Function normalize_chunks

dask/array/core.py:3064–3234

Normalize chunks to tuple of tuples This takes in a variety of input types and information and produces a full tuple-of-tuples result for chunks, suitable to be passed to Array or rechunk or any other operation that creates a Dask array. Parameters ---------- chunks: tuple,

(chunks, shape=None, limit=None, dtype=None, previous_chunks=None)

def normalize_chunks(chunks, shape=None, limit=None, dtype=None, previous_chunks=None):
    """Normalize chunks to tuple of tuples

    This takes in a variety of input types and information and produces a full
    tuple-of-tuples result for chunks, suitable to be passed to Array or
    rechunk or any other operation that creates a Dask array.

    Parameters
    ----------
    chunks: tuple, int, dict, or string
        The chunks to be normalized.  See examples below for more details
    shape: Tuple[int]
        The shape of the array
    limit: int (optional)
        The maximum block size to target in bytes,
        if freedom is given to choose
    dtype: np.dtype
    previous_chunks: Tuple[Tuple[int]] optional
        Chunks from a previous array that we should use for inspiration when
        rechunking auto dimensions.  If not provided but auto-chunking exists
        then auto-dimensions will prefer square-like chunk shapes.

    Examples
    --------
    Fully explicit tuple-of-tuples

    >>> from dask.array.core import normalize_chunks
    >>> normalize_chunks(((2, 2, 1), (2, 2, 2)), shape=(5, 6))
    ((2, 2, 1), (2, 2, 2))

    Specify uniform chunk sizes

    >>> normalize_chunks((2, 2), shape=(5, 6))
    ((2, 2, 1), (2, 2, 2))

    Cleans up missing outer tuple

    >>> normalize_chunks((3, 2), (5,))
    ((3, 2),)

    Cleans up lists to tuples

    >>> normalize_chunks([[2, 2], [3, 3]])
    ((2, 2), (3, 3))

    Expands integer inputs 10 -> (10, 10)

    >>> normalize_chunks(10, shape=(30, 5))
    ((10, 10, 10), (5,))

    Expands dict inputs

    >>> normalize_chunks({0: 2, 1: 3}, shape=(6, 6))
    ((2, 2, 2), (3, 3))

    The values -1 and None get mapped to full size

    >>> normalize_chunks((5, -1), shape=(10, 10))