stellaR accesses and manipulates publicly available stellar evolutionary tracks and isochrones from the Pisa low-mass database. It retrieves and plots the required calculations from CDS, constructs by interpolation tracks or isochrones of compositions different to the ones available in the database, constructs isochrones for age not included in the database, and extracts relevant evolutionary points from tracks or isochrones.

stella creates and trains a neural network to identify stellar flares. Within stella, users can simulate flares as a training set, run a neural network, and feed in their own data to the neural network model. The software returns a probability at each data point as to whether that data point is part of a flare; the code can also characterize the flares identified.

STELLA is a one-dimensional multi-group radiation hydrodynamics code. STELLA incorporates implicit hydrodynamics coupled to a multi-group non-equilibrium radiative transfer for modeling SN II-L light curves. The non-equilibrium description of radiation is crucial for this problem since the presupernova envelope may be of low mass and very dilute. STELLA implicitly treats time dependent equations of the angular moments of intensity averaged over a frequency bin. Local thermodynamic equilibrium is assumed to determine the ionization levels of materials.

The STELA (Sampling Time for Even Lightcurve Analysis) Toolkit interpolates gappy, irregular, or noisy light curves using Gaussian Processes, enabling the computation of a wide range of time-domain and frequency-domain data products. STELA supports standard Fourier frequency-resolved products such as power spectra, cross spectra, lag spectra, and coherence, as well as lags via the Cross-Correlation Function (CCF), interpolated with GPs or traditional linear interpolation.

STECKMAP stands for STEllar Content and Kinematics via Maximum A Posteriori likelihood. It is a tool for interpreting galaxy spectra in terms of their stellar populations through the derivation of their star formation history, age-metallicity relation, kinematics and extinction. The observed spectrum is projected onto a temporal sequence of models of single stellar populations, so as to determine a linear combination of these models that best fits the observed spectrum. The weights of the various components of this linear combination indicate the stellar content of the population. This procedure is regularized using various penalizing functions. The principles of the method are detailed in <a href="http://cdsads.u-strasbg.fr/abs/2006MNRAS.365...74O">Ocvirk et al. 2006</a>.

STDWeb performs photometry and transient detection in astronomical images through a web-based interface. It accepts FITS images and provides interactive masking, detects sources, performs astrometric calibration, and calibrates photometry with supported catalogs and filters. The software subtracts templates using either automatic or user-provided images and performs forced photometry on targets in original or difference images. STDWeb includes experimental transient detection with basic artifact rejection. The code relies on STDPipe (ascl:2112.006) and uses several external tools, including SExtractor (ascl:1010.064), HOTPANTS (ascl:1504.004), and Astrometry.net (ascl:1208.001). STDWeb runs as a web service with a back-end task queue suitable for deployment in observatory or archive environments.

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.

statpl estimates the parameter of power-law distributed data and calculates goodness-of-fit tests for them. Many objects studied in astronomy follow a power-law distribution function (DF), for example the masses of stars or star clusters. Such data is often analyzed by generating a histogram and fitting a straight line to it. The parameters obtained in this way can be severely biased, and the properties of the underlying DF, such as its shape or a possible upper limit, are difficult to extract. statpl is an (effectively) bias-free estimator for the exponent and the upper limit.

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.

STATCONT determines the continuum emission level in line-rich spectral data by inspecting the intensity distribution of a given spectrum by using different statistical approaches. The sigma-clipping algorithm provides the most accurate continuum level determination, together with information on the uncertainty in its determination; this uncertainty is used to correct the final continuum emission level. In general, STATCONT obtains accuracies of < 10 % in the continuum determination, and < 5 % in most cases. The main products of the software are the continuum emission level, together with its uncertainty, and data cubes containing only spectral line emission, i.e. continuum-subtracted data cubes. STATCONT also includes the option to estimate the spectral index or variation of the continuum emission with frequency.

Smoothed Particle Hydrodynamics (SPH) is a Lagrangian particle method that approximates a continuous fluid as discrete nodes, each carrying various parameters such as mass, position, velocity, pressure, and temperature. In an SPH simulation the resolution scales with the particle density; StarSmasher is able to handle both equal-mass and equal number-density particle models. StarSmasher solves for hydro forces by calculating the pressure for each particle as a function of the particle's properties - density, internal energy, and internal properties (e.g. temperature and mean molecular weight). The code implements variational equations of motion and libraries to calculate the gravitational forces between particles using direct summation on NVIDIA graphics cards. Using a direct summation instead of a tree-based algorithm for gravity increases the accuracy of the gravity calculations at the cost of speed. The code uses a cubic spline for the smoothing kernel and an artificial viscosity prescription coupled with a Balsara Switch to prevent unphysical interparticle penetration. The code also implements an artificial relaxation force to the equations of motion to add a drag term to the calculated accelerations during relaxation integrations. Initially called StarCrash, StarSmasher was developed originally by Rasio.

The Matlab starsense_algorithms package evaluates the performance of various star sensors through the implementation of centroiding, geometric voting and QUEST algorithms. The physical parameters of a star sensor are parametrized and by changing these parameters, performance estimators such as sky coverage, memory requirement, and timing requirements can be estimated for the selected star sensor.

The 3D grid-based Monte Carlo code STaRS (Sejong Radiative Transfer through Raman and Rayleigh Scattering with atomic hydrogen) traces radiative transfer through Raman and Rayleigh scattering. This can be used to investigate line formation of Raman-scattered features in a thick neutral region illuminated by a strong far-UV emission source. Favorable conditions for Raman scattering with atomic hydrogen are easily met in symbiotic stars, young planetary nebulae, and active galactic nuclei.

