forbes-0023
dc742bfc82
- test_types.py: JointType enum values/count, dataclass defaults/isolation - test_pebble_game.py: DOF accounting, rigidity, classification, edge results - test_jacobian.py: Jacobian shape per joint type, rank, parallel axis degeneracy - test_analysis.py: demo scenarios (revolute, fixed, triangle, parallel axes) - test_generator.py: chain/rigid/overconstrained generation, training batch Bug fixes found during testing: - JointType enum: duplicate int values caused aliasing (SLIDER=REVOLUTE etc). Changed to (ordinal, dof) tuple values with a .dof property. - pebble_game.py: .value -> .dof for constraint count - analysis.py: classify from effective DOF (not raw pebble game with virtual ground body skew) 105 tests, all passing. Closes #6
kindred-solver
Assembly constraint prediction via GNN. Produces a trained model embedded in a FreeCAD workbench (Kindred Create library), later integrated into vanilla Create.
Overview
kindred-solver predicts whether assembly constraints (joints) are independent or redundant using graph neural networks. Given an assembly graph where bodies are nodes and joints are edges, the model classifies each constraint and reports degrees of freedom per body.
Repository Structure
kindred-solver/
├── solver/ # Core library
│ ├── datagen/ # Synthetic data generation (pebble game)
│ ├── datasets/ # PyG dataset adapters
│ ├── models/ # GNN architectures (GIN, GAT, NNConv)
│ ├── training/ # Training loops and configs
│ ├── evaluation/ # Metrics and visualization
│ └── inference/ # Runtime prediction API
├── freecad/ # FreeCAD integration
│ ├── workbench/ # FreeCAD workbench addon
│ ├── bridge/ # FreeCAD <-> solver interface
│ └── tests/ # Integration tests
├── export/ # Model packaging for Create
├── configs/ # Hydra configs (dataset, model, training, export)
├── scripts/ # CLI utilities
├── data/ # Datasets (not committed)
├── tests/ # Unit and integration tests
└── docs/ # Documentation
Setup
Install (development)
pip install -e ".[train,dev]"
pre-commit install
pre-commit install --hook-type commit-msg
Using Make
make help # show all targets
make dev # install all deps + pre-commit hooks
make test # run tests
make lint # run ruff linter
make type-check # run mypy
make check # lint + type-check + test
make train # run training
make data-gen # generate synthetic data
make export # export model
Using Docker
# GPU training
docker compose up train
# Run tests (CPU)
docker compose up test
# Generate data
docker compose up data-gen
License
Apache 2.0