jax.numpy.hstack¶

jax.numpy.hstack(tup)[source]¶

Stack arrays in sequence horizontally (column wise).

LAX-backend implementation of hstack(). Original docstring below.

This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.

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 ndarrays) – The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
Returns: stacked – The array formed by stacking the given arrays.
Return type: ndarray

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).
dstack(): Stack arrays in sequence depth wise (along third axis).
column_stack(): Stack 1-D arrays as columns into a 2-D array.
hsplit(): Split an array into multiple sub-arrays horizontally (column-wise).

Examples

>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
       [2, 3],
       [3, 4]])