DaCHS, the Data Center Helper Suite, is an integrated package for publishing astronomical data sets to the Virtual Observatory. Network-facing, it speaks the major VO protocols (SCS, SIAP, SSAP, TAP, Datalink, etc). Operator-facing, many input formats, including FITS/WCS, ASCII files, and VOTable, can be processed to publication-ready data. DaCHS puts particular emphasis on integrated metadata handling, which facilitates a tight integration with the VO's Registry
We present AstroCV, a computer vision library for processing and analyzing big astronomical datasets.
The goal of AstroCV is to provide a community repository of high performance Python and C++ algorithms used in the areas of image processing and computer vision.
The current AstroCV library includes methods for the tasks of object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.
The underlying models were trained using convolutional neural networks and deep learning techniques, which provide better results than methods based on manual feature engineering and SVMs.
The detection and classification methods were trained end-to-end using public datasets such as the Sloan Digital Sky Survey (SDSS), the Galaxy Zoo, and private datasets such as the Next Generation Virgo (NGVS) and Fornax (NGFS) surveys.
Training results are strongly bound to the conversion method from raw FITS data for each band into a 3-channel color image. Therefore, we propose data augmentation for the training using 5 conversion methods. This greatly improves the overall galaxy detection and classification for images produced from different instruments, bands and data reduction procedures.
The detection and classification methods were trained using the deep learning framework DARKNET and the real-time object detection system YOLO. These methods are implemented in C and CUDA languages and makes intensive use of graphical processing units (GPU). Using a single high-end Nvidia GPU card, it can process a SDSS image in 50 milliseconds and a DECam image in less than 3 seconds.
We provide the open source code, documentation, pre-trained networks, python tutorials for using the AstroCV library and train your own datasets.
MulensModel calculates light curves of microlensing events. Both single and binary lens events are modeled and various higher-order effects can be included: extended source (with limb-darkening), annual microlensing parallax, and satellite microlensing parallax. The code is object-oriented and written in Python3, and requires AstroPy (ascl:1304.002).
Kadenza enables time-critical data analyses to be carried out using NASA's Kepler Space Telescope. It enables users to convert Kepler's raw data files into user-friendly Target Pixel Files upon downlink from the spacecraft. The primary motivation for this tool is to enable the microlensing, supernova, and exoplanet communities to create quicklook lightcurves for transient events which require rapid follow-up.
nanopipe is a data reduction pipeline for calibration, RFI removal, and pulse time-of-arrival measurement from radio pulsar data. It was developed primarily for use by the NANOGrav project. nanopipe is written in Python, and depends on the PSRCHIVE (ascl:1105.014) library.
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.
CIFOG is a versatile MPI-parallelised semi-numerical tool to perform simulations of the Epoch of Reionization. From a set of evolving cosmological gas density and ionizing emissivity fields, it computes the time and spatially dependent ionization of neutral hydrogen (HI), neutral (HeI) and singly ionized helium (HeII) in the intergalactic medium (IGM). The code accounts for HII, HeII, HeIII recombinations, and provides different descriptions for the photoionization rate that are used to calculate the residual HI fraction in ionized regions. This tool has been designed to be coupled to semi-analytic galaxy formation models or hydrodynamical simulations. The modular fashion of the code allows the user to easily introduce new descriptions for recombinations and the photoionization rate.
PyVO is a package providing access to remote data and services of the Virtual observatory (VO) using Python. It takes advantage VO standards to interface to tens of thousands of catalogs, data archives, information services, and analysis tools. PyVO is built on top of Astopy (and numpy).
The VO protocols support by pyVO include the Table Access Protocol TAP, the Simple Image and Spectra Access Protocols (SIAP, SSAP), Simple Cone Search (SCS), the VO services interface VOSI, and Datalink.
DaMaSCUS-CRUST determines the critical cross-section for strongly interacting DM for various direct detection experiments systematically and precisely using Monte Carlo simulations of DM trajectories inside the Earth's crust, atmosphere, or any kind of shielding. Above a critical dark matter-nucleus scattering cross section, any terrestrial direct detection experiment loses sensitivity to dark matter, since the Earth crust, atmosphere, and potential shielding layers start to block off the dark matter particles. This critical cross section is commonly determined by describing the average energy loss of the dark matter particles analytically. However, this treatment overestimates the stopping power of the Earth crust; therefore, the obtained bounds should be considered as conservative. DaMaSCUS-CRUST is a modified version of DaMaSCUS (ascl:1706.003) that accounts for shielding effects and returns a precise exclusion band.
eqpair computes the electron energy distribution resulting from a balance between heating and direct acceleration of particles, and cooling processes. Electron-positron pair balance, bremstrahlung, and Compton cooling, including external soft photon input, are among the processes considered, and the final electron distribution can be hybrid, thermal, or non-thermal.
mrpy calculates the MRP parameterization of the Halo Mass Function. It calculates basic statistics of the truncated generalized gamma distribution (TGGD) with the TGGD class, including mean, mode, variance, skewness, pdf, and cdf. It generates MRP quantities with the MRP class, such as differential number counts and cumulative number counts, and offers various methods for generating normalizations. It can generate the MRP-based halo mass function as a function of physical parameters via the mrp_b13 function, and fit MRP parameters to data in the form of arbitrary curves and in the form of a sample of variates with the SimFit class. mrpy also calculates analytic hessians and jacobians at any point, and allows the user to alternate parameterizations of the same form via the reparameterize module.
collapse calculates the spherical−collapse for standard cosmological models as well as for dark energy models when the dark energy can be taken to be spatially homogeneous. The calculation is valid on sub−horizon scales and takes a top−hat perturbation to exist in an otherwise featureless cosmos and follows its evolution into the non−linear regime where it reaches a maximum size and then recollapses. collapse provides the user with the linear−collapse threshold (delta_c) and the virial overdensity (Delta_v) for the collapsed halo over a range of cosmic scale factors.
BHMcalc provides renditions of the instantaneous circumbinary habital zone (CHZ) and also calculates BHM properties of the system including those related to the rotational evolution of the stellar components and the combined XUV and SW fluxes as measured at different distances from the binary. Moreover, it provides numerical results that can be further manipulated and used to calculate other properties.
PyOSE is a fully numerical orbital sampling effect (OSE) simulator that can model arbitrary inclinations of the transiting moon orbit. It can be used to search for exomoons in long-term stellar light curves such as those by Kepler and the upcoming PLATO mission.
runDM calculates the running of the couplings of Dark Matter (DM) to the Standard Model (SM) in simplified models with vector mediators. By specifying the mass of the mediator and the couplings of the mediator to SM fields at high energy, the code can calculate the couplings at low energy, taking into account the mixing of all dimension-6 operators. runDM can also extract the operator coefficients relevant for direct detection, namely low energy couplings to up, down and strange quarks and to protons and neutrons.
Glimpse, also known as Glimpse2D, is a weak lensing mass-mapping tool that relies on a robust sparsity-based regularization scheme to recover high resolution convergence from either gravitational shear alone or from a combination of shear and flexion. Including flexion allows the supplementation of the shear on small scales in order to increase the sensitivity to substructures and the overall resolution of the convergence map. To preserve all available small scale information, Glimpse avoids any binning of the irregularly sampled input shear and flexion fields and treats the mass-mapping problem as a general ill-posed inverse problem, regularized using a multi-scale wavelet sparsity prior. The resulting algorithm incorporates redshift, reduced shear, and reduced flexion measurements for individual galaxies and is made highly efficient by the use of fast Fourier estimators.
astroplan is a flexible toolbox for observation planning and scheduling. It is powered by Astropy (ascl:1304.002); it works for Python beginners and new observers, and is powerful enough for observatories preparing nightly and long-term schedules as well. It calculates rise/set/meridian transit times, alt/az positions for targets at observatories anywhere on Earth, and offers built-in plotting convenience functions for standard observation planning plots (airmass, parallactic angle, sky maps). It can also determine the observability of sets of targets given an arbitrary set of constraints (i.e., altitude, airmass, moon separation/illumination, etc.).
AntiparticleDM calculates the prospects of future direct detection experiments to discriminate between Majorana and Dirac Dark Matter (i.e., to determine whether Dark Matter is its own antiparticle). Direct detection event rates and mock data generation are dealt with by a variation of the WIMpy code.
HiGal SED Fitter fits modified blackbody SEDs to Herschel data, specifically targeted at Herschel Hi-Gal data.
muLAn is a Python modeling software developed to analyze and fit light curves of gravitational microlensing events. The software has been designed to be easy to use even for the newcomer in microlensing, thanks to 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. It also comes with many useful routines (export publication-quality figures, data formatting and cleaning) and state-of-the-art statistical tools. The result of the modeling 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 includes all classical microlensing models (single and binary microlenses, ground- and space-based parallax effects, orbital motion, finite-source effects, limb-darkening, etc.) which 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 allows to inspect the light curve before any modeling work. The code is modular: users can add their own model's computation or minimization routines by directly adding their Python files without modifying the main code. This flexibility aims to offer a valuable framework to develop automated open-source microlensing modeling codes.
VISIBLE applies approximated matched filters to interferometric data, allowing line detection directly in visibility space. The filter can be created from a FITS image or RADMC3D output image, and the weak line data can be a CASA MS or uvfits file. The filter response spectrum can be output either to a .npy file or returned back to the user for scripting.
Verne calculates the Earth-stopping effect for super-heavy Dark Matter (DM). The code allows you to calculate the speed distribution (and DM signal rate) at an arbitrary detector location on the Earth. The calculation takes into account the full anisotropic DM velocity distribution and the full velocity dependence of the DM-nucleus cross section. Results can be obtained for any DM mass and cross section, though the results are most reliable for very heavy DM particles.
The Opik method gives the mean probability of collision of a small body with a given planet. It is a statistical value valid for an orbit with given (a,e,i) and undefined argument of perihelion. In some cases, the planet can eject the small body from the solar system; in these cases, the program estimates the mean time for the ejection. The Opik method does not take into account other perturbers than the planet considered, so it only provides an idea of the timescales involved.
The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data. ARTIP starts with raw data, i.e. a measurement set and goes through multiple stages, such as flux calibration, bandpass calibration, phase calibration, and imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs. It is written using standard python libraries and the CASA package. The pipeline can deal with datasets with multiple spectral windows and also multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators.
CMacIonize simulates the self-consistent evolution of HII regions surrounding young O and B stars, or other sources of ionizing radiation. The code combines a Monte Carlo photoionization algorithm that uses a complex mix of hydrogen, helium and several coolants in order to self-consistently solve for the ionization and temperature balance at any given time, with a standard first order hydrodynamics scheme. The code can be run as a post-processing tool to get the line emission from an existing simulation snapshot, but can also be used to run full radiation hydrodynamical simulations. Both the radiation transfer and the hydrodynamics are implemented in a general way that is independent of the grid structure that is used to discretize the system, allowing it to be run both as a standard fixed grid code and also as a moving-mesh code.
venice reads a mask file (DS9 or fits type) and a catalogue of objects (ascii or fits type) to create a pixelized mask, find objects inside/outside a mask, or generate a random catalogue of objects inside/outside a mask. The program reads the mask file and checks if a point, giving its coordinates, is inside or outside the mask, i.e. inside or outside at least one polygon of the mask.
PyCCF emulates a Fortran program written by B. Peterson for use with reverberation mapping. The code cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. In addition, it is possible to run Monto Carlo iterations using flux randomization and random subset selection (RSS) to produce cross-correlation centroid distributions to estimate the uncertainties in the cross correlation results.
FAC calculates various atomic radiative and collisional processes, including radiative transition rates, collisional excitation and ionization by electron impact, energy levels, photoionization, and autoionization, and their inverse processes radiative recombination and dielectronic capture. The package also includes a collisional radiative model to construct synthetic spectra for plasmas under different physical conditions.
RadVel models Keplerian orbits in radial velocity (RV) time series. The code is written in Python with a fast Kepler's equation solver written in C. It provides a framework for fitting RVs using maximum a posteriori optimization and computing robust confidence intervals by sampling the posterior probability density via Markov Chain Monte Carlo (MCMC). RadVel can perform Bayesian model comparison and produces publication quality plots and LaTeX tables.
GABE (Grid And Bubble Evolver) evolves scalar fields (as well as other purposes) on an expanding background for non-canonical and non-linear classical field theory. GABE is based on the Runge-Kutta method.
DICE is a C++ template library designed to solve collisionless fluid dynamics in 6D phase space using massively parallel supercomputers via an hybrid OpenMP/MPI parallelization. ColDICE, based on DICE, implements a cosmological and physical VLASOV-POISSON solver for cold systems such as dark matter (CDM) dynamics.
