Stack, Concatenate, and Block

Often we have many arrays stored on disk that we want to stack together and think of as one large array. This is common with geospatial data in which we might have many HDF5/NetCDF files on disk, one for every day, but we want to do operations that span multiple days.

To solve this problem we use the functions da.stack, da.concatenate, and da.block.

Stack

We stack many existing Dask arrays into a new array, creating a new dimension as we go.

>>> import dask.array as da

>>> arr0 = da.from_array(np.zeros((3, 4)), chunks=(1, 2))
>>> arr1 = da.from_array(np.ones((3, 4)), chunks=(1, 2))

>>> data = [arr0, arr1]

>>> x = da.stack(data, axis=0)
>>> x.shape
(2, 3, 4)

>>> da.stack(data, axis=1).shape
(3, 2, 4)

>>> da.stack(data, axis=-1).shape
(3, 4, 2)

This creates a new dimension with length equal to the number of slices

Concatenate

We concatenate existing arrays into a new array, extending them along an existing dimension

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

>>> arr0 = da.from_array(np.zeros((3, 4)), chunks=(1, 2))
>>> arr1 = da.from_array(np.ones((3, 4)), chunks=(1, 2))

>>> data = [arr0, arr1]

>>> x = da.concatenate(data, axis=0)
>>> x.shape
(6, 4)

>>> da.concatenate(data, axis=1).shape
(3, 8)

Block

We can handle a larger variety of cases with da.block as it allows concatenation to be applied over multiple dimensions at once. This is useful if your chunks tile a space, for example if small squares tile a larger 2-D plane.

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

>>> arr0 = da.from_array(np.zeros((3, 4)), chunks=(1, 2))
>>> arr1 = da.from_array(np.ones((3, 4)), chunks=(1, 2))

>>> data = [
...     [arr0, arr1],
...     [arr1, arr0]
... ]

>>> x = da.block(data)
>>> x.shape
(6, 8)