Results 1-50 of 1905 (1875 ASCL, 30 submitted)
The FITS format (Flexible Image Transport System) is a widely used format to store astronomical data. It is used to store a lot of different types of data such as 1D or 2D spectra, 3D data cubes. It has been developed in the late 1970 to reach its final form almost two decades ago. FITS files are built with two components. The data themselves are stored as tables and contains any types of data. A header is built containing set of keywords-value pairs aiming at describing the data themselves.
Accessing and displaying metadata inside FITS files is important in order to get an overview of their content properties without having to read the data themselves. It is particularly useful when dealing with large amount of files at once. Tools have been already publicly available for years with the dfits and fitsort algorithms (the documentation is available here https://www.eso.org/sci/software/eclipse/eug/eug/node8.html). The main limitation is that they are stand-alone programs useable only in a terminal. They can not be used natively inside another program.
The python module presented here, dfitspy, is a project that migrates the main dfits and fitsort capabilities to python. It is a metadata searcher/displayer for FITS files. As dfits and fitsort, dfitspy is able to display in the terminal the result of a metadata search and is able to grep certain values of keywords inside large samples of files. Therefore it can be used directly with the command line interface. Nevertheless, dfitspy can be, and it is its strength, imported as a python module and the user can use these functionnalities inside another python code or the python interpretor.
We present allesfitter, a tool for flexible and robust inference of stars and exoplanets given photometric and radial velocity (RV) data. allesfitter offers a rich selection of orbital and transit models, accommodating multiple exoplanets, multi-star systems, star spots, stellar flares, and various noise models. It features both parameter estimation and model selection. A graphical user interface allows to specify input parameters, and to easily run a nested sampling or Markov Chain Monte Carlo (MCMC) fit, producing publication-ready tables, LaTex code, and plots. For all this, allesfitter provides an inference framework that unites the versatile packages ellc (light curve and RV models; Maxted 2016), aflare (flare model; Davenport et al. 2014), dynesty (static and dynamic nested sampling; https://github.com/joshspeagle/dynesty), emcee (MCMC sampling; Foreman-Mackey et al. 2013) and celerite (Gaussian Process models; Foreman-Mackey et al. 2017). The code is publicly available at https://github.com/MNGuenther/allesfitter.
PyP-BEAGLE enables one to post-process the BEAGLE outputs to create different type of publication-quality plots, as well as to prepare LaTeX tables that can be included in scientific publications.
brutifus contains a set of Python routines that can aid to post-process datacubes from integral field spectrographs. The spirit of brutifus is to deal with generic tasks with as little user interactions as possible: for example, the registration of a datacube WCS solution with the Gaia catalogue, the correction of Galactic reddening, or the subtraction of the nebular/stellar continuum on a spaxel-per-spaxel basis. brutifus is modular, in that the order in which the post-processing routines are run is entirely customizable.
Specutils provides a basic interface for the loading, manipulation, and common forms of analysis of spectroscopic data. Its generic data containers and accompanying modules can be used to build a particular scientific workflow or higher-level analysis tool. It is an AstroPy (ascl:1304.002) affiliated package, and SpecViz (ascl:1902.011), which is built on top of Specutils, provides a visual, interactive interface to its analysis capabilities.
SpecViz interactively visualizes and analyzes 1D astronomical spectra. It reads data from FITS and ASCII tables and allows spectra to be easily plotted and examined. It supports instrument-specific data quality handling, flexible spectral units conversions, custom plotting attributes, plot annotations, tiled plots, among other features. SpecViz includes a measurement tool for spectral lines for performing and recording measurements and a model fitting capability for creating simple (e.g., single Gaussian) or multi-component models (e.g., multiple Gaussians for emission and absorption lines in addition to regions of flat continua). SpecViz is built on top of the Specutils (ascl:1902.012) Astropy-affiliated python library, providing a visual, interactive interface to the analysis capabilities in that library.
dyPolyChord implements dynamic nested sampling using the efficient PolyChord (ascl:1502.011) sampler to provide state-of-the-art nested sampling performance. Any likelihoods and priors which work with PolyChord can be used (Python, C++ or Fortran), and the output files produced are in the PolyChord format.
