The SAMI (Sydney-AAO Multi-object Integral field spectrograph) pipeline reduces data from the Sydney-AAO Multi-object Integral field spectrograph (SAMI) for the SAMI Galaxy Survey. The python code organizes SAMI data and, along with the AAO 2dfdr package, carries out all steps in the data reduction, from raw data to fully calibrated datacubes. The principal steps are: data management, use of 2dfdr to produce row-stacked spectra, flux calibration, correction for telluric absorption, removal of atmospheric dispersion, alignment of dithered exposures, and drizzling onto a regular output grid. Variance and covariance information is tracked throughout the pipeline. Some quality control routines are also included.

samiDB is an archive, database, and query engine to serve the spectra, spectral hypercubes, and high-level science products that make up the SAMI Galaxy Survey. Based on the versatile Hierarchical Data Format (HDF5), samiDB does not depend on relational database structures and hence lightens the setup and maintenance load imposed on science teams by metadata tables. The code, written in Python, covers the ingestion, querying, and exporting of data as well as the automatic setup of an HTML schema browser. samiDB serves as a maintenance-light data archive for Big Science and can be adopted and adapted by science teams that lack the means to hire professional archivists to set up the data back end for their projects.

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

SAMUS (Simulator of Asteroid Malformation Under Stress) simulates the deformation of minor bodies, assuming that they are homogenous incompressible fluid masses. They are initialized as ellipsoids and the Navier-Stokes equations are interatively solved to investigate the deformation of the body over time. The software is modular and allows for user-defined output functions, size, and trajectories. Structured as a single large class, SAMUS can store variables and handle arbitrary function calls, which eases debugging and investigation, especially for lengthy high-fidelity simulation runs.

The Search And Non-Destroy (SAND) is a VLBI data reduction pipeline composed of a set of Python programs based on the AIPS interface provided by ObitTalk. It is designed for the massive data reduction of multi-epoch VLBI monitoring research. It can automatically investigate calibrated visibility data, search all the radio emissions above a given noise floor and do the model fitting either on the CLEANed image or directly on the uv data. It then digests the model-fitting results, intelligently identifies the multi-epoch jet component correspondence, and recognizes the linear or non-linear proper motion patterns. The outputs including CLEANed image catalogue with polarization maps, animation cube, proper motion fitting and core light curves. For uncalibrated data, a user can easily add inline modules to do the calibration and self-calibration in a batch for a specific array.

SAOImage DS9 is an astronomical imaging and data visualization application. DS9 supports FITS images and binary tables, multiple frame buffers, region manipulation, and many scale algorithms and colormaps. It provides for easy communication with external analysis tasks and is highly configurable and extensible via XPA and SAMP. DS9 is a stand-alone application. It requires no installation or support files. Versions of DS9 currently exist for Solaris, Linux, MacOSX, and Windows. All versions and platforms support a consistent set of GUI and functional capabilities. DS9 supports advanced features such as multiple frame buffers, mosaic images, tiling, blinking, geometric markers, colormap manipulation, scaling, arbitrary zoom, rotation, pan, and a variety of coordinate systems. DS9 also supports FTP and HTTP access. The GUI for DS9 is user configurable. GUI elements such as the coordinate display, panner, magnifier, horizontal and vertical graphs, button bar, and colorbar can be configured via menus or the command line. DS9 is a Tk/Tcl application which utilizes the SAOTk widget set. It also incorporates the X Public Access (XPA) mechanism to allow external processes to access and control its data, GUI functions, and algorithms.

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

Sapporo mimics the behavior of GRAPE hardware and uses the GPU to perform high-precision gravitational N-body simulations. It makes use of CUDA and therefore only works on NVIDIA GPUs. N-body codes currently running on GRAPE-6 can switch to Sapporo by a simple relinking of the library. Sapporo's precision is comparable to that of GRAPE-6, even though internally the GPU hardware is limited to single precision arithmetics. This limitation is effectively overcome by emulating double precision for calculating the distance between particles.

SARA-PPD is a proof of concept MATLAB implementation of an acceleration strategy for a recently proposed primal-dual distributed algorithm. The algorithm optimizes resolution by accounting for the correct noise statistics, leverages natural weighting in the definition of the minimization problem for image reconstruction, and optimizes sensitivity by enabling accelerated convergence through a preconditioning strategy incorporating sampling density information. This algorithm offers efficient processing of large-scale data sets that will be acquired by next generation radio-interferometers such as the Square Kilometer Array.

