Results 1001-1100 of 3429 (3345 ASCL, 84 submitted)

[ascl:2204.010]
FBCTrack: Fragmentation and bulk composition tracking

The fragmentation and bulk composition tracking package contains two codes. The fragmentation code models fragmentation in collisions for the C version of REBOUND (ascl:1110.016). This code requires setting two global parameters. It automatically produces a collision report that details the time of every collision, the bodies involved, how the collision was resolved, and how many fragments were produced; collision outcomes are assigned a numerical value. The bulk composition tracking code tracks the composition change as a function of mass exchange for bodies with a homogenous composition. It is a post-processing code that works in conjunction with the fragmentation code, and requires the collision report generated by the fragmentation code.

[ascl:1712.011]
FBEYE: Analyzing Kepler light curves and validating flares

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.

[ascl:2302.015]
FCFC: C toolkit for computing correlation functions from pair counts

FCFC (Fast Correlation Function Calculator) computes correlation functions from pair counts. It supports the isotropic 2-point correlation function, anisotropic 2PCF, 2-D 2PCF, and 2PCF Legendre multipoles, among others. Written in C, FCFC takes advantage of three parallelisms that can be used simultaneously, distributed-memory processes via Message Passing Interface (MPI), shared-memory threads via Open Multi-Processing (OpenMP), and single instruction, multiple data (SIMD).

[ascl:1505.014]
FCLC: Featureless Classification of Light Curves

FCLC (Featureless Classification of Light Curves) software describes the static behavior of a light curve in a probabilistic way. Individual data points are converted to densities and consequently probability density are compared instead of features. This gives rise to an independent classification which can corroborate the usefulness of the selected features.

[ascl:1806.027]
fcmaker: Creating ESO-compliant finding charts for Observing Blocks on p2

fcmaker creates astronomical finding charts for Observing Blocks (OBs) on the p2 web server from the European Southern Observatory (ESO). It automates the creation of ESO-compliant finding charts for Service Mode and/or Visitor Mode OBs at the Very Large Telescope (VLT). The design of the fcmaker finding charts, based on an intimate knowledge of VLT observing procedures, is fine-tuned to best support night time operations. As an automated tool, fcmaker also allows observers to independently check visually, for the first time, the observing sequence coded inside an OB. This includes, for example, the signs of telescope and position angle offsets.

[ascl:1705.012]
fd3: Spectral disentangling of double-lined spectroscopic binary stars

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.

[ascl:1705.011]
FDBinary: A tool for spectral disentangling of double-lined spectroscopic binary stars

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).

[ascl:1606.011]
FDIPS: Finite Difference Iterative Potential-field Solver

FDIPS is a finite difference iterative potential-field solver that can generate the 3D potential magnetic field solution based on a magnetogram. It is offered as an alternative to the spherical harmonics approach, as when the number of spherical harmonics is increased, using the raw magnetogram data given on a grid that is uniform in the sine of the latitude coordinate can result in inaccurate and unreliable results, especially in the polar regions close to the Sun. FDIPS is written in Fortran 90 and uses the MPI library for parallel execution.

[ascl:1604.011]
FDPS: Framework for Developing Particle Simulators

Iwasawa, Masaki; Tanikawa, Ataru; Hosono, Natsuki; Nitadori, Keigo; Muranushi, Takayuki; Makino, Junichiro

FDPS provides the necessary functions for efficient parallel execution of particle-based simulations as templates independent of the data structure of particles and the functional form of the interaction. It is used to develop particle-based simulation programs for large-scale distributed-memory parallel supercomputers. FDPS includes templates for domain decomposition, redistribution of particles, and gathering of particle information for interaction calculation. It uses algorithms such as Barnes-Hut tree method for long-range interactions; methods to limit the calculation to neighbor particles are used for short-range interactions. FDPS reduces the time and effort necessary to write a simple, sequential and unoptimized program of O(N^2) calculation cost, and produces compiled programs that will run efficiently on large-scale parallel supercomputers.

[ascl:1806.001]
feets: feATURE eXTRACTOR FOR tIME sERIES

feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).

[ascl:2110.018]
FEniCS: Computing platform for solving partial differential equations

FEniCS solves partial differential equations (PDEs) and enables users to quickly translate scientific models into efficient finite element code. With the high-level Python and C++ interfaces to FEniCS, it is easy to get started, but FEniCS offers also powerful capabilities for more experienced programmers. FEniCS runs on a multitude of platforms ranging from laptops to high-performance clusters, and each component of the FEniCS platform has been fundamentally designed for parallel processing. This framework allows for rapid prototyping of finite element formulations and solvers on laptops and workstations, and the same code may then be deployed on large high-performance computers.

[ascl:1203.004]
FERENGI: Full and Efficient Redshifting of Ensembles of Nearby Galaxy Images

Bandpass shifting and the (1+z)5 surface brightness dimming (for a fixed width filter) make standard tools for the extraction of structural parameters of galaxies wavelength dependent. If only few (or one) observed high-res bands exist, this dependence has to be corrected to make unbiased statements on the evolution of structural parameters or on galaxy subsamples defined by morphology. FERENGI artificially redshifts low-redshift galaxy images to different redshifts by applying the correct cosmological corrections for size, surface brightness and bandpass shifting. A set of artificially redshifted galaxies in the range 0.1<z<1.1 using a set of ~100 SDSS low-redshift (v<7000 km s-1) images as input has been created to use as a training set of realistic images of galaxies of diverse morphologies and a large range of redshifts for the GEMS and COSMOS galaxy evolution projects. This training set allows other studies to investigate and quantify the effects of cosmological redshift on the determination of galaxy morphologies, distortions, and other galaxy properties that are potentially sensitive to resolution, surface brightness, and bandpass issues. The data sets are also available for download from the FERENGI website.

[ascl:2201.008]
fermi-gce-flows: Infer the Galactic Center gamma-ray excess

fermi-gce-flows uses a machine learning-based technique to characterize the contribution of modeled components, including unresolved point sources, to the GCE. It can perform posterior parameter estimation while accounting for pixel-to-pixel spatial correlations in the gamma-ray map. On application to Fermi data, the method generically attributes a smaller fraction of the GCE flux to unresolved point source-like emission when compared to traditional approaches.

[ascl:1812.006]
Fermipy: Fermi-LAT data analysis package

Wood, M.; Caputo, R.; Charles, E.; Di Mauro, M.; Magill, J.; Perkins, J. S.; Fermi-LAT Collaboration

Fermipy facilitates analysis of data from the Large Area Telescope (LAT) with the Fermi Science Tools. It is built on the pyLikelihood interface of the Fermi Science Tools and provides a set of high-level tools for performing common analysis tasks, including data and model preparation with the gt-tools, extracting a spectral energy distribution (SED) of a source, and generating TS and residual maps for a region of interest. Fermipy also finds new source candidates and can localize a source or fit its spatial extension. The package uses a configuration-file driven workflow in which the analysis parameters (data selection, IRFs, and ROI model) are defined in a YAML configuration file. Analysis is executed through a python script that calls the methods of GTAnalysis to perform different analysis operations.

