diff --git a/pystencils/datahandling/datahandling_interface.py b/pystencils/datahandling/datahandling_interface.py
index 8b426ce5be1205aacdceafbc15017e18bc4acb39..ba960edc17b2e3409dc465c257e23d9e9394dc5c 100644
--- a/pystencils/datahandling/datahandling_interface.py
+++ b/pystencils/datahandling/datahandling_interface.py
@@ -3,7 +3,7 @@ from typing import Callable, Dict, Iterable, Optional, Sequence, Tuple, Union
import numpy as np
-from pystencils.field import Field
+from pystencils.field import Field, FieldType
class DataHandling(ABC):
@@ -36,7 +36,7 @@ class DataHandling(ABC):
@abstractmethod
def add_array(self, name: str, values_per_cell, dtype=np.float64,
latex_name: Optional[str] = None, ghost_layers: Optional[int] = None, layout: Optional[str] = None,
- cpu: bool = True, gpu: Optional[bool] = None, alignment=False, staggered=False) -> Field:
+ cpu: bool = True, gpu: Optional[bool] = None, alignment=False, field_type=FieldType.GENERIC) -> Field:
"""Adds a (possibly distributed) array to the handling that can be accessed using the given name.
For each array a symbolic field is available via the 'fields' dictionary
diff --git a/pystencils/datahandling/parallel_datahandling.py b/pystencils/datahandling/parallel_datahandling.py
index 0fd8d71df11cd8569221b869db016223dba51d52..54f26806be318f6ef91a5ca11a9888a59524fb0c 100644
--- a/pystencils/datahandling/parallel_datahandling.py
+++ b/pystencils/datahandling/parallel_datahandling.py
@@ -7,7 +7,7 @@ import waLBerla as wlb
from pystencils.datahandling.blockiteration import block_iteration, sliced_block_iteration
from pystencils.datahandling.datahandling_interface import DataHandling
-from pystencils.field import Field
+from pystencils.field import Field, FieldType
from pystencils.kernelparameters import FieldPointerSymbol
from pystencils.utils import DotDict
@@ -90,7 +90,7 @@ class ParallelDataHandling(DataHandling):
self._custom_data_names.append(name)
def add_array(self, name, values_per_cell=1, dtype=np.float64, latex_name=None, ghost_layers=None,
- layout=None, cpu=True, gpu=None, alignment=False, staggered=False):
+ layout=None, cpu=True, gpu=None, alignment=False, field_type=FieldType.GENERIC):
if ghost_layers is None:
ghost_layers = self.default_ghost_layers
if gpu is None:
@@ -141,7 +141,7 @@ class ParallelDataHandling(DataHandling):
self.fields[name] = Field.create_generic(name, self.dim, dtype, index_dimensions, layout,
index_shape=(values_per_cell,) if index_dimensions > 0 else None,
- staggered=staggered)
+ field_type=field_type)
self.fields[name].latex_name = latex_name
self._field_name_to_cpu_data_name[name] = name
if gpu:
diff --git a/pystencils/datahandling/serial_datahandling.py b/pystencils/datahandling/serial_datahandling.py
index be9a3f9672335e51e18123bb53c2861f77a7e36a..f8b0a4a1d8b56e8533eb99c6c7f915c00f307419 100644
--- a/pystencils/datahandling/serial_datahandling.py
+++ b/pystencils/datahandling/serial_datahandling.py
@@ -7,7 +7,7 @@ import numpy as np
from pystencils.datahandling.blockiteration import SerialBlock
from pystencils.datahandling.datahandling_interface import DataHandling
from pystencils.field import (
- Field, create_numpy_array_with_layout, layout_string_to_tuple, spatial_layout_string_to_tuple)
+ Field, FieldType, create_numpy_array_with_layout, layout_string_to_tuple, spatial_layout_string_to_tuple)
from pystencils.slicing import normalize_slice, remove_ghost_layers
from pystencils.utils import DotDict
@@ -76,7 +76,7 @@ class SerialDataHandling(DataHandling):
return self._field_information[name]['values_per_cell']
def add_array(self, name, values_per_cell=1, dtype=np.float64, latex_name=None, ghost_layers=None, layout=None,
- cpu=True, gpu=None, alignment=False, staggered=False):
+ cpu=True, gpu=None, alignment=False, field_type=FieldType.GENERIC):
if ghost_layers is None:
ghost_layers = self.default_ghost_layers
if layout is None:
@@ -129,7 +129,7 @@ class SerialDataHandling(DataHandling):
