diff --git a/src/pystencils/field.py b/src/pystencils/field.py
index 2a74824c4e523026f5370acdb889f0ff3a11d375..5d7cb95cc4e6b6275f7fb55edb31c8ef950448c5 100644
--- a/src/pystencils/field.py
+++ b/src/pystencils/field.py
@@ -8,6 +8,7 @@ import re
from enum import Enum
from itertools import chain
from typing import List, Optional, Sequence, Set, Tuple, Union
+from warnings import warn
import numpy as np
import sympy as sp
@@ -151,11 +152,15 @@ class Field:
field_type=FieldType.GENERIC,
) -> "Field":
"""
- Creates a generic field where the field size is not fixed i.e. can be called with arrays of different sizes
+ Creates a generic field where the field size is not fixed i.e. can be called with arrays of different sizes.
+
+ **Field Element Type** By default, the data type of the field entries is left undetermined until
+ code generation, at which point it is set to the default numerical type of the kernel.
+ You can specify a concrete type using the `dtype` parameter.
Args:
field_name: symbolic name for the field
- dtype: numpy data type of the array the kernel is called with later
+ dtype: Data type of the field entries
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
@@ -228,7 +233,7 @@ class Field:
array: np.ndarray,
index_dimensions: int = 0,
field_type=FieldType.GENERIC,
- ) -> "Field":
+ ) -> Field:
"""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.
@@ -272,13 +277,14 @@ class Field:
field_name: str,
shape: tuple[int, ...],
index_dimensions: int = 0,
- dtype: UserTypeSpec = np.float64,
+ dtype: UserTypeSpec | DynamicType = DynamicType.NUMERIC_TYPE,
layout: str | tuple[int, ...] = "numpy",
+ memory_strides: None | Sequence[int] = None,
strides: Optional[Sequence[int]] = None,
field_type=FieldType.GENERIC,
- ) -> "Field":
+ ) -> Field:
"""
- Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout
+ Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout.
Args:
field_name: symbolic name for the field
@@ -286,14 +292,31 @@ class Field:
index_dimensions: 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: full layout of array, not only spatial dimensions
- strides: strides in bytes or None to automatically compute them from shape (assuming no padding)
+ memory_strides: Linearization strides for each dimension;
+ i.e. the number of elements to skip to get from one index to the next in the respective dimension.
field_type: kind of field
"""
- if isinstance(dtype, DynamicType):
- raise ValueError(
- "Parameter `dtype` to `Field.create_fixed_size` does not accept `DynamicType`."
+ if strides is not None:
+ warn(
+ "The `strides` parameter to `Field.create_fixed_size` is deprecated "
+ "and will be removed in pystencils 2.1. "
+ "Use `memory_strides` instead; "
+ "beware that `memory_strides` takes the number of *elements* to skip, "
+ "instead of the number of bytes.",
+ FutureWarning
)
+ if memory_strides is not None:
+ raise ValueError("Cannot specify `memory_strides` and deprecated parameter `strides` at the same time.")
+
+ if isinstance(dtype, DynamicType):
+ raise ValueError("Cannot specify the deprecated parameter `strides` together with a `DynamicType`. "
+ "Set `memory_strides` instead.")
+
+ np_type = create_type(dtype).numpy_dtype
+ assert np_type is not None
+ memory_strides = tuple([s // np_type.itemsize for s in strides])
+
spatial_dimensions = len(shape) - index_dimensions
assert spatial_dimensions >= 1
@@ -302,7 +325,8 @@ class Field:
layout, spatial_dimensions + index_dimensions
)
- dtype = create_type(dtype)
+ if not isinstance(dtype, DynamicType):
+ dtype = create_type(dtype)
shape_tuple = tuple(int(s) for s in shape)
strides_tuple: tuple[int, ...]
