The SPEDAS (Space Physics Environment Data Analysis Software) framework supports multi-mission data ingestion, analysis and visualization for the Space Physics community. It standardizes the retrieval of data from distributed repositories, the scientific processing with a powerful set of legacy routines, the quick visualization with full output control and the graph creation for use in papers and presentations. SPEDAS includes a GUI for ease of use by novice users, works on multiple platforms, and though based on IDL, can be used with or without an IDL license. The framework supports plugin modules for multiple projects such as THEMIS, MMS, and WIND, and provides interfaces for software modules developed by the individual teams of those missions. A Python implementation of the framework, PySPEDAS (ascl:2405.005), is also available.

SPECX is a general purpose line data reduction system. It can read and write FITS data cubes but has specialist support for the GSD format data from the James Clerk Maxwell Telescope. It includes commands to store and retrieve intermediate spectra in storage registers and perform the fitting and removal of polynomial, harmonic and Gaussian baselines. SPECX can filter and edit spectra and list and display spectra on a graphics terminal. It is able to perform Fourier transform and power spectrum calculations, process up to eight spectra (quadrants) simultaneously with either the same or different center, and assemble a number of reduced individual spectra into a map file and contour or greyscale any plane or planes of the resulting cube. Two versions of SPECX are distributed. Version 6.x is the VMS and Unix version and is distributed as part of the <a href="http://ascl.net/1110.012">Starlink</a> software collection. Version 7.x is a complete rewrite of SPECX distributed for Windows.

SpecViz interactively visualizes and analyzes 1D astronomical spectra. It reads data from FITS and ASCII tables and allows spectra to be easily plotted and examined. It supports instrument-specific data quality handling, flexible spectral units conversions, custom plotting attributes, plot annotations, tiled plots, among other features. SpecViz includes a measurement tool for spectral lines for performing and recording measurements and a model fitting capability for creating simple (e.g., single Gaussian) or multi-component models (e.g., multiple Gaussians for emission and absorption lines in addition to regions of flat continua). SpecViz is built on top of the Specutils (ascl:1902.012) Astropy-affiliated python library, providing a visual, interactive interface to the analysis capabilities in that library. The functionality of SpecViz is now actively developed as part of Jdaviz (ascl:2307.001).

Specview is a tool for 1-D spectral visualization and analysis of astronomical spectrograms. Written in Java, it is capable of reading all the Hubble Space Telescope spectral data formats as well as data from several other instruments (such as IUE, FUSE, ISO, FORS and SDSS), preview spectra from MAST, and data from generic FITS and ASCII tables. It can read data from Virtual Observatory servers, and read and write spectrogram data in Virtual Observatory SED format. It can also read files in the SPC Galactic format used in the chemistry field. Once ingested, data can be plotted and examined with a large selection of custom settings. Specview supports instrument-specific data quality handling, flexible spectral units conversions, custom plotting attributes, plot annotations, tiled plots, hardcopy to JPEG files and PostScript file or printer, etc. Specview can be used to build wide-band SEDs, overplotting or combining data from the same astronomical source taken with different instruments and/or spectral bands. Data can be further processed with averaging, splicing, detrending, and Fourier filtering tools. Specview has a spectral model fitting capability that enables the user to work with multi-component models (including user-defined models) and fit models to data.

Specutils provides a basic interface for the loading, manipulation, and common forms of analysis of spectroscopic data. Its generic data containers and accompanying modules can be used to build a particular scientific workflow or higher-level analysis tool. It is an AstroPy (ascl:1304.002) affiliated package, and SpecViz (ascl:1902.011), which is built on top of Specutils, provides a visual, interactive interface to its analysis capabilities.

Spectuner identifies spectral lines of interstellar molecules automatically. The code uses XCLASS (ascl:1810.016) for the spectral line model and <a href="https://scipy.org/">SciPy</a> for the peak finder. Spectral fitting is performed using article swarm optimization and the peak matching loss function. From frequency in a unit of MHz and temperature in a unit of K, Spectuner returns the combined spectrum, identification of the combined spectrum, and the identification of all candidates.

SPECTRUM ((C) Richard O. Gray, 1992-2008) is a stellar spectral synthesis program which runs on a number of platforms, including most flavors of UNIX and LINUX. It will also run under Windwos 9x/ME/NT/2000/XP using the Cygwin tools or the distributed Windows binaries. The code for SPECTRUM has been written in the "C" language. SPECTRUM computes the LTE synthetic spectrum given a stellar atmosphere model. SPECTRUM can use as input the fully blanketed stellar atmosphere models of Robert Kurucz including the new models of Castelli and Kurucz, but any other stellar atmosphere model which can be cast into the format of Kurucz's models can be used as well. SPECTRUM can be programmed with "command-line switches" to give a number of different outputs. In the default mode, SPECTRUM computes the stellar-disk-integrated normalized-intensity spectrum, but in addition, SPECTRUM will compute the absolute monochromatic flux from the stellar atmosphere or the specific intensity from any point on the stellar surface.

This project presents a comprehensive spectroscopic analysis of O and B-type stars, neutron stars, and white dwarfs, with a focus on the detection of helium (He) and oxygen (O) in stellar atmospheres. By leveraging data from the Sloan Digital Sky Survey (SDSS) and utilizing tools such as Astropy, Astroquery, and Specutils, the project aims to identify key spectral lines of helium and oxygen, as well as the formation of heliox (OHe) molecules. The methodology involves querying SDSS for relevant spectral data, filtering and analyzing it based on stellar classification, and visualizing the results using advanced techniques. The findings contribute to the understanding of stellar evolution, chemical processes, and the role of these elements in various stellar classes. Additionally, the project incorporates interactive data exploration with Aladin Lite and Simbad, offering a robust framework for future astrophysical research.

