massmappy recovers convergence mass maps on the celestial sphere from weak lensing cosmic shear observations. It relies on SSHT (ascl:2207.034) and HEALPix (ascl:1107.018) to handle sampled data on the sphere. The spherical Kaiser-Squires estimator is implemented.

SSHT performs fast and exact spin spherical harmonic transforms; functionality is also provided to perform fast and exact adjoint transforms, forward and inverse transforms, and spherical harmonic transforms for a number of alternative sampling schemes. The code can interface with DUCC (ascl:2008.023) and use it as a backend for spherical harmonic transforms and rotations.

piXedfit provides a self-contained set of tools for analyzing spatially resolved properties of galaxies using imaging data or a combination of imaging data and the integral field spectroscopy (IFS) data. piXedfit has six modules that can handle all tasks in the analysis of the spatially resolved SEDs of galaxies, including images processing, a spatial-matching between reduced broad-band images with an IFS data cube, pixel binning, performing SED fitting, and making visualization plots for the SED fitting results.

gwdet computes the probability of detecting a gravitational-wave signal from compact binaries averaging over sky-location and source inclination. The code has two classes, averageangles and detectability. averageangles computes the detection probability, averaged over all angles (such as sky location, polarization, and inclination), as a function of the projection parameter. detectability computes the detection probability of a non-spinning compact binary.

BANZAI (Beautiful Algorithms to Normalize Zillions of Astronomical Images) processes raw data taken from Las Cumbres Observatory and produces science quality data products. It is capable of reducing single or multi-extension fits files. For historical data, BANZAI can also reduce the data cubes that were produced by the Sinistro cameras.

This code analyzes a dipole axis in the distribution of galaxy spin directions. The code takes as input a list of galaxies, their equatorial coordinates, and their spin directions. It then determines the statistical significance of possible dipole axis at any point in the sky by comparing the cosine dependence of the spin directions to the mean and standard deviation of the cosine dependence after 2000 runs with random spin directions. A code to analyze the binomial distribution of the spin directions using Monte Carlo simulation is also available.

ParticleGridMapper.jl interpolates particle data onto either a Cartesian (uniform) grid or an adaptive mesh refinement (AMR) grid where each cell contains no more than one particle. The AMR grid can be trimmed with a user-defined maximum level of refinement. Three different interpolation schemes are supported: nearest grid point (NGP), smoothed-particle hydrodynamics (SPH), and Meshless finite mass (MFM). It is multi-threading parallel.

disksurf measures the height of optically thick emission or photosphere in moderately inclined protoplanetary disks. The package is dependent on AstroPy (ascl:1304.002) and uses GoFish (ascl:2011.016) to retrieve data from FITS data cubes and user-specified parameters to return a surface object containing the disk-centric coordinates of the surface and the gas temperature and rotation velocity at those locations. disksurf provides clipping, smoothing, and diagnostic functions as well.

ConeRot extracts velocity perturbations in protoplanetary disks from observed line centroids maps ν∘, by creating axially-symmetric centroid maps. It also derives 3D rotation curves in disk-centered cylindrical coordinates, and can estimate the disk orientation based on line data alone. It approximates the unit opacity surface of an axially symmetric disc by a series of cones whose orientations are fit to the observed velocity centroid in concentric radial domains, or regions, with the disc orientation and the rotation curve both optimized to fit ν∘ in each region. ConeRot extracts the perturbations directly from observations without strong assumptions about the underlying disk model and employs a reduced number of free parameters.

pdspy fits Monte Carlo radiative transfer models for protostellar/protoplanetary disks to ALMA continuum and spectral line datasets using Markov Chain Monte Carlo fitting. It contains two tools, one to fit ALMA continuum visibilities and broadband spectral energy distributions (SEDs) with full radiative transfer models, and another to fit ALMA spectral line visibilities with protoplanetary disk models that include a vertically isothermal, power law temperature distribution. No radiative equilibrium calculation is done.

