ASCL.net

Astrophysics Source Code Library

Making codes discoverable since 1999

Browsing Codes

Results 1-100 of 2075 (2041 ASCL, 34 submitted)

Order
Title Date
 
Mode
Abstract Compact
Per Page
50100250All
[submitted] Parallelized rotation and flipping INvariant Kohonen maps (PINK)

Morphological classification is one of the most demanding challenges in astronomy. With the advent of all-sky surveys, an enormous amount of imaging data is publicly available. These data are typically analyzed by experts or encouraged amateur volunteers. For upcoming surveys with billions of objects, however, such an approach is not feasible anymore. We present a simple yet effective variant of a rotation-invariant self-organizing map that is suitable for many analysis tasks in astronomy. We show how to reduce the computational complexity via modern GPUs and apply the resulting framework to galaxy data for morphological analysis.

[submitted] Magnetizer: computing magnetic fields of evolving galaxies

Computes time and radial dependent magnetic fields for a sample of galaxies in the output of a semi-analytic model of galaxy formation. The magnetic field is obtained by numerically solving the galactic dynamo equations throughout history of each galaxy. Stokes parameters and Faraday rotation measure can also be computed along a random line-of-sight for each galaxy.

[submitted] HaloAnalysis: read and analyze halo catalogs and merger trees

HaloAnalysis reads and analyzes halo/galaxy catalogs, generated from Rockstar (ascl:1210.008) or AHF (ascl:1102.009), and merger trees generated from Consistent Trees (ascl:1210.011). Written in Python, it offers the following functionality: reads halo/galaxy/tree catalogs from multiple file formats; assigns baryonic particles and galaxy properties to dark-matter halos; combines and re-generates halo/galaxy/tree files in hdf5 format; analyzes properties of halos/galaxies; selects halos to generate zoom-in initial conditions. Includes a Jupyter notebook tutorial.

[submitted] GizmoAnalysis: read and analyze Gizmo simulations

GizmoAnalysis reads and analyzes N-body simulations run with the Gizmo code (ascl:1410.003). Written in Python, we developed it primarily to analyze FIRE simulations, though it is useable with any Gizmo snapshot files. It offers the following functionality: reads snapshot files and converts particle data to physical units; provides a flexible dictionary class to store particle data and compute derived quantities on the fly; plots images and properties of particles; generates region files for input to MUSIC (ascl:1311.011) to generate cosmological zoom-in initial conditions; computes rates of supernovae and stellar winds, including their nucleosynthetic yields, as used in FIRE simulations. Includes a Jupyter notebook tutorial.

[submitted] qnm: A Python package for calculating Kerr quasinormal modes, separation constants, and spherical-spheroidal mixing coefficients

qnm is an open-source Python package for computing the Kerr quasinormal mode frequencies, angular separation constants, and spherical-spheroidal mixing coefficients. The qnm package includes a Leaver solver with the Cook-Zalutskiy spectral approach to the angular sector, and a caching mechanism to avoid repeating calculations. We provide a large cache of low ℓ, m, n modes, which can be downloaded and installed with a single function call, and interpolated to provide good initial guess for root-polishing at new values of spin.

[submitted] uvplot

A simple package to make nice plots of interferometric visibilities, often called uvplots.

[ascl:1909.014] fgivenx: Functional posterior plotter

fgivenx plots a predictive posterior of a function, dependent on sampled parameters, for a Bayesian posterior Post(theta|D,M) described by a set of posterior samples {theta_i}~Post. If there is a function parameterized by theta y=f(x;theta), this script produces a contour plot of the conditional posterior P(y|x,D,M) in the (x,y) plane.

[ascl:1909.013] EPOS: Exoplanet Population Observation Simulator

EPOS (Exoplanet Population Observation Simulator) simulates observations of exoplanet populations. It provides an interface between planet formation simulations and exoplanet surveys such as Kepler. EPOS can also be used to estimate planet occurrence rates and the orbital architectures of planetary systems.

[ascl:1909.012] HISS: HI spectra stacker

HISS stacks HI (emission and absorption) spectra in a consistent and reliable manner to enable statistical analysis of average HI properties. It provides plots of the stacked spectrum and reference spectrum with any fitted function, of the stacked noise response, and of the distribution of the integrated fluxes when calculating the uncertainties. It also produces a table containing the integrated flux calculated from the fitted functions and the stacked spectrum, among other output files.

[ascl:1909.011] WVTICs: SPH initial conditions using Weighted Voronoi Tesselations

WVTICs generates glass-like initial conditions for Smoothed Particle Hydrodynamics. Relaxation of the particle distribution is done using an algorithm based on Weighted Voronoi Tesselations; additional particle reshuffling can be enabled to improve over- and undersampled maxima/minima. The WBTICs package includes a full suite of analytical test problems.

[ascl:1909.010] AREPO: Cosmological magnetohydrodynamical moving-mesh simulation code

AREPO is a massively parallel gravity and magnetohydrodynamics code for astrophysics, designed for problems of large dynamic range. It employs a finite-volume approach to discretize the equations of hydrodynamics on a moving Voronoi mesh, and a tree-particle-mesh method for gravitational interactions. AREPO is originally optimized for cosmological simulations of structure formation, but has also been used in many other applications in astrophysics.

[ascl:1909.009] CLOVER: Convolutional neural network spectra identifier and kinematics predictor

CLOVER (Convnet Line-fitting Of Velocities in Emission-line Regions) is a convolutional neural network (ConvNet) trained to identify spectra with two velocity components along the line of sight and predict their kinematics. It works with Gaussian emission lines (e.g., CO) and lines with hyperfine structure (e.g., NH3). CLOVER has two prediction steps, classification and parameter prediction. For the first step, CLOVER segments the pixels in an input data cube into one of three classes: noise (i.e., no emission), one-component (emission line with single velocity component), and two-component (emission line with two velocity components). For the pixels identified as two-components in the first step, a second regression ConvNet is used to predict centroid velocity, velocity dispersion, and peak intensity for each velocity component.

