pystencils.grids.tensor_field.TensorField

pystencils.grids.tensor_field.TensorField#

class pystencils.grids.tensor_field.TensorField(name, spatial_rank, tensor_shape=(), *, dtype=DynamicType.NUMERIC_TYPE, layout=MemoryLayout.RIGHTMOST)#

Tensor field mapping each point of a \(d\)-dimensional index space to a rank \(n\) tensor.

A tensor field is a function

\[f: I \to T^{k_1 \times \cdots \times k_n}\]

from an index space \(I \subset \mathbb{Z}^{d}\) to a tensor space over \(T\). \(T\) may be \(\mathbb{R}\) or a specific numeric data type.

Note

Degenerate tensor shapes (i.e. \(n_i = 1\) for any \(i\)) are not supported.

Accessing Values Field entries can be accessed using the [] and () operators. Spatial offsets must be given in []; they are interpreted relative to the current node. Tensor indices are passed to ().

Examples:

  • Access vector entry 1 at the current node:

f(1)

  • Access scalar entry at the eastern neighbor node:

f[1, 0]()

  • Access tensor entry (0, 0) at the north-west neighbor node:

f[-1, 1](0, 0)
Parameters:

  • name (str) – Name of the tensor field

  • spatial_rank (int) – Dimensionality of the index space \(I\)

  • tensor_shape (tuple[int, ...]) – Shape of the field’s tensors

  • dtype (str | type | dtype | PsType | DynamicType) – Data type of the field’s tensor entries

  • layout (str | MemoryLayout) – Memory layout of the field’s memory buffers at runtime

property name: str#

The field’s name

property dtype: PsNumericType | DynamicType#

Data type of tensor entries

property layout: MemoryLayout#

Memory layout of runtime buffers

property spatial_rank: int#

Dimensionality of the spatial index space

property tensor_shape: tuple[int, ...]#

Shape of the field’s tensors

property tensor_rank: int#

Rank of the field’s tensors

get_buffer_spec()#

Return the buffer specification defining the field’s memory properties

Return type:

FieldBufferSpec

get_iteration_limits()#

Return the iteration limits for kernels operating on this field

Return type:

IterationLimits

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