casa_cube provides an interface to data cubes generated by CASA (ascl:1107.013) or Gildas (ascl:1305.010). It performs simple tasks such as plotting given channel maps, moment maps, and line profile in various units, and also corrects for cloud extinction, reconvolves with a beam taper, and permits quick and easy comparisons with models.

pymcfost provides an interface to and can be used to visualize results from the 3D radiative transfer code MCFOST (ascl:2207.023). pymcfost can set up continuum and line models, read a single model or library of models, plot basic quantities such as density structures and temperature maps, and plot observables, including SEDs, polarization maps, visibilities, and channels maps (with spatial and spectral convolution). It can also convert units (e.g. W.m-2 to Jy or brightness temperature), and it provides an interface to the ALMA CASA simulator (ascl:1107.013).

MCFOST is a 3D continuum and line radiative transfer code based on an hybrid Monte Carlo and ray-tracing method. It is mainly designed to study the circumstellar environment of young stellar objects, but has been used for a wide range of astrophysical problems. The calculations are done exactly within the limitations of the Monte Carlo noise and machine precision, i.e., no approximation are used in the calculations. The code has been strongly optimized for speed.

MCFOST is primarily designed to study protoplanetary disks. The code can reproduce most of the observations of disks, including SEDs, scattered light images, IR and mm visibilities, and atomic and molecular line maps. As the Monte Carlo method is generic, any complex structure can be handled by MCFOST and its use can be extended to other astrophysical objects. For instance, calculations have succesfully been performed on infalling envelopes and AGB stars. MCFOST also includes a non-LTE line transfer module, and NLTE level population are obtained via iterations between Monte Carlo radiative transfer calculations and statistical equilibrium.

triple-stability uses a simple form of an artificial neural network, a multi-layer perceptron, to check whether a given configuration of a triple-star system is dynamically stable. The code is written in Python and the MLP classifier can be imported to other custom Python3 scripts.

BAYGAUD (BAYesian GAUssian Decomposer) implements the decomposition of velocity profiles in a data cube and subsequent classification. It uses MultiNest (ascl:1109.006) for calculating the posterior distribution and the evidence for a given likelihood function. The code models a given line profile with an optimal number of Gaussians based on the Bayesian Markov Chain Monte Carlo (MCMC) techniques. BAYGAUD is parallelized using the Message-Passing Interface (MPI) standard, which reduces the time needed to calculate the evidence using MCMC techniques.

vKompth fits the energy-dependent rms-amplitude and phase-lag spectra of low-frequency quasi-periodic oscillations in low mass black-hole X-ray binaries using a variable Comptonization model. The accretion disc is modeled as a multi-temperature blackbody source emitting soft photons which are then Compton up-scattered in a spherical corona, including feedback of Comptonized photons that return to the disc.

walter calculates the number density of stars detected in a given observation aiming to resolve a stellar population. The code also calculates the exposure time needed to reach certain population features, such as the horizontal branch, and provides an estimate of the crowding limit. walter was written with the expectation that such calculations will be very useful for planning surveys with the Nancy Grace Roman Space Telescope (RST, formerly WFIRST).

pocoMC performs Bayesian inference, including model comparison, for challenging scientific problems. The code utilizes a normalizing flow to precondition the target distribution by removing any correlations between its parameters. pocoMC then generates posterior samples, used for parameter estimation, with a powerful adaptive Sequential Monte Carlo algorithm manifesting a sampling efficiency that can be orders of magnitude higher than without precondition. Furthermore, pocoMC also provides an unbiased estimate of the model evidence that can be used for the task of Bayesian model comparison. The code is designed to excel in demanding parameter estimation problems that include multimodal and highly non–Gaussian target distributions.

LOTUS (non-LTE Optimization Tool Utilized for the derivation of atmospheric Stellar parameters) derives stellar parameters via Equivalent Width (EW) method with the assumption of 1D non-local thermodynamic equilibrium. It mainly applies on the spectroscopic data from high resolution spectral survey. It can provide extremely accurate measurement of stellar parameters compared with non-spectroscopic analysis from benchmark stars. LOTUS provides a fast optimizer for obtaining stellar parameters based on Differential Evolution algorithm, well constrained uncertainty of derived stellar parameters from slice-sampling MCMC from PyMC3 (ascl:1610.016), and can interpolate the Curve of Growth from theoretical EW grid under the assumptions of LTE and Non-LTE. It also visualizes excitation and ionization balance when at the optimal combination of stellar parameters.

DustPy simulates the radial evolution of gas and dust in protoplanetary disks, involving viscous evolution of the gas disk and advection and diffusion of the dust disk, as well as dust growth by solving the Smoluchowski equation. The package provides a standard simulation and the ability to plot results, and also allows modification of the initial conditions for dust, gas, the grid, and the central star.

calviacat calibrates star photometry by comparison to a catalog, including PanSTARRS 1, ATLAS-RefCat2, and SkyMapper catalogs. Catalog queries are cached so that subsequent calibrations of the same or similar fields can be more quickly executed.

petitRADTRANS (pRT) calculates transmission and emission spectra of exoplanets for clear and cloudy planets. It also incorporates an easy subpackage for running retrievals with nested sampling. It allows the calculation of emission or transmission spectra, at low or high resolution, clear or cloudy, and includes a retrieval module to fit a petitRADTRANS model to spectral data. pRT has two different opacity treatment modes. The low resolution mode runs calculations at λ/Δλ ≤ 1000 using the so-called correlated-k treatment for opacities. The high resolution mode runs calculations at λ/Δλ ≤ 10⁶, using a line-by-line opacity treatment.

MuSCAT2_transit_pipeline provides photometry and transit analysis pipelines for MuSCAT2. It consists of a set of executable scripts and two Python packages: muscat2ph for photometry, and muscat2ta for transit analysis. The MuSCAT2 photometry can be carried out using the scripts only. The transit analysis can also in most cases be done using the main transit analysis script m2fit, but the muscat2ta package also offers high-level classes that can be used to carry out more customized transit analysis as a Python script (or Jupyter notebook).

The Exoplanet Characterization ToolKit (ExoCTK) focuses primarily on the atmospheric characterization of exoplanets and provides tools for time-series observation planning, forward modeling, data reduction, limb darkening, light curve fitting, and retrievals. It contains calculators for contamination, visibility, integrations and groups, and includes several Jupyter Notebooks to aid in learning how to use the various tools included in the ExoCTK package.

The samsam package provides two samplers, a scaled adaptive metropolis algorithm to robustly obtain samples from a target distribution, and a covariance importance sampling algorithm to efficiently compute the model evidence (or other integrals). It also includes tools to assess the convergence of the sam sampler and a few commonly used prior distributions.

Helios-r2 performs atmospheric retrieval of brown dwarf and exoplanet spectra. It uses a Bayesian statistics approach by employing a nested sampling method to generate posterior distributions and calculate the Bayesian evidence. The nested sampling itself is done by Multinest (ascl:1109.006). The computationally most demanding parts of the model have been written in NVIDIA's CUDA language for an increase in computational speed. Successful applications include retrieval of brown dwarf emission spectra and secondary eclipse measurements of exoplanets.

SolAster provides querying, analysis, and calculation methods to independently derive 'sun-as-a-star' RV variations using SDO/HMI data for any time span since SDO has begun observing. Scaling factors are provided in order to calculate RVs comparable to magnitudes measured by ground-based spectrographs (HARPS-N and NEID). In addition, there are routines to calculate magnetic observables to compare with RV variations and determine what is driving Solar activity.

TESS_PRF displays the TESS pixel response function (PRF) at any location on the detector. The package is primarily for estimating how the light from a point source is distributed given its position in a TESS Target Pixel File (TPF) or TESScut postage stamp. By default, it accesses the relevant PRF files on MAST, but can also reference files on a local directory. TESS_PRF assumes the PRF doesn't change considerably within a small TPF. The PRF model can be positioned by passing the relative row and column location within the TPF to the "resample" method. The pixel locations follow WCS convention, that an integer value corresponds to the center of a pixel.

Pyriod provides basic period detection and fitting routines for astronomical time series. Written in Python and designed to be run interactively in a Jupyter notebook, it displays and allows the user to interact with time series data, fit frequency solutions, and save figures from the toolbar. It can display original or residuals time series, fold the time series on some frequency, add selected peaks from the periodogram to the model, and refine the fit by computing a least-squared fit of the model using Lmfit (ascl:1606.014).

MultiModes extracts the most significant frequencies of a sample of classical pulsating stars. The code takes a directory with light curves and initial parameters as input. For every light curve, the code calculates the frequencies spectrum, or periodogram, with the Fast Lomb Scargle algorithm, extracts the higher amplitude peak, and evaluates whether it is a real signal or noise. It fits frequency, amplitude, and phase through non-linear optimization, using a multisine function. This function is redefined with the new calculated parameters. MultiModes then does a simultaneous fit of a number of peaks (20 by default), subtracts them from the original signal, and goes back to the beginning of the loop with the residual, repeating the same process until the stop criterion is reached. After that, the code can filter suspicious spurious frequencies, those of low amplitude below the Rayleigh resolution, and possible combined frequencies.

Echelle diagrams are used mainly in asteroseismology, where they function as a diagnostic tool for estimating Δν, the separation between modes of the same degree ℓ; the amplitude spectrum of a star is stacked in equal slices of Δν, the large separation. The echelle Python code creates and manipulates echelle diagrams. The code provides the ability to dynamically change Δν for rapid identification of the correct value. echelle features performance optimized dynamic echelle diagrams and multiple backends for supporting Jupyter or terminal usage.

Cosmic-kite performs a fast estimation of the TT Cosmic Microwave Background (CMB) power spectra corresponding to a set of cosmological parameters; it can also estimate the maximum-likelihood cosmological parameters from a power spectra. This software is an auto-encoder that was trained and calibrated using power spectra from random cosmologies computed with the CAMB code (ascl:1102.026).

MeSsI performs an automatic classification between merging and relaxed clusters. This method was calibrated using mock catalogues constructed from the millennium simulation, and performs the classification using some machine learning techniques, namely random forest for classification and mixture of gaussians for the substructure identification.

pynucastro interfaces to the nuclear reaction rate databases, including the JINA Reaclib nuclear reactions database. This set of Python interfaces enables interactive exploration of rates and collection of rates (networks) in Jupyter notebooks and easy creation of the righthand side routines for reaction network integration (the ODEs) for use in simulation codes.

MULTIGRIS (also called mgris) uses the sequential Monte Carlo method in PyMC (ascl:1506.005) to extract the posterior distributions of primary grid parameters and predict unobserved parameters/observables. The code accepts either a discrete number of components and/or continuous (e.g., power-law, normal) distributions for any given parameter. MULTIGRIS, written in Python, infers the posterior probability functions of parameters in a multidimensional potentially incomplete grid with some observational tracers defined for each parameter set. Observed values and their potentially asymmetric uncertainties are used to calculate a likelihood which, together with predefined or custom priors, produces the posterior distributions. Linear combinations of parameter sets may be used with inferred mixing weights and nearest neighbor or linear interpolation may be used to sample the parameter space.

Modern cosmological surveys are delivering datasets characterized by unprecedented quality and statistical completeness. In order to maximally extract cosmological information from these observations, matching theoretical predictions are needed. In the nonlinear regime of structure formation, cosmological simulations are the primary means of obtaining the required information but the computational cost of sufficiently resolved large-volume simulations makes it prohibitive to run very large ensembles. Nevertheless, precision emulators built on a tractable number of high-quality simulations can be used to build very fast prediction schemes to enable a variety of cosmological inference studies. The "Mira-Titan Universe" simulation suite covers the standard six cosmological parameters and, in addition, includes massive neutrinos and a dynamical dark energy equation of state. It is based on 111 cosmological simulations, each covering a (2.1Gpc)^3 volume and evolving 3200^3 particles, and augments these higher-resolution simulations with an additional set of 1776 lower-resolution simulations and TimeRG perturbation theory results to cover scales straddling the linear to mildly nonlinear regimes. The emulator built on this suite, the CosmicEmu, provides predictions at the two to three percent level of accuracy over a wide range of cosmological parameters. Presented in: https://arxiv.org/abs/2207.12345.

The software used to transform the tabular USNO/AE98 asteroid ephemerides into a Chebyshev polynomial representations, and evaluate them at an arbitrary time is available. The USNO/AE98 consisted of the ephemerides of fifteen of the largest asteroids, and were used in The Astronomical Almanac from 2000 through 2015. These ephemerides are outdated and no longer available, but the software used to store and evaluate them is still available and provides a robust method for storing compact ephemerides of solar system bodies.

The object of the software is to provide a compact binary representation of solar system bodies with eccentric orbits, which can produce the body's position and velocity at an arbitrary instant within the ephemeris' time span. It uses a modification of the Newhall (1989) algorithm to achieve this objective. The Newhall algorithm is used to store both the Jet Propulsion Laboratory DE and the Institut de mécanique céleste et de calcul des éphémérides INPOP high accuracy planetary ephemerides. The Newhall algorithm breaks an ephemeris into a number time contiguous segments, and each segment is stored as a set of Chebyshev polynomial coefficients. The length of the time segments and the maximum degree Chebyshev polynomial coefficient is fixed for each body. This works well for bodies with small eccentricities, but it becomes inefficient for a body in a highly eccentric orbit. The time segment length and maximum order Chebyshev polynomial coefficient must be chosen to accommodate the strong curvature and fast motion near pericenter, while the body spends most of its time either moving slowly near apocenter or in the lower curvature mid-anomaly portions of its orbit. The solution is to vary the time segment length and maximum degree Chebyshev polynomial coefficient with the body's position. The portion of the software that converts tabular ephemerides into a Chebyshev polynomial representation (CPR) performs this compaction automatically, and the portion that evaluates that representation requires only a modest increase in the evaluation time.

The software also allows the user to choose the required tolerance of the CPR. Thus, if less accuracy is required a more compact, somewhat quicker to evaluate CPR can be manufactured and evaluated. Numerical tests show that a fractional precision of 4e-16 may be achieved, only a factor of 4 greater than the 1e-16 precision of a 64-bit IEEE (2019) compliant floating point number.

The software is written in C and designed to work with the C edition of the Naval Observatory Vector Astrometry Software (NOVAS). The programs may be used to convert tabular ephemerides of other solar system bodies as well. The included READ.ME file provides the details of the software and how to use it.

REFERENCES

IEEE Computer Society 2019, IEEE Standard for Floating-Point Arithmetic. IEEE STD 754-2019, IEEE, pp. 1–84

Newhall, X X 1989, 'Numerical Representation of Planetary Ephemerides,' Celest. Mech., 45, 305 - 310

The Spritz code is a fully general relativistic magnetohydrodynamic code based on the Einstein Toolkit (ascl:1102.014). The code solves the GRMHD equations in 3D Cartesian coordinates and on a dynamical spacetime. Spritz supports tabulated equations of state, takes finite temperature effects into account and allows for the inclusion of neutrino radiation.

DustFilaments paints filaments in the Celestial Sphere to generate a full sky map of the Thermal Dust emission at millimeter frequencies by integrating a population of 3D filaments. The code requires a magnetic field cube, which can be calculated separately or by DustFilaments. With the magnetic field cube as input, the package creates a random filament population with a given seed, and then paints a filament into a healpix map provided as input; the healpix map is updated in place.

ShapePipe processes single-exposure images and stacked images. Input images have to be calibrated beforehand for astrometry and photometry. The code can handle different image and file types, such as single-exposure mosaic, single-exposure single-CCD, stacked images, database catalog files, and PSF files, some of which are created by the pipeline during the analysis, among others. The end product of ShapePipe is a final catalog containing information for each galaxy, including its shape parameters and the ellipticity components :math:e_1 and :math:e_2. This catalog also contains shapes of artificially sheared images. This information is used in post-processing to compute calibrated shear estimates via metacalibration.

