py_visual_algo

Interactive Algorithm Visualizer for Python

Overview

py_visual_algo is a Python library designed for visualizing and understanding algorithms, including sorting, searching, graph traversal, and evolutionary algorithms. Using real-time visualizations and step-by-step animations, it transforms complex algorithmic concepts into intuitive, interactive experiences. Perfect for educators, students, and developers, py_visual_algo bridges the gap between theoretical concepts and practical implementation.

Features

• Real-Time Visualizations
  Watch algorithms execute in real-time with live updates and insights.

• Step-by-Step Animations
  Follow every decision, iteration, and operation of an algorithm.

• Graph Integration
  Leverage networkx to visualize and work with graph-based algorithms.

• Evolutionary Algorithms
  Explore optimization problems with cutting-edge techniques like genetic algorithms, particle swarm optimization, and more.

• Extensive Algorithm Coverage
  Includes a wide range of algorithms such as:

  • Sorting: Bubble Sort, Quick Sort, Merge Sort.
  • Searching: Linear Search, Binary Search, Jump Search.
  • Graph Algorithms: BFS, DFS, Dijkstra, A*.
  • Evolutionary Algorithms: Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization.

• Customizable API
  Tailor algorithm visualizations, themes, and execution parameters to fit your unique requirements.

• Educational Focus
  Designed for teaching, learning, and presentations, making it the ideal tool for instructors and students.

Whether you're an educator aiming to simplify algorithmic concepts, a student trying to grasp algorithms interactively, or a developer experimenting with optimization techniques, py_visual_algo is your go-to solution.

Algorithms Included in py_visual_algo

Sorting Algorithms

• bubble_sort(data) - Bubble Sort
• merge_sort(data) - Merge Sort
• quick_sort(data) - Quick Sort
• insertion_sort(data) - Insertion Sort
• selection_sort(data) - Selection Sort
• heap_sort(data) - Heap Sort
• radix_sort(data) - Radix Sort
• counting_sort(data) - Counting Sort
• bucket_sort(data) - Bucket Sort
• shell_sort(data) - Shell Sort

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Searching Algorithms

• linear_search(data, target) - Linear Search
• binary_search(data, target) - Binary Search
• jump_search(data, target) - Jump Search
• exponential_search(data, target) - Exponential Search
• interpolation_search(data, target) - Interpolation Search

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Graph Algorithms

• bfs(graph, start) - Breadth-First Search (BFS)
• dfs(graph, start) - Depth-First Search (DFS)
• bfs_with_edges(graph, start) - BFS with Edge Visualization
• dfs_with_edges(graph, start) - DFS with Edge Visualization
• dijkstra(graph, start) - Dijkstra's Algorithm
• a_star(graph, start, goal, heuristic) - A* Algorithm
• bellman_ford(graph, start) - Bellman-Ford Algorithm
• prim(graph, start) - Prim's Algorithm (Minimum Spanning Tree)
• kruskal(graph) - Kruskal's Algorithm (Minimum Spanning Tree)
• floyd_warshall(graph) - Floyd-Warshall Algorithm
• topological_sort(graph) - Topological Sorting

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Tree Algorithms

• inorder_traversal(root) - Inorder Traversal
• preorder_traversal(root) - Preorder Traversal
• postorder_traversal(root) - Postorder Traversal
• level_order_traversal(root) - Level Order Traversal (BFS for Trees)

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Evolutionary and Optimization Algorithms

• genetic_algorithm(...) - Genetic Algorithm
• simulated_annealing(...) - Simulated Annealing
• particle_swarm_optimization(...) - Particle Swarm Optimization (PSO)
• differential_evolution(...) - Differential Evolution
• ant_colony_optimization(...) - Ant Colony Optimization (ACO)
• memetic_algorithm(...) - Memetic Algorithm
• artificial_bee_colony(...) - Artificial Bee Colony Optimization (ABC)
• harmony_search(...) - Harmony Search
• cultural_algorithm(...) - Cultural Algorithm
• tabu_search(...) - Tabu Search

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Key Features

• Real-Time Visualizations: Step-by-step execution for better understanding.
• Graph Integration: Supports visualization of graph-based algorithms using networkx.
• Customizable API: Adapt algorithm execution and visualizations to your needs.
• Educational Focus: Ideal for teaching and learning.

This comprehensive set of algorithms makes py_visual_algo a versatile library for various applications, including education, research, and development.

Installation

pip install py-visual-algo

Usage

Example of visualizing Bubble Sort:

from py_visual_algo.algorithms.sorting import bubble_sort
from py_visual_algo.visualizer.plotter import plot_sorting

data = [10, 3, 7, 2, 5]
generator = bubble_sort(data)
plot_sorting(generator)

Running Examples

The py_visual_algo library includes a variety of examples to demonstrate its capabilities. These examples are located in the examples/ directory.

