def __getitem__(self, key):
"""
return the subtensors of size "key" for every element in content. (e.g. submatrices [1:5,1:5] of the affine expansion of A)
"""
it = AffineExpansionStorageContent_Iterator(
self._content,
flags=["multi_index", "refs_ok"],
op_flags=["readonly"])
slices = slice_to_array(
self._content[it.multi_index], key,
self._component_name_to_basis_component_length,
self._component_name_to_basis_component_index)
if slices in self._precomputed_slices:
return self._precomputed_slices[slices]
else:
output = _AffineExpansionStorage.__new__(
type(self), *self._content.shape)
output.__init__(*self._content.shape)
while not it.finished:
# Slice content and assign
output[it.multi_index] = self._do_slicing(
self._content[it.multi_index], key)
# Increment
it.iternext()
self._precomputed_slices[slices] = output
return output
def _load_dicts(self, full_directory):
assert DictIO.exists_file(
full_directory, "component_name_to_basis_component_index")
self._component_name_to_basis_component_index = DictIO.load_file(
full_directory,
"component_name_to_basis_component_index",
globals={
"ComponentNameToBasisComponentIndexDict":
ComponentNameToBasisComponentIndexDict
})
assert DictIO.exists_file(
full_directory, "component_name_to_basis_component_length")
self._component_name_to_basis_component_length = DictIO.load_file(
full_directory,
"component_name_to_basis_component_length",
globals={"OnlineSizeDict": OnlineSizeDict})
it = AffineExpansionStorageContent_Iterator(
self._content,
flags=["multi_index", "refs_ok"],
op_flags=["readonly"])
while not it.finished:
if self._component_name_to_basis_component_index is not None:
self._content[
it.
multi_index]._component_name_to_basis_component_index = self._component_name_to_basis_component_index
if self._component_name_to_basis_component_length is not None:
self._content[
it.
multi_index]._component_name_to_basis_component_length = self._component_name_to_basis_component_length
it.iternext()
def load(self, directory, filename):
if self._content is not None: # avoid loading multiple times
if self._content.size > 0:
it = AffineExpansionStorageContent_Iterator(
self._content,
flags=["multi_index", "refs_ok"],
op_flags=["readonly"])
while not it.finished:
if self._content[
it.
multi_index] is not None: # ... but only if there is at least one element different from None
if isinstance(self._content[it.multi_index],
AbstractFunctionsList):
if len(
self._content[it.multi_index]
) > 0: # ... unless it is an empty FunctionsList
return False
elif isinstance(self._content[it.multi_index],
AbstractBasisFunctionsMatrix):
if sum(
self._content[it.multi_index].
_component_name_to_basis_component_length
.values()
) > 0: # ... unless it is an empty BasisFunctionsMatrix
return False
else:
return False
it.iternext()
# Get full directory name
full_directory = Folders.Folder(
os.path.join(str(directory), filename))
# Exit in the trivial case of empty affine expansion
if self._content.size is 0:
return True
# Load content item type and shape
reference_item = self._load_content_item_type_shape(full_directory)
# Initialize iterator
it = AffineExpansionStorageContent_Iterator(
self._content, flags=["c_index", "multi_index", "refs_ok"])
# Load content
self._load_content(reference_item, it, full_directory)
# Load dicts
self._load_dicts(full_directory)
# Reset precomputed slices
self._precomputed_slices.clear()
self._prepare_trivial_precomputed_slice(reference_item)
# Return
return True
def save(self, directory, filename):
# Get full directory name
full_directory = Folders.Folder(os.path.join(str(directory), filename))
full_directory.create()
# Export depending on type
TypeIO.save_file(self._type, full_directory, "type")
assert self._type in ("basis_functions_matrix", "empty", "error_estimation_operators_11", "error_estimation_operators_21", "error_estimation_operators_22", "functions_list", "operators")
if self._type in ("basis_functions_matrix", "functions_list"):
# Save delayed functions
delayed_functions = self._content[self._type]
