SparseTimeFunction¶

class
devito.types.
SparseTimeFunction
(*args, **kwargs)[source]¶ Bases:
devito.types.sparse.AbstractSparseTimeFunction
,devito.types.sparse.SparseFunction
Tensor symbol representing a space and timevarying sparse array in symbolic equations.
Like SparseFunction, SparseTimeFunction carries multidimensional data that are not aligned with the computational grid. As such, each data value is associated some coordinates. A SparseTimeFunction provides symbolic interpolation routines to convert between TimeFunctions and sparse data points. These are based upon standard [bi,tri]linear interpolation.
 Parameters
name (str) – Name of the symbol.
npoint (int) – Number of sparse points.
nt (int) – Number of timesteps along the time dimension.
grid (Grid) – The computational domain from which the sparse points are sampled.
coordinates (np.ndarray, optional) – The coordinates of each sparse point.
space_order (int, optional) – Discretisation order for space derivatives. Defaults to 0.
time_order (int, optional) – Discretisation order for time derivatives. Defaults to 1.
shape (tuple of ints, optional) – Shape of the object. Defaults to
(nt, npoint)
.dimensions (tuple of Dimension, optional) – Dimensions associated with the object. Only necessary if the SparseFunction defines a multidimensional tensor.
dtype (datatype, 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.
Examples
Creation
>>> from devito import Grid, SparseTimeFunction >>> grid = Grid(shape=(4, 4)) >>> sf = SparseTimeFunction(name='sf', grid=grid, npoint=2, nt=3) >>> sf sf(time, p_sf)
Inspection
>>> sf.data Data([[0., 0.], [0., 0.], [0., 0.]], dtype=float32) >>> sf.coordinates sf_coords(p_sf, d) >>> sf.coordinates_data array([[0., 0.], [0., 0.]], dtype=float32)
Symbolic interpolation routines
>>> from devito import TimeFunction >>> f = TimeFunction(name='f', grid=grid) >>> exprs0 = sf.interpolate(f) >>> exprs1 = sf.inject(f, sf)
Notes
The parameters must always be given as keyword arguments, since SymPy uses
*args
to (re)create the dimension arguments of the symbolic object.
data
¶ The domain data values, as a numpy.ndarray.
Elements are stored in rowmajor 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.

data_domain
¶ The domain data values.
Elements are stored in rowmajor 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.

data_ro_domain
¶ Readonly view of the domain data values.

data_ro_with_halo
¶ Readonly view of the domain+outhalo data values.

data_with_halo
¶ The domain+outhalo data values.
Elements are stored in rowmajor 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.

dimensions
¶ Tuple of Dimensions representing the object indices.

dtype
¶ The data type of the object.

grid
¶ The Grid on which the discretization occurred.

gridpoints
¶ The reference grid point corresponding to each sparse point.
Notes
When using MPI, this property refers to the physically owned sparse points.

guard
(expr=None, offset=0)¶ Generate guarded expressions, that is expressions that are evaluated by an Operator only if certain conditions are met. The introduced condition, here, is that all grid points in the support of a sparse value must fall within the grid domain (i.e., not on the halo).
 Parameters
expr (exprlike, optional) – Input expression, from which the guarded expression is derived. If not specified, defaults to
self
.offset (int, optional) – Relax the guard condition by introducing a tolerance offset.

inject
(field, expr, offset=0, u_t=None, p_t=None)[source]¶ Generate equations injecting an arbitrary expression into a field.
 Parameters
field (Function) – Input field into which the injection is performed.
expr (exprlike) – Injected expression.
offset (int, optional) – Additional offset from the boundary.
u_t (exprlike, optional) – Time index at which the interpolation is performed.
p_t (exprlike, optional) – Time index at which the result of the interpolation is stored.

interpolate
(expr, offset=0, u_t=None, p_t=None, increment=False)[source]¶ Generate equations interpolating an arbitrary expression into
self
. Parameters
expr (exprlike) – Input expression to interpolate.
offset (int, optional) – Additional offset from the boundary.
u_t (exprlike, optional) – Time index at which the interpolation is performed.
p_t (exprlike, optional) – Time index at which the result of the interpolation is stored.
increment (bool, optional) – If True, generate increments (Inc) rather than assignments (Eq).

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.

space_order
¶ The space order.

time_order
¶ The time order.