Gnuastro (GNU Astronomy Utilities) manipulates and analyzes astronomical data. It is an official GNU package of a large collection of programs and C/C++ library functions. Command-line programs perform arithmetic operations on images, convert FITS images to common types like JPG or PDF, convolve an image with a given kernel or matching of kernels, perform cosmological calculations, crop parts of large images (possibly in multiple files), manipulate FITS extensions and keywords, and perform statistical operations. In addition, it contains programs to make catalogs from detection maps, add noise, make mock profiles with a variety of radial functions using monte-carlo integration for their centers, match catalogs, and detect objects in an image among many other operations. The command-line programs share the same basic command-line user interface for the comfort of both the users and developers. Gnuastro is written to comply fully with the GNU coding standards and integrates well with all Unix-like operating systems. This enables astronomers to expect a fully familiar experience in the source code, building, installing and command-line user interaction that they have seen in all the other GNU software that they use. Gnuastro's extensive library is included for users who want to build their own unique programs.
BOND determines oxygen and nitrogen abundances in giant H II regions by comparison with a large grid of photoionization models. The grid spans a wide range in O/H, N/O and ionization parameter U, and covers different starburst ages and nebular geometries. Unlike other statistical methods, BOND relies on the [Ar III]/[Ne III] emission line ratio to break the oxygen abundance bimodality. By doing so, it can measure oxygen and nitrogen abundances without assuming any a priori relation between N/O and O/H. BOND takes into account changes in the hardness of the ionizing radiation field, which can come about due to the ageing of H II regions or the stochastically sampling of the IMF. The emission line ratio He I/Hβ, in addition to commonly used strong lines, constrains the hardness of the ionizing radiation field. BOND relies on the emission line ratios [O III]/Hβ, [O II]/Hβ and [N II]/Hβ, [Ar III]/Hβ, [Ne III]/Hβ, He I/Hβ as its input parameters, while its output values are the measurements and uncertainties for O/H and N/O.
cambmag is a modification to CAMB (ascl:1102.026) that calculates the compensated magnetic mode in the scalar, vector and tensor case. Previously CAMB included code only for the vectors. It also corrects for tight-coupling issues and adds in the ability to include massive neutrinos when calculating vector modes.
DecouplingModes calculates the amplitude of the passive modes, which requires solving the Einstein equations on superhorizon scales sourced by the anisotropic stress from the magnetic fields (prior to neutrino decoupling), and the magnetic and neutrino stress (after decoupling). The code is available as a Mathematica notebook.
InitialConditions finds the initial series solutions for perturbations in our Universe. This includes all scalar (1 adiabatic, 4 isocurvature and 2 magnetic modes), vector (1 vorticity mode, 1 magnetic mode), and tensor (1 gravitational wave mode and 1 magnetic mode) perturbations including terms up to second order in the neutrino mass. It can handle the standard species (cdm, baryons, photons), and two neutrino mass eigenstates (1 light, 1 heavy).
hh0 is a Bayesian hierarchical model (BHM) that describes the full distance ladder, from nearby geometric-distance anchors through Cepheids to SNe in the Hubble flow. It does not rely on any of the underlying distributions being Gaussian, allowing outliers to be modeled and obviating the need for any arbitrary data cuts.
Stan facilitates statistical inference at the frontiers of applied statistics and provides both a modeling language for specifying complex statistical models and a library of statistical algorithms for computing inferences with those models. These components are exposed through interfaces in environments such as R, Python, and the command line.
iWander assesses the origin of interstellar small bodies such as asteroids and comets. It includes a series of databases and tools that can be used in general for studying the dynamics of an interstellar vagabond object (small−body, interstellar spaceship and even stars).
BANYAN_Sigma calculates the membership probability that a given astrophysical object belongs to one of the currently known 27 young associations within 150 pc of the Sun, using Bayesian inference. This tool uses the sky position and proper motion measurements of an object, with optional radial velocity (RV) and distance (D) measurements, to derive a Bayesian membership probability. By default, the priors are adjusted such that a probability threshold of 90% will recover 50%, 68%, 82% or 90% of true association members depending on what observables are input (only sky position and proper motion, with RV, with D, with both RV and D, respectively). The algorithm is implemented in a Python package, in IDL, and is also implemented as an interactive web page.
Two neural networks were designed to identify hazardous planetesimals that were trained on object trajectories calculated in a cloud computing environment. The first neural network was fully-connected and was trained on the orbital elements (OEs) of real/simulated planetesimals, while the second was a 1-dimensional convolutional neural network that was trained on the position Cartesian coordinates of real/simulated planetesimals. Ultimately, the network trained on OEs had a better performance by identifying one-third of known potentially hazardous objects including the 3 asteroids with the highest chance of impact with Earth (2009 FD, 1999 RQ36, 1950 DA) as established by NASA's Monte Carlo based Sentry system.
loci is a shared library for interpolations in up to 4 dimensions. It is written in C and can be used with C/C++, Python and others. In order to calculate the coefficients of the cubic polynom, only local values are used: The data itself and all combinations of first-order derivatives, i.e. in 2D f_x, f_y and f_xy. This is in contrast to splines, where the coefficients are not calculated using derivatives, but non-local data, which can lead to over-smoothing the result.
LgrbWorldModel is written in Fortran 90 and attempts to model the population distribution of the Long-duration class of Gamma-Ray Bursts (LGRBs) as detected by the NASA's now-defunct Burst And Transient Source Experiment (BATSE) onboard the Compton Gamma Ray Observatory (CGRO). It is assumed that the population distribution of LGRBs is well fit by a multivariate log-normal distribution. The best-fit parameters of the distribution are then found by maximizing the likelihood of the observed data by BATSE detectors via a native built-in Adaptive Metropolis-Hastings Markov-Chain Monte Carlo (AMH-MCMC) Sampler.
SgrbWorldModel, written in Fortran 90, presents an attempt at modeling the population distribution of the Short-duration class of Gamma-Ray Bursts (SGRBs) as detected by the NASA's now-defunct Burst And Transient Source Experiment (BATSE) onboard the Compton Gamma Ray Observatory (CGRO). It is assumed that the population distribution of SGRBs is well fit by a multivariate log-normal distribution, whose differential cosmological rate of occurrence follows the Star-Formation-Rate (SFR) convolved with a log-normal binary-merger delay-time distribution. The best-fit parameters of the model are then found by maximizing the likelihood of the observed data by the BATSE detectors via a native built-in Adaptive Metropolis-Hastings Markov-Chain Monte Carlo (AMH-MCMC)Sampler that is part of the code. A model for the detection algorithm of the BATSE detectors is also provided.
QATS detects transiting extrasolar planets in time-series photometry. It relaxes the usual assumption of strictly periodic transits by permitting a variable, but bounded, interval between successive transits.
Photodynam facilitates so-called "photometric-dynamical" modeling. This model is quite simple and this is reflected in the code base. A N-body code provides coordinates and the photometric code produces light curves based on coordinates.
MadDM computes dark matter relic abundance and dark matter nucleus scattering rates in a generic model. The code is based on the existing MadGraph 5 architecture and as such is easily integrable into any MadGraph collider study. A simple Python interface offers a level of user-friendliness characteristic of MadGraph 5 without sacrificing functionality. MadDM is able to calculate the dark matter relic abundance in models which include a multi-component dark sector, resonance annihilation channels and co-annihilations. The direct detection module of MadDM calculates spin independent / spin dependent dark matter-nucleon cross sections and differential recoil rates as a function of recoil energy, angle and time. The code provides a simplified simulation of detector effects for a wide range of target materials and volumes.
FBEYE, the "Flares By-Eye" detection suite, is written in IDL and analyzes Kepler light curves and validates flares. It works on any 3-column light curve that contains time, flux, and error. The success of flare identification is highly dependent on the smoothing routine, which may not be suitable for all sources.
Flux Tube is a nonlinear, two-dimensional, numerical simulation of magneto-acoustic wave propagation in the photosphere and chromosphere of small-scale flux tubes with internal structure. Waves with realistic periods of three to five minutes are studied, after horizontal and vertical oscillatory perturbations are applied to the equilibrium model. Spurious reflections of shock waves from the upper boundary are minimized by a special boundary condition.
RODRIGUES (RATT Online Deconvolved Radio Image Generation Using Esoteric Software) is a web-based radio telescope simulation and reduction tool. From a technical perspective it is a web based parameterized docker container scheduler with a result set viewer.
Cosmology Applications (CosApps) provides tools to simulate gravitational lensing using two different techniques, ray tracing and shear calculation. The tool ray_trace_ellipse calculates deflection angles on a grid for light passing a deflecting mass distribution. Using MPI, ray_trace_ellipse may calculate deflection in parallel across network connected computers, such as cluster. The program physcalc calculates the gravitational lensing shear using the relationship of convergence and shear, described by a set of coupled partial differential equations.
SFoF is a friends-of-friends galaxy cluster detection algorithm that operates in either spectroscopic or photometric redshift space. The linking parameters, both transverse and along the line-of-sight, change as a function of redshift to account for selection effects.
Nyx code solves equations of compressible hydrodynamics on an adaptive grid hierarchy coupled with an N-body treatment of dark matter. The gas dynamics in Nyx use a finite volume methodology on an adaptive set of 3-D Eulerian grids; dark matter is represented as discrete particles moving under the influence of gravity. Particles are evolved via a particle-mesh method, using Cloud-in-Cell deposition/interpolation scheme. Both baryonic and dark matter contribute to the gravitational field. In addition, Nyx includes physics for accurately modeling the intergalactic medium; in optically thin limits and assuming ionization equilibrium, the code calculates heating and cooling processes of the primordial-composition gas in an ionizing ultraviolet background radiation field.
draco analyzes transit radio data with the m-mode formalism. It is telescope agnostic, and is used as part of the analysis and simulation pipeline for the CHIME (Canadian Hydrogen Intensity Mapping Experiment) telescope. It can simulate time stream data from maps of the sky (using the m-mode formalism) and add gain fluctuations and correctly correlated instrumental noise (i.e. Wishart distributed). Further, it can perform various cuts on the data and make maps of the sky from data using the m-mode formalism.
Bitshuffle rearranges typed, binary data for improving compression; the algorithm is implemented in a python/C package within the Numpy framework. The library can be used alongside HDF5 to compress and decompress datasets and is integrated through the dynamically loaded filters framework. Algorithmically, Bitshuffle is closely related to HDF5's Shuffle filter except it operates at the bit level instead of the byte level. Arranging a typed data array in to a matrix with the elements as the rows and the bits within the elements as the columns, Bitshuffle "transposes" the matrix, such that all the least-significant-bits are in a row, etc. This transposition is performed within blocks of data roughly 8kB long; this does not in itself compress data, but rearranges it for more efficient compression. A compression library is necessary to perform the actual compression. This scheme has been used for compression of radio data in high performance computing.
Py-SPHViewer visualizes and explores N-body + Hydrodynamics simulations. The code interpolates the underlying density field (or any other property) traced by a set of particles, using the Smoothed Particle Hydrodynamics (SPH) interpolation scheme, thus producing not only beautiful but also useful scientific images. Py-SPHViewer enables the user to explore simulated volumes using different projections. Py-SPHViewer also provides a natural way to visualize (in a self-consistent fashion) gas dynamical simulations, which use the same technique to compute the interactions between particles.
MPI_XSTAR parallelizes execution of multiple XSTAR runs using Message Passing Interface (MPI). XSTAR (ascl:9910.008), part of the HEASARC's HEAsoft (ascl:1408.004) package, calculates the physical conditions and emission spectra of ionized gases. MPI_XSTAR invokes XSTINITABLE from HEASoft to generate a job list of XSTAR commands for given physical parameters. The job list is used to make directories in ascending order, where each individual XSTAR is spawned on each processor and outputs are saved. HEASoft's XSTAR2TABLE program is invoked upon the contents of each directory in order to produce table model FITS files for spectroscopy analysis tools.
WiseView is a browser-based tool for rendering image blinks of Wide-field Infrared Survey Explorer (WISE) coadds spanning a multi-year time baseline. WiseView allows for easy visual identification of motion and variability for sources far beyond the single-frame detection limit, a key threshold not surmounted by many studies. WiseView transparently gathers small image cutouts drawn from many terabytes of unWISE coadds, facilitating access to this large and unique dataset. Users need only input the coordinates of interest, and can interactively tune parameters including the image stretch, colormap and blink rate. WiseView was developed in the context of the Backyard Worlds: Planet 9 citizen science project, and has already enabled hundreds of brown dwarf candidate discoveries made by citizen scientists and professional astronomers alike.
The Kinematic Distance utilities (KDUtils) calculate kinematic distances and kinematic distance uncertainties. The package includes methods to calculate "traditional" kinematic distances as well as a Monte Carlo method to calculate kinematic distances and uncertainties.
