Results 201-250 of 2431 (2396 ASCL, 35 submitted)
TATTER (Two-sAmple TesT EstimatoR) performs two-sample hypothesis test. The two-sample hypothesis test is concerned with whether distributions p(x) and q(x) are different on the basis of finite samples drawn from each of them. This ubiquitous problem appears in a legion of applications, ranging from data mining to data analysis and inference. This implementation can perform the Kolmogorov-Smirnov test (for one-dimensional data only), Kullback-Leibler divergence, and Maximum Mean Discrepancy (MMD) test. The module performs a bootstrap algorithm to estimate the null distribution and compute p-value.
CosmoLike analyzes cosmological data sets and forecasts future missions. It has been used in the analysis of the Dark Energy Survey and to optimize the Large Synoptic Survey Telescope and the Wide-Field Infrared Survey Telescope, and is useful for innovative theory projects that test new concepts and methods to enhance the constraining power of cosmological analyses.
2D-FFTLog takes the FFTLog algorithm for 1D Hankel transforms and generalizes it for 2D Hankel transforms. The algorithm is useful for efficiently computing non-Gaussian covariance matrices of cosmological 2-point statistics in configuration space from Fourier space covariances. Fast bin-averaging method is also developed for both the logarithmic binning and general binning choices. C and Python versions of the code are available.
The KinMS (KINematic Molecular Simulation) package simulates observations of arbitrary molecular/atomic cold gas distributions from interferometers and line observations from integral field units. This modeling tool is optimized for situations where one has analytic forms for e.g. the rotation curve and/or surface brightness profiles (and may want to fit the parameters of these parametric models). It can, however, also be used as a tilted-ring modelling code. The routines are flexible and have been used in various different applications, including investigating the kinematics of molecular gas in early-type galaxies and determining supermassive black-hole masses from CO interferometric observations. They are also useful for creating mock observations from hydrodynamic simulations, and input data-cubes for further simulation in, for example, CASA's (ascl:1107.013) sim_observe tool. Interactive Data Language (IDL) and Python versions of the code are available.
PRIISM images radio interferometry data using the sparse modeling technique. In addition to generating an image, PRIISM can choose the best image from a range of processing parameters using cross validation. User can obtain statistically optimal images by providing the visibility data with some configuration parameters. The software is implemented as a Python module.
HEARSAY computes simulations of the causal contacts between emitters in the Galaxy. It implements the Stochastic Constrained Causal Contact Network (SC3Net) model and explores the parameter space of the model for the emergence of communicating nodes through Monte Carlo simulations and analyzes their causal connections. This model for the abundance and duration of civilizations is based on minimal assumptions and three free parameters, with focus on the statistical properties of empirical models instead of an interpretable model with variables to be determined by observation.
HIPSTER (HIgh-k Power Spectrum EstimatoR) computes small-scale power spectra and isotropic bispectra for cosmological simulations and galaxy surveys of arbitrary shape. The code computes the Legendre multipoles of the power spectrum, Pℓ(k), or bispectrum Bℓ(k1,k2), by computing weighted pair counts over the simulation box or survey, truncated at some maximum radius. The code can be run either in 'aperiodic' or 'periodic' mode for galaxy surveys or cosmological simulations respectively. HIPSTER also supports weighted spectra, for example when tracer particles are weighted by their mass in a multi-species simulation. Generalization to anisotropic bispectra is straightforward (and requires no additional computing time) and can be added on request.
MCRaT (Monte Carlo Radiation Transfer) analyzes the radiation signature expected from astrophysical outflows. MCRaT injects photons in a FLASH (ascl:1010.082) simulation and individually propagates and compton scatters the photons through the fluid until the end of the simulation. This process of injection and propagating occurs for a user specified number of times until there are no more photons to be injected. Users can then construct light curves and spectra with the MCRaT calculated results. The hydrodynamic simulations used with this version of MCRaT must be in 2D; however, the photon propagation and scattering is done in 3D by assuming cylindrical symmetry. Additionally, MCRaT uses the full Klein–Nishina cross section including the effects of polarization, which can be fully simulated in the code. MCRaT works with FLASH hydrodynamic simulations and PLUTO (ascl:1010.045) AMR simulations, with both 2D spherical (r, equation) and 2D cartesian ((x,y) and (r,z)).