@@ -311,14 +335,7 @@ class Field:
strides_tuple = compute_strides(shape_tuple, layout)
else:
assert len(strides) == len(shape_tuple)
- np_type = dtype.numpy_dtype
-
- if np_type is None:
- raise ValueError(
- f"Cannot create fixed-size field of data type {dtype} which has no NumPy representation."
- )
-
- strides_tuple = tuple([s // np_type.itemsize for s in strides])
+ strides_tuple = tuple(strides)
if isinstance(dtype, PsStructType):
if index_dimensions != 0:
diff --git a/tests/frontend/test_field.py b/tests/frontend/test_field.py
index 56c3bdabbbf348d1f0de19404db7a84289294126..6ac76f3c643b3f20c8bdb77b2e443fe0649e441a 100644
--- a/tests/frontend/test_field.py
+++ b/tests/frontend/test_field.py
@@ -42,7 +42,7 @@ def test_field_basic():
assert f1.ndim == f.ndim
assert f1.values_per_cell() == f.values_per_cell()
- f = Field.create_fixed_size("f", (10, 10), strides=(80, 8), dtype=np.float64)
+ f = Field.create_fixed_size("f", (10, 10), strides=(10, 1), dtype=np.float64)
assert f.spatial_strides == (10, 1)
assert f.index_strides == ()
assert f.center_vector == sp.Matrix([f.center])
@@ -61,13 +61,13 @@ def test_field_basic():
neighbor = field_access.neighbor(coord_id=0, offset=-2)
assert neighbor.offsets == (-1, 1)
assert "_" in neighbor._latex("dummy")
- assert f.dtype == create_type("float64")
+ assert f.dtype == DynamicType.NUMERIC_TYPE
f = Field.create_fixed_size("f", (8, 8, 2, 2, 2), index_dimensions=3)
assert f.center_vector == sp.Array(
[[[f(i, j, k) for k in range(2)] for j in range(2)] for i in range(2)]
)
- assert f.dtype == create_type("float64")
+ assert f.dtype == DynamicType.NUMERIC_TYPE
f = Field.create_generic("f", spatial_dimensions=5, index_dimensions=2)
field_access = f[1, -1, 2, -3, 0](1, 0)
diff --git a/tests/nbackend/transformations/test_canonicalize_symbols.py b/tests/nbackend/transformations/test_canonicalize_symbols.py
index 2758d123417eb8e3015ed1d6b4d8cf0ba7c14611..dbc4ba10b71668af43d3a352d9cc49a8c9d61140 100644
--- a/tests/nbackend/transformations/test_canonicalize_symbols.py
+++ b/tests/nbackend/transformations/test_canonicalize_symbols.py
@@ -17,7 +17,7 @@ def test_deduplication():
factory = AstFactory(ctx)
canonicalize = CanonicalizeSymbols(ctx)
- f = Field.create_fixed_size("f", (5, 5), strides=(5, 1))
+ f = Field.create_fixed_size("f", (5, 5), memory_strides=(5, 1))
x, y, z = sp.symbols("x, y, z")
ispace = FullIterationSpace.create_from_slice(ctx, make_slice[:, :], f)
diff --git a/tests/nbackend/transformations/test_hoist_invariants.py b/tests/nbackend/transformations/test_hoist_invariants.py
index daa2760c0b376dc0bb1f2ca59703a15efc5c2312..1f27a5a4cd17d9b20e54b3c44d1e733f8374f947 100644
--- a/tests/nbackend/transformations/test_hoist_invariants.py
+++ b/tests/nbackend/transformations/test_hoist_invariants.py
@@ -33,7 +33,7 @@ def test_hoist_multiple_loops():
canonicalize = CanonicalizeSymbols(ctx)
hoist = HoistLoopInvariantDeclarations(ctx)
- f = Field.create_fixed_size("f", (5, 5), strides=(5, 1))
+ f = Field.create_fixed_size("f", (5, 5), memory_strides=(5, 1))
x, y, z = sp.symbols("x, y, z")
ispace = FullIterationSpace.create_from_slice(ctx, make_slice[:, :], f)