def __init__(self,
config_section=None,
key_generator=None,
import_=None,
export=None,
filename_generator=None):
self._config_section = config_section
if self._config_section is None:
self._storage = dict()
self._key_generator = None
self._import = None
self._export = None
self._filename_generator = None
else:
from rbnics.utils.config import config # cannot import at global scope
cache_options = config.get(self._config_section, "cache")
assert isinstance(cache_options, set)
if "RAM" in cache_options:
cache_size = config.get(self._config_section,
"RAM cache limit")
assert isinstance(cache_size, str)
if cache_size == "unlimited":
self._storage = dict()
else:
assert cache_size.isdigit()
cache_size = int(cache_size)
assert cache_size > 0
self._storage = lrucache(cache_size)
assert key_generator is not None
self._key_generator = key_generator
else:
self._storage = DisabledStorage()
self._key_generator = key_generator
if "disk" in cache_options:
cache_size = config.get(self._config_section,
"disk cache limit")
assert isinstance(cache_size, str)
assert cache_size == "unlimited"
assert import_ is not None
self._import = import_
assert export is not None
self._export = export
assert filename_generator is not None
self._filename_generator = filename_generator
else:
self._import = None
self._export = None
self._filename_generator = None
def __init__(self, truth_problem, parametrized_expression, folder_prefix,
basis_generation):
# Call the parent initialization
ParametrizedProblem.__init__(self, folder_prefix)
# Store the parametrized expression
self.parametrized_expression = parametrized_expression
self.truth_problem = truth_problem
assert basis_generation in ("Greedy", "POD")
self.basis_generation = basis_generation
# $$ ONLINE DATA STRUCTURES $$ #
# Online reduced space dimension
self.N = 0
# Define additional storage for EIM
self.interpolation_locations = parametrized_expression.create_interpolation_locations_container(
) # interpolation locations selected by the greedy (either a ReducedVertices or ReducedMesh)
self.interpolation_matrix = OnlineAffineExpansionStorage(
1) # interpolation matrix
# Solution
self._interpolation_coefficients = None # OnlineFunction
# $$ OFFLINE DATA STRUCTURES $$ #
self.snapshot = None # will be filled in by Function, Vector or Matrix as appropriate in the EIM preprocessing
self.snapshot_cache = dict() # of Function, Vector or Matrix
# Basis functions container
self.basis_functions = parametrized_expression.create_basis_container()
# I/O
self.folder["basis"] = os.path.join(self.folder_prefix, "basis")
self.folder["cache"] = os.path.join(self.folder_prefix, "cache")
self.folder["reduced_operators"] = os.path.join(
self.folder_prefix, "reduced_operators")
self.cache_config = config.get("EIM", "cache")
def __init__(self, V, **kwargs):
# Call to parent
ParametrizedProblem.__init__(self, self.name())
# Input arguments
self.V = V
# Form names and order (to be filled in by child classes)
self.terms = list()
self.terms_order = dict()
self.components = list()
# Number of terms in the affine expansion
self.Q = dict() # from string to integer
# Matrices/vectors resulting from the truth discretization
self.OperatorExpansionStorage = AffineExpansionStorage
self.operator = dict() # from string to OperatorExpansionStorage
self.inner_product = None # AffineExpansionStorage (for problems with one component) or dict of AffineExpansionStorage (for problem with several components), even though it will contain only one matrix
self._combined_inner_product = None
self.projection_inner_product = None # AffineExpansionStorage (for problems with one component) or dict of AffineExpansionStorage (for problem with several components), even though it will contain only one matrix
self._combined_projection_inner_product = None
self.dirichlet_bc = None # AffineExpansionStorage (for problems with one component) or dict of AffineExpansionStorage (for problem with several components)
self.dirichlet_bc_are_homogeneous = None # bool (for problems with one component) or dict of bools (for problem with several components)
self._combined_and_homogenized_dirichlet_bc = None
# Solution
self._solution = Function(self.V)
self._solution_cache = dict() # of Functions
self._output = 0
self._output_cache = dict() # of Numbers
self._output_cache__current_cache_key = None
# I/O
self.folder["cache"] = os.path.join(self.folder_prefix, "cache")
self.cache_config = config.get("problems", "cache")
def __init__(self, truth_problem, term, multiply_by_theta, spectrum, eigensolver_parameters, folder_prefix):
# Call the parent initialization
ParametrizedProblem.__init__(self, folder_prefix) # this class does not export anything
self.truth_problem = truth_problem
# Matrices/vectors resulting from the truth discretization
self.term = term
assert isinstance(self.term, (tuple, str))
if isinstance(self.term, tuple):
assert len(self.term) == 2
isinstance(self.term[0], str)
isinstance(self.term[1], int)
self.multiply_by_theta = multiply_by_theta
assert isinstance(self.multiply_by_theta, bool)
self.operator = None # AffineExpansionStorage
self.inner_product = None # AffineExpansionStorage, even though it will contain only one matrix
self.spectrum = spectrum
self.eigensolver_parameters = eigensolver_parameters
# Avoid useless computations
self._eigenvalue = 0.
