Results 1951-2000 of 2516 (2473 ASCL, 43 submitted)

[ascl:1907.013]
RVSpecFit: Radial velocity and stellar atmospheric parameter fitting

RVSpecFit determines radial velocities and stellar atmospheric parameters from spectra by direct pixel fitting by interpolated stellar templates. The code doesn't require spectrum normalization and can deal with non-flux calibrated spectra. RVSpecFit is able to fit multiple spectra simultaneously.

[ascl:1606.008]
s2: Object oriented wrapper for functions on the sphere

The s2 package can represent any arbitrary function defined on the sphere. Both real space map and harmonic space spherical harmonic representations are supported. Basic sky representations have been extended to simulate full sky noise distributions and Gaussian cosmic microwave background realisations. Support for the representation and convolution of beams is also provided. The code requires HEALPix (ascl:1107.018) and CFITSIO (ascl:1010.001).

[ascl:1110.013]
S2HAT: Scalable Spherical Harmonic Transform Library

Many problems in astronomy and astrophysics require a computation of the spherical harmonic transforms. This is in particular the case whenever data to be analyzed are distributed over the sphere or a set of corresponding mock data sets has to be generated. In many of those contexts, rapidly improving resolutions of both the data and simulations puts increasingly bigger emphasis on our ability to calculate the transforms quickly and reliably.

The scalable spherical harmonic transform library S2HAT consists of a set of flexible, massively parallel, and scalable routines for calculating diverse (scalar, spin-weighted, etc) spherical harmonic transforms for a class of isolatitude sky grids or pixelizations. The library routines implement the standard algorithm with the complexity of O(n^3/2), where n is a number of pixels/grid points on the sphere, however, owing to their efficient parallelization and advanced numerical implementation, they achieve very competitive performance and near perfect scalability. S2HAT is written in Fortran 90 with a C interface. This software is a derivative of the spherical harmonic transforms included in the HEALPix package and is based on both serial and MPI routines of its version 2.01, however, since version 2.5 this software is fully autonomous of HEALPix and can be compiled and run without the HEALPix library.

[ascl:1211.001]
S2LET: Fast wavelet analysis on the sphere

S2LET provides high performance routines for fast wavelet analysis of signals on the sphere. It uses the SSHT code built on the MW sampling theorem to perform exact spherical harmonic transforms on the sphere. The resulting wavelet transform implemented in S2LET is theoretically exact, i.e. a band-limited signal can be recovered from its wavelet coefficients exactly and the wavelet coefficients capture all the information. S2LET also supports the HEALPix sampling scheme, in which case the transforms are not theoretically exact but achieve good numerical accuracy. The core routines of S2LET are written in C and have interfaces in Matlab, IDL and Java. Real signals can be written to and read from FITS files and plotted as Mollweide projections.

[ascl:1103.003]
S2PLOT: Three-dimensional (3D) Plotting Library

We present a new, three-dimensional (3D) plotting library with advanced features, and support for standard and enhanced display devices. The library - S2PLOT - is written in C and can be used by C, C++ and FORTRAN programs on GNU/Linux and Apple/OSX systems. S2PLOT draws objects in a 3D (x,y,z) Cartesian space and the user interactively controls how this space is rendered at run time. With a PGPLOT inspired interface, S2PLOT provides astronomers with elegant techniques for displaying and exploring 3D data sets directly from their program code, and the potential to use stereoscopic and dome display devices. The S2PLOT architecture supports dynamic geometry and can be used to plot time-evolving data sets, such as might be produced by simulation codes. In this paper, we introduce S2PLOT to the astronomical community, describe its potential applications, and present some example uses of the library.

[ascl:2005.009]
s3PCF: Compute the 3-point correlation function in the squeezed limit

s3PCF computes the 3-point correlation function (3PCF) in the squeezed limit given galaxy positions and pair positions. The code is currently written specifically for the Abacus simulations, but the main functionalities can be also easily adapted for other galaxy catalogs with the appropriate properties.