We have developed a detailed stellar evolution code capable of following the simultaneous evolution of both stars in a binary system, together with their orbital properties. To demonstrate the capabilities of the code we investigate potential progenitors for the Type IIb supernova 1993J, which is believed to have been an interacting binary system prior to its primary exploding. We use our detailed binary stellar evolution code to model this system to determine the possible range of primary and secondary masses that could have produced the observed characteristics of this system, with particular reference to the secondary. Using the luminosities and temperatures for both stars (as determined by Maund et al. 2004) and the remaining mass of the hydrogen envelope of the primary at the time of explosion, we find that if mass transfer is 100 per cent efficient the observations can be reproduced by a system consisting of a 15 solar mass primary and a 14 solar mass secondary in an orbit with an initial period of 2100 days. With a mass transfer efficiency of 50 per cent, a more massive system consisting of a 17 solar mass primary and a 16 solar mass secondary in an initial orbit of 2360 days is needed. We also investigate some of the uncertainties in the evolution, including the effects of tidal interaction, convective overshooting and thermohaline mixing.

STARRY computes light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more. By modeling celestial body surface maps as sums of spherical harmonics, STARRY does all this analytically and is therefore fast, stable, and differentiable. Coded in C++ but wrapped in Python, STARRY is easy to install and use.

starry_process implements an interpretable Gaussian process (GP) for modeling stellar light curves. The code's hyperparameters are physically interpretable, and include the radius of the spots, the mean and variance of the latitude distribution, the spot contrast, and the number of spots, among others. The rotational period of the star, the limb darkening parameters, and the inclination (or marginalize over the inclination if it is not known) can also be specified.

StarPy derives the quenching star formation history (SFH) of a single galaxy through the Bayesian Markov Chain Monte Carlo method code <i>emcee</i> (ascl:1303.002). The sample function implements the emcee EnsembleSampler function for the galaxy colors input. Burn-in is run and calculated for the length specified before the sampler is reset and then run for the length of steps specified. StarPy provides the ability to use the look-up tables provided or creating your own.

STARMAN is a stellar photometry package designed for the reduction of data from imaging systems. Its main components are crowded-field photometry programs, aperture photometry programs, a star finding program, and a CCD reduction program. Image and table handling are served by a large number of programs which have a general use in photometry and other types of work. The package is a coherent whole, for use in the entire process of stellar photometry from raw images to the final standard-system magnitudes and their plotting as color-magnitude and color-color diagrams. It was distributed as part of the Starlink software collection (ascl:1110.012).

Starlink has many applications within it to meet a variety of needs; it includes: <ul><li>a general astronomical image viewer;</li><li>data reduction tools, including programs for reducing CCD-like data;</li><li>general-purpose data-analysis and visualisation tools;</li><li>image processing, data visualisation, and manipulating NDF components;</li><li>a flexible and powerful library for handling World Coordinate Systems (partly based on the SLALIB library);</li><li>a library of routines intended to make accurate and reliable positional-astronomy applications easier to write; and </li><li>and a Hierarchical Data System that is portable and flexible for storing and retrieving data.</li></ul>

Starlink Figaro is an independently-maintained fork of Figaro (ascl:1203.013) that runs in the Starlink software environment (ascl:1110.012). It is a general-purpose data reduction package targeted mainly at optical/IR spectroscopy. It uses the NDF data format and the ADAM libraries for parameters and messaging.

The study of stellar populations in galaxies is entering a new era with the availability of large and high quality databases of both observed galactic spectra and state-of-the-art evolutionary synthesis models. The power of spectral synthesis can be investigated as a mean to estimate physical properties of galaxies. Spectral synthesis is nothing more than the decomposition of an observed spectrum in terms of a superposition of a base of simple stellar populations of various ages and metallicities, producing astrophysically interesting output such as the star-formation and chemical enrichment histories of a galaxy, its extinction and velocity dispersion. This is what the STARLIGHT spectral synthesis code does.

Traditionally, a simulation of a dense stellar system required choosing an initial model, running an integrator, and analyzing the output. Almost all of the effort went into writing a clever integrator that could handle binaries, triples and encounters between various multiple systems efficiently. Recently, the scope and complexity of these simulations has increased dramatically, for three reasons: 1) the sheer size of the data sets, measured in Terabytes, make traditional 'awking and grepping' of a single output file impractical; 2) the addition of stellar evolution data brings qualitatively new challenges to the data reduction; 3) increased realism of the simulations invites realistic forms of 'SOS': Simulations of Observations of Simulations, to be compared directly with observations. We are now witnessing a shift toward the construction of archives as well as tailored forms of visualization including the use of virtual reality simulators and planetarium domes, and a coupling of both with budding efforts in constructing virtual observatories. This review describes these new trends, presenting Starlab as the first example of a full software environment for realistic large-scale simulations of dense stellar systems.

Starkiller simulates and subtracts integral field unit (IFU) data for all cataloged stars. If no catalog is provided, Gaia DR3 is automatically downloaded. Point spread functions (PSFs) are modeled from the input data cube and can include trailing. Stellar spectra are extracted using PSF photometry, matched to the CK model stellar spectral library, and initially normalized to catalog photometry (Gaia G by default). The normalized spectra are injected into a simulated data cube, and a secondary flux correction adjusts the IFU to best match calibration sources. Starkiller subtractes the simulated scene from the calibrated IFU to produce a synthetic differenced cube, which is saved as a FITS file.

Starfish is a set of tools used for spectroscopic inference. It robustly determines stellar parameters using high resolution spectral models and uses Markov Chain Monte Carlo (MCMC) to explore the full posterior probability distribution of the stellar parameters. Additional potential applications include other types of spectra, such as unresolved stellar clusters or supernovae spectra.

