jax.numpy.r_#

jax.numpy.r_=<jax._src.numpy.index_tricks.RClassobject>#

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

LAX-backend implementation ofnumpy.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] generatesjnp.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 forstep will create ajnp.linspace object instead,which includes the right endpoint:

>>>jnp.r_[-1:1:6j,0,jnp.array([1,2,3])]Array([-1. , -0.6, -0.2,  0.2,  0.6,  1. ,  0. ,  1. ,  2. ,  3. ],      dtype=float32)

Use a string directive of the form"axis,dims,trans1d" as the first argument tospecify concatenation axis, minimum number of dimensions, and the position of theupgraded 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 outputArray([[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 frontArray([[1],       [2],       [3],       [4],       [5],       [6]], dtype=int32)

Negative values fortrans1d offset the last axis towards the startof 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 inputsto 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.