it = NonAffineExpansionStorageContent_Iterator(delayed_functions, flags=["c_index", "multi_index", "refs_ok"], op_flags=["readonly"])
while not it.finished:
delayed_function = delayed_functions[it.multi_index]
delayed_function.save(full_directory, "delayed_functions_" + str(it.index))
it.iternext()
elif self._type == "empty":
pass
elif self._type in ("error_estimation_operators_11", "error_estimation_operators_21", "error_estimation_operators_22"):
# Save delayed functions
delayed_function_type = {
DelayedBasisFunctionsMatrix: "DelayedBasisFunctionsMatrix",
DelayedLinearSolver: "DelayedLinearSolver"
}
assert len(self._content["delayed_functions"]) is 2
for (index, delayed_functions) in enumerate(self._content["delayed_functions"]):
it = NonAffineExpansionStorageContent_Iterator(delayed_functions, flags=["c_index", "refs_ok"], op_flags=["readonly"])
while not it.finished:
delayed_function = delayed_functions[it.index]
DelayedFunctionsTypeIO.save_file(delayed_function_type[type(delayed_function)], full_directory, "delayed_functions_" + str(index) + "_" + str(it.index) + "_type")
DelayedFunctionsProblemNameIO.save_file(delayed_function.get_problem_name(), full_directory, "delayed_functions_" + str(index) + "_" + str(it.index) + "_problem_name")
delayed_function.save(full_directory, "delayed_functions_" + str(index) + "_" + str(it.index) + "_content")
it.iternext()
ErrorEstimationInnerProductIO.save_file(get_reduced_problem_from_error_estimation_inner_product(self._content["inner_product_matrix"]).truth_problem.name(), full_directory, "inner_product_matrix_problem_name")
elif self._type == "operators":
# Save truth content
it = NonAffineExpansionStorageContent_Iterator(self._content["truth_operators"], flags=["c_index", "multi_index", "refs_ok"], op_flags=["readonly"])
while not it.finished:
operator = self._content["truth_operators"][it.multi_index]
assert isinstance(operator, (AbstractParametrizedTensorFactory, NumericForm))
if isinstance(operator, AbstractParametrizedTensorFactory):
problem_name = get_problem_from_parametrized_operator(operator).name()
(term, index) = get_term_and_index_from_parametrized_operator(operator)
TruthContentItemIO.save_file("ParametrizedTensorFactory", full_directory, "truth_operator_" + str(it.index) + "_type")
TruthContentItemIO.save_file((problem_name, term, index), full_directory, "truth_operator_" + str(it.index))
elif isinstance(operator, NumericForm):
TruthContentItemIO.save_file("NumericForm", full_directory, "truth_operator_" + str(it.index) + "_type")
TruthContentItemIO.save_file(operator, full_directory, "truth_operator_" + str(it.index))
else:
raise TypeError("Invalid operator type")
it.iternext()
assert "truth_operators_as_expansion_storage" in self._content
# Save basis functions content
assert len(self._content["basis_functions"]) in (0, 1, 2)
BasisFunctionsContentLengthIO.save_file(len(self._content["basis_functions"]), full_directory, "basis_functions_length")
for (index, basis_functions) in enumerate(self._content["basis_functions"]):
BasisFunctionsProblemNameIO.save_file(get_reduced_problem_from_basis_functions(basis_functions).truth_problem.name(), full_directory, "basis_functions_" + str(index) + "_problem_name")
BasisFunctionsProblemNameIO.save_file(basis_functions._components_name, full_directory, "basis_functions_" + str(index) + "_components_name")
else:
raise ValueError("Invalid type")
def load(self, directory, filename):
if self._type != "empty": # avoid loading multiple times
if self._type in ("basis_functions_matrix", "functions_list"):
delayed_functions = self._content[self._type]
it = NonAffineExpansionStorageContent_Iterator(
delayed_functions,
flags=["c_index", "multi_index", "refs_ok"],
op_flags=["readonly"])
while not it.finished:
if isinstance(delayed_functions[it.multi_index],
DelayedFunctionsList):
assert self._type == "functions_list"
if len(
delayed_functions[it.multi_index]
) > 0: # ... unless it is an empty FunctionsList
return False
elif isinstance(delayed_functions[it.multi_index],
DelayedBasisFunctionsMatrix):
assert self._type == "basis_functions_matrix"
if sum(
delayed_functions[it.multi_index].
