def solve_adjoint_supremizer(self, solution):
(cache_key, cache_file) = self._supremizer_cache_key_and_file()
if "RAM" in self.cache_config and cache_key in self._adjoint_supremizer_cache:
log(PROGRESS, "Loading adjoint supremizer from cache")
assign(self._adjoint_supremizer,
self._adjoint_supremizer_cache[cache_key])
elif "Disk" in self.cache_config and self.import_supremizer(
self.folder["cache"],
cache_file,
self._adjoint_supremizer,
component="r"):
log(PROGRESS, "Loading adjoint supremizer from file")
if "RAM" in self.cache_config:
self._adjoint_supremizer_cache[cache_key] = copy(
self._adjoint_supremizer)
else: # No precomputed adjoint supremizer available. Truth adjoint supremizer solve is performed.
log(PROGRESS, "Solving adjoint supremizer problem")
self._solve_adjoint_supremizer(solution)
if "RAM" in self.cache_config:
self._adjoint_supremizer_cache[cache_key] = copy(
self._adjoint_supremizer)
self.export_supremizer(
self.folder["cache"],
cache_file,
self._adjoint_supremizer,
component="r"
) # Note that we export to file regardless of config options, because they may change across different runs
return self._adjoint_supremizer
def solve(self, N=None, **kwargs):
"""
Perform an online solve. self.N will be used as matrix dimension if the default value is provided for N.
:param N : Dimension of the reduced problem
:type N : integer
:return: reduced solution
"""
N, kwargs = self._online_size_from_kwargs(N, **kwargs)
N += self.N_bc
self._latest_solve_kwargs = kwargs
self._solution = OnlineFunction(N)
if N == 0: # trivial case
return self._solution
try:
assign(self._solution, self._solution_cache[
self.mu, N,
kwargs]) # **kwargs is not supported by __getitem__
except KeyError:
assert not hasattr(self, "_is_solving")
self._is_solving = True
self._solve(N, **kwargs)
delattr(self, "_is_solving")
self._solution_cache[self.mu, N, kwargs] = copy(self._solution)
return self._solution
def patched_truth_compute_output(self_):
other_truth_problem.compute_output()
assign(self.truth_problem._output,
other_truth_problem._output)
assign(self.truth_problem._output_over_time,
other_truth_problem._output_over_time)
return self.truth_problem._output_over_time
def get_residual_norm_squared(self):
residual_norm_squared_over_time = TimeSeries(self._solution_over_time)
assert len(self._solution_over_time) == len(self._solution_dot_over_time)
for (k, t) in enumerate(self._solution_over_time.stored_times()):
if not isclose(t, self.t0, self.dt/2.):
# Set current time
self.set_time(t)
# Set current solution and solution_dot
assign(self._solution, self._solution_over_time[k])
assign(self._solution_dot, self._solution_dot_over_time[k])
# Compute the numerator of the error bound at the current time, first
# by computing residual of elliptic part
elliptic_residual_norm_squared = ParabolicCoerciveRBReducedProblem_Base.get_residual_norm_squared(self)
# ... and then adding terms related to time derivative
N = self._solution.N
theta_m = self.compute_theta("m")
theta_a = self.compute_theta("a")
theta_f = self.compute_theta("f")
residual_norm_squared_over_time.append(
elliptic_residual_norm_squared
+ 2.0*(transpose(self._solution_dot)*sum(product(theta_m, self.error_estimation_operator["m", "f"][:N], theta_f)))
+ 2.0*(transpose(self._solution_dot)*sum(product(theta_m, self.error_estimation_operator["m", "a"][:N, :N], theta_a))*self._solution)
+ transpose(self._solution_dot)*sum(product(theta_m, self.error_estimation_operator["m", "m"][:N, :N], theta_m))*self._solution_dot
)
else:
# Error estimator on initial condition does not use the residual
residual_norm_squared_over_time.append(0.)
