Function concatenate

dask/array/_array_expr/_collection.py:1466–1581

Concatenate arrays along an existing axis Given a sequence of dask Arrays form a new dask Array by stacking them along an existing dimension (axis=0 by default) Parameters ---------- seq: list of dask.arrays axis: int Dimension along which to align all of the a

(seq, axis=0, allow_unknown_chunksizes=False)

def concatenate(seq, axis=0, allow_unknown_chunksizes=False):
    """
    Concatenate arrays along an existing axis

    Given a sequence of dask Arrays form a new dask Array by stacking them
    along an existing dimension (axis=0 by default)

    Parameters
    ----------
    seq: list of dask.arrays
    axis: int
        Dimension along which to align all of the arrays. If axis is None,
        arrays are flattened before use.
    allow_unknown_chunksizes: bool
        Allow unknown chunksizes, such as come from converting from dask
        dataframes.  Dask.array is unable to verify that chunks line up.  If
        data comes from differently aligned sources then this can cause
        unexpected results.

    Examples
    --------

    Create slices

    >>> import dask.array as da
    >>> import numpy as np

    >>> data = [da.from_array(np.ones((4, 4)), chunks=(2, 2))
    ...          for i in range(3)]

    >>> x = da.concatenate(data, axis=0)
    >>> x.shape
    (12, 4)

    >>> da.concatenate(data, axis=1).shape
    (4, 12)

    Result is a new dask Array

    See Also
    --------
    stack
    """
    from dask.array import wrap

    seq = [asarray(a, allow_unknown_chunksizes=allow_unknown_chunksizes) for a in seq]

    if not seq:
        raise ValueError("Need array(s) to concatenate")

    if axis is None:
        seq = [a.flatten() for a in seq]
        axis = 0

    seq_metas = [meta_from_array(s) for s in seq]
    _concatenate = concatenate_lookup.dispatch(
        type(max(seq_metas, key=lambda x: getattr(x, "__array_priority__", 0)))
    )