SOXS creates simulated X-ray observations of astrophysical sources. The package provides a comprehensive set of tools to design source models and convolve them with simulated models of X-ray observatories. In particular, SOXS is the primary simulation tool for simulations of Lynx and Line Emission Mapper observations. SOXS provides facilities for creating spectral models, simple spatial models for sources, astrophysical background and foreground models, as well as a Python implementation of the SIMPUT file format.

PoWR (Potsdam Wolf-Rayet Models) calculates synthetic spectra for Wolf-Rayet and OB stars from model atmospheres which account for Non-LTE, spherical expansion and metal line blanketing. The model data is provided through a web interface and includes Spectral Energy Distribution, line spectrum in high resolution for different wavelength bands, and atmosphere stratification. For Wolf-Rayet stars of the nitrogen subclass, there are grids of hydrogen-free models and of models with a specified mass fraction of hydrogen. The iron-group and total CNO mass fractions correspond to the metallicity of the Galaxy, the Large Magellanic Cloud, or the Small Magellanic Cloud, respectively. The source code is available as a tarball on the same web interface.

GalCEM (GALactic Chemical Evolution Model) tracks isotope masses as a function of time in a given galaxy. The list of tracked isotopes automatically adapts to the complete set provided by the input yields. The prescription includes massive stars, low-to-intermediate mass stars, and Type Ia supernovae as enrichment channels. Multi-dimensional interpolation curves are extracted from the input yield tables with a preprocessing tool; these interpolation curves improve the computation speeds of the full convolution integrals, which are computed for each isotope and for each enrichment channel. GalCEM also provides tools to track the mass rate change of individual isotopes on a typical spiral galaxy with a final baryonic mass of 5×1010M⊙.

WALDO (Waveform AnomaLy DetectOr) flags possible anomalous Gravitational Waves from Numerical Relativity catalogs using deep learning. It uses a U-Net architecture to learn the waveform features of a dataset. After computing the mismatch between those waveforms and the neural predictions, WALDO isolates high mismatch evaluations for anomaly search.

void-dwarf-analysis analyzes Keck Cosmic Web Imager datacubes to produce maps of kinematic properties (velocity and velocity dispersion), emission line fluxes, and gas-phase metallicities of void dwarf galaxies.

KCWI_DRP, written in Python and based on kderp (ascl:2301.018), is the official DRP for the Keck Cosmic Web Imager at the W. M. Keck Observatory. It provides all of the functionality of the older pipeline and has three execution modes: multi-threading for CPU intensive tasks such as wavelength calibration, and multi-processing for large datasets. It offers vacuum to air and heliocentric or barycentric correction and the ability to use KOA file names or original file names. KCWI_DRP also improves the provenance and traceability of DRP versions and execution steps in the headers over kderp, and has versatile sky subtraction modes including using external sky frames and ability of masking regions.

kderp (KCWI Data Extraction and Reduction Pipeline) reduces data for the Keck Cosmic Web Imager. Written in IDL, it performs basic CCD reduction on raw images to produce bias and overscan subtracted, gain-corrected, trimmed and cosmic ray removed images; it can also subtract the sky. It defines the geometric transformations required to map each pixel in the 2d image into slice, postion, and wavelength, and performs flat field and illumination corrections. It generates cubes, applying the transformations previously solved to the object intensity, variance and mask images output from any of the previous stages, and uses a standard star observation to generate an inverse sensitivity curve which is applied to the corresponding observations to flux calibrate them.

This pipeline has been superseded by KCWI_DRP (ascl:2301.019).

ReACT extends the Copter (ascl:1304.022) and MG-Copter packages, which calculate redshift and real space large scale structure observables for a wide class of gravity and dark energy models. Additions to Copter include spherical collapse in modified gravity, halo model power spectrum for general theories, and real and redshift space LSS 2 point statistics for modified gravity and dark energy. ReACT also includes numerical perturbation theory kernel solvers, real space bispectra in modified gravity, and a numerical perturbation theory kernel solver up to 4th order for 1-loop bispectrum.