CuspCore describes the formation of flat cores in dark matter haloes and ultra-diffuse galaxies from feedback-driven outflow episodes. The halo response is divided into an instantaneous change of potential at constant velocities followed by an energy-conserving relaxation. The core assumption of the model is that the total energy E=U+K is conserved for each shell enclosing a given dark matter mass, which is treated in the code as a least-square minimization of the difference between the final and the initial energy of each shell.

Wavetrack recognizes and tracks CME shock waves, filaments, and other solar objects. The code creates base images by averaging а series of images a few minutes prior to the start of the eruption and constructs base difference images by subtracting base images from the current raw image of the sequence. This enhances the change in intensity caused by coronal bright fronts, omits static details, and reduces noise. Wavetrack then chooses an appropriate intensity interval and decomposes the base difference or running difference image with an A-Trous wavelet transform, where each wavelet coefficient is obtained by convolving the image array with a corresponding iteration of the wavelet kernel. When the maximum value of the wavelet coefficients for a connected set of pixels satisfies certain conditions, this region is considered as a structure on the respective wavelet coefficient. Separate stand-alone object masks are obtained with a clustering algorithm and objects are renumbered according to the number of the quadrant they belong at each iteration.

The spectroscopy analysis pipeline pyPipe3D produces coherent and easy to distribute and compare parameters of stellar populations and ionized gas; it is suited in particular for data from the most recent optical IFS surveys. The pipeline is build using pyFIT3D, which is the main spectral fitting module included in this package.

RealSim-IFS generates survey-realistic integral-field spectroscopy (IFS) observations of galaxies from numerical simulations of galaxy formation. The tool is designed primarily to emulate current and experimental observing strategies for IFS galaxy surveys in astronomy, and can reproduce both the flux and variance propagation of real galaxy spectra to cubes. RealSim-IFS has built-in functions supporting SAMI and MaNGA IFU footprints, but supports any fiber-based IFU design, in general.

PyCASSO runs the STARLIGHT code (ascl:1108.006) in integral field spectra (IFS). Cubes from various instruments are supported, including PMAS/PPAK (CALIFA), MaNGA, GMOS and MUSE. Emission lines can be measured using DOBBY, which is included in the package. The package also includes tools for IFS cubes analysis and plotting.

CCDLAB provides graphical user interface functionality for FITS image viewing and data reduction based on the JPFITS FITS-file interface. It can view, manipulate, and save FITS primary image data and image extensions, view and manipulate FITS image headers, and view FITS Bintable extensions. The code enables batch processing, viewing, and saving of FITS images and searching FITS files on disk. CCDLAB also provides general image reduction techniques, source detection and characterization, and can create World Coordinate Solutions automatically or manually for FITS images.

The population synthesis code SEVN (Stellar EVolution for N-body) includes up-to-date stellar evolution (through look-up tables), binary evolution, and different recipes for core-collapse supernovae. SEVN also provides an up-to-date formalism for pair-instability and pulsational pair-instability supernovae, and is designed to interface with direct-summation N-body codes such as STARLAB (ascl:1010.076) and HiGPUs (ascl:1207.002).

MADYS (Manifold Age Determination for Young Stars) determines the age and mass of young stellar and substellar objects. The code automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives age and mass estimates for individual objects through isochronal fitting. MADYS harmonizes the heterogeneity of publicly-available isochrone grids and the user can choose amongst several models, some of which have customizable astrophysical parameters. Particular attention has been dedicated to the categorization of these models, labeled through a four-level taxonomical classification.

atoMEC simulates high energy density phenomena such as in warm dense matter. It uses Kohn-Sham density functional theory, in combination with an average-atom approximation, to solve the electronic structure problem for single-element materials at finite temperature.

wdwarfdate derives the Bayesian total age of a white dwarf from an effective temperature and a surface gravity. It runs a chain of models assuming single star evolution and estimates the following parameters and their uncertainties: total age of the object, mass and cooling age of the white dwarf, and mass and lifetime of the progenitor star.