LawSynthGitHub

Getting started

LawSynth turns time-series observations into executable mathematical worlds: interpretable law systems you can read, simulate, forecast, compare, and share. The whole product is one loop, and every step is deterministic and offline:

observe (CSV) → discover (laws) → understand (explain) → use (simulate /
forecast / intervene) → compare → share (report / .lsworld bundle) → organize

A discovery is a portable .lsworld bundle; everything downstream — CLI, Python SDK, Studio, and the HTTP services — operates on that same artifact.

  1. installation — build the lawsynth CLI and the Python SDK.
  2. quickstart — run the core loop end to end.
  3. concepts — datasets, worlds, laws, and bundles.
  4. your first world — discover, inspect, and reuse a bundle.
  5. Pick a surface: CLI, Python, or Studio.

Then go deeper with task-oriented, copy-pasteable material:

  1. Cookbook — short recipes for each task (discover,

clean, forecast, validate, monitor, export, organize, pipeline, service, Jupyter), each with a CLI recipe *and* its Python-SDK equivalent.

  1. Tutorials — longer end-to-end walkthroughs that

chain many features into one narrative (messy sensor → trusted model, predator–prey ecology, monitoring a running system).

The checked-in examples and the workspace tests are executable and are the source of truth for supported inputs. Discovery finds a sparse fit from the implemented feature library — it is not evidence that an inferred relation is causal or that extrapolation beyond the observed window is valid.