[ascl:1111.003]
Saada: A Generator of Astronomical Database

Saada transforms a set of heterogeneous FITS files or VOtables of various categories (images, tables, spectra, etc.) in a powerful database deployed on the Web. Databases are located on your host and stay independent of any external server. This job doesn’t require writing code. Saada can mix data of various categories in multiple collections. Data collections can be linked each to others making relevant browsing paths and allowing data-mining oriented queries. Saada supports 4 VO services (Spectra, images, sources and TAP) . Data collections can be published immediately after the deployment of the Web interface.

[ascl:1306.001]
SAC: Sheffield Advanced Code

The Sheffield Advanced Code (SAC) is a fully non-linear MHD code designed for simulations of linear and non-linear wave propagation in gravitationally strongly stratified magnetized plasma. It was developed primarily for the forward modelling of helioseismological processes and for the coupling processes in the solar interior, photosphere, and corona; it is built on the well-known VAC platform that allows robust simulation of the macroscopic processes in gravitationally stratified (non-)magnetized plasmas. The code has no limitations of simulation length in time imposed by complications originating from the upper boundary, nor does it require implementation of special procedures to treat the upper boundaries. SAC inherited its modular structure from VAC, thereby allowing modification to easily add new physics.

[submitted]
Sacc: Save All Correlations and Covariances

Zuntz, J.; Slosar, A.; Alonso, D.; Becker, M.; Broussard, A.; McClintock, T.; Nicola, A.; Miyatake, H.; Sanchez, J.; Neveu, J.

SACC (Save All Correlations and Covariances) is a format and reference library for general storage

of summary statistic measurements for the Dark Energy Science Collaboration (DESC) within and from the Large Synoptic Survey Telescope (LSST) project's Dark Energy Science Collaboration.

[ascl:1601.006]
SAGE: Semi-Analytic Galaxy Evolution

Croton, Darren J.; Stevens, Adam R. H.; Tonini, Chiara; Garel, Thibault; Bernyk, Maksym; Bibiano, Antonio; Hodkinson, Luke; Mutch, Simon J.; Poole, Gregory B.; Shattow, Genevieve M.

SAGE (Semi-Analytic Galaxy Evolution) models galaxy formation in a cosmological context. SAGE has been rebuilt to be modular and customizable. The model runs on any dark matter cosmological N-body simulation whose trees are organized in a supported format and contain a minimum set of basic halo properties.

[ascl:1203.011]
SALT2: Spectral Adaptive Lightcurve Template

SALT (Spectral Adaptive Lightcurve Template) is a package for Type Ia Supernovae light curve fitting. Its main purpose is to provide a distance estimator but it can also be used for photometric redshifts, and spectroscopic + photometric identification. This code is also known by the name snfit.

[ascl:1407.006]
SAMI: Sydney-AAO Multi-object Integral field spectrograph pipeline

Allen, J. T.; Green, A. W.; Fogarty, L. M. R.; Sharp, R.; Nielsen, J.; Konstantopoulos, I.; Taylor, E. N.; Scott, N.; Cortese, L.; Richards, S. N.; Croom, S.; Owers, M. S.; Bauer, A. E.; Sweet, S. M.; Bryant, J. J.

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.

[ascl:1504.011]
samiDB: A Prototype Data Archive for Big Science Exploration

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.

[ascl:1605.015]
SAND: Automated VLBI imaging and analyzing pipeline

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.

[ascl:0003.002]
SAOImage DS9: A utility for displaying astronomical images in the X11 window environment

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.

[ascl:1210.029]
Sapporo: N-body simulation library for GPUs

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.

[ascl:1907.005]
SARA-PPD: Preconditioned primal-dual algorithm for radio-interferometric imaging

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.

[ascl:1904.020]
SARAH: SUSY and non-SUSY model builder and analyzer

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.

[ascl:1404.004]
SAS: Science Analysis System for XMM-Newton observatory

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.

[ascl:1707.002]
SASRST: Semi-Analytic Solutions for 1-D Radiative Shock Tubes

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.

[ascl:2103.005]
satcand: Orbital stability and tidal migration constraints for KOI exomoon candidates

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.

[ascl:1309.005]
SATMC: SED Analysis Through Monte Carlo

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.

[ascl:1601.012]
SavGolFilterCov: Savitzky Golay filter for data with error covariance

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.

[ascl:1904.015]
SBGAT: Small Bodies Geophysical Analysis Tool

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.