FERRE matches physical models to observed data, taking a set of observations and identifying the model parameters that best reproduce the data, in a chi-squared sense. It solves the common problem of having numerical parametric models that are costly to evaluate and need to be used to interpret large data sets. FERRE provides flexibility to search for all model parameters, or hold constant some of them while searching for others. The code is written to be truly N-dimensional and fast. Model predictions are to be given as an array whose values are a function of the model parameters, i.e., numerically. FERRE holds this array in memory, or in a direct-access binary file, and interpolates in it. The code returns, in addition to the optimal set of parameters, their error covariance, and the corresponding model prediction. The code is written in FORTRAN90.

SOAP-GPU is a revision of SOAP 2 (ascl:1504.021), which simulates spectral time series with the effect of active regions (spot, faculae or both). In addition to the traditional outputs of SOAP 2.0 (the cross-correlation function and extracted parameters: radial velocity, bisector span, full width at half maximum), SOAP-GPU generates the integrated spectra at each phase for given input spectra and spectral resolution. Additional capabilities include fast spectral simulation of stellar activity due to GPU acceleration, simulation of more complicated active region structures with superposition between active regions, and more realistic line bisectors, based on solar observations, that varies as function of mu angle for both quiet and active regions. In addition, SOAP-GPU accepts any input high resolution observed spectra. The PHOENIX synthetic spectral library are already implemented at the code level which allows users to simulate stellar activity for stars other than the Sun. Furthermore, SOAP-GPU simulates realistic spectral time series with either spot number/SDO image as additional inputs. The code is written in C and provides python scripts for input pre-processing and output post-processing.

LBL derives velocity measurements from high-resolution (R>50 000) datasets by accounting for outliers in the spectra data. It is tailored for fiber-fed multi-order spectrographs, both in optical and near-infrared (up to 2.5µm) domains. The domain is split into individual units (lines) and the velocity and its associated uncertainty are measured within each line and combined through a mixture model to allow for the presence of spurious values. In addition to the velocity, other quantities are also derived, the most important being a value (dW) that can be understood (for a Gaussian line) as a change in the line FWHM. These values provide useful stellar activity indicators. LBL works on data from a variety of instruments, including SPIRou, NIRPS, HARPS, and ESPRESSO. The code's output is an rdb table that can be uploaded to the online DACE pRV analysis tool.

pyExoRaMa visualizes and manipulates data related to exoplanets and their host stars in a multi-dimensional parameter space. It enables statistical studies based on the large and constantly increasing number of detected exoplanets, identifies possible interdependence among several physical parameters, and compares observables with theoretical models describing the exoplanet composition and structure.

XGA (X-ray: Generate and Analyse) analyzes X-ray sources observed by the XMM-Newton Space telescope. It is based around declaring different types of source and sample objects which correspond to real X-ray sources, finding all available data, and then insulating the user from the tedious generation and basic analysis of X-ray data products. XGA generates photometric products and spectra for individual sources, or whole samples, with just a few lines of code. Though not a pipeline, pipelines for complex analysis can be built on top of it. XGA provides an easy to use (and parallelized) Python interface with XMM's Science Analysis System (ascl:1404.004), as well as with XSPEC (ascl:9910.005). All XMM products and fit results are read into an XGA source storage structure, thus avoiding the need to leave a Python environment at any point during the analysis. This module also supports more complex analyses for specific object types such as the easy generation of scaling relations, the measurement of gas masses for galaxy clusters, and the PSF correction of images.

Rosetta runs tasks for resource-intensive, interactive data analysis as software containers. The code's architecture frames user tasks as microservices – independent and self-contained units – which fully support custom and user-defined software packages, libraries and environments. These include complete remote desktop and GUI applications, common analysis environments such as the Jupyter Notebooks. Rosetta relies on Open Container Initiative containers, allowing for safe, effective and reproducible code execution. It can use a number of container engines and runtimes and seamlessly supports several workload management systems, thus enabling containerized workloads on a wide range of computing resources.

Fastcc returns color corrections for different spectra for various Cosmic Microwave Background experiments. Available in both Python and IDL, the script is easy to use when analyzing radio spectra of sources with data from multiple wide-survey CMB experiments in a consistent way across multiple experiments.

Xpol computes angular power spectra based on cross-correlation between maps and covariance matrices. The code is written in C and is fully MPI parallelized in CPU and memory using spherical transform by s2hat (ascl:1110.013). It has been used to derive CMB and dust power spectra for Archeops and CMB, dust, CIB, SZ, SZ-CIB for Planck, among others.

HiLLiPoP is a multifrequency CMB likelihood for Planck data. The likelihood is a spectrum-based Gaussian approximation for cross-correlation spectra from Planck 100, 143 and 217GHz split-frequency maps, with semi-analytic estimates of the Cl covariance matrix based on the data. The cross-spectra are debiased from the effects of the mask and the beam leakage using Xpol (ascl:2301.009) before being compared to the model, which includes CMB and foreground residuals. They cover the multipoles from ℓ=30 to ℓ=2500. HiLLiPoP is interfaced with the Cobaya (ascl:1910.019) MCMC sampler.

LoLLiPoP is a Planck low-l polarization likelihood based on cross-power-spectra for which the bias is zero when the noise is uncorrelated between maps. It uses a modified approximation to apply to cross-power spectra and is interfaced with the Cobaya (ascl:1910.019) MCMC sampler. Cross-spectra are computed on the CMB maps from Commander component separation applied on each detset-split Planck frequency maps.

Self-cal produces radio-interferometric images of an astrophysical object. The code is an adaptation of the self-calibration algorithm to optical/infrared long-baseline interferometry, especially to make use of differential phases and differential visibilities. It works together with the Mira image reconstruction software and has been used mainly on VLTI data. Self-cal, written in Yorick, is also available as part of fitsOmatic (ascl:2301.005).

The fitOmatic model-fitting prototyping tool tests multi-wavelength model-fitting and exploits VLTI data. It provides tools to define simple geometrical models and conveniently adjust the model's parameters. Written in Yorick, it takes optical interferometry FITS (oifits) files as input and allows the user to define a model of the source from a set of pre-defined models, which can be combined to make more complicated models. fitOmatic then computes the Fourier Transform of the modeled brightness distribution and synthetic observables are computed at the wavelengths and projected baselines of the observations. fitomatic's strength is its ability to define vector-parameters, i.e., parameters that may depend on wavelength and/or time. The self-cal (ascl:2301.006) component of fitOmatic is also available as a separate code.

HEADSS (HiErArchical Data Splitting and Stitching) facilitates clustering at scale, unlike clustering algorithms that scale poorly with increased data volume or that are intrinsically non-distributed. HEADSS automates data splitting and stitching, allowing repeatable handling, and removal, of edge effects. Implemented in conjunction with scikit's HDBSCAN, the code achieves orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. HEADSS also establishes a hierarchy of features by using a subset of clustering features to split the data.

WF4Py implements frequency-domain gravitational wave waveform models in pure Python, thus enabling parallelization over multiple events at a time. Waveforms in WF4Py are built as classes; the functions take dictionaries containing the parameters of the events to analyze as input and provide Fourier domain waveform models. All the waveforms are accurately checked with their implementation in LALSuite (ascl:2012.021) and are a core element of GWFAST (ascl:2212.001).

Pyxel hosts and pipelines models (analytical, numerical, statistical) simulating different types of detector effects on images produced by Charge-Coupled Devices (CCD), Monolithic, and Hybrid CMOS imaging sensors. Users can provide one or more input images to Pyxel, set the detector and model parameters, and select which effects to simulate, such as cosmic rays, detector Point Spread Function (PSF), electronic noises, Charge Transfer Inefficiency (CTI), persistence, dark current, and charge diffusion, among others. The output is one or more images including the simulated detector effects combined. The Pyxel framework, written in Python, provides basic image analysis tools, an input image generator, and a parametric mode to perform parametric and sensitivity analysis. It also offers a model calibration mode to find optimal values of its parameters based on a target dataset the model should reproduce.

CALSAGOS (Clustering ALgorithmS Applied to Galaxies in Overdense Systems) selects cluster members and searches, finds, and identifies substructures and galaxy groups in and around galaxy clusters using the redshift and position in the sky of the galaxies. The package offers two ways to determine cluster members, ISOMER and CLUMBERI. The ISOMER (Identifier of SpectrOscopic MembERs) function selects the spectroscopic cluster members by defining cluster members as those galaxies with a peculiar velocity lower than the escape velocity of the cluster. The CLUMBERI (CLUster MemBER Identifier) function select the cluster members using a 3D-Gaussian Mixture Modules (GMM). Both functions remove the field interlopers by using a 3-sigma clipping algorithm. CALSAGOS uses the function LAGASU (LAbeller of GAlaxies within SUbstructures) to search, find, and identify substructures and groups in and around a galaxy cluster; this function is based on clustering algorithms (GMM and DBSCAN), which search areas with high density to define a substructure or groups.

unWISE-verse is an integrated Python pipeline for downloading sets of unWISE time-resolved coadd cutouts from the WiseView image service and uploading subjects to Zooniverse.org for use in astronomical citizen science research. This software was initially designed for the Backyard Worlds: Cool Neighbors research project and is optimized for target sets containing low luminosity brown dwarf candidates. However, unWISE-verse can be applied to other future astronomical research projects that seek to make use of unWISE infrared sky maps, such as studies of infrared variable/transient sources.

Spender establishes a restframe for galaxy spectra that has higher resolution and larger wavelength range than the spectra from which it is trained. The model can be trained from spectra at different redshifts or even from different instruments without the need to standardize the observations. Spender also has an explicit, differentiable redshift dependence, which can be coupled with a redshift estimator for a fully data-driven spectrum analysis pipeline. The code describes the restframe spectrum by an autoencoder and transforms the restframe model to the observed redshift; it also matches the spectral resolution and line spread function of the instrument.

CONTROL (CUTE autONomous daTa ReductiOn pipeLine) produces science-quality output with a single command line with zero user interference for CUTE (Colorado Ultraviolet Transit Experiment) data. It can be used for any single order spectral data in any wavelength without any modification. The pipeline is governed by a parameter file, which is available with this distribution. CONTROL is fully automated and works in a series of steps following standard CCD reduction techniques. It creates a reduction log to track processes carried out and any parameters used.

Burning Arrow determines the destabilization of massive particle circular orbits due to thermal radiation, emitted in X-ray, from the hot accretion disk material. This code requires the radiation forces exerted on the material at the point of interest found by running the code Infinity (ascl:2212.021). Burning Arrow begins by assuming a target particle in the disk that moves in a circular orbit. It then introduces the recorded radiation forces from Infinity code for the target region. The forces are subsequently introduced into the target particle equations of motion and the trajectory is recalculated. Burning Arrow then produces images of the black hole - accretion disk system that includes the degenerated particle trajectories that obey the assorted velocity profiles.

Tranquillity creates an observing screen looking toward a black hole - accretion disk system, seeks the object, then searches and locates its contour. Subsequently, it attempts to locate the first Einstein "echo" ring and its location. Finally, it collates the retrieved information and draws conclusions; these include the accretion disk level inclination compared to the line of sight and the main disk and the first echo median. The displacement, and thus the divergence of the latter two, is the required information in order to construct the divergence plots. Other programs can later on automatically read these plots and provide estimations of the central black hole spin.

Elysium creates an observing screen at the desirable distance away from a black hole system. Observers set on every pixel of this screen then photograph the area toward the black hole - accretion disk system and report back what they record. This can be the accretion disk (incoming photons bring in radiation and thus energy), the black hole event horizon, or the empty space outside and beyond the system (there are no incoming photons or energy). The central black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user.

Infinity sets an observer in a black hole - accretion disk system. The black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user. This observer can be on the surface of the disk, in its exterior or its interior (if the disk is not opaque). Infinity then scans the entire sky around the observer and investigates whether photons emitted by the hot accretion disk material can reach them. After recording the incoming radiation, the program calculates the stress-energy tensor of the radiation. Afterwards, the program calculates the radiation flux and hence, the radiation force exerted on target particles of various velocity profiles.

Omega solves the photon equations of motion in the environment surrounding a black hole. This black hole can be either Schwarzschild (nonrotating) or Kerr (rotating) by choice of the user. The software offers numerous options, such as the geometrical setup of the accretion disk around the black hole (including no disk, band, slab, wedge, among others, the spin parameter of the central black hole, and the thickness of the accretion disk. Other options that can be set includ the azimuthal angle of the photon emission/reception, the poloidal angle of the photon emission/reception, and how far away or close to the system to look.

m2mcluster performs made-to-measure modeling of star clusters, and can fit target observations of a Galactic globular cluster's 3D density profile and individual kinematic properties, including proper motion velocity dispersion, and line of sight velocity dispersion. The code uses AMUSE (ascl:1107.007) to model the gravitational N-body evolution of the system between time steps; GalPy (ascl:1411.008) is also required.

SourceXtractor++ extracts a catalog of sources from astronomical images; it is the successor to SExtractor (ascl:1010.064). SourceXtractor++ has been completely rewritten in C++ and improves over its predecessor in many ways. It provides support for multiple “measurement” images, has an optimized multi-object, multi-frame model-fitting engine, and can define complex priors and dependencies for model parameters. It also offers efficient image data caching and multi-threaded processing, and has a modular design with support for third-party plug-ins.

powspec provides functions to compute power and cross spectral density of 2D arrays. Units are properly taken into account. It can, for example, create fake Gaussian field images, compute power spectra P(k) of each image, shrink a mask with regard to a kernel, generate a Gaussian field, and plot various results.

The AbundanceMatching Python module creates (interpolates and extrapolates) abundance functions and also provides fiducial deconvolution and abundance matching.

SImMER (Stellar Image Maturation via Efficient Reduction) reduces astronomical imaging data. It performs standard dark-subtraction and flat-fielding operations on data from, for example, the ShARCS camera on the Shane 3-m telescope at Lick Observatory and the PHARO camera on the Hale 5.1-m telescope at Palomar Observatory; its object-oriented design allows the software to be extended to other instruments. SImMER can also perform sky-subtraction, image registration, FWHM measurement, and contrast curve calculation, and can generate tables and plots. For widely separated stars which are of somewhat equal brightness, a “wide binary” mode allows the user to selects which star is the primary around which each image should be centered.

pyTANSPEC extracts XD-mode spectra automatically from data collected by the TIFR-ARIES Near Infrared Spectrometer (TANSPEC) on India's ground-based 3.6-m Devasthal Optical Telescope at Nainital, India. The TANSPEC offers three modes of observations, imaging with various filters, spectroscopy in the low-resolution prism mode with derived R~ 100-400 and the high-resolution cross-dispersed mode (XD-mode) with derived median R~ 2750 for a slit of width 0.5 arcsec. In the XD-mode, ten cross-dispersed orders are packed in the 2048 x 2048 pixels detector to cover the full wavelength regime. The XD-mode is most utilized; pyTANSPEC provides a dedicated pipeline for consistent data reduction for all orders and to reduces data reduction time. The code requires nominal human intervention only for the quality assurance of the reduced data. Two customized configuration files are used to guide the data reduction. The pipeline creates a log file for all the fits files in a given data directory from its header, identifies correct frames (science, continuum and calibration lamps) based on the user input, and offers an option to the user for eyeballing and accepting/removing of the frames, does the cleaning of raw science frames and yields final wavelength calibrated spectra of all orders simultaneously.

