The code is applied to a special case in which the system consists of one massive object and many low-mass objects. The interaction between one massive object and low-mass objects is calculated by post-Newtonian approximation. On the other hand, the interaction between low-mass objects is calculated by Newtonian gravity. This code is based on sticky9 code. On old CUDA environment (version 4.2 or earlier), the code can be accelerated by GPU.
Recently, there have been two landmark discoveries of gravitationally lensed supernovae: the first multiply-imaged SN, 'Refsdal', and the first Type Ia SN resolved into multiple images, SN iPTF16geu. Fitting the multiple light curves of such objects can deliver measurements of the lensing time delays, which are the difference in arrival times for the separate images. These measurements provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Over the next decade, accurate time delay measurements will be needed for the tens to hundreds of lensed SNe to be found by wide-field time-domain surveys such as LSST and WFIRST. We have developed an open source software package for simulations and time delay measurements of multiply-imaged SNe, including an improved characterization of the uncertainty caused by microlensing. We describe simulations using the package that suggest a before-peak detection of the leading image enables a more accurate time delay measurement by ~4 days compared to an after-peak detection. We also conclude that fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data, but that employing a Gaussian Process Regression (GPR) technique is sufficient for determining the uncertainty due to microlensing.
Over the past several decades, high precision pulsar-timing experiments have continued to advance, reaching precisions of ∼10 ns where many subtle phenomena can be observed. At this level of precision, extremely careful data handling and sophisticated timing models are required. In this paper, we present a modern Python-based pulsar timing package, called PINT (from PINT Is Not Tempo3), which is designed to analyze high-precision pulsar timing data in a wide variety of applications. PINT is a well-tested, validated, object-oriented, and modular package, enabling interactive data analysis and providing an extensible and flexible development platform for timing applications. PINT utilizes well-debugged public Python packages and modern software development schemes (e.g., the NumPy and Astropy libraries, version control and development with git and GitHub, and various types of testing) for increased development efficiency and enhanced stability. PINT has been developed and implemented completely independently from traditional pulsar timing software (e.g.TEMPO/Tempo2) and is, therefore, a robust tool for cross-checking timing analyses and simulating data. We describe the design, usage, and validation of PINT, and compare timing results between it and TEMPO and Tempo2.
stellarWakes uses stellar kinematic data to search for dark matter (DM) subhalos through their gravitational perturbations to the stellar phase-space distribution.
Bilby provides a user-friendly interface to perform parameter estimation. It is primarily designed and built for inference of compact binary coalescence events in interferometric data, such as analysis of compact binary mergers and other types of signal model including supernovae and the remnants of binary neutron star mergers, but it can also be used for more general problems. The software is flexible, allowing the user to change the signal model, implement new likelihood functions, and add new detectors. Bilby can also be used to do population studies using hierarchical Bayesian modelling.
The Python suite eddy recovers precise rotation profiles of protoplanetary disks from Doppler shifted line emission, providing an easy way to fit first moment maps and the inference of a rotation velocity from an annulus of spectra.
bettermoments measures precise line-of-sight velocities from Doppler shifted lines to determine small scale deviations indicative of, for example, embedded planets.
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.
Photon makes simple 1D plots in python. It uses mainly matplotlib and PyQt5 and has been build to be fully customizable, allowing the user to change the fontstyle, fontsize, fontcolors, linewidth of the axes, thickness, and other parameters, and see the changes directly in the plot. Once a customization is created, it can be saved in a configuration file and reloaded for future use, allowing reuse of the customization for other plots. The main tool is a graphical user interface and it is started using a command line interface.
The ssos pipeline detects and identifies known and unknown Solar System Objects (SSOs) in astronomical images. ssos requires at least 3 images with overlapping field-of-views in the sky taken within a reasonable amount of time (e.g., 2 hours, 1 night). SSOs are detected mainly by judging the apparent motion of all sources in the images. The pipeline serves as a wrapper for the SExtractor (ascl:1010.064) and SCAMP (ascl:1010.063) software suites and allows different source extraction strategies to be chosen. All sources in the images are subject to a highly configurable filter pipeline. ssos is a versatile, light-weight, and easy-to-use software for surveys or PI-observation campaigns lacking a dedicated SSO detection pipeline.
Galaxia_wrap is a python wrap around the popular Galaxia tool (ascl:1101.007) for generating mock stellar surveys, such as a magnitude limited survey, using a built-in Galaxy model or directly from n-body data. It also offers n-body functionality and has been used to infer the age distribution of a specific stellar tracer population.
The unwise_psf Python module renders point spread function (PSF) models appropriate for use in modeling of unWISE coadd images. unwise_psf translates highly detailed single-exposure WISE PSF models in detector coordinates to the corresponding pixelized PSF models in coadd space, accounting for subtleties including the WISE scan direction and its considerable variation near the ecliptic poles. Applications of the unwise_psf module include performing forced photometry on unWISE coadds, constructing WISE-selected source catalogs based on unWISE coadds and masking unWISE coadd regions contaminated by bright stars.
The Core Cosmology Library (CCL) computes basic cosmological observables and provides predictions for many cosmological quantities, including distances, angular power spectra, correlation functions, halo bias and the halo mass function through state-of-the-art modeling prescriptions. Fiducial specifications for the expected galaxy distributions for the Large Synoptic Survey Telescope (LSST) are also included, together with the capability of computing redshift distributions for a user-defined photometric redshift model. Predictions for correlation functions of galaxy clustering, galaxy-galaxy lensing and cosmic shear are within a fraction of the expected statistical uncertainty of the observables for the models and in the range of scales of interest to LSST. CCL is written in C and has a python interface.