This module implements an ad-hoc grism-based spectrograph optical model. It provides a flexible chromatic mapping between the input focal plane and the output detector plane, based on an effective simplified ray-tracing model of the key optical elements defining the spectrograph (collimator, prism, grating, camera), described by a restricted number of physically-motivated distortion parameters.

Spectroflat flat fields spectro-polarimetric data. It can be plugged into existing Python-based data reduction pipelines or used as a standalone calibration and performance analysis tool. The code includes smile distortion correction and flat field extraction. The library expects the spatial domain on the vertical-axis and the spectral domain on the horizontal axis. Spectroflat does not include any file reading/writing routines and expects numpy arrays as input.

SpectRes efficiently resamples spectra and their associated uncertainties onto an arbitrary wavelength grid. The Python function works with any grid of wavelength values, including non-uniform sampling, and preserves the integrated flux. This may be of use for binning data to increase the signal to noise ratio, obtaining synthetic photometry, or resampling model spectra to match the sampling of observational data.

SpECTRE solves multi-scale, multi-physics problems in astrophysics and gravitational physics, such as those associated with the multi-messenger astrophysics of neutron star mergers, core-collapse supernovae, and gamma-ray bursts. It runs at petascale and is designed for future exascale computers.

SPECTRE's chief purpose is the manipulation of single-order spectra, and it performs many of the tasks contained in such IRAF routines as "splot" and "rv". It is not meant to replace the much more general capabilities of IRAF, but does some functions in a manner that some might find useful. A brief list of SPECTRE tasks are: spectrum smoothing; equivalent width calculation; continuum rectification; noise spike excision; and spectrum comparison. SPECTRE was written to manipulate coude spectra, and thus is probably most useful for working on high dispersion spectra. Echelle spectra can be gathered from various observatories, reduced to singly-dimensioned spectra using IRAF, then written out as FITS files, thus becoming accessible to SPECTRE. Three different spectra may be manipulated and displayed simultaneously. SPECTRE, written in standard FORTRAN77, can be used only with the SM graphics package.

SpectraPy collects algorithms and methods for data reduction of astronomical spectra obtained by a through slits spectrograph. It produces two-dimensional wavelength calibrated spectra corrected by instrument distortions. The library is designed to be spectrograph independent and can be used on both longslit (LS) and multi object spectrograph (MOS) data. SpectraPy comes with a set of already configured spectrographs, but it can be easily configured to reduce data of other instruments.

SpectralRadex runs RADEX (ascl:1010.075) directly from Python and creates model spectra from RADEX outputs. The package uses F2PY (Fortran to Python interface generator) to compile a version of RADEX written in modern Fortran, most importantly dropping the use of common blocks. As a result, running a RADEX model creates no subprocesses and can be parallelized. SpectralRadex uses the RADEX calculated line opacities and excitation temperatures to calculate the brightness temperature as a function of frequency. This allows observed spectra to be modeled in Python in a non-LTE fashion.

Spectral-cube provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It is a versatile data container for building custom analysis routines. It provides a uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild, and allows easy extraction of cube sub-regions using physical coordinates. It has the ability to create, combine, and apply masks to datasets and is designed to work with datasets too large to load into memory, and provide basic summary statistic methods like moments and array aggregates.

Spectractor extracts spectra from slitless spectrophotometric images and measures the atmospheric transmission on the line of sight if standard stars are targeted. It has been optimized on CTIO images but can be configured to analyze any kind of slitless data that contains the order 0 and the order 1 of a spectrum. In particular, it can be used to estimate the atmospheric transmission of the Vera Rubin Observatory site using the dedicated Auxiliary Telescope.

Spectra calculates the power spectrum of a time series equally spaced or not based on the Spectral Correlation Coefficient (Ferraz-Mello 1981, Astron. Journal 86 (4), 619). It is very efficient for detection of low frequencies.

Spectra-Without-Windows (formerly called BOSS-Without-Windows) analyzes Baryon Oscillation Spectroscopic Survey (BOSS) DR12 data using quadratic and cubic estimators. It contains analysis codes to estimate unwindowed power spectra and unwindowed bispectra. It also supplies the raw power and bispectrum spectrum measurements of BOSS and 999 Patchy simulations, and contains a utility function to generate the background number density, n(r) from the survey mask and n(z) distribution. This code has been replaced by the newer and more powerful 3D polyspectrum code PolyBin3D (ascl:2404.006).

The spectools_ir suite analyzes medium/high-resolution IR molecular astronomical spectra. It has three main sub-modules (flux_calculator, slabspec, and slab_fitter) and also offers a sub-module (utils) with a few additional functions. Written with infrared medium/high-resolution molecular spectroscopy in mind, spectools_ir generally assumes spectra are in units of Jy and microns and uses information from the HITRAN molecular database. Some routines are more general, but users interested in other applications should proceed with caution.

Spectool processes astronomical spectral data, offering a collection of common spectral analysis algorithms. The toolkit includes functions for spectral resampling, spectral flattening, radial velocity measurements, spectral convolution broadening, among others. Each function in the package is implemented independently, allowing users to select and utilize the desired features as needed. Spectool's functions have simple and intuitive interfaces, ensuring ease of use for various data sets and analysis tasks.

Studies of astrophysical non-LTE media require the combination of atomic and molecular spectroscopic and collisional data often described differently in various databases. SPECTCOL is a tool that implements <a href="http://www.vamdc.org">VAMDC</a> standards, retrieve relevant information from different databases such as CDMS, HITRAN, BASECOL, and can upload local files. All transfer of data between the client and the databases use the <a href="http://www.vamdc.eu/documents/standards/">VAMDC-XSAMS schema</a>. The spectroscopic and collisional information is combined and useful outputs (ascii or xsams) are provided for the study of the interstellar medium.

Specstack creates stacked spectra using a simple algorithm with sigma-clipping to combine the spectra of galaxies in the rest-frame into a single averaged spectrum. Though written originally for galaxy spectra, it also works for other types of objects. It is written in Python and is started from the command-line.