[ascl:1909.008] RascalC: Fast code for galaxy covariance matrix estimation

RascalC quickly estimates covariance matrices from two- or three-point galaxy correlation functions. Given an input set of random particle locations and a two-point correlation function (or input set of galaxy positions), RascalC produces an estimate of the associated covariance for a given binning strategy, with non-Gaussianities approximated by a ‘shot-noise-rescaling’ parameter. For the 2PCF, the rescaling parameter can be calibrated by dividing the particles into jackknife regions and comparing sample to theoretical jackknife covariance. RascalC can also be used to compute Legendre-binned covariances and cross-covariances between different two-point correlation functions.

[ascl:1909.007] EBHLIGHT: General relativistic radiation magnetohydrodynamics with Monte Carlo transport

EBHLIGHT solves the equations of general relativistic radiation magnetohydrodynamics in stationary spacetimes. Fluid integration is performed with the second order shock-capturing scheme HARM (ascl:1209.005) and frequency-dependent radiation transport is performed with the second order Monte Carlo code grmonty (ascl:1306.002). Fluid and radiation exchange four-momentum in an explicit first-order operator-split fashion.

[ascl:1909.006] ChempyMulti: Multi-star Bayesian inference with Chempy

ChempyMulti is an update to Chempy (ascl:1702.011) and provides yield table scoring and multi-star Bayesian inference. This replaces the ChempyScoring package in Chempy. Chempy is a flexible one-zone open-box chemical evolution model, incorporating abundance fitting and stellar feedback calculations. It includes routines for parameter optimization for simulations and observational data and yield table scoring.

[ascl:1909.005] HADES: Hexadecapolar Analysis for Dust Estimation in Simulations (of CMB B-mode thermal dust emission)

HADES analyzse dust levels in simulated CMB galactic dust maps with realistic experimental noise and lensing configurations. It allows detection of dust via its anisotropy properties in CMB B-modes. It also includes techniques for computing null-tests and a rudimentary technique for dedusting.

[ascl:1909.004] TPI: Test Particle Integrator

TPI computes the gravitational dynamics of particles orbiting a supermassive black hole (SBH). A distinction is made to two types of particles: test particles and field particles. Field particles are assumed to move in quasi-static Keplerian orbits around the SBH that precess due to the enclosed mass (Newtonian 'mass precession') and relativistic effects. Otherwise, field-particle-field-particle interactions are neglected. Test particles are integrated in the time-dependent potential of the field particles and the SBH. Relativistic effects are included in the equations of motion (including the effects of SBH spin), and test-particle-test-particle interactions are neglected.

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

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

[ascl:1909.002] MultiColorFits: Colorize and combine multiple fits images for visually aesthetic scientific plots

MultiColorFits is a tool to colorize and combine multiple fits images for making visually aesthetic scientific plots. The standard method to make color composites by combining fits images programmatically in python is to assign three images as separate red, green, and blue channels. This can produce unsatisfactory results for a variety of reasons, such as when less than three images are available, or additional images are desired to be shown. MultiColorFits breaks these limitations by allowing users to apply any color to a given image, not just red, green, or blue. Composites can then be created from an arbitrary number of images. Controls are included for stretching brightness scales with common functions.

[ascl:1909.001] Auto-multithresh: Automated masking for clean

Auto-multithresh implements an automated masking algorithm for clean. It operates on the residual image within the minor cycle of clean to identify and mask regions of significant emission. It then cascades these significant regions down to lower signal to noise. It includes features to pad the mask to avoid sharp edges and to remove small regions that are unlikely to be significant emission. The algorithm described by this code was incorporated into the tclean task within CASA as auto-multithresh.

[submitted] CR-SISTEM: Symplectic integrator for lunar core-mantle and orbital dynamics

This integrator is based on the algorithm of Touma and Wisdom (2001, http://ui.adsabs.harvard.edu/abs/2001AJ....122.1030T). The triaxial Moon has a triaxial liquid core, and is perturbed by the Sun and Earth's oblateness. Orbits of the Moon and Earth are fully integrated, and other planets (or additional point-mass satellites) may be included in the integration. Lunar and solar tides on Earth, eccentricity and obliquity tides on the Moon, and lunar core-mantle friction and all included. The tides on Earth and the Moon are treated in the same way Cuk et al (2016, http://ui.adsabs.harvard.edu/abs/2016Natur.539..402C) and many details of their closely-related code can be found in the online supplement of that paper. In the posted version, the lunar core-mantle friction torque is directly proportional to the core-mantle differential rotation, with a fixed damping timescale of 10,000 present-day sidereal months (120 yrs, after Pavlov et al. (2016, https://ui.adsabs.harvard.edu/abs/2016CeMDA.126...61P).

[ascl:1908.025] FastCSWT: Fast directional Continuous Spherical Wavelet Transform

FastCSWT performs a directional continuous wavelet transform on the sphere. The transform is based on the construction of the continuous spherical wavelet transform (CSWT) developed by Antoine and Vandergheynst (1999). A fast implementation of the CSWT (based on the fast spherical convolution developed by Wandelt and Gorski 2001) is also provided.

[ascl:1908.024] PYSAT: Python Satellite Data Analysis Toolkit

The Python Satellite Data Analysis Toolkit (pysat) provides a simple and flexible interface for downloading, loading, cleaning, managing, processing, and analyzing space science data. The toolkit supports in situ satellite observations and many different types of ground- and space-based measurements. Its analysis routines are independent of instrument and data source.

[ascl:1908.023] FIRST Classifier: Automated compact and extended radio sources classifier

FIRST Classifier is an on-line system for automated classification of compact and extended radio sources. It is developed based on a trained Deep Convolutional Neural Network Model to automate the morphological classification of compact and extended radio sources observed in the FIRST radio survey. FIRST Classifier is able to predict the morphological class for a single source or for a list of sources as Compact or Extended (FRI, FRII and BENT).

[ascl:1908.022] YMW16: Electron-density model

YMW16 models the distribution of free electrons in the Galaxy, the Magellanic Clouds and the inter-galactic medium and can be used to estimate distances for real or simulated pulsars and fast radio bursts (FRBs) based on their position and dispersion measure. The Galactic model is based on 189 pulsars that have independently determined distances as well as dispersion measures, whereas simpler models are used for the electron density in the MC and the IGM.

[ascl:1908.021] bias_emulator: Halo bias emulator

bias_emulator models the clustering of halos on large scales. It incorporates the cosmological dependence of the bias beyond the mapping of halo mass to peak height. Precise measurements of the halo bias in the simulations are interpolated across cosmological parameter space to obtain the halo bias at any point in parameter space within the simulation cloud. A tool to produce realizations of correlated noise for propagating the modeling uncertainty into error budgets that use the emulator is also provided.

[ascl:1908.020] QLF: Luminosity function analysis code

QLF derives full posterior distributions for and analyzes luminosity functions models; it also models hydrogen and helium reionization. Used with the included homogenized data, the derived luminosity functions can be easily compared with theoretical models or future data sets.

[ascl:1908.019] MAESTROeX: Low Mach number stellar hydrodynamics code

MAESTROeX solves the equations of low Mach number hydrodynamics for stratified atmospheres or stars with a general equation of state. It includes reactions and thermal diffusion and can be used on anything from a single core to 100,000s of processor cores with MPI + OpenMP. MAESTROeX maintains the accuracy of its predecessor MAESTRO (ascl:1010.044) while taking advantage of a simplified temporal integration scheme and leveraging the AMReX software framework for block-structured adaptive mesh refinement (AMR) applications.

[ascl:1908.018] EBAI: Eclipsing Binaries with Artificial Intelligence

Eclipsing Binaries via Artificial Intelligence (EBAI) automates the process of solving light curves of eclipsing binary stars. EBAI is based on the back-propagating neural network paradigm and is highly flexible in construction of neural networks. EBAI comes in two flavors, serial (ebai) and multi-processor (ebai.mpi), and can be run in training, continued training, and recognition mode.

[ascl:1908.017] JPLephem: Jet Propulsion Lab ephemerides package

JPLephem loads and uses standard Jet Propulsion Laboratory (JPL) ephemerides for predicting the position and velocity of a planet or other Solar System body. It is one of the foundations of the Skyfield (ascl:1907.024) astronomy library for Python, and can also be used as a standalone package to generate raw vectors.

[ascl:1908.016] DustCharge: Charge distribution for a dust grain

DustCharge calculates the equilibrium charge distribution for a dust grain of a given size and composition, depending on the local interstellar medium conditions, such as density, temperature, ionization fraction, local radiation field strength, and cosmic ray ionization fraction.

[ascl:1908.015] Analysator: Quantitative analysis of Vlasiator files

Analysator analyzes vlsv files produced by Vlasiator (ascl:1908.014). The code facilitates studies of particle paths, pitch angle distributions, velocity distributions, and more. It can read and write VLSV files and do calculations with the data, plot the real space from VLSV files with Mayavi (ascl:1205.008), and plot the velocity space (both blocks and iso surface) from VLSV files. It can also take cut-throughs, pitch angle distributions, gyrophase angle, and 3d slices, plot variables with sub plots in a clean format, and fit 1D polynomials to data.

[ascl:1908.014] Vlasiator: Hybrid-Vlasov simulation code

Vlasiator is a 6-dimensional Vlasov theory-based simulation. It simulates the entire near-Earth space at a global scale using the kinetic hybrid-Vlasov approach, to study fundamental plasma processes (reconnection, particle acceleration, shocks), and to gain a deeper understanding of space weather.

[ascl:1908.013] BEAST: Bayesian Extinction And Stellar Tool

BEAST (Bayesian Extinction and Stellar Tool) fits the ultraviolet to near-infrared photometric SEDs of stars to extract stellar and dust extinction parameters. The stellar parameters are age (t), mass (M), metallicity (M), and distance (d). The dust extinction parameters are dust column (Av), average grain size (Rv), and mixing between type A and B extinction curves (fA).

[ascl:1908.012] oscode: Oscillatory ordinary differential equation solver

oscode solves oscillatory ordinary differential equations efficiently. It is designed to deal with equations of the form x¨(t)+2γ(t)x˙(t)+ω2(t)x(t)=0, where γ(t) and ω(t) can be given as explicit functions or sequence containers (Eigen::Vectors, arrays, std::vectors, lists) in C++ or as numpy.arrays in Python. oscode makes use of an analytic approximation of x(t) embedded in a stepping procedure to skip over long regions of oscillations, giving a reduction in computing time. The approximation is valid when the frequency changes slowly relative to the timescales of integration, it is therefore worth applying when this condition holds for at least some part of the integration range.

[ascl:1908.011] NuRadioMC: Monte Carlo simulation package for radio neutrino detectors

NuRadioMC simulates ultra-high energy neutrino detectors that rely on the radio detection method, which exploits the radio emission generated in the electromagnetic component of a particle shower following a neutrino interaction. The code simulates the neutrino interaction in a medium, subsequent Askaryan radio emission, propagation of the radio signal to the detector and the detector response. NuRadioMC is a Monte Carlo framework that combines flexibility in detector design with user-friendliness. It includes an event generator, improved modeling of the radio emission, a revisited approach to signal propagation, and increased flexibility and precision in the detector simulation.

[ascl:1908.010] SNAPDRAGONS: Stellar Numbers And Parameters Determined Routinely And Generated Observing N-body Systems

SNAPDRAGONS (Stellar Numbers And Parameters Determined Routinely And Generated Observing N-body Systems) is a simplified version of the population synthesis code Galaxia (ascl:1101.007), using a different process to generate the stellar catalog. It splits each N-body particle from the galaxy simulation into an appropriate number of stellar particles to create a mock catalog of observable stars from the N-body model. SNAPDRAGON uses the same isochrones and extinction map as Galaxia.

[ascl:1908.009] PyRADS: Python RADiation model for planetary atmosphereS

The 1D radiation code PyRADS provides line-by-line spectral resolution. For Earth-like atmospheres, PyRADS currently uses HITRAN 2016 line lists and the MTCKD continuum model. A version for shortwave radiation (scattering) is also available.

