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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.