SARAH builds and analyzes SUSY and non-SUSY models. It calculates all vertices, mass matrices, tadpoles equations, one-loop corrections for tadpoles and self-energies, and two-loop RGEs for a given model. SARAH writes model files for a variety of other software packages for dark matter studies, includes many SUSY and non-SUSY models, and makes implementing new models efficient and straightforward. Written in Mathematica, SARAH can also use output from Vevacious (ascl:1904.019) to check for the global minimum for a given model and parameter point.

The Science Analysis System (SAS) is an extensive suite of software tasks developed to process the data collected by the XMM-Newton Observatory. The SAS extracts standard (spectra, light curves) and/or customized science products, and allows reproductions of the reduction pipelines run to get the PPS products from the ODFs files. SAS includes a powerful and extensive suite of FITS file manipulation packages based on the Data Access Layer library.

SASHIMI-C calculates various subhalo properties efficiently using semi-analytical models for cold dark matter (CDM), providing a full catalog of dark matter subhalos in a host halo with arbitrary mass and redshift. Each subhalo is characterized by its mass and density profile both at accretion and at the redshift of interest, accretion redshift, and effective number (or weight) corresponding to that particular subhalo. SASHIMI-C computes the subhalo mass function without making any assumptions such as power-law functional forms; the only assumed power law is that for the primordial power spectrum predicted by inflation. The code is not limited to numerical resolution nor to Poisson shot noise, and its results are well in agreement with those from numerical N-body simulations.

SASHIMI-W calculates various subhalo properties efficiently using semi-analytical models for warm dark matter (WDM); the code is based on the extended Press-Schechter formalism and subhalos' tidal evolution prescription. The calculated constraints are independent of physics of galaxy formation and free from numerical resolution and the Poisson noise, and its results are well in agreement with those from numerical N-body simulations.

SASRST, a small collection of Python scripts, attempts to reproduce the semi-analytical one-dimensional equilibrium and non-equilibrium radiative shock tube solutions of Lowrie & Rauenzahn (2007) and Lowrie & Edwards (2008), respectively. The included code calculates the solution for a given set of input parameters and also plots the results using Matplotlib. This software was written to provide validation for numerical radiative shock tube solutions produced by a radiation hydrodynamics code.

satcand applies theoretical constraints of orbital stability and tidal migration to KOI exomoon candidates. The package can evaluate the tidal migration within a Sun-Earth-Moon system, plot angular velocity over time, and calculate the migration time scale (T1) and the total migration time scale, among other things. In addition to the theoretical constraints, observational constraints can be applied.

SATCHEL (Search Algorithm for Transits in the Citizen science Hunt for Exoplanets in Lightcurves) searches for individual signals of interest in time-series data classified through crowdsourcing. The pipeline was built for the purpose of finding long-period exoplanet transit signals in Kepler photometric time-series data, but may be adapted for searches for any kind of one-dimensional signals in crowdsourced classifications.

SatGen generates satellite-galaxy populations for host halos of desired mass and redshift. It combines halo merger trees, empirical relations for galaxy-halo connection, and analytic prescriptions for tidal effects, dynamical friction, and ram-pressure stripping. It emulates zoom-in cosmological hydrosimulations in certain ways and outperforms simulations regarding statistical power and numerical resolution.

SATMC is a general purpose, MCMC-based SED fitting code written for IDL and Python. Following Bayesian statistics and Monte Carlo Markov Chain algorithms, SATMC derives the best fit parameter values and returns the sampling of parameter space used to construct confidence intervals and parameter-parameter confidence contours. The fitting may cover any range of wavelengths. The code is designed to incorporate any models (and potential priors) of the user's choice. The user guide lists all the relevant details for including observations, models and usage under both IDL and Python.

SAVED21cm extracts the 21cm signal from the simulated mock observation for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH). Though built for the REACH experiment, this 21cm signal extraction pipeline can in principle can be utilized for any global 21cm experiment. The toolkit is based on a pattern recognition framework using the Singular Value Decomposition (SVD) of the 21cm and foreground training set. SAVED21cm finds the patterns in the training sets and properly models the chromatic distortions with a better basis than the polynomials.