[ascl:1905.011]
Fermitools: Fermi Science Tools

Fermi Science Tools is a suite of tools for the analysis of both the Large-Area Telescope (LAT) and the Gamma-ray Burst Monitor (GBM) data, including point source analysis for generating maps, spectra, and light curves, pulsar timing analysis, and source identification.

[ascl:2301.016]
FERRE: Match physical models to measurements

FERRE matches physical models to observed data, taking a set of observations and identifying the model parameters that best reproduce the data, in a chi-squared sense. It solves the common problem of having numerical parametric models that are costly to evaluate and need to be used to interpret large data sets. FERRE provides flexibility to search for all model parameters, or hold constant some of them while searching for others. The code is written to be truly N-dimensional and fast. Model predictions are to be given as an array whose values are a function of the model parameters, *i.e.*, numerically. FERRE holds this array in memory, or in a direct-access binary file, and interpolates in it. The code returns, in addition to the optimal set of parameters, their error covariance, and the corresponding model prediction. The code is written in FORTRAN90.

[ascl:2005.014]
FETCH: Fast Extragalactic Transient Candidate Hunter

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.

[ascl:1208.011]
Fewbody: Numerical toolkit for simulating small-N gravitational dynamics

Fewbody is a numerical toolkit for simulating small-N gravitational dynamics. It is a general N-body dynamics code, although it was written for the purpose of performing scattering experiments, and therefore has several features that make it well-suited for this purpose. Fewbody uses the 8th-order Runge-Kutta Prince-Dormand integration method with 9th-order error estimate and adaptive timestep to advance the N-body system forward in time. It integrates the usual formulation of the N-body equations in configuration space, but allows for the option of global pairwise Kustaanheimo-Stiefel (K-S) regularization (Heggie 1974; Mikkola 1985). The code uses a binary tree algorithm to classify the N-body system into a set of independently bound hierarchies, and performs collisions between stars in the “sticky star” approximation. Fewbody contains a collection of command line utilities that can be used to perform individual scattering and N-body interactions, but is more generally a library of functions that can be used from within other codes.

[ascl:2005.006]
FFANCY: Fast Folding Algorithm for pulsar searching

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.

[ascl:2208.010]
FFD: Flare Frequency Distribution

FFD (Flare Frequency Distribution) fits power-laws to FFDs. FFDs relate the frequency (*i.e.*, occurrence rate) of flares to their energy, peak flux, photometric equivalent width, or other parameters. This module was created to handle disparate datasets between which the flare detection limit varies; in essence, the number of flares detected is treated as following a Poisson distribution while the flare energies are treated as following a power law.

[ascl:1911.022]
FFTLog-and-beyond: Generalized FFTLog algorithm

FFTLog-and-beyond takes the FFTLog algorithm for single-Bessel integrals and generalizes it for integrals containing a derivative of the Bessel function to solve the non-Limber integrals. The full non-Limber angular power spectrum integral is simplified by noting the small contribution from unequal-time nonlinear terms; this significantly reduces the computation and avoids the double-Bessel integral. The original FFTLog algorithm is also extended to compute integrals containing derivatives of Bessel functions, which can be used to efficiently compute angular power spectra including redshift-space distortions (RSD) and Doppler effects. C and Python versions of the code are available.

[ascl:1512.017]
FFTLog: Fast Fourier or Hankel transform

FFTLog is a set of Fortran subroutines that compute the fast Fourier or Hankel (= Fourier-Bessel) transform of a periodic sequence of logarithmically spaced points. FFTLog can be regarded as a natural analogue to the standard Fast Fourier Transform (FFT), in the sense that, just as the normal FFT gives the exact (to machine precision) Fourier transform of a linearly spaced periodic sequence, so also FFTLog gives the exact Fourier or Hankel transform, of arbitrary order m, of a logarithmically spaced periodic sequence.

[ascl:1201.015]
FFTW: Fastest Fourier Transform in the West

FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and complex data (as well as of even/odd data, i.e. the discrete cosine/sine transforms or DCT/DST).

Benchmarks performed on a variety of platforms show that FFTW's performance is typically superior to that of other publicly available FFT software, and is even competitive with vendor-tuned codes. In contrast to vendor-tuned codes, however, FFTW's performance is portable: the same program will perform well on most architectures without modification.

The FFTW library is required by other codes such as StarCrash (ascl:1010.074) and Hammurabi (ascl:1201.014).

[ascl:2307.021]
FGBuster: Parametric component separation for Cosmic Microwave Background observations

FGBuster (ForeGroundBuster) separates frequency maps into component maps and forecasts component separation both when the model is correct and when it is incorrect. FGBuster can be used for SED evaluation, intermediate component separation, multi-resolution separation, and forecasting, among other tasks.

[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:2205.014]
FHD: Fast Holographic Deconvolution

Sullivan, Ian; Barry, Nichole; Byrne, Ruby L.; Morales, Miguel F.; Hazelton, Bryna; Beardsley, Adam; Lanman, Adam

FHD is an open-source imaging algorithm for radio interferometers and is written in IDL. The three main use-cases for FHD are efficient image deconvolution for general radio astronomy, fast-mode Epoch of Reionization analysis, and simulation. FHD inputs beam models, calibration files, and sky model catalogs and requires input data to be in uvfits format.

[ascl:1603.014]
fibmeasure: Python/Cython module to find the center of back-illuminated optical fibers in metrology images

fibmeasure finds the precise locations of the centers of back-illuminated optical fibers in images. It was developed for astronomical fiber positioning feedback via machine vision cameras and is optimized for high-magnification images where fibers appear as resolvable circles. It was originally written during the design of the WEAVE pick-and-place fiber positioner for the William Herschel Telescope.

[ascl:1111.013]
FIBRE-pac: FMOS Image-based Reduction Package

Iwamuro, F.; Moritani, Y.; Yabe, K.; Sumiyoshi, M.; Kawate, K.; Tamura, N.; Akiyama, M.; Kimura, M.; Takato, N.; Tait, P.; Ohta, K.; Totani, T.; Suzuki, Y.; Tonegawa, M.

The FIBRE-pac (FMOS image-based reduction package) is an IRAF-based reduction tool for the fiber multiple-object spectrograph (FMOS) of the Subaru telescope. To reduce FMOS images, a number of special techniques are necessary because each image contains about 200 separate spectra with airglow emission lines variable in spatial and time domains, and with complicated throughput patterns for the airglow masks. In spite of these features, almost all of the reduction processes except for a few steps are carried out automatically by scripts in text format making it easy to check the commands step by step. Wavelength- and flux-calibrated images together with their noise maps are obtained using this reduction package.

