numpy.arccosh() in Python
Last Updated :
29 Nov, 2018
numpy.arccosh() : This mathematical function helps user to calculate inverse hyperbolic cosine, element-wise for all arr.
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.
-> If provided, it must have a shape that the inputs broadcast to.
-> 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 i.e. array elements.
Note :
2pi Radians = 360 degrees
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 : Working
import numpy as np
in_array = [ 2 , 1 , 10 , 100 ]
print ( "Input array : \n" , in_array)
arccosh_Values = np.arccosh(in_array)
print ( "\nInverse hyperbolic Cosine values : \n" , arccosh_Values)
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Output :
Input array :
[2, 1, 10, 100]
Inverse hyperbolic Cosine values :
[ 1.3169579 0. 2.99322285 5.29829237]
Code #2 : Graphical representation
% 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 ( "\nout_array with cos : " , out_array1)
print ( "\nout_array with arccosh : " , out_array2)
plt.plot(in_array, out_array1,
color = 'blue' , marker = "." )
plt.plot(in_array, out_array2,
color = 'red' , marker = "+" )
plt.title( "blue : numpy.cos() \nred : numpy.arccosh()" )
plt.xlabel( "X" )
plt.ylabel( "Y" )
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Output :
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 3.01561662 3.14159265]
out_array with cos : [ 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 -0.92943115 -0.96842762 -0.99207551 -1. ]
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 1.81152627]
)
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