dstack(tup)

This is equivalent to concatenation along the third axis after 2-D arrays of shape :None:None:`(M,N)` have been reshaped to :None:None:`(M,N,1)` and 1-D arrays of shape :None:None:`(N,)` have been reshaped to :None:None:`(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

tup : sequence 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

stacked : ndarray

The array formed by stacking the given arrays, will be at least 3-D.

Stack arrays in sequence depth wise (along third axis).

See Also

block

Assemble an nd-array from nested lists of blocks.

column_stack

Stack 1-D arrays as columns into a 2-D array.

concatenate

Join a sequence of arrays along an existing axis.

dsplit

Split array along third axis.

hstack

Stack arrays in sequence horizontally (column wise).

stack

Join a sequence of arrays along a new axis.

vstack

Stack arrays in sequence vertically (row wise).

Examples

>>> 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]]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.hstack scipy.interpolate._polyint.KroghInterpolator dask.array.routines.dstack numpy.ma.extras.hstack numpy.block numpy.concatenate numpy.core._multiarray_umath.concatenate numpy.vstack numpy.split numpy.ma.extras.vstack

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