RFCDE provides an implementation of random forests designed for conditional density estimation. It computes a kernel density estimate of y with nearest neighbor weightings defined by the location of the evaluation point x relative to the leaves in the random forest.
cdetools provides tools for evaluating conditional density estimates and has applications to photometric redshift estimation and likelihood-free cosmological inference. Available in R and Python, it provides functions for computing a so-called CDE loss function for tuning and assessing the quality of individual probability density functions (PDFs) and diagnostic functions that probe the population-level performance of the PDFs.
RAPP is a robust automated photometry pipeline for blurred images. RAPP requires that the observed images contain at least three stars and applies bias, dark, and flat field correction on blurred observational raw data; it also uses the median of adjacent pixels to eliminate outliers. It also uses star enhancement and robust image matching, extracts stars, and performs aperture photometry to extract information from blurred images.
AMPEL provides an analysis framework for high-throughput surveys and is suited for streamed data. The package combines the functionality of an alert broker with a generic framework capable of hosting user-contributed code; it encourages provenance and keeps track of the varying information states that a transient displays. The latter concept includes information gathered over time and data policies such as access or calibration levels.
FETCH (Fast Extragalactic Transient Candidate Hunter) provides real-time classification of candidates from single pulse search pipelines. The package takes in a candidate file of frequency-time and DM-time data and, for each candidate and choice of model, provides the probability that the candidate is an FRB. FETCH also provides a framework for fine-tuning the models to further improve its performance for particular backends.
qubefit fits an observed data cube to generate a model cube from a user-defined emission model. The model cube is convolved with the observed beam, after which residuals between the convolved model and the observed data cube are minimized using a Markov chain Monte Carlo approach. qubefit also determines estimates of the uncertainty for each parameter of the model.
2DBAT implements Bayesian fits of 2D tilted-ring models to derive rotation curves of galaxies. It performs 2D tilted-ring analysis based on a Bayesian Markov Chain Monte Carlo (MCMC) technique, thus quantifying the kinematic geometry of galaxy discs, and deriving high-quality rotation curves that can be used for mass modeling of baryons and dark matter halos.
gotetra uses phase-space tesselation techniques to extract information about cosmological N-body simulations. The key applications of this Go-based code are the measurement of splashback shells around halos and the generation of high resolution images of density fields. The package includes routines to generates 3D and 2D (projected) density fields from a particle snapshot generated by a cosmological N-body simulation, measure density along lines of sight from the center of halos, and compresse the position space data from cosmological N-body simulations. Included are two helper libraries with functions for calculating cosmological quantities and computing a number of useful mathematical functions.
NNKCDE is a simple and easily interpretable Conditional Density Estimation (CDE) method. It computes a kernel density estimate of y using the k nearest neighbors of the evaluation point x. The model has only two tuning parameters: the number of nearest neighbors k and the bandwidth h of the smoothing kernel in y-space. Both tuning parameters are chosen in a principled way by minimizing the CDE loss on validation data.
s3PCF computes the 3-point correlation function (3PCF) in the squeezed limit given galaxy positions and pair positions. The code is currently written specifically for the Abacus simulations, but the main functionalities can be also easily adapted for other galaxy catalogs with the appropriate properties.
HiFLEx reduces echelle data taken with a single or bifurcated fiber input. It takes a FITS image file (i.e., a CCD image) and runs data reduction steps, extracts out orders from an Echelle spectrograph (regardless of separation and curvature, as long as orders are distinguishable from one-another), applies the wavelength correction, measures the radial velocity, and performs further calibration steps.
Carpyncho browses catalogs to search for and characterize time variable data of the Vista Variables in the Via Lactea (VVV) Survey. The stacked pawprint data from the Cambridge Astronomical Science Unit's (CASU) Vista Data Flow System (VDFS) v>= 1.3 catalogs have been crossed matched with the VDFS CASU v1.3 tile catalogs into Parquet files, allowing detection and classification of periodic variables within this dataset.
FFANCY uses the Fast Folding Algorithm (FFA) on a distributed-computing framework to search for pulsars in time-domain series data. This enables the algorithm to be applied to all-sky blind pulsar surveys. The package runs an implementation of the FFA on real or simulated pulsar time series data in either SIGPROC (ascl:1107.016) or PRETSO (ascl:1107.017) format with a choice of additional algorithms to be used in the evaluation of each folded profile and outputs a periodogram along with other output threads used for testing. It also contains routines that convert the periodogram output into a list of pulsar candidates with options for candidate grouping and harmonic matching, generate simulated pulsar profiles for use in testing profile evaluation algorithms independent of the FFA, provide basic statistics for the folded profiles produced by progeny, test individual profiles using profiles produced by progeny, and other complementary functions.
