field.py 31.4 KB
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from enum import Enum
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from itertools import chain
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from typing import Tuple, Sequence, Optional, List, Set
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import numpy as np
import sympy as sp
from sympy.core.cache import cacheit
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from pystencils.alignedarray import aligned_empty
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from pystencils.data_types import create_type, StructType
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from pystencils.kernelparameters import FieldShapeSymbol, FieldStrideSymbol
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from pystencils.stencils import offset_to_direction_string, direction_string_to_offset
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from pystencils.sympyextensions import is_integer_sequence
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__all__ = ['Field', 'fields', 'FieldType']

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def fields(description=None, index_dimensions=0, layout=None, **kwargs):
    """Creates pystencils fields from a string description.

    Examples:
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        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,))
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        Create an integer field of shape (10, 20):
            >>> f = fields("f : int32[10, 20]")
            >>> f.has_fixed_shape, f.shape
            (True, (10, 20))
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        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,)
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        Format string can be left out, field names are taken from keyword arguments.
            >>> fields(f1=arr_s, f2=arr_s)
            [f1, f2]

        The keyword names ``index_dimension`` and ``layout`` 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)
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    """
    result = []
    if description:
        field_descriptions, dtype, shape = _parse_description(description)
        layout = 'numpy' if layout is None else layout
        for field_name, idx_shape in field_descriptions:
            if field_name in kwargs:
                arr = kwargs[field_name]
                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))
            elif isinstance(shape, tuple):
                f = Field.create_fixed_size(field_name, shape + idx_shape, dtype=dtype,
                                            index_dimensions=len(idx_shape), layout=layout)
            elif isinstance(shape, int):
                f = Field.create_generic(field_name, spatial_dimensions=shape, dtype=dtype,
                                         index_shape=idx_shape, layout=layout)
            elif shape is None:
                f = Field.create_generic(field_name, spatial_dimensions=2, dtype=dtype,
                                         index_shape=idx_shape, layout=layout)
            else:
                assert False
            result.append(f)
    else:
        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))

    if len(result) == 0:
        return None
    elif len(result) == 1:
        return result[0]
    else:
        return result


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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
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    # 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
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    @staticmethod
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    def is_generic(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.GENERIC
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    @staticmethod
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    def is_indexed(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.INDEXED
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    @staticmethod
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    def is_buffer(field):
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        assert isinstance(field, Field)
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        return field.field_type == FieldType.BUFFER
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    @staticmethod
    def is_custom(field):
        assert isinstance(field, Field)
        return field.field_type == FieldType.CUSTOM

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class Field:
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    """
    With fields one can formulate stencil-like update rules on structured grids.
    This Field class knows about the dimension, memory layout (strides) and optionally about the size of an array.

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    Creating Fields:
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        The preferred method to create fields is the `fields` function.
        Alternatively one can use one of the static functions `Field.create_generic`, `Field.create_from_numpy_array`
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        and `Field.create_fixed_size`. Don't instantiate the Field directly!
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        Fields can be created with known or unknown shapes:

        1. 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 just-in-time compilation directly from Python is done.
           (see `Field.create_from_numpy_array`
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        2. create a more general kernel that works for variable array sizes. This can be used to create kernels
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           beforehand for a library. (see `Field.create_generic`)
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    Dimensions and Indexing:
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        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
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        looped over. Additionally N values are stored per cell. In this case spatial_dimensions is two or three,
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        and index_dimensions equals N. If you want to store a matrix on each point in a two dimensional grid, there
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        are four dimensions, two spatial and two index dimensions: ``len(arr.shape) == spatial_dims + index_dims``
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        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.
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        First specify the spatial offsets in [], then in case index_dimension>0 the indices in ()
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        e.g. ``f[-1,0,0](7)``
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    Example using no index dimensions:
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        >>> a = np.zeros([10, 10])
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        >>> f = Field.create_from_numpy_array("f", a, index_dimensions=0)
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        >>> jacobi = (f[-1,0] + f[1,0] + f[0,-1] + f[0,1]) / 4
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    Examples for index dimensions to create LB field and implement stream pull:
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        >>> from pystencils import Assignment
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        >>> stencil = np.array([[0,0], [0,1], [0,-1]])
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        >>> src, dst = fields("src(3), dst(3) : double[2D]")
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        >>> assignments = [Assignment(dst[0,0](i), src[-offset](i)) for i, offset in enumerate(stencil)];
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    """
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    @staticmethod
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    def create_generic(field_name, spatial_dimensions, dtype=np.float64, index_dimensions=0, layout='numpy',
                       index_shape=None, field_type=FieldType.GENERIC) -> 'Field':
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        """
        Creates a generic field where the field size is not fixed i.e. can be called with arrays of different sizes