return residual_norm_squared_over_time
def _solve(self):
if self.multiply_by_theta:
assert isinstance(self.term, str) # method untested otherwise
O = sum(product(self.truth_problem.compute_theta(self.term), self.operator)) # noqa
else:
assert isinstance(self.term, tuple) # method untested otherwise
assert len(self.operator) == 1
O = self.operator[0] # noqa
assert len(self.inner_product) == 1
inner_product = self.inner_product[0]
if self.truth_problem.dirichlet_bc is not None:
dirichlet_bcs_sum = sum(product((0., )*len(self.truth_problem.dirichlet_bc), self.truth_problem.dirichlet_bc))
eigensolver = EigenSolver(self.truth_problem.V, O, inner_product, dirichlet_bcs_sum)
else:
eigensolver = EigenSolver(self.truth_problem.V, O, inner_product)
eigensolver_parameters = dict()
eigensolver_parameters["problem_type"] = "gen_hermitian"
assert self.spectrum is "largest" or self.spectrum is "smallest"
eigensolver_parameters["spectrum"] = self.spectrum + " real"
if self.eigensolver_parameters is not None:
if "spectral_transform" in self.eigensolver_parameters and self.eigensolver_parameters["spectral_transform"] == "shift-and-invert":
eigensolver_parameters["spectrum"] = "target real"
eigensolver_parameters.update(self.eigensolver_parameters)
eigensolver.set_parameters(eigensolver_parameters)
eigensolver.solve(1)
r, c = eigensolver.get_eigenvalue(0) # real and complex part of the eigenvalue
r_vector, c_vector = eigensolver.get_eigenvector(0) # real and complex part of the eigenvectors
assert isclose(c, 0), "The required eigenvalue is not real"
self._eigenvalue = r
assign(self._eigenvector, r_vector)
def solve(self):
problem = self.problem
solver = TimeStepping(self, problem._solution, problem._solution_dot)
solver.set_parameters(problem._time_stepping_parameters)
(_, problem._solution_over_time, problem._solution_dot_over_time) = solver.solve()
assign(problem._solution, problem._solution_over_time[-1])
assign(problem._solution_dot, problem._solution_dot_over_time[-1])
def solve(self, **kwargs):
"""
Perform a truth solve in case no precomputed solution is imported.
"""
(cache_key,
cache_file) = self._cache_key_and_file_from_kwargs(**kwargs)
if "RAM" in self.cache_config and cache_key in self._solution_cache:
log(PROGRESS, "Loading truth solution from cache")
assign(self._solution, self._solution_cache[cache_key])
elif "Disk" in self.cache_config and self.import_solution(
self.folder["cache"], cache_file):
log(PROGRESS, "Loading truth solution from file")
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
else: # No precomputed solution available. Truth solve is performed.
log(PROGRESS, "Solving truth problem")
assert not hasattr(self, "_is_solving")
self._is_solving = True
assign(self._solution, Function(self.V))
self._solve(**kwargs)
delattr(self, "_is_solving")
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
self.export_solution(
self.folder["cache"], cache_file
) # Note that we export to file regardless of config options, because they may change across different runs
return self._solution
def _solve(self):
assert self.operator["stability_factor_left_hand_matrix"] is not None
if self.expansion_index is None:
A = sum(product(
self.truth_problem.compute_theta("stability_factor_left_hand_matrix"),
self.operator["stability_factor_left_hand_matrix"]))
else:
assert len(self.operator["stability_factor_left_hand_matrix"]) == 1
A = self.operator["stability_factor_left_hand_matrix"][0]
assert self.operator["stability_factor_right_hand_matrix"] is not None
assert len(self.operator["stability_factor_right_hand_matrix"]) == 1
B = self.operator["stability_factor_right_hand_matrix"][0]
if self.dirichlet_bc is not None:
dirichlet_bcs_sum = sum(product((0., ) * len(self.dirichlet_bc), self.dirichlet_bc))
eigensolver = EigenSolver(self.truth_problem.stability_factor_V, A, B, dirichlet_bcs_sum)
else:
