Contributing to fastrad

We welcome community contributions, particularly optimizations mapping novel tensor operations substituting slower scalar implementations!

The PR Process and Gotchas

1. Fork the fastrad repository natively.

2. Initialize your local experimental branch: git checkout -b feature/your-feature

3. Commit localized logic iterations securely. Ensure standard Python PEP8 formatting using flake8 or black.

4. Run the comprehensive benchmark tests (python benchmarks/report_generator.py) guaranteeing PyRadiomics compliance parity before submitting. Pull requests failing numerical IBSI bounds identical to `PyRadiomics` will not be accepted.

5. Push your feature branch and initialize a Pull Request.

Submitting Bug Reports

When creating issues for extraction exceptions or unexpected behavior, please provide: * The exact geometric dimensions and modality properties of your MedicalImage. * The FeatureSettings initialization blueprint (especially the target device: string). * Standardized terminal outputs or specific PyTorch OutOfMemoryError tracebacks.

We do not require local patient scan uploads. Synthetic tensor scripts reliably modeling identical data boundaries (using torch.rand) are ideal for issue replications securely protecting patient PHI limits.