ExPRES (Exoplanetary and Planetary Radio Emission Simulator) reproduces the occurrence of CMI-generated radio emissions from planetary magnetospheres, exoplanets or star-planet interacting systems in time-frequency plane, with special attention given to computation of the radio emission beaming at and near its source. Physical information drawn from such radio observations may include the location and dynamics of the radio sources, the type of current system leading to electron acceleration and their energy and, for exoplanetary systems, the magnetic field strength, the orbital period of the emitting body and the rotation period, tilt and offset of the planetary magnetic field. Most of these parameters can be remotely measured only via radio observations. ExPRES code provides the proper framework of analysis and interpretation for past (Cassini, Voyager, Galileo), current (Juno, ground-based radio telescopes) and future (BepiColombo, Juice) observations of planetary radio emissions, as well as for future detection of radio emissions from exoplanetary systems.
Radynversion infers solar atmospheric properties during a solar flare. The code is based on an Invertible Neural Network (INN) that is trained to learn an approximate bijective mapping between the atmospheric properties of electron density, temperature, and bulk velocity (all as a function of altitude), and the observed Hα and Ca II λ8542 line profiles. As information is lost in the forward process of radiation transfer, this information is injected back into the model during the inverse process by means of a latent space; the training allows this latent space to be filled using an n-dimensional unit Gaussian distribution, where n is the dimensionality of the latent space. The code is based on a model trained by simulations made by RADYN, a 1D non-equilibrium radiation hydrodynamic model with good optically thick radiation treatment that does not consider magnetic effects.
BELLAMY is a cross-matching algorithm designed primarily for radio images, that aims to match all sources in the supplied target catalogue to sources in a reference catalogue by calculating the probability of a match. BELLAMY utilises not only the position of a source on the sky, but also the flux data to calculate this probability, determining the most probable match in the reference catalog to the target source. Additionally, BELLAMY attempts to undo any spatial distortion that may be affecting the target catalogue, by creating a model of the offsets of matched sources which is then applied to unmatched sources. This combines to produce an iterative cross-matching algorithm that provides the user with an obvious measure of how confident they should be with the results of a cross-match.
PINT (PINT Is Not Tempo3) analyzes high-precision pulsar timing data, 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 practices (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 such as TEMPO (ascl:1509.002) and Tempo2 (ascl:1210.015) and is a robust tool for cross-checking timing analyses and simulating data.
The RPFITS data file format records synthesis visibility data obtained from the Australia Telescope Compact Array (ATCA) at Narrabri, NSW. It is also used for single-dish spectral line data obtained from Parkes and Mopra, including Parkes multibeam data. RPFITS superficially resembles random group FITS, but differs in important respects, making it incompatible with standard FITS software such as FITSIO (ascl:1010.001) and FTOOLS (ascl:9912.002) and, in particular, it precludes the use of fv (ascl:1205.005). The RPFITS Fortran library contains routines for reading and writing RPFITS files. A header file, RPFITS.h, is provided to facilitate usage by C and C++ applications. Also included is rpfhdr, a utility for viewing RPFITS headers (it also works for standard FITS), and rpfex for extracting selected scans from an RPFITS file.
LiveData is a multibeam single-dish data reduction system for bandpass calibration and gridding. It is used for processing Parkes multibeam and Mopra data.
GraviDy performs N-body 3D visualizations; it is a GPU, direct-summation N-body integrator based on the Hermite scheme and includes relativistic corrections for sources of gravitational radiation. The software is modular, allowing users to readily introduce new physics, and exploits available computational resources. The software can be used in parallel on multiple CPUs and GPUs, with a considerable speed-up benefit. The single-GPU version is between one and two orders of magnitude faster than the single-CPU version.
PyMF performs spatial filtering (matched filter, matched multifilter, constrained matched filter and constrained matched mutifilter) image processing that provides optimal reduction of the contamination introduced by sources that can be approximated by templates. These techniques use the flat-sky approximation.
The Limited Post-Newtonian N-body code (LPNN) simulates post-Newtonian interactions between a massive object and many low-mass objects. The interaction between one massive object and low-mass objects is calculated by post-Newtonian approximation, and the interaction between low-mass objects is calculated by Newtonian gravity. This code is based on the sticky9 code, and can be accelerated with the use of GPU in a CUDA (version 4.2 or earlier) environment.
Supernova Time Delays (SNTD) simulates and measures time delay of multiply-imaged supernovae, and offers an improved characterization of the uncertainty caused by microlensing. Lensing time delays can be determined by fitting the multiple light curves of these objects; measuring these delays provide precise tests of lens models or constraints on the Hubble constant and other cosmological parameters that are independent of the local distance ladder. Fitting the effects of microlensing without an accurate prior often leads to biases in the time delay measurement and over-fitting to the data; this can be mitigated by using a Gaussian Process Regression (GPR) technique to determine the uncertainty due to microlensing. SNTD can produce accurate simulations for wide-field time domain surveys such as LSST and WFIRST.
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.
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