[ascl:2202.012]
fiducial_flare: Spectra and lightcurves of a standardized far ultraviolet flare

fiducial_flare generates a reasonable approximation of the UV emission of M dwarf stars over a single flare or a series of them. The simulated radiation is resolved in both wavelength and time. The intent is to provide consistent input for applications requiring time-dependent stellar UV radiation fields that balances simplicity with realism, namely for simulations of exoplanet atmospheres.

[ascl:1307.004]
FieldInf: Field Inflation exact integration routines

FieldInf is a collection of fast modern Fortran routines for computing exactly the background evolution and primordial power spectra of any single field inflationary models. It implements reheating without any assumptions through the "reheating parameter" R allowing robust inflationary parameter estimations and inference on the reheating energy scale. The underlying perturbation code actually deals with N fields minimally-coupled and/or non-minimally coupled to gravity and works for flat FLRW only.

[ascl:1708.009]
FIEStool: Automated data reduction for FIber-fed Echelle Spectrograph (FIES)

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.

[ascl:1203.013]
Figaro: Data Reduction Software

Figaro (sometimes referred to as "standalone Figaro") is a data reduction system that originated at Caltech and whose development continued at the Anglo-Australian Observatory. Although it is intended to be able to deal with any sort of data, almost all its applications to date are geared towards processing optical and infrared data. Figaro uses hierarchical data structures to provide flexibility in its data file formats. Figaro was originally written to run under DEC's VMS operating system, but is now available both for VAX/VMS (by special request) and for various flavors of UNIX including Linux and MacOS.

A variant of Figaro (ascl:1411.022) is incorporated into the Starlink package (ascl:1110.012).

[ascl:1608.009]
FilFinder: Filamentary structure in molecular clouds

FilFinder extracts and analyzes filamentary structure in molecular clouds. In particular, it is capable of uniformly extracting structure over a large dynamical range in intensity. It returns the main filament properties: local amplitude and background, width, length, orientation and curvature. FilFinder offers additional tools to, for example, create a filament-only image based on the properties of the radial fits. The resulting mask and skeletons may be saved in FITS format, and property tables may be saved as a CSV, FITS or LaTeX table.

[ascl:1602.007]
FilTER: Filament Trait-Evaluated Reconstruction

FilTER (Filament Trait-Evaluated Reconstruction) post-processes output from DisPerSE (ascl:1302.015

[ascl:2202.016]
Find_Orb: Orbit determination from observations

Find_Orb takes a set of observations of an asteroid, comet, or natural or artificial satellite given in the MPC (Minor Planet Center) format, the ADES astrometric format, and/or the NEODyS or AstDyS formats, and finds the corresponding orbit.

[ascl:2210.004]
Finder_charts: Create finder charts from image data of various sky surveys

Finder_charts creates multi-band finder charts from image data of various partial- and all-sky surveys such as DSS, 2MASS, WISE, UKIDSS, VHS, Pan-STARRS, and DES. It also creates a WISE time series of image data acquired between 2010 and 2021. All images are reprojected so that north is up and east is to the left. The resulting finder charts can be overplotted with corresponding catalog positions. All catalog entries within the specified field of view can be saved in a variety of formats, including ipac, csv, and tex, as can the finder charts in png, pdf, eps, and other common graphics formats. Finder_charts consists of a single Python module, which depends only on well-known packages, making it easy to install.

[ascl:2004.013]
Finesse: Frequency domain INterfErometer Simulation SoftwarE

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.

[ascl:1808.006]
Fips: An OpenGL based FITS viewer

FIPS is a cross-platform FITS viewer with a responsive user interface. Unlike other FITS viewers, FIPS uses GPU hardware via OpenGL to provide functionality such as zooming, panning and level adjustments. OpenGL 2.1 and later is supported. FIPS supports all 2D image formats except floating point formats on OpenGL 2.1. FITS image extension has basic limited support.

[ascl:2202.006]
FIRE Studio: Movie making utilities for the FIRE simulations

FIRE Studio is a Python interface for C libraries that project Smoothed Particle Hydrodynamic (SPH) datasets. These C libraries can, in principle, be applied to any SPH dataset; the Python interface is specialized to conveniently load and format Gadget-derivative datasets such as GIZMO (ascl:1410.003). FIRE Studio is fast, memory efficient, and parallelizable. In addition to producing "1-color" projection maps for SPH datasets, the interface can produce "2-color" maps, where the pixel saturation is set by one projected quantity and the hue is set by another, and "3-color" maps, where three quantities are projected simultaneously and remapped into an RGB colorspace. FIRE Studio can model stellar emission and dust extinction to produce mock Hubble images (by default) or to model surface brightness maps for thirteen of the most common bands (plus the bolometric luminosity). It produces publication quality static images of simulation datasets and provides interpolation scripts to create movies that smoothly evolve in time (provided multiple snapshots in time of the data exist), view the dataset from different perspectives (taking advantage of shared memory buffers to allow massive parallelization), or both.

[ascl:2108.010]
FIREFLY: Chi-squared minimization full spectral fitting code

FIREFLY (Fitting IteRativEly For Likelihood analYsis) derives stellar population properties of stellar systems, whether observed galaxy or star cluster spectra or model spectra from simulations. The code fits combinations of single-burst stellar population models to spectroscopic data following an iterative best-fitting process controlled by the Bayesian Information Criterion without applying priors. Solutions within a statistical cut are retained with their weight, which is arbitrary. No additive or multiplicative polynomia are used to adjust the spectral shape and no regularization is imposed. This fitting freedom allows mapping of the effect of intrinsic spectral energy distribution (SED) degeneracies, such as age, metallicity, dust reddening on stellar population properties, and quantifying the effect of varying input model components on such properties.

[ascl:1810.021]
Firefly: Interactive exploration of particle-based data

Firefly provides interactive exploration of particle-based data in the browser. The user can filter, display vector fields, and toggle the visibility of their customizable datasets all on-the-fly. Different Firefly visualizations, complete with preconfigured data and camera view-settings, can be shared by URL. As Firefly is written in WebGL, it can be hosted online, though Firefly can also be used locally, without an internet connection. Firefly was developed with simulations of galaxy formation in mind but is flexible enough to display any particle-based data. Other features include a stereoscopic 3D picture mode and mobile compatibility.

[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:1202.014]
FISA: Fast Integrated Spectra Analyzer

FISA (Fast Integrated Spectra Analyzer) permits fast and reasonably accurate age and reddening determinations for small angular diameter open clusters by using their integrated spectra in the (3600-7400) AA range and currently available template spectrum libraries. This algorithm and its implementation help to achieve astrophysical results in shorter times than from other methods. FISA has successfully been applied to integrated spectroscopy of open clusters, both in the Galaxy and in the Magellanic Clouds, to determine ages and reddenings.

