numpy.cosh() in Python

numpy.cosh(x[, out]) = ufunc ‘cos’) : This mathematical function 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).

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

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# 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)

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Output : 


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

cosine Hyperbolic values : 
 [  1.           2.50917848   1.60028686  11.59195328]



 

Code #2 : Graphical representation











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# 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() 



















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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]




 

References :  https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.cosh.html#numpy.cosh

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