Field¶

class
Field
(field_name, field_type, dtype, layout, shape, strides)¶ With fields one can formulate stencillike update rules on structured grids. This Field class knows about the dimension, memory layout (strides) and optionally about the size of an array.
 Creating Fields:
The preferred method to create fields is the
fields
function. Alternatively one can use one of the static functionsField.create_generic
,Field.create_from_numpy_array
andField.create_fixed_size
. Don’t instantiate the Field directly! Fields can be created with known or unknown shapes:If you want to create a kernel with fixed loop sizes i.e. the shape of the array is already known. This is usually the case if justintime compilation directly from Python is done. (see
Field.create_from_numpy_array
create a more general kernel that works for variable array sizes. This can be used to create kernels beforehand for a library. (see
Field.create_generic
)
 Dimensions and Indexing:
A field has spatial and index dimensions, where the spatial dimensions come first. The interpretation is that the field has multiple cells in (usually) two or three dimensional space which are looped over. Additionally N values are stored per cell. In this case spatial_dimensions is two or three, and index_dimensions equals N. If you want to store a matrix on each point in a two dimensional grid, there are four dimensions, two spatial and two index dimensions:
len(arr.shape) == spatial_dims + index_dims
The shape of the index dimension does not have to be specified. Just use the ‘index_dimensions’ parameter. However, it is good practice to define the shape, since out of bounds accesses can be directly detected in this case. The shape can be passed with the ‘index_shape’ parameter of the field creation functions.
When accessing (indexing) a field the result is a
Field.Access
which is derived from sympy Symbol. First specify the spatial offsets in [], then in case index_dimension>0 the indices in () e.g.f[1,0,0](7)
 Staggered Fields:
Staggered fields are used to store a value on a second grid shifted by half a cell with respect to the usual grid.
The first index dimension is used to specify the position on the staggered grid (e.g. 0 means halfway to the eastern neighbor, 1 is halfway to the northern neighbor, etc.), while additional indices can be used to store multiple values at each position.
 Example using no index dimensions:
>>> a = np.zeros([10, 10]) >>> f = Field.create_from_numpy_array("f", a, index_dimensions=0) >>> jacobi = (f[1,0] + f[1,0] + f[0,1] + f[0,1]) / 4
 Examples for index dimensions to create LB field and implement stream pull:
>>> from pystencils import Assignment >>> stencil = np.array([[0,0], [0,1], [0,1]]) >>> src, dst = fields("src(3), dst(3) : double[2D]") >>> assignments = [Assignment(dst[0,0](i), src[offset](i)) for i, offset in enumerate(stencil)];

static
create_generic
(field_name, spatial_dimensions, dtype=<class 'numpy.float64'>, index_dimensions=0, layout='numpy', index_shape=None, field_type=<FieldType.GENERIC: 0>)¶ Creates a generic field where the field size is not fixed i.e. can be called with arrays of different sizes
 Parameters
field_name – symbolic name for the field
dtype – numpy data type of the array the kernel is called with later
spatial_dimensions – see documentation of Field
index_dimensions – see documentation of Field
layout – tuple specifying the loop ordering of the spatial dimensions e.g. (2, 1, 0 ) means that the outer loop loops over dimension 2, the second outer over dimension 1, and the inner loop over dimension 0. Also allowed: the strings ‘numpy’ (0,1,..d) or ‘reverse_numpy’ (d, …, 1, 0)
index_shape – optional shape of the index dimensions i.e. maximum values allowed for each index dimension, has to be a list or tuple
field_type – besides the normal GENERIC fields, there are INDEXED fields that store indices of the domain that should be iterated over, BUFFER fields that are used to generate communication packing/unpacking kernels, and STAGGERED fields, which store values halfway to the next cell
 Return type

static
create_from_numpy_array
(field_name, array, index_dimensions=0, field_type=<FieldType.GENERIC: 0>)¶ Creates a field based on the layout, data type, and shape of a given numpy array.
Kernels created for these kind of fields can only be called with arrays of the same layout, shape and type.

static
create_fixed_size
(field_name, shape, index_dimensions=0, dtype=<class 'numpy.float64'>, layout='numpy', strides=None, field_type=<FieldType.GENERIC: 0>)¶ Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout
 Parameters
field_name (
str
) – symbolic name for the fieldindex_dimensions (
int
) – how many of the trailing dimensions are interpreted as index (as opposed to spatial)dtype – numpy data type of the array the kernel is called with later
layout (
str
) – full layout of array, not only spatial dimensionsstrides (
Optional
[Sequence
[int
]]) – strides in bytes or None to automatically compute them from shape (assuming no padding)field_type – kind of field
 Return type

neighbor_vector
(offset)¶ Like neighbor, but returns the entire vector/tensor stored at offset.

