4.1.7 pde.backends.base module

Defines base class of backends that implement computations.

class BackendBase(config: MutableMapping[str, Any] | Config | None, *, name: str | None = None)[source]

Bases: Generic

Basic backend from which all other backends inherit.

The generic type parameter TNativeArray determines the type of the native data representation of the backend.

Initialize the backend.

Parameters:

config (Config) – Configuration data for the backend
name (str) – The name of the backend

compile_function(func: TFunc, **kwargs) → TFunc[source]

General method that compiles a user function.

Parameters:

func (callable) – The function that needs to be compiled for this backend
**kwargs – Additional arguments affecting how the function is compiled

classmethod from_args(config: MutableMapping[str, Any] | Config | None, args: str = '', *, name: str | None = None) → Self[source]

Initialize backend with extra arguments.

Parameters:

config (Config) – Configuration data for the backend
args (str) – Additional arguments that determine how the backend is initialized
name (str) – The name of the backend

get_operator_info(grid: GridBase, operator: str | OperatorInfo) → OperatorInfo[source]

Return an operator for a particular grid.

Parameters:

grid (GridBase) – Grid for which the operator is needed
operator (str) – Identifier for the operator. Some examples are ‘laplace’, ‘gradient’, or ‘divergence’. The registered operators for this grid can be obtained from the operators attribute.

Returns:

information for the operator

Return type:

OperatorInfo

get_registered_operators(grid_id: GridBase | type[GridBase]) → set[str][source]

Returns all operators defined for a grid.

Parameters:: grid_id (GridBase or its type) – Grid or grid class for which the operators need to be returned

property info: dict[str, Any]

relevant information about the backend

Type:: dict

make_data_setter(grid: GridBase, rank: int, bcs: BoundariesBase | None = None) → DataSetter[source]

Create a function to set the valid part of a full data array.

Parameters:

grid (GridBase) – Grid for which the data setter is defined
rank (int) – Rank of the data represented on the grid
bcs (BoundariesBase, optional) – If supplied, the returned function also enforces boundary conditions by setting the ghost cells to the correct values

Returns:

Takes two numpy arrays, setting the valid data in the first one, using the second array. The arrays need to be allocated already and they need to have the correct dimensions, which are not checked. If bcs are given, a third argument is allowed, which sets arguments for the BCs.

Return type:

callable

make_expression_function(expression: ExpressionBase, *, single_arg: bool = False, user_funcs: dict[str, Callable] | None = None) → Callable[..., NumberOrArray][source]

Return a function evaluating an expression.

Parameters:

expression (ExpressionBase) – The expression that is converted to a function
single_arg (bool) – Determines whether the returned function accepts all variables in a single argument as an array or whether all variables need to be supplied separately.
user_funcs (dict) – Additional functions that can be used in the expression.

Returns:

the function

Return type:

function

make_full_data_setter(bcs: BoundariesBase) → DataSetter[source]

Create a function to set the valid part of a full data array.

Parameters:: bcs (BoundariesBase, optional) – If supplied, the returned function also enforces boundary conditions by setting the ghost cells to the correct values
Returns:: Takes two numpy arrays, setting the valid data in the first one, using the second array. The arrays need to be allocated already and they need to have the correct dimensions, which are not checked. If bcs are given, a third argument is allowed, which sets arguments for the BCs.
Return type:: callable

make_gaussian_noise(field: TField, *, rng: np.random.Generator) → Callable[[], TNativeArray][source]

Create a function generating Gaussian white noise.

Parameters:

field (FieldBase) – An example for the field from which the grid and other information can be extracted
rng (Generator) – Random number generator (default: default_rng()).

make_ghost_cell_setter(bcs: BoundariesBase) → GhostCellSetter[source]

Return function that sets the ghost cells on a full array.