OCFit fits and analyzes O-C diagrams using Genetic Algorithms and Markov chain Monte Carlo methods. The MC method is used to determine a very good estimation of errors of the parameters. Unlike some other fitting routines, OCFit does not need any initial values of fitted parameters. An intuitive graphic user interface is provided for ease of fitting, and nine common models of periodic O-C changes are included.
The Core Flight Executive is a portable, platform-independent embedded system framework that is the basis for flight software for satellite data systems and instruments; cFE can be used on other embedded systems as well. The Core Flight Executive is written in C and depends on the software library Operating System Abstraction Layer (OSAL), which is available at https://sourceforge.net/projects/osal/.
The OctApps library provides various functions, written in Octave, for performing searches for the weak signatures of continuous gravitational waves from rapidly-rotating neutron stars amidst the instrumental noise of the LIGO and Virgo detectors.
psrqpy directly queries the Australia Telescope National Facility (ATNF) Pulsar Catalogue by downloading and parsing the full catalog database, which is cached and can be reused. The module assists astronomers who want access to the latest pulsar information via a script rather than through the standard web interface.
Juliet essentially serves as a wrapper of other tools, including Batman (ascl:1510.002), George (ascl:1511.015), Dynesty (ascl:1809.013) and AstroPy (ascl:1304.002), to analyze and model transits, radial-velocities, or both from multiple instruments at the same time. Using nested sampling algorithms, it performs a thorough sampling of the parameter space and a model comparison via Bayesian evidences. Juliet also fits transiting and non-transiting multi-planetary systems, and Gaussian Processes (GPs) which might share hyperparameters between the photometry and radial-velocities simultaneously (e.g., stellar rotation periods).
AUTOSPEC provides fast, automated extraction of high quality 1D spectra from astronomical datacubes with minimal user effort. AutoSpec takes an integral field unit (IFU) datacube and a simple parameter file in order to extract a 1D spectra for each object in a supplied catalogue. A custom designed cross-correlation algorithm improves signal to noise as well as isolates sources from neighboring contaminants.
Astrochemistry database of chemical species.
GENGA (Gravitational ENcounters with Gpu Acceleration) integrates planet and planetesimal dynamics in the late stage of planet formation and stability analyses of planetary systems. It uses mixed variable integration when the motion is a perturbed Kepler orbit and combines this with a direct N-body Bulirsch-Stoer method during close encounters. It supports three simulation modes: 1.) integration of up to 2048 massive bodies; 2.) integration with up to a million test particles; and 3.) parallel integration of a large number of individual planetary systems.
Lightkurve analyzes astronomical flux time series data, in particular the pixels and light curves obtained by NASA’s Kepler, K2, and TESS exoplanet missions. This community-developed Python package is designed to be user friendly to lower the barrier for students, astronomers, and citizen scientists interested in analyzing data from these missions. Lightkurve provides easy tools to download, inspect, and analyze time series data and its documentation is supported by a large syllabus of tutorials.
distlink computes the minimum orbital intersection distance (MOID), or global minimum of the distance between the points lying on two Keplerian ellipses by finding all stationary points of the distance function, based on solving an algebraic polynomial equation of 16th degree. The program tracks numerical errors and carefully treats nearly degenerate cases, including practical cases with almost circular and almost coplanar orbits. Benchmarks confirm its high numeric reliability and accuracy, and even with its error-controlling overheads, this algorithm is a fast MOID computation method that may be useful in processing large catalogs. Written in C++, the library also includes auxiliary functions.
GRAND-HOD (GeneRalized ANd Differentiable Halo Occupation Distribution) takes a generalized Halo Occupation Distribution (HOD) prescription as input and outputs the corresponding mock galaxy catalogs in binary files. The code is differentiable and incorporates various generalizations to the standard HOD. It is written for the Abacus simulations, but the main functionalities can be easily adapted for other halo catalogs with the appropriate properties.
PynPoint processes and analyzes high-contrast imaging data of exoplanets and circumstellar disks. The generic, end-to-end pipeline's modular architecture separates the core functionalities and the pipeline modules. These modules have specific tasks such as background subtraction, frame selection, centering, PSF subtraction with principal component analysis, estimation of detection limits, and photometric and astrometric analysis. All modules store their results in a central database. Management of the available hardware by the backend of the pipeline is in particular an advantage for data sets containing thousands of images, as is common in the mid-infrared wavelength regime. This version of PynPoint is a significant rewrite of the earlier PynPoint package (ascl:1501.001).
The stellar classification code galclassify is a stand-alone version of Galaxia (ascl:1101.007). It classifies and generates a synthetic population for each star using input containing observables in a fixed format rather than using a precomputed population over a large field. It is suitable for individual stellar classifications, but slow if you want to classify large samples of stars.
easyaccess facilitates access to astronomical catalogs stored in SQL Databases. It is an enhanced command line interpreter and provides a custom interface with custom commands and was specifically designed to access data from the Dark Energy Survey Oracle database, including autocompletion of tables, columns, users and commands, simple ways to upload and download tables using csv, fits and HDF5 formats, iterators, search and description of tables among others. It can easily be extended to other surveys or SQL databases. The package is written in Python and supports customized addition of commands and functionalities.
ExoGAN (Exoplanets Generative Adversarial Network) analyzes exoplanetary atmospheres using an unsupervised deep-learning algorithm that recognizes molecular features, atmospheric trace-gas abundances, and planetary parameters. After training, ExoGAN can be applied to a large number of instruments and planetary types and can be used either as a final atmospheric analysis or to provide prior constraints to subsequent retrieval.