RoLo (Roche Lobe) calculates the radius and potential of the Roche Lobe for any specified direction, and also gives some other commonly used quantities (such as the Lagrange points). The calculator is valid for any mass ratio q between 0.01 and 100. The coordinates are spherical-polar (R, theta, phi) centered on one star (M1), with the x-axis (theta=pi/2, phi=0) pointing towards the other star (M2). The mass ratio is defined as q=M2/M1. Distances are given in units of the binary separation, a. A circular orbit is assumed.
Time series are commonly unevenly spaced in time make it difficult to obtain an accurate estimate of their typical red-noise spectrum. REDFIT overcomes this problem by fitting a first-order autoregressive (AR1) process directly to unevenly spaced time series. Hence, interpolation in the time domain and its inevitable bias can be avoided. The program can be used to test if peaks in the spectrum of a time series are significant against the red-noise background from an AR1 process.
RM-Tools analyzes radio polarization data, specifically the use of Faraday rotation measure synthesis and Stokes QU model fitting. It contains routines for both single-pixel 1D polarized spectra as well as 3D polarization cubes. RM-Tools is intended to serve as a toolkit for studies of polarized radio sources and measurements of their Faraday rotation. RM-Tools is the core package for the pipelines used for the POlarized Sky Survey of the Universe's Magnetism (POSSUM) and the polarization component of the Very Large Array Sky Survey (VLASS). The package is maintained by the Canadian Initiative for Radio Astronomy Data Analysis (CIRADA; cirada.org).
michi2 fits combinations of arbitrary numbers of libraries/components to a given observational data. Written in C++ and Python, this chi-square fitting tool can fit a galaxy's spectral energy distribution (SED) with stellar, active galactic nuclear, dust and radio SED templates, and fit a galaxy's spectral line energy distribution (SLED) with one or more gas components using radiative transfer LVG model grid libraries.
michi2 first samples the high-dimensional parameter space (N1*N2*N3*..., where N is the number of independent templates in each library, and 1/2/3 is the ID of components) in an optimized way for a few thousand or tens of thousand times to compute the chi-square to the input observational data, then uses Python scripts to analyze the chi-square distribution and derive the best-fit, median, lower and higher 1-sigma values for each parameter in each library/component. This tool is useful for fitting larger number of templates and arbitrary combinations of libraries/components, including some constraining of one library/component onto another.
RID (Relativistic Image Doubling in water Cherenkov detectors) uses Monte Carlo simulations to find the relative fraction of charged, relativistic particles entering a HAWC-like Water Cherenkov Detector that can cause a Relativistic Image Doubling (RID) effect visible to at least one of the internal detectors. The technique is available in C++ and Fortran; RID also includes python code for the horizontal incidence of the muon inside the tank.
PRECISION reduces astronomical IR imaging data. Written with SPHERE data in mind, it provides a fast and easy reduction of bright sources suitable for science. While it may not extract the absolute maximum amount of science, the objective is to provide a means to get science-ready data with minimal computing time or human interaction.
IRDAP (IRDIS Data reduction for Accurate Polarimetry) accurately reduces SPHERE-IRDIS polarimetric data. It is a highly-automated end-to-end pipeline; its core feature is model-based correction of the instrumental polarization effects. IRDAP handles data taken both in field- and pupil-tracking mode and using the broadband filters Y, J, H and Ks. Data taken with the narrowband filters can be reduced as well, although with a somewhat worse accuracy. For pupil-tracking observations IRDAP can additionally apply angular differential imaging.
The Python wrapper PyKat extends the optical interferometer modeling software Finesse (ascl:2004.013). It provides an efficient GUI for conducting complex numerical simulations and manipulating and viewing simulation setups, and enables the use of Python's extensive scientific software ecosystem.
Finesse is a numeric simulation for laser interferometers and models parametric instabilities, easily providing the required mechanical-to-optical transfer functions in imperfect and arbitrary interferometer configurations using Hermite-Gaussian beams. The code has been used to apply limits to the number and type of higher order modes used in simulation and investigate the potential use of higher order Laguerre-Gauss modes to reduce thermal noise in future gravitational wave detector designs. The PyKat wrapper (ascl:2004.014) helps automate complex Finesse tasks.