[ascl:1908.008] TRISTAN-MP: TRIdimensional STANford - Massively Parallel code

TRISTAN-MP is a fully relativistic Particle-In-Cell (PIC) code for plasma physics computations and self-consistently solves the full set of Maxwell’s equations, along with the relativistic equations of motion for the charged particles. Fields are discretized on a finite 3D or 2D mesh, the computational grid; the code then uses time-centered and space-centered finite difference schemes to advance the equations in time via the Lorentz force equation, and to calculate spatial derivatives, so that the algorithm is second order accurate in space and time. The charges and currents derived from the particles' velocities and positions are then used as source terms to re-calculate the electromagnetic fields. TRISTAN-MP is based on the original TRISTAN code by O. Buneman (1999).

[ascl:1908.007] MosfireDRP: MOSFIRE Data Reduction Pipeline

MosfireDRP reduces data from the MOSFIRE spectrograph of the Keck Observatory; it produces flat-fielded, wavelength calibrated, rectified, and stacked 2D spectrograms for each slit on a given mask in nearly real time. Background subtraction is performed in two states: a simple pairwise subtraction of interleaved stacks, and then fitting a 2D b-spline model to the background residuals.

[ascl:1908.006] GBKFIT: Galaxy kinematic modeling

GBKFIT performs galaxy kinematic modeling. It can be used to extract morphological and kinematical properties of galaxies by fitting models to spatially resolved kinematic data. The software can also take beam smearing into account by using the knowledge of the line and point spread functions. GBKFIT can take advantage of many-core and massively parallel architectures such as multi-core CPUs and Graphics Processing Units (GPUs), making it suitable for modeling large-scale surveys of thousands of galaxies within a very seasonable time frame. GBKFIT features an extensible object-oriented architecture that supports arbitrary models and optimization techniques in the form of modules; users can write custom modules without modifying GBKFIT’s source code. The software is written in C++ and conforms to the latest ISO standards.

[ascl:1908.005] dips: Detrending periodic signals in timeseries

dips detrends timeseries of strictly periodic signals. It does not assume any functional form for the signal or the background or the noise; it disentangles the strictly periodic component from everything else. It has been used for detrending Kepler, K2 and TESS timeseries of periodic variable stars, eclipsing binary stars, and exoplanets.

[ascl:1908.004] Gramsci: GRAph Made Statistics for Cosmological Information

Gramsci (GRAph Made Statistics for Cosmological Information) computes the general N-point spatial correlation functions of any discrete point set embedded within an Euclidean space of ℝ^n. It uses kd-trees and graph databases to count all possible N-tuples in binned configurations within a given length scale, e.g. all pairs of points or all triplets of points with side lengths. Gramsci can run in serial, OpenMP, MPI and hybrid parallel schemes. It is useful for performing domain decomposition of input catalogs, especially if the catalogs are large or the Rmax value is too large.

[ascl:1908.003] ActSNClass: Active learning for supernova photometric classification

ActSNClass uses a parametric feature extraction method, Random Forest classifier and two learning strategies (uncertainty sampling and random sampling) to performs active learning for supernova photometric classification.

[ascl:1908.002] Molsoft: Molonglo Telescope Observing Software

Molsoft operates, monitors and schedules observations, both through predetermined schedule files and fully dynamically, at the refurbished Molonglo Observatory Synthesis Radio Telescope (MOST). It was developed as part of the UTMOST upgrade of the facility. The software includes a large-scale pulsar timing program; the autonomous observing system and the dynamic scheduler has increased the observing efficiency by a factor of 2-3 in comparison with static scheduling.

[ascl:1908.001] QAC: Quick Array Combinations front end to CASA

QAC (Quick Array Combinations) is a front end to CASA (ascl:1107.013) and calls tools and tasks to help in combining data from a single dish and interferometer. QAC hides some of the complexity of writing CASA scripts and provide a simple interface to array combination tools and tasks in CASA. This project was conceived alongside the TP2VIS (ascl:1904.021) project, where it was used to provide an easier way to call CASA and perform regression tests.

[ascl:1907.032] Astro-SCRAPPY: Speedy Cosmic Ray Annihilation Package in Python

Astro-SCRAPPY detects cosmic rays in images (numpy arrays), based on Pieter van Dokkum's L.A.Cosmic algorithm and originally adapted from cosmics.py written by Malte Tewes. This implementation is optimized for speed, resulting in slight difference from the original code, such as automatic recognition of saturated stars (rather than treating such stars as large cosmic rays, and use of a separable median filter instead of the true median filter. Astro-SCRAPPY is an AstroPy (ascl:1304.002) affiliated package.

[ascl:1907.031] MGB: Interactive spectral classification code

MGB (Marxist Ghost Buster) attacks spectral classification by using an interactive comparison with spectral libraries. It allows the user to move along the two traditional dimensions of spectral classification (spectral subtype and luminosity classification) plus the two additional ones of rotation index and spectral peculiarities. Double-lined spectroscopic binaries can also be fitted using a combination of two standards. The code includes OB2500 v2.0, a standard grid of blue-violet R ~ 2500 spectra of O stars from the Galactic O-Star Spectroscopic Survey, but other grids can be added to MGB.

[ascl:1907.030] Wōtan: Stellar detrending methods

Wōtan provides free and open source algorithms to remove trends from time-series data automatically as an aid to to search efficiently for transits in stellar light curves from surveys. The toolkit helps determine empirically the best tool for a given job, serving as a one-stop solution for various smoothing tasks.

[ascl:1907.029] XDF-GAN: Mock astronomical survey generator

XDF-GAN generates mock galaxy surveys with a Spatial Generative Adversarial Network (SGAN)-like architecture. Mock galaxy surveys are generated from data that is preprocessed as little as possible (preprocessing is only a 99.99th percentile clipping). The outputs can also be tessellated together to create a very large survey, limited in size only by the RAM of the generation machine.

[ascl:1907.028] ROHSA: Separation of diffuse sources in hyper-spectral data

ROHSA (Regularized Optimization for Hyper-Spectral Analysis) reveals the statistical properties of interstellar gas through atomic and molecular lines. It uses a Gaussian decomposition algorithm based on a multi-resolution process from coarse to fine grid to decompose any kind of hyper-spectral observations into a sum of coherent Gaussian. Optimization is performed on the whole data cube at once to obtain a solution with spatially smooth parameters.

