numpy.arccosh() in Python
Last Updated :
29 Nov, 2018
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numpy.arccosh() : This mathematical function helps user to calculate inverse hyperbolic cosine, element-wise for all arr.
Syntax :
Python
Output :
Python
Output :
)
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
# Python program explaining
# arccosh() function
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)
Input array : [2, 1, 10, 100] Inverse hyperbolic Cosine values : [ 1.3169579 0. 2.99322285 5.29829237]Code #2 : Graphical representation
# Python program showing
# Graphical representation
# of arccosh() function
%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)
#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() \nred : numpy.arccosh()")
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 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]