StarFISH is a suite of programs designed to determine the star formation history (SFH) of a stellar population, given multicolor stellar photometry and a library of theoretical isochrones. It constructs a library of synthetic color-magnitude diagrams from the isochrones, which includes the effects of extinction, photometric errors and completeness, and binarity. A minimization routine is then used to determine the linear combination of synthetic CMDs that best matches the observed photometry. The set of amplitudes modulating each synthetic CMD describes the star formation history of the observed stellar population.

StarFinder is an IDL code for the deep analysis of stellar fields, designed for Adaptive Optics well-sampled images with high and low Strehl ratio. The Point Spread Function is extracted directly from the frame, to take into account the actual structure of the instrumental response and the atmospheric effects. The code is written in IDL language and organized in the form of a self-contained widget-based application, provided with a series of tools for data visualization and analysis. A description of the method and some applications to Adaptive Optics data are presented.

The deep learning model Starduster emulates dust radiative transfer simulations, which significantly accelerates the computation of dust attenuation and emission. Starduster contains two specific generative models, which explicitly take into account the features of the dust attenuation curves and dust emission spectra. Both generative models should be trained by a set of characteristic outputs of a radiative transfer simulation. The obtained neural networks can produce realistic galaxy spectral energy distributions that satisfy the energy balance condition of dust attenuation and emission. Applications of Starduster include SED-fitting and SED-modeling from semi-analytic models.

Stardust extracts galaxy properties by fitting their multiwavelength data to a set of linearly combined templates. This Python package brings three different families of templates together: 1.) UV+Optical emission from dust unobscured stellar light; 2.) AGN heated dust in the MIR; and 3.) IR dust reprocessed stellar light in the NIR-FIR. Stardust's template fitting does not rely on energy balance. As a result, the total luminosity of dust obscured and dust unobscured stellar light do not rely on each other, and it is possible to fit objects such as SMGs where the energy balance approach might not be applicable.

STARDIS performs local thermodynamic equilibrium (LTE) radiative transfer calculations to generate synthetic stellar spectra from input model atmospheres. Built in part on structures from TARDIS (ascl:1402.018), it ingests configuration files and atomic data, computes opacity and radiation transport through a spherically symmetric plasma, and produces spectral outputs over user-specified wavelength grids. The package includes tools for setting up environments, running model configurations, and plotting resulting spectra in interactive workflows. STARDIS supports modular experimentation with opacity sources, transport options, and model parameters to facilitate generation and analysis of synthetic spectra.

stardate measures precise stellar ages by combining isochrone fitting with gyrochronology (rotation-based ages) to increase the precision of stellar ages on the main sequence. The best possible ages provided by stardate will be for stars with rotation periods, though ages can also be predicted for stars without rotation periods. stardate is an extension to isochrones that incorporates gyrochronology and the code reverts back to isochrones when no rotation period is provided.

StarCrash is a parallel fortran code based on Smoothed Particle Hydrodynamics (SPH) techniques to calculate the 3-d evolution of self-gravitating fluid systems. The code in particularly suited to the study of stellar interactions, such as mergers of binary star systems and stellar collisions. The StarCrash code comes with several important features, including: <ul><li>Several routines which construct the initial conditions appropriate to a wide variety of physical systems </li><li>An efficient parallel neighbor-finding algorithm for calculating hydrodynamic quantities </li><li>A parallel gravitational field solver based on FFT convolution techniques, which uses the FFTW software libraries </li><li>Relaxation Techniques for single stars and synchronized binaries </li><li>Three different artificial viscosity treatments to calculate the thermodynamic evolution of the matter </li><li>An optional gravitational radiation back-reaction treatment, which calculates the damping force from gravity wave losses to lowest relativistic order in a spatially accurate way</li></ul>

StarcNet (STAR Cluster classification NETwork) classifies star clusters from galaxy images taken by the Hubble Space Telescope (HST); it uses a convolutional neural network (CNN) trained to classify five-band galaxy images into four morphological classes. Written in PyTorch, StarcNet runs using mosaics (.fits files with the galaxy photometric information) and catalogs (.tab files with object coordinates), and includes the option to also download the galaxy mosaics from a single .tar.gz file per galaxy (as from the Legacy ExtraGalactic UV Survey).

StarburstPy is a python wrapper for Starburst99 (ascl:1104.003). The code contains methods for setting all inputs, running Starburst99, and reading output data into python dictionaries.

Starburst99 is a comprehensive set of model predictions for spectrophotometric and related properties of galaxies with active star formation. The models are presented in a homogeneous way for five metallicities between Z = 0.040 and 0.001 and three choices of the initial mass function. The age coverage is 10^6 to 10^9 yr. Spectral energy distributions are used to compute colors and other quantities.

The python photometry suite StarbugII provides accurate photometry on point-like sources embedded in complex diffuse emissions. The tool has a simple modular interface with a wide range of photometric routines including embedded source detection, aperture and PSF photometry, diffuse background emission estimation, catalog matching and artificial star testing. The core is built around Photutils (ascl:1609.011).

STARBLADE (STar and Artefact Removal with a Bayesian Lightweight Algorithm from Diffuse Emission) separates superimposed point-like sources from a diffuse background by imposing physically motivated models as prior knowledge. The algorithm can also be used on noisy and convolved data, though performing a proper reconstruction including a deconvolution prior to the application of the algorithm is advised; the algorithm could also be used within a denoising imaging method. STARBLADE learns the correlation structure of the diffuse emission and takes it into account to determine the occurrence and strength of a superimposed point source.

Database management is an increasingly important part of astronomical data analysis. Astronomers need easy and convenient ways of storing, editing, filtering, and retrieving data about data. Commercial databases do not provide good solutions for many of the everyday and informal types of database access astronomers need. The Starbase database system with simple data file formatting rules and command line data operators has been created to answer this need. The system includes a complete set of relational and set operators, fast search/index and sorting operators, and many formatting and I/O operators. Special features are included to enhance the usefulness of the database when manipulating astronomical data. The software runs under UNIX, MSDOS and IRAF.