eigensolver = EigenSolver(self.truth_problem.stability_factor_V, A, B)
eigensolver_parameters = dict()
assert self.spectrum == "largest" or self.spectrum == "smallest"
eigensolver_parameters["spectrum"] = self.spectrum + " real"
eigensolver_parameters.update(self.eigensolver_parameters)
eigensolver.set_parameters(eigensolver_parameters)
eigensolver.solve(1)
r, c = eigensolver.get_eigenvalue(0) # real and complex part of the eigenvalue
r_vector, c_vector = eigensolver.get_eigenvector(0) # real and complex part of the eigenvectors
assert isclose(c, 0.), "The required eigenvalue is not real"
self._eigenvalue = r
assign(self._eigenvector, r_vector)
def solve_supremizer(self, solution):
kwargs = self._latest_solve_kwargs
try:
assign(self._supremizer, self._supremizer_cache[self.mu, kwargs]) # **kwargs is not supported by __getitem__
except KeyError:
self._solve_supremizer(solution)
self._supremizer_cache[self.mu, kwargs] = copy(self._supremizer)
return self._supremizer
def export_error(self, folder=None, filename=None, component=None, suffix=None, **kwargs):
self.truth_problem.solve(**kwargs)
assert len(self.truth_problem._solution_over_time) == len(self._solution_over_time)
for (k, t) in enumerate(self.truth_problem._solution_over_time.stored_times()):
self.set_time(t)
assign(self._solution, self._solution_over_time[k])
assign(self.truth_problem._solution, self.truth_problem._solution_over_time[k])
assert suffix is None
self.truth_problem.export_solution(folder, filename, self.truth_problem._solution - self.basis_functions[:self._solution.N]*self._solution, component=component, suffix=k)
def solve(self):
cache_key = self._cache_key()
try:
self._eigenvalue = self._eigenvalue_cache[cache_key]
assign(self._eigenvector, self._eigenvector_cache[cache_key])
except KeyError:
self._solve()
self._eigenvalue_cache[cache_key] = self._eigenvalue
self._eigenvector_cache[cache_key] = copy(self._eigenvector)
return (self._eigenvalue, self._eigenvector)
def _compute_relative_error(self, absolute_error_over_time, **kwargs):
relative_error_over_time = TimeSeries(self._solution_over_time)
assert len(self._solution_over_time) == len(absolute_error_over_time)
for (k, t) in enumerate(self.truth_problem._solution_over_time.stored_times()):
self.set_time(t)
assign(self.truth_problem._solution, self.truth_problem._solution_over_time[k])
absolute_error = self._convert_error_at_time(k, absolute_error_over_time)
relative_error = ParametrizedReducedDifferentialProblem_DerivedClass._compute_relative_error(self, absolute_error, **kwargs)
relative_error_over_time.append(relative_error)
relative_error_over_time = self._convert_error_over_time(relative_error_over_time)
return relative_error_over_time
def _compute_error(self, **kwargs):
error_over_time = list()
for (k, (truth_solution, reduced_solution)) in enumerate(
zip(self.truth_problem._solution_over_time,
self._solution_over_time)):
self.set_time(k * self.dt)
assign(self._solution, reduced_solution)
assign(self.truth_problem._solution, truth_solution)
error = ParametrizedReducedDifferentialProblem_DerivedClass._compute_error(
self, **kwargs)
error_over_time.append(error)
error_over_time = self._convert_error_over_time(error_over_time)
return error_over_time
def solve(self, N=None, **kwargs):
N, kwargs = self.reduced_problem._online_size_from_kwargs(N, **kwargs)
N += self.reduced_problem.N_bc
self._eigenvector = OnlineFunction(N)
cache_key = self._cache_key(**kwargs)
try:
self._eigenvalue = self._eigenvalue_cache[cache_key]
assign(self._eigenvector, self._eigenvector_cache[cache_key])
except KeyError:
self._solve(N, **kwargs)
self._eigenvalue_cache[cache_key] = self._eigenvalue
self._eigenvector_cache[cache_key] = copy(self._eigenvector)
return (self._eigenvalue, self._eigenvector)
def solve(self, **kwargs):
"""
Perform a truth solve in case no precomputed solution is imported.
