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[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

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

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

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

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

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

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

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

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

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

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

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.

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