SpecPro is an interactive program for viewing and analyzing spectra, particularly in the context of modern imaging surveys. In addition to displaying the 1D and 2D spectrum, SpecPro can simultaneously display available stamp images as well as the spectral energy distribution of a source. This extra information can help significantly in assessing a spectrum.

SpecMatch-Emp extracts the fundamental properties of a star (effective temperature, radius, and metallicity) by comparing a target star's spectrum to a library of spectra from stars with known properties. The spectral library comprises high-resolution, high signal-to-noise observed spectra from Keck/HIRES for 404 touchstone stars with well-determined stellar parameters derived from interferometry, asteroseismology, and spectrophotometry. The code achieves accuracies of 100K, 15%, and 0.09 dex in Teff, Rstar, and [Fe/H] respectively for FGKM dwarfs.

speclib provides a lightweight Python interface for loading, manipulating, and analyzing stellar spectra and model grids. The code can load a spectral grid into memory and linearly interpolate between temperature grid points to generate component spectra. speclib includes utilities for photometric synthesis, spectral resampling, and SED construction using stellar spectral libraries.

species (<b>spe</b>ctral <b>c</b>haracterization and <b>i</b>nference for <b>e</b>xoplanet <b>s</b>cience) provides a coherent framework for spectral and photometric analysis of directly imaged exoplanets and brown dwarfs which builds on publicly-available data and models from various resources. species contains tools for grid and free retrievals using Bayesian inference, synthetic photometry, interpolating a variety atmospheric and evolutionary model grids (including the possibility to add a custom grid), color-magnitude and color-color diagrams, empirical spectral analysis, spectral and photometric calibration, and analysis of emission lines.

special (SPEctral Characterization of directly ImAged Low-mass companions) characterizes low-mass (M, L, T) dwarfs down to giant planets at optical/IR wavelengths. It can also be used more generally to characterize any type of object with a measured spectrum, provided a relevant input model grid, regardless of the observational method used to obtain the spectrum (direct imaging or not) and regardless of the format of the spectra (multi-band photometry, low-resolution or medium-resolution spectrum, or a combination thereof). It analyzes measured spectra, calculating the spectral correlation between channels of an IFS datacube and empirical spectral indices for MLT-dwarfs. It fits input spectra to either photo-/atmospheric model grids or a blackbody model, including additional parameters such as (extra) black body component(s), extinction and total-to-selective extinction ratio, and can use emcee (ascl:1303.002), nestle (ascl:2103.022), or UltraNest (ascl:1611.001) samplers infer posterior distributions on spectral model parameters in a Bayesian framework, among other tasks.

The IDL code Special-Blurring compares models of quantum-foam-induced blurring with the full dataset of gamma-ray burst localizations available from the NASA High Energy Astrophysics Science Research Archive (as of 1 November 2022). This includes GRB221009A, which was especially bright and detected in extremely high energy TeV gamma-rays. An upper limit of the parameter alpha (giving the maximal strength of quantum blurring) can be entered, which is scaled in the model of blurring (called "Phi") operating much like "seeing" from the ground in the optical, and those calculations are plotted against the observations.

Specdre performs spectroscopy data reduction and analysis. General features of the package include data cube manipulation, arc line calibration, resampling and spectral fitting. Particular care is taken with error propagation, including tracking covariance. SPECDRE is distributed as part of the Starlink software collection (<a href="http://ascl.net/1110.012">ascl:1110.012</a>).

The DEEP2 DEIMOS Data Reduction Pipeline ("spec2d") is an IDL-based, automated software package designed to reduce Keck/DEIMOS multi-slit spectroscopic observations, collected as part of the DEEP2 Galaxy Redshift Survey. The pipeline is best suited for handling data taken with the 1200 line/mm grating tilted towards the red (lambda_c ~ 7800Å). The spec2d reduction package takes the raw DEIMOS data as its input and produces a variety of outputs including 2-d slit spectra and 1-d object spectra.

SpDust is an IDL program that evaluates the spinning dust emissivity for user-provided environmental conditions. A new version of the code became available in March, 2010.

SpcAudace processes long slit spectra with automated pipelines and performs astrophysical analysis of the latter data. These powerful pipelines do all the required steps in one pass: standard preprocessing, masking of bad pixels, geometric corrections, registration, optimized spectrum extraction, wavelength calibration and instrumental response computation and correction. Both high and low resolution long slit spectra are managed for stellar and non-stellar targets. Many types of publication-quality figures can be easily produced: pdf and png plots or annotated time series plots. Astrophysical quantities can be derived from individual or large amount of spectra with advanced functions: from line profile characteristics to equivalent width and periodogram. More than 300 documented functions are available and can be used into TCL scripts for automation. SpcAudace is based on Audela open source software.

SPCA (Spitzer Phase Curve Analysis) analyzes Spitzer/IRAC observations of exoplanets. It implements 2D polynomial, Pixel Level Decorrelation, BiLinearly-Interpolated Sub-pixel Sensitivity mapping, and Gaussian Process decorrelation methods, allowing the user to change techniques by setting a single variable. The code's modular structure enables integration of custom astrophysical models and decorrelation methods. SPCA can reduce and decorrelate multiple datasets with a single command.

SPARTAN fits the spectroscopy and photometry of distant galaxies. The code implements multiple interfaces to help in the configuration of the fitting and the inspection of the results. SPARTAN relies on pre-computed input files (such as stellar population and IGM extinction), available for download, to save time in the fitting process.

SPARTA is a post-processing framework for particle-based cosmological simulations. The code is written in pure, MPI-parallelized C and is optimized for high performance. The main purpose of SPARTA is to understand the formation of structure in a dynamical sense, namely by analyzing the trajectories (or orbits) of dark matter particles around their halos. Within this framework, the user can add analysis modules that operate on individual trajectories or entire halos. The initial goal of SPARTA was to compute the splashback radius of halos, but numerous other applications have been implemented as well, including spherical overdensity calculations and tracking subhalos via their constituent particles.

