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This allows for testing the results against each other as well as checking the performance of each.\n\n## Docker Usage\n\n- First build the desired image \n  - node: `docker build -f Dockerfile.node -t maestro-shapes-node .`\n  - python `docker build -f Dockerfile.python -t maestro-shapes-python .`\n\n### Depth-First Search (DFS) \n- Run the Node DFS script with small data: `docker run --rm maestro-shapes-node node dfs.js data_small.txt`\n\n```\nNumber of connected shapes (DFS): 13\nExecution time (DFS): 0s 0.572252ms\nMemory used (DFS): 2.0546875 KB\n```\n\n- Run the DFS script with large data: `docker run --rm maestro-shapes-node node dfs.js data_large.txt`\n\n```\nNumber of connected shapes (DFS): 663\nExecution time (DFS): 0s 2.250764ms\nMemory used (DFS): 2.109375 KB\n```\n\n- Run the Python DFS script with small data: `docker run --rm maestro-shapes-python python dfs.py data_small.txt`\n\n```\nNumber of connected shapes (DFS): 13\nExecution time (DFS): 0.420570s\nMemory used (DFS): 0.0000 KB\n```\n\n Run the Python DFS script with large data: `docker run --rm maestro-shapes-python python dfs.py data_large.txt`\n\n ```\nNumber of connected shapes (DFS): 663\nExecution time (DFS): 5.587816s\nMemory used (DFS): 0.0000 KB\n ```\n\n### Breadth-First Search (BFS)\n- Run the Node BFS script with small data: `docker run --rm maestro-shapes-node node bfs.js data_small.txt`\n\n```\nNumber of connected shapes (BFS): 13\nExecution time (BFS): 0s 0.816396ms\nMemory used (BFS): 333.328125 KB\n```\n\n- Run the Node BFS script with large data: `docker run --rm maestro-shapes-node node bfs.js data_large.txt`\n\n```\nNumber of connected shapes (BFS): 663\nExecution time (BFS): 0s 10.894949ms\nMemory used (BFS): 542.9765625 KB\n```\n\n- Run the Python BFS script with small data: `docker run --rm maestro-shapes-python python bfs.py data_small.txt`\n\n```\nNumber of connected shapes (BFS): 13\nExecution time (BFS): 0.373125s\nMemory used (BFS): 0.0000 KB\n```\n\n Run the Python BFS script with large data: `docker run --rm maestro-shapes-python python bfs.py data_large.txt`\n\n ```\nNumber of connected shapes (BFS): 663\nExecution time (BFS): 5.525112s\nMemory used (BFS): 0.0000 KB\n ```\n\n### Union Find\n- Run the Union Find script  with small data: `docker run --rm maestro-shapes-node node unionFind.js data_small.txt`\n\n```\nNumber of connected shapes (Union-Find): 13\nExecution time (Union-Find): 0s 0.978859ms\nMemory used (Union-Find): 26.3046875 KB\n```\n\n- Run the Union Find script with large data: `docker run --rm maestro-shapes-node node unionFind.js data_large.txt`\n\n```\nNumber of connected shapes (Union-Find): 663\nExecution time (Union-Find): 0s 4.008323ms\nMemory used (Union-Find): 203.921875 KB\n```\n\n## Client Animation Usage\n\nThis was an attempt to make a visualization of how each algorithm traverses the grid.\nIt isn't functioning as I had hoped yet.\n\nI'll host a version of the app [here](https://codecurio.us/grid-traversal/)\n\nInstall dependencies `npm install`\n\nBuild the production bundle running `npm run build`\n\nFor Development use `npm run start`\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskwid138%2Fmaestro-ai-programming-puzzle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fskwid138%2Fmaestro-ai-programming-puzzle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fskwid138%2Fmaestro-ai-programming-puzzle/lists"}