numpy.sinh () in Python



Equivalent to 1/2 * (np.exp (x) — np.exp (-x)) or -1j * np.sin (1j * x).

Parameters:

array: [array_like] elements are in radians.
2pi Radians = 36o degrees

Return: An array with hyperbolic sine of x for all x ie array elements

Code # 1: Work

# Python3 program explaining
# sinh function ()

 

import numpy as np

import math

 

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

print ( "Input array:" , in_array)

 

Sinh_Values ​​ = np.sinh (in_array)

print ( "Sine Hyperbolic values:" , Sinh_Values)

Exit:

 Input array: [0, 1.5707963267948966, 1.0471975511965976, 3.141592653589793] Sine Hyperbolic values: [0. 2.3012989 1.24936705 11.54873936] 

Code # 2: Graphic representation Adding

# Python program showing graphical
# function representation sinh ( )

import numpy as np

import matplotlib.pyplot as plt

 

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

out_array = np.sinh ( in_array)

 

print ( "in_array : " , in_array)

print ( "out_array:" , out_array)

  
# red for numpy.sinh ()

plt.plot (in_array, out_array, color = `red` , marker = "o" )

plt.title ( "numpy.sinh ()" )

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: [-116.548 -964 -654. 0.28949778 0.28949778 0.96554336 1.9652737 3.62383424 6.49723393 11.54873936]