Parameters:: bcs (BoundariesBase) – Defines the boundary conditions for a particular grid, for which the setter should be defined.
Returns:: Callable with signature (data_full: NumericArray, args=None), which sets the ghost cells of the full data, potentially using additional information in args (e.g., the time t during solving a PDE)

make_inner_prod_operator(field: DataFieldBase, *, conjugate: bool = True) → _BinaryOperatorImplType[source]

Return operator calculating the dot product between two fields.

This supports both products between two vectors as well as products between a vector and a tensor.

Parameters:

field (DataFieldBase) – Field for which the inner product is defined
conjugate (bool) – Whether to use the complex conjugate for the second operand

Returns:

Function that takes two instance of native data arrays, which contain the discretized data of the two operands. An optional third argument can specify the output array to which the result is written.

make_integrator(grid: GridBase) → Callable[[TNativeArray], TNativeArray][source]

Return function that integrates discretized data over a grid.

Note that this function takes and returns data in the native representation of the backend. If this function is used in a multiprocessing run (using MPI), the integrals are performed on all subgrids and then accumulated. Each process then receives the same value representing the global integral.

Parameters:: grid (GridBase) – Grid for which the operator is needed
Returns:: A function that takes a numpy array and returns the integral with the correct weights given by the cell volumes.

make_interpolator(field: DataFieldBase, *, fill: Number | None = None, with_ghost_cells: bool = False) → Callable[[FloatingArray, NumericArray], NumberOrArray][source]

Returns a function that can be used to interpolate values.

Parameters:

field (DataFieldBase) – Field for which the interpolator is defined
fill (Number, optional) – Determines how values out of bounds are handled. If None, a ValueError is raised when out-of-bounds points are requested. Otherwise, the given value is returned.
with_ghost_cells (bool) – Flag indicating that the interpolator should work on the full data array that includes values for the ghost points. If this is the case, the boundaries are not checked and the coordinates are used as is.

Returns:

A function which returns interpolated values when called with arbitrary positions within the space of the grid.

make_mpi_synchronizer(operator: int | str = 'MAX') → Callable[[float], float][source]

Return function that synchronizes values between multiple MPI processes.

Warning

The default implementation does not synchronize anything. This is simply a hook, which can be used by backends that support MPI

Parameters:: operator (str or int) – Flag determining how the value from multiple nodes is combined. Possible values include “MAX”, “MIN”, and “SUM”.
Returns:: Function that can be used to synchronize values across nodes

make_operator(grid: GridBase, operator: str | OperatorInfo, *, bcs: BoundariesBase, dtype: DTypeLike | None = None, **kwargs) → OperatorType[source]

Return a compiled function applying an operator with boundary conditions.

Parameters:

grid (GridBase) – Grid for which the operator is needed
operator (str) – Identifier for the operator. Some examples are ‘laplace’, ‘gradient’, or ‘divergence’. The registered operators for this grid can be obtained from the operators attribute.
bcs (BoundariesBase) – The boundary conditions used before the operator is applied
dtype (numpy dtype) – The data type of the field.
**kwargs – Specifies extra arguments influencing how the operator is created.

The returned function takes the discretized data on the grid as an input and returns the data to which the operator operator has been applied. The function only takes the valid grid points and allocates memory for the ghost points internally to apply the boundary conditions specified as bc. Note that the function supports an optional argument out, which if given should provide space for the valid output array without the ghost cells. The result of the operator is then written into this output array.

The function also accepts an optional parameter args, which is forwarded to set_ghost_cells. This allows setting boundary conditions based on external parameters, like time.

Backends can internally implement the raw operator in either style op(arr, args=None) -> result (functional) or op(arr, out, args=None) (in-place). The backend-specific implementation of make_operator() adapts this internal style to the public call convention (arr, *, out=None, args=None).

Returns:: the function that applies the operator. This function has the signature (arr: NumericArray, out: NumericArray = None, args=None).
Return type:: callable

make_operator_no_bc(grid: GridBase, operator: str | OperatorInfo, *, dtype: DTypeLike | None = None, **kwargs) → OperatorType[source]

Return a compiled function applying an operator without boundary conditions.