"""
self._latest_solve_kwargs = kwargs
try:
assign(self._solution, self._solution_cache[self.mu, kwargs]) # **kwargs is not supported by __getitem__
except KeyError:
assert not hasattr(self, "_is_solving")
self._is_solving = True
assign(self._solution, Function(self.V))
self._solve(**kwargs) # will also add to cache
delattr(self, "_is_solving")
return self._solution
def patched_truth_solve(self_, **kwargs_):
other_truth_problem.solve(**kwargs_)
assign(self.truth_problem._solution, other_truth_problem._solution)
assign(self.truth_problem._solution_dot, other_truth_problem._solution_dot)
assign(self.truth_problem._solution_over_time, other_truth_problem._solution_over_time)
assign(self.truth_problem._solution_dot_over_time, other_truth_problem._solution_dot_over_time)
return self.truth_problem._solution_over_time
def solve(self, N=None, **kwargs):
N, kwargs = self._online_size_from_kwargs(N, **kwargs)
N += self.N_bc
self._solution = OnlineFunction(N)
self._solution_dot = OnlineFunction(N)
cache_key = self._cache_key_from_N_and_kwargs(N, **kwargs)
assert ((cache_key in self._solution_cache) ==
(cache_key in self._solution_dot_cache) ==
(cache_key in self._solution_over_time_cache) ==
(cache_key in self._solution_dot_over_time_cache))
if "RAM" in self.cache_config and cache_key in self._solution_cache:
log(PROGRESS, "Loading reduced solution from cache")
assign(self._solution, self._solution_cache[cache_key])
assign(self._solution_dot, self._solution_dot_cache[cache_key])
assign(self._solution_over_time,
self._solution_over_time_cache[cache_key])
assign(self._solution_dot_over_time,
self._solution_dot_over_time_cache[cache_key])
else:
log(PROGRESS, "Solving reduced problem")
assert not hasattr(self, "_is_solving")
self._is_solving = True
self._solve(N, **kwargs)
delattr(self, "_is_solving")
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
self._solution_dot_cache[cache_key] = copy(
self._solution_dot)
self._solution_over_time_cache[cache_key] = copy(
self._solution_over_time)
self._solution_dot_over_time_cache[cache_key] = copy(
self._solution_dot_over_time)
return self._solution_over_time
def _compute_relative_error(self, absolute_error_over_time, **kwargs):
relative_error_over_time = list()
for (k, truth_solution) in enumerate(
self.truth_problem._solution_over_time):
self.set_time(k * self.dt)
assign(self.truth_problem._solution, truth_solution)
absolute_error = self._convert_error_at_time(
k, absolute_error_over_time)
relative_error = ParametrizedReducedDifferentialProblem_DerivedClass._compute_relative_error(
self, absolute_error, **kwargs)
relative_error_over_time.append(relative_error)
relative_error_over_time = self._convert_error_over_time(
relative_error_over_time)
return relative_error_over_time
def solve(self, N=None, **kwargs):
N, kwargs = self._online_size_from_kwargs(N, **kwargs)
N += self.N_bc
self._latest_solve_kwargs = kwargs
self._solution = OnlineFunction(N)
self._solution_over_time.clear()
self._solution_dot = OnlineFunction(N)
self._solution_dot_over_time.clear()
if N == 0: # trivial case
self._solution_over_time.extend([
self._solution
for _ in self._solution_over_time.expected_times()
])
self._solution_dot_over_time.extend([
self._solution_dot
for _ in self._solution_dot_over_time.expected_times()
])
return self._solution_over_time
try:
assign(self._solution_over_time,
self._solution_over_time_cache[self.mu, N, kwargs])
# **kwargs is not supported by __getitem__
assign(self._solution_dot_over_time,
self._solution_dot_over_time_cache[self.mu, N, kwargs])
except KeyError:
assert not hasattr(self, "_is_solving")
self._is_solving = True
self._solve(N, **kwargs)
delattr(self, "_is_solving")
assign(self._solution, self._solution_over_time[-1])
assign(self._solution_dot, self._solution_dot_over_time[-1])
return self._solution_over_time
def solve_supremizer(self, solution):
N_us = OnlineSizeDict(solution.N) # create a copy
del N_us["p"]
cache_key = self._supremizer_cache_key_from_N_and_kwargs(N_us)