[ascl:1907.027] intensitypower: Spectrum multipoles modeler

intensitypower measures and models the auto- and cross-power spectrum multipoles of galaxy catalogs and radio intensity maps presented in spherical coordinates. It can also convert the multipoles to power spectrum wedges P(k,mu) and 2D power spectra P(k_perp,k_par). The code assumes the galaxy catalog is a set of discrete points and the radio intensity map is a pixelized continuous field which includes angular pixelization using healpix, binning in redshift channels, smoothing by a Gaussian telescope beam, and the addition of a Gaussian noise in each cell. The galaxy catalog and radio intensity map are transferred onto an FFT grid, and power spectrum multipoles are measured including curved-sky effects. Both maps include redshift-space distortions.

[ascl:1907.026] MCRGNet: Morphological Classification of Radio Galaxy Network

MCRGNet (Morphological Classification of Radio Galaxy Network) classifies radio galaxies of different morphologies. It is based on the Convolutional Neural Network (CNN), which is trained and applied under a three-step framework: 1.) pretraining the network unsupervisedly with unlabeled samples, 2.) fine-tuning the pretrained network parameters supervisedly with labeled samples, and 3.) classifying a new radio galaxy by the trained network. The code uses a dichotomous tree classifier composed of cascaded CNN based subclassifiers.

[ascl:1907.025] GIST: Galaxy IFU Spectroscopy Tool

GIST (Galaxy IFU Spectroscopy Tool) provides a convenient all-in-one framework for the scientific analysis of fully reduced, (integral-field) spectroscopic data, conducting all the steps from the preparation of input data to the scientific analysis and to the production of publication-quality plots. In its basic set-up, the GIST pipeline extracts stellar kinematics, performs an emission-line analysis, and derives stellar population properties from full spectral fitting and via the measurement of absorption line-strength indices by exploiting pPXF (ascl:1210.002)and GandALF routines. The pipeline is not specific to any instrument or analysis technique, and includes a dedicated visualization routine with a sophisticated graphical user interface for fully interactive plotting of all measurements, spectra, fits, and residuals, as well as star formation histories and the weight distribution of the models.

[ascl:1907.024] Skyfield: High precision research-grade positions for planets and Earth satellites generator

Skyfield computes positions for the stars, planets, and satellites in orbit around the Earth. Its results should agree with the positions generated by the United States Naval Observatory and their Astronomical Almanac to within 0.0005 arcseconds (which equals half a “mas” or milliarcsecond). It computes geocentric coordinates or topocentric coordinates specific to your location on the Earth’s surface. Skyfield accepts AstroPy (ascl:1304.002) time objects as input and can return results in native AstroPy units but is not dependend on AstroPy nor its compiled libraries.

[ascl:1907.023] REVOLVER: REal-space VOid Locations from suVEy Reconstruction

REVOLVER reconstructs real space positions from redshift-space tracer data by subtracting RSD through FFT-based reconstruction (optional) and applies void-finding algorithms to create a catalogue of voids in these tracers. The tracers are normally galaxies from a redshift survey but could also be halos or dark matter particles from a simulation box. Two void-finding routines are provided. The first is based on ZOBOV (ascl:1304.005) and uses Voronoi tessellation of the tracer field to estimate the local density, followed by a watershed void-finding step. The second is a voxel-based method, which uses a particle-mesh interpolation to estimate the tracer density, and then uses a similar watershed algorithm. Input data files can be in FITS format, or ASCII- or NPY-formatted data arrays.

[ascl:1907.022] CMDPT: Color Magnitude Diagrams Plot Tool

CMD Plot Tool calculates and plots Color Magnitude Diagrams (CMDs) from astronomical photometric data, e.g. of a star cluster observed in two filter bandpasses. It handles multiple file formats (plain text, DAOPHOT .mag files, ACS Survey of Galactic Globular Clusters .zpt files) to generate professional and customized plots without a steep learning curve. It works “out of the box” and does not require any installation of development environments, additional libraries, or resetting of system paths. The tool is available as a single application/executable file with the source code. Sample data is also bundled for demonstration. CMD Plot Tool can also convert DAOPHOT magnitude files to CSV format.

[ascl:1907.021] PRISM: Probabilistic Regression Instrument for Simulating Models

PRISM analyzes scientific models using the Bayes linear approach, the emulation technique, and history matching to construct an approximation ('emulator') of any given model. The software facilitates and enhances existing MCMC methods by restricting plausible regions and exploring parameter space efficiently and can be used as a standalone alternative to MCMC for model analysis, providing insight into the behavior of complex scientific models. PRISM stores results in HDF5-files and can be executed in serial or MPI on any number of processes. It accepts any type of model and comparison data and can reduce relevant parameter space by factors over 100,000 using only a few thousand model evaluations.

[ascl:1907.020] GaussPy+: Gaussian decomposition package for emission line spectra

GaussPy+ is a fully automated Gaussian decomposition package for emission line spectra. It is based on GaussPy (ascl:1907.019) and offers several improvements, including automating preparatory steps and providing an accurate noise estimation, improving the fitting routine, and providing a routine to refit spectra based on neighboring fit solutions. GaussPy+ handles complex emission and low to moderate signal-to-noise values.

[ascl:1907.019] GaussPy: Python implementation of the Autonomous Gaussian Decomposition algorithm

GaussPy implements the Autonomous Gaussian Decomposition (AGD) algorithm, which uses computer vision and machine learning techniques to provide optimized initial guesses for the parameters of a multi-component Gaussian model automatically and efficiently. The speed and adaptability of AGD allow it to interpret large volumes of spectral data efficiently. Although it was initially designed for applications in radio astrophysics, AGD can be used to search for one-dimensional Gaussian (or any other single-peaked spectral profile)-shaped components in any data set. To determine how many Gaussian functions to include in a model and what their parameters are, AGD uses a technique called derivative spectroscopy. The derivatives of a spectrum can efficiently identify shapes within that spectrum corresponding to the underlying model, including gradients, curvature and edges.

