dask.array.dstack
dask.array.dstack¶
- dask.array.dstack(tup, allow_unknown_chunksizes=False)[source]¶
Stack arrays in sequence depth wise (along third axis).
This docstring was copied from numpy.dstack.
Some inconsistencies with the Dask version may exist.
This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.
This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.
- Parameters
- tupsequence of arrays
The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.
- Returns
- stackedndarray
The array formed by stacking the given arrays, will be at least 3-D.
See also
concatenate
Join a sequence of arrays along an existing axis.
stack
Join a sequence of arrays along a new axis.
block
Assemble an nd-array from nested lists of blocks.
vstack
Stack arrays in sequence vertically (row wise).
hstack
Stack arrays in sequence horizontally (column wise).
column_stack
Stack 1-D arrays as columns into a 2-D array.
dsplit
Split array along third axis.
Examples
>>> import numpy as np >>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.dstack((a,b)) array([[[1, 2], [2, 3], [3, 4]]])
>>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]])