disnht computes the absorption spectrum for a user-defined distribution of column densities. The input is a file including the array of column density values; a python routine is provided that can make logarithmic distribution of column density that can be used as an input. Other optional inputs are a cross-section file that includes the 2-d array [energy, cross-section]; a script is provided for computing cross sections for different abundance model for the interstellar medium (solar values). Other boolean flags can be used for input and output description, rebin, plot or save.

MAGRATHEA solves planet interiors and considers the case of fully differentiated interiors. The code integrates the hydrostatic equation in order to determine the correct planet radius given the mass in each layer. The code returns the pressure, temperature, density, phase, and radius at steps of enclosed mass. The code support four layers: core, mantle, hydrosphere, and atmosphere. Each layer has a phase diagram with equations of state chosen for each phase.

COWS (COsmic Web Skeleton) implements the cosmic filament finder COsmic Web Skeleton (COWS). Written in Python, the cosmic filament finder works on Hessian-based cosmic web identifiers (such as the V-web) and returns a catalogue of filament spines. The code identifies the medial axis, or skeleton, of cosmic web filaments and then separates this skeleton into individual filaments.

statmorph calculates non-parametric morphological diagnostics of galaxy images (e.g., Gini-M_{20} and CAS statistics), and fits 2D Sérsic profiles. Given a background-subtracted image and a corresponding segmentation map indicating the source(s) of interest, statmorph calculates the following morphological statistics for each source:
- Gini-M20 statistics;
- Concentration, Asymmetry and Smoothness (CAS) statistics;
- Multimode, Intensity and Deviation (MID) statistics;
- outer asymmetry and shape asymmetry;
- Sérsic index; and,
- several shape and size measurements associated to the above statistics, such as ellipticity, Petrosian radius, and half-light radius, among others.

AltaiPony de-trend light curves from Kepler, K2, and TESS missions, and searches them for flares. The code also injects and recovers synthetic flares to account for de-trending and noise loss in flare energy and determines energy-dependent recovery probability for every flare candidate. AltaiPony uses K2SC (ascl:1605.012), AstroPy (ascl:1304.002) and lightkurve (ascl:1812.013) in addition to other common codes, and extensive documentation and tutorials are provided for the software.

fermi-gce-flows uses a machine learning-based technique to characterize the contribution of modeled components, including unresolved point sources, to the GCE. It can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the gamma-ray map. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point source-like emission when compared to traditional approaches.

tellrv measures absolute radial velocities for low-resolution NIR spectra. It uses telluric features to provide absolute wavelength calibration, and then cross-correlates with a standard star. Observations of a standard star are included for convenience; the code also requires both the telluric and non-telluric-corrected spectra.

dark-photons-perturbations determines constraints from Cosmic Microwave Background photons oscillating into dark photons, and from heating of the primordial plasma due to dark photon dark matter converting into low-energy photons in an inhomogeneous universe.

AllStarFit analyzes optical and infrared images and includes functions for:
- object detection and image segmentation using the ProFound package (ascl:1804.006);
- PSF determination using the ProFit package (ascl:1612.004) to fit multiple stars in the field simultaneously; and
- galaxy modelling with ProFit, using the previously determined PSF and user-specified models.

AllStarFit supports a variety of optimization methods (provided by external packages), including maximum-likelihood and Markov chain Monte Carlo (MCMC).

FitsMap visualizes astronomical image and catalog data. Implemented in Python, the software is a simple, lightweight tool, requires only a simple web server, and can scale to over gigapixel images with tens of millions of sources. Further, the web-based visualizations can be viewed performantly on mobile devices.

BLOSMapping determines the line-of-sight component of magnetic fields associated with molecular clouds. The code uses Faraday rotation measure catalogs along with an on-off approach based on relative measurements to estimate the rotation measure caused by molecular clouds. It then uses the outputs from a chemical evolution code along with extinction maps to determine the line-of-sight magnetic field strength and direction.

AstroToolBox identifies and classifies astronomical objects with a focus on low-mass stars and ultra-cool dwarfs. It can search numerous catalogs, including SIMBAD (measurements & references), AllWISE, Gaia, SDSS, among others, evaluates spectral type for main sequence stars including brown dwarfs, and provides SED fitting for ultra-cool and white dwarfs. AstroToolBox draws Gaia color-magnitude diagrams (CMD) with overplotted M0-M9 spectral types, and can draw Montreal Cooling Sequences on the white dwarf branch of the Gaia CMD. The tool can also blink images from different epochs in an image viewer, thus allowing visual identification of the motion or variability of objects. The software displays time series (static or animated) using infrared and optical images of various surveys and contains a photometric classifier. It also includes astrometric calculators and converters, an ADQL query interface (IRSA, VizieR, NOAO) and a batch spectral type lookup feature that uses a CSV file with object coordinates as input. The ToolBox also has a file browser linked to the image viewer, which makes it possible to check a large list of objects in a convenient way, and can save interesting finds in an object collection for later use.

EzTao models time series as a continuous-time autoregressive moving-average (CARMA) process. EzTao utilizes celerite (ascl:1709.008), a fast and scalable Gaussian Process Regression library, to evaluate the likelihood function. On average, EzTao is ten times faster than other tools relying on a Kalman filter for likelihood computation.

JexoSim 2.0 (JWST Exoplanet Observation Simulator) simulates exoplanet transit observations using all four instruments of the James Webb Space Telescope, and is designed for the planning and validation of science cases for JWST. The code generates synthetic spectra that capture the full impact of complex noise sources and systematic trends, allowing for assessment of both accuracy and precision in the final spectrum. JexoSim does not contain all known systematics for the various instruments, but is a good starting point to investigate the effects of systematics, and has the framework to incorporate more systematics in the future.

HoloSim-ML performs beam simulation and analysis of radio holography data from complex optical systems. The code uses machine learning to efficiently determine the position of hundreds of mirror adjusters on multiple mirrors with few micron accuracy.

The Fast Template Periodogram extends the Generalised Lomb Scargle periodogram (Zechmeister and Kurster 2009) for arbitrary (periodic) signal shapes. A template is first approximated by a truncated Fourier series of length H. The Nonequispaced Fast Fourier Transform NFFT is used to efficiently compute frequency-dependent sums. Template fitting can now be done in NlogN time, improving existing algorithms by an order of magnitude for even small datasets. The FTP can be used in conjunction with gradient descent to accelerate a non-linear model fit, or be used in place of the multi-harmonic periodogram for non-sinusoidal signals with a priori known shapes.

The l1 periodogram searches for periodicities in unevenly sampled time series. It can be used similarly as a Lomb-Scargle periodogram, and retrieves a figure which has a similar aspect but has fewer peaks due to aliasing. It is primarily designed for the search of exoplanets in radial velocity data, but can be also used for other purposes. The principle of the algorithm is to search for a representation of the input signal as a sum of a small number of sinusoidal components, that is a representation which is sparse in the frequency domain. Here, "small number" means small compared to the number of observations.

wpca, written in Python, offers several implementations of Weighted Principal Component Analysis and uses an interface similar to scikit-learn's sklearn.decomposition.PCA. Implementations include a direct decomposition of a weighted covariance matrix to compute principal vectors, and then a weighted least squares optimization to compute principal components, and an iterative expectation-maximization approach to solve simultaneously for the principal vectors and principal components of weighted data. It also includes a standard non-weighted PCA implemented using the singular value decomposition, primarily to be useful for testing.

hankl implements the FFTLog algorithm in lightweight Python code. The FFTLog algorithm can be thought of as the Fast Fourier Transform (FFT) of a logarithmically spaced periodic sequence (= Hankel Transform). hankl consists of two modules, the General FFTLog module and the Cosmology one. The latter is suited for modern cosmological application and relies heavily on the former to perform the Hankel transforms. The accuracy of the method usually improves as the range of integration is enlarged; FFTlog prefers an interval that spans many orders of magnitude. Resolution is important, as low resolution introduces sharp features which in turn causes ringing.

GRIT (Gravitational Rigid-body InTegrators) simulaties the coupled dynamics of both spin and orbit of N gravitationally interacting rigid bodies. The code supports tidal forces and general relativity correction are supported, and multiple schemes with different orders of convergences and splitting strategies are available. Multiscale splittings boost the simulation speed, and force evaluations can be parallelized. In addition, each body can be set to be a rigid body or just a point mass, and the floating-point format can be customized as float, double, or long double globally.

BayesicFitting fits models to data. Data in this context means a set of (measured) points x and y. The model provides some (mathematical) relation between the x and y. Fitting adapts the model such that certain criteria are optimized. The BayesicFitting toolbox also determines whether one model fits the data better than another, making the toolbox particularly powerful. The package consists of more than 100 Python classes, of which one third are model classes. Another third are fitters in one guise or another along with additional tools, and the remaining third is used for Nested Sampling.

The O'TRAIN package identifies transients in astronomical images based on a Convolutional Neural Network (CNN). It works on images from different telescopes and, through the use of Docker, can be deployed on different operating systems. O'TRAIN uses image cutouts containing real and false transients provided by the user to train a CNN algorithm implemented with Keras. Built-in diagnostics help to characterize the accuracy of the training, and a trained model is used to classify any new cutouts.

Optab, written in Fortran90, generates ideal-gas opacity tables. It computes opacity based on user-provided chemical equilibrium abundances, and outputs mean opacities as well as monochromatic opacities, thus providing opacity tables that are consistent with one's equation of state for radiation hydrodynamics simulations. For convenience, Optab also provides interfaces for FastChem (ascl:1804.025) or TEA (ascl:1505.031) for computing chemical abundances.

deeplenstronomy simulates large datasets for applying deep learning to strong gravitational lensing. It wraps the functionalities of lenstronomy (ascl:1804.012) in a convenient yaml-style interface to generate training datasets. The code can use built-in astronomical surveys, realistic galaxy colors, real images of galaxies, and physically motivated distributions of all parameters to train the neural network to create a simulated dataset.

TESSreduce builds on lightkurve (ascl:1812.013) to reduce TESS data while preserving transient signals. It takes a TPF as input (supplied or constructed with TESScut (https://mast.stsci.edu/tesscut/). The background subtraction accounts for the smooth background and detector straps. In addition to background subtraction, TESSreduce also aligns images, performs difference imaging, detects transient events, and by using PS1 data, can calibrate TESS counts to physical flux or AB magnitudes.

The SAPHIRES (Stellar Analysis in Python for HIgh REsolution Spectroscopy) suite contains functions for analyzing high-resolution stellar spectra. Though most of its functionality is aimed at deriving radial velocities (RVs), the suite also includes capabilities to measure projected rotational velocities (vsini) and determine spectroscopic flux ratios in double-lined binary systems (SB2s). These measurements are made primarily by computing spectral-line broadening functions. More traditional techniques such as Fourier cross-correlation, and two-dimensional cross-correlation (TODCOR) are also included.

Qwind3 models radiation-driven winds originating from accretion discs. An improvement over Qwind (ascl:2112.013), it derives the wind initial conditions and has significantly improved ray-tracing to calculate the wind absorption self consistently given the extended nature of the UV emission. It also corrects the radiation flux for relativistic effects, and assesses the impact of this on the wind velocity.

Qwind simulates the launching and acceleration phase of line-driven winds in the context of AGN accretion discs. The wind is modeled as a set of streamlines originating on the surface of the AGN accretion disc, and evolved following their equation of motion, given by the balance between radiative and gravitational force.

DiracVsMajorana determines the statistical significance with which a successful electron scattering experiment could reject the Majorana hypothesis -- that dark matter (DM) particles are their own anti-particles, a so-called Majorana fermion -- using the likelihood ratio test in favor of the hypothesis of Dirac DM. The code assumes that the DM interacts with the photon via higher-order electromagnetic moments. It requires tabulated atomic response functions, which can be computed with DarkARC (ascl:2112.011), to compute ionization spectra and predictions for signal event rates.

DarkARC computes and tabulates atomic response functions for direct sub-GeV dark matter (DM) searches. The tabulation of the atomic response functions is separated into two steps: 1.) the computation and tabulation of three radial integrals, and 2.) their combination into the response function tables. The computations are performed in parallel using the multiprocessing library.

WIMpy_NREFT (also known as WIMpy) calculates Dark Matter-Nucleus scattering rates in the framework of non-relativistic effective field theory (NREFT). It currently supports operators O1 to O11, as well as millicharged and magnetic dipole Dark Matter. It can be used to generate spectra for Xenon, Argon, Carbon, Germanium, Iodine and Fluorine targets. WIMpy_NREFT also includes functionality to calculate directional recoil spectra, as well as signals from coherent neutrino-nucleus scattering (including fluxes from the Sun, atmosphere and diffuse supernovae).

The Bayesian-based Gaussian Process model AsteroGaP (Asteroid Gaussian Processes) fits sparsely-sampled asteroid light curves. By utilizing a more flexible Gaussian Process framework for modeling asteroid light curves, it is able to represent light curves in a periodic but non-sinusoidal manner.

MISTTBORN can simultaneously fit multiple types of data within an MCMC framework. It handles photometric transit/eclipse, radial velocity, Doppler tomographic, or individual line profile data, for an arbitrary number of datasets in an arbitrary number of photometric bands for an arbitrary number of planets and allows the use of Gaussian process regression to handle correlated noise in photometric or Doppler tomographic data. The code can include dilution due to a nearby unresolved star in the transit fits, and an additional line component due to another star or scattered sun/moonlight in Doppler tomographic or line profile fits. It can also be used for eclipsing binary fits, including a secondary eclipse and radial velocities for both stars. MISTTBORN produces diagnostic plots showing the data and best-fit models and the associated code MISTTBORNPLOTTER produces publication-quality plots and tables.

NeutrinoFog calculates the neutrino floor based on the derivative of a hypothetical experimental discovery limit as a function of exposure, and leads to a neutrino floor that is only influenced by the systematic uncertainties on the neutrino flux normalizations.

STDPipe is a set of Python routines for astrometry, photometry and transient detection related tasks, intended for quick and easy implementation of custom pipelines, as well as for interactive data analysis. It is implemented as a library of routines covering most common tasks and operates on standard Python objects, including NumPy arrays for images and Astropy (ascl:1304.002) tables for catalogs and object lists. The pipeline does not re-implement code already implemented in other Python packages; instead, it transparently wraps external codes, such as SExtractor (ascl:1010.064), SCAMP (ascl:1010.063), PSFEx (ascl:1301.001), HOTPANTS (ascl:1504.004), and Astrometry.Net (ascl:1208.001), that do not have their own Python interfaces. STDPipe operates on temporary files, keeping nothing after the run unless something is explicitly requested.

Interferopy analyzes datacubes from radio-to-submm observations. It provides a homogenous interface to common tasks, making it easy to go from reduced datacubes to essential measurements and publication-quality plots. Its core functionalities are widely applicable and have been successfully tested on (but are not limited to) ALMA, NOEMA, VLA and JCMT data.

Defringe corrects fringe artifacts in near-infrared astronomical images taken with old generation CCD cameras. It essentially solves a robust PCA problem, masking out astrophysical sources, and models the contaminants as a linear superposition of (unknown) modes, with (unknown) projection coefficients. The problem uses nuclear norm regularization, which acts as a convex proxy for rank minimization. The code is written in python, using cupy for GPU acceleration, but will also work on CPUs.

The Python package SCORPIO retrieves images and associated data of galaxy pairs based on their position, facilitating visual analysis and data collation of multiple archetypal systems. The code ingests information from SDSS, 2MASS and WISE surveys based on the available bands and is designed for studies of galaxy pairs as natural laboratories of multiple astrophysical phenomena for, among other things, tidal force deformation of galaxies, pressure gradient induced star formation regions, and morphological transformation.

QUESTFIT fit the Spitzer mid-infrared spectra of the QUEST (Quasar ULIRG and Evolution STudy) sample. It uses two PAH templates atop an extincted and absorbed continuum model to fit the mid-IR spectra of galaxies that are heavily-absorbed. It also fits AGN with silicate models. The current version of QUESTFIT is optimized for processing spectra from the CASSIS (Combined Atlas of Sources with Spitzer IRS Spectra) portal to produce PAH fluxes for heavily absorbed sources.

pyCELP (aka "pi-KELP") calculates Coronal Emission Line Polarization. It forward synthesizes the polarized emission of ionized atoms formed in the solar corona and calculates the atomic density matrix elements for a single ion under coronal equilibrium conditions and excited by a prescribed radiation field and thermal collisions. pyCELP solves a set of statistical equilibrium equations in the spherical statistical tensor representation for a multi-level atom for the no-coherence case. This approximation is useful in the case of forbidden line emission by visible and infrared lines, such as Fe XIII 1074.7 nm and Si X 3934 nm.

DIPol-UF provides tools for remote control and operation of DIPol-UF, an optical (BVR) imaging CCD polarimeter. The project contains libraries that handle low-level interoperation with ANDOR SDK (provided by the CCD manufacturer), communication with stepper motors (which perform plate rotations), FITS file serialization/deserialization, over-network communication between different system components (each CCD is connected to a standalone PC), as well as provide GUI (built with WPF).