[ascl:1907.014]
sbpy: Small-body planetary astronomy

Mommert, Michael; Kelley, Michael S. P.; de Val-Borro, Miguel; Li, Jian-Yang; Guzman, Giannina; Sipőcz, Brigitta; Ďurech, Josef; Granvik, Mikael; Grundy, Will; Moskovitz, Nick; Penttilä, Antti; Samarasinha, Nalin

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.

[ascl:1010.063]
SCAMP: Automatic Astrometric and Photometric Calibration

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.

[ascl:2002.006]
ScamPy: Sub-halo Clustering and Abundance Matching Python interface

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.

[ascl:1209.012]
Scanamorphos: Maps from scan observations made with bolometer arrays

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.

[ascl:1803.003]
scarlet: Source separation in multi-band images by Constrained Matrix Factorization

Melchior, Peter; Moolekamp, Fred; Jerdee, Maximilian; Armstrong, Robert; Sun, Ai-Lei; Bosch, James; Lupton, Robert

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.

[ascl:1505.008]
SCEPtER: Stellar CharactEristics Pisa Estimation gRid

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.

[ascl:2103.013]
schNell: Fast calculation of N_ell for GW anisotropies

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.

[ascl:1907.001]
schwimmbad: Parallel processing pools interface

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.

[ascl:1311.001]
SciDB: Open Source DMAS for Scientific Research

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.

[ascl:1609.006]
SCIMES: Spectral Clustering for Interstellar Molecular Emission Segmentation

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.

[ascl:2011.019]
Scintools: Pulsar scintillation data tools

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.

[ascl:1601.003]
SCOUSE: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

Henshaw, J. D.; Longmore, S. N.; Kruijssen, J. M. D.; Davies, B.; Bally, J.; Barnes, A.; Battersby, C.; Burton, M.; Cunningham, M. R.; Dale, J. E.; Ginsburg, A.; Immer, K.; Jones, P. A.; Kendrew, S.; Mills, E. A. C.; Molinari, S.; Moore, T. J. T.; Ott, J.; Pillai, T.; Rathborne, J.; Schilke, P.; Schmiedeke, A.; Testi, L.; Walker, D.; Walsh, A.; Zhang, Q.

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.

[ascl:2003.004]
scousepy: Semi-automated multi-COmponent Universal Spectral-line fitting Engine

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.

[ascl:2002.001]
SDAR: Slow-Down Algorithmic Regularization code for solving few-body problems

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.

[submitted]
SDSS Dual Active Nuclei Galaxy Detection Pipeline

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.

[ascl:2012.015]
seaborn: Statistical data visualization

Waskom, Michael; Botvinnik, Olga; Gelbart, Maoz; Ostblom, Joel; Hobson, Paul; Lukauskas, Saulius; Gemperline, David C; Augspurger, Tom; Halchenko, Yaroslav; Warmenhoven, Jordi; Cole, John B.; De Ruiter, Julian; Vanderplas, Jake; Hoyer, Stephan; Pye, Cameron; Miles, Alistair; Swain, Corban; Meyer, Kyle; Martin, Marcel; Bachant, Pete Quintero, Eric; Kunter, Gero; Villalba, Santi; Brian; Fitzgerald, Clark; Evans, C. G.; Williams, Mike Lee; O'Kane, Drew; Yarkoni, Tal; Brunner, Thomas

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.

[ascl:1210.012]
SearchCal: The JMMC Evolutive Search Calibrator Tool

SearchCal builds an evolutive catalog of stars suitable as calibrators within any given user-defined angular distance and magnitude around a scientific target. SearchCal can select suitable bright calibration stars (V ≤ 10; K ≤ 5.0) for obtaining the ultimate precision of current interferometric instruments like the VLTI and faint calibration stars up to K ~ 15 around the scientific target. Star catalogs available at the CDS are searched via web requests and provide the useful astrometric and photometric informations for selecting calibrators. The missing photometries are computed with an accuracy of about 0.1 mag. The stellar angular diameter is estimated with a precision of about 10% through newly determined surface-brightness versus color-index relations based on the I, J, H and K magnitudes. For each star the squared visibility is computed taking into account the central wavelength and the maximum baseline of the predicted observations.