SPARTA analyzes periodically-variable spectroscopic observations. Intended for common astronomical uses, SPARTA facilitates analysis of single- and double-lined binaries, high-precision radial velocity extraction, and periodicity searches in complex, high dimensional data. It includes two modules, UNICOR and USuRPER. UNICOR analyzes spectra using 1-d CCF. It includes maximum-likelihood analysis of multi-order spectra and detection of systematic shifts. USuRPER (Unit Sphere Representation PERiodogram) is a phase-distance correlation (PDC) based periodogram and is designed for very high-dimensional data such as spectra.

SparsePZ uses sparse basis representation to fully represent individual photometric redshift probability density functions (PDFs). This approach requires approximately half the parameters for the same multi-Gaussian fitting accuracy, and has the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function. Only 10-20 points per galaxy are needed to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. This basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution or accuracy.

SparseBLS uses the Box-fitting Least Squares (BLS) algorithm to detect transiting exoplanets in photometric data. SparseBLS does not bin data into phase bins and does not use a phase grid. Because its detection efficiency does not depend on the transit phase, it is significantly faster than BLS for sparse data and is well-suited for large photometric surveys producing unevenly-sampled sparse light curves, such as Gaia.

Sparkling identifies spherical cosmic voids in numerical N-body simulations and galaxy surveys. The algorithm uses a Voronoi tessellation to estimate the density field and selects underdense candidates by iteratively computing the integrated density contrast inside spheres of increasing radius until a specific threshold is reached. Void centers are refined through repeated random displacements that are accepted only when they increase the candidate radius, yielding well-defined centers for subsequent analysis. Overlapping spheres are rejected to produce a non-redundant void catalog suitable for computing stacking statistics such as correlation functions and radial density profiles.

SPARK (Software Package for Astronomical Reduction with KMOS), also called kmos-kit, reduces data from the K-band Multi Object Spectrograph (KMOS) for the VLT. In many cases, science data can be processed using a single recipe; alternately, all functions this recipe provides can be performed using other recipes provided as tools. Among the functions the recipes provide are sky subtraction, cube reconstruction with the application of flexure corrections, dividing out the telluric spectrum, applying an illumination correction, aligning the cubes, and then combinging them. The result is a set of files which contain the combined datacube and associated noise cube for each of the 24 integral field unit (IFUs). The pipeline includes simple error propagation.

SpArcFiRe takes as input an image of a galaxy in FITS, JPG, or PNG format, identifies spiral arms, and extracts structural information about the spiral arms. Pixels in each arm segment are listed, enabling image analysis on each segment. The automated method also performs a least-squares fit of a logarithmic spiral arc to the pixels in that segment, giving per-arc parameters, such as the pitch angle, arm segment length, and location, and outputs images showing the steps SpArcFire took to detect arm segments.

The Seismic Propagation through Active Regions and Convection (SPARC) code was developed by S. Hanasoge. The acoustic wavefield in SPARC is simulated by numerically solving the linearised 3-D Euler equations in Cartesian geometry (e.g., see Hanasoge, Duvall and Couvidat (2007)). Spatial derivatives are calculated using sixth-order compact finite differences (Lele,1992) and time evolution is achieved through the repeated application of an optimized second-order five-stage Runge-Kutta scheme (Hu, 1996). Periodic horizontal boundaries are used.

SPAN (SPectral ANalysis) is a cross-platform graphical user interface (GUI) application for extracting, manipulating, and analyzing astronomical spectra. It is optimized for the study of galaxy spectra across the near-ultraviolet (NUV) to near-infrared (NIR) atmospheric windows. SPAN extracts 1D spectra from FITS images and datacubes, performs spectral processing (e.g., Doppler correction, continuum modelling, denoising), and supports analyses such as line-strength measurements, stellar and gas kinematics, and stellar population studies, using both built-in routines and the widely adopted pPXF algorithm (ascl:1210.002) for full spectral fitting. It runs on Windows, Linux, macOS, and Android, and features an intuitive, task-oriented interface. The goal of SPAN is to unify essential tools for modern spectral analysis into a single, user-friendly application that offers a flexible and accessible environment while maintaining scientific accuracy.

SPAMMS (Spectroscopic PAtch Model for Massive Stars), designed with geometrically deformed systems in mind, combines the eclipsing binary modelling code PHOEBE 2 (ascl:1106.002) and the NLTE radiative transfer code FASTWIND to produce synthetic spectra for systems at given phases, orientations and geometries. SPAMMS reproduces the morphology of observed spectral line profiles for overcontact systems and the Rossiter-Mclaughlin and Struve-Sahade effects.

SPAMCART generates synthetic spectral energy distributions and intensity maps from smoothed particle hydrodynamics simulation snapshots. It follows discrete luminosity packets as they propagate through a density field, and computes the radiative equilibrium temperature of the ambient dust from their trajectories. The sources can be extended and/or embedded, and discrete and/or diffuse. The density is not mapped on to a grid, and therefore the calculation is performed at exactly the same resolution as the hydrodynamics. The code strictly adheres to Kirchhoff's law of radiation. The algorithm is based on the Lucy Monte Carlo radiative transfer method and is fairly simple to implement, as it uses data structures that are already constructed for other purposes in modern particle codes

SPAM is a extension to AIPS for reducing high-resolution, low-frequency radio interferometric observations. Direction-dependent ionospheric calibration and image-plane ripple suppression are among the features that help to make high-quality sub-GHz images. Data reductions are captured in well-tested Python scripts that execute AIPS tasks directly (mostly during initial data reduction steps), call high-level functions that make multiple AIPS or ParselTongue calls, and require few manual operations.