_component_name_to_basis_component_length.
values()
) > 0: # ... unless it is an empty BasisFunctionsMatrix
return False
else:
raise TypeError("Invalid delayed functions")
it.iternext()
else:
return False
# Get full directory name
full_directory = Folders.Folder(os.path.join(str(directory), filename))
# Detect trivial case
assert TypeIO.exists_file(full_directory, "type")
imported_type = TypeIO.load_file(full_directory, "type")
self._type = imported_type
assert self._type in ("basis_functions_matrix", "empty",
"error_estimation_operators_11",
"error_estimation_operators_21",
"error_estimation_operators_22",
"functions_list", "operators")
if self._type in ("basis_functions_matrix", "functions_list"):
# Load delayed functions
assert self._type in self._content
delayed_functions = self._content[self._type]
it = NonAffineExpansionStorageContent_Iterator(
delayed_functions, flags=["c_index", "multi_index", "refs_ok"])
while not it.finished:
delayed_function = delayed_functions[it.multi_index]
delayed_function.load(full_directory,
"delayed_functions_" + str(it.index))
it.iternext()
elif self._type == "empty":
pass
elif self._type in ("error_estimation_operators_11",
"error_estimation_operators_21",
"error_estimation_operators_22"):
# Load delayed functions
assert "delayed_functions" not in self._content
self._content["delayed_functions"] = [
NonAffineExpansionStorageContent_Base(self._shape[0],
dtype=object),
NonAffineExpansionStorageContent_Base(self._shape[1],
dtype=object)
]
for (index, delayed_functions) in enumerate(
self._content["delayed_functions"]):
it = NonAffineExpansionStorageContent_Iterator(
delayed_functions, flags=["c_index", "refs_ok"])
while not it.finished:
assert DelayedFunctionsTypeIO.exists_file(
full_directory, "delayed_functions_" + str(index) +
"_" + str(it.index) + "_type")
delayed_function_type = DelayedFunctionsTypeIO.load_file(
full_directory, "delayed_functions_" + str(index) +
"_" + str(it.index) + "_type")
assert DelayedFunctionsProblemNameIO.exists_file(
full_directory, "delayed_functions_" + str(index) +
"_" + str(it.index) + "_problem_name")
delayed_function_problem_name = DelayedFunctionsProblemNameIO.load_file(
full_directory, "delayed_functions_" + str(index) +
"_" + str(it.index) + "_problem_name")
delayed_function_problem = get_problem_from_problem_name(
delayed_function_problem_name)
assert delayed_function_type in (
"DelayedBasisFunctionsMatrix", "DelayedLinearSolver")
if delayed_function_type == "DelayedBasisFunctionsMatrix":
delayed_function = DelayedBasisFunctionsMatrix(
delayed_function_problem.V)
delayed_function.init(
delayed_function_problem.components)
elif delayed_function_type == "DelayedLinearSolver":
delayed_function = DelayedLinearSolver()
else:
raise ValueError("Invalid delayed function")
delayed_function.load(
full_directory, "delayed_functions_" + str(index) +
"_" + str(it.index) + "_content")
delayed_functions[it.index] = delayed_function
it.iternext()
# Load inner product
assert ErrorEstimationInnerProductIO.exists_file(
full_directory, "inner_product_matrix_problem_name")
inner_product_matrix_problem_name = ErrorEstimationInnerProductIO.load_file(
full_directory, "inner_product_matrix_problem_name")
inner_product_matrix_problem = get_problem_from_problem_name(
inner_product_matrix_problem_name)
inner_product_matrix_reduced_problem = get_reduced_problem_from_problem(
inner_product_matrix_problem)
self._content[
"inner_product_matrix"] = inner_product_matrix_reduced_problem._error_estimation_inner_product