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        Args:
            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
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                    over dimension 0. Also allowed: the strings 'numpy' (0,1,..d) or 'reverse_numpy' (d, ..., 1, 0)
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            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
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        """
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        if index_shape is not None:
            assert index_dimensions == 0 or index_dimensions == len(index_shape)
            index_dimensions = len(index_shape)
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        if isinstance(layout, str):
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            layout = spatial_layout_string_to_tuple(layout, dim=spatial_dimensions)
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        total_dimensions = spatial_dimensions + index_dimensions
        if index_shape is None or len(index_shape) == 0:
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            shape = tuple([FieldShapeSymbol([field_name], i) for i in range(total_dimensions)])
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        else:
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            shape = tuple([FieldShapeSymbol([field_name], i) for i in range(spatial_dimensions)] + list(index_shape))
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        strides = tuple([FieldStrideSymbol(field_name, i) for i in range(total_dimensions)])
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        np_data_type = np.dtype(dtype)
        if np_data_type.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        return Field(field_name, field_type, dtype, layout, shape, strides)
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    @staticmethod
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    def create_from_numpy_array(field_name: str, array: np.ndarray, index_dimensions: int = 0) -> 'Field':
        """Creates a field based on the layout, data type, and shape of a given numpy array.

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        Kernels created for these kind of fields can only be called with arrays of the same layout, shape and type.
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        Args:
            field_name: symbolic name for the field
            array: numpy array
            index_dimensions: see documentation of Field
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        """
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        spatial_dimensions = len(array.shape) - index_dimensions
        if spatial_dimensions < 1:
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            raise ValueError("Too many index dimensions. At least one spatial dimension required")

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        full_layout = get_layout_of_array(array)
        spatial_layout = tuple([i for i in full_layout if i < spatial_dimensions])
        assert len(spatial_layout) == spatial_dimensions
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        strides = tuple([s // np.dtype(array.dtype).itemsize for s in array.strides])
        shape = tuple(int(s) for s in array.shape)
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        numpy_dtype = np.dtype(array.dtype)
        if numpy_dtype.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        return Field(field_name, FieldType.GENERIC, array.dtype, spatial_layout, shape, strides)
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    @staticmethod
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    def create_fixed_size(field_name: str, shape: Tuple[int, ...], index_dimensions: int = 0,
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                          dtype=np.float64, layout: str = 'numpy', strides: Optional[Sequence[int]] = None) -> 'Field':
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        """
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        Creates a field with fixed sizes i.e. can be called only with arrays of the same size and layout
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        Args:
            field_name: symbolic name for the field
            shape: overall shape of the array
            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)
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        """
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        spatial_dimensions = len(shape) - index_dimensions
        assert spatial_dimensions >= 1
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        if isinstance(layout, str):
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            layout = layout_string_to_tuple(layout, spatial_dimensions + index_dimensions)
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        shape = tuple(int(s) for s in shape)
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        if strides is None:
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            strides = compute_strides(shape, layout)
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        else:
            assert len(strides) == len(shape)
            strides = tuple([s // np.dtype(dtype).itemsize for s in strides])
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        numpy_dtype = np.dtype(dtype)
        if numpy_dtype.fields is not None:
            if index_dimensions != 0:
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                raise ValueError("Structured arrays/fields are not allowed to have an index dimension")
            shape += (1,)
            strides += (1,)