[ascl:1010.070]
Fisher.py: Fisher Matrix Manipulation and Confidence Contour Plotting

Fisher.py allows you to combine constraints from multiple experiments (e.g., weak lensing + supernovae) and add priors (e.g., a flat universe) simply and easily. Calculate parameter uncertainties and plot confidence ellipses. Fisher matrix expectations for several experiments are included as calculated by myself (time delays) and the Dark Energy Task Force (WL/SN/BAO/CL/CMB), or provide your own.

[ascl:1201.007]
Fisher4Cast: Fisher Matrix Toolbox

The Fisher4Cast suite, which requires MatLab, provides a standard, tested tool set for general Fisher Information matrix prediction and forecasting for use in both research and education. The toolbox design is robust and modular, allowing for easy additions and adaptation while keeping the user interface intuitive and easy to use. Fisher4Cast is completely general but the default is coded for cosmology. It provides parameter error forecasts for cosmological surveys providing distance, Hubble expansion and growth measurements in a general, curved FLRW background.

[ascl:2308.015]
FishLSS: Fisher forecasting for Large Scale Structure surveys

FishLSS computes the Fisher information matrix for a set of observables and model parameters. It can model the redshift-space power spectrum of any biased tracer of the CDM+baryon field and the post-reconstruction galaxy power spectrum. The code also models the projected cross-correlation of galaxies with the CMB lensing convergence, the projected galaxy power spectrum, and the CMB lensing convergence power spectrum. FishLSS requires pyFFTW (ascl:2109.009), velocileptors (ascl:2308.014), and CLASS (ascl:1106.020).

[ascl:1609.004]
FISHPACK: Efficient FORTRAN Subprograms for the Solution of Separable Elliptic Partial Differential Equations

The FISHPACK collection of Fortran77 subroutines solves second- and fourth-order finite difference approximations to separable elliptic Partial Differential Equations (PDEs). These include Helmholtz equations in cartesian, polar, cylindrical, and spherical coordinates, as well as more general separable elliptic equations. The solvers use the cyclic reduction algorithm. When the problem is singular, a least-squares solution is computed. Singularities induced by the coordinate system are handled, including at the origin r=0 in cylindrical coordinates, and at the poles in spherical coordinates. A modernization of FISHPACK is available as FISHPACK90 (ascl:1609.005).

[ascl:1609.005]
FISHPACK90: Efficient FORTRAN Subprograms for the Solution of Separable Elliptic Partial Differential Equations

FISHPACK90 is a modernization of the original FISHPACK (ascl:1609.004), employing Fortran90 to slightly simplify and standardize the interface to some of the routines. This collection of Fortran programs and subroutines solves second- and fourth-order finite difference approximations to separable elliptic Partial Differential Equations (PDEs). These include Helmholtz equations in cartesian, polar, cylindrical, and spherical coordinates, as well as more general separable elliptic equations. The solvers use the cyclic reduction algorithm. When the problem is singular, a least-squares solution is computed. Singularities induced by the coordinate system are handled, including at the origin r=0 in cylindrical coordinates, and at the poles in spherical coordinates. Test programs are provided for the 19 solvers. Each serves two purposes: as a template to guide you in writing your own codes utilizing the FISHPACK90 solvers, and as a demonstration on your computer that you can correctly produce FISHPACK90 executables.

[ascl:1601.016]
Fit Kinematic PA: Fit the global kinematic position-angle of galaxies

Fit kinematic PA measures the global kinematic position-angle (PA) from integral field observations of a galaxy stellar or gas kinematics; the code is available in IDL and Python.

[ascl:1609.015]
FIT3D: Fitting optical spectra

Sánchez, S. F.; Pérez, E.; Sánchez-Blázquez, P.; González, J. J.; Rosales-Ortega, F. F.; Cano-Díaz, M.; López-Cobá, C.; Marino, R. A.; Gil de Paz, A.; Mollá, M.; López-Sánchez, A. R.; Ascasibar, Y.; Barrera-Ballesteros, J.

FIT3D fits optical spectra to deblend the underlying stellar population and the ionized gas, and extract physical information from each component. FIT3D is focused on the analysis of Integral Field Spectroscopy data, but is not restricted to it, and is the basis of Pipe3D, a pipeline used in the analysis of datasets like CALIFA, MaNGA, and SAMI. It can run iteratively or in an automatic way to derive the parameters of a large set of spectra.

[ascl:1305.011]
FITDisk: Cataclysmic Variable Accretion Disk Demonstration Tool

FITDisk models accretion disk phenomena using a fully three-dimensional hydrodynamics calculation, and data can either be visualized as they are computed or stored to hard drive for later playback at a fast frame rate. Simulations are visualized using OpenGL graphics and the viewing angle can be changed interactively. Pseudo light curves of simulated systems can be plotted along with the associated Fourier amplitude spectrum. It provides an easy to use graphical user interface as well as 3-D interactive graphics. The code computes the evolution of a CV accretion disk, visualizes results in real time, records and plays back simulations, and generates and plots pseudo light curves and associated power spectra. FITDisk is the Windows executable form of this software; its Fortran source code is also available as DiskSim (ascl:1811.013).

[ascl:2301.005]
fitOmatic: Interferometric data modeling

The fitOmatic model-fitting prototyping tool tests multi-wavelength model-fitting and exploits VLTI data. It provides tools to define simple geometrical models and conveniently adjust the model's parameters. Written in Yorick, it takes optical interferometry FITS (oifits) files as input and allows the user to define a model of the source from a set of pre-defined models, which can be combined to make more complicated models. fitOmatic then computes the Fourier Transform of the modeled brightness distribution and synthetic observables are computed at the wavelengths and projected baselines of the observations. fitomatic's strength is its ability to define vector-parameters, *i.e.*, parameters that may depend on wavelength and/or time. The self-cal (ascl:2301.006) component of fitOmatic is also available as a separate code.

[ascl:1206.002]
FITS Liberator: Image processing software

Lindberg Christensen, Lars; Nielsen, Lars Holm; Nielsen, Kaspar K.; Johansen, Teis; Hurt, Robert; de Martin, David

The ESA/ESO/NASA FITS Liberator makes it possible to process and edit astronomical science data in the FITS format to produce stunning images of the universe. Formerly a plugin for Adobe Photoshop, the current version of FITS Liberator is a stand-alone application and no longer requires Photoshop. This image processing software makes it possible to create color images using raw observations from a range of telescopes; the FITS Liberator continues to support the FITS and PDS formats, preferred by astronomers and planetary scientists respectively, which enables data to be processed from a wide range of telescopes and planetary probes, including ESO’s Very Large Telescope, the NASA/ESA Hubble Space Telescope, NASA’s Spitzer Space Telescope, ESA’s XMM–Newton Telescope and Cassini–Huygens or Mars Reconnaissance Orbiter.

