dpnp.copysign

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

Composes a floating-point value with the magnitude of \(x1_i\) and the sign of \(x2_i\) for each element of the input array x1.

For full documentation refer to numpy.copysign.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have a real-valued floating-point data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have a real-valued floating-point data type.

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) --

    Output array to populate. Array must have the correct shape and the expected data type.

    Default: None.

  • order ({None, "C", "F", "A", "K"}, optional) --

    Memory layout of the newly output array, if parameter out is None.

    Default: "K".

Returns:

out -- An array containing the element-wise results. 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.negative

Return the numerical negative of each element of x.

dpnp.positive

Return the numerical positive of each element of x.

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

Examples

>>> import dpnp as np
>>> np.copysign(np.array(1.3), np.array(-1))
array(-1.3)
>>> 1 / np.copysign(np.array(0), 1)
array(inf)
>>> 1 / np.copysign(np.array(0), -1)
array(-inf)

>>> x = np.array([-1, 0, 1])
>>> np.copysign(x, -1.1)
array([-1., -0., -1.])
>>> np.copysign(x, np.arange(3) - 1)
array([-1., 0., 1.])