dpnp.atanh
- dpnp.atanh(x, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)
Computes hyperbolic inverse tangent for each element \(x_i\) for input array x.
The inverse of
dpnp.tanh
, so that if \(y = tanh(x)\) then \(x = atanh(y)\). Note thatdpnp.arctanh
is an alias ofdpnp.atanh
.For full documentation refer to
numpy.atanh
.- 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 tangent, 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.tanh
Hyperbolic tangent, element-wise.
dpnp.asinh
Hyperbolic inverse sine, element-wise.
dpnp.acosh
Hyperbolic inverse cosine, element-wise.
dpnp.atan
Trigonometric inverse tangent, element-wise.
Notes
dpnp.atanh
is a multivalued function: for each x there are infinitely many numbers z such that \(tanh(z) = x\). 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.atanh
always returns real output. For each value that cannot be expressed as a real number or infinity, it yieldsNaN
.For complex floating-point input data types,
dpnp.atanh
is a complex analytic function that has, by convention, the branch cuts \((-\infty, -1]\) and \([1, \infty)\) and is continuous from above on the former and from below on the latter.The inverse hyperbolic tangent is also known as \(tanh^{-1}\).
Examples
>>> import dpnp as np >>> x = np.array([0, -0.5]) >>> np.atanh(x) array([0.0, -0.54930614])