Skip to content

AGENTS.md

Latest commit

 

History

History
91 lines (67 loc) · 3.84 KB

AGENTS.md

File metadata and controls

91 lines (67 loc) · 3.84 KB
Raw

PyAutoFit Workspace Test — Agent Instructions

This is the integration-test suite for PyAutoFit, run on the build server to verify the core library works end-to-end. It is not a user-facing workspace — see ../autofit_workspace for examples and tutorials. These are the canonical, agent-agnostic instructions for this repo.

Dependencies: autofit (and autonerves).

Repository Structure

scripts/                     Integration-test scripts run on the build server
  searches/                  Non-linear search tests (Dynesty, Emcee, Nautilus, LBFGS, …)
  features/                  Feature tests (assertions, latent variables, minimal output, …)
  database/                  Database tests (directory, scrape, session)
  graphical/                 Graphical-model tests (EP, hierarchical, simultaneous)
  model_composition/         Model-composition tests
  prior_correctness/         Prior-behaviour correctness tests
  jax_assertions/            JAX assertion tests
  simulators/                Simulator utilities used by the other scripts
failed/                      One .txt log per failing script (written by run_all_scripts.sh)
dataset/ config/ output/     Input data, YAML config, runtime fit results

Running Scripts

Run a single script directly from the repo root — with no env applied, the non-linear search runs for real (sampler limits like n_like_max keep it short):

python scripts/searches/DynestyStatic.py

Testing

On CI, smoke_tests.yml gates every PR on Python 3.12 and 3.13. The gate runs the smoke runner (the definition of green):

python .github/scripts/run_smoke.py

It executes the curated entries in smoke_tests.txt, applying per-entry environment from config/build/env_vars.yaml. That file sets PYAUTO_TEST_MODE=2 (skip the sampler) as the default, but search and feature tests that must validate real inference unset it (or set PYAUTO_TEST_MODE=1, reduced iterations with a real sampler) — those are the cases where bypassing the sampler would defeat the test. So the test-mode story is per-script, not a blanket "runs for real." A failure under these flags signals a real problem.

For a local full sweep of every script under scripts/, use the stateless runner (logs each failure to failed/<script_path>.txt):

bash run_all_scripts.sh

Sandboxed / restricted runs

If numba or matplotlib cannot write to the default cache locations, point them at writable dirs:

NUMBA_CACHE_DIR=/tmp/numba_cache MPLCONFIGDIR=/tmp/matplotlib python scripts/searches/DynestyStatic.py

Bulk-edit safety

When editing the same region across many scripts in one pass, only rewrite the targeted region. Never produce a whole-file write unless you have read the entire current file — a whole-file write from a header skim silently deletes every section below the header.

Related Repos

  • Source libs: ../PyAutoFit, ../PyAutoNerves.
  • ../autofit_workspace — the user-facing workspace; ../HowToFit — the tutorial series.
  • ../PyAutoHands — CI / build tooling.

Task Workflows

When a library change lands, run the smoke suite, read any [FAIL] entries, and update the affected test scripts to the new API (preserving intent). Never edit a script to mask a real regression — if a library bug surfaces, flag it for PyAutoFit rather than papering over it. Note in your PR any change that affects sibling repos (autofit_workspace, the source libraries).

Never rewrite history

Never rewrite pushed history on any repo with a remote — no git init over a tracked repo, no force-push to main, no fresh-start "Initial commit", no filter-repo / filter-branch / rebase -i on pushed branches. To get a clean tree: git fetch origin && git reset --hard origin/main && git clean -fd.