Fermipy facilitates analysis of data from the Large Area Telescope (LAT) with the Fermi Science Tools. It is built on the pyLikelihood interface of the Fermi Science Tools and provides a set of high-level tools for performing common analysis tasks, including data and model preparation with the gt-tools, extracting a spectral energy distribution (SED) of a source, and generating TS and residual maps for a region of interest. Fermipy also finds new source candidates and can localize a source or fit its spatial extension. The package uses a configuration-file driven workflow in which the analysis parameters (data selection, IRFs, and ROI model) are defined in a YAML configuration file. Analysis is executed through a python script that calls the methods of GTAnalysis to perform different analysis operations.
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
aesop (ARC Echelle Spectroscopic Observation Pipeline) analyzes echelle spectra for observations made by the Astrophysics Research Consortium (ARC) Echelle Spectrograph on the ARC 3.5 m Telescope at Apache Point Observatory. It is a high resolution spectroscopy software toolkit that picks up where the traditional IRAF reduction scripts leave off, and offers blaze function normalization by polynomial fits to observations of early-type stars, a robust least-squares normalization method, and radial velocity measurements (or offset removals) via cross-correlation with model spectra, including barycentric radial velocity calculations. It also concatenates multiple echelle orders into a simple 1D spectrum and provides approximate flux calibration.
PFANT computes a synthetic spectrum assuming local thermodynamic equilibrium from a given stellar model atmosphere and lists of atomic and molecular lines; it provides large wavelength coverage and line lists from ultraviolet through the visible and near-infrared. PFANT has been optimized for speed, offers error reporting, and command-line configuration options.
GLADIS (GLobal Accretion Disk Instability Simulation) computes the time-dependent evolution of a black hole accretion disk, in one-dimensional, axisymmetric, vertically integrated scheme. The code solves two partial-differential equations of hydrodynamics for surface density and temperature evolution, i.e., given by viscous diffusion and energy conservation. Accretion disks can be subject to radiation-pressure instability if the stress tensor is proportional to the total (gas plus radiation) pressure. In the gas-pressure dominated case there is no instability. An intermediate case is provided in the code by the square root of the gas and total pressures. GLADIS is parallelized with MPI, and sample .ini and run command files are provided with the code.
WISP (Wenger Interferometry Software Package) is a radio interferometry calibration, reduction, imaging, and analysis package. WISP is a collection of Python code implemented through CASA (ascl:1107.013). Its generic and modular framework is designed to handle any continuum or spectral line radio interferometry data.
This Python package allows the user to setup and run an agent-based simulation of a SETI survey. The package allows the creation of a population of observing and transmitting civilisations. Each transmitter and observer conducts their activities according to an input strategy. The success of observers and transmitters can then be recorded, and multiple simulations can be run for Monte Carlo Realisation.
This package is therefore a flexible framework in which to simulate and test different SETI strategies, both as an Observer and as a Transmitter. It is primarily designed with radio SETI in mind, but is sufficiently flexible to simulate all forms of electromagnetic SETI, and potentially neutrino and gravitational wave SETI.
cuvarbase provides a Python (2.7+) library for performing period finding (Lomb-Scargle, Phase Dispersion Minimization, Conditional Entropy, Box-least squares) on astronomical time-series datasets. Speedups over CPU implementations depend on the algorithm, dataset, and GPU capabilities but are typically ~1-2 orders of magnitude and are especially high for BLS and Lomb-Scargle. Unit tested and available via pip or from source at GitHub.
Pulsarhunter searches for and confirms pulsars; it provides a set of time domain optimization tools for processing timeseries data produced by SIGPROC (ascl:1107.016). The software can natively write candidate lists for JReaper (included in the package), removing the need to manually import candidates into JReaper; JReaper also reads the PulsarHunter candidate file format.
PENTACLE calculates gravitational interactions between particles within a cut-off radius and a Barnes-Hut tree method for gravity from particles beyond. It uses FDPS (ascl:1604.011) to parallelize a Barnes-Hut tree algorithm for a memory-distributed supercomputer. The software can handle 1-10 million particles in a high-resolution N-body simulation on CPU clusters for collisional dynamics, including physical collisions in a planetesimal disc.
gdr2_completeness queries Gaia DR2 TAP services and divides the queries into sub-queries chunked into arbitrary healpix bins. Downloaded data are formatted into arrays. Internal completeness is calculated by dividing the total starcount and starcounts with an applied cut (e.g., radial velocity measurement and good parallax). Independent determination of the external GDR2 completeness per healpix (level 6) and G magnitude bin (3 coarse bins: 8-12,12-15,15-18) is inferred from a crossmatch with 2MASS data. The overall completeness of a specific GDR2 sample can be approximated by multiplying the internal with the external completeness map, which is useful when data are compared to models thereof. Jupyter notebooks showcasing both utilities enable the user to easily construct the overall completeness for arbitrary samples of the GDR2 catalogue.
Vplanet simulates planetary system evolution with a focus on habitability. Physical models, typically consisting of ordinary differential equations for stellar, orbital, tidal, rotational, atmospheric, internal, magnetic, climate, and galactic evolution, are coupled together to simulate evolution for the age of a system.
