Path2Finding is a pathfinding algorithm visualizer that focuses on teaching the user how pathfinding algorithm work. It allows the user to modify the maze, change the algorithm, and see how each of them perform. We also offer quick information about each algorithm to try and give a deeper understanding of the logic behind them.

• Start/stop the visualization
• Select an algorithm (BFS, DFS, A*, Dijkstra's)
• Set a visualization speed (x0.5, x1, x1.5, x2)
• Change the grid size (minimum 20 x 20)
• Move start and end points
• Randomize wall placement
• Place/remove walls
• Clear the grid
• Save and load a maze
• View information about each algorithm

1. Install Node.js and Yarn

2. Clone this repository to your local computer and install its dependencies

   > git clone https://github.com/path2finding/visualizer.git
   > cd visualizer
   > yarn

1. From the root project directory, start the application

   > yarn start

2. A browser window should open once started but if not you can access it here: http://localhost:3000/

https://youtu.be/6Yoa2gQcspA

In the project directory, you can run:

Runs the app in the development mode.
Open http://localhost:3000 to view it in the browser.

The page will reload if you make edits.
You will also see any lint errors in the console.

Launches the test runner in the interactive watch mode.
See the section about running tests for more information.

Launches the test runner in the ci mode which prevents them from hanging on watch mode.
See the section about running tests for more information.

Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.

The build is minified and the filenames include the hashes.
Your app is ready to be deployed!

See the section about deployment for more information.

Builds the React UI in production production and assembles a docker image to host the build using Nginx.

Runs the Nginx server hosting the React UI on port 80. Access the running server at http://localhost

Thanks goes to these wonderful people (emoji key):

Mike Upton 💻 🚇

Shawn Toubeau 💻 🚇 🎨 📖

Jame Coyne 💻 🎨 📖 🚇

Harry Gordenstein 💻 🚇 📖

This project follows the all-contributors specification. Contributions of any kind welcome!