5 Best Ways to Reset a Polygon to Its Initial State in Python

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How to Reset a Polygon to Its Initial State in Python

πŸ’‘ Problem Formulation: In geometrical programming and graphical applications, it is often required to reset a polygon object to its original state. This entails reverting the vertices and properties of the polygon to the state it was when it was first created. If a polygon was defined as Polygon([(0, 0), (1, 0), (0, 1)]), the goal is to return it to this initial configuration after various transformations, translations, rotations, or scalings have been applied.

Method 1: Using an Object-Oriented Approach

This method encapsulates the polygon within a class, storing the initial vertices upon creation. It provides a reset method that can be called to revert the polygon to its original state. This approach adheres to the principles of object-oriented programming, making it a clean and maintainable solution.

Here’s an example:

class Polygon:
    def __init__(self, vertices):
        self.original_vertices = vertices
        self.vertices = vertices.copy()

    def reset(self):
        self.vertices = self.original_vertices.copy()

# Create a polygon and apply transformations
polygon = Polygon([(0, 0), (1, 0), (0, 1)])
# Reset to original vertices

The output of the code snippet is the polygon object reverted to its original vertices.

In the code example above, we define a Polygon class that initializes with the given vertices and stores a copy of them. The reset function allows us to restore the polygon’s current vertices to the initial state by copying the original vertices back to the current ones.

Method 2: Using a Functional Approach

The functional approach utilizes functions to manage the state of the polygon. A reset function is defined that receives the original and current state of a polygon and returns the polygon in its original state. This is suitable for those who prefer functional programming paradigms.

Here’s an example:

def reset_polygon(original_vertices, current_vertices):
    return original_vertices.copy()

# Initial vertices
original_vertices = [(0, 0), (1, 0), (0, 1)]
# Current vertices can be altered
current_vertices = original_vertices.copy()
# Eventually, reset to the initial state
current_vertices = reset_polygon(original_vertices, current_vertices)

The reset function returns the polygon’s vertices in their initial configuration.

With the approach outlined here, the reset_polygon function serves as a utility to reset the current state of a polygon to its initial state. The vertices are not stored within an object, and separate variables are used to hold the original and current vertices.

Method 3: Using a Global Variable

By setting the initial polygon state as a global variable, functions or methods can reference and revert to this state. This is a straightforward way but might lead to issues with global variable management in larger programs.

Here’s an example:

ORIGINAL_VERTICES = [(0, 0), (1, 0), (0, 1)]

def reset_polygon_to_initial(vertices):
    return ORIGINAL_VERTICES.copy()

# Current vertices
vertices = ORIGINAL_VERTICES.copy()
# Reset polygon to initial state
vertices = reset_polygon_to_initial(vertices)

The reset function outputs the vertices as set in the global variable ORIGINAL_VERTICES.

This method employs a globally accessed constant that represents the initial state. Although less advisable in a modular or large-scale application due to potential global state conflicts, it is a simple solution for smaller scripts or examples where namespace pollution is not a concern.

Method 4: Using a Closure to Encapsulate State

Closures provide a way to encapsulate the state within a function scope. By creating a closure, we hide the initial state of the polygon within the reset function itself, avoiding global state. This method provides both encapsulation and avoids the need for a class structure.

Here’s an example:

def polygon_resetter(vertices):
    original_vertices = vertices.copy()
    def reset():
        nonlocal original_vertices
        return original_vertices.copy()
    return reset

# Create a reset function specifically for the initial state
reset_to_initial = polygon_resetter([(0, 0), (1, 0), (0, 1)])
# Use the closure to reset
vertices = reset_to_initial()

The closure outputs the original vertices each time the reset_to_initial function is called.

Our polygon_resetter function returns a reset closure that can be used to revert polygons to their initial state. The closure has access to the original state and provides a clear functional approach with state encapsulation.

Bonus One-Liner Method 5: Python’s Multiple Assignment

When dealing with simple polygon reassignments, Python’s multiple assignment feature can be used to reset the polygon’s vertices in a one-liner function. This is not a common way to handle complex state resets, but it exhibits Python’s versatile syntax capabilities for simpler cases.

Here’s an example:

original_vertices = [(0, 0), (1, 0), (0, 1)]
current_vertices = original_vertices.copy()
# Polygon transformations happen here...
# Reset to initial state in one line
current_vertices, = original_vertices,

This one-liner effectively resets current_vertices to original_vertices.

The example showcases Python’s tuple unpacking and repacking to reset the current_vertices. It assumes that the variable original_vertices still contains the original polygon’s state which is less practical for more complex scenarios but demonstrates Python’s assignment tricks.


  • Method 1: Object-Oriented Approach. Maintains encapsulation and adheres to OOP principles. It might be an overkill for simple reset tasks.
  • Method 2: Functional Approach. Separates data and behavior without side effects. Requires careful tracking of the original state outside of the utility function.
  • Method 3: Global Variable. Quick and easy setup for small scripts. Not suitable for larger, more complex applications due to global state management issues.
  • Method 4: Closures. Provides encapsulation without using classes. Usage might be less intuitive for developers not familiar with closures or functional programming techniques.
  • Method 5: Python’s Multiple Assignment. A concise syntax for simple scenarios. Not practical for managing state in applications where the original state can be easily lost or modified.