A Savitzky–Golay filter is often applied to data to smooth the data without greatly distorting the signal; however, almost all data inherently comes with noise, and the noise properties can differ from point to point. This python script improves upon the traditional Savitzky-Golay filter by accounting for error covariance in the data. The inputs and arguments are modeled after scipy.signal.savgol_filter.

SBGAT (Small Body Geophysical Analysis Tool) generates simulated data originating from small bodies shape models, combined with advanced shape-modification properties. It uses polyhedral shape models from which can be computed mass properties such as volume, center of mass, and inertia, synthetic observations such as lightcurves and radar, and which can be used within dynamical models, such as spherical harmonics and polyhedron gravity modeling. SBGAT can generate spherical harmonics expansions from constant-density polyhedra (and export them to JSON) and evaluate the spherical harmonics expansions. It can also generate YORP coefficients, multi-threaded Polyhedron Gravity Model gravity and potential evaluations, and synthetic light-curve and radar observations for single/primary asteroids.

SBGAT has two distinct packages: a dynamic library SBGAT Core that contains the data structure and algorithm backbone of SBGAT, and SBGAT Gui, which wraps the former inside a VTK, Qt user interface to facilitate user/data interaction. SBGAT Core can be used without the SBGAT Gui wrapper.

Simulation-based inference is the process of finding parameters of a simulator from observations. The PyTorch package sbi performs simulation-based inference by taking a Bayesian approach to return a full posterior distribution over the parameters, conditional on the observations. This posterior can be amortized (i.e. useful for any observation) or focused (i.e.tailored to a particular observation), with different computational trade-offs. The code offers a simple interface for one-line posterior inference.

sbpy, an Astropy affiliated package, supplements functionality provided by Astropy (ascl:1304.002) with functions and methods that are frequently used for planetary astronomy with a clear focus on asteroids and comets. It offers access tools for various databases for orbital and physical data, spectroscopy analysis tools and models, photometry models for resolved and unresolved observations, ephemerides services, and other tools useful for small-body planetary astronomy.

Astrometric and photometric calibrations have remained the most tiresome step in the reduction of large imaging surveys. SCAMP has been written to address this problem. The program efficiently computes accurate astrometric and photometric solutions for any arbitrary sequence of FITS images in a completely automatic way. SCAMP is released under the GNU General Public License.

ScamPy "paints" an observed population of cosmological objects on top of the DM-halo/subhalo hierarchy obtained from DM-only simulations. The method combines the Halo Occupation Distribution (HOD) method with sub-halo abundance matching (SHAM); the two processes together are dubbed Sub-halo clustering and abundance matching (SCAM). The procedure requires applying the two methods in sequence; by applying the HOD scheme, the host sub-haloes are selected, and the SHAM algorithm associates an observable property of choice to each sub-halo. The provided python interface allows users to load and populate DM halos and sub-halos obtained by FoF and SUBFIND algorithms applied to DM snapshots at any redshift. The software is highly-optimized and flexible.

Scanamorphos is an IDL program to build maps from scan observations made with bolometer arrays. Scanamorphos can post-process scan observations performed with the Herschel photometer arrays. This post-processing mainly consists in subtracting the total low-frequency noise (both its thermal and non-thermal components), masking cosmic ray hit residuals, and projecting the data onto a map. Although it was developed for Herschel, it is also applicable with minimal adjustment to scan observations made with other bolometer arrays provided they entail sufficient redundancy; it was successfully applied to P-Artemis, an instrument operating on the APEX telescope. Scanamorphos does not assume any particular noise model and does not apply any Fourier-space filtering to the data. It is an empirical tool using only the redundancy built in the observations, taking advantage of the fact that each portion of the sky is sampled at multiple times by multiple bolometers. The user is allowed to optionally visualize and control results at each intermediate step, but the processing is fully automated.

SCARLET performs source separation (aka "deblending") on multi-band images. It is geared towards optical astronomy, where scenes are composed of stars and galaxies, but it is straightforward to apply it to other imaging data. Separation is achieved through a constrained matrix factorization, which models each source with a Spectral Energy Distribution (SED) and a non-parametric morphology, or multiple such components per source. The code performs forced photometry (with PSF matching if needed) using an optimal weight function given by the signal-to-noise weighted morphology across bands. The approach works well if the sources in the scene have different colors and can be further strengthened by imposing various additional constraints/priors on each source. Because of its generic utility, this package provides a stand-alone implementation that contains the core components of the source separation algorithm. However, the development of this package is part of the LSST Science Pipeline; the meas_deblender package contains a wrapper to implement the algorithms here for the LSST stack.