[ascl:1505.029]
fits2hdf: FITS to HDFITS conversion

fits2hdf ports FITS files to Hierarchical Data Format (HDF5) files in the HDFITS format. HDFITS allows faster reading of data, higher compression ratios, and higher throughput. HDFITS formatted data can be presented transparently as an in-memory FITS equivalent by changing the import lines in Python-based FITS utilities. fits2hdf includes a utility to port MeasurementSets (MS) to HDF5 files.

[ascl:2309.014]
fitScalingRelation: Fit galaxy cluster scaling relations using MCMC

fitScalingRelation fits galaxy cluster scaling relations using orthogonal or bisector regression and MCMC. It takes into account errors on both variables and intrinsic scatter. Although it geared for fitting galaxy cluster scaling relations of all kinds, it can be used for any kind of regression problem with errors on both variables and intrinsic scatter.

[ascl:1710.018]
FITSFH: Star Formation Histories

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.

[ascl:1111.014]
FITSH: Software Package for Image Processing

FITSH provides a standalone environment for analysis of data acquired by imaging astronomical detectors. The package provides utilities both for the full pipeline of subsequent related data processing steps (including image calibration, astrometry, source identification, photometry, differential analysis, low-level arithmetic operations, multiple image combinations, spatial transformations and interpolations, etc.) and for aiding the interpretation of the (mainly photometric and/or astrometric) results. The package also features a consistent implementation of photometry based on image subtraction, point spread function fitting and aperture photometry and provides easy-to-use interfaces for comparisons and for picking the most suitable method for a particular problem. The utilities in the package are built on the top of the commonly used UNIX/POSIX shells (hence the name of the package), therefore both frequently used and well-documented tools for such environments can be exploited and managing massive amount of data is rather convenient.

[ascl:1107.003]
FITSManager: Management of Personal Astronomical Data

Cui, Chenzhou; Fan, Dongwei; Zhao, Yongheng; Kembhavi, Ajit; He, Boliang; Cao, Zihuang; Li, Jian; Nandrekar, Deoyani

With the increase of personal storage capacity, it is easy to find hundreds to thousands of FITS files in the personal computer of an astrophysicist. Because Flexible Image Transport System (FITS) is a professional data format initiated by astronomers and used mainly in the small community, data management toolkits for FITS files are very few. Astronomers need a powerful tool to help them manage their local astronomical data. Although Virtual Observatory (VO) is a network oriented astronomical research environment, its applications and related technologies provide useful solutions to enhance the management and utilization of astronomical data hosted in an astronomer's personal computer. FITSManager is such a tool to provide astronomers an efficient management and utilization of their local data, bringing VO to astronomers in a seamless and transparent way. FITSManager provides fruitful functions for FITS file management, like thumbnail, preview, type dependent icons, header keyword indexing and search, collaborated working with other tools and online services, and so on. The development of the FITSManager is an effort to fill the gap between management and analysis of astronomical data.

[ascl:2201.004]
FitsMap: Interactive astronomical image and catalog data visualizer

FitsMap visualizes astronomical image and catalog data. Implemented in Python, the software is a simple, lightweight tool, requires only a simple web server, and can scale to over gigapixel images with tens of millions of sources. Further, the web-based visualizations can be viewed performantly on mobile devices.

[ascl:1905.012]
Fitsverify: FITS file format-verification tool

Fitsverify rigorously checks whether a FITS (Flexible Image Transport System) data file conforms to the requirements defined in Version 3.0 of the FITS Standard document; it is a standalone version of the ftverify and fverify tasks that are distributed as part of the ftools (ascl:9912.002) software package. The source code must be compiled and linked with the CFITSIO (ascl:1010.001) library. An interactive web is also available that can verify the format of any FITS data file on a local computer or on the Web.

[ascl:1709.011]
FLaapLUC: Fermi-LAT automatic aperture photometry light curve

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.

[ascl:1710.007]
FLAG: Exact Fourier-Laguerre transform on the ball

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 (ascl:2207.034). 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.

[ascl:1112.007]
FLAGCAL: FLAGging and CALlibration Pipeline for GMRT Data

FLAGging and CALlibration (FLAGCAL) is a software pipeline developed for automatic flagging and calibration of the GMRT data. This pipeline can be used for preprocessing (before importing the data in AIPS) any other interferromteric data also (given that the data file is in FITS format and contains multiple channels & scans).There are also a few GUI based tools which can be used for quick visualization of the data.

[ascl:2305.010]
FLAGLET: Fast and exact wavelet transform on the ball

FLAGLET computes flaglet transforms with arbitrary spin direction, probing the angular features of this generic wavelet transform for rapid analysis of signals from wavelet coefficients. The code enables the decomposition of a band-limited signal into a set of flaglet maps that capture all information contained in the initial band-limited map, and it can reconstruct the individual flaglets at varying resolutions. FLAGLET relies upon the SSHT (ascl:2207.034), S2LET (ascl:1211.001), and SO3 codes to provide angular transforms and sampling theorems, as well as the FFTW (ascl:1201.015) code to compute Fourier transforms.

[ascl:1811.007]
Flame: Near-infrared and optical spectroscopy data reduction pipeline

Flame reduces near-infrared and optical multi-object spectroscopic data. Although the pipeline was created for the LUCI instrument at the Large Binocular Telescope, Flame, written in IDL, is modular and can be adapted to work with data from other instruments. The software uses 2D transformations, thus using one interpolation step to wavelength calibrate and rectify the data. The γ(x, y) transformation also includes the spatial misalignment between frames, which can be measured from a reference star observed simultaneously with the science targets; sky subtraction can be performed via nodding and/or modelling of the sky spectrum.

[submitted]
FLARE: Synthetic Fast Radio Burst catalog generator

FLARE, a parallel code written in Python, generates 100,000 Fast Radio Bursts (FRB) using the Monte Carlo method. The FRB population is diverse and includes sporadic FRBs, repeaters, and periodic repeaters. However, less than 200 FRBs have been detected to date, which makes understanding the FRB population difficult. To tackle this problem, FLARE uses a Monte Carlo method to generate 100,000 realistic FRBs, which can be analyzed later on for further research. It has the capability to simulate FRB distances (based on the observed FRB distance range), energies (based on the "flaring magnetar model" of FRBs), fluences, multi-wavelength counterparts (based on x-ray to radio fluence ratio of FRB 200428), and other properties. It analyzes the resulting synthetic FRB catalog and displays the distribution of their properties. It is fast (as a result of parallel code) and requires minimal human interaction. FLARE is, therefore, able to give a broad picture of the FRB population.