NOD3 processes and analyzes maps from single-dish observations affected by scanning effects from clouds, receiver instabilities, or radio-frequency interference. Its “basket-weaving” tool combines orthogonally scanned maps into a final map that is almost free of scanning effects. A restoration tool for dual-beam observations reduces the noise by a factor of about two compared to the NOD2 version. Combining single-dish with interferometer data in the map plane ensures the full recovery of the total flux density.
HBT+ is a hybrid subhalo finder and merger tree builder for cosmological simulations. It comes as an MPI edition that can be run on distributed clusters or shared memory machines and is MPI/OpenMP parallelized, and also as an OpenMP edition that can be run on shared memory machines and is only OpenMP parallelized. This version is more memory efficient than the MPI branch on shared memory machines, and is more suitable for analyzing zoomed-in simulations that are difficult to balance on distributed clusters. Both editions support hydro simulations with gas/stars.
HBT is a Hierarchical Bound-Tracing subhalo finder and merger tree builder, for numerical simulations in cosmology. It tracks haloes from birth and continues to track them after mergers, finding self-bound structures as subhaloes and recording their merger histories as merger trees.
Bifrost is a stream processing framework that eases the development of high-throughput processing CPU/GPU pipelines. It is designed for digital signal processing (DSP) applications within radio astronomy. Bifrost uses a flexible ring buffer implementation that allows different signal processing blocks to be connected to form a pipeline. Each block may be assigned to a CPU core, and the ring buffers are used to transport data to and from blocks. Processing blocks may be run on either the CPU or GPU, and the ring buffer will take care of memory copies between the CPU and GPU spaces.
MARXS (Multi-Architecture-Raytrace-Xraymission-Simulator) simulates X-ray observatories. Primarily designed to simulate X-ray instruments on astronomical X-ray satellites and sounding rocket payloads, it can also be used to ray-trace experiments in the laboratory. MARXS performs polarization Monte-Carlo ray-trace simulations from a source (astronomical or lab) through a collection of optical elements such as mirrors, baffles, and gratings to a detector.
SPIDERMAN calculates exoplanet phase curves and secondary eclipses with arbitrary surface brightness distributions in two dimensions. The code uses a geometrical algorithm to solve exactly the area of sections of the disc of the planet that are occulted by the star. Approximately 1000 models can be generated per second in typical use, which makes making Markov Chain Monte Carlo analyses practicable. The code is modular and allows comparison of the effect of multiple different brightness distributions for a dataset.
LExTeS (Link Extraction and Testing Suite) extracts hyperlinks from PDF documents, tests the extracted links to see which are broken, and tabulates the results. Though written to support a particular set of PDF documents, the dataset and scripts can be edited for use on other documents.
FATS facilitates and standardizes feature extraction for time series data; it quickly and efficiently calculates a compilation of many existing light curve features. Users can characterize or analyze an astronomical photometric database, though this library is not necessarily restricted to the astronomical domain and can also be applied to any kind of time series data.
Thindisk computes the line emission from a geometrically thin protoplanetary disk. It creates a datacube in FITS format that can be processed with a data reduction package (such as GILDAS, ascl:1305.010) to produce synthetic images and visibilities. These synthetic data can be compared with observations to determine the properties (e.g. central mass or inclination) of an observed disk. The disk is assumed to be in Keplerian rotation at a radius lower than the centrifugal radius (which can be set to a large value, for a purely Keplerian disk), and in infall with rotation beyond the centrifugal radius.
rec-2d models the distribution of water vapor in protoplanetary disks. Given a distribution of gas and dust, rac-2d first solves the dust temperature distribution with a Monte Carlo method and then solves the gas temperature distribution and chemical composition. Although the geometry is symmetric with respect to rotation around the central axis and reflection about the midplane, the photon propagation is done in full three dimensions. After establishing the dust temperature distribution, the disk chemistry is evolved for 1 Myr; the heating and cooling processes are coupled with chemistry, allowing the gas temperature to be evolved in tandem with chemistry based on the heating and cooling rates.
Gammapy analyzes gamma-ray data and creates sky images, spectra and lightcurves, from event lists and instrument response information; it can also determine the position, morphology and spectra of gamma-ray sources. It is used to analyze data from H.E.S.S., Fermi-LAT, and the Cherenkov Telescope Array (CTA).
HO-CHUNK calculates radiative equilibrium temperature solution, thermal and PAH/vsg emission, scattering and polarization in protostellar geometries. It is useful for computing spectral energy distributions (SEDs), polarization spectra, and images.
megaman is a scalable manifold learning package implemented in python. It has a front-end API designed to be familiar to scikit-learn but harnesses the C++ Fast Library for Approximate Nearest Neighbors (FLANN) and the Sparse Symmetric Positive Definite (SSPD) solver Locally Optimal Block Precodition Gradient (LOBPCG) method to scale manifold learning algorithms to large data sets. It is designed for researchers and as such caches intermediary steps and indices to allow for fast re-computation with new parameters.
galkin is a compilation of kinematic measurements tracing the rotation curve of our Galaxy, together with a tool to treat the data. The compilation is optimized to Galactocentric radii between 3 and 20 kpc and includes the kinematics of gas, stars and masers in a total of 2780 measurements collected from almost four decades of literature. The user-friendly software provided selects, treats and retrieves the data of all source references considered. This tool is especially designed to facilitate the use of kinematic data in dynamical studies of the Milky Way with various applications ranging from dark matter constraints to tests of modified gravity.
galstreams provides a compilation of spatial information for known stellar streams and overdensities in the Milky Way and includes Python tools for visualizing them. ASCII tables are also provided for quick viewing of the stream's footprints using TOPCAT (ascl:1101.010).
Lightning is a spectral energy distribution (SED) fitting procedure that quickly and reliably recovers star formation history (SFH) and extinction parameters. The SFH is modeled as discrete steps in time. The code consists of a fully vectorized inversion algorithm to determine SFH step intensities and combines this with a grid-based approach to determine three extinction parameters.
clustep generates a snapshot in GADGET-2 (ascl:0003.001) format containing a galaxy cluster halo in equilibrium; this snapshot can also be read in RAMSES (ascl:1011.007) using the DICE patch. The halo is made of a dark matter component and a gas component, with the latter representing the ICM. Each of these components follows a Dehnen density profile, with gamma=0 or gamma=1. If gamma=1, then the profile corresponds to a Hernquist profile.
galstep generates initial conditions for disk galaxy simulations with GADGET-2 (ascl:0003.001), RAMSES (ascl:1011.007) and GIZMO (ascl:1410.003), including a stellar disk, a gaseous disk, a dark matter halo and a stellar bulge. The first two components follow an exponential density profile, and the last two a Dehnen density profile with gamma=1 by default, corresponding to a Hernquist profile.
RGW is a lightweight R-language implementation of the affine-invariant Markov Chain Monte Carlo sampling method of Goodman & Weare (2010). The implementation is based on the description of the python package emcee (ascl:1303.002).
correlcalc calculates two-point correlation function (2pCF) of galaxies/quasars using redshift surveys. It can be used for any assumed geometry or Cosmology model. Using BallTree algorithms to reduce the computational effort for large datasets, it is a parallelised code suitable for running on clusters as well as personal computers. It takes redshift (z), Right Ascension (RA) and Declination (DEC) data of galaxies and random catalogs as inputs in form of ascii or fits files. If random catalog is not provided, it generates one of desired size based on the input redshift distribution and mangle polygon file (in .ply format) describing the survey geometry. It also calculates different realisations of (3D) anisotropic 2pCF. Optionally it makes healpix maps of the survey providing visualization.
BayesVP offers a Bayesian approach for modeling Voigt profiles in absorption spectroscopy. The code fits the absorption line profiles within specified wavelength ranges and generates posterior distributions for the column density, Doppler parameter, and redshifts of the corresponding absorbers. The code uses publicly available efficient parallel sampling packages to sample posterior and thus can be run on parallel platforms. BayesVP supports simultaneous fitting for multiple absorption components in high-dimensional parameter space. The package includes additional utilities such as explicit specification of priors of model parameters, continuum model, Bayesian model comparison criteria, and posterior sampling convergence check.
FTbg performs Fourier transforms on FITS images and separates low- and high-spatial frequency components by a user-specified cut. Both components are then inverse Fourier transformed back to image domain. FTbg can remove large-scale background/foreground emission in many astrophysical applications. FTbg has been designed to identify and remove Galactic background emission in Herschel/Hi-GAL continuum images, but it is applicable to any other (e.g., Planck) images when background/foreground emission is a concern.
The inhomog library provides Raychaudhuri integration of cosmological domain-wise average scale factor evolution using an analytical formula for kinematical backreaction Q_D evolution. The inhomog main program illustrates biscale examples. The library routine lib/Omega_D_precalc.c is callable by RAMSES (ascl:1011.007) using the RAMSES extension ramses-scalav.
SpcAudace processes long slit spectra with automated pipelines and performs astrophysical analysis of the latter data. These powerful pipelines do all the required steps in one pass: standard preprocessing, masking of bad pixels, geometric corrections, registration, optimized spectrum extraction, wavelength calibration and instrumental response computation and correction. Both high and low resolution long slit spectra are managed for stellar and non-stellar targets. Many types of publication-quality figures can be easily produced: pdf and png plots or annotated time series plots. Astrophysical quantities can be derived from individual or large amount of spectra with advanced functions: from line profile characteristics to equivalent width and periodogram. More than 300 documented functions are available and can be used into TCL scripts for automation. SpcAudace is based on Audela open source software.
pred_loggs models the entire PGF probability density field, enabling iterative statistical modeling of upper limits and prediction of full G/S probability distributions for individual galaxies.
LIMEPY solves distribution function (DF) based lowered isothermal models. It solves Poisson's equation used on input parameters and offers fast solutions for isotropic/anisotropic, single/multi-mass models, normalized DF values, density and velocity moments, projected properties, and generates discrete samples.
The galario library exploits the computing power of modern graphic cards (GPUs) to accelerate the comparison of model predictions to radio interferometer observations. It speeds up the computation of the synthetic visibilities given a model image (or an axisymmetric brightness profile) and their comparison to the observations.
OSIRIS Toolbox reduces data for the Keck OSIRIS instrument, an integral field spectrograph that works with the Keck Adaptive Optics System. It offers real-time reduction of raw frames into cubes for display and basic analysis. In this real-time mode, it takes about one minute for a preliminary data cube to appear in the “quicklook” display package. The reduction system also includes a growing set of final reduction steps including correction of telluric absorption and mosaicing of multiple cubes.
PSPLINE is a collection of Spline and Hermite interpolation tools for 1D, 2D, and 3D datasets on rectilinear grids. Spline routines give full control over boundary conditions, including periodic, 1st or 2nd derivative match, or divided difference-based boundary conditions on either end of each grid dimension. Hermite routines take the function value and derivatives at each grid point as input, giving back a representation of the function between grid points. Routines are provided for creating Hermite datasets, with appropriate boundary conditions applied. The 1D spline and Hermite routines are based on standard methods; the 2D and 3D spline or Hermite interpolation functions are constructed from 1D spline or Hermite interpolation functions in a straightforward manner. Spline and Hermite interpolation functions are often much faster to evaluate than other representations using e.g. Fourier series or otherwise involving transcendental functions.
Gasoline solves the equations of gravity and hydrodynamics in astrophysical problems, including simulations of planets, stars, and galaxies. It uses an SPH method that features correct mixing behavior in multiphase fluids and minimal artificial viscosity. This method is identical to the SPH method used in the ChaNGa code (ascl:1105.005), allowing users to extend results to problems requiring >100,000 cores. Gasoline uses a fast, memory-efficient O(N log N) KD-Tree to solve Poisson's Equation for gravity and avoids artificial viscosity in non-shocking compressive flows.
FITSFH derives star formation histories from photometry of resolved stellar populations by populating theoretical isochrones according to a chosen stellar initial mass function (IMF) and searching for the linear combination of isochrones with different ages and metallicities that best matches the data. In comparing the synthetic and real data, observational errors and incompleteness are taken into account, and a rudimentary treatment of the effect of unresolved binaries is also implemented. The code also allows for an age-dependent range of extinction values to be included in the modelling.
ATLAS9 computes model atmospheres using a fixed set of pretabulated opacities, allowing one to work on huge numbers of stars and interpolate in large grids of models to determine parameters quickly. The code works with two different sets of opacity distribution functions (ODFs), one with “big” wavelength intervals covering the whole spectrum and the other with 1221 “little” wavelength intervals covering the whole spectrum. The ODFs use a 12-step representation; the radiation field is computed starting with the highest step and working down. If a lower step does not matter because the line opacity is small relative to the continuum at all depths, all the lower steps are lumped together and not computed to save time.