STAR-MELT extracts and identifies emission lines from FITS files by matching to a compiled reference database of lines. Line profiles are fitted and quantified, allowing for calculations of physical properties across each individual observation. Temporal variations in lines can readily be displayed and quantified. STAR-MELT is also useful for different applications of spectral analysis where emission line identification is required. Standard data formats for spectra are automatically compatible, with user-defined custom formats also available. Any reference database (atomic or molecular) can also be used for line identification.

star_shadow automatically analyzes space based light curves of eclipsing binaries and provide a measurement of eccentricity, among other parameters. It measures the timings of eclipses using the time derivatives of the light curves, using a model of orbital harmonics obtained from an initial iterative prewhitening of sinusoids. Since the algorithm extracts the harmonics from the rest of the sinusoidal variability eclipse timings can be measured even in the presence of other (astrophysical) signals, thus determining the orbital eccentricity automatically from the light curve along with information about the other variability present in the light curve. The output includes, but is not limited to, a sinusoid plus linear model of the light curve, the orbital period, the eccentricity, argument of periastron, and inclination.

Stan facilitates statistical inference at the frontiers of applied statistics and provides both a modeling language for specifying complex statistical models and a library of statistical algorithms for computing inferences with those models. These components are exposed through interfaces in environments such as R, Python, and the command line.

The modular Stagger code can run simulations of deep stellar atmospheres, sunspot formation, stellar chromospheres and coronae, proto-stellar disks, star formation from giant molecular clouds, and galaxy formation. First described in 1995 for model star formation, the code has evolved and handles simulations with large ranges of both spatial and temporal scales. Stagger is efficient and highly parallelizable, provides accurate magnetohydrodynamic (MHD) solvers, handles simulations with large ranges of both spatial and temporal scales, and is adaptable to many kinds of astrophysical systems.

STACKER stacks sources in interferometric data, <i>i.e.</i>, averaging emission from different sources. The library allows stacking to be done directly on visibility data as well as in the image domain. The code is in format of a CASA (ascl:1107.013) task and implements uv- and image-stacking algorithms; it also provides several useful tasks for stacking related data processing. It allows introduction and stacking of random sources to estimate bias and noise, and also allows removal of a model of bright sources from the data.

sstrax provides fast simulations of Milky Way stellar stream formation. Using JAX (ascl:2111.002) acceleration to support code compilation, sstrax forward models all aspects of stream formation, including evolution in gravitational potentials, tidal disruption and observational models, in a fully modular way. Although sstrax is a standalone python package, it was also developed to integrate directly with the Albatross (ascl:2306.009) inference pipeline, which performs inference on all relevant aspects of the stream model.

SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.

The ssos pipeline detects and identifies known and unknown Solar System Objects (SSOs) in astronomical images. ssos requires at least 3 images with overlapping field-of-views in the sky taken within a reasonable amount of time (<i>e.g.</i>, 2 hours, 1 night). SSOs are detected mainly by judging the apparent motion of all sources in the images. The pipeline serves as a wrapper for the SExtractor (ascl:1010.064) and SCAMP (ascl:1010.063) software suites and allows different source extraction strategies to be chosen. All sources in the images are subject to a highly configurable filter pipeline. ssos is a versatile, light-weight, and easy-to-use software for surveys or PI-observation campaigns lacking a dedicated SSO detection pipeline.

StellarSpectraObservationFitting (SSOF) measures radial velocities and creates data-driven models (with fast, physically-motivated Gaussian Process regularization) for the time-variable spectral features for both the telluric transmission and stellar spectrum measured by Extremely Precise Radial Velocity (EPRV) spectrographs (while accounting for the wavelength-dependent instrumental line-spread function). Written in Julia, SSOF provides two methods for estimating the uncertainties on the RVs and model scores based on the photon uncertainties in the original data. For quick estimates of the uncertainties, the code looks at the local curvature of the likelihood space; the second method for estimating errors is via bootstrap resampling.

SSMM (Slotted Symbolic Markov Modeling) reduces time-domain stellar variable observations to classify stellar variables. The method can be applied to both folded and unfolded data, and does not require time-warping for waveform alignment. Written in Matlab, the performance of the supervised classification code is quantifiable and consistent, and the rate at which new data is processed is dependent only on the computational processing power available.

sslf is a simple, effective and useful spectral line finder for 1D data. It utilizes the continuous wavelet transform from SciPy, which is a productive way to find even weak spectral lines.

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.

SSE is a rapid single-star evolution (SSE) code; these analytical formulae cover all phases of evolution from the zero-age main-sequence up to and including remnant phases. It is valid for masses in the range 0.1-100 Msun and metallicity can be varied. The SSE package contains a prescription for mass loss by stellar winds. It also follows the evolution of rotational angular momentum for the star.

Scaling Relations Finder finds the scaling relations between magnetic field properties and observables for a model of galactic magnetic fields. It uses observable quantities as input: the galaxy rotation curve, the surface densities of the gas, stars and star formation rate, and the gas temperature to create galactic dynamo models. These models can be used to estimate parameters of the random and mean components of the magnetic field, as well as the gas scale height, root-mean-square velocity and the correlation length and time of the interstellar turbulence, in terms of the observables.

squishyplanet produces realistic lightcurves and phase curves of non-spherical exoplanets. The code generates models of triaxial planets; fitting for the triaxial shape can provide additional constraints on the planet’s interior properties and evolution. squishyplanet also handles complex limb darkening profiles while also accounting for the planet’s non-circular, potentially time-varying, projected shape.

SpyDust models spinning‑dust emission in astrophysical environments using a Python‑based framework. Building upon SPDUST (ascl:1010.016), SpyDust offers enhanced capabilities and corrections. It implements extended treatments of grain shape, dipole radiation back‑reaction, and plasma. Users supply environmental parameters (<i>e.g.</i>, hydrogen density, temperature, ionization fractions) and the code computes emission spectra across specified frequency ranges. SpyDust supports optional MPI‑based parallel execution and provides Jupyter notebook examples for workflow demonstration.

SPTCLASS assigns semi-automatic spectral types to a sample of stars. The main code includes three spectral classification schemes: the first one is optimized to classify stars in the mass range of TTS (K5 or later, hereafter LATE-type scheme); the second one is optimized to classify stars in the mass range of IMTTS (F late to K early, hereafter Gtype scheme), and the third one is optimized to classify stars in the mass range of HAeBe (F5 or earlier, hereafter HAeBe scheme). SPTCLASS has an interactive module that allows the user to select the best result from the three schemes and analyze the input spectra.

The SPT lensing likelihood code, written in Fortran90, performs a Gaussian likelihood based upon the lensing potential power spectrum using a file from CAMB (ascl:1102.026) which contains the normalization required to get the power spectrum that the likelihood call is expecting.

The SPS software simulates the operation of the Spectral and Photometric Imaging Receiver on-board the ESA’s Herschel Space Observatory. It is coded using the Interactive Data Language (IDL), and produces simulated data at the level-0 stage (non-calibrated data in digitised units). The primary uses for the simulator are to: - optimize and characterize the photometer observing functions - aid in the development, validation, and characterization of the SPIRE data pipeline - provide a realistic example of SPIRE data, and thus to facilitate the development of specific analysis tools for specific science cases. It should be noted that the SPS is not an officially supported product of the SPIRE ICC, and was originally developed for ICC use only. Consequently the SPS can be supported only on a "best efforts" basis.

The presence of human-made interference mimicking the behavior of celestial radio pulses is a major challenge when searching for radio pulses emitted on millisecond timescales by celestial radio sources such as pulsars and fast radio bursts due to the highly imbalanced samples. Single-pulse Searcher (SpS) reduces the presence of radio interference when processing standard output from radio single-pulse searches to produce diagnostic plots useful for selecting good candidates. The modular software allows modifications for specific search characteristics. LOTAAS Single-pulse Searcher (L-SpS) is an implementation of different features of the software (such as a machine-learning approach) developed for a particular study: the LOFAR Tied-Array All-Sky Survey (LOTAAS).

The finite volume hydro code Sprout uses a simple expanding Cartesian grid to track outflows for several orders of magnitudes in expansion. It captures shocks whether they are aligned or misaligned with the grid, and provides second-order convergence for smooth flows. The code's expanding mesh capability reduces numerical diffusion drastically for outflows, especially when the analytic nature of the bulk flow is known beforehand. Sprout can be used to study fluid instabilities in expanding flows, such as in SN explosions and jets; it resolves fine fluid structures at small length scales and expand the mesh gradually as the structures grow.

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.

SPRITE (Sparse Recovery of InstrumenTal rEsponse) computes a well-resolved compact source image from several undersampled and noisy observations. The algorithm is based on sparse regularization; adding a sparse penalty in the recovery leads to far better accuracy in terms of ellipticity error, especially at low S/N.

Spright predicts planetary masses, densities, and radial velocity semi-amplitudes given a small planet's radius or planetary radii given the small planet's mass. The package contains two relations: one for small planets orbiting M dwarfs and another for planets orbiting FGK stars. The radial velocity semi-amplitude can be predicted given the planet's radius, orbital period, orbital eccentricity (optional), and the host star mass. Spright offers both a command line script and a set of Python classes. The command line script can directly create publication-quality plots, and the classes offer a full access to the predicted numerical distributions.

SPOTROD is a model for planetary transits of stars with an arbitrary limb darkening law and a number of homogeneous, circular spots on their surface. It facilitates analysis of anomalies due to starspot eclipses, and is a free, open source implementation written in C with a Python API.

spops is a database of populations synthesis simulations of spinning black-hole binary systems, together with a python module to query it. Data are obtained with the startrack and precession [ascl:1611.004] numerical codes to consistently evolve binary stars from formation to gravitational-wave detection. spops allows quick exploration of the interplay between stellar physics and black-hole spin dynamics.

Splotch is a light and fast, publicly available, ray-tracer software tool which supports the effective visualization of cosmological simulations data. The algorithm it relies on is designed to deal with point-like data, optimizing the ray-tracing calculation by ordering the particles as a function of their 'depth', defined as a function of one of the coordinates or other associated parameters. Realistic three-dimensional impressions are reached through a composition of the final colour in each pixel properly calculating emission and absorption of individual volume elements.

SPLAT is a graphical tool for displaying, comparing, modifying and analyzing astronomical spectra stored in NDF, FITS and TEXT files as well as in NDX format. It can read in many spectra at the same time and then display these as line plots. Display windows can show one or several spectra at the same time and can be interactively zoomed and scrolled, centered on specific wavelengths, provide continuous coordinate readout, produce printable hardcopy and be configured in many ways. Analysis facilities include the fitting of a polynomial to selected parts of a spectrum, the fitting of Gaussian, Lorentzian and Voigt profiles to emission and absorption lines and the filtering of spectra using average, median and line-shape window functions as well as wavelet denoising. SPLAT also supports a full range of coordinate systems for spectra, which allows coordinates to be displayed and aligned in many different coordinate systems (wavelength, frequency, energy, velocity) and transformed between these and different standards of rest (topocentric, heliocentric, dynamic and kinematic local standards of rest, etc). SPLAT is distributed as part of the <a href="http://ascl.net/1110.012">Starlink</a> (ascl:1110.012) software collection.

