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PyAutoFit

PyAutoFit is a Python probabilistic programming language for model fitting and Bayesian inference.

  • Authors: James Nightingale, Richard Hayes
  • Requires Python >= 3.12
  • Package name: autofit

Dependency Graph

PyAutoFit depends on autoconf (shared configuration and utilities). PyAutoFit does NOT depend on PyAutoArray, PyAutoGalaxy, or PyAutoLens. Never import from autoarray, autogalaxy, or autolens in this repo. Shared utilities (e.g. test_mode, jax_wrapper) belong in autoconf.

Repository Structure

  • autofit/ - Main package
    • non_linear/ - Non-linear search algorithms
      • search/mcmc/ - MCMC (emcee, zeus)
      • search/mle/ - Maximum likelihood (LBFGS, BFGS, drawer)
      • search/nest/ - Nested sampling (dynesty, nautilus)
      • samples/ - Posterior samples handling
      • paths/ - Output path management
      • analysis/ - Analysis base classes
    • mapper/ - Model and prior machinery
      • prior/ - Prior distributions
      • prior_model/ - Prior model composition
      • model.py - Core model class
    • graphical/ - Graphical models and expectation propagation
    • aggregator/ - Results aggregation across runs
    • database/ - SQLAlchemy-based results database
    • interpolator/ - Model interpolation
    • config/ - Default config files packaged with library
  • test_autofit/ - Test suite (pytest)
  • docs/ - Sphinx documentation

Key Dependencies

  • dynesty==2.1.5 - Nested sampling
  • emcee>=3.1.6 - MCMC
  • scipy<=1.14.0 - Optimisation
  • SQLAlchemy==2.0.32 - Database backend
  • anesthetic==2.8.14 - Posterior analysis/plotting
  • Optional: nautilus-sampler, zeus-mcmc, getdist

Running Tests

pytest test_autofit
pytest test_autofit/non_linear
pytest test_autofit/mapper

Codex / sandboxed runs

When running Python from Codex or any restricted environment, set writable cache directories so numba and matplotlib do not fail on unwritable home or source-tree paths:

NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib pytest test_autofit

This workspace is often imported from /mnt/c/... and Codex may not be able to write to module __pycache__ directories or /home/jammy/.cache, which can cause import-time numba caching failures without this override.

Shell Commands

  • Prefer simple shell commands
  • Avoid chaining with && or pipes; run commands separately

Related Repos

  • autofit_workspace (tutorials/examples): ../autofit_workspace

Never rewrite history

NEVER perform these operations on any repo with a remote:

  • git init in a directory already tracked by git
  • rm -rf .git && git init
  • Commit with subject "Initial commit", "Fresh start", "Start fresh", "Reset for AI workflow", or any equivalent message on a branch with a remote
  • git push --force to main (or any branch tracked as origin/HEAD)
  • git filter-repo / git filter-branch on shared branches
  • git rebase -i rewriting commits already pushed to a shared branch

If the working tree needs a clean state, the only correct sequence is:

git fetch origin
git reset --hard origin/main
git clean -fd

This applies equally to humans, local Claude Code, cloud Claude agents, Codex, and any other agent. The "Initial commit — fresh start for AI workflow" pattern that appeared independently on origin and local for three workspace repos is exactly what this rule prevents — it costs ~40 commits of redundant local work every time it happens.