dpnp.asinh

dpnp.asinh(x, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Computes inverse hyperbolic sine for each element \(x_i\) for input array x.

The inverse of dpnp.sinh, so that if \(y = sinh(x)\) then \(x = asinh(y)\). Note that dpnp.arcsinh is an alias of dpnp.asinh.

For full documentation refer to numpy.asinh.

Parameters:

  • x ({dpnp.ndarray, usm_ndarray}) -- Input array, expected to have a 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 inverse hyperbolic sine, in radians. 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.sinh

Hyperbolic sine, element-wise.

dpnp.atanh

Hyperbolic inverse tangent, element-wise.

dpnp.acosh

Hyperbolic inverse cosine, element-wise.

dpnp.asin

Trigonometric inverse sine, element-wise.

Notes

dpnp.asinh is a multivalued function: for each x there are infinitely many numbers z such that \(sin(z) = x\). The convention is to return the angle z whose the imaginary part lies in the interval \([-\pi/2, \pi/2]\).

For real-valued floating-point input data types, dpnp.asinh always returns real output. For each value that cannot be expressed as a real number or infinity, it yields NaN.

For complex floating-point input data types, dpnp.asinh is a complex analytic function that has, by convention, the branch cuts \((-\infty j, -j)\) and \((j, \infty j)\) and is continuous from the left on the former and from the right on the latter.

The inverse hyperbolic sine is also known as \(sinh^{-1}\).

Examples

>>> import dpnp as np
>>> x = np.array([np.e, 10.0])
>>> np.asinh(x)
array([1.72538256, 2.99822295])