# Recompute shape
assert "delayed_functions_shape" not in self._content
self._content["delayed_functions_shape"] = DelayedTransposeShape(
(self._content["delayed_functions"][0][0],
self._content["delayed_functions"][1][0]))
# Prepare precomputed slices
self._precomputed_slices.clear()
self._prepare_trivial_precomputed_slice()
elif self._type == "empty":
pass
elif self._type == "operators":
# Load truth content
assert "truth_operators" not in self._content
self._content[
"truth_operators"] = NonAffineExpansionStorageContent_Base(
self._shape, dtype=object)
it = NonAffineExpansionStorageContent_Iterator(
self._content["truth_operators"],
flags=["c_index", "multi_index", "refs_ok"])
while not it.finished:
assert TruthContentItemIO.exists_file(
full_directory,
"truth_operator_" + str(it.index) + "_type")
operator_type = TruthContentItemIO.load_file(
full_directory,
"truth_operator_" + str(it.index) + "_type")
assert operator_type in ("NumericForm",
"ParametrizedTensorFactory")
if operator_type == "NumericForm":
assert TruthContentItemIO.exists_file(
full_directory, "truth_operator_" + str(it.index))
value = TruthContentItemIO.load_file(
full_directory, "truth_operator_" + str(it.index))
self._content["truth_operators"][
it.multi_index] = NumericForm(value)
elif operator_type == "ParametrizedTensorFactory":
assert TruthContentItemIO.exists_file(
full_directory, "truth_operator_" + str(it.index))
(problem_name, term, index) = TruthContentItemIO.load_file(
full_directory, "truth_operator_" + str(it.index))
truth_problem = get_problem_from_problem_name(problem_name)
self._content["truth_operators"][
it.multi_index] = truth_problem.operator[term][index]
else:
raise ValueError("Invalid operator type")
it.iternext()
assert "truth_operators_as_expansion_storage" not in self._content
self._prepare_truth_operators_as_expansion_storage()
# Load basis functions content
assert BasisFunctionsContentLengthIO.exists_file(
full_directory, "basis_functions_length")
basis_functions_length = BasisFunctionsContentLengthIO.load_file(
full_directory, "basis_functions_length")
assert basis_functions_length in (0, 1, 2)
assert "basis_functions" not in self._content
self._content["basis_functions"] = list()
for index in range(basis_functions_length):
assert BasisFunctionsProblemNameIO.exists_file(
full_directory,
"basis_functions_" + str(index) + "_problem_name")
basis_functions_problem_name = BasisFunctionsProblemNameIO.load_file(
full_directory,
"basis_functions_" + str(index) + "_problem_name")
assert BasisFunctionsProblemNameIO.exists_file(
full_directory,
"basis_functions_" + str(index) + "_components_name")
basis_functions_components_name = BasisFunctionsProblemNameIO.load_file(
full_directory,
"basis_functions_" + str(index) + "_components_name")
basis_functions_problem = get_problem_from_problem_name(
basis_functions_problem_name)
basis_functions_reduced_problem = get_reduced_problem_from_problem(
basis_functions_problem)
basis_functions = basis_functions_reduced_problem.basis_functions
if basis_functions_components_name != basis_functions_problem.components:
basis_functions = basis_functions[
basis_functions_components_name]
self._content["basis_functions"].append(basis_functions)
# Recompute shape
self._content["basis_functions_shape"] = DelayedTransposeShape(
self._content["basis_functions"])
# Reset precomputed slices
self._precomputed_slices.clear()
self._prepare_trivial_precomputed_slice()
else:
raise ValueError("Invalid type")
return True