self._eigenvalue_cache = dict()
self._eigenvector = Function(truth_problem.V)
self._eigenvector_cache = dict()
self.folder["cache"] = os.path.join(folder_prefix, "cache")
self.cache_config = config.get("problems", "cache")
def __init__(self, truth_problem, folder_prefix, **kwargs):
# Call the parent initialization
ParametrizedProblem.__init__(self, folder_prefix)
# Store the parametrized problem object and the bc list
self.truth_problem = truth_problem
# Define additional storage for SCM
self.B_min = BoundingBoxSideList(
) # minimum values of the bounding box mathcal{B}. Vector of size Q
self.B_max = BoundingBoxSideList(
) # maximum values of the bounding box mathcal{B}. Vector of size Q
self.training_set = None # SCM algorithm needs the training set also in the online stage
self.greedy_selected_parameters = GreedySelectedParametersList(
) # list storing the parameters selected during the training phase
self.greedy_selected_parameters_complement = dict(
) # dict, over N, of list storing the complement of parameters selected during the training phase
self.UB_vectors = UpperBoundsList(
) # list of Q-dimensional vectors storing the infimizing elements at the greedily selected parameters
self.N = 0
self.M_e = kwargs[
"M_e"] # integer denoting the number of constraints based on the exact eigenvalues, or None
self.M_p = kwargs[
"M_p"] # integer denoting the number of constraints based on the previous lower bounds, or None
# I/O
self.folder["cache"] = os.path.join(self.folder_prefix,
"reduced_cache")
self.cache_config = config.get("SCM", "cache")
self.folder["reduced_operators"] = os.path.join(
self.folder_prefix, "reduced_operators")
# Coercivity constant eigen problem
self.exact_coercivity_constant_calculator = ParametrizedCoercivityConstantEigenProblem(
truth_problem, "a", True, "smallest",
kwargs["coercivity_eigensolver_parameters"], self.folder_prefix)
# Store here input parameters provided by the user that are needed by the reduction method
self._input_storage_for_SCM_reduction = dict()
self._input_storage_for_SCM_reduction[
"bounding_box_minimum_eigensolver_parameters"] = kwargs[
"bounding_box_minimum_eigensolver_parameters"]
self._input_storage_for_SCM_reduction[
"bounding_box_maximum_eigensolver_parameters"] = kwargs[
"bounding_box_maximum_eigensolver_parameters"]
# Avoid useless linear programming solves
self._alpha_LB = 0.
self._alpha_LB_cache = dict()
self._alpha_UB = 0.