LGMCA (Local-Generalized Morphological Component Analysis) is an extension to GMCA (ascl:1710.015). Similarly to GMCA, it is a Blind Source Separation method which enforces sparsity. The novel aspect of LGMCA, however, is that the mixing matrix changes across pixels allowing LGMCA to deal with emissions sources which vary spatially. These IDL scripts compute the CMB map from WMAP and Planck data; running LGMCA on the WMAP9 temperature products requires the main script and a selection of mandatory files, algorithm parameters and map parameters.
GMCALab solves Blind Source Separation (BSS) problems from multichannel/multispectral/hyperspectral data. In essence, multichannel data provide different observations of the same physical phenomena (e.g. multiple wavelengths), which are modeled as a linear combination of unknown elementary components or sources. Written as a set of Matlab toolboxes, it provides a generic framework that can be extended to tackle different matrix factorization problems.
GBART is an improved version of the code for determining the orbital elements for spectroscopic binaries originally written by Bertiau & Grobben (1968).
RamsesGPU is a reimplementation of RAMSES (ascl:1011.007) which drops the adaptive mesh refinement (AMR) features to optimize 3D uniform grid algorithms for modern graphics processor units (GPU) to provide an efficient software package for astrophysics applications that do not need AMR features but do require a very large number of integration time steps. RamsesGPU provides an very efficient C++/CUDA/MPI software implementation of a second order MUSCL-Handcock finite volume fluid solver for compressible hydrodynamics as a magnetohydrodynamics solver based on the constraint transport technique. Other useful modules includes static gravity, dissipative terms (viscosity, resistivity), and forcing source term for turbulence studies, and special care was taken to enhance parallel input/output performance by using state-of-the-art libraries such as HDF5 and parallel-netcdf.
The fake spectra flux extractor generates simulated quasar absorption spectra from a particle or adaptive mesh-based hydrodynamic simulation. It is implemented as a python module. It can produce both hydrogen and metal line spectra, if the simulation includes metals. The cloudy table for metal ionization fractions is included. Unlike earlier spectral generation codes, it produces absorption from each particle close to the sight-line individually, rather than first producing an average density in each spectral pixel, thus substantially preserving more of the small-scale velocity structure of the gas. The code supports both Gadget (ascl:0003.001) and AREPO.
mTransport computes the 2-point-correlation function of the curvature and tensor perturbations in multifield models of inflation in the presence of a curved field space. It is a Mathematica implementation of the transport method which encompasses scenarios with violations of slow-roll conditions and turns of the trajectory in field space. It can be used for an arbitrary mass spectrum, including massive modes, particle production and models with quasi-single-field dynamics.
PyTransport calculates the 2-point and 3-point function of inflationary perturbations produced during multi-field inflation. The core of PyTransport is C++ code which is automatically edited and compiled into a Python module once an inflationary potential is specified. This module can then be called to solve the background inflationary cosmology as well as the evolution of correlations of inflationary perturbations. PyTransport includes two additional modules written in Python, one to perform the editing and compilation, and one containing a suite of functions for common tasks such as looping over the core module to construct spectra and bispectra.
CppTransport solves the 2- and 3-point functions of the perturbations produced during an inflationary epoch in the very early universe. It is implemented for models with canonical kinetic terms, although the underlying method is quite general and could be scaled to handle models with a non-trivial field-space metric or an even more general non-canonical Lagrangian.
Binary computes the evolution of an accretion disc interacting with a binary system. It has been developed and used to study the coupled evolution of supermassive BH binaries and gaseous accretion discs.
FLAG is a fast implementation of the Fourier-Laguerre Transform, a novel 3D transform exploiting an exact quadrature rule of the ball to construct an exact harmonic transform in 3D spherical coordinates. The angular part of the Fourier-Laguerre transform uses the MW sampling theorem and the exact spherical harmonic transform implemented in the SSHT code. The radial sampling scheme arises from an exact quadrature of the radial half-line using damped Laguerre polynomials. The radial transform can in fact be used to compute the spherical Bessel transform exactly, and the Fourier-Laguerre transform is thus closely related to the Fourier-Bessel transform.
MOSFiT (Modular Open-Source Fitter for Transients) downloads transient datasets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light curve fits to those datasets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT helps bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result.
The general-purpose nuclear reaction network SkyNet evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear reactions. SkyNet is modular, permitting new or existing physics, such as nuclear reactions or equations of state, to be easily added or modified.
SPIPS (Spectro-Photo-Interferometry of Pulsating Stars) combines radial velocimetry, interferometry, and photometry to estimate physical parameters of pulsating stars, including presence of infrared excess, color excess, Teff, and ratio distance/p-factor. The global model-based parallax-of-pulsation method is implemented in Python. Derived parameters have a high level of confidence; statistical precision is improved (compared to other methods) due to the large number of data taken into account, accuracy is improved by using consistent physical modeling and reliability of the derived parameters is strengthened by redundancy in the data.
EXOFASTv2 improves upon EXOFAST (ascl:1207.001) for exoplanet modeling. It uses a differential evolution Markov Chain Monte Carlo code to fit an arbitrary number of transits (each with their own error scaling, normalization, TTV, and/or detrending parameters), an arbitrary number of RV sources (each with their own zero point and jitter), and an arbitrary number of planets, changing nothing but command line arguments and configuration files. The global model includes integrated isochrone and SED models to constrain the stellar properties and can accept priors on any fitted or derived quantities (e.g., parallax from Gaia). It is easily extensible to add additional effects or parameters.
rfpipe supports Python-based analysis of radio interferometric data (especially from the Very Large Array) and searches for fast radio transients. This extends on the rtpipe library (ascl:1706.002) with new approaches to parallelization, acceleration, and more portable data products. rfpipe can run in standalone mode or be in a cluster environment.
The vysmaw client library facilitates the development of code for processes to tap into the fast visibility stream on the National Radio Astronomy Observatory's Very Large Array correlator back-end InfiniBand network. This uses the vys protocol to allows loose coupling to clients that need to remotely access memory over an Infiniband network.
Most high energy sources detected with Fermi-LAT are blazars, which are highly variable sources. High cadence long-term monitoring simultaneously at different wavelengths being prohibitive, the study of their transient activities can help shed light on our understanding of these objects. The early detection of such potentially fast transient events is the key for triggering follow-up observations at other wavelengths. FLaapLUC (Fermi-LAT automatic aperture photometry Light C↔Urve) uses the simple aperture photometry approach to effectively detect relative flux variations in a set of predefined sources and alert potential users. Such alerts can then be used to trigger observations of these sources with other facilities. The FLaapLUC pipeline is built on top of the Science Tools provided by the Fermi-LAT collaboration and quickly generates short- or long-term Fermi-LAT light curves.
MagIC simulates fluid dynamics in a spherical shell. It solves for the Navier-Stokes equation including Coriolis force, optionally coupled with an induction equation for Magneto-Hydro Dynamics (MHD), a temperature (or entropy) equation and an equation for chemical composition under both the anelastic and the Boussinesq approximations. MagIC uses either Chebyshev polynomials or finite differences in the radial direction and spherical harmonic decomposition in the azimuthal and latitudinal directions. The time-stepping scheme relies on a semi-implicit Crank-Nicolson for the linear terms of the MHD equations and a Adams-Bashforth scheme for the non-linear terms and the Coriolis force.
bmcmc is a general purpose Markov Chain Monte Carlo package for Bayesian data analysis. It uses an adaptive scheme for automatic tuning of proposal distributions. It can also handle Bayesian hierarchical models by making use of the Metropolis-Within-Gibbs scheme.
celerite provides fast and scalable Gaussian Process (GP) Regression in one dimension and is implemented in C++, Python, and Julia. The celerite API is designed to be familiar to users of george and, like george, celerite is designed to efficiently evaluate the marginalized likelihood of a dataset under a GP model. This is then be used alongside a non-linear optimization or posterior inference library for the best results.
Multi-Source Self-Calibration (MSSC) provides direction-dependent calibration to standard phase referencing. The code combines multiple faint sources detected within the primary beam to derive phase corrections. Each source has its CLEAN model divided into the visibilities which results in multiple point sources that are stacked in the uv plane to increase the S/N, thus permitting self-calibration. This process applies only to wide-field VLBI data sets that detect and image multiple sources within one epoch.
Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
DanIDL provides IDL functions and routines for many standard astronomy needs, such as searching for matching points between two coordinate lists of two-dimensional points where each list corresponds to a different coordinate space, estimating the full-width half-maximum (FWHM) and ellipticity of the PSF of an image, calculating pixel variances for a set of calibrated image data, and fitting a 3-parameter plane model to image data. The library also supplies astrometry, general image processing, and general scientific applications.
The DAOSPEC Output Optimizer pipeline (DOOp) runs efficient and convenient equivalent widths measurements in batches of hundreds of spectra. It uses a series of BASH scripts to work as a wrapper for the FORTRAN code DAOSPEC (ascl:1011.002) and uses IRAF (ascl:9911.002) to automatically fix some of the parameters that are usually set by hand when using DAOSPEC. This allows batch-processing of quantities of spectra that would be impossible to deal with by hand. DOOp was originally built for the large quantity of UVES and GIRAFFE spectra produced by the Gaia-ESO Survey, but just like DAOSPEC, it can be used on any high resolution and high signal-to-noise ratio spectrum binned on a linear wavelength scale.
MeshLab processes and edits 3D triangular meshes. It includes tools for editing, cleaning, healing, inspecting, rendering, texturing and converting meshes, and offers features for processing raw data produced by 3D digitization tools and devices and for preparing models for 3D printing.
Phantom is a smoothed particle hydrodynamics and magnetohydrodynamics code focused on stellar, galactic, planetary, and high energy astrophysics. It is modular, and handles sink particles, self-gravity, two fluid and one fluid dust, ISM chemistry and cooling, physical viscosity, non-ideal MHD, and more. Its modular structure makes it easy to add new physics to the code.
SPHYNX addresses subsonic hydrodynamical instabilities and strong shocks; it is Newtonian, grounded on the Euler-Lagrange formulation of the smoothed-particle hydrodynamics technique, and density based. SPHYNX uses an integral approach for estimating gradients, a flexible family of interpolators to suppress pairing instability, and incorporates volume elements to provides better partition of the unity.
GAMBIT (Global And Modular BSM Inference Tool) performs statistical global fits of generic physics models using a wide range of particle physics and astrophysics data. Modules provide native simulations of collider and astrophysics experiments, a flexible system for interfacing external codes (the backend system), a fully featured statistical and parameter scanning framework, and additional tools for implementing and using hierarchical models.
iSEDfit uses Bayesian inference to extract the physical properties of galaxies from their observed broadband photometric spectral energy distribution (SED). In its default mode, the inputs to iSEDfit are the measured photometry (fluxes and corresponding inverse variances) and a measurement of the galaxy redshift. Alternatively, iSEDfit can be used to estimate photometric redshifts from the input photometry alone.
After the priors have been specified, iSEDfit calculates the marginalized posterior probability distributions for the physical parameters of interest, including the stellar mass, star-formation rate, dust content, star formation history, and stellar metallicity. iSEDfit also optionally computes K-corrections and produces multiple "quality assurance" (QA) plots at each stage of the modeling procedure to aid in the interpretation of the prior parameter choices and subsequent fitting results. The software is distributed as part of the impro IDL suite.
ANA calculates the likelihood function for a model comprised of two components to the astrophysical neutrino flux detected by IceCube. The first component is extragalactic. Since point sources have not been found and there is increasing evidence that one source catalog cannot describe the entire data set, ANA models the extragalactic flux as isotropic. The second component is galactic. A variety of catalogs of interest are also provided. ANA takes the galactic contribution to be proportional to the matter density of the universe. The likelihood function has one free parameter fgal that is the fraction of the astrophysical flux that is galactic. ANA finds the best fit value of fgal and scans over 0
empiriciSN generates realistic supernova parameters given photometric observations of a potential host galaxy, based entirely on empirical correlations measured from supernova datasets. It is intended to be used to improve supernova simulation for DES and LSST. It is extendable such that additional datasets may be added in the future to improve the fitting algorithm or so that additional light curve parameters or supernova types may be fit.
XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.
Extinction-distances uses the number of foreground stars and a Galactic model of the stellar distribution to estimate the distance to dark clouds. It exploits the relatively narrow range of intrinsic near-infrared colors of stars to separate foreground from background stars. An advantage of this method is that the distribution of stellar colors in the Galactic model need not be precisely correct, only the number density as a function of distance from the Sun.
