ThemePark Simulation using OOP

Coursework project for STAT0040 Object-Oriented Programming

Introduction

This project simulates the operations of a theme park using Object-Oriented Programming (OOP) principles. The simulation helps analyze customer behavior, ride usage, and overall park performance.

Main Python Scripts

• Themepark_classes.py: Custom module containing the core classes and methods for the simulation.
• themepark_simulation.py: Produces a new dataset of simulation output using input data from ride_info.csv, arrival_rates.csv, and ride_transitions.csv.
• example_output_aggregation.py: Summarizes the simulation output.
• test_code.py: Contains test codes that produce a different, verbose output each time, which visualises the simulation process.

Insights from the Theme Park Simulation Output

The file summary_output.csv generated using example_output_aggregation.py provides the following insights:

• By aggregating the output data from Monday to Sunday across 4 weeks, the simulation illustrates that the number of customers visiting is much higher on weekends than weekdays, and it peaks on Sundays.
• The average rides taken per customer appears lower during peak times (e.g., Sundays) and higher on days with fewer visitors (e.g., Tuesdays).
• Customers tend to spend more time waiting during peak times (from Fridays to Sundays), even though the average time they spend enjoying the rides is shorter (e.g., 0.84 hours on Sundays).

A Report on My Solutions in Themepark_classes.py

Object-Oriented Programming

• Encapsulation: Each class bundles relevant attributes with methods that operate on the data. Getters with property decorators control read-only access to private attributes while preventing unauthorized modifications.
• Inheritance: The Ride class inherits from deque, reusing existing methods like append() and popleft(), and adds specialized attributes/methods, maintaining a clean interface.
• Polymorphism: The Ride class customizes the append() method, overriding it to perform different actions based on the data type (either deque or Ride).
• Leveraging Existing Packages: Using existing packages reduces redundancy in class designs.

Data Structures for Certain Attributes

1. PriorityQueue.queue: Uses a list to store a sequence of immutable tuples representing events, ordered based on the first element.
2. Customer and ThemePark Attributes: Lists are used for attributes like Customer.path, .ride_times, .wait_times, and ThemePark.customers to allow for duplicates and easy mutability.
3. Dictionary: Maps each customer_id to their queue_entry_times, allowing fast retrieval when processing a customer.

Could have considered these to make the simulation more realistic:

1. Height Restrictions:

   • Customer: Add a height attribute.
   • Ride: Add a min_height attribute.
   • ThemePark.simulate(): Check if the customer's height exceeds the ride's minimum height before adding them to the queue.

2. Maximum Queue Lengths:

   • Ride: Add a max_queue_length attribute.
   • ThemePark: Track and retrieve queue lengths for each ride_id using a dictionary.
   • ThemePark.simulate(): Check the queue length before adding a customer. If the queue is full, route the customer to a different ride.

The modular design and encapsulation of the current four classes make it easy to integrate and maintain additional attributes and methods.