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 = arcsinh(y). Note that dpnp.asinh is an alias of dpnp.arcsinh.

For full documentation refer to numpy.arcsinh.

Parameters:

x ({dpnp.ndarray, usm_ndarray}) -- Input array, expected to have numeric 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. 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.arctanh: Hyperbolic inverse tangent, element-wise.
dpnp.arccosh: Hyperbolic inverse cosine, element-wise.
dpnp.arcsin: Trigonometric inverse sine, element-wise.

Notes

dpnp.arcsinh 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 real part lies in [-pi/2, pi/2].

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

For complex-valued input, dpnp.arcsinh is a complex analytic function that has, by convention, the branch cuts [1j, infj] and [`1j, -infj] and is continuous from above on the former and from below on the latter.

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

Examples

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