self._supremizer = OnlineFunction(N_us)
if "RAM" in self.cache_config and cache_key in self._supremizer_cache:
log(PROGRESS, "Loading reduced supremizer from cache")
assign(self._supremizer, self._supremizer_cache[cache_key])
else:
log(PROGRESS, "Solving supremizer reduced problem")
self._solve_supremizer(solution)
if "RAM" in self.cache_config:
self._supremizer_cache[cache_key] = copy(self._supremizer)
return self._supremizer
def solve_supremizer(self, solution):
N_us = OnlineSizeDict(solution.N) # create a copy
del N_us["p"]
kwargs = self._latest_solve_kwargs
self._supremizer = OnlineFunction(N_us)
try:
assign(self._supremizer, self._supremizer_cache[
self.mu, N_us,
kwargs]) # **kwargs is not supported by __getitem__
except KeyError:
self._solve_supremizer(solution)
self._supremizer_cache[self.mu, N_us,
kwargs] = copy(self._supremizer)
return self._supremizer
def compute_output(self):
N = self._solution.N
kwargs = self._latest_solve_kwargs
try:
assign(self._output_over_time, self._output_over_time_cache[self.mu, N, kwargs]) # **kwargs is not supported by __getitem__
except KeyError:
try:
self._compute_output(N)
except ValueError: # raised by compute_theta if output computation is optional
self._output_over_time.clear()
self._output_over_time.extend([NotImplemented]*len(self._solution_over_time))
self._output = NotImplemented
self._output_over_time_cache[self.mu, N, kwargs] = self._output_over_time
else:
self._output = self._output_over_time[-1]
return self._output_over_time
def _solve(self, N, **kwargs):
assert self.operator["stability_factor_left_hand_matrix"] is not None
A = sum(product(self.reduced_problem.compute_theta("stability_factor_left_hand_matrix"), self.operator["stability_factor_left_hand_matrix"][:N, :N]))
assert self.operator["stability_factor_right_hand_matrix"] is not None
assert len(self.operator["stability_factor_right_hand_matrix"]) == 1
B = self.operator["stability_factor_right_hand_matrix"][0][:N, :N]
eigensolver = OnlineEigenSolver(self.reduced_problem.stability_factor_basis_functions, A, B)
eigensolver_parameters = dict()
assert self.spectrum == "largest" or self.spectrum == "smallest"
eigensolver_parameters["spectrum"] = self.spectrum + " real"
eigensolver_parameters.update(self.eigensolver_parameters)
eigensolver.set_parameters(eigensolver_parameters)
eigensolver.solve(1)
r, c = eigensolver.get_eigenvalue(0) # real and complex part of the eigenvalue
r_vector, c_vector = eigensolver.get_eigenvector(0) # real and complex part of the eigenvectors
assert isclose(c, 0.), "The required eigenvalue is not real"
self._eigenvalue = r
assign(self._eigenvector, r_vector)
def solve(self):
(cache_key, cache_file) = self._cache_key_and_file()
assert (
(cache_key in self._eigenvalue_cache)
==
(cache_key in self._eigenvector_cache)
)
if "RAM" in self.cache_config and cache_key in self._eigenvalue_cache:
log(PROGRESS, "Loading coercivity constant from cache")
self._eigenvalue = self._eigenvalue_cache[cache_key]
assign(self._eigenvector, self._eigenvector_cache[cache_key])
elif "Disk" in self.cache_config and self.import_solution(self.folder["cache"], cache_file):
log(PROGRESS, "Loading coercivity constant from file")
if "RAM" in self.cache_config:
self._eigenvalue_cache[cache_key] = self._eigenvalue
self._eigenvector_cache[cache_key] = copy(self._eigenvector)
else: # No precomputed solution available. Truth solve is performed.
log(PROGRESS, "Solving coercivity constant eigenproblem")
self._solve()
if "RAM" in self.cache_config:
self._eigenvalue_cache[cache_key] = self._eigenvalue
self._eigenvector_cache[cache_key] = copy(self._eigenvector)
self.export_solution(self.folder["cache"], cache_file) # Note that we export to file regardless of config options, because they may change across different runs
return (self._eigenvalue, self._eigenvector)
def solve(self, N=None, **kwargs):
"""
Perform an online solve. self.N will be used as matrix dimension if the default value is provided for N.
