numpoly.tile

numpoly.tile(A: numpoly.typing.PolyLike, reps: Union[numpy._typing._array_like._SupportsArray[numpy.dtype], numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype]], bool, int, float, complex, str, bytes, numpy._typing._nested_sequence._NestedSequence[Union[bool, int, float, complex, str, bytes]]]) → numpoly.baseclass.ndpoly

Construct 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).

Args:

A:

The input array.

reps:

The number of repetitions of A along each axis.

Return:

The tiled output array.

Example:

>>> q0 = numpoly.variable()
>>> numpoly.tile(q0, 4)
polynomial([q0, q0, q0, q0])
>>> poly = numpoly.polynomial([[1, q0-1], [q0**2, q0]])
>>> numpoly.tile(poly, 2)
polynomial([[1, q0-1, 1, q0-1],
            [q0**2, q0, q0**2, q0]])
>>> numpoly.tile(poly, [2, 1])
polynomial([[1, q0-1],
            [q0**2, q0],
            [1, q0-1],
            [q0**2, q0]])