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Designed to facilitate integration with machine learning and deep learning workflows, VoxelForge is ideal for applications that require complex spatial data processing and analysis.\n\n## Key Features\n\n- Efficient voxel manipulation and storage using C++.\n- Spatial indexing with octree structures for fast query performance.\n- Integration of voxel data into graph structures for advanced modeling and analysis.\n- Flexible data identity for voxels, supporting simple integers by default or complex data types like strings and dictionaries.\n\n## Installation\n\nInstall VoxelForge easily using pip:\n\n```sh\npip install VoxelForge\n```\n\n## Building from Source\n\nIf you prefer to build VoxelForge from source, detailed instructions are available in the [BUILD.md](BUILD.md) file.\n\nIn summary, you'll need to:\n\n1. Clean the build directory.\n2. Configure the project using CMake.\n3. Build the project with `make`.\n4. Reinstall the package locally using `pip install .`.\n\nFor complete step-by-step instructions, please refer to the [BUILD.md](BUILD.md) file.\n\n## Usage\n\n### Basic Voxel Operations\n\n```python\nimport VoxelForge as vf\n\n# Create a new VoxelGrid and add a voxel with default identity\ngrid = vf.VoxelGrid()\ngrid.addVoxel(1, 2, 3)  # Adds a voxel with default identity 1\n\n# Retrieve and display voxel information\nvoxels = grid.getVoxels()\nfor voxel in voxels:\n    print(f'Voxel at ({voxel.x}, {voxel.y}, {voxel.z})')\n```\n\n### Handling Various Data Identities\n\n```python\n# Create another VoxelGrid for handling different identities\nadvanced_grid = vf.VoxelGrid()\nadvanced_grid.addVoxel(6, 6, 6, \"Santa\")\nadvanced_grid.addVoxel(7, 8, 9, {\"color\": \"#001230\", \"alpha\": 0.12, \"gravity\": 0.81  })\n\n# Retrieve and display voxel information with identities\nadvanced_voxels = advanced_grid.getVoxels()\nfor voxel in advanced_voxels:\n    print(f'Voxel at ({voxel.x}, {voxel.y}, {voxel.z}) with data {voxel.data}')\n```\n\n### Advanced Graph Features\n\n```python\n# Convert VoxelGrid to a graph structure with specified dimensions and neighboring radius\ngraph_data = advanced_grid.toTorchGraph(xDim=10, yDim=10, zDim=10, neighboring_radius=1.0)\n\n# Access node features and edge indexes from the graph\nprint(\"Node Features:\", graph_data['x'])\nprint(\"Edge Index:\", graph_data['edge_index'])\n```\n\n### Using Octree for Spatial Indexing\n\n```python\n# Initialize an Octree with a specific size and depth\noctree = vf.Octree(origin=np.array([0.0, 0.0, 0.0]), size=10.0, max_depth=3)\n\n# Insert points and locate leaf nodes\noctree.insert_point(np.array([1.0, 1.0, 1.0]))\nleaf_node = octree.locate_leaf_node(np.array([1.0, 1.0, 1.0]))\n\nif leaf_node:\n    print(f\"Leaf node found at {leaf_node.get_point()}\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewrgarcia%2Fvoxelforge","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fandrewrgarcia%2Fvoxelforge","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fandrewrgarcia%2Fvoxelforge/lists"}