ComEst calculates the completeness of CCD images conducted in astronomical observations saved in the FITS format. It estimates the completeness of the source finder SExtractor (ascl:1010.064) on the optical and near-infrared (NIR) imaging of point sources or galaxies as a function of flux (or magnitude) directly from the image itself. It uses PyFITS (ascl:1207.009) and GalSim (ascl:1402.009) to perform the end-to-end estimation of the completeness and can also estimate the purity of the source detection.
ExoSOFT provides orbital analysis of exoplanets and binary star systems. It fits any combination of astrometric and radial velocity data, and offers four parameter space exploration techniques, including MCMC. It is packaged with an automated set of post-processing and plotting routines to summarize results, and is suitable for performing orbital analysis during surveys with new radial velocity and direct imaging instruments.
Naima computes non-thermal radiation from relativistic particle populations. It includes tools to perform MCMC fitting of radiative models to X-ray, GeV, and TeV spectra using emcee (ascl:1303.002), an affine-invariant ensemble sampler for Markov Chain Monte Carlo. Naima is an Astropy (ascl:1304.002) affiliated package.
4DAO launches DAOSPEC (ascl:1011.002) for a large sample of spectra. Written in Fortran, the software allows one to easily manage the input and output files of DAOSPEC, optimize the main DAOSPEC parameters, and mask specific spectral regions. It also provides suitable graphical tools to evaluate the quality of the solution and provides final, normalized, zero radial velocity spectra.
The SINFONI pipeline reduces data from the Very Large Telescope's SINFONI (Spectrograph for INtegral Field Observations in the Near Infrared) instrument. It can evaluate the detector linearity and generate a corresponding non linear pixel map, create a master dark and a hot-pixel map, a master flat and a map of pixels which have intensities greater than a given threshold. It can also compute the optical distortions and slitlets distances, and perform wavelength calibration, PSF, telluric standard and other science data reduction, and can coadd bad pixel maps, collapse a cube to an image over a given wavelength range, perform cube arithmetics, among other useful tasks.
CuTEx analyzes images in the infrared bands and extracts sources from complex backgrounds, particularly star-forming regions that offer the challenges of crowding, having a highly spatially variable background, and having no-psf profiles such as protostars in their accreting phase. The code is composed of two main algorithms, the first an algorithm for source detection, and the second for flux extraction. The code is originally written in IDL language and it was exported in the license free GDL language. CuTEx could be used in other bands or in scientific cases different from the native case.
This software is also available as an on-line tool from the Multi-Mission Interactive Archive web pages dedicated to the Herschel Observatory.
Light Curves Classifier uses data mining and machine learning to obtain and classify desired objects. This task can be accomplished by attributes of light curves or any time series, including shapes, histograms, or variograms, or by other available information about the inspected objects, such as color indices, temperatures, and abundances. After specifying features which describe the objects to be searched, the software trains on a given training sample, and can then be used for unsupervised clustering for visualizing the natural separation of the sample. The package can be also used for automatic tuning parameters of used methods (for example, number of hidden neurons or binning ratio).
Trained classifiers can be used for filtering outputs from astronomical databases or data stored locally. The Light Curve Classifier can also be used for simple downloading of light curves and all available information of queried stars. It natively can connect to OgleII, OgleIII, ASAS, CoRoT, Kepler, Catalina and MACHO, and new connectors or descriptors can be implemented. In addition to direct usage of the package and command line UI, the program can be used through a web interface. Users can create jobs for ”training” methods on given objects, querying databases and filtering outputs by trained filters. Preimplemented descriptors, classifier and connectors can be picked by simple clicks and their parameters can be tuned by giving ranges of these values. All combinations are then calculated and the best one is used for creating the filter. Natural separation of the data can be visualized by unsupervised clustering.
pyLCSIM simulates X-ray lightcurves from coherent signals and power spectrum models. Coherent signals can be specified as a sum of one or more sinusoids, each with its frequency, pulsed fraction and phase shift; or as a series of harmonics of a fundamental frequency (each with its pulsed fraction and phase shift). Power spectra can be simulated from a model of the power spectrum density (PSD) using as a template one or more of the built-in library functions. The user can also define his/her custom models. Models are additive.
KERTAP computes the strong lensing effects of Kerr black holes, including the effects on polarization. The key ingredients of KERTAP are a graphic user interface, a backward ray-tracing algorithm, a polarization propagator dealing with gravitational Faraday rotation, and algorithms computing observables such as flux magnification and polarization angles.
TWO-POP-PY runs a two-population dust evolution model that follows the upper end of the dust size distribution and the evolution of the dust surface density profile and treats dust surface density, maximum particle size, small and large grain velocity, and fragmentation. It derives profiles that describe the dust-to-gas ratios and the dust surface density profiles well in protoplanetary disks, in addition to the radial flux by solid material rain out.
PACSman provides an alternative for several reduction and analysis steps performed in HIPE (ascl:1111.001) on PACS spectroscopic data; it is written in IDL. Among the operations possible with it are transient correction, line fitting, map projection, and map analysis, and unchopped scan, chop/nod, and the decommissioned wavelength switching observation modes are supported.
GMM (Gaussian Mixture Modeling) tests the existence of bimodality in globular cluster color distributions. GMM uses three indicators to distinguish unimodal and bimodal distributions: the kurtosis of the distribution, the separation of the peaks, and the probability of obtaining the same χ2 from a unimodal distribution.
GANDALF (Gas AND Absorption Line Fitting) accurately separates the stellar and emission-line contributions to observed spectra. The IDL code includes reddening by interstellar dust and also returns formal errors on the position, width, amplitude and flux of the emission lines. Example wrappers that make use of pPXF (ascl:1210.002) to derive the stellar kinematics are included.
RM-CLEAN reads in dirty Q and U cubes, generates rmtf based on the frequencies given in an ASCII file, and cleans the RM spectra following the algorithm given by Brentjens (2007). The output cubes contain the clean model components and the CLEANed RM spectra. The input cubes must be reordered with mode=312, and the output cubes will have the same ordering and thus must be reordered after being written to disk. RM-CLEAN runs as a MIRIAD (ascl:1106.007) task and a Python wrapper is included with the code.
BAGEMASS calculates the posterior probability distribution for the mass and age of a star from its observed mean density and other observable quantities using a grid of stellar models that densely samples the relevant parameter space. It is written in Fortran and requires FITSIO (ascl:1010.001).
FIEStool automatically reduces data obtained with the FIber-fed Echelle Spectrograph (FIES) at the Nordic Optical Telescope, a high-resolution spectrograph available on a stand-by basis, while also allowing the basic properties of the reduction to be controlled in real time by the user. It provides a Graphical User Interface and offers bias subtraction, flat-fielding, scattered-light subtraction, and specialized reduction tasks from the external packages IRAF (ascl:9911.002) and NumArray. The core of FIEStool is instrument-independent; the software, written in Python, could with minor modifications also be used for automatic reduction of data from other instruments.
ALCHEMIC solves chemical kinetics problems, including gas-grain interactions, surface reactions, deuterium fractionization, and transport phenomena and can model the time-dependent chemical evolution of molecular clouds, hot cores, corinos, and protoplanetary disks.
PBMC (Pre-Conditioned Backward Monte Carlo) solves the vector Radiative Transport Equation (vRTE) and can be applied to planetary atmospheres irradiated from above. The code builds the solution by simulating the photon trajectories from the detector towards the radiation source, i.e. in the reverse order of the actual photon displacements. In accounting for the polarization in the sampling of photon propagation directions and pre-conditioning the scattering matrix with information from the scattering matrices of prior (in the BMC integration order) photon collisions, PBMC avoids the unstable and biased solutions of classical BMC algorithms for conservative, optically-thick, strongly-polarizing media such as Rayleigh atmospheres.
DISORT (DIScrete Ordinate Radiative Transfer) solves the problem of 1D scalar radiative transfer in a single optical medium, such as a planetary atmosphere. The code correctly accounts for multiple scattering by an isotropic or plane-parallel beam source, internal Planck sources, and reflection from a lower boundary. Provided that polarization effects can be neglected, DISORT efficiently calculates accurate fluxes and intensities at any user-specified angle and location within the user-specified medium.
STools contains a variety of simple tools for spectroscopy, such as reading an IRAF-formatted (multispec) echelle spectrum in FITS, measuring the wavelength of the center of a line, Gaussian convolution, deriving synthetic photometry from an input spectrum, and extracting and interpolating a MARCS model atmosphere (standard composition).
Astroquery allows users to access online astronomical data from a wide range of sources; it is an Astropy-affiliated package. Each web service has its own sub-package for interfacing with a particular data source.
CRISPRED reduces data from the CRISP imaging spectropolarimeter at the Swedish 1 m Solar Telescope (SST). It performs fitting routines, corrects optical aberrations from atmospheric turbulence as well as from the optics, and compensates for inter-camera misalignments, field-dependent and time-varying instrumental polarization, and spatial variation in the detector gain and in the zero level offset (bias). It has an object-oriented IDL structure with computationally demanding routines performed in C subprograms called as dynamically loadable
CINE calculates infrared pumping efficiencies that can be applied to the most common molecules found in cometary comae such as water, hydrogen cyanide or methanol. One of the main mechanisms for molecular excitation in comets is the fluorescence by the solar radiation followed by radiative decay to the ground vibrational state. This command-line tool calculates the effective pumping rates for rotational levels in the ground vibrational state scaled by the heliocentric distance of the comet. Fluorescence coefficients are useful for modeling rotational emission lines observed in cometary spectra at sub-millimeter wavelengths. Combined with computational methods to solve the radiative transfer equations based, e.g., on the Monte Carlo algorithm, this model can retrieve production rates and rotational temperatures from the observed emission spectrum.
The ATOOLS package of applications provides an interface to the AST library (ascl:1404.016), allowing quick experiments to be performed from the shell. It manipulates descriptions of coordinate frames and mappings in the form of AST objects and performs other functions, with each application within the package corresponding closely to one of the functions in the AST library.
SWOT (Super W Of Theta) computes two-point statistics for very large data sets, based on “divide and conquer” algorithms, mainly, but not limited to data storage in binary trees, approximation at large scale, parellelization (open MPI), and bootstrap and jackknife resampling methods “on the fly”. It currently supports projected and 3D galaxy auto and cross correlations, galaxy-galaxy lensing, and weighted histograms.
Gala is a Python package (and Astropy affiliated package) for Galactic astronomy and gravitational dynamics. The bulk of the package centers around implementations of gravitational potentials, numerical integration, nonlinear dynamics, and astronomical velocity transformations (i.e. proper motions). Gala uses the Astropy units and coordinates subpackages extensively to provide a clean, pythonic interface to these features but does any heavy-lifting in C and Cython for speed.
PyMOC manipulates Multi-Order Coverage (MOC) maps. It supports reading and writing the three encodings mentioned in the IVOA MOC recommendation: FITS, JSON and ASCII.
CCFpams allows the measurement of stellar temperature, metallicity and gravity within a few seconds and in a completely automated fashion. Rather than performing comparisons with spectral libraries, the technique is based on the determination of several cross-correlation functions (CCFs) obtained by including spectral features with different sensitivity to the photospheric parameters. Literature stellar parameters of high signal-to-noise (SNR) and high-resolution HARPS spectra of FGK Main Sequence stars are used to calibrate the stellar parameters as a function of CCF areas.
Pyaneti is a multi-planet radial velocity and transit fit software. The code uses Markov chain Monte Carlo (MCMC) methods with a Bayesian approach and a parallelized ensemble sampler algorithm in Fortran which makes the code fast. It creates posteriors, correlations, and ready-to-publish plots automatically, and handles circular and eccentric orbits. It is capable of multi-planet fitting and handles stellar limb darkening, systemic velocities for multiple instruments, and short and long cadence data, and offers additional capabilities.
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.
HII Region Models fits HII region models to observed radio recombination line and radio continuum data. The algorithm includes the calculations of departure coefficients to correct for non-LTE effects. HII Region Models has been used to model star formation in the nucleus of IC 342.
KeplerSolver solves Kepler's equation for arbitrary epoch and eccentricity, using continued fractions. It is written in C and its speed is nearly the same as the SWIFT routines, while achieving machine precision. It comes with a test program to demonstrate usage.
PyPulse handles PSRFITS files and performs subsequent analyses on pulse profiles.
EXOSIMS generates and analyzes end-to-end simulations of space-based exoplanet imaging missions. The software is built up of interconnecting modules describing different aspects of the mission, including the observatory, optical system, and scheduler (encoding mission rules) as well as the physical universe, including the assumed distribution of exoplanets and their physical and orbital properties. Each module has a prototype implementation that is inherited by specific implementations for different missions concepts, allowing for the simulation of widely variable missions.
KERN is a bi-annually released set of radio astronomical software packages. It should contain most of the standard tools that a radio astronomer needs to work with radio telescope data. The goal of KERN to is to save time and frustration in setting up of scientific pipelines, and to assist in achieving scientific reproducibility.
We present Kliko, a Docker based container specification for running one or multiple related compute jobs. The key concepts of Kliko is the encapsulation of data processing software into a container and the formalisation of the input, output and task parameters. Formalisation is realised by bundling a container with a Kliko file, which describes the IO and task parameters. This Kliko container can then be opened and run by a Kliko runner. The Kliko runner will parse the Kliko definition and gather the values for these parameters, for example by requesting user input or pre defined values in a script. Parameters can be various primitive types, for example float, int or the path to a file. This paper will also discuss the implementation of a support library named Kliko which can be used to create Kliko containers, parse Kliko definitions, chain Kliko containers in workflows using, for example, Luigi a workflow manager. The Kliko library can be used inside the container interact with the Kliko runner. Finally this paper will discuss two reference implementations based on Kliko: RODRIGUES, a web based Kliko container schedular and output visualiser specifically for astronomical data, and VerMeerKAT, a multi container workflow data reduction pipeline which is being used as a prototype pipeline for the commisioning of the MeerKAT radio telescope.
sick infers astrophysical parameters from noisy observed spectra. Phenomena that can alter the data (e.g., redshift, continuum, instrumental broadening, outlier pixels) are modeled and simultaneously inferred with the astrophysical parameters of interest. This package relies on emcee (ascl:1303.002); it is best suited for situations where a grid of model spectra already exists, and one would like to infer model parameters given some data.
the-wizz clusters redshift estimates for any photometric unknown sample in a survey. The software is composed of two main parts: a pair finder and a pdf maker. The pair finder finds spatial pairs and stores the indices of all closer pairs around target reference objects in an output HDF5 data file. Users then query this data file using the indices of their unknown sample to produce an output clustering-z.
Encube is a qualitative, quantitative and comparative visualization and analysis framework, with application to high-resolution, immersive three-dimensional environments and desktop displays, providing a capable visual analytics experience across the display ecology. Encube includes mechanisms for the support of: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. The framework is modular, allowing additional functionalities to be included as required.
GenPK generates the 3D matter power spectra for each particle species from a Gadget snapshot. Written in C++, it requires both FFTW3 and GadgetReader.
LMC is a Markov Chain Monte Carlo engine in Python that implements adaptive Metropolis-Hastings and slice sampling, as well as the affine-invariant method of Goodman & Weare, in a flexible framework. It can be used for simple problems, but the main use case is problems where expensive likelihood evaluations are provided by less flexible third-party software, which benefit from parallelization across many nodes at the sampling level. The parallel/adaptive methods use communication through MPI, or alternatively by writing/reading files, and mostly follow the approaches pioneered by CosmoMC (ascl:1106.025).
DARK SAGE is a semi-analytic model of galaxy formation that focuses on detailing the structure and evolution of galaxies' discs. The code-base, written in C, is an extension of SAGE (ascl:1601.006) and maintains the modularity of SAGE. DARK SAGE runs on any N-body simulation with trees organized in a supported format and containing a minimum set of basic halo properties.
DaMaSCUS calculates the density and velocity distribution of dark matter (DM) at any detector of given depth and latitude to provide dark matter particle trajectories inside the Earth. Provided a strong enough DM-matter interaction, the particles scatter on terrestrial atoms and get decelerated and deflected. The resulting local modifications of the DM velocity distribution and number density can have important consequences for direct detection experiments, especially for light DM, and lead to signatures such as diurnal modulations depending on the experiment's location on Earth. The code involves both the Monte Carlo simulation of particle trajectories and generation of data as well as the data analysis consisting of non-parametric density estimation of the local velocity distribution functions and computation of direct detection event rates.
rtpipe (real-time pipeline) analyzes radio interferometric data with an emphasis on searching for transient or variable astrophysical sources. The package combines single-dish concepts such as dedispersion and filters with interferometric concepts, including images and the uv-plane. In contrast to time-domain data recorded with large single-dish telescopes, visibilities from interferometers can precisely localize sources anywhere in the entire field of view. rtpipe opens interferometers to the study of fast transient sky, including sources like pulsars, stellar flares, rotating radio transients, and fast radio bursts. Key portions of the search pipeline, such as image generation and dedispersion, have been accelerated. That, in combination with its multi-threaded, multi-node design, makes rtpipe capable of searching millisecond timescale data in real time on small compute clusters.
The simple, straightforward Exotrending code detrends exoplanet transit light curves given a light curve (flux versus time) and good ephemeris (epoch of first transit and orbital period). The code has been tested with Kepler and K2 light curves and should work with any other light curve.
Supernovae classifies supernovae using their light curves directly as inputs to a deep recurrent neural network, which learns information from the sequence of observations. Observational time and filter fluxes are used as inputs; since the inputs are agnostic, additional data such as host galaxy information can also be included.
astroABC is a Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation. astroABC allows for massive parallelization using MPI, a framework that handles spawning of processes across multiple nodes. It has the ability to create MPI groups with different communicators, one for the sampler and several others for the forward model simulation, which speeds up sampling time considerably. For smaller jobs the Python multiprocessing option is also available.
WeirdestGalaxies finds the weirdest galaxies in the Sloan Digital Sky Survey (SDSS) by using a basic outlier detection algorithm. It uses an unsupervised Random Forest (RF) algorithm to assign a similarity measure (or distance) between every pair of galaxy spectra in the SDSS. It then uses the distance matrix to find the galaxies that have the largest distance, on average, from the rest of the galaxies in the sample, and defined them as outliers.
NPTFit is a specialized Python/Cython package that implements Non-Poissonian Template Fitting (NPTF), originally developed for characterizing populations of unresolved point sources. It offers fast evaluation of likelihoods for NPTF analyses and has an easy-to-use interface for performing non-Poissonian (as well as standard Poissonian) template fits using MultiNest (ascl:1109.006) or other inference tools. It allows inclusion of an arbitrary number of point source templates, with an arbitrary number of degrees of freedom in the modeled flux distribution, and has modules for analyzing and plotting the results of an NPTF.
PSOAP (Precision Spectroscopic Orbits A-Parametrically) uses Gaussian processes to infer component spectra of single-lined and double-lined spectroscopic binaries, while simultaneously exploring the posteriors of the orbital parameters and the spectra themselves. PSOAP accounts for the natural λ-covariances in each spectrum, thus providing a natural "de-noising" of the spectra typically offered by Fourier techniques.
The spectral disentangling technique can be applied on a time series of observed spectra of a spectroscopic double-lined binary star (SB2) to determine the parameters of orbit and reconstruct the spectra of component stars, without the use of template spectra. fd3 disentangles the spectra of SB2 stars, capable also of resolving the possible third companion. It performs the separation of spectra in the Fourier space which is faster, but in several respects less versatile than the wavelength-space separation. (Wavelength-space separation is implemented in the twin code CRES.) fd3 is written in C and is designed as a command-line utility for a Unix-like operating system. fd3 is a new version of FDBinary (ascl:1705.011), which is now deprecated.
FDBinary disentangles spectra of SB2 stars. The spectral disentangling technique can be applied on a time series of observed spectra of an SB2 to determine the parameters of orbit and reconstruct the spectra of component stars, without the use of template spectra. The code is written in C and is designed as a command-line utility for a Unix-like operating system. FDBinary uses the Fourier-space approach in separation of composite spectra. This code has been replaced with the newer fd3 (ascl:1705.012).
Written in Python, PROFILER analyzes the radial surface brightness profiles of galaxies. It accurately models a wide range of galaxies and galaxy components, such as elliptical galaxies, the bulges of spiral and lenticular galaxies, nuclear sources, discs, bars, rings, and spiral arms with a variety of parametric functions routinely employed in the field (Sérsic, core-Sérsic, exponential, Gaussian, Moffat and Ferrers). In addition, Profiler can employ the broken exponential model (relevant for disc truncations or antitruncations) and two special cases of the edge-on disc model: namely along the major axis (in the disc plane) and along the minor axis (perpendicular to the disc plane).
LensPop simulates observations of the galaxy-galaxy strong lensing population in the Dark Energy Survey (DES), the Large Synoptic Survey Telescope (LSST), and Euclid surveys.
MBProj2 obtains thermodynamic profiles of galaxy clusters. It forward-models cluster X-ray surface brightness profiles in multiple bands, optionally assuming hydrostatic equilibrium. The code is a set of Python classes the user can use or extend. When modelling a cluster assuming hydrostatic equilibrium, the user chooses a form for the density profile (e.g. binning or a beta model), the metallicity profile, and the dark matter profile (e.g. NFW). If hydrostatic equilibrium is not assumed, a temperature profile model is used instead of the dark matter profile. The code uses the emcee Markov Chain Monte Carlo code (ascl:1303.002) to sample the model parameters, using these to produce chains of thermodynamic profiles.
HHTpywrapper is a python interface to call the Hilbert–Huang Transform (HHT) MATLAB package. HHT is a time-frequency analysis method to adaptively decompose a signal, that could be generated by non-stationary and/or nonlinear processes, into basis components at different timescales, and then Hilbert transform these components into instantaneous phases, frequencies and amplitudes as functions of time. HHT has been successfully applied to analyzing X-ray quasi-periodic oscillations (QPOs) from the active galactic nucleus RE J1034+396 (Hu et al. 2014) and two black hole X-ray binaries, XTE J1550–564 (Su et al. 2015) and GX 339-4 (Su et al. 2017). HHTpywrapper provides examples of reproducing HHT analysis results in Su et al. (2015) and Su et al. (2017). This project is originated from the Astro Hack Week 2015.
getimages performs background derivation and image flattening for high-resolution images obtained with space observatories. It is based on median filtering with sliding windows corresponding to a range of spatial scales from the observational beam size up to a maximum structure width X. The latter is a single free parameter of getimages that can be evaluated manually from the observed image. The median filtering algorithm provides a background image for structures of all widths below X. The same median filtering procedure applied to an image of standard deviations derived from a background-subtracted image results in a flattening image. Finally, a flattened image is computed by dividing the background-subtracted by the flattening image. Standard deviations in the flattened image are now uniform outside sources and filaments. Detecting structures in such radically simplified images results in much cleaner extractions that are more complete and reliable. getimages also reduces various observational and map-making artifacts and equalizes noise levels between independent tiles of mosaicked images. The code (a Bash script) uses FORTRAN utilities from getsources (ascl:1507.014), which must be installed.
Light curves from the Kepler telescope rely on "postage stamp" cutouts of a few pixels near each of 200,000 target stars. These light curves are optimized for the detection of short-term signals like planet transits but induce systematics that overwhelm long-term variations in stellar flux. Longer-term effects can be recovered through analysis of the Full Frame Images, a set of calibration data obtained monthly during the Kepler mission. The Python package f3 analyzes the Full Frame Images to infer long-term astrophysical variations in the brightness of Kepler targets, such as magnetic activity or sunspots on slowly rotating stars.
SPTCLASS assigns semi-automatic spectral types to a sample of stars. The main code includes three spectral classification schemes: the first one is optimized to classify stars in the mass range of TTS (K5 or later, hereafter LATE-type scheme); the second one is optimized to classify stars in the mass range of IMTTS (F late to K early, hereafter Gtype scheme), and the third one is optimized to classify stars in the mass range of HAeBe (F5 or earlier, hereafter HAeBe scheme). SPTCLASS has an interactive module that allows the user to select the best result from the three schemes and analyze the input spectra.
PCAT (Probabilistic Cataloger) samples from the posterior distribution of a metamodel, i.e., union of models with different dimensionality, to compare the models. This is achieved via transdimensional proposals such as births, deaths, splits and merges in addition to the within-model proposals. This method avoids noisy estimates of the Bayesian evidence that may not reliably distinguish models when sampling from the posterior probability distribution of each model.
The code has been applied in two different subfields of astronomy: high energy photometry, where transdimensional elements are gamma-ray point sources; and strong lensing, where light-deflecting dark matter subhalos take the role of transdimensional elements.
demc2, also abbreviated as DE-MCMC, is a differential evolution Markov Chain parameter estimation library written in R for adaptive MCMC on real parameter spaces.
DMATIS (Dark Matter ATtenuation Importance Sampling) calculates the trajectories of DM particles that propagate in the Earth's crust and the lead shield to reach the DAMIC detector using an importance sampling Monte-Carlo simulation. A detailed Monte-Carlo simulation avoids the deficiencies of the SGED/KS method that uses a mean energy loss description to calculate the lower bound on the DM-proton cross section. The code implementing the importance sampling technique makes the brute-force Monte-Carlo simulation of moderately strongly interacting DM with nucleons computationally feasible. DMATIS is written in Python 3 and MATHEMATICA.
