) # Ice base is the greater of the
# bedrock topography or the base of
# the shelf
D = softplus(-Bhat, df.Constant(0)) # Water depth
S = B + H_c # Ice surface = Ice base + thickness
####################################################
############ FUNCTION INITIALIZATION #############
####################################################
# Initialize thickness and length to reasonable values. It is particularly
T = dolfin.PETScMatrix()
print('before assemble: {:}s'.format(time.time() - t_ini))
dolfin.assemble(s_ij, tensor=S)
print('first assembly')
dolfin.assemble(t_ij, tensor=T)
#%%
print("starting the boundary conditions")
t_ini = time.time()
markers = dolfin.MeshFunction('size_t', mesh, 2)
vals = np.zeros(markers.array().shape, dtype=int)
vals[boundary_facets] = int(BBOX)
markers.set_values(vals)
electric_wall = dolfin.DirichletBC(vector_space,
dolfin.Constant((0.0, 0.0, 0.0)), markers,
BBOX)
electric_wall.apply(S)
electric_wall.apply(T)
print('boundary conditions took {:} s'.format(time.time() - t_ini))
#%%
print("starting the solving")
solver = dolfin.SLEPcEigenSolver(S, T)
solver.parameters["solver"] = "krylov-schur"
solver.parameters["problem_type"] = "gen_hermitian"
solver.parameters["spectrum"] = "target magnitude"
solver.parameters["spectral_transform"] = "shift-and-invert"
solver.parameters["spectral_shift"] = 30.
n_to_solve = 1
nx = 32
ny = 32
mesh = dl.UnitSquareMesh(dl.mpi_comm_self(), nx, ny)
Vh2 = dl.FunctionSpace(mesh, 'Lagrange', 1)
Vh1 = dl.FunctionSpace(mesh, 'Lagrange', 1)
Vh = [Vh2, Vh1, Vh2]
ndofs = [Vh[STATE].dim(), Vh[PARAMETER].dim(), Vh[ADJOINT].dim()]
if rank == 0:
print(sep, "Set up the mesh and finite element spaces", sep)
print(
"Number of dofs: STATE={0}, PARAMETER={1}, ADJOINT={2}".format(*ndofs))
u_bdr = dl.Expression("x[1]", degree=1)
u_bdr0 = dl.Constant(0.0)
bc = dl.DirichletBC(Vh[STATE], u_bdr, u_boundary)
bc0 = dl.DirichletBC(Vh[STATE], u_bdr0, u_boundary)
pde = PDEVariationalProblem(Vh, pde_varf, bc, bc0, is_fwd_linear=True)
if dlversion() <= (1, 6, 0):
pde.solver = dl.PETScKrylovSolver("cg", amg_method())
pde.solver_fwd_inc = dl.PETScKrylovSolver("cg", amg_method())
pde.solver_adj_inc = dl.PETScKrylovSolver("cg", amg_method())
else:
pde.solver = dl.PETScKrylovSolver(mesh.mpi_comm(), "cg", amg_method())
pde.solver_fwd_inc = dl.PETScKrylovSolver(mesh.mpi_comm(), "cg",
amg_method())
pde.solver_adj_inc = dl.PETScKrylovSolver(mesh.mpi_comm(), "cg",
amg_method())
pde.solver.parameters["relative_tolerance"] = 1e-15
n = extH1.dim()
# test and trial functions
A = dlfn.TrialFunction(intHCurl)
B = dlfn.TestFunction(intHCurl)
u = dlfn.TrialFunction(intH1)
phi = dlfn.TrialFunction(extH1)
psi = dlfn.TestFunction(extH1)
# measures and normal vectors
dA, dV, normals = dict(), dict(), dict()
for i in subIds:
dA[i] = dlfn.Measure("ds",
subMeshes[i],
subdomain_data=facetSubMeshFuns[i])
dV[i] = dlfn.Measure("dx", subMeshes[i])
normals[i] = dlfn.FacetNormal(subMeshes[i])
intV = dlfn.assemble(dlfn.Constant(1.0) * dV[intId])
extV = dlfn.assemble(dlfn.Constant(1.0) * dV[extId])
#------------------------------------------------------------------------------#
# solution functions
#------------------------------------------------------------------------------#
sol_A = dlfn.Function(intHCurl)
sol_A0 = dlfn.Function(intHCurl)
sol_phi = dlfn.Function(extH1)
#------------------------------------------------------------------------------#
# weak forms and assembly in interior
#------------------------------------------------------------------------------#
# linear forms in interior domain
from dolfin import curl, inner, dot, grad
id_int = dlfn.Constant(1. / dt) * dot(A, B) * dV[intId]
a_int = dlfn.Constant(0.5 * eta) * inner(curl(A), curl(B)) * dV[intId]
b_int = u * dot(normals[intId], curl(B)) * dA[intId](intrfcId)
def pde_varf(u, m, p):
return dl.exp(m) * dl.inner(dl.nabla_grad(u), dl.nabla_grad(
p)) * dl.dx - dl.Constant(0.0) * p * dl.dx
def __init__(self, V, sub_domain, method="topological"):
df.DirichletBC.__init__(self, V, df.Constant(0), sub_domain, method)
u_e = df.Expression(("A*exp(B*x[0]) + C"), A=A, B=B, C=C, degree=2)
for j, N in enumerate(Ns):
mesh = df.UnitSquareMesh(N, N)
V = df.FunctionSpace(mesh, 'P', 1)
v = df.TestFunction(V)
u_ = df.Function(V)
u = df.TrialFunction(V)
if SUPG_stabilization:
alpha = 2
h = 1 / N
magnitude = 1
Pe = magnitude * h / (2.0 * mu)
tau = h / (2.0 * magnitude) * (1.0 / np.tanh(Pe) - 1.0 / Pe)
beta = df.Constant(tau * alpha)
v = v + beta * h * v.dx(0)
F = df.Constant(mu) * inner(grad(u), grad(v)) * dx + inner(u.dx(0),
v) * dx
bc1 = df.DirichletBC(V, df.Constant(0), left)
bc2 = df.DirichletBC(V, df.Constant(1), right)
# Neumann on y = 0 and y = 1 enforced implicitly
df.solve(df.lhs(F) == df.rhs(F), u_, bcs=[bc1, bc2])
# interpolate(u_e, VV)
# u_numerical = u_.vector().vec().array
# X = V.tabulate_dof_coordinates()
X = mesh.coordinates()
u_numerical = u_.compute_vertex_values(mesh)
sources=cpp_sources,
include_dirs=["."])
#Generate a mesh and some matrices
nx = 32
ny = 32
mesh = dl.UnitSquareMesh(nx, ny)
Vh = dl.FunctionSpace(mesh, 'Lagrange', 1)
ndof = Vh.dim()
uh, vh = dl.TrialFunction(Vh), dl.TestFunction(Vh)
A = dl.assemble( dl.inner( dl.nabla_grad(uh), dl.nabla_grad(vh) ) *dl.dx )
M = dl.assemble( dl.inner( uh, vh ) *dl.dx )
ones = dl.interpolate(dl.Constant(1.), Vh).vector()
Mones = M*ones
s = Mones
s.set_local( np.ones(ndof) / Mones.array() )
M.zero()
M.set_diagonal(s)
myla = cpp_module.cpp_linalg()
B = myla.MatPtAP(M, A)
x = dl.Vector()
B.init_vector(x,1)
#This works fine
B.mult(s,x)