def get_basis_functions(self, prob=None):
"""
Assemble a dictionary containing lists of Okada basis functions on each subfault.
Each basis function associated with a subfault has active controls set to zero on all other
subfaults and only one non-zero active control on the subfault itself, set to one. All passive
controls retain the value that they hold before assembly.
"""
from adapt_utils.unsteady.solver import AdaptiveProblem
prob = prob or AdaptiveProblem(self)
self._basis_functions = {}
# Stash the control parameters and zero out all active ones
tmp = self.control_parameters.copy()
for control in self.active_controls:
self.control_parameters[control] = np.zeros(self.num_subfaults)
# Loop over active controls on each subfault and compute the associated basis functions
msg = "INIT: Assembling Okada basis function array with active controls {:}..."
print_output(msg.format(self.active_controls))
msg = "INIT: Assembling '{:s}' basis function on subfault {:d}/{:d}..."
for control in self.active_controls:
self._basis_functions[control] = []
for i, subfault in enumerate(self.subfaults):
self.print_debug(msg.format(control, i, self.num_subfaults),
mode='full')
self.control_parameters[control][i] = 1
self.set_initial_condition(prob,
annotate_source=False,
subtract_from_bathymetry=False)
self._basis_functions[control].append(
prob.fwd_solutions[0].copy(deepcopy=True))
self.control_parameters[control][i] = 0
self.control_parameters = tmp
'ny': fac_y,
'plot_pvd': True,
'input_dir': inputdir,
'output_dir': outputdir,
'nonlinear_method': 'relaxation',
'r_adapt_rtol': tol_value,
# Spatial discretisation
'family': 'dg-dg',
'stabilisation': None,
'stabilisation_sediment': None,
'friction': 'manning'
}
op = BeachOptions(**kwargs)
assert op.num_meshes == 1
swp = AdaptiveProblem(op)
def gradient_interface_monitor(mesh,
alpha=alpha,
beta=beta,
gamma=gamma,
K=kappa):
"""
Monitor function focused around the steep_gradient (budd acta numerica)
NOTE: Defined on the *computational* mesh.
"""
P1 = FunctionSpace(mesh, "CG", 1)
'ny': fac_y,
'plot_pvd': True,
'input_dir': inputdir,
'output_dir': outputdir,
# Spatial discretisation
'family': 'dg-dg',
'stabilisation': None,
'stabilisation_sediment': None,
'friction': 'manning'
}
op = BeachOptions(**kwargs)
if os.getenv('REGRESSION_TEST') is not None:
op.dt_per_export = 18
op.end_time = op.dt * op.dt_per_export
swp = AdaptiveProblem(op)
t1 = time.time()
swp.solve_forward()
t2 = time.time()
if os.getenv('REGRESSION_TEST') is not None:
sys.exit(0)
print(t2 - t1)
new_mesh = RectangleMesh(880, 20, 220, 10)
bath = Function(FunctionSpace(new_mesh, "CG",
1)).project(swp.fwd_solutions_bathymetry[0])
fpath = "hydrodynamics_beach_bath_fixed_{:d}_{:d}".format(
op = TohokuBoxBasisOptions(**kwargs)
op.di = di
op.plot_pvd = plot_pvd
# Bookkeeping for storing total variation
num_cells.append(op.default_mesh.num_cells())
gauges = list(op.gauges.keys())
if errors['timeseries'] == {}:
errors['timeseries'] = {gauge: [] for gauge in gauges}
errors['timeseries_smooth'] = {gauge: [] for gauge in gauges}
N = int(np.ceil(np.sqrt(len(gauges))))
# Interpolate initial condition from [Saito et al. 2011] into a P1 space on the current mesh
op_saito = TohokuOptions(mesh=op.default_mesh, **kwargs)
swp_saito = AdaptiveProblem(op_saito,
nonlinear=nonlinear,
print_progress=op.debug)
ic_saito = op_saito.set_initial_condition(swp_saito)
# Project Saito's initial condition into the box basis
swp_box = AdaptiveProblem(op, nonlinear=nonlinear, print_progress=op.debug)
