slice_with_int_dask_array_aggregate(idx, chunk_outputs, x_chunks, axis)

Note that there is no combine function, as a recursive aggregation (e.g. with split_every) would not give any benefit.

Parameters

idx: ndarray, ndim=1, dtype=any integer :

j-th chunk of idx

chunk_outputs: ndarray :

concatenation along axis of the outputs of slice_with_int_dask_array for all chunks of x and the j-th chunk of idx

x_chunks: tuple :

dask chunks of the x da.Array along axis, e.g. (3, 3, 2)

axis: int :

normalized axis to take elements from (0 <= axis < x.ndim)

Returns

Selection from all chunks of x for the j-th chunk of idx, in the correct

order

Final aggregation function of slice_with_int_dask_array_on_axis . Aggregate all chunks of x by one chunk of idx, reordering the output of slice_with_int_dask_array .

Examples

See :

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

File: /dask/array/chunk.py#345
type: <class 'function'>
Commit: