ACor (Automatic Correlation Information System) automates correlation, processing, and monitoring of radio interferometric data for time-domain studies of variable astrophysical radio sources. The software ingests interferometer observations, performs correlation and calibration steps, and produces time-series products suitable for detecting variability in radio continuum and maser emission. It is designed to support high-cadence, unattended observing campaigns, emphasizing robustness, repeatability, and minimal manual intervention. ACor forms part of an automated data-processing pipeline developed for monitoring variability in high-mass protostars with a single-baseline radio interferometer.

ztfquery enables programmatic access to data products from the Zwicky Transient Facility. The package queries and downloads images, catalogs, metadata, and observing logs through the IRSA interface. It includes tools to retrieve light curves and source information from ZTF services and to access selected follow-up data products. ztfquery organizes retrieved data into analysis-ready formats and supports integration with standard Python workflows for time-domain astronomy.

xmm_simulator generates mock XMM-Newton images and spectra from 3D galaxy cluster simulations from hydrodynamical simulations. It generates particle background spectra from filter-wheel-closed data, a realistic sky background model, energy-dependent vignetting, energy redistribution, and PSF convolution. Users can extract lists of simulated events, corresponding photon images, exposure maps, non-X-ray background maps, and spectra with redistribution matrices and weighted ancillary responses. xmm_simulator also includes tools to sum maps from multiple instruments and to organize simulation outputs into FITS files suitable for further analysis.

Polarpy processes and analyzes data from the POLAR gamma-ray polarimeter, which records spectral and polarization measurements in ROOT format. The package includes utilities to convert raw POLAR data into HDF5 format suitable for use in analysis frameworks and workflows. It supports integration with the 3ML (ascl:2506.018) modeling environment for downstream analysis of spectral and polarization information. Polarpy’s conversion tools streamline handling of POLAR instrument outputs and facilitate preparation of data for scientific modeling. The repository includes example scripts and code to assist users in preparing and analyzing polarization-related datasets.

PAOS (Physical Optics Simulator) performs physical optics propagation simulations using Fourier optics and the Fresnel approximation to model the behavior of optical fields through complex optical systems. It combines paraxial ray tracing with wavefront propagation methods to analyze diffraction, wavefront aberrations, and imaging effects in user-defined optical configurations. The package accepts configurable input systems via files or interfaces, supports modeling of apertures, optical elements, and aberrations, and outputs propagated fields and related diagnostic data. PAOS includes command-line and library interfaces along with interactive notebooks for exploration and visualization of simulation results. Its modular design allows users to apply different propagation and aberration models, generate surface error fields, and retrieve metrics such as wavefront properties and optical system responses.

tilsotua calculates the world-coordinate system (WCS) positions of slits in multislit mask designs used for the Low-Resolution Imaging Spectrograph (LRIS). The package ingests slit coordinate information stored in FITS files from mask design archives or autoslit outputs and reconstructs the corresponding slit positions in sky coordinates. It applies optional astrometric corrections by comparing alignment star slit locations to modern catalogs to refine the slit WCS positions. tilsotua outputs updated FITS files with filled slit center coordinates, CSV tables of slit corner and center positions, DS9 region files for visualization, and quick-look plots of mask geometries. The software supports reusable Python interfaces and command-line workflows for processing observational mask data.

STARDIS performs local thermodynamic equilibrium (LTE) radiative transfer calculations to generate synthetic stellar spectra from input model atmospheres. Built in part on structures from TARDIS (ascl:1402.018), it ingests configuration files and atomic data, computes opacity and radiation transport through a spherically symmetric plasma, and produces spectral outputs over user-specified wavelength grids. The package includes tools for setting up environments, running model configurations, and plotting resulting spectra in interactive workflows. STARDIS supports modular experimentation with opacity sources, transport options, and model parameters to facilitate generation and analysis of synthetic spectra.

SolarZip compresses solar extreme-ultraviolet image data using error-bounded lossy compression techniques designed to reduce data volume while controlling reconstruction error. The software applies interpolation-based predictors and configurable absolute or relative error constraints to image data prior to compression. It includes command-line tools for processing image datasets and producing compressed outputs along with reconstructed products for evaluation. SolarZip supports batch processing workflows and provides utilities to assess compression performance through distortion and reconstruction metrics.

RVSNUpy measures spectroscopic redshifts using inverse-variance-weighted cross-correlation between an observed spectrum and a library of rest-frame template spectra. It ingests spectra (e.g., SDSS FITS spectra and MMT/Hectospec raw spectra) and runs single-object or batch redshift measurements through a reusable measurement object initialized with a chosen template set. RVSNUpy reports redshift solutions across templates and identifies a best measurement within its returned results table. The package includes notebooks demonstrating typical workflows for redshift measurement and inspection of cross-correlation outputs.

PANCAKE (Python bAsed Numerical Color-magnitude-diagram Analysis pacKagE) analyzes stellar populations in nearby galaxies or star clusters by fitting observed color–magnitude diagrams (CMDs) with synthetic models using numerical methods. The package reads photometric observations, constructs template CMDs from theoretical isochrones, grids the CMD data, and executes model-free fitting to infer parameters such as star formation history and stellar population characteristics. It includes routines to preprocess input data, handle gridding and fitting workflows, and assess results such as residual maps and parameter estimates. PANCAKE supports multiple gridding strategies, integrates with Python scientific libraries for array handling and optimization, and can be applied to a variety of stellar population CMD datasets.