self._alpha_UB_cache = dict()
def __init__(self, truth_problem, **kwargs):
# Call to parent
ParametrizedProblem.__init__(self, truth_problem.name())
# $$ ONLINE DATA STRUCTURES $$ #
# Online reduced space dimension
self.N = None # integer (for problems with one component) or dict of integers (for problem with several components)
self.N_bc = None # integer (for problems with one component) or dict of integers (for problem with several components)
self.dirichlet_bc = None # bool (for problems with one component) or dict of bools (for problem with several components)
self.dirichlet_bc_are_homogeneous = None # bool (for problems with one component) or dict of bools (for problem with several components)
self._combined_and_homogenized_dirichlet_bc = None
# Form names and order
self.terms = truth_problem.terms
self.terms_order = truth_problem.terms_order
self.components = truth_problem.components
# Number of terms in the affine expansion
self.Q = dict() # from string to integer
# Reduced order operators
self.OperatorExpansionStorage = OnlineAffineExpansionStorage
self.operator = dict() # from string to OperatorExpansionStorage
self.inner_product = None # AffineExpansionStorage (for problems with one component) or dict of AffineExpansionStorage (for problem with several components), even though it will contain only one matrix
self._combined_inner_product = None
self.projection_inner_product = None # AffineExpansionStorage (for problems with one component) or dict of AffineExpansionStorage (for problem with several components), even though it will contain only one matrix
self._combined_projection_inner_product = None
# Solution
self._solution = None # OnlineFunction
self._solution_cache = dict() # of Functions
self._output = 0
self._output_cache = dict() # of Numbers
self._output_cache__current_cache_key = None
# $$ OFFLINE DATA STRUCTURES $$ #
# High fidelity problem
self.truth_problem = truth_problem
# Basis functions matrix
self.basis_functions = None # BasisFunctionsMatrix
# I/O
self.folder["basis"] = os.path.join(self.folder_prefix, "basis")
self.folder["reduced_operators"] = os.path.join(
self.folder_prefix, "reduced_operators")
self.cache_config = config.get("reduced problems", "cache")
getattr(sys.modules[__name__ + "." + backend + ".wrapping"], class_or_function_impl))
if hasattr(sys.modules[module_name], "__all__"):
if class_or_function_name not in sys.modules[module_name].__all__:
sys.modules[module_name].__all__.append(class_or_function_name)
# Make sure that backends do not contain dispatcher functions (but rather, actual functions and classes)
import inspect
for backend in required_backends:
for class_or_function_name in sys.modules[__name__ + "." + backend].__all__:
class_or_function = getattr(sys.modules[__name__ + "." + backend], class_or_function_name)
assert inspect.isclass(class_or_function) or inspect.isfunction(class_or_function)
# In contrast, make sure that this module only contains dispatcher objects
from rbnics.utils.decorators.dispatch import Dispatcher
for dispatcher_name in sys.modules[__name__].__all__:
dispatcher = getattr(sys.modules[__name__], dispatcher_name)
# if there was at least a concrete implementation by @BackendFor or @backend_for
if isinstance(getattr(backends_cache, dispatcher_name), Dispatcher):
assert isinstance(dispatcher, Dispatcher)
# Store some additional classes, defined in the abstract module, which are base classes but not backends,
# and thus have not been processed by @BackendFor and @backend_for decorators
for extra_class in ("LinearProblemWrapper", "NonlinearProblemWrapper", "TimeDependentProblemWrapper"):
assert not hasattr(sys.modules[__name__], extra_class)
setattr(sys.modules[__name__], extra_class, getattr(sys.modules[__name__ + ".abstract"], extra_class))
sys.modules[__name__].__all__.append(extra_class)
# Load required backends
load_backends(config.get("backends", "required backends"))
# Import (current) online backend
importlib.import_module(__name__ + "." + online_backend)
importlib.import_module(__name__ + "." + online_backend + ".wrapping")
# Copy all classes and functions from (current) online backend to this module with "Online" and "online_" prefixes, respectively
for class_or_function_name in sys.modules[__name__ + "." +
online_backend].__all__:
class_or_function = getattr(
sys.modules[__name__ + "." + online_backend],
class_or_function_name)
assert inspect.isclass(class_or_function) or inspect.isfunction(
class_or_function)
if class_or_function_name[0].isupper(
): # less restrictive than inspect.isclass(class_or_function), because for instance OnlineMatrix is implemented as function rather than a class
prefix = "Online"
else: # less restrictive than inspect.isfunction(class_or_function)
prefix = "online_"
setattr(sys.modules[__name__], prefix + class_or_function_name,
class_or_function)
sys.modules[__name__].__all__.append(prefix + class_or_function_name)
# Also copy the online wrapping module, without prefixes
sys.modules[__name__ + ".wrapping"] = sys.modules[__name__ + "." +
online_backend].wrapping
# Get the online backend name
from rbnics.utils.config import config
set_online_backend(config.get("backends", "online backend"))