SPLAT-VO is an extension of the <a href="http://ascl.net/1402.007">SPLAT</a> (Spectral Analysis Tool, ascl:1402.007) graphical tool for displaying, comparing, modifying and analyzing astronomical spectra; it includes facilities that allow it to work as part of the Virtual Observatory (VO). SPLAT-VO comes in two different forms, one for querying and downloading spectra from SSAP servers and one for interoperating with VO tools, such as <a href="http://ascl.net/1101.010">TOPCAT</a> (ascl:1101.010).

SPLASH (formerly SUPERSPHPLOT) visualizes output from (astrophysical) simulations using the Smoothed Particle Hydrodynamics (SPH) method in one, two and three dimensions. Written in Fortran 90, it uses the PGPLOT graphics subroutine library for plotting. It is based around a command-line menu structure but utilizes the interactive capabilities of PGPLOT to manipulate data interactively in the plotting window. SPLASH is fully interactive; visualizations can be changed rapidly at the touch of a button (e.g. zooming, rotating, shifting cross section positions etc). Data is read directly from the code dump format giving rapid access to results and the visualization is advanced forwards and backwards through timesteps by single keystrokes. SPLASH uses the SPH kernel to render plots of not only density but other physical quantities, giving a smooth representation of the data.

SPISEA (Stellar Population Interface for Stellar Evolution and Atmospheres) generates single-age, single-metallicity populations (<i>i.e.</i>, star clusters). The software (formerly called PyPopStar) provides control over different parameters, including cluster characteristics (age, metallicity, mass, distance); total extinction, differential extinction, and extinction law; stellar evolution and atmosphere models; stellar multiplicity and Initial Mass Function; and photometric filters. SPISEA can be used to create a cluster isochrone in many filters using different stellar models, generate a star cluster at any age with an unusual IMF and unresolved multiplicity, and make a spectrum of a star cluster in integrated light.

Spirality measures spiral arm pitch angles by fitting galaxy images to spiral templates of known pitch. Written in MATLAB, the code package also includes GenSpiral, which produces FITS images of synthetic spirals, and SpiralArmCount, which uses a one-dimensional Fast Fourier Transform to count the spiral arms of a galaxy after its pitch is determined.

SPIPS (Spectro-Photo-Interferometry of Pulsating Stars) combines radial velocimetry, interferometry, and photometry to estimate physical parameters of pulsating stars, including presence of infrared excess, color excess, Teff, and ratio distance/p-factor. The global model-based parallax-of-pulsation method is implemented in Python. Derived parameters have a high level of confidence; statistical precision is improved (compared to other methods) due to the large number of data taken into account, accuracy is improved by using consistent physical modeling and reliability of the derived parameters is strengthened by redundancy in the data.

SpinSpotter calculates stellar rotation periods from high-cadence photometry. The code uses the autocorrelation function (ACF) to identify stellar rotation periods up to one-third the observational baseline of the data. SpinSpotter includes diagnostic tools that describe features in the ACF and allows tuning of the tolerance with which to accept a period detection.

SPINspiral analyzes gravitational-wave signals from stellar-mass binary inspirals detected by ground-based interferometers such as LIGO and Virgo. It performs parameter estimation on these signals using Markov-chain Monte-Carlo (MCMC) techniques. This analysis includes the spins of the binary components. Written in C, the package is modular; its main routine is as small as possible and calls other routines, which perform tasks such as reading input, choosing and setting (starting or injection) parameters, and handling noise. Other routines compute overlaps and likelihoods, contain the MCMC core, and manage more general support functions and third-party routines.

spinsfast is a fast spin-s spherical harmonic transform algorithm, which is flexible and exact for band-limited functions. It permits the computation of several distinct spin transforms simultaneously. Specifically, only one set of special functions is computed for transforms of quantities with any spin, namely the Wigner d matrices evaluated at π/2, which may be computed with efficient recursions. For any spin, the computation scales as O(L^3), where L is the band limit of the function.

SPInS (Stellar Parameters INferred Systematically) provides the age, mass, and radius of a star, among other parameters, from a set of photometric, spectroscopic, interferometric, and/or asteroseismic observational constraints; it also generates error bars and correlations. Derived from AIMS (ascl:1611.014), it relies on a stellar model grid and uses a Bayesian approach to find the PDF of stellar parameters from a set of classical constraints. The heart of SPInS is a MCMC solver coupled with interpolation within a pre-computed stellar model grid. The code can consider priors such as the IMF or SFR and can characterize single stars or coeval stars, such as members of binary systems or of stellar clusters.

spinosaurus computes two‑point correlation functions of galaxy shapes and densities using Lagrangian perturbation theory (LPT). Given cosmological input (<i>e.g.</i>, matter power spectrum) and bias parameters, it returns shape–shape and density–shape power spectra (and related statistics) including the effects of long-wavelength displacement resummation. The code relies on numpy, scipy, and pyFFTW (ascl:2109.009) for efficient Fourier transforms and supports both a library interface and example notebooks for common use cases.

spinOS calculates binary orbital elements. Given a set of radial velocity measurements of a spectroscopic binary and/or relative position measurement of an astrometric binary, spinOS fits an orbital model by minimizing a chi squared metric. These routines are neatly packaged in a graphical user interface, developed using tkinter, facilitating use. Minimization is achieved by default using a Levenberg-Marquardt algorithm from lmfit [ascl:1606.014]. A Markov Chain Monte Carlo option is available to sample the posterior probability distribution in order to estimate errors on the orbital elements.

Spinifex is a pure Python tooling for ionospheric corrections in radio astronomy, <i>e.g.</i>, getting total electron content and rotation measures. The code is in part a re-write of RMextract (ascl:1806.024). All existing features of RMextract have been re-implemented, but spinifex is not directly backwards compatible with RMextract.

spike generates and drizzles point-spread functions (PSFs) for space-based imaging instruments. It supports model and empirical PSF creation for instruments aboard the Hubble Space Telescope (HST), James Webb Space Telescope (JWST), and Nancy Grace Roman Space Telescope (Roman). Spike handles image ingestion, coordinate reading, PSF generation via TinyTim (ascl:1010.057) or WebbPSF/HST empirical methods, and correct drizzling or resampling to match final image processing.

