📊 Sorting Algorithm Visualizer in Python

This project implements and visualizes four classic sorting algorithms — Bubble Sort, Quick Sort, Merge Sort, and Radix Sort using Python and matplotlib animation.

Unlike standard implementations, this project can run multiple algorithms side-by-side in a single execution, allowing users to compare performance, execution steps, and visual behavior in real time.

---

🧠 Project Overview

The main goal of this project is to combine algorithm analysis with interactive visualization. By tracking comparisons, swaps, pivots, and merges at each step, this project provides an educational tool to observe:

• Internal mechanics of sorting algorithms
• Relative execution performance on the same dataset
• Visualization of algorithm operations using animated bar charts

This makes it ideal for learning, teaching, and analyzing sorting strategies.

---

⚙️ Implemented Sorting Algorithms

🔹 Bubble Sort

• Iteratively compares adjacent elements.
• Demonstrates step-by-step comparisons and swaps.
• Simple but inefficient (O(n²)).

🔹 Quick Sort

• Divide-and-conquer strategy using pivots.
• Highlights partitioning and recursive sorting.
• Average O(n log n) time, worst-case O(n²).

🔹 Merge Sort

• Recursive divide + merge approach.
• Tracks element copies, comparisons, and merges.
• Stable, always O(n log n) performance.

🔹 Radix Sort

• Non-comparative, digit-level sorting.
• Works for non-negative integers only.
• Complexity: O(nk) where k is number of digits.

---

🔁 Why Multiple Algorithms Together?

Running all four algorithms simultaneously emphasizes their differences:

Feature	Bubble	Quick	Merge	Radix
Complexity	O(n²)	O(n log n) avg	O(n log n)	O(nk)
In-place	✅	✅	❌	✅
Stable	✅	❌	✅	✅
Visualization	Comparisons & swaps	Pivot & partitions	Divide + merge	Digit passes

Side-by-side execution allows a direct performance and behavior comparison.

---

🧩 Architecture Overview

The Python script is divided into these components:

• Number Generator: generates random numbers or reads from a file.
• Sorting Implementations: each algorithm logs its internal operations as a sequence of "states" for visualization.
• State Tracker: stores operations (compare, swap, pivot, copy, copy_back, merged, digit, pass_complete, complete) for animation.
• Matplotlib Animation: subplots display each chosen algorithm in real time.

Color Legend (Animation)

Color	Meaning
🟨 Yellow	Comparison (element(s) under comparison)
🟥 Red	Swap / Copy (elements being swapped or copied)
🟩 Green	Pivot or merged section
🟦 Blue	Default / Idle
🟩 Light Green	Sorted / Pass complete

---

📷 Example Visualization

Run the script with:

python py sorting.py --mode random --n 50 --min_val 0 --max_val 100 --algo bubble --algo quick --algo merge --algo radix

---

⚡ Terminal Output

This output shows algorithm efficiency, number of steps, maximum frames for animation, and overall sorting performance comparison.

---

📊 Performance Summary Table

Rank	Algorithm	Steps	Time (ms)
1	Radix Sort	153	0.07
2	Quick Sort	378	0.15
3	Merge Sort	621	0.26
4	Bubble Sort	1863	2.72

• ✅ Fastest: Radix Sort (0.07 ms)
• ❌ Slowest: Bubble Sort (2.72 ms)

This table provides an at-a-glance comparison of algorithm performance and steps.

---

📁 Project Files

File	Description
sorting.py	Main Python script with sorting algorithms, state instrumentation, and visualization
numbers.txt	Optional input/output file for dataset (one number per line)
images/	Example GIFs or PNGs of visualization output

---

▶️ Usage Instructions

Run with command-line arguments:

# Randomly generate numbers
python sorting.py --mode random --n <number_of_elements> --algo <algorithm_names>

# Use numbers from a file
python sorting.py --mode file --file <file_path> --algo <algorithm_names>

# Customize animation speed (interval in ms)
python sorting.py --mode random --n <number_of_elements> --algo <algorithm_names> --interval <milliseconds>

Command-Line Arguments

Argument	Description
--mode	Input mode: random or file
--algo	Sorting algorithm(s) to visualize (bubble, quick, merge, radix)
--n	Number of elements (for random mode)
--min_val / --max_val	Range of generated numbers (for random mode)
--file	File path for input/output numbers
--interval	Animation frame interval in ms

---

🛠️ Tools Used

Tool	Purpose
Python 3	Programming language
matplotlib	Visualization and animation
argparse	Command-line argument parsing
time	Performance measurement

---

✅ Conclusion

This project serves as a learning resource and visual tool for understanding sorting algorithms:

• Shows internal mechanics of four classic sorting methods
• Highlights trade-offs between simplicity, speed, and stability
• Provides interactive, side-by-side visual comparison of multiple algorithms

It demonstrates how algorithm choice impacts execution beyond theoretical complexity.

---

📝 License

Open for educational and personal use under the MIT License