dpnp.power

dpnp.power(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Calculates x1_i raised to x2_i for each element x1_i of the input array x1 with the respective element x2_i of the input array x2.

For full documentation refer to numpy.power.

Parameters:

• x1 ({dpnp.ndarray, usm_ndarray}) – First input array, expected to have numeric data type.

• x2 ({dpnp.ndarray, usm_ndarray}) – Second input array, also expected to have numeric data type.

• out ({None, dpnp.ndarray}, optional) – Output array to populate. Array must have the correct shape and the expected data type.

• order ({"C", "F", "A", "K"}, optional) – Output array, if parameter out is None. Default: “K”.

Returns:

out – An array containing the result of element-wise of raising each element to a specified power. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.fmax

Element-wise maximum of array elements.

dpnp.fmin

Element-wise minimum of array elements.

dpnp.fmod

Calculate the element-wise remainder of division.

Examples

>>> import dpnp as dp
>>> a = dp.arange(6)
>>> dp.power(a, 3)
array([  0,   1,   8,  27,  64, 125])

Raise the bases to different exponents.

>>> b = dp.array([1.0, 2.0, 3.0, 3.0, 2.0, 1.0])
>>> dp.power(a, b)
array([ 0.,  1.,  8., 27., 16.,  5.])

The effect of broadcasting.

>>> c = dp.array([[1, 2, 3, 3, 2, 1], [1, 2, 3, 3, 2, 1]])
>>> dp.power(a, c)
array([[ 0,  1,  8, 27, 16,  5],
        [ 0,  1,  8, 27, 16,  5]])

The ** operator can be used as a shorthand for power on dpnp.ndarray.

>>> b = dp.array([1, 2, 3, 3, 2, 1])
>>> a = dp.arange(6)
>>> a ** b
array([ 0,  1,  8, 27, 16,  5])

Negative values raised to a non-integral value will result in nan.

>>> d = dp.array([-1.0, -4.0])
>>> dp.power(d, 1.5)
array([nan, nan])