PACMAN (Planetary Atmosphere, Crust, and MANtle geochemical evolution) runs a coupled redox-geochemical-climate evolution model. It runs Monte Carlo calculations over nominal parameter ranges, including number of iterations and number of cores for parallelization, which can be altered to reproduce different scenarios and sensitivity tests. Model outputs and corresponding input parameters are saved in separate files which are used to plot results; the the user can choose which outputs to plot, including all successful outputs, nominal Earth outputs, waterworld false positives, desertworld false positives, and high CO2:H2O false positives. Among other functions, PACMAN contains functions for interpolating the pre-computed Outgoing Longwave Radiation (OLR) grid, the atmosphere-ocean partitioning grid, and the stratospheric water vapor grid, calculating bond albedo and outgassing fluxes.

The BANZAI-NRES pipeline processes data from the Network of Robotic Echelle Spectrographs (NRES) on the Las Cumbres Observatory network and provides extracted, wavelength calibrated spectra. If the target is a star, it provides stellar classification parameters (e.g., effective temperature and surface gravity) and a radial velocity measurement. The automated radial velocity measurements from this pipeline have a precision of ~ 10 m/s for high signal-to-noise observations. The data flow and infrastructure of this code relies heavily on BANZAI (ascl:2207.031), enabling BANZAI-NRES to focus on analysis that is specific to spectrographs. The wavelength calibration is primarily done using xwavecal (ascl:2212.011). The pipeline propagates an estimate of the formal uncertainties from all of the data processing stages and includes these in the output data products. These are used as weights in the cross correlation function to measure the radial velocity.

The xwavecal library automatically wavelength calibrates echelle spectrographs for high precision radial velocity work. The routines are designed to operate on data with extracted 1D spectra. The library provides a convienience function which returns a list of wavelengths from just a list of spectral feature coordinates (pixel and order) and a reference line list. The returned wavelengths are the wavelengths of the measured spectral features under the best fit wavelength model. xwavecal also provides line identification and spectral reduction utilities. The library is modular; each step of the wavelength calibration is a stage which can be disabled by removing the associated line in the config.ini file. Wavelength calibrating data which already have spectra means only using the wavelength calibration stages. Using the full experimental pipeline means enabling the other data reduction stages, such as overscan subtraction.

sf_deconvolve performs PSF deconvolution using a low-rank approximation and sparsity. It can handle a fixed PSF for the entire field or a stack of PSFs for each galaxy position. The code accepts Numpy binary files or FITS as input, takes the observed (i.e. with PSF effects and noise) stack of galaxy images and a known PSF, and attempts to reconstruct the original images. sf_deconvolve can be run in a terminal or in an active Python session, and includes options for initialization, optimization, low-Rank approximation, sparsity, PSF estimation, and other attributes.

Hazma enables indirect detection of sub-GeV dark matter. It computes gamma-ray and electron/positron spectra from dark matter annihilations, sets limits on sub-GeV dark matter using existing gamma-ray data, and determines the discovery reach of future gamma-ray detectors. The code also derives accurate CMB constraints. Hazma comes with several sub-GeV dark matter models, for which it provides functions to compute dark matter annihilation cross sections and mediator decay widths. A variety of low-level tools are provided to make it straightforward to define new models.

panco2 extracts measurements of the pressure profile of the hot gas inside galaxy clusters from millimeter-wave observations. The extraction is performed using forward modeling the millimeter-wave signal of clusters and MCMC sampling of a posterior distribution for the parameters given the input data. Many characteristic features of millimeter-wave observations can be taken into account, such as filtering (both through PSF smearing and transfer functions), point source contamination, and correlated noise.

