numpy.ma.dstack¶

numpy.ma.dstack(tup) = <numpy.ma.extras._fromnxfunction_seq instance at 0x52d1dd4c>¶

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

Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing.

This function continues to be supported for backward compatibility, but you should prefer np.concatenate or np.stack. The np.stack function was added in NumPy 1.10.

Parameters:

Parameters:	tup : sequence of arrays Arrays to stack. All of them must have the same shape along all but the third axis.
Returns:	stacked : ndarray The array formed by stacking the given arrays.

tup : sequence of arrays

Arrays to stack. All of them must have the same shape along all but the third axis.

Returns:

stacked : ndarray

The array formed by stacking the given arrays.

See also

stack: Join a sequence of arrays along a new axis.
vstack: Stack along first axis.
hstack: Stack along second axis.
concatenate: Join a sequence of arrays along an existing axis.
dsplit: Split array along third axis.

Notes

The function is applied to both the _data and the _mask, if any.

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]]])