VoigtFit fits Voigt profiles to absorption lines. It fits multiple components for various atomic lines simultaneously, allowing parameters to be tied and fixed, and can automatically fit a polynomial continuum model together with the line profiles. A physical model can be used to constrain thermal and turbulent broadening of absorption lines as well as implementing molecular excitation models. The code uses a χ2 minimization approach to find the best solution and offers interactive features such as manual continuum placement locally around each line, manual masking of undesired fitting regions, and interactive definition of velocity components for various elements, improving the ease of estimating initial guesses.
radon performs a Fast Radon Transform (FRT) on image data for streak detection. The software finds short streaks and multiple streaks, convolves the images with a given PSF, and tracks the best S/N results and find a automatic threshold. It also calculates the streak parameters in the input image and the streak parameters in the input image. radon has a simulator that can make multiple streaks of different intensities and coordinates, and can simulate random streaks with parameters chosen uniformly in a user-defined range.
pygad provides a framework for dealing with Gadget snapshots. The code reads any of the many different Gadget (ascl:0003.001) formats, allows easy masking snapshots to particles of interest, decorates the data blocks with units, allows to add automatically updating derived blocks, and provides several binning and plotting routines, among other tasks, to provide convenient, intuitive handling of the Gadget data without the need to worry about technical details. pygad provides access to single stellar population (SSP) models, has an interface to Rockstar (ascl:1210.008) output files, provides its own friends-of-friends (FoF) finder, calculates spherical overdensities, and has a sub-module to generate mock absorption lines.
DiskSim is a source-code distribution of the SPH accretion disk modeling code previously released in a Windows executable form as FITDisk (ascl:1305.011). The code released now is the full research code in Fortran and can be modified as needed by the user.
muLAn analyzes and fits light curves of gravitational microlensing events. The code includes all classical microlensing models (for example, single and binary microlenses, ground- and space-based parallax effects, orbital motion, finite-source effects, and limb-darkening); these can be combined into several time intervals of the analyzed light curve. Minimization methods include an Affine-Invariant Ensemble Sampler to generate a multivariate proposal function while running several Markov Chain Monte Carlo (MCMC) chains, for the set of parameters which is chosen to be fit; non-fitting parameters can be either kept fixed or set on a grid defined by the user. Furthermore, the software offers a model-free option to align all data sets together and allow inspection the light curve before any modeling work. It also comes with many useful routines (export publication-quality figures, data formatting and cleaning) and state-of-the-art statistical tools.
Modeling results can be interpreted using an interactive html page which contains all information about the light curve model, caustics, source trajectory, best-fit parameters and chi-square. Parameters uncertainties and statistical properties (such as multi-modal features of the posterior density) can be assessed from correlation plots. The code is modular, allowing the addition of other computation or minimization routines by directly adding their Python files without modifying the main code. The software has been designed to be easy to use even for the newcomer in microlensing, with external, synthetic and self-explanatory setup files containing all important commands and option settings. The user may choose to launch the code through command line instructions, or to import muLAn within another Python project like any standard Python package.
The SIM5 library contains routines for relativistic raytracing and radiation transfer in GR. Written C with a Python interface, it has a special focus on raytracing from accretion disks, tori, hot spots or any other 3D configuration of matter in Kerr geometry, but it can be used with any other metric as well. It handles both optically thick and thin sources as well as transport of polarization of the radiation and calculates the propagation of light rays from the source to an observer through a curved spacetime. It supports parallelization and runs on GPUs.
MillCgs clusters galaxies from the semi-analytic models run on top of the Millennium Simulation to identify Compact Groups. MillCgs uses a machine learning clustering algorithm to find the groups and then runs analytics to filter out the groups that do not fit the user specified criteria. The package downloads the data, processes it, and then creates graphs of the data.
RLOS (Relativistic Line Of Sight) uses hydrocode output data, such as that from PLUTO (ascl:1010.045), to create synthetic images depicting what a model relativistic astrophysical jet looks like to a stationary observer. The approximate time-delayed imaging algorithm used is implemented within existing line-of-sight code. The software has the potential to study a variety of dynamical astrophysical phenomena in collaboration with other imaging and simulation tools.
Pylians facilitates the analysis of numerical simulations (both N-body and hydro). This set of libraries, written in python, cython and C, compute power spectra, bispectra, and correlation functions, identifies voids, and populates halos with galaxies using an HOD. Pylians can also apply HI+H2 corrections to the output of hydrodynamic simulations, makes 21cm maps, computes DLAs column density distribution functions, and plots density fields.
Flame reduces near-infrared and optical multi-object spectroscopic data. Although the pipeline was created for the LUCI instrument at the Large Binocular Telescope, Flame, written in IDL, is modular and can be adapted to work with data from other instruments. The software uses 2D transformations, thus using one interpolation step to wavelength calibrate and rectify the data. The γ(x, y) transformation also includes the spatial misalignment between frames, which can be measured from a reference star observed simultaneously with the science targets; sky subtraction can be performed via nodding and/or modelling of the sky spectrum.
QuickSip quickly projects Survey Image Properties (e.g. seeing, sky noise, airmass) into Healpix sky maps with flexible weighting schemes. It was initially designed to produce observing condition "systematics" maps for the Dark Energy Survey (DES), but will work with any multi-epoch survey and images with valid WCS. QuickSip can reproduce the Mangle (ascl:1202.005) magnitude limit maps at sub-percent accuracy but doesn't support additional masks (stars, trails, etc), in which case Mangle should be used. Thus, QuickSip can be seen as a simplified Mangle to project image properties into Healpix maps in a fast and more flexible manner.
Shark is a flexible semi-analytic galaxy formation model for easy exploration of different physical processes. Shark has been implemented with several models for gas cooling, active galactic nuclei, stellar and photo-ionization feedback, and star formation (SF). The software can determine the stellar mass function and stellar–halo mass relation at z=0–4; cosmic evolution of the star formation rate density, stellar mass, atomic and molecular hydrogen; local gas scaling relations; and structural galaxy properties. It performs particularly well for the mass–size relation for discs/bulges, the gas–stellar mass and stellar mass–metallicity relations. Shark is written in C++11 and has been parallelized with OpenMP.