SPIDERz (SuPport vector classification for IDEntifying Redshifts) applies powerful support vector machine (SVM) optimization and statistical learning techniques to custom data sets to obtain accurate photometric redshift (photo-z) estimations. It is written for the IDL environment and can be applied to traditional data sets consisting of photometric band magnitudes, or alternatively to data sets with additional galaxy parameters (such as shape information) to investigate potential correlations between the extra galaxy parameters and redshift.

SPIDERMAN calculates exoplanet phase curves and secondary eclipses with arbitrary surface brightness distributions in two dimensions. The code uses a geometrical algorithm to solve exactly the area of sections of the disc of the planet that are occulted by the star. Approximately 1000 models can be generated per second in typical use, which makes making Markov Chain Monte Carlo analyses practicable. The code is modular and allows comparison of the effect of multiple different brightness distributions for a dataset.

SpiceyPy is a Python wrapper for the NAIF C SPICE Toolkit (ascl:1903.015). It is compatible with Python 2 and 3, and was written using ctypes.

The SPICE (Spacecraft Planet Instrument C-matrix [“Camera matrix”] Events) toolkit offers a set of building blocks for constructing tools supporting multi-mission, international space exploration programs and research in planetary science, heliophysics, Earth science, and for observations from terrestrial observatories. It computes many kinds of observation geometry parameters, including the ephemerides, orientations, sizes, and shapes of planets, satellites, comets and asteroids. It can also compute the orientation of a spacecraft, its various moving structures, and an instrument's field-of-view location on a planet's surface or atmosphere. It can determine when a specified geometric event occurs, such as when an object is in shadow or is in transit across another object. The SPICE toolkit is available in FORTRAN 77, ANSI C, IDL, and MATLAB.

SPIBACK (SPIral arms & Bar bACKward integrations) generates Milky Way models through the backward integration method. The code allows users to plot the 2D local velocity space distribution at the Sun's position, as well as median Galactocentric radial velocity maps across the area of the Galactic disk probed with Gaia DR3. This can be done for different bar and spiral arms parameters. The parameters are set by default to be those of the "fiducial model" and can be adjusted as needed.

SPHYNX addresses subsonic hydrodynamical instabilities and strong shocks; it is Newtonian, grounded on the Euler-Lagrange formulation of the smoothed-particle hydrodynamics technique, and density based. SPHYNX uses an integral approach for estimating gradients, a flexible family of interpolators to suppress pairing instability, and incorporates volume elements to provides better partition of the unity.

SPHRAY, a Smoothed Particle Hydrodynamics (SPH) ray tracer, is designed to solve the 3D, time dependent, radiative transfer (RT) equations for arbitrary density fields. The SPH nature of SPHRAY makes the incorporation of separate hydrodynamics and gravity solvers very natural. SPHRAY relies on a Monte Carlo (MC) ray tracing scheme that does not interpolate the SPH particles onto a grid but instead integrates directly through the SPH kernels. Given initial conditions and a description of the sources of ionizing radiation, the code will calculate the non-equilibrium ionization state (HI, HII, HeI, HeII, HeIII, e) and temperature (internal energy/entropy) of each SPH particle. The sources of radiation can include point like objects, diffuse recombination radiation, and a background field from outside the computational volume. The MC ray tracing implementation allows for the quick introduction of new physics and is parallelization friendly. A quick Axis Aligned Bounding Box (AABB) test taken from computer graphics applications allows for the acceleration of the raytracing component. We present the algorithms used in SPHRAY and verify the code by performing all the test problems detailed in the recent Radiative Transfer Comparison Project of Iliev et. al. The Fortran 90 source code for SPHRAY and example SPH density fields are made available online.

SPHGR (Smoothed-Particle Hydrodynamics Galaxy Reduction) is a python based open-source framework for analyzing smoothed-particle hydrodynamic simulations. Its basic form can run a baryonic group finder to identify galaxies and a halo finder to identify dark matter halos; it can also assign said galaxies to their respective halos, calculate halo & galaxy global properties, and iterate through previous time steps to identify the most-massive progenitors of each halo and galaxy. Data about each individual halo and galaxy is collated and easy to access. SPHGR supports a wide range of simulations types including N-body, full cosmological volumes, and zoom-in runs. Support for multiple SPH code outputs is provided by pyGadgetReader (ascl:1411.001), mainly Gadget (ascl:0003.001) and TIPSY (ascl:1111.015).

Spheroid determines the size distribution of polarizing interstellar dust grains based on electromagnetic scattering by spheroidal particles. It contains subroutines to treat the case of complex refractive indices, and also includes checks for some limiting cases.

spherimatch performs efficient cross-matching and self-matching of astronomical catalogs in spherical coordinates. Designed for use in astrophysics, where data is naturally distributed on the celestial sphere, the package enables fast matching with an algorithmic complexity of <i>O</i> (<i>N</i> log <i>N</i>). It supports Friends-of-Friends (FoF) group identification and duplicate removal in spherical coordinates, and integrates easily with common data processing tools such as pandas.

The Spherical Library provides an efficient and accurate mathematical representation of shapes on the celestial sphere, such as sky coverage and footprints. Shapes of arbitrary complexity and size can be dynamically created from simple building blocks, whose exact area is also analytically computed. This methodology is also perfectly suited for censoring problematic parts of datasets, e.g., bad seeing, satellite trails or diffraction spikes of bright stars.

sphereint calculates the numerical volume in a sphere. It provides a weight for each grid position based on whether or not it is in (weight = 1), out (weight = 0), or partially in (weight in between 0 and 1) a sphere of a given radius. A cubic cell is placed around each grid position and the volume of the cell in the sphere (assuming a flat surface in the cell) is calculated and normalized by the cell volume to obtain the weight.