Scatfit models observed burst signals of impulsive time domain radio signals ( e.g., Fast Radio Bursts (FRBs) or pulsars pulses), which usually are convolution products of various effects, and fits them to the experimental data. It includes several models for scattering and instrumental effects. The code loads the experimental time domain radio data, cleans them, fits an aggregate scattering model to the data, and robustly estimates the model parameters and their uncertainties. Additionally, scatfit determines the scaling of the scattering time with frequency, i.e. the scattering index, and the scattering-corrected dispersion measure of the burst or pulse.

SCEPtER (Stellar CharactEristics Pisa Estimation gRid) estimates the stellar mass and radius given a set of observable quantities; the results are obtained by adopting a maximum likelihood technique over a grid of pre-computed stellar models. The code is quite flexible since different observables can be used, depending on their availability, as well as different grids of models.

The fast N-body code SCF-FDPS (Self-Consistent Field-Framework for Developing Particle Simulators) simulates disk-halo systems. It combines a self-consistent field (SCF) code, which provides scalability, and a tree code that is parallelized using the Framework for Developing Particle Simulators (FDPS) (ascl:1604.011). SCF-FDPS handles a wide variety of halo profiles and can be used to study extensive dynamical problems of disk-halo systems.

schNell computes basic map-level noise properties for generic networks of gravitational wave interferometers, primarily the noise power spectrum "N_ell", but this lightweight python module that can also be used for, for example, antenna patterns, overlap functions, and inverse variance maps, among other tasks. The code has three main classes; detectors contain information about each individual detector of the network, such as their positions, noise properties, and orientation. NoiseCorrelations describes the noise-level correlation between pairs of detectors, and the MapCalculators class combines a list of Detectors into a network (potentially together with a NoiseCorrelation object) and computes the corresponding map-level noise properties arising from their correlations.

schwimmbad provides a uniform interface to parallel processing pools and enables switching easily between local development (e.g., serial processing or with multiprocessing) and deployment on a cluster or supercomputer (via, e.g., MPI or JobLib). The utilities provided by schwimmbad require that tasks or data be “chunked” and that code can be “mapped” onto the chunked tasks.

SciCatalog handles catalogs of scientific data in a way that is easily extensible, including the ability to create nicely formatted AASTex deluxe tables for use in AAS Publishing manuscripts. It handles catalogs of values, their positive and negative uncertainties, and references for those values with methods for easily adding columns and changing values. The catalog is also backed up every time it is loaded under the assumption that it is about to be modified.

SciDB is a DMAS (Data Management and Analytics Software System) optimized for data management of big data and for big analytics. SciDB is organized around multidimensional array storage, a generalization of relational tables, and is designed to be scalable up to petabytes and beyond. Complex analytics are simplified with SciDB because arrays and vectors are first-class objects with built-in optimized operations. Spatial operators and time-series analysis are easy to express. Interfaces to common scientific tools like R as well as programming languages like C++ and Python are provided.

SCIMES identifies relevant molecular gas structures within dendrograms of emission using the spectral clustering paradigm. It is useful for decomposing objects in complex environments imaged at high resolution.

SCINTOOLS (SCINtillation TOOLS) simulates and analyzes pulsar scintillation data. The code can be used for processing observed dynamic spectra, computing secondary spectra and ACFs, measuring scintillation arcs, simulating dynamic spectra, and modeling pulsar transverse velocities through scintillation arcs or diffractive timescales.

SCONCE-SCMS detects cosmic web structures, primarily cosmic filaments and the associated cosmic nodes, from a collection of discrete observations with the extended subspace constrained mean shift (SCMS) algorithms on the unit (hyper)sphere (in most cases, the 2D (RA,DEC) celestial sphere), and the directional-linear products space (most commonly, the 3D (RA,DEC,redshift) light cone).