SEP (Source Extraction and Photometry) makes the core algorithms of Source Extractor (ascl:1010.064) available as a library of standalone functions and classes. These operate directly on in-memory arrays (no FITS files or configuration files). The code is derived from the Source Extractor code base (written in C) and aims to produce results compatible with Source Extractor whenever possible. SEP consists of a C library with no dependencies outside the standard library and a Python module that wraps the C library in a Pythonic API. The Python wrapper operates on NumPy arrays with NumPy as its only dependency. It is generated using Cython.
From Source Extractor, SEP includes background estimation, image segmentation (including on-the-fly filtering and source deblending), aperture photometry in circular and elliptical apertures, and source measurements such as Kron radius, "windowed" position fitting, and half-light radius. It also adds the following features that are not available in Source Extractor: optimized matched filter for variable noise in source extraction; circular annulus and elliptical annulus aperture photometry functions; local background subtraction in shape consistent with aperture in aperture photometry functions; exact pixel overlap mode in all aperture photometry functions; and masking of elliptical regions on images.
binaryBHexp (binary black hole explorer) uses surrogate models of numerical simulations to generate on-the-fly interactive visualizations of precessing binary black holes. These visualizations can be generated in a few seconds and at any point in the 7-dimensional parameter space of the underlying surrogate models. These visualizations provide a valuable means to understand and gain insights about binary black hole systems and gravitational physics such as those detected by the LIGO gravitational wave detector.
DRAGONS (Data Reduction for Astronomy from Gemini Observatory North and South) is Gemini's Python-based data reduction platform. DRAGONS offers an automation system that allows for hands-off pipeline reduction of Gemini data, or of any other astronomical data once configured. The platform also allows researchers to control input parameters and in some cases will offer to interactively optimize some data reduction steps, e.g. change the order of fit and visualize the new solution.
Synphot simulates photometric data and spectra, observed or otherwise. It can incorporate the user's filters, spectra, and data, and use of a pre-defined standard star (Vega), bandpass, or extinction law. synphot can also construct complicated composite spectra using different models, simulate observations, and compute photometric properties such as count rate, effective wavelength, and effective stimulus. It can manipulate a spectrum by, for example, applying redshift, or normalize it to a given flux value in a given bandpass. Synphot can also sample a spectrum at given wavelengths, plot a quick-view of a spectrum, and perform repetitive operations such as simulating the observations of multiple type of sources through multiple bandpasses. Synphot understands Astropy (ascl:1304.002) models and units and is an Astropy affiliated package.
OCD (O'Connell Effect Detector using Push-Pull Learning) detects eclipsing binaries that demonstrate the O'Connell Effect. This time-domain signature extraction methodology uses a supporting supervised pattern detection algorithm. The methodology maps stellar variable observations (time-domain data) to a new representation known as Distribution Fields (DF), the properties of which enable efficient handling of issues such as irregular sampling and multiple values per time instance. Using this representation, the code applies a metric learning technique directly on the DF space capable of specifically identifying the stars of interest; the metric is tuned on a set of labeled eclipsing binary data from the Kepler survey, targeting particular systems exhibiting the O’Connell Effect. This code is useful for large-scale data volumes such as that expected from next generation telescopes such as LSST.
stginga customizes Ginga to aid data analysis for the data supported by STScI (e.g., HST or JWST). For instance, it provides plugins and configuration files that understand HST and JWST data products.
An extension to synphot (ascl:1811.001), stsynphot implements synthetic photometry package for HST and JWST support. The software constructs spectra from various grids of model atmosphere spectra, parameterized spectrum models, and atlases of stellar spectrophotometry. It also simulates observations specific to HST and JWST, computes photometric calibration parameters for any supported instrument mode, and plots instrument-specific sensitivity curves and calibration target spectra.
Firefly provides interactive exploration of particle-based data in the browser. The user can filter, display vector fields, and toggle the visibility of their customizable datasets all on-the-fly. Different Firefly visualizations, complete with preconfigured data and camera view-settings, can be shared by URL. As Firefly is written in WebGL, it can be hosted online, though Firefly can also be used locally, without an internet connection. Firefly was developed with simulations of galaxy formation in mind but is flexible enough to display any particle-based data. Other features include a stereoscopic 3D picture mode and mobile compatibility.
DDS simulates scattered light and thermal reemission in arbitrary optically dust distributions with spherical, homogeneous grains where the dust parameters (optical properties, sublimation temperature, grain size) and SED of the illuminating/ heating radiative source can be arbitrarily defined. The code is optimized for studying circumstellar debris disks where large grains (i.e., with large size parameters) are expected to determine the far-infrared through millimeter dust reemission spectral energy distribution. The approach to calculate dust temperatures and dust reemission spectra is only valid in the optically thin regime. The validity of this constraint is verified for each model during the runtime of the code. The relative abundances of different grains can be arbitrarily chosen, but must be constant outside the dust sublimation region., i.e., the shape of the (arbitrary) radial dust density distribution outside the dust sublimation region is the same for all grain sizes and chemistries.
Miex calculates Mie scattering coefficients and efficiency factors for broad grain size distributions and a very wide wavelength range (λ ≈ 10-10-10-2m) of the interacting radiation and incorporates standard solutions of the scattering amplitude functions. The code handles arbitrary size parameters, and single scattering by particle ensembles is calculated by proper averaging of the respective parameters.