interpolated_access
(offset, interpolation_mode='linear', address_mode='BORDER', allow_textures=True)¶ Provides access to field values at noninteger positions
interpolated_access
is similar toField.absolute_access()
except that it allows noninteger offsets and automatic handling of outofbound accesses. Parameters
offset (
Tuple
) – Tuple of spatial coordinates (can be floats)interpolation_mode – One of
pystencils.interpolation_astnodes.InterpolationMode
address_mode – How boundaries are handled can be ‘border’, ‘wrap’, ‘mirror’, ‘clamp’
allow_textures – Allow implementation by texture accesses on GPUs

staggered_access
(offset, index=None)¶ If this field is a staggered field, it can be accessed using halfinteger offsets. For example, an offset of
(0, sp.Rational(1,2))
or"E"
corresponds to the staggered point to the east of the cell center, i.e. halfway to the easternnext cell. If the field stores more than one value per staggered point (e.g. a vector or a tensor), the index (integer or tuple of integers) refers to which of these values to access.

staggered_vector_access
(offset)¶ Like staggered_access, but returns the entire vector/tensor stored at offset.

class
Access
(name, *args, **kwargs)¶ Class representing a relative access into a
Field
.This class behaves like a normal sympy Symbol, it is actually derived from it. One can built up sympy expressions using field accesses, solve for them, etc.
Examples
>>> vector_field_2d = fields("v(2): double[2D]") # create a 2D vector field >>> northern_neighbor_y_component = vector_field_2d[0, 1](1) >>> northern_neighbor_y_component v_N^1 >>> central_y_component = vector_field_2d(1) >>> central_y_component v_C^1 >>> central_y_component.get_shifted(1, 0) # move the existing access v_E^1 >>> central_y_component.at_index(0) # change component v_C^0

property
nr_of_coordinates
¶

property
offset_name
¶ Spatial offset as string, EastWest for x, NorthSouth for y and TopBottom for z coordinate.
Example
>>> f = fields("f: double[2D]") >>> f[1, 1].offset_name # northeast 'NE'
 Return type

property
index
¶ Value of index coordinates as tuple.

neighbor
(coord_id, offset)¶ Returns a new Access with changed spatial coordinates.
 Parameters
Example
>>> f = fields('f: [2D]') >>> f[0,0].neighbor(coord_id=1, offset=1) f_S
 Return type

get_shifted
(*shift)¶ Returns a new Access with changed spatial coordinates
Example
>>> f = fields("f: [2D]") >>> f[0,0].get_shifted(1, 1) f_NE
 Return type

at_index
(*idx_tuple)¶ Returns new Access with changed index.
Example
>>> f = fields("f(9): [2D]") >>> f(0).at_index(8) f_C^8
 Return type

property
is_absolute_access
¶ Indicates if a field access is relative to the loop counters (this is the default) or absolute
 Return type

property

fields
(description=None, index_dimensions=0, layout=None, field_type=<FieldType.GENERIC: 0>, **kwargs)¶ Creates pystencils fields from a string description.
Examples
 Create a 2D scalar and vector field:
>>> s, v = fields("s, v(2): double[2D]") >>> assert s.spatial_dimensions == 2 and s.index_dimensions == 0 >>> assert (v.spatial_dimensions, v.index_dimensions, v.index_shape) == (2, 1, (2,))
 Create an integer field of shape (10, 20):
>>> f = fields("f : int32[10, 20]") >>> f.has_fixed_shape, f.shape (True, (10, 20))
 Numpy arrays can be used as template for shape and data type of field:
>>> arr_s, arr_v = np.zeros([20, 20]), np.zeros([20, 20, 2]) >>> s, v = fields("s, v(2)", s=arr_s, v=arr_v) >>> assert s.index_dimensions == 0 and s.dtype.numpy_dtype == arr_s.dtype >>> assert v.index_shape == (2,)
 Format string can be left out, field names are taken from keyword arguments.
>>> fields(f1=arr_s, f2=arr_s) [f1: double[20,20], f2: double[20,20]]
 The keyword names
index_dimension
andlayout
have special meaning, don’t use them for field names >>> f = fields(f=arr_v, index_dimensions=1) >>> assert f.index_dimensions == 1 >>> f = fields("pdfs(19) : float32[3D]", layout='fzyx') >>> f.layout (2, 1, 0)

class
FieldType
(value)¶ An enumeration.