Steps to Run Examples

1. Clone the repository:

git clone https://github.com/tirotir-ir/py_visual_algo.git
cd py_visual_algo

2. Navigate to the examples/ directory:

cd examples

‍‍‍‍‍‍‍3. Run an example script: ‍‍‍

    python sorting1.py

Available Examples

• Sorting Algorithms: Demonstrates sorting algorithms like Bubble Sort, Merge Sort, and Quick Sort. bubble_sort1.py
• Searching Algorithms: Shows Linear Search, Binary Search, and Jump Search. linear_search.py
• Graph Algorithms: Visualizes BFS, DFS, Dijkstra, and A* algorithms. graph1.py
• Tree Traversals: Explores Inorder, Preorder, and Postorder traversals. tree_traversal1.py
• Optimization Algorithms: Demonstrates Particle Swarm Optimization, Ant Colony Optimization, and more. ant_colony_optimization.py

examples/ ├── sorting/ │ ├── bubble_sort1.py │ ├── bubble_sort2.py │ ├── quick_sort.py │ ├── merge_sort.py │ ├── sorting1.py │ └── sorting2.py ├── searching/ │ ├── linear_search.py │ ├── binary_search.py │ ├── jump_search.py │ └── searching1.py ├── graph_algorithms/ │ ├── a_star1.py │ ├── bellman_ford.py │ ├── bfs1.py │ ├── bfs_with_edges.py │ ├── dfs1.py │ ├── dfs_with_edges.py │ ├── dijkstra1.py │ ├── dijkstra2.py │ ├── graph1.py │ ├── graph2.py │ └── graph3.py ├── tree_algorithms/ │ ├── inorder_traversal.py │ ├── postorder_traversal.py │ ├── preorder_traversal.py │ ├── tree_traversal1.py │ └── tree_traversal2.py ├── evolutionary_algorithms/ │ ├── ant_colony_optimization.py │ ├── artificial_bee_colony.py │ ├── cultural_algorithm.py │ ├── differential_evolution.py │ ├── genetic_algorithm.py │ ├── harmony_search.py │ ├── memetic_algorithm.py │ ├── pso_example.py │ ├── simulated_annealing.py │ └── evolutionary1.py └── pathfinding/ ├── pathfinding1.py └── pathfinding2.py

Using the Graphical User Interface (GUI)

The py_visual_algo library includes a user-friendly Graphical User Interface (GUI) to browse and run examples interactively. Follow these steps to use the GUI:

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Steps to Run the GUI

1. Activate the Virtual Environment

Activate your virtual environment to use the installed library:

• On Windows:

  <virtual_env_root>\Scripts\activate

• Example:

  python -m venv env
  env\Scripts\activate

• On macOS/Linux:

  source <virtual_env_root>/bin/activate

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2. Install the Library (if not already installed)

If the library is not already installed in the virtual environment, install it using pip:

pip install py_visual_algo

Ensure the examples folder is either included in the installed package or downloaded separately there (see Step 3).

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3. Ensure the examples Folder is in the Correct Location

The examples folder must be located two levels above the GUI script (gui.py). If the library is installed in a virtual environment, place the examples folder under:

<virtual_env_root>/Lib/site-packages/examples

For example, in a typical virtual environment, the directory structure should look like this:

<virtual_env_root>/
├── Lib/
│   ├── site-packages/
│   │   ├── py_visual_algo/
│   │   │   ├── ui/
│   │   │   │   └── gui.py
│   │   ├── examples/
│   │   │   ├── bfs1.py
│   │   │   ├── bubble_sort1.py
│   │   │   └── ...

Move the examples folder to the correct location by running:

move <path_to_downloaded_examples> <virtual_env_root>/Lib/site-packages/

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4. Run the GUI

Once the directory structure is correct and the virtual environment is active, run the GUI:

python -m py_visual_algo.ui.gui

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5. Select and Run Examples

When the GUI launches:

• It will dynamically load available examples from the examples folder.
• Select an example from the list displayed in the GUI.
• Click Run Example to execute it.

Ensure the examples folder contains valid Python files (e.g., bfs1.py, bubble_sort1.py). The output or visualization will appear after the example runs successfully.

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Running Examples Separately

If you prefer to run examples individually without using the GUI, follow these steps:

Find the Example Files

The examples folder contains Python scripts for various algorithms. Ensure the examples folder is downloaded and placed in the correct location:

<virtual_env_root>/Lib/site-packages/examples

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3. Run an Example

Navigate to the examples folder and execute any example script directly. For example:

• Run BFS Example:

  python <virtual_env_root>/Lib/site-packages/examples/bfs1.py

• Run Bubble Sort Example:

  python <virtual_env_root>/Lib/site-packages/examples/bubble_sort1.py

Replace <virtual_env_root> with the path to your virtual environment.

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Troubleshooting

1. No Examples Displayed in the GUI:

   • Verify the examples folder is in the correct location (<virtual_env_root>/Lib/site-packages/examples).
   • Confirm the examples folder contains .py files.

2. Virtual Environment Not Activated:

   • Ensure the virtual environment is activated before running the GUI or examples.

By following these steps, you can explore and run the library's examples interactively or individually.

Contributing

See CONTRIBUTING.md for guidelines.

License

This project is licensed under the MIT License. See the LICENSE file for details.