dask.array.tile

dask.array.tile¶

dask.array.tile(A, reps)[source]¶

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

This docstring was copied from numpy.tile.

Some inconsistencies with the Dask version may exist.

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).

Note : Although tile may be used for broadcasting, it is strongly recommended to use numpy’s broadcasting operations and functions.

Parameters

Aarray_like: The input array.
repsarray_like: The number of repetitions of A along each axis.

Returns

cndarray: The tiled output array.

See also

repeat: Repeat elements of an array.
broadcast_to: Broadcast an array to a new shape

Examples

>>> a = np.array([0, 1, 2])  
>>> np.tile(a, 2)  
array([0, 1, 2, 0, 1, 2])
>>> np.tile(a, (2, 2))  
array([[0, 1, 2, 0, 1, 2],
       [0, 1, 2, 0, 1, 2]])
>>> np.tile(a, (2, 1, 2))  
array([[[0, 1, 2, 0, 1, 2]],
       [[0, 1, 2, 0, 1, 2]]])

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

>>> c = np.array([1,2,3,4])  
>>> np.tile(c,(4,1))  
array([[1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4],
       [1, 2, 3, 4]])

dask.array.tensordot

dask.array.topk