PrecomputedSparseTimeFunction
- class devito.types.PrecomputedSparseTimeFunction(*args)[source]
Bases:
AbstractSparseTimeFunction
,PrecomputedSparseFunction
Tensor symbol representing a space- and time-varying sparse array in symbolic equations; unlike SparseTimeFunction, PrecomputedSparseTimeFunction uses externally-defined data for interpolation.
- Parameters:
name (str) – Name of the symbol.
npoint (int) – Number of sparse points.
grid (Grid) – The computational domain from which the sparse points are sampled.
r (int) – Number of gridpoints in each dimension to interpolate a single sparse point to. E.g.
r=2
for linear interpolation.gridpoints (np.ndarray, optional) – An array carrying the reference grid point corresponding to each sparse point. Of all the gridpoints that one sparse point would be interpolated to, this is the grid point closest to the origin, i.e. the one with the lowest value of each coordinate dimension. Must be a two-dimensional array of shape
(npoint, grid.ndim)
.interpolation_coeffs (np.ndarray, optional) – An array containing the coefficient for each of the r^2 (2D) or r^3 (3D) gridpoints that each sparse point will be interpolated to. The coefficient is split across the n dimensions such that the contribution of the point (i, j, k) will be multiplied by
interpolation_coeffs[..., i]*interpolation_coeffs[..., j]*interpolation_coeffs[...,k]
. So forr=6
, we will store 18 coefficients per sparse point (instead of potentially 216). Must be a three-dimensional array of shape(npoint, grid.ndim, r)
.space_order (int, optional) – Discretisation order for space derivatives. Defaults to 0.
time_order (int, optional) – Discretisation order for time derivatives. Default to 1.
shape (tuple of ints, optional) – Shape of the object. Defaults to
(npoint,)
.dimensions (tuple of Dimension, optional) – Dimensions associated with the object. Only necessary if the SparseFunction defines a multi-dimensional tensor.
dtype (data-type, optional) – Any object that can be interpreted as a numpy data type. Defaults to
np.float32
.initializer (callable or any object exposing the buffer interface, optional) – Data initializer. If a callable is provided, data is allocated lazily.
allocator (MemoryAllocator, optional) – Controller for memory allocation. To be used, for example, when one wants to take advantage of the memory hierarchy in a NUMA architecture. Refer to default_allocator.__doc__ for more information.
Notes
The parameters must always be given as keyword arguments, since SymPy uses
*args
to (re-)create the dimension arguments of the symbolic object.- property data
The domain data values, as a numpy.ndarray.
Elements are stored in row-major format.
Notes
With this accessor you are claiming that you will modify the values you get back. If you only need to look at the values, use
data_ro()
instead.
- property data_domain
The domain data values.
Elements are stored in row-major format.
Notes
Alias to
self.data
.With this accessor you are claiming that you will modify the values you get back. If you only need to look at the values, use
data_ro_domain()
instead.
- property data_ro_domain
Read-only view of the domain data values.
- property data_ro_with_halo
Read-only view of the domain+outhalo data values.
- property data_with_halo
The domain+outhalo data values.
Elements are stored in row-major format.
Notes
With this accessor you are claiming that you will modify the values you get back. If you only need to look at the values, use
data_ro_with_halo()
instead.
- property dimensions
Tuple of Dimensions representing the object indices.
- property dtype
The data type of the object in the generated code, represented as a Python class:
numpy.dtype: basic data types. For example, np.float64 -> double.
ctypes: composite objects (e.g., structs), foreign types.
- property grid
The Grid on which the discretization occurred.
- property gridpoints
The reference grid point corresponding to each sparse point.
Notes
When using MPI, this property refers to the physically owned sparse points.
- inject(*args, **kwargs)
Implement an injection operation from a sparse point onto the grid
- interpolate(expr, offset=0, u_t=None, p_t=None, increment=False)[source]
Generate equations interpolating an arbitrary expression into
self
.- Parameters:
expr (expr-like) – Input expression to interpolate.
offset (int, optional) – Additional offset from the boundary.
u_t (expr-like, optional) – Time index at which the interpolation is performed.
p_t (expr-like, optional) – Time index at which the result of the interpolation is stored.
increment (bool, optional) – If True, generate increments (Inc) rather than assignments (Eq).
- property name
The name of the object.
- shape
Shape of the domain region. The domain constitutes the area of the data written to by an Operator.
Notes
In an MPI context, this is the local domain region shape.
- property space_order
The space order.
- property time_order
The time order.