API reference guide

The Python reference describes the public objects and their arguments. Begin with Quickstart for a complete fit, or Advanced Topics for practical choices and interpretation. The API pages supply exact signatures once you know which operation you need.

Most workflows start with BezierSpace, Samples and fit. Use Points when cell identity must travel with coordinates, and use the returned FitResult.model for prediction. The common types live in torch_bsf.core and are also available from the package root.

For numerical methods, consult fitting and solvers for regression, or optimization for differentiable costs. Sampling, validation and active learning build on the same space and model. Plotting, scikit-learn, Lightning and MLflow integrations are optional; install the relevant extra before executing their examples.

geometry describes mutable topology construction, while a compiled BezierSpace records immutable geometry and coefficient layout. The archive interface in io preserves that layout with model coefficients. Private modules beginning with an underscore are implementation details.