numpy.arcsinh() in Python (original) (raw)

Last Updated : 29 Nov, 2018

numpy.arcsinh() : This mathematical function helps user to calculate inverse hyperbolic sine, element-wise for all arr.

Syntax : numpy.arcsinh(arr, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, ufunc ‘arcsinh’)
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 sine 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/2, pi/2].

Code #1 : Working

import numpy as np

in_array = [ 2 , 1 , 10 , 100 ]

print ( "Input array : \n" , in_array)

arcsinh_Values = np.arcsinh(in_array)

print ( "\nInverse hyperbolic sine values of input array : \n" , arcsinh_Values)

Output :

Input array : [2, 1, 10, 100]

Inverse hyperbolic sine values of input array : [ 1.44363548 0.88137359 2.99822295 5.29834237]

Code #2 : Graphical representation

import numpy as np

import matplotlib.pyplot as plt

in_array = np.linspace( 1 , np.pi, 18 )

out_array1 = np.sin(in_array)

out_array2 = np.arcsinh(in_array)

print ( "in_array : " , in_array)

print ( "\nout_array with sin : " , out_array1)

print ( "\nout_array with arcsinh : " , out_array2)

plt.plot(in_array, out_array1,

`` color = 'blue' , marker = "." )

plt.plot(in_array, out_array2,

`` color = 'red' , marker = "+" )

plt.title( "blue : numpy.sin() \nred : numpy.arcsinh()" )

plt.xlabel( "X" )

plt.ylabel( "Y" )

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 sin : [ 8.41470985e-01 9.02688009e-01 9.49598344e-01 9.81458509e-01 9.97763553e-01 9.98255056e-01 9.82925230e-01 9.52017036e-01 9.06020338e-01 8.45664137e-01 7.71905017e-01 6.85911986e-01 5.89047946e-01 4.82848093e-01 3.68995589e-01 2.49294878e-01 1.25643097e-01 1.22464680e-16]

out_array with arcsinh : [ 0.88137359 0.96770792 1.04881189 1.12508571 1.1969269 1.26471422 1.32879961 1.38950499 1.44712201 1.50191335 1.55411486 1.60393799 1.65157228 1.69718777 1.74093713 1.78295772 1.82337333 1.86229574]

Similar Reads