TriPoDPy simulates the evolution of gas and dust in protoplanetary disks using a parametric dust-evolution model. It models the radial evolution of disk material, including viscous evolution of the gas and the advection, diffusion, and growth of dust particles. It implements the TriPoD dust model to represent dust size distributions and track their evolution across the disk. The code uses Simframe (ascl:2603.018) for running simulations, and its Simulation class inherits attributes from DustPy (ascl:2207.016). TriPoDPy can be used to explore the structure and dynamics of protoplanetary disks and the processes that influence dust growth and transport during planet formation.

Simframe facilitates setting up and running scientific simulations. The package organizes simulation data into fields derived from NumPy arrays, allowing users to define variables, parameters, and differential equations within a unified simulation frame. It includes numerical integration schemes—both explicit and implicit—to evolve model variables and supports adaptive step sizes and custom integration methods. simframe also provides tools for structuring simulation components, updating variables during integration, and writing or reading output data files. Example applications include solving coupled differential equations and modeling dynamical systems such as N-body problems or compartmental models.

easyspec streamlines long-slit spectroscopy. The code reduces raw long-slit spectroscopic data, and extracts and calibrates spectra in wavelegnth and flux. It can fit a model to each line of the spectrum with a MCMC approach and recover physical quantities such as redshift, dispersion velocity, FWHM, and line flux. easyspec can also be used to fit two Gaussian components, explore the MCMC posterior distributions, and stack several spectra of the same target collected with different instruments.

Raccoon cleans low-frequency sinusoidal artifacts (“wiggles”) caused by resampling noise or aliasing in JWST-NIRSpec integral field spectroscopy (IFS) data. The package provides tools to process affected data and to visualize spectra before and after wiggle cleaning. Users can tailor settings to the source morphology or astronomical scene and to characteristics of the observed data (e.g., wavelength range).

THOR (Tracklet-less Heliocentric Orbit Recovery) links astronomical detections across multiple epochs to identify observations belonging to the same moving Solar System object. The software searches detection catalogs by testing heliocentric orbit hypotheses and associating observations consistent with each candidate orbit. This approach enables recovery of asteroids and other moving objects without requiring intranight tracklets or a predefined observing cadence. THOR can therefore analyze survey data sets with irregular temporal sampling and link observations separated by days to months. The code supports large-scale processing of survey catalogs to aid discovery and orbit determination of asteroids and other minor bodies.

SECRET (Stochasticity Emulator for Cosmic Ray Electrons) models the flux of cosmic-ray electrons using machine-learning density estimation techniques. The code represents the joint probability distribution of electron spectra across cosmic-ray transport parameter space using masked autoregressive models. This approach enables interpolation of predicted spectra for different propagation conditions without running a full numerical simulation for each parameter set. SECRET can therefore accelerate studies of cosmic-ray electron spectra and facilitate comparisons between theoretical models and observational data.

PyMGal generates optical mock observations from cosmological simulations. Incorporating methods from EzGal (ascl:1208.021), the program infers the spectral energy distributions (SEDs) of stellar particles within a simulation snapshot using customizable simple stellar population (SSP) models. These SEDs are used to calculate particle brightness through selected telescope filters. The results can be projected into realistic 2D images. The software is compatible with different simulation formats, including GADGET, GIZMO, and AREPO. Tested on IllustrisTNG, SIMBA, EAGLE, and The Three Hundred hydrodynamical simulations, PyMGal should work on other cosmological simulations as well.

exoatlas accesses, organizes, and visualizes populations of exoplanets and Solar System bodies. The toolkit retrieves and updates planetary data from online archives such as the NASA Exoplanet Archive and JPL Solar System Dynamics, organizing these data into population objects that provide convenient access to planetary and stellar properties. Built-in utilities compare planetary populations and generate visual summaries of their characteristics, enabling exploration of exoplanet datasets and comparative visualizations for research and education.

BAHAMAS (BAyesian inference with HAmiltonian Montecarlo for Astrophysical Stochastic background) performs Bayesian inference of stochastic gravitational-wave background signals in simulated data for the Laser Interferometer Space Antenna. The code simulates frequency-domain data streams corresponding to the A and E time-delay interferometry channels and estimates source parameters using Hamiltonian Monte Carlo sampling implemented with NumPyro. It supports the analysis of stationary stochastic processes, such as power-law backgrounds, and can include overlapping sources and mission data gaps to evaluate their effects on signal reconstruction. BAHAMAS provides command-line tools for data generation, preprocessing, and parameter estimation from simulated or challenge datasets. The software produces parameter estimates and diagnostics for stochastic background models from full-resolution or coarse-grained frequency data.

RapidGBM inspects and analyzes transient events using data from the Fermi Gamma-ray Burst Monitor (GBM) on the Fermi Gamma-ray Space Telescope. The web-based tool checks whether a source at a specified sky position and time is observable by GBM, accounting for Earth occultation and spacecraft passages through the South Atlantic Anomaly using spacecraft position history files. The code retrieves time-tagged event data, generates light curves, estimates backgrounds, and computes signal-to-noise ratios to assess candidate signals. RapidGBM extracts source and background spectra and produces detector response matrices using the GBM Response Generator. Spectral fitting is performed through PyXspec (ascl:2101.014), enabling rapid evaluation of spectral parameters such as photon index, peak energy, and energy flux.