COSMOS (Carnegie Observatories System for MultiObject Spectroscopy) reduces multislit spectra obtained with the IMACS and LDSS3 spectrographs on the Magellan Telescopes. It can be used for the quick-look analysis of data at the telescope as well as for pipeline reduction of large data sets. COSMOS is based on a precise optical model of the spectrographs, which allows (after alignment and calibration) an accurate prediction of the location of spectra features. This eliminates the line search procedure which is fundamental to many spectral reduction programs, and allows a robust data pipeline to be run in an almost fully automatic mode, allowing large amounts of data to be reduced with minimal intervention.
Multipoles, written in Python, calculates the quadrupole and hexadecapole approximations of the finite-source magnification: quadrupole (Wk,rho,Gamma) and hexadecapole (Wk,rho,Gamma). The code is efficient and faster than previously available methods, and could be generalized for use on large portions of the light curves.
The Difference-smoothing MATLAB code measures the time delay from the light curves of images of a gravitationally lendsed quasar. It uses a smoothing timescale free parameter, generates more realistic synthetic light curves to estimate the time delay uncertainty, and uses X2 plot to assess the reliability of a time delay measurement as well as to identify instances of catastrophic failure of the time delay estimator. A systematic bias in the measurement of time delays for some light curves can be eliminated by applying a correction to each measured time delay.
XID+ is a prior-based source extraction tool which carries out photometry in the Herschel SPIRE (Spectral and Photometric Imaging Receiver) maps at the positions of known sources. It uses a probabilistic Bayesian framework that provides a natural framework in which to include prior information, and uses the Bayesian inference tool Stan to obtain the full posterior probability distribution on flux estimates.
VULCAN describes gaseous chemistry from 500 to 2500 K using a reduced C-H-O chemical network with about 300 reactions. It uses eddy diffusion to mimic atmospheric dynamics and excludes photochemistry, and can be used to examine the theoretical trends produced when the temperature-pressure profile and carbon-to-oxygen ratio are varied.
A-Track is a fast, open-source, cross-platform pipeline for detecting moving objects (asteroids and comets) in sequential telescope images in FITS format. The moving objects are detected using a modified line detection algorithm.
Photo-z-SQL is a flexible template-based photometric redshift estimation framework that can be seamlessly integrated into a SQL database (or DB) server and executed on demand in SQL. The DB integration eliminates the need to move large photometric datasets outside a database for redshift estimation, and uses the computational capabilities of DB hardware. Photo-z-SQL performs both maximum likelihood and Bayesian estimation and handles inputs of variable photometric filter sets and corresponding broad-band magnitudes.
Transit calculates the transmission or emission spectrum of a planetary atmosphere with application to extrasolar-planet transit and eclipse observations, respectively. It computes the spectra by solving the one-dimensional line-by-line radiative-transfer equation for an atmospheric model.
PySM generates full-sky simulations of Galactic foregrounds in intensity and polarization relevant for CMB experiments. The components simulated are thermal dust, synchrotron, AME, free-free, and CMB at a given Nside, with an option to integrate over a top hat bandpass, to add white instrument noise, and to smooth with a given beam. PySM is based on the large-scale Galactic part of Planck Sky Model code and uses some of its inputs
Quickclump finds clumps in a 3D FITS datacube. It is a fast, accurate, and automated tool written in Python. Though Quickclump is primarily intended for decomposing observations of interstellar clouds into individual clumps, it can also be used for finding clumps in any 3D rectangular data.
VaST (Variability Search Toolkit) finds variable objects on a series of astronomical images in FITS format. The software performs object detection and aperture photometry using SExtractor (ascl:1010.064) on each image, cross-matches lists of detected stars, performs magnitude calibration with respect to the first (reference) image and constructs a lightcurve for each object. The sigma-magnitude, Stetson's L variability index, Robust Median Statistic (RoMS) and other plots may be used to visually identify variable star candidates. The two distinguishing features of VaST are its ability to perform accurate aperture photometry of images obtained with non-linear detectors and to handle complex image distortions. VaST can be used in cases of unstable PSF (e.g., bad guiding or with digitized wide-field photographic images), and has been successfully applied to images obtained with telescopes ranging from 0.08 to 2.5m in diameter equipped with a variety of detectors including CCD, CMOS, MIC and photographic plates.
STATCONT determines the continuum emission level in line-rich spectral data by inspecting the intensity distribution of a given spectrum by using different statistical approaches. The sigma-clipping algorithm provides the most accurate continuum level determination, together with information on the uncertainty in its determination; this uncertainty is used to correct the final continuum emission level. In general, STATCONT obtains accuracies of < 10 % in the continuum determination, and < 5 % in most cases. The main products of the software are the continuum emission level, together with its uncertainty, and data cubes containing only spectral line emission, i.e. continuum-subtracted data cubes. STATCONT also includes the option to estimate the spectral index or variation of the continuum emission with frequency.
Shwirl visualizes spectral data cubes with meaningful coloring methods. The program has been developed to investigate transfer functions, which combines volumetric elements (or voxels) to set the color, and graphics shaders, functions used to compute several properties of the final image such as color, depth, and/or transparency, as enablers for scientific visualization of astronomical data. The program uses Astropy (ascl:1304.002) to handle FITS files and World Coordinate System, Qt (and PyQt) for the user interface, and VisPy, an object-oriented Python visualization library binding onto OpenGL.
UDAT is a pattern recognition tool for mass analysis of various types of data, including image and audio. Based on its WND-CHARM (ascl:1312.002) prototype, UDAT computed a large set of numerical content descriptors from each file it analyzes, and selects the most informative features using statistical analysis. The tool can perform automatic classification of galaxy images by training with annotated galaxy images. It also has unsupervised learning capabilities, such as query-by-example of galaxies based on morphology. That is, given an input galaxy image of interest, the tool can search through a large database of images to retrieve the galaxies that are the most similar to the query image. The downside of the tool is its computational complexity, which in most cases will require a small or medium cluster.
pwkit is a collection of miscellaneous astronomical utilities in Python, with an emphasis on radio astronomy, reading and writing various data formats, and convenient command-line utilities. Utilities include basic astronomical calculations, data visualization tools such as mapping arbitrary data to color scales and tracing contours, and data input and output utilities such as streaming output from other programs.
Charm (cosmic history agnostic reconstruction method) reconstructs the cosmic expansion history in the framework of Information Field Theory. The reconstruction is performed via the iterative Wiener filter from an agnostic or from an informative prior. The charm code allows one to test the compatibility of several different data sets with the LambdaCDM model in a non-parametric way.
MC-SPAM (Monte-Carlo Synthetic-Photometry/Atmosphere-Model) generates limb-darkening coefficients from models that are comparable to transit photometry; it extends the original SPAM algorithm by Howarth (2011) by taking in consideration the uncertainty on the stellar and transit parameters of the system under analysis.
Atmospheric Athena simulates hydrodynamic escape from close-in giant planets in 3D. It uses the Athena hydrodynamics code (ascl:1010.014) with a new ionizing radiative transfer implementation to self-consistently model photoionization driven winds from the planet. The code is fully compatible with static mesh refinement and MPI parallelization and can handle arbitrary planet potentials and stellar initial conditions.
ICICLE (Initial Conditions for Isolated CoLlisionless systEms) generates stable initial conditions for isolated collisionless systems that can then be used in NBody simulations. It supports the Navarro-Frenk-White, Hernquist, King and Einasto density profiles.
QtClassify is a GUI that helps classify emission lines found in integral field spectroscopic data. Input needed is a datacube as well as a catalog with emission lines and a signal-to-noise cube, such at that created by LSDCat (ascl:1612.002). The main idea is to take each detected line and guess what line it could be (and thus the redshift of the object). You would expect to see other lines that might not have been detected but are visible in the cube if you know where to look, which is why parts of the spectrum are shown where other lines are expected. In addition, monochromatic layers of the datacube are displayed, making it easy to spot additional emission lines.
A self-organizing map (SOM) can be used to identify planetary candidates from Kepler and K2 datasets with accuracies near 90% in distinguishing known Kepler planets from false positives. TransitSOM classifies a Kepler or K2 lightcurve using a self-organizing map (SOM) created and pre-trained using PyMVPA (ascl:1703.009). It includes functions for users to create their own SOMs.
PyMVPA eases statistical learning analyses of large datasets. It offers an extensible framework with a high-level interface to a broad range of algorithms for classification, regression, feature selection, data import and export. It is designed to integrate well with related software packages, such as scikit-learn, shogun, and MDP.
Exorings is suitable for surveying entire catalogs of transiting planet candidates for exoring candidates, providing a subset of objects worthy of more detailed light curve analysis. Moreover, it is highly suited for uncovering evidence of a population of ringed planets by comparing the radius anomaly and PR-effects in ensemble studies.
Self-Interacting Dark Matter (SIDM) is a hypothetical model for cold dark matter in the Universe. A strong interaction between dark matter particles introduce a different physics inside dark-matter haloes, making the density profile cored, reduce the number of subhaloes, and trigger gravothermal collapse. sidm-nbody is an N-body simulation code with Direct Simulation Monte Carlo scattering for self interaction, and some codes to analyse gravothermal collapse of isolated haloes. The N-body simulation is based on GADGET 1.1.
SNRPy (Super Nova Remnant Python) models supernova remnant (SNR) evolution and is useful for understanding SNR evolution and to model observations of SNR for obtaining good estimates of SNR properties. It includes all phases for the standard path of evolution for spherically symmetric SNRs and includes alternate evolutionary models, including evolution in a cloudy ISM, the fractional energy loss model, and evolution in a hot low-density ISM. The graphical interface takes in various parameters and produces outputs such as shock radius and velocity vs. time, SNR surface brightness profile and spectrum.
The Matlab starsense_algorithms package evaluates the performance of various star sensors through the implementation of centroiding, geometric voting and QUEST algorithms. The physical parameters of a star sensor are parametrized and by changing these parameters, performance estimators such as sky coverage, memory requirement, and timing requirements can be estimated for the selected star sensor.
PHOTOMETRYPIPELINE (PP) provides calibrated photometry from imaging data obtained with small to medium-sized observatories. PP uses Source Extractor (ascl:1010.064) and SCAMP (ascl:1010.063) to register the image data and perform aperture photometry. Calibration is obtained through matching of field stars with reliable photometric catalogs. PP has been specifically designed for the measurement of asteroid photometry, but can also be used to obtain photometry of fixed sources.
Corrfunc is a suite of high-performance clustering routines. The code can compute a variety of spatial correlation functions on Cartesian geometry as well Landy-Szalay calculations for spatial and angular correlation functions on a spherical geometry and is useful for, for example, exploring the galaxy-halo connection. The code is written in C and can be used on the command-line, through the supplied python extensions, or the C API.
COCOA (Cluster simulatiOn Comparison with ObservAtions) creates idealized mock photometric observations using results from numerical simulations of star cluster evolution. COCOA is able to present the output of realistic numerical simulations of star clusters carried out using Monte Carlo or N-body codes in a way that is useful for direct comparison with photometric observations. The code can simulate optical observations from simulation snapshots in which positions and magnitudes of objects are known. The parameters for simulating the observations can be adjusted to mimic telescopes of various sizes. COCOA also has a photometry pipeline that can use standalone versions of DAOPHOT (ascl:1104.011) and ALLSTAR to produce photometric catalogs for all observed stars.
Larch is an open-source library and toolkit written in Python for processing and analyzing X-ray spectroscopic data. The primary emphasis is on X-ray spectroscopic and scattering data collected at modern synchrotron sources. Larch provides a wide selection of general-purpose processing, analysis, and visualization tools for processing X-ray data; its related target application areas include X-ray absorption fine structure (XAFS), micro-X-ray fluorescence (XRF) maps, quantitative X-ray fluorescence, X-ray absorption near edge spectroscopy (XANES), and X-ray standing waves and surface scattering. Larch provides a complete set of XAFS Analysis tools and has support for visualizing and analyzing XRF maps and spectra, and additional tools for X-ray spectral analysis, data handling, and general-purpose data modeling.
KSTAT calculates the 2 and 3-point correlation functions in discreet point data. These include the two-point correlation function in 2 and 3-dimensions, the anisotripic 2PCF decomposed in either sigma-pi or Kazin's dist. mu projection.
The 3-point correlation function can also work in anisotropic coordinates (currently under development). The code is based on kd-tree structures and is parallelized using a mixture of MPI and OpenMP.
