geodiff 0.8.0

Differentiable geometry representations for shape parameterization and optimization.

Differentiable geometry representations for shape parameterization and optimization.

Project Plan

Stage 1: Initial Setup

• Add Github Actions workflow for Github Pages.
• Create first cut User Docs using Jupyter Books and MyST markdown.
  • What is this package for?
  • Add .gitignore for MyST markdown.
• Launch Github Discussions for the project.
  • Create introductory dicussion post.
• Add MIT License.
• Update pyproject.toml.
  • Maintainers, license, license-file, keywords, classifiers, project urls.
• Add Github Actions workflow for Github Release and PyPI publishing.
• Add CHANGELOG.md to maintain release details.
• Create first tag and push it to initiate first release and publish.

Stage 2: Implement Geometry Representations

• Install necessary dependencies
  • numpy, matplotlib and pytorch.
• Implement loss functions.
  • Start with Chamfer loss.
• Hicks-Henne bump functions.
  • Implement the Hicks-Henne class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• CST parameterization.
  • Implement the CST class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• NICE normalizing flow parameterization.
  • Implement the NICE class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• RealNVP normalizing flow parameterization.
  • Implement the RealNVP class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• NIGnet parameterization.
  • Implement the NIGnet class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• NeuralODE parameterization.
  • Implement the NeuralODE class.
  • Add visualization method.
  • Add type hints and docstrings.
  • Add test script.
  • Add documentation.
  • Merge with main branch.
  • Create a tag and push it to create a release.
• Make Pre-Aux net modular by defining it separately from the invertible networks.
  • Make Pre-Aux net modular for NICE.
  • Change test script for NICE.
  • Make Pre-Aux net modular for all representations.
  • Change test scripts for all representations.
  • Update documentation for all representations.
    • Fix random seed for replicating results.
  • Merge with main branch.
  • Create a tag and push it to create a release.