A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error

(C) 2024 by Congrong Ren. See LICENSE in top-level directory.

Dependencies

To run this project, you need to have the following Python packages installed:

• numpy: For numerical operations
• networkx: For creating and manipulating complex networks
• trimesh: For handling triangular meshes
• dahuffman: For Huffman coding (data compression)
• zstd: For Zstandard compression
• matplotlib: For plotting and visualization

Testing Examples

To run this project, execute the following command in your terminal:

$ python compress_decompress.py -dataset <dataset_name> -attribute <attribute_name>

For example:

$ python compress_decompress.py -dataset syn -attribute data

See the results in results/<dataset_name>/<attribute_name>.

Citation

Please including the following citation if you use the code:

• Ren, C., Liang, X., and Guo, H., "A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error," Computer Graphics Forum, vol. 43, no. 3, 2024, p. e15097.