roll(a, shift, axis=None)

Elements that roll beyond the last position are re-introduced at the first.

Notes

Supports rolling over multiple dimensions simultaneously.

Parameters

a : array_like

Input array.

shift : int or tuple of ints

The number of places by which elements are shifted. If a tuple, then :None:None:`axis` must be a tuple of the same size, and each of the given axes is shifted by the corresponding number. If an int while :None:None:`axis` is a tuple of ints, then the same value is used for all given axes.

axis : int or tuple of ints, optional

Axis or axes along which elements are shifted. By default, the array is flattened before shifting, after which the original shape is restored.

Returns

res : ndarray

Output array, with the same shape as a.

Roll array elements along a given axis.

See Also

rollaxis

Roll the specified axis backwards, until it lies in a given position.

Examples

>>> x = np.arange(10)
... np.roll(x, 2)
array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])

>>> np.roll(x, -2)
array([2, 3, 4, 5, 6, 7, 8, 9, 0, 1])

>>> x2 = np.reshape(x, (2, 5))
... x2
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

>>> np.roll(x2, 1)
array([[9, 0, 1, 2, 3],
       [4, 5, 6, 7, 8]])

>>> np.roll(x2, -1)
array([[1, 2, 3, 4, 5],
       [6, 7, 8, 9, 0]])

>>> np.roll(x2, 1, axis=0)
array([[5, 6, 7, 8, 9],
       [0, 1, 2, 3, 4]])

>>> np.roll(x2, -1, axis=0)
array([[5, 6, 7, 8, 9],
       [0, 1, 2, 3, 4]])

>>> np.roll(x2, 1, axis=1)
array([[4, 0, 1, 2, 3],
       [9, 5, 6, 7, 8]])

>>> np.roll(x2, -1, axis=1)
array([[1, 2, 3, 4, 0],
       [6, 7, 8, 9, 5]])

>>> np.roll(x2, (1, 1), axis=(1, 0))
array([[9, 5, 6, 7, 8],
       [4, 0, 1, 2, 3]])

>>> np.roll(x2, (2, 1), axis=(1, 0))
array([[8, 9, 5, 6, 7],
       [3, 4, 0, 1, 2]])

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

dask.array.routines.roll scipy.interpolate._fitpack2.SmoothSphereBivariateSpline scipy.interpolate._fitpack2.LSQSphereBivariateSpline dask.array.core.Array.map_overlap

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