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

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

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

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)

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]

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