[ascl:1907.018] StePar: Inferring stellar atmospheric parameters using the EW method

StePar computes the stellar atmospheric parameters Teff, log g, [Fe/H], and ξ of FGK-type stars using the Equivalent Width (EW) method. The code implements a grid of MARCS model atmospheres and uses the MOOG radiative transfer code (ascl:1202.009) and TAME (ascl:1503.003). StePar uses a Downhill Simplex minimization algorithm, running it twice for any given star, to compute the stellar atmospheric parameters.

[ascl:1907.017] ZChecker: Zwicky Transient Facility moving target checker for short object lists

ZChecker finds, measures, and visualizes known comets in the Zwicky Transient Facility time-domain survey. Images of targets are identified using on-line ephemeris generation and survey metadata. The photometry of the targets are measured and the images are processed with temporal filtering to highlight morphological variations in time.

[submitted] DM phase: A novel algorithm for correcting dispersion of radio signals

Radio waves propagating in space are subject to frequency-dependent delay due to interactions with cold free electrons, which gives coherent radio emissions a unique structure known as dispersion. The study of impulsive radio signals from astronomical sources, such as those emitted by pulsars and fast radio bursts (FRBs), requires proper corrections for this effect. Moreover, the ionized medium itself can be characterized by sensitive measurements of this dispersion.
Signal dispersion is proportional to the integrated column density of free electrons along the line of sight, a quantity known as dispersion measure (DM), and inversely proportional to the observing frequency squared. Traditional methods search for the best DM value of a source by maximizing the signal-to-noise ratio (S/N) of the detected signal. While sensitive and efficient algorithms have been designed for this purpose, they are affected by two limitations. Firstly, they implicitly assume a broadband emission across the entire observing frequency bandwidth. While this is normally true for pulsars, some FRBs have been observed to have complex spectra which returned incorrect DM values. Secondly, these traditional algorithms are highly sensitive to large-amplitude events such as large noise spikes and radio interference. In order to overcome these limitations, we developed a new algorithm to maximize the coherent power of the signal instead of its intensity. Since the structure of the signal is coherent at different frequencies, this method is relatively insensitive to complex spectro-temporal shapes of the pulses. In addition, this method is more robust to noise and interference because these normally have incoherent structures and the amplitude information in each frequency channel is discarded.

[ascl:1907.016] astrodendro: Astronomical data dendrogram creator

Astrodendro, written in Python, creates dendrograms for exploring and displaying hierarchical structures in observed or simulated astronomical data. It handles noisy data by allowing specification of the minimum height of a structure and the minimum number of pixels needed for an independent structure. Astrodendro allows interactive viewing of computed dendrograms and can also produce publication-quality plots with the non-interactive plotting interface.

[ascl:1907.015] TurbuStat: Turbulence statistics in spectral-line data cubes

TurbuStat implements a variety of turbulence-based statistics described in the astronomical literature and defines distance metrics for each statistic to quantitatively compare spectral-line data cubes, as well as column density, integrated intensity, or other moment maps. The software can simulate observations of fractional Brownian Motion fields, including 2-D images and optically thin H I data cubes. TurbuStat also offers multicore fast-Fourier-transform support and provides a segmented linear model for fitting lines with a break point.

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

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

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

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

[ascl:1907.012] molly: 1D astronomical spectra analyzer

molly analyzes 1D astronomical spectra. Its prime purpose is for handling large numbers of similar spectra (e.g., time series spectroscopy), but it contains many of the standard operations used for normal spectrum analysis as well. It overlaps with the various similar programs such as dipso (ascl:1405.016) and has strengths (particularly for time series spectra) and weaknesses compared to them.

[ascl:1907.011] beamconv: Cosmic microwave background detector data simulator

beamconv simulates the scanning of the CMB sky while incorporating realistic beams and scan strategies. It uses (spin-)spherical harmonic representations of the (polarized) beam response and sky to generate simulated CMB detector signal timelines. Beams can be arbitrarily shaped. Pointing timelines can be read in or calculated on the fly; optionally, the results can be binned on the sphere.

[ascl:1907.010] OMNICAL: Redundant calibration code for low frequency radio interferometers

OMNICAL calibrates antennas in the redundant subset of the array. The code consists of two algorithms, a logarithmic method (logcal) and a linearized method (lincal). OMNICAL makes visibilities from physically redundant baselines agree with each other and also explicitly minimizes the variance within redundant visibilities.

[ascl:1907.009] Plonk: Smoothed particle hydrodynamics data analysis and visualization

Plonk analyzes and visualizes smoothed particle hydrodynamics simulation data. It is built on the scientific Python ecosystem, including NumPy, Matplotlib, Cython, h5py, SymPy, and pandas. Plock's visualization module uses Splash (ascl:1103.004) to produce images using smoothed particle hydrodynamics interpolation. The code is modular and extendible, and can be scripted or used interactively.

[submitted] Accretion Disk Radial Structure Models

A collection of radial structure models of various accretion disk solutions. Each model implements a common interface that gives the radial dependence of selected geometrical, physical and thermodynamic quantities of the accretion flow.

[ascl:1907.008] Dewarp: Distortion removal and on-sky orientation solution for LBTI detectors

Dewarp constructs pipelines to remove distortion from a detector and find the orientation with true North. It was originally written for the LBTI LMIRcam detector, but is generalizable to any project with reference sources and/or an astrometric field paired with a machine-readable file of astrometric target locations.

[ascl:1907.007] SPAM: Hu-Sawicki f(R) gravity imprints search

SPAM searches for imprints of Hu-Sawicki f(R) gravity on the rotation curves of the SPARC (Spitzer Photometry and Accurate Rotation Curves) sample using the MCMC sampler emcee (ascl:1303.002). The code provides attributes for inspecting the MCMC chains and translating names of parameters to indices. The SPAM package also contains plotting scripts.

[ascl:1907.006] POCS: PANOPTES Observatory Control System

PANOPTES (Panoptic Astronomical Networked Observatories for a Public Transiting Exoplanets Survey) is a citizen science project for low cost, robotic detection of transiting exoplanets. POCS (PANOPTES Observatory Control System) is the main software driver for the PANOPTES telescope system, responsible for high-level control of the unit. POCS defines an Observatory class that automatically controls a commercially available equatorial mount, including image analysis and corresponding mount adjustment to obtain a percent-level photometric precision.