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        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)
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    def __init__(self, field_name, field_type, dtype, layout, shape, strides):
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        """Do not use directly. Use static create* methods"""
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        self._field_name = field_name
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        assert isinstance(field_type, FieldType)
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        assert len(shape) == len(strides)
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        self.field_type = field_type
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        self._dtype = create_type(dtype)
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        self._layout = normalize_layout(layout)
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        self.shape = shape
        self.strides = strides
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        self.latex_name = None  # type: Optional[str]
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    def new_field_with_different_name(self, new_name):
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        return Field(new_name, self.field_type, self._dtype, self._layout, self.shape, self.strides)
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    @property
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    def spatial_dimensions(self) -> int:
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        return len(self._layout)

    @property
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    def index_dimensions(self) -> int:
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        return len(self.shape) - len(self._layout)
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    @property
    def layout(self):
        return self._layout

    @property
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    def name(self) -> str:
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        return self._field_name
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    @property
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    def spatial_shape(self) -> Tuple[int, ...]:
        return self.shape[:self.spatial_dimensions]
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    @property
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    def has_fixed_shape(self):
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        return is_integer_sequence(self.shape)
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    @property
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    def index_shape(self):
        return self.shape[self.spatial_dimensions:]
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    @property
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    def has_fixed_index_shape(self):
        return is_integer_sequence(self.index_shape)
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    @property
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    def spatial_strides(self):
        return self.strides[:self.spatial_dimensions]
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    @property
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    def index_strides(self):
        return self.strides[self.spatial_dimensions:]
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    @property
    def dtype(self):
        return self._dtype

    def __repr__(self):
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        return self._field_name
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    def neighbor(self, coord_id, offset):
        offset_list = [0] * self.spatial_dimensions
        offset_list[coord_id] = offset
        return Field.Access(self, tuple(offset_list))
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    def neighbors(self, stencil):
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        return [self.__getitem__(s) for s in stencil]
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    @property
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    def center_vector(self):
        index_shape = self.index_shape
        if len(index_shape) == 0:
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            return self.center
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        elif len(index_shape) == 1:
            return sp.Matrix([self(i) for i in range(index_shape[0])])
        elif len(index_shape) == 2:
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            def cb(*args):
                r = self.__call__(*args)
                return r
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            return sp.Matrix(*index_shape, cb)
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    @property
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    def center(self):
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        center = tuple([0] * self.spatial_dimensions)
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        return Field.Access(self, center)

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    def __getitem__(self, offset):
        if type(offset) is np.ndarray:
            offset = tuple(offset)
        if type(offset) is str:
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            offset = tuple(direction_string_to_offset(offset, self.spatial_dimensions))
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        if type(offset) is not tuple:
            offset = (offset,)
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        if len(offset) != self.spatial_dimensions:
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            raise ValueError("Wrong number of spatial indices: "
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                             "Got %d, expected %d" % (len(offset), self.spatial_dimensions))
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        return Field.Access(self, offset)

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    def absolute_access(self, offset, index):
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        assert FieldType.is_custom(self)
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        return Field.Access(self, offset, index, is_absolute_access=True)

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    def __call__(self, *args, **kwargs):
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        center = tuple([0] * self.spatial_dimensions)
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        return Field.Access(self, center)(*args, **kwargs)

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    def hashable_contents(self):
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        dth = hash(self._dtype)
        return self._layout, self.shape, self.strides, dth, self.field_type, self._field_name, self.latex_name
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    def __hash__(self):
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        return hash(self.hashable_contents())
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    def __eq__(self, other):
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        if not isinstance(other, Field):
            return False
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        return self.hashable_contents() == other.hashable_contents()
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    # noinspection PyAttributeOutsideInit,PyUnresolvedReferences
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    class Access(sp.Symbol):
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        """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
        """
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        def __new__(cls, name, *args, **kwargs):
            obj = Field.Access.__xnew_cached_(cls, name, *args, **kwargs)
            return obj