SPAM searches for imprints of Hu-Sawicki f(R) gravity on the rotation curves of the SPARC (Spitzer Photometry and Accurate Rotation Curves) sample using the MCMC sampler emcee (ascl:1303.002). The code provides attributes for inspecting the MCMC chains and translating names of parameters to indices. The SPAM package also contains plotting scripts.

spalipy performs detection-based astronomical image registration in Python. A source image is transformed to the pixel-coordinate system of a template image using their respective detections as tie-points by finding matching quads of detections. spalipy also includes an optional additional warping of the initial affine transformation via splines to achieve accurate registration in the case of non-homogeneous coordinate transforms. This is particularly useful in the case of optically distorted or wide field-of-view images.

SpaghettiLens allows citizen scientists to model gravitational lenses collaboratively; the software should also be easily adaptable to any other, reasonably similar problem. It lets volunteers execute a computer intensive task that cannot be easily executed client side and relies on citizen scientists collaborating. SpaghettiLens makes survey data available to citizen scientists, manages the model configurations generated by the volunteers, stores the resulting model configuration, and delivers the actual model. A model can be shared and discussed with other volunteers and revised, and new child models can be created, resulting in a branching version tree of models that explore different possibilities. Scientists can choose a collection of models; discussion among volunteers and scientists prune the tree to determine which models will receive further analysis.

SpacePy provides data analysis and visualization tools for the space science community. Written in Python, it builds on the capabilities of the NumPy and MatPlotLib packages to make basic data analysis, modeling and visualization easier. It contains modules for handling many complex time formats, obtaining data from the OMNI database, and accessing the powerful Onera library. It contains a library of commonly used empirical relationships, performs association analysis, coordinate transformations, radiation belt modeling, and CDF reading, and creates publication quality plots.

spaceKLIP reduces and analyzes JWST NIRCam and MIRI coronagraphy data. The package runs the official JWST stage 1 and 2 data reduction pipelines with several modifications that improve the quality of high-contrast imaging reductions. spaceKLIP then performs PSF subtraction based on the KLIP algorithm as implemented in pyKLIP (ascl:1506.001), outputs contrast curves, and enables forward model PSF fitting for any detected companions in order to extract their properties (offset and flux).

SpaceHub uses unique algorithms for fast precise and accurate computations for few-body problems ranging from interacting black holes to planetary dynamics. This few-body gravity integration toolkit can treat black hole dynamics with extreme mass ratios, extreme eccentricities and very close encounters. SpaceHub offers a regularized Radau integrator with round off error control down to 64 bits floating point machine precision and can handle extremely eccentric orbits and close approaches in long-term integrations.

The Solar Position Algorithm (SPA) calculates the solar zenith and azimuth angles in the period from the year -2000 to 6000, with uncertainties of +/- 0.0003 degrees based on the date, time, and location on Earth. SPA is implemented in C; in addition to being available for download, an online calculator using this code is available at <a href="https://www.nrel.gov/midc/solpos/spa.html">https://www.nrel.gov/midc/solpos/spa.html</a>.

SP_Ace (Stellar Parameters And Chemical abundances Estimator) estimates the stellar parameters Teff, log g, [M/H], and elemental abundances. It employs 1D stellar atmosphere models in Local Thermodynamic Equilibrium (LTE). The code is highly automated and suitable for analyzing the spectra of large spectroscopic surveys with low or medium spectral resolution (R = 2000-20 000). A <a href="http://dc.g-vo.org/SP_ACE">web service for calculating these values</a> with the software is also available.

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

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

SourceDetect utilizes Keras and Tensorflow to train and apply a convolutional neural network model to perform a transient event search through TESS data. The code scans imaging cutouts, computes detection likelihoods, and classifies candidate point-like events as positive, negative, or artifacts and outputs tables of detections including positions, classes, and event confidences. SourceDetect is optimized for large-scale surveys, handling vectorized image inputs and providing programmable filtering criteria to isolate transient, variable, or moving sources.

Spin-Orbit Tomography (SOT) is a retrieval technique of a two-dimensional map of an Exo-Earth from time-series data of integrated reflection light. The software provides code for the Bayesian version of the static SOT and dynamic mapping (time-varying mapping) with full Bayesian modeling, and tutorials for L2 and Bayesian SOT are available in jupyter notebooks.

SORA optimally analyzes stellar occultation data. The library includes processes starting on the prediction of such events to the resulting size, shape and position of the Solar System object and can be used to build pipelines to analyze stellar occultation data. A stellar occultation is defined by the occulting body (Body), the occulted star (Star), and the time of the occultation. On the other hand, each observational station (Observer) will be associated with their light curve (LightCurve). SORA has tasks that allow the user to determine the immersion and emersion times and project them to the tangent sky plane, using the information within the Observer, Body and Star Objects. That projection will lead to chords that will be used to obtain the object’s apparent size, shape and position at the moment of the occultation. Automatic processes optimize the reduction of typical events. However, users have full control over the parameters and methods and can make changes in every step of the process.

SOPT (Sparse OPTimisation) is a C implementation of the Sparsity Averaging Reweighted Analysis (SARA) algorithm. The approach relies on the observation that natural images exhibit strong average sparsity; average sparsity outperforms state-of-the-art priors that promote sparsity in a single orthonormal basis or redundant frame, or that promote gradient sparsity.

SOPIE (Sequential Off-Pulse Interval Estimation) provides functions to non-parametrically estimate the off-pulse interval of a source function originating from a pulsar. The technique is based on a sequential application of P-values obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

SOPHISM models astronomical instrumentation from the entrance of the telescope to data acquisition at the detector, along with software blocks dealing with, for example, demodulation, inversion, and compression. The code performs most analyses done with light in astronomy, such as differential photometry, spectroscopy, and polarimetry. The simulator offers flexibility and implementation of new effects and subsystems, making it user-adaptable for a wide variety of instruments. SOPHISM can be used for all stages of instrument definition, design, operation, and lifetime tracking evaluation.

SOPHIA (Simulations Of Photo Hadronic Interactions in Astrophysics) solves problems connected to photohadronic processes in astrophysical environments and can also be used for radiation and background studies at high energy colliders such as LEP2 and HERA, as well as for simulations of photon induced air showers. SOPHIA implements well established phenomenological models, symmetries of hadronic interactions in a way that describes correctly the available exclusive and inclusive photohadronic cross section data obtained at fixed target and collider experiments.

SONG computes the non-linear evolution of the Universe in order to predict cosmological observables such as the bispectrum of the Cosmic Microwave Background (CMB). More precisely, it is a second-order Boltzmann code, as it solves the Einstein and Boltzmann equations up to second order in the cosmological perturbations.

Sonification extends the Astronify software (ascl:2408.005) to sonify a spatially distributed dataset. The package contains scripts to convert images into scatterplots and sonifications. The reproduce_image.py script takes an image file and reproduces it as a scatterplot by converting the input image to grayscale, extracting pixel values and generating scatter data based on these values, and then plotting the scatter data to create a visual representation of the image. The sonifications script converts the scatterplot data into an audio series and adjusts the note spacing and sonification range to customize an auditory representation. Sonification accepts images in PNG and JPG formats.

SolTrack computes the position of the Sun, the rise and set times and azimuths, and transit times and altitudes. It includes corrections for aberration and parallax, and has a simple routine to correct for atmospheric refraction, taking into account local atmospheric conditions. SolTrack is derived from the Fortran library libTheSky (ascl:2209.018). The package can be used to track the Sun on a low-specs machine, such as a microcontroller or PLC, and can be used for (highly) concentrated (photovoltaic) solar power or accurate solar-energy modeling.

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.

SolarZip compresses solar extreme-ultraviolet image data using error-bounded lossy compression techniques designed to reduce data volume while controlling reconstruction error. The software applies interpolation-based predictors and configurable absolute or relative error constraints to image data prior to compression. It includes command-line tools for processing image datasets and producing compressed outputs along with reconstructed products for evaluation. SolarZip supports batch processing workflows and provides utilities to assess compression performance through distortion and reconstruction metrics.

SolarSoft is a set of integrated software libraries, data bases, and system utilities which provide a common programming and data analysis environment for Solar Physics. The SolarSoftWare (SSW) system is built from Yohkoh, SOHO, SDAC and Astronomy libraries and draws upon contributions from many members of those projects. It is primarily an IDL based system, although some instrument teams integrate executables written in other languages. The SSW environment provides a consistent look and feel at widely distributed co-investigator institutions to facilitate data exchange and to stimulate coordinated analysis. Commonalities and overlap in solar data and analysis goals are exploited to permit application of fundamental utilities to the data from many different solar instruments. The use of common libraries, utilities, techniques and interfaces minimizes the learning curve for investigators who are analyzing new solar data sets, correlating results from multiple experiments or performing research away from their home institution.

SolarKAT mitigates solar interference in MeerKAT data and recovers the visibilities rather than discarding them; this solar imaging pipeline takes 1GC calibrated data in Measurement Set format as input. Written in Python, the pipeline employs solar tracking, subtraction, and peeling techniques to enhance data quality by significantly reducing solar radio interference. This is achieved while preserving the flux measurements in the main field. SolarKAT is versatile and can be applied to general radio astronomy observations and solar radio astronomy; additionally, generated solar images can be used for weather forecasting. SolarKAT is deployed in Stimela (ascl:2305.007). It is based on existing radio astronomy software, including CASA (ascl:1107.013), breizorro (ascl:2305.009), WSclean (ascl:1408.023), Quartical (ascl:2305.006), and Astropy (ascl:1304.002).

SolarAxionFlux quantifies systematic differences and statistical uncertainties in the calculation of the solar axion flux from axion-photon and axion-electron interactions. Determining the limitations of these calculations can be used to identify potential improvements and help determine axion model parameters more accurately.

solar-vSI performs Monte Carlo integration of multi-body phase space efficiently. The calculation of solar antineutrino spectra from 8B decay requires the integration of five-body phase space. Though there is no simple analytical approach to this problem, recursive relations can be used to facilitate numerical evaluations.

Solar-MACH (Solar MAgnetic Connection HAUS) derives and visualizes the spatial configuration and solar magnetic connection of different observers (<i>i.e.</i>, spacecraft or planets) in the heliosphere at different times. It provides publication-ready figures for analyzing Solar Energetic Particle events (SEPs) or solar transients such as Coronal Mass Ejections (CMEs). Solar-MACH is available as a Python package; a Streamlit-enabled tool that runs in a browser is also available (solar-mach.github.io)

SoFT (Solar Feature Tracking) detects and tracks small-scale magnetic elements in the Sun’s atmosphere. The code analyzes their dynamics to address questions on coronal heating and solar wind acceleration. It uses threshold masking to reduce the impact of noise, identify peaks, and uses local maxima as markers and segment the image based on the EDT gradient field and other techniques to detect and identify magnetic elements. SoFT matches features across frames to determine associations, and then estimates and compiles the physical properties of magnetic structures, including barycenters, magnetic flux, velocity, and others.

SoFiAX is a web-based platform to merge and interact with the results of parallel execution of SoFiA HI source finding software [ascl:1412.001] and other steps of processing ASKAP Wallaby HI survey data.

SoFiA is a flexible source finding pipeline designed to detect and parameterize sources in 3D spectral-line data cubes. SoFiA combines several powerful source finding and parameterization algorithms, including wavelet denoising, spatial and spectral smoothing, source mask optimization, spectral profile fitting, and calculation of the reliability of detections. In addition to source catalogues in different formats, SoFiA can also generate a range of output data cubes and images, including source masks, moment maps, sub-cubes, position-velocity diagrams, and integrated spectra. The pipeline is controlled by simple parameter files and can either be invoked on the command line or interactively through a modern graphical user interface. A reimplementation of this pipeline using OpenMPI, SoFiA 2 (ascl:2109.005), is available.

