tropygal estimates entropy in the context of galactic dynamics, but can be used to estimate the entropy in any context. It focuses on nearest-neighbor estimators, and it also provides a function for density estimates. Additionally, tropygal provides functions for analytical distribution functions and density of states for dynamical models that have analytical expressions.

Particle_spray models the position and velocity distributions of newly-escaped stream particles that emerge from globular clusters (GCs). Rather than computing the detailed internal cluster dynamics, which is computationally expensive, the code directly draws tracer particles from these distributions. This algorithm is fast and accurate, and is implemented in a series of notebooks for several galactic dynamics codes, including AGAMA (ascl:1805.008) and galpy (ascl:1411.008).

naif extracts frequencies and respective amplitudes from time-series, such as that of an orbital coordinate. Based on the Numerical Analysis of Fundamental Frequencies (NAFF) algorithm and written in Python, naif offers some improvements, particularly in computation time. It also offers functions to plot the power-spectrum before extraction of each frequency, which can be useful for debugging particular orbits.

NIMBLE (Non-parametrIc jeans Modeling with B-spLinEs) inferrs the cumulative mass distribution of a gravitating system from full 6D phase space coordinates of its tracers via spherical Jeans modeling. It models the Milky Way's dark matter halo using Gaia and Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS) data. NIMBLE includes a basic inverse modeling Jeans routine that assumes perfect and complete data is available and a more complex forward modeling Jeans routine that deconvolves observational effects (uncertainties and limited survey volume) characteristic of Gaia and the DESI-MWS. It also includes tools for generating simple equilibrium model galaxies using Agama (ascl:1805.008) and imposing mock Gaia+DESI errors on 6D phase space input data.

FORSTAND constructs dynamical models of galaxies using the Schwarzschild orbit-superposition method; the method is available as part of the AGAMA (ascl:1805.008) framework. The models created are constrained by line-of-sight kinematic observations and are applicable to galaxies of all morphological types, including disks and triaxial rotating bars.