forbes-0023
78289494e2
- Add AxisStrategy type (cardinal, random, near_parallel) - Add random body orientations via scipy.spatial.transform.Rotation - Add parallel axis injection with configurable probability - Add grounded parameter on all 7 generators (grounded/floating) - Add axis sampling strategies: cardinal, random, near-parallel - Update _create_joint with orientation-aware anchor offsets - Add _resolve_axis helper for parallel axis propagation - Update generate_training_batch with axis_strategy, parallel_axis_prob, grounded_ratio parameters - Add body_orientations and grounded fields to batch output - Export AxisStrategy from datagen package - Add 28 new tests (72 total generator tests, 158 total) Closes #8
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