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PyAutoFit — Agent Instructions

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

Setup

pip install -e ".[dev]"

Running Tests

python -m pytest test_autofit/
python -m pytest test_autofit/non_linear/
python -m pytest test_autofit/mapper/

Sandboxed / Codex runs

NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib python -m pytest test_autofit/

Key Architecture

  • Non-linear searches (non_linear/search/): MCMC (emcee), nested sampling (dynesty, nautilus), MLE (LBFGS, BFGS, drawer)
  • Model composition (mapper/): af.Model, af.Collection, prior distributions
  • Analysis (non_linear/analysis/): base af.Analysis class with log_likelihood_function
  • Aggregator (aggregator/): results aggregation across runs
  • Database (database/): SQLAlchemy backend for results storage
  • Graphical models (graphical/): expectation propagation

Key Rules

  • All files must use Unix line endings (LF)
  • Format with black autofit/

Working on Issues

  1. Read the issue description and any linked plan.
  2. Identify affected files and write your changes.
  3. Run the full test suite: python -m pytest test_autofit/
  4. Ensure all tests pass before opening a PR.
  5. If changing public API, note the change in your PR description — downstream packages (PyAutoArray, PyAutoGalaxy, PyAutoLens) and workspaces may need updates.

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.