op.project(swp_box, ic_saito)
op.set_initial_condition(swp_box)
ic_box = project(swp_box.fwd_solutions[0].split()[1], swp_box.P1[0])
# Load or save timeseries, as appropriate
if plot_only:
for gauge in gauges:
for options, name in zip((op_saito, op),
('original', 'projected')):
for tt in errors:
'ny': ny,
'plot_pvd': True,
'input_dir': inputdir,
'output_dir': outputdir,
'nonlinear_method': 'relaxation',
'r_adapt_rtol': 1.0e-3,
# Spatial discretisation
'family': 'dg-dg',
'stabilisation': None,
'stabilisation_sediment': None,
'friction': 'manning'
}
op = BeachOptions(**kwargs)
assert op.num_meshes == 1
swp = AdaptiveProblem(op)
def velocity_monitor(mesh, alpha=alpha, beta=beta, gamma=gamma, K=kappa):
P1 = FunctionSpace(mesh, "CG", 1)
b = swp.fwd_solutions_bathymetry[0]
if b is not None:
abs_hor_vel_norm = Function(b.function_space()).project(conditional(b > 0.0, Constant(1.0), Constant(0.0)))
else:
abs_hor_vel_norm = Function(swp.bathymetry[0].function_space()).project(conditional(swp.bathymetry[0] > 0.0, Constant(1.0), Constant(0.0)))
comp_new = project(abs_hor_vel_norm, P1)
mon_init = project(1.0 + alpha * comp_new, P1)
return mon_init
}
if os.getenv('REGRESSION_TEST') is not None:
kwargs['end_time'] = kwargs['dt'] * kwargs['dt_per_export']
fpath = 'resolution_{:d}'.format(n_coarse)
if initial_monitor_type is not None:
fpath = os.path.join(fpath, initial_monitor_type)
fpath = os.path.join(fpath, monitor_type)
op = BoydOptions(approach='monge_ampere',
n=n_coarse,
fpath=fpath,
order=kwargs['order'])
op.update(kwargs)
# --- Initialise mesh
swp = AdaptiveProblem(op)
# Refine around equator and/or soliton
if initial_monitor is not None:
mesh_mover = MeshMover(swp.meshes[0],
initial_monitor,
method='monge_ampere',
op=op)
mesh_mover.adapt()
mesh = Mesh(mesh_mover.x)
op.__init__(mesh=mesh, **kwargs)
swp.__init__(op, meshes=[mesh])
# --- Monitor function definitions
if args.dt is not None:
op.dt = float(args.dt)
if args.end_time is not None:
op.end_time = float(args.end_time)
op.di = create_directory(os.path.join(op.di, op.hessian_time_combination))
# --- Solve the tracer transport problem
assert op.approach != 'fixed_mesh'
for n in range(int(args.min_level or 0), int(args.max_level or 5)):
op.target = 1000*2**n
op.dt = 0.01*0.5**n
op.dt_per_export = 2**n
# Run simulation
tp = AdaptiveProblem(op)
cpu_timestamp = perf_counter()
tp.run()
times = [perf_counter() - cpu_timestamp]
dofs = [Q.dof_count for Q in tp.Q]
num_cells = [mesh.num_cells() for mesh in tp.meshes]
# Assess error
final_sol = tp.fwd_solutions_tracer[-1].copy(deepcopy=True)
final_l1_norm = norm(final_sol, norm_type='L1')
final_l2_norm = norm(final_sol, norm_type='L2')
tp.set_initial_condition(i=-1)
init_sol = tp.fwd_solutions_tracer[-1].copy(deepcopy=True)
init_l1_norm = norm(init_sol, norm_type='L1')
init_l2_norm = norm(init_sol, norm_type='L2')
abs_l2_error = errornorm(init_sol, final_sol, norm_type='L2')
# Misc
'debug': bool(args.debug or False),
}
if os.getenv('REGRESSION_TEST') is not None:
kwargs['end_time'] = 1.5
op = BubbleOptions(approach='lagrangian', n=int(args.n or 1))
op.update(kwargs)
if args.dt is not None:
op.dt = float(args.dt)
if args.end_time is not None:
op.end_time = float(args.end_time)
# --- Initialise the mesh
tp = AdaptiveProblem(op)
# NOTE: We use Monge-Ampere with a monitor function indicating the initial condition
alpha = 10.0 # Parameter controlling prominance of refined region
eps = 1.0e-03 # Parameter controlling width of refined region
def monitor(mesh):