assert all(f.name != name for f in self.fields.values()), "Symbolic field with this name already exists"
self.fields[name] = Field.create_from_numpy_array(name, cpu_arr, index_dimensions=index_dimensions,
- staggered=staggered)
+ field_type=field_type)
self.fields[name].latex_name = latex_name
return self.fields[name]
diff --git a/pystencils/field.py b/pystencils/field.py
index f5f59824b81c40e4f9558f4f9ccdee2951e3f3ea..085d45675829542c3d4c4b6ced3cf194ab5acbfc 100644
--- a/pystencils/field.py
+++ b/pystencils/field.py
@@ -21,7 +21,47 @@ from pystencils.sympyextensions import is_integer_sequence
__all__ = ['Field', 'fields', 'FieldType', 'AbstractField']
-def fields(description=None, index_dimensions=0, layout=None, staggered=False, **kwargs):
+class FieldType(Enum):
+ # generic fields
+ GENERIC = 0
+ # index fields are currently only used for boundary handling
+ # the coordinates are not the loop counters in that case, but are read from this index field
+ INDEXED = 1
+ # communication buffer, used for (un)packing data in communication.
+ BUFFER = 2
+ # unsafe fields may be accessed in an absolute fashion - the index depends on the data
+ # and thus may lead to out-of-bounds accesses
+ CUSTOM = 3
+ # staggered field
+ STAGGERED = 4
+
+ @staticmethod
+ def is_generic(field):
+ assert isinstance(field, Field)
+ return field.field_type == FieldType.GENERIC
+
+ @staticmethod
+ def is_indexed(field):
+ assert isinstance(field, Field)
+ return field.field_type == FieldType.INDEXED
+
+ @staticmethod
+ def is_buffer(field):
+ assert isinstance(field, Field)
+ return field.field_type == FieldType.BUFFER
+
+ @staticmethod
+ def is_custom(field):
+ assert isinstance(field, Field)
+ return field.field_type == FieldType.CUSTOM
+
+ @staticmethod
+ def is_staggered(field):
+ assert isinstance(field, Field)
+ return field.field_type == FieldType.STAGGERED
+
+
+def fields(description=None, index_dimensions=0, layout=None, field_type=FieldType.GENERIC, **kwargs):
"""Creates pystencils fields from a string description.
Examples:
@@ -62,16 +102,16 @@ def fields(description=None, index_dimensions=0, layout=None, staggered=False, *
idx_shape_of_arr = () if not len(idx_shape) else arr.shape[-len(idx_shape):]
assert idx_shape_of_arr == idx_shape
f = Field.create_from_numpy_array(field_name, kwargs[field_name], index_dimensions=len(idx_shape),
- staggered=staggered)
+ field_type=field_type)
elif isinstance(shape, tuple):
f = Field.create_fixed_size(field_name, shape + idx_shape, dtype=dtype,
- index_dimensions=len(idx_shape), layout=layout, staggered=staggered)
+ index_dimensions=len(idx_shape), layout=layout, field_type=field_type)
elif isinstance(shape, int):
f = Field.create_generic(field_name, spatial_dimensions=shape, dtype=dtype,
- index_shape=idx_shape, layout=layout, staggered=staggered)
+ index_shape=idx_shape, layout=layout, field_type=field_type)
elif shape is None:
f = Field.create_generic(field_name, spatial_dimensions=2, dtype=dtype,
- index_shape=idx_shape, layout=layout, staggered=staggered)
+ index_shape=idx_shape, layout=layout, field_type=field_type)
else:
assert False
result.append(f)
@@ -79,7 +119,7 @@ def fields(description=None, index_dimensions=0, layout=None, staggered=False, *
assert layout is None, "Layout can not be specified when creating Field from numpy array"
for field_name, arr in kwargs.items():
result.append(Field.create_from_numpy_array(field_name, arr, index_dimensions=index_dimensions,
- staggered=staggered))
+ field_type=field_type))
if len(result) == 0:
return None
@@ -89,39 +129,6 @@ def fields(description=None, index_dimensions=0, layout=None, staggered=False, *
return result
-class FieldType(Enum):
- # generic fields
- GENERIC = 0
- # index fields are currently only used for boundary handling
- # the coordinates are not the loop counters in that case, but are read from this index field
- INDEXED = 1
- # communication buffer, used for (un)packing data in communication.
