numpy.cosh() in Python

Last Updated : 08 Mar, 2024

The numpy.cosh() is a mathematical function that helps user to calculate hyperbolic cosine for all x(being the array elements). Equivalent to 1/2 * (np.exp(x) - np.exp(-x)) and np.cos(1j*x).

Syntax : numpy.cosh(x[, out]) = ufunc 'cos') Parameters : array : [array_like] elements are in radians. 2pi Radians = 36o degrees Return : An array with hyperbolic cosine of x for all x i.e. array elements

Code #1 : Working

Python3

# Python3 program explaining
# cosh() function

import numpy as np
import math

in_array = [0, math.pi / 2, np.pi / 3, np.pi]
print ("Input array : \n", in_array)

cosh_Values = np.cosh(in_array)
print ("\ncosine Hyperbolic values : \n", cosh_Values)

Output :

Input array : 
 [0, 1.5707963267948966, 1.0471975511965976, 3.141592653589793]

cosine Hyperbolic values : 
 [  1.           2.50917848   1.60028686  11.59195328]

Code #2 : Graphical representation

Python3

# Python program showing Graphical
# representation of cosh() function
import numpy as np
import matplotlib.pyplot as plt

in_array = np.linspace(-np.pi, np.pi, 12)
out_array = np.cosh(in_array)

print("in_array : ", in_array)
print("\nout_array : ", out_array)

# red for numpy.cosh()
plt.plot(in_array, out_array, color = 'red', marker = "o")
plt.title("numpy.cosh()")
plt.xlabel("X")
plt.ylabel("Y")
plt.show()

Output :

in_array :  [-3.14159265 -2.57039399 -1.99919533 -1.42799666 -0.856798   -0.28559933
  0.28559933  0.856798    1.42799666  1.99919533  2.57039399  3.14159265]

out_array :  [ 11.59195328   6.57373932   3.75927846   2.20506252   1.39006258
   1.04106146   1.04106146   1.39006258   2.20506252   3.75927846
   6.57373932  11.59195328]