dask.array.shuffle
dask.array.shuffle¶
- dask.array.shuffle(x, indexer: list[list[int]], axis: int, chunks: Literal['auto'] = 'auto')[source]¶
Reorders one dimensions of a Dask Array based on an indexer.
The indexer defines a list of positional groups that will end up in the same chunk together. A single group is in at most one chunk on this dimension, but a chunk might contain multiple groups to avoid fragmentation of the array.
The algorithm tries to balance the chunksizes as much as possible to ideally keep the number of chunks consistent or at least manageable.
- Parameters
- x: dask array
Array to be shuffled.
- indexer: list[list[int]]
The indexer that determines which elements along the dimension will end up in the same chunk. Multiple groups can be in the same chunk to avoid fragmentation, but each group will end up in exactly one chunk.
- axis: int
The axis to shuffle along.
- chunks: “auto”
Hint on how to rechunk if single groups are becoming too large. The default is to split chunks along the other dimensions evenly to keep the chunksize consistent. The rechunking is done in a way that ensures that non all-to-all network communication is necessary, chunks are only split and not combined with other chunks.
Examples
>>> import dask.array as da >>> import numpy as np >>> arr = np.array([[1, 2, 3, 4, 5, 6, 7, 8], [9, 10, 11, 12, 13, 14, 15, 16]]) >>> x = da.from_array(arr, chunks=(2, 4))
Separate the elements in different groups.
>>> y = x.shuffle([[6, 5, 2], [4, 1], [3, 0, 7]], axis=1)
The shuffle algorihthm will combine the first 2 groups into a single chunk to keep the number of chunks small.
The tolerance of increasing the chunk size is controlled by the configuration “array.chunk-size-tolerance”. The default value is 1.25.
>>> y.chunks ((2,), (5, 3))
The array was reordered along axis 1 according to the positional indexer that was given.
>>> y.compute() array([[ 7, 6, 3, 5, 2, 4, 1, 8], [15, 14, 11, 13, 10, 12, 9, 16]])