- BUFFER = 2
- # unsafe fields may be accessed in an absolute fashion - the index depends on the data
- # and thus may lead to out-of-bounds accesses
- CUSTOM = 3
-
- @staticmethod
- def is_generic(field):
- assert isinstance(field, Field)
- return field.field_type == FieldType.GENERIC
-
- @staticmethod
- def is_indexed(field):
- assert isinstance(field, Field)
- return field.field_type == FieldType.INDEXED
-
- @staticmethod
- def is_buffer(field):
- assert isinstance(field, Field)
- return field.field_type == FieldType.BUFFER
-
- @staticmethod
- def is_custom(field):
- assert isinstance(field, Field)
- return field.field_type == FieldType.CUSTOM
-
-
class AbstractField:
class AbstractAccess:
@@ -182,7 +189,7 @@ class Field(AbstractField):
@staticmethod
def create_generic(field_name, spatial_dimensions, dtype=np.float64, index_dimensions=0, layout='numpy',
- index_shape=None, field_type=FieldType.GENERIC, staggered=False) -> 'Field':
+ index_shape=None, 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
@@ -197,9 +204,9 @@ class Field(AbstractField):
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, and BUFFER fields that are used to generate
- communication packing/unpacking kernels
- staggered: enables staggered access (with half-integer offsets) and corresponding printing
+ that should be iterated over, BUFFER fields that are used to generate communication
+ packing/unpacking kernels, and STAGGERED fields, which store values half-way to the next
+ cell
"""
if index_shape is not None:
assert index_dimensions == 0 or index_dimensions == len(index_shape)
@@ -221,14 +228,14 @@ class Field(AbstractField):
raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
shape += (1,)
strides += (1,)
- if staggered and index_dimensions == 0:
+ if field_type == FieldType.STAGGERED and index_dimensions == 0:
raise ValueError("A staggered field needs at least one index dimension")
- return Field(field_name, field_type, dtype, layout, shape, strides, staggered)
+ return Field(field_name, field_type, dtype, layout, shape, strides)
@staticmethod
def create_from_numpy_array(field_name: str, array: np.ndarray, index_dimensions: int = 0,
- staggered=False) -> 'Field':
+ field_type=FieldType.GENERIC) -> '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.