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

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

[ascl:1907.004] pyGTC: Parameter covariance plots

pyGTC creates giant triangle confusogram (GTC) plots. Triangle plots display the results of a Monte-Carlo Markov Chain (MCMC) sampling or similar analysis. The recovered parameter constraints are displayed on a grid in which the diagonal shows the one-dimensional posteriors (and, optionally, priors) and the lower-left triangle shows the pairwise projections. Such plots are useful for seeing the parameter covariances along with the priors when fitting a model to data.

[ascl:1907.003] pyuvdata: Pythonic interface to interferometric data sets

pyuvdata defines a pythonic interface to interferometric data sets; it supports the development of and interchange of data between calibration and foreground subtraction pipelines. It can read and write MIRIAD (ascl:1106.007), uvfits, and uvh5 files and reads CASA (ascl:1107.013) measurement sets and FHD (Fast Holographic Deconvolution) visibility save files. Particular focus has been paid to supporting drift and phased array modes.

[ascl:1907.002] healvis: Radio interferometric visibility simulator based on HEALpix maps

Healvis simulates radio interferometric visibility off of HEALPix shells. It generates a flat-spectrum and a GSM model and computes visibilities, and can simulates visibilities given an Observation Parameter YAML file. Healvis can perform partial frequency simulations in serial to minimize instantaneous memory loads.

[ascl:1907.001] schwimmbad: Parallel processing pools interface

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

[ascl:1906.022] pyLIMA: Microlensing modeling package

pyLIMA (python Lightcurve Identification and Microlensing Analysis) fits microlensing lightcurves and derives the physical quantities of lens systems. The package provides microlensing modeling, and the magnification estimation for high cadence lightcurves has been optimized. pyLIMA is designed to make microlensing modeling and event simulation widely available to the community.

[ascl:1906.021] centerRadon: Center determination code in stellar images

centerRadon finds the center of stars based on Radon Transform to sub-pixel precision. For a coronagraphic image of a star, it starts from a given location, then for each sub-pixel position, it interpolates the image and sums the pixels along different angles, creating a cost function. The center of the star is expected to correspond with where the cost function maximizes. The default values are set for the STIS coronagraphic images of the Hubble Space Telescope by summing over the diagonals (i.e., 45° and 135°), but it can be generally applied to other high-contrast imaging instruments with or without Adaptive Optics systems such as HST-NICMOS, P1640, or GPI.

[ascl:1906.020] LIZARD: Particle initial conditions for cosmological simulations

LIZARD (Lagrangian Initialization of Zeldovich Amplitudes for Resimulations of Displacements) creates particle initial conditions for cosmological simulations using the Zel'dovich approximation for the matter and velocity power spectrum.

[ascl:1906.019] PlasmaPy: Core Python package for plasma physics

PlasmaPy provides core functionality and a common framework for data visualization and analysis for plasma physics. It has modules for basic plasma physics calculations, running desktop-scale simulations to test preliminary ideas such as one-dimensional MHD/PIC or test particles, or comparing data from two different sources, such as simulations and spacecraft.

[ascl:1906.018] MEGAlib: Medium Energy Gamma-ray Astronomy library

The Medium Energy Gamma-ray Astronomy library (MEGAlib) simulates, calibrates, and analyzes data of hard X-ray and gamma-ray detectors, with a specialization on Compton telescopes. The library comprises all necessary data analysis steps for these telescopes, from simulation/measurements via calibration, event reconstruction to image reconstruction.

MEGAlib contains a geometry and detector description tool for the detailed modeling of different detector types and characteristics, and provides an easy to use simulation program based on Geant4 (ascl:1010.079). For different Compton telescope detector types (electron tracking, multiple Compton or time of flight based), specialized Compton event reconstruction algorithms are implemented in different approaches (Chi-square and Bayesian). The high level data analysis tools calculate response matrices, perform image deconvolution (specialized in list-mode-likelihood-based Compton image reconstruction), determine detector resolutions and sensitivities, retrieve spectra, and determine polarization modulations.

[ascl:1906.017] mcfit: Multiplicatively Convolutional Fast Integral Transforms

mcfit computes integral transforms, inverse transforms without analytic inversion, and integral kernels as derivatives. It can also transform input array along any axis, output the matrix form, an is easily extensible for other kernels.

[ascl:1906.016] PandExo: Instrument simulations for exoplanet observation planning

PandExo generates instrument simulations of JWST’s NIRSpec, NIRCam, NIRISS and NIRCam and HST WFC3 for planning exoplanet observations. It uses throughput calculations from STScI’s Exposure Time Calculator, Pandeia, and offers both an online tool and a python package.

[ascl:1906.015] OIT: Nonconvex optimization approach to optical-interferometric imaging

In the context of optical interferometry, only undersampled power spectrum and bispectrum data are accessible, creating an ill-posed inverse problem for image recovery. Recently, a tri-linear model was proposed for monochromatic imaging, leading to an alternated minimization problem; in that work, only a positivity constraint was considered, and the problem was solved by an approximated Gauss–Seidel method.

The Optical-Interferometry-Trilinear code improves the approach on three fundamental aspects. First, the estimated image is defined as a solution of a regularized minimization problem, promoting sparsity in a fixed dictionary using either an l1 or a (re)weighted-l1 regularization term. Second, the resultant non-convex minimization problem is solved using a block-coordinate forward–backward algorithm. This algorithm is able to deal both with smooth and non-smooth functions, and benefits from convergence guarantees even in a non-convex context. Finally, the model and algorithm are generalized to the hyperspectral case, promoting a joint sparsity prior through an l2,1 regularization term.

[ascl:1906.014] GPUVMEM: Maximum Entropy Method (MEM) GPU algorithm for radio astronomical image synthesis

The maximum entropy method (MEM) is a well known deconvolution technique in radio-interferometry. This method solves a non-linear optimization problem with an entropy regularization term. Other heuristics such as CLEAN are faster but highly user dependent. Nevertheless, MEM has the following advantages: it is unsupervised, it has a statistical basis, it has a better resolution and better image quality under certain conditions. GPUVMEM presents a high performance GPU version of non-gridding MEM.