PyMCCF (Python Modernized Cross Correlation Function), also known as MCCF, cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. Based on PyCCF (ascl:1805.032) and ICCF, it introduces a new parameter, MAX, to reduce the number of interpolated points used to just those which are not farther from the nearest real one than the MAX. This significantly reduces noise from interpolation errors. The estimation of the errors in PyMCCF is exactly the same as in PyCCF.

GPry efficiently obtains marginal quantities from computationally expensive likelihoods. It works best with smooth (continuous) likelihoods and posteriors that are slow to converge by other methods, which is dependent on the number of dimensions and expected shape of the posterior distribution. The likelihood should be low-dimensional (d<20 as a rule of thumb), though the code may still provide considerable improvements in speed in higher dimensions, despite an increase in the computational overhead of the algorithm. GPry is an alternative to samplers such as MCMC and Nested Sampling with a goal of speeding up inference in cosmology, though the software will work with any likelihood that can be called as a python function. It uses Cobaya's (ascl:1910.019) model framework so all of Cobaya's inbuilt likelihoods work, too.

MTNeedlet uses needlets to filter spherical (Healpix) maps and detect and analyze the maxima population using a multiple testing approach. It has been developed with the CMB in mind, but it can be applied to other spherical maps. It pivots around three basic steps: 1.) The calculation of several types of needlets and their possible use to filter maps; 2.) The detection of maxima (or minima) on spherical maps, their visualization and basic analysis; and 3.) The multiple testing approach in order to detect anomalies in the maxima population of the maps with respect to the expected behavior for a random Gaussian map. MTNeedlet relies on Healpy (ascl:2008.022) to efficiently deal with spherical maps.

FastDF (Fast Distribution Function) integrates relativistic particles along geodesics in a comoving periodic volume with forces determined by cosmological linear perturbation theory. Its main application is to set up accurate particle realizations of the linear phase-space distribution of massive relic neutrinos by starting with an analytical solution deep in radiation domination. Such particle realizations are useful for Monte Carlo experiments and provide consistent initial conditions for cosmological N-body simulations. Gravitational forces are calculated from three-dimensional potential grids, which are obtained by convolving random phases with linear transfer functions using Fast Fourier Transforms. The equations of motion are solved using a symplectic leapfrog integration scheme to conserve phase-space density and prevent the build-up of errors. Particles can be exported in different gauges and snapshots are provided in the HDF5 format, compatible with N-body codes like SWIFT (ascl:1805.020) and Gadget-4 (ascl:2204.014). The code has an interface with CLASS (ascl:1106.020) for calculating transfer functions and with monofonIC (ascl:2008.024) for setting up initial conditions with dark matter, baryons, and neutrinos.

MGCosmoPop implements a hierarchical Bayesian inference method for constraining the background cosmological history, in particular the Hubble constant, together with modified gravitational-wave propagation and binary black holes population models (mass, redshift and spin distributions) with gravitational-wave data. It includes support for loading and analyzing data from the GWTC-3 catalog as well as for generating injections to evaluate selection effects, and features a module to run in parallel on clusters.

Eventdisplay reconstructs and analyzes data from the Imaging Atmospheric Cherenkov Telescopes (IACT). It has been primarily developed for VERITAS and CTA analysis. The package calibrates and parametrizes images, event reconstruction, and stereo analysis, and provides train boosted decision trees for direction and energy reconstruction. It fills and uses lookup tables for mean scaled width and length calculation, energy reconstruction, and stereo reconstruction, and calculates radial camera acceptance from data files and instrument response functions such as effective areas, angular point-spread function, and energy resolution. Eventdisplay offers additional tools as well, including tools for calculating sky maps and spectral energy distribution, and to plot instrument response function, spectral energy distributions, light curves, and sky maps, among others.