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        def __new_stage2__(self, field, offsets=(0, 0, 0), idx=None, is_absolute_access=False):
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            field_name = field.name
            offsets_and_index = chain(offsets, idx) if idx is not None else offsets
            constant_offsets = not any([isinstance(o, sp.Basic) and not o.is_Integer for o in offsets_and_index])
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            if not idx:
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                idx = tuple([0] * field.index_dimensions)
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            if constant_offsets:
                offset_name = offset_to_direction_string(offsets)
                if field.index_dimensions == 0:
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                    superscript = None
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                elif field.index_dimensions == 1:
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                    superscript = str(idx[0])
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                else:
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                    idx_str = ",".join([str(e) for e in idx])
                    superscript = idx_str
                if field.has_fixed_index_shape and not isinstance(field.dtype, StructType):
                    for i, bound in zip(idx, field.index_shape):
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                        if i >= bound:
                            raise ValueError("Field index out of bounds")
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            else:
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                offset_name = "%0.10X" % (abs(hash(tuple(offsets_and_index))))
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                superscript = None
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            symbol_name = "%s_%s" % (field_name, offset_name)
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            if superscript is not None:
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                symbol_name += "^" + superscript
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            obj = super(Field.Access, self).__xnew__(self, symbol_name)
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            obj._field = field
            obj._offsets = []
            for o in offsets:
                if isinstance(o, sp.Basic):
                    obj._offsets.append(o)
                else:
                    obj._offsets.append(int(o))
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            obj._offsets = tuple(obj._offsets)
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            obj._offsetName = offset_name
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            obj._superscript = superscript
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            obj._index = idx

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            obj._indirect_addressing_fields = set()
            for e in chain(obj._offsets, obj._index):
                if isinstance(e, sp.Basic):
                    obj._indirect_addressing_fields.update(a.field for a in e.atoms(Field.Access))

            obj._is_absolute_access = is_absolute_access
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            return obj

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        def __getnewargs__(self):
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            return self.field, self.offsets, self.index, self.is_absolute_access
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        # noinspection SpellCheckingInspection
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        __xnew__ = staticmethod(__new_stage2__)
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        # noinspection SpellCheckingInspection
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        __xnew_cached_ = staticmethod(cacheit(__new_stage2__))

        def __call__(self, *idx):
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            if self._index != tuple([0] * self.field.index_dimensions):
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                raise ValueError("Indexing an already indexed Field.Access")

            idx = tuple(idx)
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            if self.field.index_dimensions == 0 and idx == (0,):
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                idx = ()

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            if len(idx) != self.field.index_dimensions:
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                raise ValueError("Wrong number of indices: "
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                                 "Got %d, expected %d" % (len(idx), self.field.index_dimensions))
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            return Field.Access(self.field, self._offsets, idx)

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        def __getitem__(self, *idx):
            return self.__call__(*idx)

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        def __iter__(self):
            """This is necessary to work with parts of sympy that test if an object is iterable (e.g. simplify).
            The __getitem__ would make it iterable"""
            raise TypeError("Field access is not iterable")

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        @property
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        def field(self) -> 'Field':
            """Field that the Access points to"""
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            return self._field

        @property
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        def offsets(self) -> Tuple:
            """Spatial offset as tuple"""
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            return self._offsets
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        @property
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        def required_ghost_layers(self) -> int:
            """Largest spatial distance that is accessed."""
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            return int(np.max(np.abs(self._offsets)))

        @property
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        def nr_of_coordinates(self):
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            return len(self._offsets)

        @property
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        def offset_name(self) -> str:
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            """Spatial offset as string, East-West for x, North-South for y and Top-Bottom for z coordinate.

            Example:
                >>> f = fields("f: double[2D]")
                >>> f[1, 1].offset_name  # north-east
                'NE'
            """
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            return self._offsetName

        @property
        def index(self):
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            """Value of index coordinates as tuple."""
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            return self._index

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        def neighbor(self, coord_id: int, offset: Sequence[int]) -> 'Field.Access':
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            """Returns a new Access with changed spatial coordinates.

            Args:
                coord_id: index of the coordinate to change (0 for x, 1 for y,...)
                offset: incremental change of this coordinate

            Example:
                >>> f = fields('f: [2D]')
                >>> f[0,0].neighbor(coord_id=1, offset=-1)
                f_S
            """
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            offset_list = list(self.offsets)
            offset_list[coord_id] += offset
            return Field.Access(self.field, tuple(offset_list), self.index)
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        def get_shifted(self, *shift)-> 'Field.Access':
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            """Returns a new Access with changed spatial coordinates

            Example:
                >>> f = fields("f: [2D]")
                >>> f[0,0].get_shifted(1, 1)
                f_NE
            """
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            return Field.Access(self.field, tuple(a + b for a, b in zip(shift, self.offsets)), self.index)

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        def at_index(self, *idx_tuple) -> 'Field.Access':
            """Returns new Access with changed index.

