Skip to content

README.md

Latest commit

 

History

History
44 lines (35 loc) · 2.05 KB

README.md

File metadata and controls

44 lines (35 loc) · 2.05 KB
Raw

PyAutoReduce

Data reduction of Hubble Space Telescope (and, in future, JWST and other instrument) imaging into modeling-ready datasets for PyAutoLens and PyAutoGalaxy.

Given a strong lens (or galaxy) target, PyAutoReduce downloads the archival exposures, reduces them with the instrument's standard pipeline tooling, and emits the exact products the modeling stack loads via al.Imaging.from_fits:

Product Description
data.fits Science cutout, drizzled to the modeling pixel scale
noise_map.fits Per-pixel RMS: drizzle weight-map background term + Poisson source term, correlated-noise corrected
psf.fits / psf_full.fits Drizzle-consistent PSF estimate (compact + extended)
reduction.json Full provenance: program IDs, exposures, zero-point, exposure time, pixel scale, pipeline versions

Design principles

  • Default pipelines first. Each stage uses the instrument's standard tooling (astroquery.mast, drizzlepac, photutils) with its recommended settings; deviations exist only where lens modeling requires them, and each one is documented in the design docs.
  • Disk-frugal. Full-frame exposures are transient: download per target, reduce, package the cutouts, evict. Survey-mosaic targets use MAST cutout services instead of tile downloads.
  • Standalone. PyAutoReduce emits the PyAutoLens/PyAutoGalaxy input format but does not import them; it sits directly on the astropy ecosystem.

Status

Design phase. The HST/ACS pipeline design lives in docs/design/hst_acs_pipeline.md; the longer-term roadmap (WFC3, JWST, per-exposure frame products) in docs/design/roadmap.md.

Installation

pip install autoreduce            # core (outputs + packaging only)
pip install "autoreduce[hst]"     # + the STScI HST reduction stack (drizzlepac)
pip install "autoreduce[psf]"     # + high-fidelity PSF reconstruction back-ends