SoFiA 2 is a fully automated spectral-line source finding pipeline originally intended for the detection of galaxies in large HI data cubes. It is a reimplementation of parts of the original SoFiA pipeline (ascl:1412.001) in the C programming language and uses OpenMP for multithreading, making it substantially faster and more memory-efficient than its predecessor. At its core, SoFiA 2 uses the Smooth + Clip algorithm for source finding which operates by spatially and spectrally smoothing the data on multiple scales and applying a user-defined flux threshold relative to the noise level in each iteration. A wide range of useful preconditioning and post-processing filters is available, including noise normalization, flagging of artifacts and reliability filtering. In addition to global data products and source catalogs in different formats, SoFiA 2 can also generate cutout images and spectra for each individual detection.

SOFA (Standards Of Fundamental Astronomy) is a collection of subprograms, in source-code form, that implement official IAU algorithms for fundamental astronomy computations. SOFA offers more than 160 routines for fundamental astronomy, including time scales (including dealing with leap seconds), Earth rotation, sidereal time, precession, nutation, polar motion, astrometry and transforms between various reference systems (e.g. BCRS, ICRS, GCRS, CIRS, TIRS, ITRS). The subprograms are supported by 55 vector/matrix routines, and are available in both Fortran77 and C implementations.

SOAP (Spherical Overdensity and Aperture Processor) computes halo and galaxy properties from SWIFT (ascl:1805.020) simulations after being post-processed with a subhalo finder. The package takes a subhalo catalog as input and calculates a wide array of properties for each object. It offers parallel processing capabilities for efficient handling of large datasets, and allows for consistent property calculation across multiple halo finders. SOAP supports various halo definitions, including spherical overdensities and fixed physical apertures, providing flexibility for diverse observational comparisons. The package is compatible with both dark matter-only and full hydrodynamic simulations, producing HDF5 catalogues that are integrated with the swiftsimio package for seamless unit handling.

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

SOAP (Spot Oscillation And Planet) 2.0 simulates the effects of dark spots and bright plages on the surface of a rotating star, computing their expected radial velocity and photometric signatures. It includes the convective blueshift and its inhibition in active regions.

so_noise_models is the N(ell) noise curve projection code for the Simons Observatory. The code, written in pure Python, consists of several independent sub-modules, representing each version of the noise code. The usage of the models can vary substantially from version to version. The package also includes demo code that that demonstrates usage of the noise models, such as by producing noise curve plots, effective noise power spectra for SO LAT component-separated CMB T, E, B, and Compton-y maps, and lensing noise curves from SO LAT component-separated CMB T, E, B maps.

Supernova Time Delays (SNTD) simulates and measures time delay of multiply-imaged supernovae, and offers an improved characterization of the uncertainty caused by microlensing. Lensing time delays can be determined by fitting the multiple light curves of these objects; measuring these delays provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data; this can be mitigated by using a Gaussian Process Regression (GPR) technique to determine the uncertainty due to microlensing. SNTD can produce accurate simulations for wide-field time domain surveys such as LSST and WFIRST.

SNSEDextend extrapolates core-collapse and Type Ia Spectral Energy Distributions (SEDs) into the UV and IR for use in simulations and photometric classifications. The user provides a library of existing SED templates (such as those in the authors' <a href="https://github.com/jpierel14/SNSED_Repository">SN SED Repository</a>) along with new photometric constraints in the UV and/or NIR wavelength ranges. The software then extends the existing template SEDs so their colors match the input data at all phases. SNSEDextend can also extend the SALT2 spectral time-series model for Type Ia SN for a "first-order" extrapolation of the SALT2 model components, suitable for use in survey simulations and photometric classification tools; as the code does not do a rigorous re-training of the SALT2 model, the results should not be relied on for precision applications such as light curve fitting for cosmology.

SNRPy (Super Nova Remnant Python) models supernova remnant (SNR) evolution and is useful for understanding SNR evolution and to model observations of SNR for obtaining good estimates of SNR properties. It includes all phases for the standard path of evolution for spherically symmetric SNRs and includes alternate evolutionary models, including evolution in a cloudy ISM, the fractional energy loss model, and evolution in a hot low-density ISM. The graphical interface takes in various parameters and produces outputs such as shock radius and velocity vs. time, SNR surface brightness profile and spectrum.

SNOwGLoBES (SuperNova Observatories with GLoBES) computes interaction rates and distributions of observed quantities for supernova burst neutrinos in common detector materials. The code provides a very simple and fast code and data package for tests of observability of physics signatures in current and future detectors, and for evaluation of relative sensitivities of different detector configurations. The event estimates are made using available cross-sections and parameterized detector responses. Water, argon, scintillator and lead-based configurations are included. The package makes use of GLoBES (ascl:2109.018). SNOwGLoBES is not intended to replace full detector simulations; however output should be useful for many types of studies, and simulation results can be incorporated.

Snowball models atmospheric loss in order to constrain an atmosphere's cumulative impact of historic X-ray and extreme ultraviolet radiation-driven mass loss. The escape model interpolates the BaSTI luminosity evolution grid to the observed mass and luminosity of the host star.

The SNooPy package (also known as SNpy), written in Python, contains tools for the analysis of TypeIa supernovae. It offers interactive plotting of light-curve data and models (and spectra), computation of reddening laws and K-corrections, LM non-linear least-squares fitting of light-curve data, and various types of spline fitting, including Diercx and tension. The package also includes a SNIa lightcurve template generator in the CSP passbands, estimates of Milky-Way Extinction, and a module for dealing with filters and spectra.