[ascl:1010.082]
FLASH: Adaptive Mesh Hydrodynamics Code for Modeling Astrophysical Thermonuclear Flashes

Fryxell, B.; Olson, K.; Ricker, P.; Timmes, F. X.; Zingale, M.; Lamb, D. Q.; MacNeice, P.; Rosner, R.; Truran, J. W.; Tufo, H.

The FLASH code, currently in its 4th version, is a publicly available high performance application code which has evolved into a modular, extensible software system from a collection of unconnected legacy codes. FLASH consists of inter-operable modules that can be combined to generate different applications. The FLASH architecture allows arbitrarily many alternative implementations of its components to co-exist and interchange with each other. A simple and elegant mechanism exists for customization of code functionality without the need to modify the core implementation of the source. A built-in unit test framework combined with regression tests that run nightly on multiple platforms verify the code.

[ascl:1606.015]
FLASK: Full-sky Lognormal Astro-fields Simulation Kit

FLASK (Full-sky Lognormal Astro-fields Simulation Kit) makes tomographic realizations on the sphere of an arbitrary number of correlated lognormal or Gaussian random fields; it can create joint simulations of clustering and lensing with sub-per-cent accuracy over relevant angular scales and redshift ranges. It is C++ code parallelized with OpenMP; FLASK generates fast full-sky simulations of cosmological large-scale structure observables such as multiple matter density tracers (galaxies, quasars, dark matter haloes), CMB temperature anisotropies and weak lensing convergence and shear fields. The mutiple fields can be generated tomographically in an arbitrary number of redshift slices and all their statistical properties (including cross-correlations) are determined by the angular power spectra supplied as input and the multivariate lognormal (or Gaussian) distribution assumed for the fields. Effects like redshift space distortions, doppler distortions, magnification biases, evolution and intrinsic aligments can be introduced in the simulations via the input power spectra which must be supplied by the user.

[ascl:2111.012]
flatstar: Make 2d intensity maps of limb-darkened stars

flatstar is an open-source Python tool for drawing stellar disks as numpy.ndarray objects with scientifically-rigorous limb darkening. Each pixel has an accurate fractional intensity in relation to the total stellar intensity of 1.0. It is ideal for ray-tracing simulations of stars and planetary transits. The code is fast, has the most well-known limb-darkening laws, including linear, quadratic, square-root, logarithmic, and exponential, and allows the user to implement custom limb-darkening laws. flatstar also offers supersampling for situations where both coarse arrays and precision in stellar disk intensity (*i.e.*, no hard pixel boundaries) is desired, and upscaling to save on computation time when high-resolution intensity maps are needed, though there is some precision loss in intensities.

[ascl:2308.002]
FLATW'RM: Finding flares in Kepler data using machine-learning tools

FLATW'RM (FLAre deTection With Ransac Method) detects stellar flares in light curves using a classical machine-learning method. The code tries to find a rotation period in the light curve and splits the data to detection windows. The light curve sections are fit with the robust fitting algorithm RANSAC (Random sample consensus); outlier points (flare candidates) above the pre-set detection level are marked for each section. For the given detection window, only those flare candidates that have at least a given number of consecutive points (three by default) are kept and marked as flares. When using FLATW’RM, the code's output should be checked to determine whether changes to the default settings are needed to account for light curve noise, data sampling frequency, and scientific needs.

[ascl:2203.009]
fleck: Fast starspot rotational modulation light curves

fleck simulates rotational modulation of stars due to starspots and is used to overcome the degeneracies and determine starspot coverages accurately for a sample of young stars. The code simulates starspots as circular dark regions on the surfaces of rotating stars, accounting for foreshortening towards the limb, and limb darkening. Supplied with the latitudes, longitudes, and radii of spots and the stellar inclinations from which each star is viewed, fleck takes advantage of efficient array broadcasting with numpy to return approximate light curves. For example, the code can compute rotational modulation curves sampled at ten points throughout the rotation of each star for one million stars, with two unique spots each, all viewed at unique inclinations, in about 10 seconds on a 2.5 GHz Intel Core i7 processor. This rapid computation of light curves en masse makes it possible to measure starspot distributions with techniques such as Approximate Bayesian Computation.

[ascl:2007.011]
FleCSPH: Parallel and distributed SPH implementation based on the FleCSI

Loiseau, Julien; Lim, Hyun; Kaltenborn, Mark Alexander; Korobkin, Oleg; Mauney, Christopher M.; Sagert, Irina; Even, Wesley P.; Bergen, Benjamin K.

FleCSPH is a multi-physics compact application that exercises FleCSI parallel data structures for tree-based particle methods. In particular, the software implements a smoothed-particle hydrodynamics (SPH) solver for the solution of Lagrangian problems in astrophysics and cosmology. FleCSPH includes support for gravitational forces using the fast multipole method (FMM). Particle affinity and gravitation is handled using the parallel implementation of the octree data structure provided by FleCSI.

[ascl:2009.019]
FLEET: Finding Luminous and Exotic Extragalactic Transients

Gomez, Sebastian; Berger, Edo; Blanchard, Peter K.; Hosseinzadeh, Griffin; Nicholl, Matt; Villar, V. Ashley; Yin, Yao

FLEET (Finding Luminous and Exotic Extragalactic Transients) is a machine-learning pipeline that predicts the probability of a transient to be a superluminous supernova. With light curve and contextual host galaxy information, it uses a random forest algorithm to rapidly identify SLSN-I without the need for redshift information.

[ascl:1612.006]
flexCE: Flexible one-zone chemical evolution code

flexCE (flexible Chemical Evolution) computes the evolution of a one-zone chemical evolution model with inflow and outflow in which gas is instantaneously and completely mixed. It can be used to demonstrate the sensitivity of chemical evolution models to parameter variations, show the effect of CCSN yields on chemical evolution models, and reproduce the 2D distribution in [O/Fe]{[Fe/H] by mixing models with a range of inflow and outflow histories. It can also post-process cosmological simulations to predict element distributions.

[ascl:1107.004]
Flexible DM-NRG

This code combines the spectral sum-conserving methods of Weichselbaum and von Delft and of Peters, Pruschke and Anders (both relying upon the complete basis set construction of Anders and Schiller) with the use of non-Abelian symmetries in a flexible manner: Essentially any non-Abelian symmetry can be taught to the code, and any number of such symmetries can be used throughout the computation for any density of states, and to compute any local operators' correlation function's real and imaginary parts or any thermodynamical expectation value. The code works both at zero and finite temperatures.

[ascl:1205.006]
Flexion: IDL code for calculating gravitational flexion

Gravitational flexion is a technique for measuring 2nd order gravitational lensing signals in background galaxies and radio lobes. Unlike shear, flexion directly probes variations of the potential field. Moreover, the information contained in flexion is orthogonal to what is found in the shear. Thus, we get the information "for free."

