https://github.com/who-else-but-arjun/a-star
This A* search implementation finds optimal paths by combining actual path costs with Euclidean distance estimates to the goal
https://github.com/who-else-but-arjun/a-star
a-star-algorithm graph networkx streamlit
Last synced: 3 months ago
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This A* search implementation finds optimal paths by combining actual path costs with Euclidean distance estimates to the goal
- Host: GitHub
- URL: https://github.com/who-else-but-arjun/a-star
- Owner: who-else-but-arjun
- Created: 2025-02-16T16:54:16.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-02-16T17:39:52.000Z (3 months ago)
- Last Synced: 2025-02-16T18:32:12.443Z (3 months ago)
- Topics: a-star-algorithm, graph, networkx, streamlit
- Language: Jupyter Notebook
- Homepage: https://a-star-algo.streamlit.app/
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# A* Search Visualization
A interactive visualization tool for the A* pathfinding algorithm using Streamlit and NetworkX.
## Setup
1. Install dependencies:
```bash
pip install streamlit networkx matplotlib numpy torch pillow
```
2. Create a `maps` directory in your project folder:
```bash
mkdir maps
```
3. Place your map files (`.pt` format) in the `maps` directory. Each map file should contain:
- A graph dictionary defining node connections and weights
- A positions dictionary defining node coordinates
## Usage
1. Run the application:
```bash
streamlit run a-star.py
```
2. In the web interface:
- Select a map from the dropdown
- Choose start and goal nodes
- Adjust animation speed if needed
- Click "Run Search" to start the visualization
## Requirements
- Python 3.7+
- Streamlit
- NetworkX
- Matplotlib
- NumPy
- PyTorch
- Pillow
## Project Structure
```
project/
│
├── a-atar.py # Main application file
├── maps/ # Directory for map files
│ └── *.pt # Map data files
└── temp/ # Generated visualizations
```#