            Example:
                >>> f = fields("f(9): [2D]")
                >>> f(0).at_index(8)
                f_C^8
            """
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            return Field.Access(self.field, self.offsets, idx_tuple)

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        @property
        def is_absolute_access(self) -> bool:
            """Indicates if a field access is relative to the loop counters (this is the default) or absolute"""
            return self._is_absolute_access

        @property
        def indirect_addressing_fields(self) -> Set['Field']:
            """Returns a set of fields that the access depends on.

             e.g. f[index_field[1, 0]], the outer access to f depends on index_field
             """
            return self._indirect_addressing_fields

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        def _hashable_content(self):
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            super_class_contents = super(Field.Access, self)._hashable_content()
            return (super_class_contents, self._field.hashable_contents(), *self._index, *self._offsets)
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        def _latex(self, _):
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            n = self._field.latex_name if self._field.latex_name else self._field.name
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            offset_str = ",".join([sp.latex(o) for o in self.offsets])
            if self.is_absolute_access:
                offset_str = "\\mathbf{}".format(offset_str)
            elif self.field.spatial_dimensions > 1:
                offset_str = "({})".format(offset_str)

            if self.index and self.index != (0,):
                return "{{%s}_{%s}^{%s}}" % (n, offset_str, self.index if len(self.index) > 1 else self.index[0])
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            else:
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                return "{{%s}_{%s}}" % (n, offset_str)
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        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_absolute_access:
                offset_str = "[abs]{}".format(offset_str)
            if self.index and self.index != (0,):
                return "%s[%s](%s)" % (n, offset_str, self.index if len(self.index) > 1 else self.index[0])
            else:
                return "%s[%s]" % (n, offset_str)

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def get_layout_from_strides(strides: Sequence[int], index_dimension_ids: Optional[List[int]] = None):
    index_dimension_ids = [] if index_dimension_ids is None else index_dimension_ids
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    coordinates = list(range(len(strides)))
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    relevant_strides = [stride for i, stride in enumerate(strides) if i not in index_dimension_ids]
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    result = [x for (y, x) in sorted(zip(relevant_strides, coordinates), key=lambda pair: pair[0], reverse=True)]
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    return normalize_layout(result)
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def get_layout_of_array(arr: np.ndarray, index_dimension_ids: Optional[List[int]] = None):
    """ Returns a list indicating the memory layout (linearization order) of the numpy array.

    Examples:
        >>> get_layout_of_array(np.zeros([3,3,3]))
        (0, 1, 2)
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    In this example the loop over the zeroth coordinate should be the outermost loop,
    followed by the first and second. Elements arr[x,y,0] and arr[x,y,1] are adjacent in memory.
    Normally constructed numpy arrays have this order, however by stride tricks or other frameworks, arrays
    with different memory layout can be created.
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    The index_dimension_ids parameter leaves specifies which coordinates should not be
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    """
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    index_dimension_ids = [] if index_dimension_ids is None else index_dimension_ids
    return get_layout_from_strides(arr.strides, index_dimension_ids)
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def create_numpy_array_with_layout(shape, layout, alignment=False, byte_offset=0, **kwargs):
    """Creates numpy array with given memory layout.

    Args:
        shape: shape of the resulting array
        layout: layout as tuple, where the coordinates are ordered from slow to fast
        alignment: number of bytes to align the beginning and the innermost coordinate to, or False for no alignment
        byte_offset: only used when alignment is specified, align not beginning but address at this offset
                     mostly used to align first inner cell, not ghost cells