x, y = SpatialCoordinate(mesh)
x0, y0, r = op.source_loc[0]
return conditional(le(abs((x - x0)**2 + (y - y0)**2 - r**2), eps), alpha,
1.0)
mesh_mover = MeshMover(tp.meshes[0], monitor, method='monge_ampere', op=op)
'debug': bool(args.debug or False),
}
if os.getenv('REGRESSION_TEST') is not None:
kwargs['end_time'] = 30.0
fpath = 'resolution_{:d}'.format(n_coarse)
if monitor is not None:
fpath = os.path.join(fpath, monitor_type)
op = BoydOptions(approach='ale' if ale else 'fixed_mesh',
fpath=fpath,
n=n_coarse,
order=kwargs['order'])
op.update(kwargs)
# --- Initialise mesh
swp = AdaptiveProblem(op)
# Refine around equator and/or soliton
if monitor is not None:
mesh_mover = MeshMover(swp.meshes[0],
monitor,
method='monge_ampere',
op=op)
mesh_mover.adapt()
mesh = Mesh(mesh_mover.x)
op.__init__(mesh=mesh, **kwargs)
swp.__init__(op, meshes=[mesh])
# Apply constant mesh velocity
if ale:
raise NotImplementedError # FIXME
'approach': 'fixed_mesh',
# Discretisation
'tracer_family': args.family or 'dg',
'stabilisation_tracer': args.stabilisation,
'use_limiter_for_tracers': bool(args.limiters or True),
'use_tracer_conservative_form': bool(args.conservative or False),
# Misc
'debug': bool(args.debug or False),
}
# --- Create solver and copy initial solution
ep = AdaptiveProblem(CosinePrescribedVelocityOptions(**kwargs))
ep.set_initial_condition()
init_sol = ep.fwd_solutions_tracer[0].copy(deepcopy=True)
init_norm = norm(init_sol)
# --- Eulerian interpretation
ep.solve_forward()
final_sol_eulerian = ep.fwd_solutions_tracer[-1]
relative_error_eulerian = abs(errornorm(init_sol, final_sol_eulerian)/init_norm)
print_output("Relative error in Eulerian case: {:.2f}%".format(100*relative_error_eulerian))
# --- Lagrangian interpretation
constructor = TohokuRadialBasisOptions
elif basis == 'okada':
from adapt_utils.case_studies.tohoku.options.okada_options import TohokuOkadaBasisOptions
constructor = TohokuOkadaBasisOptions
else:
raise ValueError("Basis type '{:s}' not recognised.".format(basis))
fontsize = 22
fontsize_tick = 20
# Load control parameters
fname = os.path.join(data_dir, 'discrete', 'optimisation_progress_{:s}' + '_{:d}.npy'.format(level))
kwargs['control_parameters'] = np.load(fname.format('ctrl', level))[-1]
op = constructor(**kwargs)
# Plot source over whole domain
swp = AdaptiveProblem(op)
swp.set_initial_condition()
fig, axes = plt.subplots(figsize=(8, 7))
cbar = fig.colorbar(
tricontourf(swp.fwd_solutions[0].split()[1], levels=50, cmap='coolwarm', axes=axes),
ax=axes)
cbar.set_label(r'Elevation [$\mathrm m$]', size=fontsize)
axes.axis(False)
cbar.ax.tick_params(labelsize=fontsize_tick)
plt.tight_layout()
savefig('optimised_source_{:d}'.format(level), fpath=plot_dir, extensions=plot.extensions)
# Zoom
lonlat_corners = [(138, 32), (148, 42), (138, 42)]
utm_corners = [lonlat_to_utm(*corner, 54) for corner in lonlat_corners]
xlim = [utm_corners[0][0], utm_corners[1][0]]
'nx': nx,
'ny': ny,
'plot_pvd': True,
'output_dir': outputdir,
'nonlinear_method': 'relaxation',
'r_adapt_rtol': r_tol,
# Spatial discretisation
'family': 'dg-dg',
'stabilisation': None,
'use_automatic_sipg_parameter': True,
'friction': 'quadratic'
}
op = BeachOptions(**kwargs)
assert op.num_meshes == 1
swp = AdaptiveProblem(op)
swp.shallow_water_options[0]['mesh_velocity'] = None
def gradient_interface_monitor(mesh,
mod=mod,
beta_mod=beta_mod,
alpha=alpha,
beta=beta,
gamma=gamma,
x=None):
"""
Monitor function focused around the steep_gradient (budd acta numerica)
NOTE: Defined on the *computational* mesh.