ArviZ provides backend-agnostic tools for diagnostics and visualizations of Bayesian inference by first converting inference data into xarray objects. It includes functions for posterior analysis, model checking, comparison and diagnostics. ArviZ’s functions work with NumPy arrays, dictionaries of arrays, xarray datasets, and have built-in support for PyMC3 (ascl:1610.016), PyStan, CmdStanPy, Pyro (ascl:1507.018), NumPyro, emcee (ascl:1303.002), and TensorFlow Probability objects. A Julia wrapper is also available.
FUNDPAR determines fundamental parameters of solar-type stars, by using as input the Equivalent Widths of Fe I,II lines. The code uses solar-scaled ATLAS9 model atmospheres with NEWODF opacities, together with the 2009 version of the MOOG (ascl:1202.009) program. Parameter files control different details, such as the mixing-length parameter, the overshooting, and the damping of the lines. FUNDPAR also derives the uncertainties of the parameters.
kombine is an ensemble sampler built for efficiently exploring multimodal distributions. By using estimates of ensemble’s instantaneous distribution as a proposal, it achieves very fast burnin, followed by sampling with very short autocorrelation times.
stardate measures precise stellar ages by combining isochrone fitting with gyrochronology (rotation-based ages) to increase the precision of stellar ages on the main sequence. The best possible ages provided by stardate will be for stars with rotation periods, though ages can also be predicted for stars without rotation periods. stardate is an extension to isochrones that incorporates gyrochronology and the code reverts back to isochrones when no rotation period is provided.
PPMAP provides column density mapping with extra dimensions (temperature and dust opacity index); it generate image cubes of differential column density as a function of (x,y) sky position and temperature for diffuse dusty structures. The code incorporates parallel processing using OpenMP for some of the more CPU-intensive steps. It is currently configured for the "Raven" cluster at Cardiff University and runs in a mode in which the computations are split between 16 separate nodes, each of which uses 16 cores with OpenMP.
PyCosmo provides accurate predictions for cosmological observables including background quantities, power spectra and Limber and beyond-Limber angular power spectra. The software is designed to be interactive and user-friendly. It is available for download and is also offered on an interactive platform (PyCosmo Hub), which allows users to perform their own computations using Jupyter Notebooks without installing any software.
ASTRAEUS (semi-numerical rAdiative tranSfer coupling of galaxy formaTion and Reionization in n-body dArk mattEr simUlationS) self-consistently derives the evolution of galaxies and the reionization of the IGM based on the merger trees and density fields of a DM-only N-body simulation. It models gas accretion, star formation, SN feedback, the time and spatial evolution of the ionized regions, accounting for recombinations, HI fractions and photoionization rates within ionized regions, and radiative feedback. ASTRAEUS is for studying the galaxy-reionization interplay in the first billion years. The underlying code is written in C and is MPI-parallelized; its modular design allows new physical processes and galaxy properties to be added easily. ASTRAEUS can be run on a tree-branch-by-tree-branch basis (i.e., fully vertical) or on a redshift-by-redshift basis (i.e., fully horizontal) when evolving the galaxies by using local horizontal merger trees.
PyWD2015 provides a modern graphical user interface (GUI) for the 2015 version of the Wilson-Devinney (WD) code (ascl:2004.004). The GUI is written in Python 2.7 and uses the Qt4 interface framework. At its core, PyWD2015 generates lcin and dcin files from user inputs and sends them to WD, then reads and visualizes the output in a user-friendly way. It also includes tools that make the technical aspects of the modeling process significantly easier.
Wilson-Devinney binary star modeling code (WD) is a complete package for modeling binary stars and their eclipes and consists of two main modules. The LC module generates light and radial velocity curves, spectral line profiles, images, conjunction times, and timing residuals; the DC module handles differential corrections, performing parameter adjustment of light curves, velocity curves, and eclipse timings by the Least Squares criterion. WD handles eccentric orbits and asynchronous rotation, and can compute velocity curves (with proximity and eclipse effects). It offers options for detailed reflection and nonlinear (logarithmic law) limb darkening, adjustment of spot parameters, an optional provision for spots to drift over the surface, and can follow light curve development over large numbers of orbits. Absolute flux solution allow Direct Distance Estimation (DDE) and there are improved solutions for ellipsoidal variables and for eclipsing binaries (EBs) with very shallow eclipses. Absolute flux solutions also can estimate temperatures of both EB components under suitable circumstances.
