dpnp.ldexp

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

Returns \(x1 * 2^{x2}\), element-wise.

The mantissas x1 and exponents of two x2 are used to construct floating-point numbers \(x1 * 2^{x2}\).

For full documentation refer to numpy.ldexp.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- Array of multipliers, expected to have a real-valued floating-point data type.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Array of exponents of two, expected to have an integer 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 -- The result of \(x1 * 2^{x2}\).

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.frexp

Return (y1, y2) from \(x = y1 * 2^{y2}\), inverse to dpnp.ldexp.

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).

Complex dtypes are not supported, they will raise a TypeError.

dpnp.ldexp is useful as the inverse of dpnp.frexp, if used by itself it is more clear to simply use the expression \(x1 * 2^{x2}\).

Examples

>>> import dpnp as np
>>> np.ldexp(5, np.arange(4))
array([ 5., 10., 20., 40.])