[ascl:1411.016]
Flicker: Mean stellar densities from flicker

Flicker calculates the mean stellar density of a star by inputting the flicker observed in a photometric time series. Written in Fortran90, its output may be used as an informative prior on stellar density when fitting transit light curves.

[ascl:1210.007]
FLUKA: Fully integrated particle physics Monte Carlo simulation package

Fassò, Alberto; Ferrari, Alfredo; Ranft, Johannes; Sala, Paola; Mairani, Andrea; Empl, Anton; Sommerer, Florian; Cerutti, Francesco; Battistoni, Giuseppe; Roesler, Stefan; Vlachoudis, Vasilis; Patera, Vincenzo; Aarnio, P.; Möhring, J.-H.; Stevenson, G. R.; Zazula, J. M.

FLUKA (FLUktuierende KAskade) is a general-purpose tool for calculations of particle transport and interactions with matter. FLUKA can simulate with high accuracy the interaction and propagation in matter of about 60 different particles, including photons and electrons from 1 keV to thousands of TeV, neutrinos, muons of any energy, hadrons of energies up to 20 TeV (up to 10 PeV by linking FLUKA with the DPMJET code) and all the corresponding antiparticles, neutrons down to thermal energies and heavy ions. The program, written in Fortran, can also transport polarised photons (e.g., synchrotron radiation) and optical photons. Time evolution and tracking of emitted radiation from unstable residual nuclei can be performed online.

[ascl:1105.008]
Flux Tube Model

This Fortran code computes magnetohydrostatic flux tubes and sheets according to the method of Steiner, Pneuman, & Stenflo (1986) A&A 170, 126-137. The code has many parameters contained in one input file that are easily modified. Extensive documentation is provided in README files.

[ascl:1712.010]
Flux Tube: Solar model

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.

[ascl:2110.015]
Flux: Julia machine learning library

Innes, Michael; Saba, Elliot; Fischer, Keno; Gandhi, Dhairya; Concetto Rudilosso, Marco; Mariya Joy, Neethu; Karmali, Tejan; Pal, Avik; Shah, Viral

Flux provides an elegant approach to machine learning. Written in Julia, it provides lightweight abstractions on top of Julia's native GPU and AD support. It has many useful tools built in, but also lets you use the full power of the Julia language where you need it. Flux has relatively few explicit APIs for features like regularization or embeddings; instead, writing down the mathematical form works and is fast. The package works well with Julia libraries from data frames and images to differential equation solvers, so building complex data processing pipelines that integrate Flux models is straightforward.

[ascl:1405.010]
FLUXES: Position and flux density of planets

Jenness, Tim; Privett, Grant; Matthews, Henry; Hohenkerk, Catherine; Barnard, Vicki; Tilanus, Remo; Watt, Graeme; Emerson, Jim

FLUXES calculates approximate topocentric positions of the planets and also integrated flux densities of five of them at several wavelengths. These provide calibration information at the effective frequencies and beam-sizes employed by the UKT14, SCUBA and SCUBA-2 receivers on the JCMT telescope based on Mauna Kea, Hawaii. FLUXES is part of the bundle that comprises the Starlink multi-purpose astronomy software package (ascl:1110.012).

[ascl:1011.019]
FLY: MPI-2 High Resolution code for LSS Cosmological Simulations

Cosmological simulations of structures and galaxies formations have played a fundamental role in the study of the origin, formation and evolution of the Universe. These studies improved enormously with the use of supercomputers and parallel systems and, recently, grid based systems and Linux clusters. Now we present the new version of the tree N-body parallel code FLY that runs on a PC Linux Cluster using the one side communication paradigm MPI-2 and we show the performances obtained. FLY is included in the Computer Physics Communication Program Library. This new version was developed using the Linux Cluster of CINECA, an IBM Cluster with 1024 Intel Xeon Pentium IV 3.0 Ghz. The results show that it is possible to run a 64 Million particle simulation in less than 15 minutes for each timestep, and the code scalability with the number of processors is achieved. This lead us to propose FLY as a code to run very large N-Body simulations with more than $10^{9}$ particles with the higher resolution of a pure tree code.

[ascl:2107.004]
FoF-Halo-finder: Halo location and size

FoF-Halo-finder identifies the location and size of collapsed objects (halos) within a cosmological simulation box. These halos are the host for the luminous objects in the Universe. Written in C, it is based on the friends-of-friends (FoF) algorithm, and is designed to work with PMN-body (ascl:2107.003).

[ascl:2312.010]
FORECAST: Realistic astronomical image and galaxy survey generator

Fortuni, Flaminia; Merlin, Emiliano; Fontana, Adriano; Giocoli, Carlo; Romelli, Erik; Graziani, Luca; Santini, Paola; Castellano, Marco; Charlot, Stéphane; Chevallard, Jacopo

FORECAST generates realistic astronomical images and galaxy surveys by forward modeling the output snapshot of any hydrodynamical cosmological simulation. It exploits the snapshot by constructing a lightcone centered on the observer's position; the code computes the observed fluxes of each simulated stellar element, modeled as a Single Stellar Population (SSP), in any chosen set of pass-band filters, including k-correction, IGM absorption, and dust attenuation. These fluxes are then used to create an image on a grid of pixels, to which observational features such as background noise and PSF blurring can be added. FORECAST provides customizable options for filters, size of the field of view, and survey parameters, thus allowing the synthetic images to be tailored for specific research requirements.

[submitted]
forecaster-plus

An internally overhauled but fundamentally similar version of Forecaster by Jingjing Chen and David Kipping, originally presented in arXiv:1603.08614 and hosted at https://github.com/chenjj2/forecaster.

The model itself has not changed- no new data was included and the hyperparameter file was not regenerated. All functions were rewritten to take advantage of Numpy vectorization and some additional user features were added. Now able to be installed via pip.

[ascl:1701.007]
Forecaster: Mass and radii of planets predictor

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.

[ascl:1912.009]
FORSTAND: Flexible ORbit Superposition Toolbox for ANalyzing Dynamical models

FORSTAND constructs dynamical models of galaxies using the Schwarzschild orbit-superposition method; the method is available as part of the AGAMA (ascl:1805.008) framework. The models created are constrained by line-of-sight kinematic observations and are applicable to galaxies of all morphological types, including disks and triaxial rotating bars.

[ascl:1904.011]
FortesFit: Flexible spectral energy distribution modelling with a Bayesian backbone

FortesFit efficiently explores and discriminates between various spectral energy distributions (SED) models of astronomical sources. The Python package adds Bayesian inference to a framework that is designed for the easy incorporation and relative assessment of SED models, various fitting engines, and a powerful treatment of priors, especially those that may arise from non-traditional wave-bands such as the X-ray or radio emission, or from spectroscopic measurements. It has been designed with particular emphasis for its scalability to large datasets and surveys.

