jax.numpy.dstack#

jax.numpy.dstack(tup, dtype=None)[source]#

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

LAX-backend implementation of numpy.dstack().

Original docstring below.

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:

  • 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.

  • dtype (Union[Any, str, dtype, SupportsDType, None]) –

Returns:

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

Return type:

ndarray