Snoopy is a spectral 3D code that solves the MHD and Boussinesq equations, such as compressibility, particles, and Braginskii viscosity, and several other physical effects. It's useful for turbulence study involving shear and rotation. Snoopy requires the FFTW library (ascl:1201.015), and can run on parallel machine using MPI OpenMP or both at the same time.

snmachine reads in photometric supernova light curves, extracts useful features from them, and subsequently performs supervised machine learning to classify supernovae based on their light curves. This python library is also flexible enough to easily extend to general transient classification.

SNITCH (bayeSian iNference given emIssion and absorpTion features of quenChing Histories) performs Bayesian inference of star formation histories using measured emission and absorption spectral features. The code ingests equivalent widths and spectral indices (<i>e.g.</i>, EW[Hα], Dₙ4000, Hβ, Hδₐ, Mg Fe′) and, via a pre-computed look-up table generated with FSPS models, returns posterior estimates of parameters such as metallicity, quenching time and quenching rate. It uses emcee (ascl:1303.002) to explore parameter space, produces diagnostic plots of MCMC walkers and corner plots, and is configurable to suit custom spectral parameter sets and lookup-table ranges. Designed for application to integrated‐light or IFU galaxy spectra, SNITCH enables flexible adaptation for a variety of star‐formation-history studies.

We present an algorithm to identify the type of an SN spectrum and to determine its redshift and age. This algorithm, based on the correlation techniques of Tonry & Davis, is implemented in the Supernova Identification (SNID) code. It is used by members of ongoing high-redshift SN searches to distinguish between type Ia and type Ib/c SNe, and to identify "peculiar" SNe Ia. We develop a diagnostic to quantify the quality of a correlation between the input and template spectra, which enables a formal evaluation of the associated redshift error. Furthermore, by comparing the correlation redshifts obtained using SNID with those determined from narrow lines in the SN host galaxy spectrum, we show that accurate redshifts (with a typical error less than 0.01) can be determined for SNe Ia without a spectrum of the host galaxy. Last, the age of an input spectrum is determined with a typical 3-day accuracy, shown here by using high-redshift SNe Ia with well-sampled light curves. The success of the correlation technique confirms the similarity of some SNe Ia at low and high redshifts. The SNID code, which is available to the community, can also be used for comparative studies of SN spectra, as well as comparisons between data and models.

SNEWPY uses simulated supernovae data to generate a time series of neutrino spectral fluences at Earth or the total time-integrated spectral fluence. The code can also process generated data through SNOwGLoBES (ascl:2109.019) and collate its output into the observable channels of each detector. Data from core-collapse, thermonuclear, and pair-instability supernovae simulations are included in the package.

SNEC (SuperNova Explosion Code) is a spherically-symmetric Lagrangian radiation-hydrodynamics code that follows supernova explosions through the envelope of their progenitor star, produces bolometric (and approximate multi-color) light curve predictions, and provides input to spectral synthesis codes for spectral modeling. SNEC's features include 1D (spherical) Lagrangian Newtonian hydrodynamics with artificial viscosity, stellar equation of state with a Saha solver ionization/recombination, equilibrium flux-limited photon diffusion with OPAL opacities and low-temperature opacities, and prediction of bolometric light curves and multi-color lightcurves (in the blackbody approximation).

SNCosmo synthesizes supernova spectra and photometry from SN models, and has functions for fitting and sampling SN model parameters given photometric light curve data. It offers fast implementations of several commonly used extinction laws and can be used to construct SN models that include dust. The SNCosmo library includes supernova models such as SALT2, MLCS2k2, Hsiao, Nugent, PSNID, SNANA and Whalen models, as well as a variety of built-in bandpasses and magnitude systems, and provides convenience functions for reading and writing peculiar data formats used in other packages. The library is extensible, allowing new models, bandpasses, and magnitude systems to be defined using an object-oriented interface.

SNAPDRAGONS (Stellar Numbers And Parameters Determined Routinely And Generated Observing N-body Systems) is a simplified version of the population synthesis code Galaxia (ascl:1101.007), using a different process to generate the stellar catalog. It splits each N-body particle from the galaxy simulation into an appropriate number of stellar particles to create a mock catalog of observable stars from the N-body model. SNAPDRAGON uses the same isochrones and extinction map as Galaxia.

SNANA is a general analysis package for supernova (SN) light curves that contains a simulation, light curve fitter, and cosmology fitter. The software is designed with the primary goal of using SNe Ia as distance indicators for the determination of cosmological parameters, but it can also be used to study efficiencies for analyses of SN rates, estimate contamination from non-Ia SNe, and optimize future surveys. Several SN models are available within the same software architecture, allowing technical features such as K-corrections to be consistently used among multiple models, and thus making it easier to make detailed comparisons between models. New and improved light-curve models can be easily added. The software works with arbitrary surveys and telescopes and has already been used by several collaborations, leading to more robust and easy-to-use code. This software is not intended as a final product release, but rather it is designed to undergo continual improvements from the community as more is learned about SNe.

sMV (serial MultiView) scripts provide a semi-automatic and easy-to-use workflow for serial MultiView phase plane estimation. The phase plane is iteratively rotated based on the time series of calibrator residual phases; because time-domain information is included in the iterations, phase ambiguities are accurately and automatically identified. sMV enables efficient, high-accuracy differential astrometry and artifact-reduced imaging for astrophysical studies.

SMURF reduces submillimeter single-dish continuum and heterodyne data. It is mainly targeted at data produced by the James Clerk Maxwell Telescope but data from other telescopes have been reduced using the package. SMURF is released as part of the bundle that comprises <a href="http://www.ascl.net/1110.012">Starlink</a> (ascl:1110.012) and most of the packages that use it. The two key commands are MAKEMAP for the creation of maps from sub millimeter continuum data and MAKECUBE for the creation of data cubes from heterodyne array instruments. The software can also convert data from legacy JCMT file formats to the modern form to allow it to be processed by MAKECUBE. SMURF is a core component of the <a href="http://www.ascl.net/1310.001">ORAC-DR</a> (ascl:1310.001) data reduction pipeline for JCMT.