[ascl:1405.007]
FORWARD: Forward modeling of coronal observables

Gibson, Sarah E.; Kucera, Therese A.; Casini, Roberto; Dove, James; Forland, Blake; Judge, Philip; Rachmeler, Laurel

FORWARD forward models various coronal observables and can access and compare existing data. Given a coronal model, it can produce many different synthetic observables (including Stokes polarimetry), as well as plots of model plasma properties (density, magnetic field, etc.). It uses the CHIANTI database (ascl:9911.004) and CLE polarimetry synthesis code, works with numerical model datacubes, interfaces with the PFSS module of SolarSoft (ascl:1208.013), includes several analytic models, and connects to the Virtual Solar Observatory for downloading data in a format directly comparable to model predictions.

[ascl:2102.015]
ForwardDiff: Forward mode automatic differentiation for Julia

ForwardDiff implements methods to take derivatives, gradients, Jacobians, Hessians, and higher-order derivatives of native Julia functions (or any callable object, really) using forward mode automatic differentiation (AD).While performance can vary depending on the functions you evaluate, the algorithms implemented by ForwardDiff generally outperform non-AD algorithms in both speed and accuracy.

[ascl:1204.004]
Fosite: 2D advection problem solver

Fosite implements a method for the solution of hyperbolic conservation laws in curvilinear orthogonal coordinates. It is written in Fortran 90/95 integrating object-oriented (OO) design patterns, incorporating the flexibility of OO-programming into Fortran 90/95 while preserving the efficiency of the numerical computation. Although mainly intended for CFD simulations, Fosite's modular design allows its application to other advection problems as well. Unlike other two-dimensional implementations of finite volume methods, it accounts for local conservation of specific angular momentum. This feature turns the program into a perfect tool for astrophysical simulations where angular momentum transport is crucial. Angular momentum transport is not only implemented for standard coordinate systems with rotational symmetry (i.e. cylindrical, spherical) but also for a general set of orthogonal coordinate systems allowing the use of exotic curvilinear meshes (e.g. oblate-spheroidal). As in the case of the advection problem, this part of the software is also kept modular, therefore new geometries may be incorporated into the framework in a straightforward manner.

[ascl:1610.012]
Fourierdimredn: Fourier dimensionality reduction model for interferometric imaging

Fourierdimredn (Fourier dimensionality reduction) implements Fourier-based dimensionality reduction of interferometric data. Written in Matlab, it derives the theoretically optimal dimensionality reduction operator from a singular value decomposition perspective of the measurement operator. Fourierdimredn ensures a fast implementation of the full measurement operator and also preserves the i.i.d. Gaussian properties of the original measurement noise.

[ascl:1806.030]
foxi: Forecast Observations and their eXpected Information

Using information theory and Bayesian inference, the foxi Python package computes a suite of expected utilities given futuristic observations in a flexible and user-friendly way. foxi requires a set of n-dim prior samples for each model and one set of n-dim samples from the current data, and can calculate the expected ln-Bayes factor between models, decisiveness between models and its maximum-likelihood averaged equivalent, the decisivity, and the expected Kullback-Leibler divergence (i.e., the expected information gain of the futuristic dataset). The package offers flexible inputs and is designed for all-in-one script calculation or an initial cluster run then local machine post-processing, which should make large jobs quite manageable subject to resources and includes features such as LaTeX tables and plot-making for post-data analysis visuals and convenience of presentation.

[ascl:1010.002]
fpack: FITS Image Compression Program

fpack is a utility program for optimally compressing images in the FITS data format. The associated funpack program will restore the compressed file back to its original state. These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs, except that they are specifically optimized for FITS format images and offer a wider choice of compression options.

fpack uses the tiled image compression convention for storing the compressed images. This convention can in principle support any number of of different compression algorithms; currently GZIP, Rice, Hcompress, and the IRAF pixel list compression algorithms have been implemented.

The main advantages of fpack compared to the commonly used technique of externally compressing the whole FITS file with gzip are:

- It is generally faster and offers better compression than gzip.

- The FITS header keywords remain uncompressed for fast access.

- Each HDU of a multi-extension FITS file is compressed separately, so it is not necessary to uncompress the entire file to read a single image in a multi-extension file.

- Dividing the image into tiles before compression enables faster access to small subsections of the image.

- The compressed image is itself a valid FITS file and can be manipulated by other general FITS utility software.

- Lossy compression can be used for much higher compression in cases where it is not necessary to exactly preserve the original image.

- The CHECKSUM keywords are automatically updated to help verify the integrity of the files.

- Software that supports the tiled image compression technique can directly read and write the FITS images in their compressed form.

[ascl:2311.010]
FPFS: Fourier Power Function Shaplets

FPFS (Fourier Power Function Shaplets) is a fast, accurate shear estimator for the shear responses of galaxy shape, flux, and detection. Utilizing leading-order perturbations of shear (a vector perturbation) and image noise (a tensor perturbation), the code determines shear and noise responses for both measurements and detections. Unlike methods that distort each observed galaxy repeatedly, the software employs analytical shear responses of select basis functions, including Shapelets basis and peak basis. FPFS is efficient and can process approximately 1,000 galaxies within a single CPU second, and maintains a multiplicative shear estimation bias below 0.5% even amidst blending challenges.

[ascl:2001.004]
FragMent: Fragmentation techniques for studying filaments

FragMent studies fragmentation in filaments by collating a number of different techniques, including nearest neighbour separations, minimum spanning tree, two-point correlation function, and Fourier power spectrum. It also performs model selection using a frequentist and Bayesian approach to find the best descriptor of a filament's fragmentation. While the code was designed to investigate filament fragmentation, the functions are general and may be used for any set of 2D points to study more general cases of fragmentation.

[ascl:2109.010]
Frankenstein: Flux reconstructor

Frankenstein (frank) fits the 1D radial brightness profile of an interferometric source given a set of visibilities. It uses a Gaussian process that performs the fit in <1 minute for a typical protoplanetary disc continuum dataset. Frankenstein can perform a fit in 2 ways, by running the code directly from the terminal or using the code as a Python module.

[ascl:2306.018]
FRB: Fast Radio Burst calculations, estimations, and analysis

Prochaska, J. Xavier; Simha, Sunil; Heintz, Kasper E.; Tejos, Nicolas; Mannings, Alexandra; Law, Casey

FRB performs calculations, estimations, analysis, and Bayesian inferences for Fast Radio Bursts, including dispersion measure and emission measure calculations, derived properties and spectrums, and Galactic RM.

[ascl:2011.011]
frbcat: Fast Radio Burst CATalog querying package

frbcat queries and downloads Fast Radio Burst (FRB) data from the FRBCAT Catalogue web page, the CHIME-REPEATERS web page and the Transient Name Server (TNS). It is written in Python and can be installed using pip.

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