APPLawD (Accurate Disk Potentials for Power Law Surface densities) determines the gravitational potential in the equatorial plane of a flat axially symmetric disk (inside and outside) with finite size and power law surface density profile. Potential values are computed on the basis of the density splitting method, where the residual Poisson kernel is expanded over the modulus of the complete elliptic integral of the first kind. In contrast with classical multipole expansions of potential theory, the residual series converges linearly inside sources, leading to very accurate potential values for low order truncations of the series. The code is easy to use, works under variable precision, and is written in Fortran 90 with no external dependencies.
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.
XCLASS (eXtended CASA Line Analysis Software Suite) extends CASA (ascl:1107.013) with new functions for modeling interferometric and single dish data. It provides a tool for calculating synthetic spectra by solving the radiative transfer equation for an isothermal object in one dimension, taking into account the finite source size and dust attenuation. It also includes an interface for MAGIX (ascl:1303.009) to find the parameter set that most closely reproduces the data.
cuFFS (CUDA-accelerated Fast Faraday Synthesis) performs Faraday rotation measure synthesis; it is particularly well-suited for performing RM synthesis on large datasets. Compared to a fast single-threaded and vectorized CPU implementation, depending on the structure and format of the data cubes, cuFFs achieves an increase in speed of up to two orders of magnitude. The code assumes that the pixels values are IEEE single precision floating points (BITPIX=-32), and the input cubes must have 3 axes (2 spatial dimensions and 1 frequency axis) with frequency axis as NAXIS1. A package is included to reformat data with individual stokes Q and U channel maps to the required format. The code supports both the HDFITS format and the standard FITS format, and is written in C with GPU-acceleration achieved using Nvidia's CUDA parallel computing platform.
STiC is a MPI-parallel non-LTE inversion code for observed full-Stokes observations. The code processes lines from multiple atoms in non-LTE, including partial redistribution effects of scattered photons in angle and frequency of scattered photons (PRD), and can be used with model atmospheres that have a complex depth stratification without introducing artifacts.
The catsHTM package quickly accesses and cross-matches large astronomical catalogs that have been reformatted into the HDF5-based file format. It performs efficient cone searches at resolutions from a few arc-seconds to degrees within a few milliseconds time, cross-match numerous catalogs, and can do general searches.
GiRaFFE leverages the Einstein Toolkit's (ascl:1102.014) highly-scalable infrastructure to create large-scale simulations of magnetized plasmas in strong, dynamical spacetimes on adaptive-mesh refinement (AMR) grids. It is based on IllinoisGRMHD, a user-friendly, open-source, dynamical-spacetime GRMHD code, and is highly scalable, to tens of thousands of cores.
Eclairs calculates matter power spectrum based on standard perturbation theory and regularized pertubation theory. The codes are written in C++ with a python wrapper which is designed to be easily combined with MCMC samplers.
ODTBX (Orbit Determination Toolbox) provides orbit determination analysis, advanced mission simulation, and analysis for concept exploration, proposal, early design phase, and/or rapid design center environments. The core ODTBX functionality is realized through a set of estimation commands that incorporate Monte Carlo data simulation, linear covariance analysis, and measurement processing at a generic level; its functions and utilities are combined in a flexible architecture to allow modular development of navigation algorithms and simulations. ODTBX is written in Matlab and Java.
PyUltraLight computes non-relativistic ultralight dark matter dynamics in a static spacetime background. It uses pseudo-spectral methods to compute the evolution of a complex scalar field governed by the Schrödinger-Poisson system of coupled differential equations. Computations are performed on a fixed-grid with periodic boundary conditions, allowing for a decomposition of the field in momentum space by way of the discrete Fourier transform. The field is then evolved through a symmetrized split-step Fourier algorithm, in which nonlinear operators are applied in real space, while spatial derivatives are computed in Fourier space. Fourier transforms within PyUltraLight are handled using the pyFFTW pythonic wrapper around FFTW (ascl:1201.015).
The pycraf Python package provides functions and procedures for spectrum-management compatibility studies, such as calculating the interference levels at a radio telescope produced from a radio broadcasting tower. It includes an implementation of ITU-R Recommendation P.452-16 for calculating path attenuation for the distance between an interferer and the victim service. It supports NASA's Shuttle Radar Topography Mission (SRTM) data for height-profile generation, includes a full implementation of ITU-R Rec. P.676-10, which provides two atmospheric models to calculate the attenuation for paths through Earth's atmosphere, and provides various antenna patterns necessary for compatibility studies (e.g., RAS, IMT, fixed-service links). The package can also convert power flux densities, field strengths, transmitted and received powers at certain distances and frequencies into each other.
The 3D Monte Carlo radiative transfer code ARTES calculates reflected light and thermal radiation in a spherical grid with a parameterized distribution of gas, clouds, hazes, and circumplanetary material. Designed specifically for (polarized) scattered light simulations of planetary atmospheres, it can compute both reflected stellar light and thermal emission from the planet for an arbitrary atmospheric structure and distribution of opacity sources. Multiple scattering, absorption, and polarization are fully treated and the output includes an image, spectrum, or phase curve. Several tools are included to create opacities and scattering matrices for molecules and clouds.
Echelle++ simulates realistic raw spectra based on the Zemax model of any spectrograph, with a particular emphasis on cross-dispersed Echelle spectrographs. The code generates realistic spectra of astronomical and calibration sources, with accurate representation of optical aberrations, the shape of the point spread function, detector characteristics, and photon noise. It produces high-fidelity spectra fast, an important feature when testing data reduction pipelines with a large set of different input spectra, when making critical choices about order spacing in the design phase of the instrument, or while aligning the spectrograph during construction. Echelle++ also works with low resolution, low signal to noise, multi-object, IFU, or long slit spectra, for simulating a wide array of spectrographs.
