Skip to content
Related Articles

Related Articles

Improve Article
Python – math.acosh() function
  • Last Updated : 28 May, 2020

Math module contains a number of functions which is used for mathematical operations. The math.acosh() function returns the hyperbolic arc cosine value of a number. The value passed in this function should be greater than or equal to 1.

Syntax: math.acosh(x)

Parameter:This method accepts only single parameters.

  • x :This parameter is the value to be passed to acosh()

Returns:This function returns the hyperbolic arc cosine value of a number.

Below examples illustrate the use of above function:



Example 1:




# Python code to implement
# the acosh()function
        
# importing "math"
# for mathematical operations  
import math  
       
a = math.pi / 6
        
# Return the hyperbolic arc cosine value of numbers 
print (math.acosh(7))
print (math.acosh(56))
print (math.acosh(2.45))
print (math.acosh(1))

Output:

2.6339157938496336
4.718419142372879
1.5447131178707394
0.0

Example 2:




# Python code implementation of 
# the acosh() function
import math 
import numpy as np 
import matplotlib.pyplot as plt  
     
in_array = np.linspace(1, np.pi**2, 30
     
out_array = [] 
     
for i in range(len(in_array)): 
    out_array.append(math.acosh(in_array[i])) 
    i += 1
      
print("Input_Array : \n", in_array)  
print("\nOutput_Array : \n", out_array)  
   
   
plt.plot(in_array, out_array, "go-")  
plt.title("math.acosh()")  
plt.xlabel("X")  
plt.ylabel("Y")  
plt.show() 

Output:

Input_Array : 
 [1.         1.30584843 1.61169686 1.91754528 2.22339371 2.52924214
 2.83509057 3.14093899 3.44678742 3.75263585 4.05848428 4.3643327
 4.67018113 4.97602956 5.28187799 5.58772641 5.89357484 6.19942327
 6.5052717  6.81112012 7.11696855 7.42281698 7.72866541 8.03451384
 8.34036226 8.64621069 8.95205912 9.25790755 9.56375597 9.8696044 ]

Output_Array : 
 [0.0, 0.7634351653684978, 1.0562772501126303, 1.2679873925813194, 1.4372757745859863, 
1.5794735761470122, 1.7025573669627803, 1.8113067645313763, 1.9088495232436826,
1.997360544554533, 2.07842113836573, 2.1532211217708626, 2.2226804635542514, 
2.287526464855001, 2.348344844358015, 2.405614746886384, 2.4597334430301796, 
2.51103419200721, 2.559799438447933, 2.6062707446710016, 2.6506563890658725, 
2.693137263795659, 2.7338715120762482, 2.7729982170653664, 2.8106403673544613, 
2.8469072638299315, 2.8818964902724367, 2.9156955397451294, 2.9483831668303044, 
2.9800305196125625]

 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. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course




My Personal Notes arrow_drop_up
Recommended Articles
Page :