Function reshape

dask/array/reshape.py:283–375

Reshape array to new shape Parameters ---------- shape : int or tuple of ints The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length. One shape dimension can be -1. In this case, the value is

(x, shape, merge_chunks=True, limit=None)

def reshape(x, shape, merge_chunks=True, limit=None):
    """Reshape array to new shape

    Parameters
    ----------
    shape : int or tuple of ints
        The new shape should be compatible with the original shape. If
        an integer, then the result will be a 1-D array of that length.
        One shape dimension can be -1. In this case, the value is
        inferred from the length of the array and remaining dimensions.
    merge_chunks : bool, default True
        Whether to merge chunks using the logic in :meth:`dask.array.rechunk`
        when communication is necessary given the input array chunking and
        the output shape. With ``merge_chunks==False``, the input array will
        be rechunked to a chunksize of 1, which can create very many tasks.
    limit: int (optional)
        The maximum block size to target in bytes. If no limit is provided,
        it defaults to using the ``array.chunk-size`` Dask config value.

    Notes
    -----
    This is a parallelized version of the ``np.reshape`` function with the
    following limitations:

    1.  It assumes that the array is stored in `row-major order`_
    2.  It only allows for reshapings that collapse or merge dimensions like
        ``(1, 2, 3, 4) -> (1, 6, 4)`` or ``(64,) -> (4, 4, 4)``

    .. _`row-major order`: https://en.wikipedia.org/wiki/Row-_and_column-major_order

    When communication is necessary this algorithm depends on the logic within
    rechunk.  It endeavors to keep chunk sizes roughly the same when possible.

    See :ref:`array-chunks.reshaping` for a discussion the tradeoffs of
    ``merge_chunks``.

    See Also
    --------
    dask.array.rechunk
    numpy.reshape
    """
    # Sanitize inputs, look for -1 in shape
    from dask.array.slicing import sanitize_index

    shape = tuple(map(sanitize_index, shape))
    known_sizes = [s for s in shape if s != -1]
    if len(known_sizes) < len(shape):
        if len(shape) - len(known_sizes) > 1:
            raise ValueError("can only specify one unknown dimension")
        # Fastpath for x.reshape(-1) on 1D arrays, allows unknown shape in x
        # for this case only.
        if len(shape) == 1 and x.ndim == 1:
            return x
        missing_size = sanitize_index(x.size / reduce(mul, known_sizes, 1))
        shape = tuple(missing_size if s == -1 else s for s in shape)

    _sanity_checks(x, shape)