STARRY computes light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more. By modeling celestial body surface maps as sums of spherical harmonics, STARRY does all this analytically and is therefore fast, stable, and differentiable. Coded in C++ but wrapped in Python, STARRY is easy to install and use.
VaeX (Visualization and eXploration) interactively visualizes and explores big tabular datasets. It can calculate statistics such as mean, sum, count, and standard deviation on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots, and 3d volume rendering, allowing interactive exploration of big data. VaeX uses memory mapping, zero memory copy policy and lazy computations for best performance, and integrates well with the Jupyter/IPython notebook/lab ecosystem.
JETGET accesses, visualizes, and analyses (magnetized-)fluid dynamics data stored in Hierarchical Data Format (HDF) and ASCII files. Although JETGET has been optimized to handle data output from jet simulations using the Zeus code (ascl:1306.014) from NCSA, it is also capable of analyzing other data output from simulations using other codes. JETGET can select variables from the data files, render both two- and three-dimensional graphics and analyze and plot important physical quantities. Graphics can be saved in encapsulated Postscript, JPEG, VRML, or saved into an MPEG for later visualization and/or presentations. The strength of JETGET in extracting the physics underlying such phenomena is demonstrated as well as its capabilities in visualizing the 3-dimensional features of the simulated magneto-hydrodynamic jets. The JETGET tool is written in Interactive Data Language (IDL) and uses a graphical user interface to manipulate the data. The tool was developed on a LINUX platform and can be run on any platform that supports IDL.
Barcode (BAyesian Reconstruction of COsmic DEnsity fields) samples the primordial density fields compatible with a set of dark matter density tracers after cosmic evolution observed in redshift space. It uses a redshift space model based on the analytic solution of coherent flows within a Hamiltonian Monte Carlo posterior sampling of the primordial density field; this method is applicable to analytically derivable structure formation models, such as the Zel'dovich approximation, but also higher order schemes such as augmented Lagrangian perturbation theory or even particle mesh models. The algorithm is well-suited for analysis of the dark matter cosmic web implied by the observed spatial distribution of galaxy clusters, such as obtained from X-ray, SZ or weak lensing surveys, as well as that of the intergalactic medium sampled by the Lyman alpha forest. In these cases, virialized motions are negligible and the tracers cannot be modeled as point-like objects. Barcode can be used in all of these contexts as a baryon acoustic oscillation reconstruction algorithm.
galfast generates catalogs for arbitrary, user-supplied Milky Way models, including empirically derived ones. The built-in model set is based on fits to SDSS stellar observations over 8000 deg2 of the sky and includes a three-dimensional dust distribution map. Because of the capability to use empirically derived models, galfast typically produces closer matches to the actual observed counts and color-magnitude diagrams. In particular, galfast-generated catalogs are used to derive the stellar component of “Universe Model” catalogs used by the LSST Project. A key distinguishing characteristic of galfast is its speed. Galfast uses the GPU (with kernels written in NVIDIA C/C++ for CUDA) to offload compute intensive model sampling computations to the GPU, enabling the generation of realistic catalogs to full LSST depth in hours (instead of days or weeks), making it possible to study proposed science cases with high precision.
RequiSim computes the Variance Weighted Overlap, which is a measure of the bias on the lensing signal from power spectrum modelling bias for any non-linear model. It assumes that the bias on the power spectrum is Gaussian with a covariance described by a user-provided knowledge matrix that describes the covariance in the bias on the power spectrum. The data from the Euclid wide-field survey are included.
MrMoose (Multi-Resolution Multi-Object/Origin Spectral Energy) fits user-defined models onto a set of multi-wavelength data using a Bayesian framework. The code can handle blended sources, large variation in resolution, and even upper limits consistently. It also generates a series of outputs allowing for an quick interpretation of the results. The code uses emcee (ascl:1303.002), and saves the emcee sampler object, thus allowing users to transfer the output to a personal graphical interface.
stepped_luneburg investigates the scattered light properties of a Luneburg lens approximated as a series of concentric shells with discrete refractive indices. The optical Luneburg lens has promising applications for low-cost, continuous all-sky monitoring to obtain transit light curves of bright, nearby stars. This code implements a stack-based algorithm that tracks all reflected and refracted rays generated at each optical interface of the lens as described by Snell's law. The Luneburg lens model parameters, such as number of lens layers, the power-law that describes the refractive indices, the number of incident rays, and the initial direction of the incident wavefront can be altered to optimize lens performance. The stepped_luneburg module can be imported within the Python environment or used with scripting, and it is accompanied by two other modules, enc_int and int_map, that help the user to determine the resolving power of the lens and the strength of scattered light haloes for the purpose of quality assessment.
dynesty is a Dynamic Nested Sampling package for estimating Bayesian posteriors and evidences. dynesty samples from a given distribution when provided with a loglikelihood function, a prior_transform function (that transforms samples from the unit cube to the target prior), and the dimensionality of the parameter space.
Nestcheck analyzes nested sampling runs and estimates numerical uncertainties on calculations using them. The package can load results from a number of nested sampling software packages, including MultiNest (ascl:1109.006), PolyChord (ascl:1502.011), dynesty (ascl:1809.013) and perfectns (ascl:1809.005), and offers the flexibility to add input functions for other nested sampling software packages. Nestcheck utilities include error analysis, diagnostic tests, and plots for nested sampling calculations.
qp manipulates parametrizations of 1-dimensional probability distribution functions, as suitable for photo-z PDF compression. The code helps determine a parameterization for storing a catalog of photo-z PDFs that balances the available storage resources against the accuracy of the photo-z PDFs and science products reconstructed from the stored parameters.
