# numpy.arccosh () in Python

Syntax :

numpy.arccosh (arr, /, out = None, *, where = True,
casting = ` same_kind `, order =` K `, dtype = None, ufunc` arccosh `)

Parameters:

arr: array_like
Input array.
out: [ndarray, optional] A location into which the result is stored.
– & gt; If provided, it must have a shape that the inputs broadcast to.
– & gt; If not provided or None, a freshly-allocated array is returned.
where: array_like, optional
Values ​​of True indicate to calculate the ufunc at that position, values ​​of False indicate to leave the value in the output alone.
** kwargs: Allows to pass keyword variable length of argument to a function. Used when we want to handle named argument in a function.

Return: An array with inverse hyperbolic cosine of arr
for all arr ie array elements.

Note:

The convention is to return the angle of arr whose imaginary part lies in [-pi, pi] and the real part in [0, inf].

Code # 1: Work

` `

``` # Python program explaining # arccosh () function    import numpy as np   in_array = [ 2 , 1  , 10 , 100 ] print ( "Input array:" , in_array)   arccosh_Values ​​ = np.arccosh (in_array) print ( "Inverse hyperbolic Cosine values:" , arccosh_Values) ```

Output:

` Input array: [2, 1, 10, 100] Inverse hyperbolic Cosine values: [1.3169579 0. 2.99322285 5.29829237] `

Code # 2: Graphical representation

 ` # Show Python program ` ` # Graphical view ` ` # arccosh () functions ` `% ` ` matplotlib inline ` ` import ` ` numpy as np ` ` import ` ` matplotlib.pyplot as plt ` ` in_array ` ` = ` ` np.linspace (` ` 1 ` `, np.pi, ` ` 18 ` `) ` ` out_array1 ` ` = ` ` np.cos (in_array) ` ` out_array2 ` ` = ` ` np.arccosh (in_array) `   ` print ` ` (` `" in_array: "` `, in_array) ` ` print ` ` (` ` "out_array with cos:" ` `, out_array1) ` ` print ` ` ( "out_array with arccosh:" , out_array2) ```` # blue for numpy.cosh () # red for numpy.arccosh () plt.plot (in_array, out_array1, color = `blue` , marker = "." )   plt.plot (in _array, out_array2, color = `red` , marker = "+" )   plt.title ( "blue: numpy.cos () red: numpy.arccosh ()" ) plt.xlabel ( " X " ) plt.ylabel ( "Y" ) ```

Exit:

``` in_array: [1. 1.12597604 1.25195208 1.37792812 1.50390415 1.62988019 1.75585623 1.88183227 2.00780831 2.13378435 2.25976038 2.38573642 2.51171246 2.6376885 2.76366454 2.88964058 3.0156652. : [0.54030231 0.43029566 0.31346927 0.19167471 0.0668423 -0.0590495 -0.18400541 -0.30604504 -0.42323415 -0.53371544 -0.63573787 -0.72768451 -0.80809809 -0.87570413 -0.92942711575 ] out_array with arccosh: [0. 0.49682282 0.69574433 0.84411504 0.96590748 1.07053332 1.16287802 1.24587516 1.32145434 1.39096696 1.45540398 1.51551804 1.57189678 1.62500948 1.67523791 1.7228975 1.76825238 " jpg "/>)

```