The non-parametric Jeans code GravSphere models discrete data and can be used to model dark matter distributions in galaxies. It can also recover the density ρ(r) and velocity anisotropy β(r) of spherical stellar systems, assuming only that they are in a steady state. Real or mock data are prepared by using the included binulator.py code; the repository also includes many examples for exploring the GravSphere's capabilities.

SWIFTGalaxy analyzes particles belonging to individual simulated galaxies. The code provides a software abstraction of simulated galaxies produced by the SWIFT smoothed particle hydrodynamics code (ascl:1805.020) and extends the SWIFTSimIO module. SWIFTGalaxy inherits from and extends the functionality of the SWIFTDataset. It understands the output of halo finders and therefore which particles belong to a galaxy and its integrated properties. The particles occupy a coordinate frame that is enforced to be consistent, such that particles loaded on-the-fly will, for example, match rotations and translations of particles already in memory. Intuitive masking of particle datasets is also enabled. Finally, SWIFTGalaxy provides utilities that make working in cylindrical and spherical coordinate systems more convenient.

MARTINI (Mock APERTIF-like Radio Telescope Interferometry of the Neutal ISM) creates synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies. The various aspects of the mock-observing process are divided logically into sub-modules handling the data cube, source, beam, noise, spectral model and SPH kernel. MARTINI is object-oriented: each sub-module provides a class (or classes) which can be configured as desired. For most sub-modules, base classes are provided to allow for straightforward customization. Instances of each sub-module class are then given as parameters to the Martini class. A mock observation is then constructed by calling a handful of functions to execute the desired steps in the mock-observing process.