autoconf 2026.7.15.1

PyAuto Configration

PyAutoConf

🧬 PyAutoScientist → https://github.com/PyAutoLabs/PyAutoScientist — this repo is one organ of the PyAuto organism.

📖 Full documentation → https://pyautoscientist.readthedocs.io — the whole PyAutoScientist organism, including how to fork and run your own.

PyAutoConf (package autoconf) is the configuration, serialization, and I/O foundation of the PyAuto ecosystem. It provides a layered configuration system with workspace overrides, dict / JSON / CSV serialization of arbitrary objects, and FITS I/O.

PyAutoFit, PyAutoArray, PyAutoGalaxy, and PyAutoLens all depend on autoconf: it supplies their packaged default config, the object-serialization used to persist models and results, and shared utilities (test_mode, jax_wrapper). Centralising these here keeps a single, consistent config and I/O layer beneath every library. Within the PyAutoScientist organism it is the Nerves — the layer connecting the workspace's conventions to every library.

Install

pip install autoconf

Examples

Layered config — read a directory of YAML into a queryable Config:

from autoconf.conf import Config

config = Config("path/to/config")          # directory of YAML files
value = config["general"]["model"]["section"]["value"]

JSON serialization — round-trip arbitrary Python objects:

from autoconf.dictable import output_to_json, from_json

data = {"sersic_index": 4.0, "centre": [0.0, 0.0]}
output_to_json(data, "model.json")
restored = from_json("model.json")         # == data

FITS I/O — write and read a NumPy array:

import numpy as np
from autoconf.fitsable import output_to_fits, ndarray_via_fits_from

arr = np.arange(12.0).reshape(3, 4)
output_to_fits(values=arr, file_path="demo.fits", overwrite=True)
loaded = ndarray_via_fits_from(file_path="demo.fits", hdu=0)   # np.allclose(arr, loaded)

Links

• Source & tests: autoconf/, test_autoconf/
• Agent/contributor instructions: AGENTS.md
• Ecosystem: PyAutoLabs on GitHub