# nlcpy.tile

nlcpy.tile(A, reps)[ソース]

Constructs an array by repeating A the number of times given by reps.

If reps has length `d`, the result will have dimension of `max(d, A.ndim)`.

If `A.ndim < d` , A is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-D replication. If this is not the desired behavior, promote A to d-dimensions manually before calling this function.

If `A.ndim > d`, reps is promoted to A.ndim by pre-pending 1's to it. Thus for an A of shape (2, 3, 4, 5), a reps of (2, 2) is treated as (1, 1, 2, 2).

Parameters
Aarray_like

The input array.

repsarray_like

The number of repetitions of A along each axis.

Returns
cndarray

The tiled output array.

`broadcast_to`

Broadcasts an array to a new shape.

Although tile may be used for broadcasting, it is strongly recommended to use nlcpy's broadcasting operations and functions.

Examples

```>>> import nlcpy as vp
>>> a = vp.array([0, 1, 2])
>>> vp.tile(a, 2)
array([0, 1, 2, 0, 1, 2])
>>> vp.tile(a, (2, 2))
array([[0, 1, 2, 0, 1, 2],
[0, 1, 2, 0, 1, 2]])
>>> vp.tile(a, (2, 1, 2))
array([[[0, 1, 2, 0, 1, 2]],

[[0, 1, 2, 0, 1, 2]]])
>>> b = vp.array([[1, 2], [3, 4]])
>>> vp.tile(b, 2)
array([[1, 2, 1, 2],
[3, 4, 3, 4]])
>>> vp.tile(b, (2, 1))
array([[1, 2],
[3, 4],
[1, 2],
[3, 4]])
>>> c = vp.array([1,2,3,4])
>>> vp.tile(c,(4,1))
array([[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4],
[1, 2, 3, 4]])
```