The numpy.sinh() is a mathematical function that helps user to calculate hyperbolic sine for all x(being the array elements).
Equivalent to 1/2 * (np.exp(x) – np.exp(-x)) or -1j * np.sin(1j*x).
Syntax: numpy.sinh(x[, out]) = ufunc ‘sin’)
Parameters :array : [array_like] elements are in radians.
2pi Radians = 36o degreesReturn : An array with hyperbolic sine of x for all x i.e. array elements
Code #1 : Working
# 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 : \n" , in_array) Sinh_Values = np.sinh(in_array) print ( "\nSine Hyperbolic values : \n" , Sinh_Values) |
Output :
Input array : [0, 1.5707963267948966, 1.0471975511965976, 3.141592653589793] Sine Hyperbolic values : [ 0. 2.3012989 1.24936705 11.54873936]
Code #2 : Graphical representation
# Python program showing Graphical # representation of sinh() function 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 ( "\nout_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 : [-11.54873936 -6.49723393 -3.62383424 -1.9652737 -0.96554336 -0.28949778 0.28949778 0.96554336 1.9652737 3.62383424 6.49723393 11.54873936]
References :
https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.sinh.html#numpy.sinh
.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.