The subspace constrained mean shift (SCMS) algorithm is a gradient ascent typed method dealing with the estimation of local principal curves, more widely known as density ridges. The one-dimensional density ridge traces over the curves where observational data are highly concentrated and thus serves as a natural model for cosmic filaments in our Universe. Modeling cosmic filaments as density ridges enables efficient estimation by the kernel density estimator (KDE) and the subsequent SCMS algorithm in a statistically consistent way. While the standard SCMS algorithm can identify the density ridges in any "flat" Euclidean space, it exhibits large bias in estimating the density ridges on the data space with a non-linear curvature. The extended SCMS algorithms used in SCONCE-SCMS are adaptive to the spherical and conic geometries and resolve the estimation bias of the standard SCMS algorithm on a 2D (RA,DEC) celestial sphere or 3D (RA,DEC,redshift) light cone.

An attempt at creating a common pythonic framework for visual and infrared telescope instrument data simulators.

A reference database for astronomical instrument and telescope characteristics for all types of visual and infrared systems. Instrument packages are used in conjunction with the ScopeSim instrument data simulator.

Templates and helper functions for creating on-sky Source description objects for the ScopeSim instrument data simulation engine.

The Shape COnstraint REstoration algorithm (SCORE) is a proximal algorithm based on sparsity and shape constraints to restore images. Its main purpose is to restore images while preserving their shape information. It can, for example, denoise a galaxy image by instanciating SCORE and using its denoise method and then visualize the results, and can deconvolve multiple images with different parameter values.

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

The Semi-automated multi-COmponent Universal Spectral-line fitting Engine (SCOUSE) is a spectral line fitting algorithm that fits Gaussian files to spectral line emission. It identifies the spatial area over which to fit the data and generates a grid of spectral averaging areas (SAAs). The spatially averaged spectra are fitted according to user-provided tolerance levels, and the best fit is selected using the Akaike Information Criterion, which weights the chisq of a best-fitting solution according to the number of free-parameters. A more detailed inspection of the spectra can be performed to improve the fit through an iterative process, after which SCOUSE integrates the new solutions into the solution file.

scousepy is a Python implementation of spectral line-fitting IDL code SCOUSE (ascl:1601.003). It fits a large amount of complex astronomical spectral line data in a systematic way.

Scri manipulates time-dependent functions of spin-weighted spherical harmonics. It implements the BMS transformations of the most common gravitational waveforms, including the Newman-Penrose quantity ψ4, the Bondi news function, the shear spin coefficient σ, and the transverse-traceless metric perturbation h, as well as the remaining Newman-Penrose quantities ψ0 through ψ3.

SCRIPT (Semi-numerical Code for ReIonization with PhoTon-conservation) generates the ionization field during the epoch of cosmological reionization using a photon-conserving algorithm. The code depends on density and velocity files obtained using a N-body simulation, which can be generated with a 2LPT code such as MUSIC (ascl:1311.011).

Sculptor manipulates, models and analyzes spectroscopic data; the code facilitates reproducible scientific results and easy to inspect model fits. A built-in graphical user interface around LMFIT (ascl:1606.014) offers interactive control to set up and combine multiple spectral models to fully fit the spectrum of choice. Alternatively, all core functionality can be scripted to facilitate the design of spectral fitting and analysis pipelines.

SDAR (Slow-Down Algorithmic Regularization) simulates the long-term evolution of few-body systems such as binaries and triples. The algorithm used provides a few orders of magnitude faster performance than the classical N-body method. The secular evolution of hierarchical systems, e.g. Kozai-Lidov oscillation, can be well reproduced. The code is written in the C++ language and can be used either as a stand-alone tool or a library to be plugged in other N-body codes. The high precision of the floating point to 62 digits is also supported.

Dual Active Nuclei Galaxies (DAGNs) are rare occurrences in the sky. Until now, most AGNs have been described to be found serendipitously, or by manual observation. In recent years, there has been an increasing interest in such dual AGNs and their astrophysical properties. Their study is important to the understanding of galaxy formation, star formation and these objects are the precursors to Gravitational Wave Sources.

Hence, we have devised a pipeline, that along with systematic data collection, can detect such dual AGN candidates. A novel algorithm 'Graph-Boosted Gradient Ascent' has been devised to detect whether an R-band image of a galaxy is a potential candidate for a DAGN or not. The pipeline can be cloned to a user's machine, and by joining the AstrIRG_DAGN group on SciServer, astronomers can collectively contribute to the mining of DAGNs.

Seaborn provides a high-level interface for drawing attractive statistical graphics. Written in Python, it builds on matplotlib and integrates closely with pandas data structures. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API allows the user to focus on what the different elements of the plots mean, rather than on the details of how to draw them.