[ascl:1906.013] MORPHEUS: A 3D Eulerian Godunov MPI-OpenMP hydrodynamics code with multiple grid geometries

MORPHEUS (Manchester Omni-geometRical Program for Hydrodynamical EUlerian Simulations) is a 3D hydrodynamical code used to simulate astrophysical fluid flows. It has three different grid geometries (cartesian, spherical, and cylindrical) and uses a second-order Godunov method to solve the equations of hydrodynamics. Physical modules also include radiative cooling and gravity, and a hybrid MPI-OpenMP parallelization allows computations to be run on large-scale architectures. MORPHEUS is written in Fortran90 and does not require any libraries (apart from MPI) to run.

[ascl:1906.012] Morpheus: Library to generate morphological semantic segmentation maps of astronomical images

Morpheus generates pixel level morphological classifications of astronomical sources by leveraging advances in deep learning to perform source detection, source segmentation, and morphological classification pixel-by-pixel via a semantic segmentation algorithm adopted from the field of computer vision. By utilizing morphological information about the flux of real astronomical sources during object detection, Morpheus shows resiliency to false positive identifications of sources.

[ascl:1906.011] Lizard: An extensible Cyclomatic Complexity Analyzer

Lizard is an extensible Cyclomatic Complexity Analyzer for imperative programming languages including C/C++/C#, Python, Java, and Javascript. It counts the nloc (lines of code without comments) and CCN (cyclomatic complexity number), and takes a token count of functions and a parameter count of functions. It also does copy-paste detection (code clone detection/code duplicate detection) and many other forms of static code analysis. Lizard is often used in software-related research and calculates how complex the code looks rather than how complex the code really is; thought it's often very hard to get all the included folders and files right when they are complicated, that accuracy is not needed to determine cyclomatic complexity, which can be useful for measuring the maintainability of a software package.

[ascl:1906.010] PyA: Python astronomy-related packages

The PyA (PyAstronomy) suite of astronomy-related packages includes a convenient fitting package that provides support for minimization and MCMC sampling, a set of astrophysical models (e.g., transit light-curve modeling), and algorithms for timing analysis such as the Lomb-Scargle and the Generalized Lomb-Scargle periodograms.

[ascl:1906.009] PyMORESANE: Python MOdel REconstruction by Synthesis-ANalysis Estimators

PyMORESANE is a Python and pyCUDA-accelerated implementation of the MORESANE deconvolution algorithm, a sparse deconvolution algorithm for radio interferometric imaging. It can restore diffuse astronomical sources which are faint in brightness, complex in morphology and possibly buried in the dirty beam’s side lobes of bright radio sources in the field.

[ascl:1906.008] T-RECS: Tiered Radio Extragalactic Continuum Simulation

T-RECS produces radio sources catalogs with user-defined frequencies, area and depth. It models two main populations of radio galaxies, Active Galactic Nuclei (AGNs) and Star-Forming Galaxies (SFGs), and corresponding sub-populations. T-RECS is not computationally demanding and can be run multiple times, using the same catalog inputs, to project the simulated sky onto different fields.

[ascl:1906.007] limb-darkening: Limb-darkening coefficients generator

Limb-darkening generates limb-darkening coefficients from ATLAS and PHOENIX model atmospheres using arbitrary response functions. The code uses PyFITS (ascl:1207.009) and has several other dependencies, and produces a folder of results with descriptions of the columns contained in each file.

[ascl:1906.006] turboSETI: Python-based SETI search algorithm

TurboSETI analyzes filterbank data (frequency vs. time) for narrow band drifting signals; its main purpose is to search for signals of extraterrestrial origin. TurboSETI can search the data for hundreds of drift rates (in Hz/sec) and handles either .fil or .h5 file formats. It has several dependencies, including Blimpy (ascl:1906.002) and Astropy (ascl:1304.002).

[ascl:1906.005] Kalman: Forecasts and interpolations for ALMA calibrator variability

Kalman models an inhomogeneous time series of measurements at different frequencies as noisy sampling from a finite mixture of Gaussian Ornstein-Uhlenbeck processes to try to reproduce the variability of the fluxes and of the spectral indices of the quasars used as calibrators in the Atacama Large Millimeter/Sub-millimeter Array (ALMA), assuming sensible parameters are provided to the model (obtained, for example, from maximum likelihood estimation). One routine in the Kalman Perl module calculates best forecast estimations based on a state space representation of the stochastic model using Kalman recursions, and another routine calculates the smoothed estimation (or interpolations) of the measurements and of the state space also using Kalman recursions. The code does not include optimization routines to calculate best fit parameters for the stochastic processes.

[ascl:1906.004] The Exo-Striker: Transit and radial velocity interactive fitting tool for orbital analysis and N-body simulations

The Exo-Striker analyzes exoplanet orbitals, performs N-body simulations, and models the RV stellar reflex motion caused by dynamically interacting planets in multi-planetary systems. It offers a broad range of tools for detailed analysis of transit and Doppler data, including power spectrum analysis for Doppler and transit data; Keplerian and dynamical modeling of multi-planet systems; MCMC and nested sampling; Gaussian Processes modeling; and a long-term stability check of multi-planet systems. The Exo-Striker can also analyze Mean Motion Resonance (MMR) analysis, create fast fully interactive plots, and export ready-to-use LaTeX tables with best-fit parameters, errors, and statistics. It combines Fortran efficiency and Python flexibility and is cross-platform compatible (MAC OS, Linux, Windows). The tool relies on a number of open-source packages, including RVmod engine, emcee (ascl:1303.002), batman (ascl:1510.002), celerite (ascl:1709.008), and dynesty (ascl:1809.013).

[ascl:1906.003] FREDDA: A fast, real-time engine for de-dispersing amplitudes

FREDDA detects Fast Radio Bursts (FRBs) in power data. It is optimized for use at ASKAP, namely GHz frequencies with 10s of beams, 100s of channels and millisecond integration times. The code is written in CUDA for NVIDIA Graphics Processing Units.

Would you like to view a random code?