'ny': 1 if res < 4 else 2,
'plot_pvd': True,
'input_dir': inputdir,
'output_dir': outputdir,
'nonlinear_method': 'relaxation',
'r_adapt_rtol': rtol,
# Spatial discretisation
'family': 'dg-dg',
'stabilisation': 'lax_friedrichs',
'stabilisation_sediment': 'lax_friedrichs',
}
op = TrenchSedimentOptions(**kwargs)
op.dt_per_mesh_movement = freq
assert op.num_meshes == 1
swp = AdaptiveProblem(op)
def frobenius_monitor(mesh):
"""
Frobenius norm taken component-wise.
"""
P1 = FunctionSpace(mesh, "CG", 1)
b = project(swp.fwd_solutions_bathymetry[0], P1)
H = recovery.recover_hessian(b, op=op)
frob = sqrt(H[0, 0]**2 + H[0, 1]**2 + H[1, 0]**2 + H[1, 1]**2)
return 1 + alpha_const*frob/interpolate(frob, P1).vector().gather().max()
def frobenius_monitor(mesh, x=None): # NOQA: Version above not smooth enough
"""
'stabilisation_tracer': args.stabilisation or 'supg',
'use_limiter_for_tracers': bool(args.limiters or False),
'debug': bool(args.debug or False),
}
l2_error = []
cons_error = []
times = []
num_cells = []
dofs = []
for level in range(4):
# Setup
op = BubbleOptions(approach='fixed_mesh', n=level)
op.update(kwargs)
op.dt_per_export = 2**level
tp = AdaptiveProblem(op)
dofs.append(tp.Q[0].dof_count)
num_cells.append(tp.mesh.num_cells())
tp.set_initial_condition()
init_l1_norm = norm(tp.fwd_solutions_tracer[0], norm_type='L1')
init_l2_norm = norm(tp.fwd_solutions_tracer[0], norm_type='L2')
init_sol = tp.fwd_solutions_tracer[0].copy(deepcopy=True)
# Solve forward problem
cpu_timestamp = perf_counter()
tp.solve_forward()
times.append(perf_counter() - cpu_timestamp)
# Compare initial and final tracer concentrations
final_l1_norm = norm(tp.fwd_solutions_tracer[0], norm_type='L1')
final_l2_norm = norm(tp.fwd_solutions_tracer[0], norm_type='L2')
num_meshes = int(args.num_meshes or 50)
op.end_time /= num_meshes
dt_per_mesh = int(op.end_time / op.dt)
end_time = op.end_time
dtc = Constant(op.dt)
if metric_advection:
op.di = os.path.join(op.di, 'metric_advection')
else:
op.di = os.path.join(op.di, 'on_the_fly')
if plot_pvd:
tracer_file = File(os.path.join(op.di, 'tracer.pvd'))
theta = Constant(0.5)
# Generate initial mesh
tic = perf_counter()
tp = AdaptiveProblem(op)
for i in range(op.num_adapt):
print("INITIAL MESH STEP {:d}".format(i))
tp.set_initial_condition()
c = tp.fwd_solution_tracer
M = steady_metric(c,
V=tp.P1_ten[0],
normalise=True,
enforce_constraints=True,
op=op)
tp = AdaptiveProblem(op, meshes=adapt(tp.mesh, M))
tp.set_initial_condition()
# Time loop
dofs = []
num_cells = []
