Astrophysical turbulent flows display an intrinsically multi-scale nature, making their numerical simulation and the subsequent analyses of simulated data a complex problem. In particular, two fundamental steps in the study of turbulent velocity fields are the Helmholtz-Hodge decomposition (compressive+solenoidal; HHD) and the Reynolds decomposition (bulk+turbulent; RD). These problems are relatively simple to perform numerically for uniformly-sampled data, such as the one emerging from Eulerian, fix-grid simulations; but their computation is remarkably more complex in the case of non-uniformly sampled data, such as the one stemming from particle-based or meshless simulations. In this paper, we describe, implement and test vortex-p, a publicly available tool evolved from the vortex code, to perform both these decompositions upon the velocity fields of particle-based simulations, either from smoothed particle hydrodynamics (SPH), moving-mesh or meshless codes. The algorithm relies on the creation of an ad-hoc adaptive mesh refinement (AMR) set of grids, on which the input velocity field is represented. HHD is then addressed by means of elliptic solvers, while for the RD we adapt an iterative, multi-scale filter. We perform a series of idealised tests to assess the accuracy, convergence and scaling of the code. Finally, we present some applications of the code to various SPH and meshless finite-mass (MFM) simulations of galaxy clusters performed with OpenGadget3, with different resolutions and physics, to showcase the capabilities of the code.

GalCraft creates mock integral-field spectroscopic (IFS) observations of the Milky Way and other hydrodynamical/N-body simulations. It conducts all the procedures from inputting data and spectral templates to the output of IFS data cubes in FITS format. The produced mock data cubes can be analyzed in the same way as real IFS observations by many methods, particularly codes like Voronoi binning (ascl:1211.006), pPXF (ascl:1210.002), line-strength indices, or a combination of them (e.g., the GIST pipeline, ascl:1907.025). The code is implemented using Python-native parallelization. GalCraft will be particularly useful for directly comparing the Milky Way with other MW-like galaxies in terms of kinematics and stellar population parameters and ultimately linking the Galactic and extragalactic to study galaxy evolution.

pyRRG measures the 2nd and 4th order moments using a TinyTim model to correct for PSF distortions. The code is invariant to the number exposures and orientation of the drizzle images. pyRRG uses a machine learning algorithm to automatically classify stars and galaxies; this can also be done manually if greater accuracy is needed.

Padé simulates protoplanetary disk hydrodynamics in cylindrical coordinates. Written in Fortran90, it is a finite-difference code and the compact 4th-order standard Padé scheme is used for spatial differencing. Padé differentiation is known to have spectral-like resolving power. The z direction can be periodic or non-periodic. The 4th order Runge-Kutta is used for time advancement. Padé implements a version of the FARGO technique to eliminate the time-step restriction imposed by Keplerian advection, and capturing of shocks that are not too strong can be done by using artificial bulk viscosity.

PySR performs Symbolic Regression; it uses machine learning to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. Symbolic regression works best on low-dimensional datasets, but one can also extend these approaches to higher-dimensional spaces by using "Symbolic Distillation" of Neural Networks. Here, one essentially uses symbolic regression to convert a neural net to an analytic equation. Thus, these tools simultaneously present an explicit and powerful way to interpret deep neural networks.

WISE2MBH uses infrared cataloged data from the Wide-field Infrared Survey Explorer (WISE) to estimate the mass of supermassive black holes (SMBH). It implements a Monte Carlo approach for error propagation, considering mean photometric errors from WISE magnitudes, errors in fits of scaling relations used and scatter of those relations, if available.

PyExoCross, a Python adaptation of ExoCross (ascl:1803.014), post-processes molecular line lists generated by ExoMol, HITRAN, and HITEMP and other similar initiatives. It generates absorption and emission spectra and other properties, including partition functions, specific heats, and cooling functions, based on molecular line lists. The code also calculates cross sections with four line profiles: Doppler, Gaussian, Lorentzian, and Voigt. PyExoCross can convert data format between ExoMol and HITRAN, and supports importing and exporting line lists in the ExoMol and HITRAN/HITEMP formats.

GASTLI (GAS gianT modeL for Interiors) calculates the interior structure models for gas giants exoplanets. The code computes mass-radius curves, thermal evolution curves, and interior composition retrievals to fit a interior structure model to your mass, radius, age, and if available, atmospheric metallicity data. GASTLI can also plot the results, including internal and atmospheric profiles, a pressure-temperature diagram, mass-radius relations, and thermal evolution curves.

symbolic_pofk provides simple Python functions and a Fortran90 routine for precise symbolic emulations of the linear and non-linear matter power spectra and for the conversion σ 8 ↔ A s as a function of cosmology. These can be easily copied, pasted, and modified to other languages. Outside of a tested k range, the fit includes baryons by default; however, this can be switched off.

planetMagFields accesses and analyzes information about magnetic fields of planets in our solar system and visualizes them in both 2D and 3D. The code provides access to properties of a planet, such as dipole tilt, Gauss coefficients, and computed radial magnetic field at surface, and has methods to plot the field and write a vts file for 3D visualization. planetMagFields can be used to produce both 2D and 3D visualizations of a planetary field; it also provides the option of potential extrapolation.