IllinoisGRMHD is an open-source, highly-extensible rewrite of the original closed-source general relativistic (ideal) magnetohydrodynamics (GRMHD) code of the Illinois Numerical Relativity (ILNR) Group. Reducing the learning curve was the primary focus of this rewrite, with the goal of facilitating community involvement in the code's use and development, as well as reducing the human effort necessary to generate new science. IllinoisGRMHD also saves computer time, generating roundoff-precision identical output to the original code on adaptive-mesh grids while being nearly twice as fast at scales of hundreds to thousands of cores.
Tangra performs scientific grade data reduction of GPS time-tagged video observations, including reduction of stellar occultation light curves and astrometry of close flybys of Near Earth Objects. It offers Dark and Flat frame image correction, PSF and aperture photometry, multiple methods for deriving a background as well as extensibility via add-ins. Tangra is actively developed for Windows and the current version of the software supports UCAC2, UCAC3, UCAC4, NOMAD, PPMXL and Gaia DR2 star catalogues for astrometry. The software can perform motion-fitting for fast objects and derive a mini-normal astrometric positions. The supported video file formats are AVI, SER, ADV and AAV. Tangra can be also used with observations recorded as a sequence of FITS files. There are also versions for Linux and OS-X with more limited functionality.
Locus implements the Locus Algorithm, which maximizes the performance of differential photometry systems by optimizing the number and quality of reference stars in the Field of View with the target.
Torch simulates coupled gas and N-body dynamics in astrophysical systems such as newly forming star clusters. It combines the FLASH (ascl:1010.082) code for gas dynamics and the ph4 code for direct N-body evolution via the AMUSE framework.
AstroHOG compares extended spectral-line observations (PPV cubes); the histogram of oriented gradients (HOG) technique takes as input two PPV cubes and provides an estimate of their spatial correlation across velocity channels to study spatial correlation between different tracers of the ISM.
PYSOLATOR removes the orbital modulation from a binary pulsar and/or its companion. In essence, it subtracts the predicted Roemer delay for the given orbit and then resamples the time series so as to make the signal appear as if it were emitted from the barycenter of the binary system, making the search for pulses easier and faster.
HOMER (Helper Of My Eternal Retrievals) is a machine-learning-accelerated Bayesian inverse modeling code. Given some data and uncertainties, the code determines the posterior distribution of a model. HOMER uses MC3 (ascl:1610.013) for its MCMC; its forward model is a neural network surrogate model trained by MARGE (ascl:2003.010). The code produces plots of the 1D marginalized posteriors, 2D pairwise posteriors, and parameter history traces, and can also overplot the 1D and 2D posteriors for comparison with another posterior. HOMER computes the Bhattacharyya coefficient to compare the similarity of two 1D marginalized posteriors.
MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) generates exoplanet spectra across a defined parameter space, processes the output, and trains, validates, and tests machine learning models as a fast approximation to radiative transfer. It uses BART (ascl:1608.004) for spectra generation and modifies BART’s Bayesian sampler (MC3, ascl:1610.013) with a random uniform sampler to propose models within a defined parameter space. More generally, MARGE provides a framework for training neural network models to approximate a forward, deterministic process.
TOASTER is a pulse times-of-arrival (TOA) tracker. It stores reduced/folded observations, meta data, templates, parfiles, TOAs, and timefiles in an organized manner using an SQL database. TOASTER also provides a full-featured python toolkit for reliably interacting with the data and database, and provides scripts that, for example, list and summarize the TOAs in the data base, and generate TOA files in multiple formats. The framework can also be used to generate TOAs from observations using flexible and reproducible plugins referred to as "manipulators".
CoastGuard reduces Effelsberg data; it is written in python and based on PSRCHIVE (ascl:1105.014). Though primarily designed for Effelsberg PSRIX data, it contains components sufficiently general for use with psrchive-compatible data files from other observing systems. In particular, the radio frequency interference (RFI) removal algorithm has been applied to data from the Parkes Telescope and has also been adopted by the LOFAR pulsar timing data reduction pipeline.
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