An internally overhauled but fundamentally similar version of Forecaster by Jingjing Chen and David Kipping, originally presented in arXiv:1603.08614 and hosted at https://github.com/chenjj2/forecaster.

The model itself has not changed- no new data was included and the hyperparameter file was not regenerated. All functions were rewritten to take advantage of Numpy vectorization and some additional user features were added. Now able to be installed via pip.

The caustic technique is a powerful method to infer cluster mass profiles to clustrocentric distances well beyond the virial radius. It relies in the measure of the escape velocity of the sistem using only galaxy redshift information. This method was introduced by Diaferio & Geller (1997) and Diaferio (1999). This code allows the caustic mass estimation for galaxy clusters, as well as outlier identification as a side effect. However, a pre-cleaning of interlopers is recommended, using e.g., the shifting-gapper technique.

GWToolbox simulates gravitational wave observations for various detectors. The package is composed of three modules, namely the ground-based detectors (and their targets), the space-borne detectors (and their targets) and pulsar timing arrays (PTA). These three modules work independently and have different dependencies on other packages and libraries; failed dependencies met in one module will not influence the usage of another module. GWToolbox can accessed with a web interface (gw-universe.org) or as a python package (https://bitbucket.org/radboudradiolab/gwtoolbox).

pySYD detects solar-like oscillations and measures global asteroseismic parameters. The code is a python-based implementation of the IDL-based SYD pipeline by Huber et al. (2009), which was extensively used to measure asteroseismic parameters for Kepler stars, and adapts the well-tested methodology from SYD and also improves these existing analyses. It also provides additional capabilities, including an automated best-fit background model selection, parallel processing, the ability to samples for further analyses, and an accessible and command-line friendly interface. PySYD provides best-fit values and uncertainties for the granulation background, frequency of maximum power, large frequency separation, and mean oscillation amplitudes.

SteParSyn infers stellar atmospheric parameters (Teff, log g, [Fe/H], and Vbroad) of FGKM-type stars using the spectral synthesis method. The code uses the MCMC sampler emcee (ascl:1303.002) in conjunction with an spectral emulator that can interpolate spectra down to a precision < 1%. A grid of synthetic spectra that allow the user to characterize the spectra of FGKM-type stars with parameters in the range of 3500 to 7000 K in Teff, 0.0 to 5.5 dex in log g, and −2.0 to 1.0 dex in [Fe/H] is also provided.

gCMCRT globally processes 3D atmospheric data, and as a fully 3D model, it avoids the biases and assumptions present when using 1D models to process 3D structures. It is well suited to performing the post-processing of large parameter GCM model grids, and provides simple pipelines that convert the 3D GCM structures from many well used GCMs in the community to the gCMCRT format, interpolating chemical abundances (if needed) and performing the required spectra calculation. The high-resolution spectra modes of gCMCRT provide an additional highly useful capability for 3D modellers to directly compare output to high-resolution spectral data.

The data analysis UniMAP (Unicorn Multi-window Anomaly Detection Pipeline) leverages the Temporal Outlier Factor (TOF) method to find anomalies in LVC data. The pipeline requires a target detector and a start and stop GPS time describing a time interval to analyze, and has three outputs: 1.) an array of GPS times corresponding to TOF detections; 2.) a long q-transform of the entire data interval with visualizations of the TOF detections in the time series; and 3.) q-transforms of the data windows that triggered TOF detections.

Astrosat calculates which satellites can be seen by a given observer in a given field of view at a given observation time and observation duration. This includes the geometry of the satellite and observer but also estimates the expected apparent brightness of the satellite to aid astronomers in assessing the impact on their observations.

flatstar is an open-source Python tool for drawing stellar disks as numpy.ndarray objects with scientifically-rigorous limb darkening. Each pixel has an accurate fractional intensity in relation to the total stellar intensity of 1.0. It is ideal for ray-tracing simulations of stars and planetary transits. The code is fast, has the most well-known limb-darkening laws, including linear, quadratic, square-root, logarithmic, and exponential, and allows the user to implement custom limb-darkening laws. flatstar also offers supersampling for situations where both coarse arrays and precision in stellar disk intensity (i.e., no hard pixel boundaries) is desired, and upscaling to save on computation time when high-resolution intensity maps are needed, though there is some precision loss in intensities.