How to clamp floating numbers in Python?

Clamping refers to limiting a number to a specific range, ensuring that the number lies between the minimum and maximum values. This method is used in applications like graphics and statistical computations, as it requires data to stick to specific limits.

Creating a User-Defined Function

Since Python has no built-in clamp function, we can create our own clamp() function that takes three parameters − n (number to be clamped), min_val (minimum value), and max_val (maximum value) ?

def clamp(n, min_val, max_val): 
    if n < min_val: 
        return min_val
    elif n > max_val: 
        return max_val
    else: 
        return n 
  
print(clamp(7.5, 5.0, 20.0)) 
print(clamp(-1.23, 11.0, 20.0)) 
print(clamp(15.44, 9.0, 12.0))

The above code returns the number if it is already within the range, min_val if n is less than min_val, and max_val if n is greater than max_val ?

7.5
11.0
12.0

Using NumPy clip() Method

NumPy provides a built-in numpy.clip() function that clips values in an array. Values outside the interval are clamped to the interval edges ?

import numpy as np

clamped_number = np.clip(7.5, 5.0, 20.0)
print(clamped_number)  

clamped_number = np.clip(-3.4, 0.0, 5.7)
print(clamped_number) 

clamped_number = np.clip(6.89, 0.0, 5.7)
print(clamped_number)

The output shows the clamped values ?

7.5
0.0
5.7

Using PyTorch clamp() Method

PyTorch provides torch.clamp() method to clamp all elements in a tensor within the range [min, max] ?

import torch 
  
num = torch.tensor(7.5) 
num = torch.clamp(num, min=5.3, max=20.5) 
print(num.item())

The output for the above code is ?

7.5

Comparison of Methods

Conclusion

Use a custom function for simple clamping without dependencies. Choose NumPy's clip() for array operations or PyTorch's clamp() for tensor-based computations.