The returned function has public signature (arr, out=None, args=None) -> result.

Note

The resulting function does not check whether the ghost cells of the input array have been supplied with sensible values. It is the responsibility of the user to set the values of the ghost cells beforehand. Use this function only if you absolutely know what you’re doing. In all other cases, make_operator() is probably the better choice.

Backends can choose whether their internal operator implementation uses a functional style op(arr, *, args=None) -> result or an in-place style op(arr, out, *, args=None). This wrapper must thus normalizes both styles to the public signature above.

Parameters:

grid (GridBase) – Grid for which the operator is needed
operator (str) – Identifier for the operator. Some examples are ‘laplace’, ‘gradient’, or ‘divergence’. The registered operators for this grid can be obtained from the operators attribute.
dtype (numpy dtype) – The data type of the field.
**kwargs – Specifies extra arguments influencing how the operator is created.

Returns:

the function that applies the operator. This function has the signature (arr: NumericArray, out: NumericArray=None, args=None).

Return type:

callable

make_outer_prod_operator(field: DataFieldBase) → _BinaryOperatorImplType[source]

Return operator calculating the outer product between two fields.

This supports typically only supports products between two vector fields.

Parameters:: field (DataFieldBase) – Field for which the outer product is defined
Returns:: Function that takes two instance of native data arrays, which contain the discretized data of the two operands. An optional third argument can specify the output array to which the result is written.

make_pde_rhs(eq: PDEBase, state: TField) → Callable[[TNativeArray, float], TNativeArray][source]

Return a function for evaluating the right hand side of the PDE.

Parameters:

eq (PDEBase) – The object describing the differential equation
state (FieldBase) – An example for the state from which information can be extracted

Returns:

Function returning deterministic part of the right hand side of the PDE

make_stepper(solver: SolverBase, state: TField) → StepperType[source]

Create a field-based stepping function for a given solver.

Parameters:

solver (SolverBase) – The solver instance, which determines how the stepper is constructed
state (FieldBase) – An example for the state from which the grid and other information can be extracted

Returns:

Function that can be called to advance the state from time t_start to time t_end.

make_valid_data_setter(grid: GridBase, rank: int) → DataSetter[source]

Create a function to set the valid part of a full data array.

Parameters:

grid (GridBase) – Grid for which the data setter is defined
rank (int) – Rank of the data represented on the grid

Returns:

Takes two numpy arrays, setting the valid data in the first one, using the second array. The arrays need to be allocated already and they need to have the correct dimensions, which are not checked. If bcs are given, a third argument is allowed, which sets arguments for the BCs.

Return type:

callable

native_to_numpy(value: TNativeArray) → ndarray[Any, dtype[number]][source]

native_to_numpy(value: TValue) → TValue

Convert native values to numpy representation.

Parameters:: value – The value to convert to numpy representation

numpy_to_native(value: ndarray[Any, dtype[number]]) → TNativeArray[source]

numpy_to_native(value: TValue) → TValue

Convert values from numpy to native representation.

Parameters:: value – The value to convert from numpy representation

classmethod register_operator(grid_cls: type[GridBase], name: str, factory_func: OperatorFactory | None = None, *, rank_in: int = 0, rank_out: int = 0)[source]

Register an operator for a particular grid.

Example

The method can either be used directly:

BackendClass.register_operator(grid_class, "operator", make_operator)

or as a decorator for the factory function:

@BackendClass.register_operator(grid_class, "operator")
def make_operator(grid: GridBase): ...

Parameters:

grid_cls (GridBase) – Grid class for which the operator is defined
name (str) – The name of the operator to register
factory_func (callable) – A function with signature (grid: GridBase, **kwargs), which takes a grid object and optional keyword arguments and returns an implementation of the given operator. This implementation is a function that takes a ndarray of discretized values as arguments and returns the resulting discretized data in a ndarray after applying the operator.
rank_in (int) – The rank of the input field for the operator
rank_out (int) – The rank of the field that is returned by the operator