numpy.cosh () in Python



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 ie array elements

Code # 1: Work

# Python3 explainer
# cosh () function

 

import numpy as np

import math

 

in_array = [ 0 , math.pi / 2 , np.pi / 3 , np.pi]

print ( " Input array: " , in_array)

  

cosh_Values ​​ = np.cosh (in_array)

print ( "cosine Hyperbolic values:" , cosh_Values)

Output:

 Input array: [0, 1.5707963267948966, 1.0471975511965976, 3.141592653589793] cosine Hyperbolic values: [1. 2.50917848 1.60028686 11.59195328] 

Code # 2 : Graphic simple view

# Python program showing the graphical
# cosh function 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 ( " out_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 3.207756 3.207506 1.04106146 1.04106146 1.39006258 2.20506252 3.75927846 6.57373932 11.59195328] 

“img srcodeg / s. / figure>

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