Changelog

Notable changes to hst123. Version numbering follows semantic versioning for full releases.

Pre-releases (historical)

  • v1.00 (2019-02-07) — Base: download, tweakreg, drizzle, dolphot parameters.

  • v1.01 (2019-02-15) — Run dolphot and scrape dolphot output.

  • v1.02 (2019-02-22) — Fake star injection.

  • v1.03 (2019-06-02) — Drizzle-all options and option/syntax cleanup.

  • v1.04 (2020-02-10) — Python 3.7.

  • v1.05 (2020-03-04) — Sanitizing reference images for tweakreg/dolphot.

  • v1.06 (2020-03-07) — Archiving (--archive) for large-volume analysis.

  • v1.07 (2020-03-08) — Archiving updates; construct_hst_archive.py helper.

Documentation (Sphinx)

  • Expanded sources: installation.rst (Python 3.12, Conda, DrizzlePac, DOLPHOT helper), user_guide.rst (work-directory layout, CLI overview, redo flags, output formats), api.rst (CLI entry point, full hst123 pipeline class, options, paths, stdio, workdir_cleanup, dolphot_catalog_hdf5, datamodels, BasePrimitive).

Releases

  • Unreleased — Python: 3.12 only (requires-python >=3.12,<3.13; NumPy 1.x / Astropy 5.x / DrizzlePac stack). Documentation: Sphinx uses MyST (Markdown) + reStructuredText, sphinx_rtd_theme, custom CSS/layout (Swift_host_subtraction-style), intersphinx (Python/NumPy/Astropy), and GitHub Pages via CI; README badges for build, docs workflow, and site. hst123.utils.dolphot_catalog_hdf5: parse DOLPHOT *.columns / *.param / *.info / *.data / *.warnings and write HDF5 (pip install h5py or pip install .[hdf5]) with the full numeric catalog plus descriptive column names and JSON metadata (merged per imgNNNN when possible). get_dolphot_column uses parse_column_index_and_description for robust column index parsing. install_psfs ends with relocate_all_legacy_psf_into_canonical_layout: merges legacy dolphot2.0/... *.psf into the canonical tree for ACS, WFC3 (wfc3/data, wfc3/IR, wfc3/UVIS), and WFPC2 (wfpc2/data), including sibling dolphot2.0/ next to dolphot3.1/ (common conda layout). relocate_acs_psf_into_canonical_layout remains for ACS-only use. hst123-install-dolphot applies apply_dolphot_source_patches before make: patches upstream dolphot.c (main stack buffer 82→4096 bytes) to avoid macOS sprintf overflow / SIGTRAP on long absolute output paths; --no-source-patches opts out. tests/test_dolphot_c_python_parity.py documents C vs Python parity for calcsky, splitgroups, acsmask, wfc3mask, wfpc2mask (optional binaries + DOLPHOT tree; marker dolphot_parity). hst123.utils.progress_log.LoggedProgress: throttled logger.info progress lines (#--- bar, %, counts, elapsed, est. total duration, remaining) for reuse; wired to calcsky stage 1 in write_sky_fits_fallback / compute_sky_map_dolphot (Python per-row updates; Numba: one full parallel pass then a single progress completion—no sequential batched kernels that ruined throughput). Disable with HST123_CALCSKY_PROGRESS=0 or HST123_PROGRESS_LOG=0. Optional pip install .[perf] adds numba; environment.yml includes numba for conda users. *drc.noise.fits (sky copy for drizzled stacks) removed in run_dolphot and get_dolphot_photometry primitive cleanup with respect_keep_artifacts=False so they are still removed when --keep-drizzle-artifacts is set (not in per-filter astrodrizzle cleanup, which would delete prior filters’ noise). Same glob listed in settings.pipeline_products for scripted cleanup; see tests/test_pipeline_cleanup_validation.py. Removed legacy CLI --make-clean, --no-large-reduction, --large-num and hst123.make_clean / check_large_reduction. CI: workflow uses pip install -e ".[test]"; _psf_already_satisfied treats ACS WFC PAM as present if either make_root or source_dir has a valid acs/data payload; scrape-dolphot unit tests supply minimal pipeline.options. Pure-Python DOLPHOT splitgroups (hst123.utils.dolphot_splitgroups); HST123_DOLPHOT_SPLITGROUPS_EXTERNAL=1 forces the C binary. DOLPHOT_REQUIRED_SCRIPTS no longer lists splitgroups. DOLPHOT installer: ACS_WFC_PAM.tar.gz extracts under dolphot2.0/acs/data/; files are now copied to acs/data/ and dolphot_acs_data_dir prefers directories that contain both PAM FITS. Python calcsky port: vectorized second pass (summed-area table); Numba first pass uses precomputed annulus offsets and a per-row scratch buffer (no per-pixel calloc).

  • v1.1.0 (2025-03-02) — Post–v1.07: refactored pipeline (utils, primitives), setuptools-scm versioning, tests and docs updates.

Full releases are added here when tagging. Run from the repo root: python scripts/update_changelog.py "Brief summary of changes"