:param N : Dimension of the reduced problem
:type N : integer
:return: reduced solution
"""
N, kwargs = self._online_size_from_kwargs(N, **kwargs)
N += self.N_bc
cache_key = self._cache_key_from_N_and_kwargs(N, **kwargs)
self._solution = OnlineFunction(N)
if "RAM" in self.cache_config and cache_key in self._solution_cache:
log(PROGRESS, "Loading reduced solution from cache")
assign(self._solution, self._solution_cache[cache_key])
else:
log(PROGRESS, "Solving reduced problem")
assert not hasattr(self, "_is_solving")
self._is_solving = True
self._solve(N, **kwargs)
delattr(self, "_is_solving")
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
return self._solution
def solve(self, N=None, **kwargs):
N, kwargs = self._online_size_from_kwargs(N, **kwargs)
N += self.N_bc
self._latest_solve_kwargs = kwargs
self._solution = OnlineFunction(N)
self._solution_dot = OnlineFunction(N)
if N == 0: # trivial case
time_interval = linspace(self.t0, self.T,
(self.T - self.t0) / self.dt)
self._solution_over_time = [
self._solution for _ in time_interval
]
self._solution_dot_over_time = [
self._solution_dot for _ in time_interval
]
return self._solution_over_time
try:
assign(self._solution_over_time,
self._solution_over_time_cache[
self.mu, N,
kwargs]) # **kwargs is not supported by __getitem__
assign(self._solution_dot_over_time,
self._solution_dot_over_time_cache[self.mu, N, kwargs])
except KeyError:
assert not hasattr(self, "_is_solving")
self._is_solving = True
self._solve(N, **kwargs)
delattr(self, "_is_solving")
self._solution_over_time_cache[self.mu, N, kwargs] = copy(
self._solution_over_time)
self._solution_dot_over_time_cache[self.mu, N, kwargs] = copy(
self._solution_dot_over_time)
else:
assign(self._solution, self._solution_over_time[-1])
assign(self._solution_dot, self._solution_dot_over_time[-1])
return self._solution_over_time
def _assign(object_to: TimeSeries, object_from: TimeSeries):
if object_from is not object_to:
from rbnics.backends import assign
assign(object_to._list, object_from._list)
def solve(self, **kwargs):
(cache_key, cache_file) = self._cache_key_and_file_from_kwargs(**kwargs)
assert (
(cache_key in self._solution_cache)
==
(cache_key in self._solution_dot_cache)
==
(cache_key in self._solution_over_time_cache)
==
(cache_key in self._solution_dot_over_time_cache)
)
if "RAM" in self.cache_config and cache_key in self._solution_cache:
log(PROGRESS, "Loading truth solution from cache")
assign(self._solution, self._solution_cache[cache_key])
assign(self._solution_dot, self._solution_dot_cache[cache_key])
assign(self._solution_over_time, self._solution_over_time_cache[cache_key])
assign(self._solution_dot_over_time, self._solution_dot_over_time_cache[cache_key])
elif "Disk" in self.cache_config and (
self.import_solution(self.folder["cache"], cache_file + "_solution", self._solution_over_time)
and
self.import_solution(self.folder["cache"], cache_file + "_solution_dot", self._solution_dot_over_time)
):
log(PROGRESS, "Loading truth solution from file")
assign(self._solution, self._solution_over_time[-1])
assign(self._solution_dot, self._solution_dot_over_time[-1])
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
self._solution_dot_cache[cache_key] = copy(self._solution_dot)
self._solution_over_time_cache[cache_key] = copy(self._solution_over_time)
self._solution_dot_over_time_cache[cache_key] = copy(self._solution_dot_over_time)
else:
log(PROGRESS, "Solving truth problem")
assert not hasattr(self, "_is_solving")
self._is_solving = True
assign(self._solution, Function(self.V))
assign(self._solution_dot, Function(self.V))
self._solve(**kwargs)
delattr(self, "_is_solving")
if "RAM" in self.cache_config:
self._solution_cache[cache_key] = copy(self._solution)
self._solution_dot_cache[cache_key] = copy(self._solution_dot)
self._solution_over_time_cache[cache_key] = copy(self._solution_over_time)
self._solution_dot_over_time_cache[cache_key] = copy(self._solution_dot_over_time)
# Note that we export to file regardless of config options, because they may change across different runs
self.export_solution(self.folder["cache"], cache_file + "_solution", self._solution_over_time)
self.export_solution(self.folder["cache"], cache_file + "_solution_dot", self._solution_dot_over_time)
return self._solution_over_time
def solve(self, **kwargs):
self._latest_solve_kwargs = kwargs
try:
assign(self._solution_over_time,
self._solution_over_time_cache[
self.mu,
kwargs]) # **kwargs is not supported by __getitem__
assign(self._solution_dot_over_time,
self._solution_dot_over_time_cache[self.mu, kwargs])
except KeyError:
assert not hasattr(self, "_is_solving")
self._is_solving = True
assign(self._solution, Function(self.V))
assign(self._solution_dot, Function(self.V))
self._solve(**kwargs)
delattr(self, "_is_solving")
self._solution_over_time_cache[self.mu, kwargs] = copy(
self._solution_over_time)
self._solution_dot_over_time_cache[self.mu, kwargs] = copy(
self._solution_dot_over_time)
else:
assign(self._solution, self._solution_over_time[-1])
assign(self._solution_dot, self._solution_dot_over_time[-1])
return self._solution_over_time
def patched_truth_solve(self_, **kwargs_):
other_truth_problem.solve(**kwargs_)
assign(self.truth_problem._solution,
other_truth_problem._solution)
return self.truth_problem._solution
