Function concatenate3

dask/array/core.py:5630–5697

Recursive np.concatenate Input should be a nested list of numpy arrays arranged in the order they should appear in the array itself. Each array should have the same number of dimensions as the desired output and the nesting of the lists. >>> x = np.array([[1, 2]]) >>> concaten

(arrays)

def concatenate3(arrays):
    """Recursive np.concatenate

    Input should be a nested list of numpy arrays arranged in the order they
    should appear in the array itself.  Each array should have the same number
    of dimensions as the desired output and the nesting of the lists.

    >>> x = np.array([[1, 2]])
    >>> concatenate3([[x, x, x], [x, x, x]])
    array([[1, 2, 1, 2, 1, 2],
           [1, 2, 1, 2, 1, 2]])

    >>> concatenate3([[x, x], [x, x], [x, x]])
    array([[1, 2, 1, 2],
           [1, 2, 1, 2],
           [1, 2, 1, 2]])
    """
    # We need this as __array_function__ may not exist on older NumPy versions.
    # And to reduce verbosity.
    NDARRAY_ARRAY_FUNCTION = getattr(np.ndarray, "__array_function__", None)

    arrays = concrete(arrays)
    if not arrays or all(el is None for el in flatten(arrays)):
        return np.empty(0)

    advanced = max(
        core.flatten(arrays, container=(list, tuple)),
        key=lambda x: getattr(x, "__array_priority__", 0),
    )

    if not all(
        NDARRAY_ARRAY_FUNCTION
        is getattr(type(arr), "__array_function__", NDARRAY_ARRAY_FUNCTION)
        for arr in core.flatten(arrays, container=(list, tuple))
    ):
        try:
            x = unpack_singleton(arrays)
            return _concatenate2(arrays, axes=tuple(range(x.ndim)))
        except TypeError:
            pass

    if concatenate_lookup.dispatch(type(advanced)) is not np.concatenate:
        x = unpack_singleton(arrays)
        return _concatenate2(arrays, axes=list(range(x.ndim)))

    ndim = ndimlist(arrays)
    if not ndim:
        return arrays
    chunks = chunks_from_arrays(arrays)
    shape = tuple(map(sum, chunks))

    def dtype(x):
        try:
            return x.dtype
        except AttributeError:
            return type(x)

    result = np.empty(shape=shape, dtype=dtype(deepfirst(arrays)))