numpy.arcsinh() in Python
Last Updated :
29 Nov, 2018
Improve
numpy.arcsinh() : This mathematical function helps user to calculate inverse hyperbolic sine, element-wise for all arr.
Python
Output :
Python
Output :
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
# Python program explaining
# arcsinh() function
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)
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
# Python program showing
# Graphical representation
# of arcsinh() function % matplotlib inline
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)
# blue for numpy.sinh()
# red for numpy.arcsinh()
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")
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]