Spheral++ provides a steerable parallel environment for performing coupled hydrodynamical and gravitational numerical simulations. Hydrodynamics and gravity are modeled using particle-based methods (SPH and N-Body). It uses an Adaptive Smoothed Particle Hydrodynamics (ASPH) algorithm, provides a total energy conserving compatible hydro mode, and performs fluid and solid material modeling and damage and fracture modeling in solids.

SpheCow explores the structure and dynamics of any spherical model for galaxies and dark matter haloes. The lightweight and flexible code automatically calculates the dynamical properties, assuming an isotropic or Osipkov-Merritt anisotropic orbital structure, of any model with either an analytical density profile or an analytical surface density profile as a starting point. SpheCow contains readily usable implementations for many standard models, including the Plummer, Hernquist, NFW, Einasto, Sérsic and Nuker models. The code is easily extendable, allowing new models to be added in a straightforward way. The code is publicly available as a set of C++ routines and as a Python module.

We describe a fast tree algorithm for gravitational N-body simulation on SIMD parallel computers. The tree construction uses fast, parallel sorts. The sorted lists are recursively divided along their x, y and z coordinates. This data structure is a completely balanced tree (i.e., each particle is paired with exactly one other particle) and maintains good spatial locality. An implementation of this tree-building algorithm on a 16k processor Maspar MP-1 performs well and constitutes only a small fraction (approximately 15%) of the entire cycle of finding the accelerations. Each node in the tree is treated as a monopole. The tree search and the summation of accelerations also perform well. During the tree search, node data that is needed from another processor is simply fetched. Roughly 55% of the tree search time is spent in communications between processors. We apply the code to two problems of astrophysical interest. The first is a simulation of the close passage of two gravitationally, interacting, disk galaxies using 65,636 particles. We also simulate the formation of structure in an expanding, model universe using 1,048,576 particles. Our code attains speeds comparable to one head of a Cray Y-MP, so single instruction, multiple data (SIMD) type computers can be used for these simulations. The cost/performance ratio for SIMD machines like the Maspar MP-1 make them an extremely attractive alternative to either vector processors or large multiple instruction, multiple data (MIMD) type parallel computers. With further optimizations (e.g., more careful load balancing), speeds in excess of today's vector processing computers should be possible.

Spextool (Spectral EXtraction tool) is an IDL-based data reduction package for SpeX, a medium resolution near-infrared spectrograph on the NASA IRTF. It performs all of the steps necessary to produce spectra ready for analysis and publication including non-linearity corrections, flat fielding, wavelength calibration, telluric correction, flux calibration, and order merging.

SPEX provides a uniform interface suitable for the X-ray spectral analysis of a number of solar (or other) instruments in the X and Gamma Ray energy ranges. Part of the SolarSoft (ascl:1208.013) library, this package is suitable for any datastream which can be placed in the form of response vs interval where the response is usually a counting rate (spectrum) and the interval is normally an accumulation over time. Together with an algorithm which can be used to relate a model input spectrum to the observed response, generally a response matrix, the dataset is amenable to analysis with this package. Currently the data from a large number of instruments, including SMM (HXRBS, GRS Gamma, GRS X1, and GRS X2), Yohkoh (HXT, HXS, GRS, and SXT,) CGRO (BATSE SPEC and BATSE LAD), WIND (TGRS), HIREX, and NEAR (PIN). SPEX's next generation software is available in OSPEX (ascl:2007.018), an object-oriented package that is also part of and dependent on SolarSoft.

SPEX is optimized for the analysis and interpretation of high-resolution cosmic X-ray spectra. The software is especially suited for fitting spectra obtained by current X-ray observatories like XMM-Newton, Chandra, and Suzaku. SPEX can fit multiple spectra with different model components simultaneously and handles highly complex models with many free parameters.

spex_to_xspec takes the output from the collisional ionisation equilibrium model in the <a href="https://ascl.net/1308.014">SPEX</a> spectral modelling and fitting package (ascl:1308.014), and converts it into a form usable by the <a href="https://ascl.net/9910.005">XSPEC</a> spectral fitting package (ascl:9910.005). For a list of temperatures it computes the line strengths and continuum spectra using SPEX. These are collated and written into an APEC-format table model which can be loaded into Xspec. By allowing SPEX models to be loaded into XSPEC, the program allows easy comparison between the results of the SPEX and APEC codes.

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.

SPEGID (Single-Pulse Event Group IDentification) identifies astrophysical pulse candidates as trial single-pulse event groups (SPEGs) by first applying Density Based Spatial Clustering of Applications with Noise (DBSCAN) on trial single-pulse events and then merging the clusters that fall within the expected DM (Dispersion Measure) and time span of astrophysical pulses. SPEGID also calculates the peak score for each SPEG in the S/N versus DM space to identify the expected peak-like shape in the signal-to-noise (S/N) ratio versus DM curve of astrophysical pulses. Additionally, SPEGID groups SPEGs that appear at a consistent DM and therefore are likely emitted from the same source. After running SPEGID, periocity.py can be used to find (or verify) the underlying periodicity among a group of SPEGs (i.e., astrophysical pulse candidates).

speedyfit fits the photometric spectral energy distribution of stars using a Markov chain Monte Carlo approach to determine the errors on the derived parameters. This command line tool searches the most common online databases for photometric observations of a target and automatically pulls archive photometry from the main surveys. The code fits theoretical atmosphere models to the obtained photometry. Speedyfit handles both single and binary stars and allows for the inclusion of constraints from other sources, such as atmosphere parameters derived from spectroscopy, distances, or reddening.