[ascl:1201.003]
SeBa: Stellar and binary evolution

The stellar and binary evolution package SeBa is fully integrated into the kira integrator, although it can also be used as a stand-alone module for non-dynamical applications. Due to the interaction between stellar evolution and stellar dynamics, it is difficult to solve for the evolution of both systems in a completely self-consistent way. The trajectories of stars are computed using a block timestep scheme, as described earlier. Stellar and binary evolution is updated at fixed intervals (every 1/64 of a crossing time, typically a few thousand years). Any feedback between the two systems may thus experience a delay of at most one timestep. Internal evolution time steps may differ for each star and binary, and depend on binary period, perturbations due to neighbors, and the evolutionary state of the star. Time steps in this treatment vary from several milliseconds up to (at most) a million years.

[ascl:1101.001]
Second-order Tight-coupling Code

Prior to recombination photons, electrons, and atomic nuclei rapidly scattered and behaved, almost, like a single tightly-coupled photon-baryon plasma. In order to solve the cosmological perturbation equations during that time, Cosmic Microwave Background (CMB) codes use the so-called tight-coupling approximation in which the problematic terms (i.e. the source of the stiffness) are expanded in inverse powers of the Thomson Opacity. Most codes only keep the terms linear in the inverse Thomson Opacity. We have developed a second-order tight-coupling code to test the validity of the usual first-order tight-coupling code. It is based on the publicly available code CAMB.

[ascl:1909.003]
SecularMultiple: Hierarchical multiple system secular evolution model

SecularMultiple computes the secular (orbit-averaged) gravitational dynamics of hierarchical multiple systems composed of nested binary orbits (simplex-type systems) with any configuration and any number of bodies. A particle can represent a binary or a body. The structure of the system is determined by linking to other particles with the attributes child1 and child2, and tidal interactions and relativistic corrections are included in an ad hoc fashion. SecularMultiple also includes routines for external perturbations such as flybys and supernovae.

[ascl:2008.013]
SEDBYS: Spectral Energy Distribution Builder for Young Stars

SEDBYS (Spectral Energy Distribution Builder for Young Stars) provides command-line tools and uses existing functions from standard packages such as Astropy (ascl:1304.002) to collate archival photometric and spectroscopic data. It also builds and inspects SEDS, and automatically collates the necessary software references.

[ascl:2011.014]
SEDkit: Spectral energy distribution construction and analysis tools

SEDkit constructs and analyzes simple spectral energy distributions (SED). This collection of pure Python modules creates individual SEDs or SED catalogs from spectra and/or photometry and calculates fundamental parameters (fbol, Mbol, Lbol, Teff, mass, log(g)).

[ascl:1901.008]
SEDobs: Observational spectral energy distribution simulation

SEDobs uses state-of-the-art theoretical galaxy SEDs (spectral energy distributions) to create simulated observations of distant galaxies. It used BC03 and M05 theoretical models and allows the user to configure the simulated observation that are needed. For a given simulated galaxy, the user is able to simulate multi-spectral and multi-photometric observations.

[ascl:2012.013]
sedop: Optimize discrete versions of common SEDs

sedop is a Monte-Carlo minimization code designed to optimally construct spectral energy distributions (SEDs) for sources of ultraviolet and X-ray radiation employed in numerical simulations of reionization and radiative feedback.

[ascl:1905.026]
SEDPY: Modules for storing and operating on astronomical source spectral energy distribution

SEDPY performs a variety of tasks for astronomical spectral energy distributions. It can generate synthetic photometry through any filter, provides detailed modeling of extinction curves, and offers basic aperture photometry algorithms. SEDPY can also store and interpolate model SEDs, convolve absolute or apparent fluxes, and calculate rest-frame magnitudes.

[ascl:1607.020]
SEEK: Signal Extraction and Emission Kartographer

Akeret, Joel; Seehars, Sebastian; Chang, Chihway; Monstein, Christian; Amara, Adam; Refregier, Alexandre

SEEK (Signal Extraction and Emission Kartographer) processes time-ordered-data from single dish radio telescopes or from the simulation pipline HIDE (ascl:1607.019), removes artifacts from Radio Frequency Interference (RFI), automatically applies flux calibration, and recovers the astronomical radio signal. With its companion code HIDE (ascl:1607.019), it provides end-to-end simulation and processing of radio survey data.

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