@@ -237,7 +244,7 @@ class Field(AbstractField):
field_name: symbolic name for the field
array: numpy array
index_dimensions: see documentation of Field
- staggered: enables staggered access (with half-integer offsets) and corresponding printing
+ field_type: kind of field
"""
spatial_dimensions = len(array.shape) - index_dimensions
if spatial_dimensions < 1:
@@ -256,15 +263,15 @@ class Field(AbstractField):
raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
shape += (1,)
strides += (1,)
- if staggered and index_dimensions == 0:
+ if field_type == FieldType.STAGGERED and index_dimensions == 0:
raise ValueError("A staggered field needs at least one index dimension")
- return Field(field_name, FieldType.GENERIC, array.dtype, spatial_layout, shape, strides, staggered)
+ return Field(field_name, field_type, array.dtype, spatial_layout, shape, strides)
@staticmethod
def create_fixed_size(field_name: str, shape: Tuple[int, ...], index_dimensions: int = 0,
dtype=np.float64, layout: str = 'numpy', strides: Optional[Sequence[int]] = None,
- staggered=False) -> 'Field':
+ field_type=FieldType.GENERIC) -> 'Field':
"""
Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout
@@ -275,7 +282,7 @@ class Field(AbstractField):
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)
- staggered: enables staggered access (with half-integer offsets) and corresponding printing
+ field_type: kind of field
"""
spatial_dimensions = len(shape) - index_dimensions
assert spatial_dimensions >= 1
@@ -296,15 +303,15 @@ class Field(AbstractField):
raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
shape += (1,)
strides += (1,)
- if staggered and index_dimensions == 0:
+ if field_type == FieldType.STAGGERED and index_dimensions == 0:
raise ValueError("A staggered field needs at least one index dimension")
spatial_layout = list(layout)
for i in range(spatial_dimensions, len(layout)):
spatial_layout.remove(i)
- return Field(field_name, FieldType.GENERIC, dtype, tuple(spatial_layout), shape, strides, staggered)
+ return Field(field_name, field_type, dtype, tuple(spatial_layout), shape, strides)
- def __init__(self, field_name, field_type, dtype, layout, shape, strides, staggered):
+ def __init__(self, field_name, field_type, dtype, layout, shape, strides):
"""Do not use directly. Use static create* methods"""
self._field_name = field_name
assert isinstance(field_type, FieldType)
@@ -319,7 +326,6 @@ class Field(AbstractField):
0 for _ in range(self.spatial_dimensions)
)) # type: tuple[float,sp.Symbol]
self.coordinate_transform = sp.eye(self.spatial_dimensions)
- self.is_staggered = staggered
def new_field_with_different_name(self, new_name):
if self.has_fixed_shape:
@@ -456,7 +462,7 @@ class Field(AbstractField):
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.
"""
- assert self.is_staggered
+ assert FieldType.is_staggered(self)
if type(offset) is np.ndarray:
offset = tuple(offset)
@@ -611,7 +617,6 @@ class Field(AbstractField):
obj._indirect_addressing_fields.update(a.field for a in e.atoms(Field.Access))
obj._is_absolute_access = is_absolute_access
- obj.is_staggered = field.is_staggered
return obj
def __getnewargs__(self):
@@ -744,7 +749,7 @@ class Field(AbstractField):
def _latex(self, _):
n = self._field.latex_name if self._field.latex_name else self._field.name
offset_str = ",".join([sp.latex(o) for o in self.offsets])
- if self.is_staggered:
+ if FieldType.is_staggered(self._field):
offset_str = ",".join([sp.latex(o - sp.Rational(int(i == self.index[0]), 2))
for i, o in enumerate(self.offsets)])
if self.is_absolute_access:
@@ -752,7 +757,7 @@ class Field(AbstractField):
elif self.field.spatial_dimensions > 1:
offset_str = "({})".format(offset_str)
- if self.is_staggered:
+ if FieldType.is_staggered(self._field):
if self.index and self.field.index_dimensions > 1:
return "{{%s}_{%s}^{%s}}" % (n, offset_str, self.index[1:]
if len(self.index) > 2 else self.index[1])