I created these codes as I have found it difficult in the past find similar ones freely available in the public domain. I hope to keep developing them, so please send me bug fixes,suggestions, comments/criticisms to email@example.com
GRIM (General Relativistic Implicit Magnetohydrodynamics) evolves a covariant extended magnetohydrodynamics model derived by treating non-ideal effects as a perturbation of ideal magnetohydrodynamics. Non-ideal effects are modeled through heat conduction along magnetic field lines and a difference between the pressure parallel and perpendicular to the field lines. The model relies on an effective collisionality in the disc from wave-particle scattering and velocity-space (mirror and firehose) instabilities. GRIM, which runs on CPUs as well as on GPUs, combines time evolution and primitive variable inversion needed for conservative schemes into a single step using only the residuals of the governing equations as inputs. This enables the code to be physics agnostic as well as flexible regarding time-stepping schemes.
Chempy models Galactic chemical evolution (GCE); it is a parametrized open one-zone model within a Bayesian framework. A Chempy model is specified by a set of 5-10 parameters that describe the effective galaxy evolution along with the stellar and star-formation physics: e.g. the star-formation history (SFH), the feedback efficiency, the stellar initial mass function (IMF) and the incidence of supernova of type Ia (SN Ia). Chempy can sample the posterior probability distribution in the full model parameter space and test data-model matches for different nucleosynthetic yield sets, performing essentially as a chemical evolution fitting tool. Chempy can be used to confront predictions from stellar nucleosynthesis with complex abundance data sets and to refine the physical processes governing the chemical evolution of stellar systems.
streamgap-pepper computes the effect of subhalo fly-bys on cold tidal streams based on the action-angle representation of streams. A line-of-parallel-angle approach is used to calculate the perturbed distribution function of a given stream segment by undoing the effect of all impacts. This approach allows one to compute the perturbed stream density and track in any coordinate system in minutes for realizations of the subhalo distribution down to 10^5 Msun, accounting for the stream's internal dispersion and overlapping impacts. This code uses galpy (ascl:1411.008) and the streampepperdf.py galpy extension, which implements the fast calculation of the perturbed stream structure.
The Hellenic Open University Reconstruction & Simulation (HOURS) software package contains a realistic simulation package of the detector response of very large (km3-scale) underwater neutrino telescopes, including an accurate description of all the relevant physical processes, the production of signal and background as well as several analysis strategies for triggering and pattern recognition, event reconstruction, tracking and energy estimation. HOURS also provides tools for simulating calibration techniques and other studies for estimating the detector sensitivity to several neutrino sources.
KEPLER is a general purpose stellar evolution/explosion code that incorporates implicit hydrodynamics and a detailed treatment of nuclear burning processes. It has been used to study the complete evolution of massive and supermassive stars, all major classes of supernovae, hydrostatic and explosive nucleosynthesis, and x- and gamma-ray bursts on neutron stars and white dwarfs.
GalaxyGAN uses Generative Adversarial Networks to reliably recover features in images of galaxies. The package uses machine learning to train on higher quality data and learns to recover detailed features such as galaxy morphology by effectively building priors. This method opens up the possibility of recovering more information from existing and future imaging data.
Written in Python, JetCurry models the 3D geometry of jets from 2-D images. JetCurry requires NumPy and SciPy and incorporates emcee (ascl:1303.002) and AstroPy (ascl:1304.002), and optionally uses VPython. From a defined initial part of the jet that serves as a reference point, JetCurry finds the position of highest flux within a bin of data in the image matrix and fits along the x axis for the general location of the bends in the jet. A spline fitting is used to smooth out the resulted jet stream.
Validation provides codes to compare several observations to simulated data with stellar mass and star formation rate, simulated data stellar mass function with observed stellar mass function from PRIMUS or SDSS-GALEX in several redshift bins from 0.01-1.0, and simulated data B band luminosity function with observed stellar mass function, and to create plots for various attributes, including stellar mass functions, and stellar mass to halo mass. These codes can model predictions (in some cases alongside observational data) to test other mock catalogs.
Juwvid performs time-frequency analysis. Written in Julia, it uses a modified version of the Wigner distribution, the pseudo Wigner distribution, and the short-time Fourier transform from MATLAB GPL programs, tftb-0.2. The modification includes the zero-padding FFT, the non-uniform FFT, the adaptive algorithm by Stankovic, Dakovic, Thayaparan 2013, the S-method, the L-Wigner distribution, and the polynomial Wigner-Ville distribution.
corner.py uses matplotlib to visualize multidimensional samples using a scatterplot matrix. In these visualizations, each one- and two-dimensional projection of the sample is plotted to reveal covariances. corner.py was originally conceived to display the results of Markov Chain Monte Carlo simulations and the defaults are chosen with this application in mind but it can be used for displaying many qualitatively different samples. An earlier version of corner.py was known as triangle.py.
ORBE performs numerical integration of an arbitrary planetary system composed by a central star and up to 100 planets and minor bodies. ORBE calculates the orbital evolution of a system of bodies by means of the computation of the time evolution of their orbital elements. It is easy to use and is suitable for educational use by undergraduate students in the classroom as a first approach to orbital integrators.
SONG computes the non-linear evolution of the Universe in order to predict cosmological observables such as the bispectrum of the Cosmic Microwave Background (CMB). More precisely, it is a second-order Boltzmann code, as it solves the Einstein and Boltzmann equations up to second order in the cosmological perturbations.
GWFrames eliminates all rotational behavior, thus simplifying the waveform as much as possible and allowing direct generalizations of methods for analyzing nonprecessing systems. In the process, the angular velocity of a waveform is introduced, which also has important uses, such as supplying a partial solution to an important inverse problem.
kcorrect fits very restricted spectral energy distribution models to galaxy photometry or spectra in the restframe UV, optical and near-infrared. The main purpose of the fits are for calculating K-corrections. The templates used for the fits may also be interpreted physically, since they are based on the Bruzual-Charlot stellar evolution synthesis codes. Thus, for each fit galaxy kcorrect can provide an estimate of the stellar mass-to-light ratio.
GrayStarServer is a stellar atmospheric modeling and spectrum synthesis code of pedagogical accuracy that is accessible in any web browser on commonplace computational devices and that runs on a timescale of a few seconds.
Forecaster predicts the mass (or radius) from the radius (or mass) for objects covering nine orders-of-magnitude in mass. It is an unbiased forecasting model built upon a probabilistic mass-radius relation conditioned on a sample of 316 well-constrained objects. It accounts for observational errors, hyper-parameter uncertainties and the intrinsic dispersions observed in the calibration sample.
MSWAVEF calculates hydrogenic and non-hydrogenic momentum-space electronic wavefunctions. Such wavefunctions are often required to calculate various collision processes, such as excitation and line broadening cross sections. The hydrogenic functions are calculated using the standard analytical expressions. The non-hydrogenic functions are calculated within quantum defect theory according to the method of Hoang Binh and van Regemorter (1997). Required Hankel transforms have been determined analytically for angular momentum quantum numbers ranging from zero to 13 using Mathematica. Calculations for higher angular momentum quantum numbers are possible, but slow (since calculated numerically). The code is written in IDL.
KAULAKYS calculates cross sections and rate coefficients for inelastic collisions between Rydberg atoms and hydrogen atoms according to the free electron model of Kaulakys (1986, 1991). It is written in IDL and requires the code MSWAVEF (ascl:1701.006) to calculate momentum-space wavefunctions. KAULAKYS can be easily adapted to collisions with perturbers other than hydrogen atoms by providing the appropriate scattering amplitudes.
CosmoSlik quickly puts together, runs, and analyzes an MCMC chain for analysis of cosmological data. It is highly modular and comes with plugins for CAMB (ascl:1102.026), CLASS (ascl:1106.020), the Planck likelihood, the South Pole Telescope likelihood, other cosmological likelihoods, emcee (ascl:1303.002), and more. It offers ease-of-use, flexibility, and modularity.
Spectra calculates the power spectrum of a time series equally spaced or not based on the Spectral Correlation Coefficient (Ferraz-Mello 1981, Astron. Journal 86 (4), 619). It is very efficient for detection of low frequencies.
Vizic is a Python visualization library that builds the connection between images and catalogs through an interactive map of the sky region. The software visualizes catalog data over a custom background canvas using the shape, size and orientation of each object in the catalog and displays interactive and customizable objects in the map. Property values such as redshift and magnitude can be used to filter or apply colormaps, and objects can be selected for further analysis through standard Python functions from inside a Jupyter notebook.
Vizic allows custom overlays to be appended dynamically on top of the sky map; included are Voronoi, Delaunay, Minimum Spanning Tree and HEALPix layers, which are helpful for visualizing large-scale structure. Overlays can be generated, added or removed dynamically with one line of code. Catalog data is kept in a non-relational database. The Jupyter Notebook allows the user to create scripts to analyze and plot the data selected/displayed in the interactive map, making Vizic a powerful and flexible interactive analysis tool. Vizic be used for data inspection, clustering analysis, galaxy alignment studies, outlier identification or simply large-scale visualizations.
Lizard is an extensible Cyclomatic Complexity Analyzer for many imperative programming languages including C/C++.
Given sparse or low-quality radial-velocity measurements of a star, there are often many qualitatively different stellar or exoplanet companion orbit models that are consistent with the data. The consequent multimodality of the likelihood function leads to extremely challenging search, optimization, and MCMC posterior sampling over the orbital parameters. The Joker is a custom-built Monte Carlo sampler that can produce a posterior sampling for orbital parameters given sparse or noisy radial-velocity measurements, even when the likelihood function is poorly behaved. The method produces correct samplings in orbital parameters for data that include as few as three epochs. The Joker can therefore be used to produce proper samplings of multimodal pdfs, which are still highly informative and can be used in hierarchical (population) modeling.
DiskSim is a source-code distribution of the SPH accretion disk modeling code described in Simpson & Wood (1998) and Wood, Thomas, & Simpson (2009). The code had been released in a Windows executable form as FITDisk (Dolence, Wood & Simpson 2005; ascl:1305.011). The code released now is the full research code in Fortran, which can be modified as needed by the user.
REPS (REscaled Power Spectra) provides accurate, one-percent level, numerical simulations of the initial conditions for massive neutrino cosmologies, rescaling the late-time linear power spectra to the simulation initial redshift.
Bayesian Technique for Multi-image Analysis (BaTMAn) characterizes any astronomical dataset containing spatial information and performs a tessellation based on the measurements and errors provided as input. The algorithm iteratively merges spatial elements as long as they are statistically consistent with carrying the same information (i.e. identical signal within the errors). The output segmentations successfully adapt to the underlying spatial structure, regardless of its morphology and/or the statistical properties of the noise. BaTMAn identifies (and keeps) all the statistically-significant information contained in the input multi-image (e.g. an IFS datacube). The main aim of the algorithm is to characterize spatially-resolved data prior to their analysis.
The chemistry and radiative cooling library Grackle provides options for primordial chemistry and cooling, photo-heating and photo-ionization from UV backgrounds, and support for user-provided arrays of volumetric and specific heating rates for astrophysical simulations and models. The library provides functions to update chemistry species; solve radiative cooling and update internal energy; and calculate cooling time, temperature, pressure, and ratio of specific heats (gamma), and has interfaces for C, C++, Fortran, and Python codes.
Trident creates synthetic absorption-line spectra from astrophysical hydrodynamics simulations. It uses the yt package (ascl:1011.022) to read in simulation datasets and extends it to provide realistic synthetic observations appropriate for studies of the interstellar, circumgalactic, and intergalactic media.
pylightcurve is a model for light-curves of transiting planets. It uses the four coefficients law for the stellar limb darkening and returns the relative flux, F(t), as a function of the limb darkening coefficients, an, the Rp/R* ratio and all the orbital parameters based on the nonlinear limb darkening model (Claret 2000).
GAMER (GPU-accelerated Adaptive MEsh Refinement) serves as a general-purpose adaptive mesh refinement + GPU framework and solves hydrodynamics with self-gravity. The code supports adaptive mesh refinement (AMR), hydrodynamics with self-gravity, and a variety of GPU-accelerated hydrodynamic and Poisson solvers. It also supports hybrid OpenMP/MPI/GPU parallelization, concurrent CPU/GPU execution for performance optimization, and Hilbert space-filling curve for load balance. Although the code is designed for simulating galaxy formation, it can be easily modified to solve a variety of applications with different governing equations. All optimization strategies implemented in the code can be inherited straightforwardly.
CELib (Chemical Evolution Library) simulates chemical evolution of galaxy formation under the simple stellar population (SSP) approximation and can be used by any simulation code that uses the SSP approximation, such as particle-base and mesh codes as well as semi-analytical models. Initial mass functions, stellar lifetimes, yields from type II and Ia supernovae, asymptotic giant branch stars, and neutron star mergers components are included and a variety of models are available for use. The library allows comparisons of the impact of individual models on the chemical evolution of galaxies by changing control flags and parameters of the library.
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