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BigONavigator Documentation

Welcome to the documentation for BigONavigator, your comprehensive guide to analyzing computational complexity in Python.

Introduction

BigONavigator is a Python package developed initially as a university project. It's designed to estimate and track the computational complexity of functions, providing a clear overview of performance in Big O notation.

Installation

To install BigONavigator, run the following command in your terminal:

pip install BigONavigator

Ensure you have Python version 3.8 or higher installed. Ideally 3.11.

Getting Started

To begin using BigONavigator, import the package and use the decorators on your functions.

Example:

from bigonavigator import O

@O['n']
def sample_function(data):
    # Your code here
    pass

Using BigONavigator

Decorators

BigONavigator provides decorators corresponding to different complexity classes. Here's how to use them:

  • @O['1']: For constant time complexity.
  • @O['n']: For linear time complexity.
  • @O['n^2']: For quadratic time complexity.
  • @O['log(n)']: For logarithmic time complexity.
  • @O['nlog(n)']: For linearithmic time complexity.
  • @O['nlog(n)^2']: For linearithmic time complexity.
  • @O['2^n']: For exponential time complexity.
  • @O['n!']: For factorial time complexity.

Tracking and Displaying Complexity

After decorating your functions, you can display their complexities:

from bigonavigator import show_complexity
show_complexity_table() # tracked statically across all instances

This function prints a table showing each function's name and its estimated complexity.