Isca provides a framework for the idealized modeling of the global circulation of planetary atmospheres at varying levels of complexity and realism. Though Isca is an outgrowth of models designed for Earth's atmosphere, it may readily be extended into other planetary regimes. Various forcing and radiation options are available. At the simple end of the spectrum a Held-Suarez case is available. An idealized grey radiation scheme, a grey scheme with moisture feedback, a two-band scheme and a multi-band scheme are also available, all with simple moist effects and astronomically-based solar forcing. At the complex end of the spectrum the framework provides a direct connection to comprehensive atmospheric general circulation models.
NEBULA performs the radiative transfer of the 3He+ hyperfine transition, radio recombination lines (RRLs), and free-free continuum emission through a model nebula. The model nebula is composed of only H and He within a three-dimension Cartesian grid with arbitrary density, temperature, and ionization structure. The 3He+ line is assumed to be in local thermodynamic equilibrium (LTE), but non-LTE effects and pressure broadening from electron impacts can be included for the RRLs. All spectra are broadened by thermal and microturbulent motions.
The Python QSO fitting code (PyQSOFit) measures spectral properties of quasars. Based on Shen's IDL version, this code decomposes different components in the quasar spectrum, e.g., host galaxy, power-law continuum, Fe II component, and emission lines. In addition, it can run Monto Carlo iterations using flux randomization to estimate the uncertainties.
surfinBH predicts the final mass, spin and recoil velocity of the remnant of a binary black hole merger. Trained directly against numerical relativity simulations, these models are extremely accurate, reproducing the results of the simulations at the same level of accuracy as the simulations themselves. Fits such as these play a crucial role in waveform modeling and tests of general relativity with gravitational waves, performed by LIGO.
spops is a database of populations synthesis simulations of spinning black-hole binary systems, together with a python module to query it. Data are obtained with the startrack and precession [ascl:1611.004] numerical codes to consistently evolve binary stars from formation to gravitational-wave detection. spops allows quick exploration of the interplay between stellar physics and black-hole spin dynamics.
perfectns performs dynamic nested sampling and standard nested sampling for spherically symmetric likelihoods and priors, and analyses the samples produced. The spherical symmetry allows the nested sampling algorithm to be followed “perfectly” - i.e. without implementation-specific errors correlations between samples. It is intended for use in research into the statistical properties of nested sampling, and to provide a benchmark for testing the performance of nested sampling software packages used for practical problems - which rely on numerical techniques to produce approximately uncorrelated samples.
VBBinaryLensing forward models gravitational microlensing events using the advanced contour integration method; it supports single and binary lenses. The lens map is inverted on a collection of points on the source boundary to obtain a corresponding collection of points on the boundaries of the images from which the area of the images can be recovered by use of Green’s theorem. The code takes advantage of a number of techniques to make contour integration much more efficient, including using a parabolic correction to increase the accuracy of the summation, introducing an error estimate on each arc of the boundary to enable defining an optimal sampling, and allowing the inclusion of limb darkening. The code is written as a C++ library and wrapped as a Python package, and can be called from either C++ or Python.
PASTA performs median stacking of astronomical sources. Written in Python, it can filter sources, provide stack statistics, generate Karma annotations, format source lists, and read information from stacked Flexible Image Transport System (FITS) images. PASTA was originally written to examine polarization stack properties and includes a Monte Carlo modeler for obtaining true polarized intensity from the observed polarization of a stack. PASTA is also useful as a generic stacking tool, even if polarization properties are not being examined.
PCCDPACK analyzes polarimetry data. The set of routines is written in CL-IRAF (including compiled Fortran codes) and analyzes dozens of point objects simultaneously on the same CCD image. A subpackage, specpol, is included to analyze spectropolarimetry data.
LEMON is a differential-photometry pipeline, written in Python, that determines the changes in the brightness of astronomical objects over time and compiles their measurements into light curves. This code makes it possible to completely reduce thousands of FITS images of time series in a matter of only a few hours, requiring minimal user interaction.
Robbie automates cataloging sources, finding variables, and identifying transients in the image domain. It works in a batch processing paradigm with a modular design so components can be swapped out or upgraded to adapt to different input data while retaining a consistent and coherent methodological approach. Robbie is based on commonly used and open software, including AegeanTools (ascl:1212.009) and STILS/TOPCAT (ascl:1101.010).
hi_class implements Horndeski's theory of gravity in the modern Cosmic Linear Anisotropy Solving System (ascl:1106.020). It can be used to compute any cosmological observable at the level of background or linear perturbations, such as cosmological distances, cosmic microwave background, matter power and number count spectra (including relativistic effects). hi_class can be readily interfaced with Monte Python (ascl:1307.002) to test Gravity and Dark Energy models.
py-sdm (Support Distribution Machines) is a Python implementation of nonparametric nearest-neighbor-based estimators for divergences between distributions for machine learning on sets of data rather than individual data points. It treats points of sets of data as samples from some unknown probability distribution and then statistically estimates the distance between those distributions, such as the KL divergence, the closely related Rényi divergence, L2 distance, or other similar distances.
PyMieDAP (Python Mie Doubling Adding Program) makes light scattering computations with Mie scattering and radiative transfer computations with full orders of scattering and taking into account the polarization of the light scattered. Full planet modeling at any phase angle is possible. With the included subpackage exopy, it is also possible to simulate systems with a star, a planet and a possible moon.
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