jax.numpy.r_

jax.numpy.r_ = <jax._src.numpy.index_tricks.RClass object>

Concatenate slices, scalars and array-like objects along the first axis.

LAX-backend implementation of numpy.r_.

See also

jnp.c_: Concatenates slices, scalars and array-like objects along the last axis.

Examples

Passing slices in the form [start:stop:step] generates jnp.arange objects:

>>> jnp.r_[-1:5:1, 0, 0, jnp.array([1,2,3])]
Array([-1,  0,  1,  2,  3,  4,  0,  0,  1,  2,  3], dtype=int32)

An imaginary value for step will create a jnp.linspace object instead, which includes the right endpoint:

>>> jnp.r_[-1:1:6j, 0, jnp.array([1,2,3])]
Array([-1.        , -0.6       , -0.20000002,  0.20000005,
       0.6       ,  1.        ,  0.        ,  1.        ,
       2.        ,  3.        ], dtype=float32)

Use a string directive of the form "axis,dims,trans1d" as the first argument to specify concatenation axis, minimum number of dimensions, and the position of the upgraded array’s original dimensions in the resulting array’s shape tuple:

>>> jnp.r_['0,2', [1,2,3], [4,5,6]] # concatenate along first axis, 2D output
Array([[1, 2, 3],
       [4, 5, 6]], dtype=int32)

>>> jnp.r_['0,2,0', [1,2,3], [4,5,6]] # push last input axis to the front
Array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]], dtype=int32)

Negative values for trans1d offset the last axis towards the start of the shape tuple:

>>> jnp.r_['0,2,-2', [1,2,3], [4,5,6]]
Array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]], dtype=int32)

Use the special directives "r" or "c" as the first argument on flat inputs to create an array with an extra row or column axis, respectively:

>>> jnp.r_['r',[1,2,3], [4,5,6]]
Array([[1, 2, 3, 4, 5, 6]], dtype=int32)

>>> jnp.r_['c',[1,2,3], [4,5,6]]
Array([[1],
       [2],
       [3],
       [4],
       [5],
       [6]], dtype=int32)

For higher-dimensional inputs (dim >= 2), both directives "r" and "c" give the same result.