    Example:
        >>> res = create_numpy_array_with_layout(shape=(2, 3, 4, 5), layout=(3, 2, 0, 1))
        >>> res.shape
        (2, 3, 4, 5)
        >>> get_layout_of_array(res)
        (3, 2, 0, 1)
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    """
    assert set(layout) == set(range(len(shape))), "Wrong layout descriptor"
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    cur_layout = list(range(len(shape)))
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    swaps = []
    for i in range(len(layout)):
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        if cur_layout[i] != layout[i]:
            index_to_swap_with = cur_layout.index(layout[i])
            swaps.append((i, index_to_swap_with))
            cur_layout[i], cur_layout[index_to_swap_with] = cur_layout[index_to_swap_with], cur_layout[i]
    assert tuple(cur_layout) == tuple(layout)
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    shape = list(shape)
    for a, b in swaps:
        shape[a], shape[b] = shape[b], shape[a]

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    if not alignment:
        res = np.empty(shape, order='c', **kwargs)
    else:
        if alignment is True:
            alignment = 8 * 4
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        res = aligned_empty(shape, alignment, byte_offset=byte_offset, **kwargs)
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    for a, b in reversed(swaps):
        res = res.swapaxes(a, b)
    return res


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def spatial_layout_string_to_tuple(layout_str: str, dim: int) -> Tuple[int, ...]:
    if layout_str in ('fzyx', 'zyxf'):
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        assert dim <= 3
        return tuple(reversed(range(dim)))
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    if layout_str in ('fzyx', 'f', 'reverse_numpy', 'SoA'):
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        return tuple(reversed(range(dim)))
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    elif layout_str in ('c', 'numpy', 'AoS'):
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        return tuple(range(dim))
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    raise ValueError("Unknown layout descriptor " + layout_str)
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def layout_string_to_tuple(layout_str, dim):
    layout_str = layout_str.lower()
    if layout_str == 'fzyx' or layout_str == 'soa':
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        assert dim <= 4
        return tuple(reversed(range(dim)))
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    elif layout_str == 'zyxf' or layout_str == 'aos':
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        assert dim <= 4
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        return tuple(reversed(range(dim - 1))) + (dim - 1,)
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    elif layout_str == 'f' or layout_str == 'reverse_numpy':
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        return tuple(reversed(range(dim)))
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    elif layout_str == 'c' or layout_str == 'numpy':
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        return tuple(range(dim))
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    raise ValueError("Unknown layout descriptor " + layout_str)
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def normalize_layout(layout):
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    """Takes a layout tuple and subtracts the minimum from all entries"""
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    min_entry = min(layout)
    return tuple(i - min_entry for i in layout)
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def compute_strides(shape, layout):
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    """
    Computes strides assuming no padding exists
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    Args:
        shape: shape (size) of array
        layout: layout specification as tuple

    Returns:
        strides in elements, not in bytes
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    """
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    dim = len(shape)
    assert len(layout) == dim
    assert len(set(layout)) == dim
    strides = [0] * dim
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    product = 1
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    for j in reversed(layout):
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        strides[j] = product
        product *= shape[j]
    return tuple(strides)
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def _parse_type_description(type_description):
    if not type_description:
        return np.float64, None
    elif '[' in type_description:
        assert type_description[-1] == ']'
        type_part, size_part = type_description[:-1].split("[", )
        if not type_part:
            type_part = "float64"
        if size_part.lower()[-1] == 'd':
            size_part = int(size_part[:-1])
        else:
            size_part = tuple(int(i) for i in size_part.split(','))
    else:
        type_part, size_part = type_description, None

    dtype = np.dtype(type_part).type
    return dtype, size_part


def _parse_field_description(description):
    if '(' not in description:
        return description, ()
    assert description[-1] == ')'
    name, index_shape = description[:-1].split('(')
    index_shape = tuple(int(i) for i in index_shape.split(','))
    return name, index_shape


def _parse_description(description):
    description = description.replace(' ', '')
    if ':' in description:
        name_descr, type_descr = description.split(':')
    else:
        name_descr, type_descr = description, ''

    # correct ',' splits inside brackets
    field_names = name_descr.split(',')
    cleaned_field_names = []
    prefix = ''
    for field_name in field_names:
        full_field_name = prefix + field_name
        if '(' in full_field_name and ')' not in full_field_name:
            prefix += field_name + ','
        else:
            prefix = ''
            cleaned_field_names.append(full_field_name)

    dtype, size = _parse_type_description(type_descr)
    fields_info = tuple(_parse_field_description(fd) for fd in cleaned_field_names)
    return fields_info, dtype, size