@@ -767,13 +772,13 @@ class Field(AbstractField):
def __str__(self):
n = self._field.latex_name if self._field.latex_name else self._field.name
offset_str = ",".join([sp.latex(o) for o in self.offsets])
- if self.is_staggered:
+ if FieldType.is_staggered(self._field):
offset_str = ",".join([sp.latex(o - sp.Rational(int(i == self.index[0]), 2))
for i, o in enumerate(self.offsets)])
if self.is_absolute_access:
offset_str = "[abs]{}".format(offset_str)
- if self.is_staggered:
+ if FieldType.is_staggered(self._field):
if self.index and self.field.index_dimensions > 1:
return "%s[%s](%s)" % (n, offset_str, self.index[1:] if len(self.index) > 2 else self.index[1])
else:
diff --git a/pystencils_tests/test_blocking_staggered.py b/pystencils_tests/test_blocking_staggered.py
index f333d4ed34a32cb16e3e7642f542c2e82ea6d2ec..76ec8abf0e3b76d30415571bb43e975f317e9a3f 100644
--- a/pystencils_tests/test_blocking_staggered.py
+++ b/pystencils_tests/test_blocking_staggered.py
@@ -5,7 +5,7 @@ import pystencils as ps
def test_blocking_staggered():
f = ps.fields("f: double[3D]")
- stag = ps.fields("stag(3): double[3D]", staggered=True)
+ stag = ps.fields("stag(3): double[3D]", field_type=ps.FieldType.STAGGERED)
terms = [
f[0, 0, 0] - f[-1, 0, 0],
f[0, 0, 0] - f[0, -1, 0],
diff --git a/pystencils_tests/test_field.py b/pystencils_tests/test_field.py
index 494cc6ab0cb3e0f7b001ba571dc1ca9bd5e4d54d..2a75f5a09f0f72855b9f4cfc7304b9dd1836c149 100644
--- a/pystencils_tests/test_field.py
+++ b/pystencils_tests/test_field.py
@@ -131,7 +131,7 @@ def test_itemsize():
def test_staggered():
- j1, j2, j3 = ps.fields('j1(2), j2(2,2), j3(2,2,2) : double[2D]', staggered=True)
+ j1, j2, j3 = ps.fields('j1(2), j2(2,2), j3(2,2,2) : double[2D]', field_type=FieldType.STAGGERED)
assert j1[0, 1](1) == j1.staggered_access((0, sp.Rational(1, 2)))
assert j1[0, 1](1) == j1.staggered_access("N")
diff --git a/pystencils_tests/test_loop_cutting.py b/pystencils_tests/test_loop_cutting.py
index 0a84db509602be16b1a3828c3a9cdc4d1b9b79e2..cd86238058b1e65e393729d9936a2f44233b4d97 100644
--- a/pystencils_tests/test_loop_cutting.py
+++ b/pystencils_tests/test_loop_cutting.py
@@ -3,7 +3,7 @@ import sympy as sp
import pystencils as ps
import pystencils.astnodes as ast
-from pystencils.field import Field
+from pystencils.field import Field, FieldType
from pystencils.astnodes import Conditional, LoopOverCoordinate, SympyAssignment
from pystencils.cpu import create_kernel, make_python_function
from pystencils.kernelcreation import create_staggered_kernel
@@ -34,9 +34,9 @@ def test_staggered_iteration():
s_arr_ref = s_arr.copy()
fields_fixed = (Field.create_from_numpy_array('f', f_arr),
- Field.create_from_numpy_array('s', s_arr, index_dimensions=1, staggered=True))
+ Field.create_from_numpy_array('s', s_arr, index_dimensions=1, field_type=FieldType.STAGGERED))
fields_var = (Field.create_generic('f', 2),
- Field.create_generic('s', 2, index_dimensions=1, staggered=True))
+ Field.create_generic('s', 2, index_dimensions=1, field_type=FieldType.STAGGERED))
for f, s in [fields_var, fields_fixed]:
# --- Manual
@@ -70,7 +70,7 @@ def test_staggered_iteration_manual():
s_arr_ref = s_arr.copy()
f = Field.create_from_numpy_array('f', f_arr)
- s = Field.create_from_numpy_array('s', s_arr, index_dimensions=1, staggered=True)
+ s = Field.create_from_numpy_array('s', s_arr, index_dimensions=1, field_type=FieldType.STAGGERED)
eqs = []
@@ -108,7 +108,7 @@ def test_staggered_iteration_manual():
def test_staggered_gpu():
dim = 2
f = ps.fields("f: double[{dim}D]".format(dim=dim))
- s = ps.fields("s({dim}): double[{dim}D]".format(dim=dim), staggered=True)
+ s = ps.fields("s({dim}): double[{dim}D]".format(dim=dim), field_type=FieldType.STAGGERED)
expressions = [(f[0, 0] + f[-1, 0]) / 2,
(f[0, 0] + f[0, -1]) / 2]
kernel_ast = ps.create_staggered_kernel(s, expressions, target='gpu', gpu_exclusive_conditions=True)