The Extensible N-Dimensional Data Format (NDF) stores bulk data in the form of N-dimensional arrays of numbers. It is typically used for storing spectra, images and similar datasets with higher dimensionality. The NDF format is based on the Hierarchical Data System (HDS) and is extensible; not only does it provide a comprehensive set of standard ancillary items to describe the data, it can also be extended indefinitely to handle additional user-defined information of any type. The NDF library is used to read and write files in the NDF format. It is distributed with the Starlink software (ascl:1110.012).

This paper presents an overview and introduction to Smoothed Particle Hydrodynamics and Magnetohydrodynamics in theory and in practice. Firstly, we give a basic grounding in the fundamentals of SPH, showing how the equations of motion and energy can be self-consistently derived from the density estimate. We then show how to interpret these equations using the basic SPH interpolation formulae and highlight the subtle difference in approach between SPH and other particle methods. In doing so, we also critique several `urban myths' regarding SPH, in particular the idea that one can simply increase the `neighbour number' more slowly than the total number of particles in order to obtain convergence. We also discuss the origin of numerical instabilities such as the pairing and tensile instabilities. Finally, we give practical advice on how to resolve three of the main issues with SPMHD: removing the tensile instability, formulating dissipative terms for MHD shocks and enforcing the divergence constraint on the particles, and we give the current status of developments in this area. Accompanying the paper is the first public release of the NDSPMHD SPH code, a 1, 2 and 3 dimensional code designed as a testbed for SPH/SPMHD algorithms that can be used to test many of the ideas and used to run all of the numerical examples contained in the paper.

NE2001p is a fully Python implementation of the NE2001 Galactic electron density model. NE2001p forward models the dispersion and scattering of compact radio sources, including pulsars, fast radio bursts, AGNs, and masers, and the model predicts the distances of radio sources that lack independent distance measures.

NEAT is a fully automated code which carries out a complete analysis of lists of emission lines to estimate the amount of interstellar extinction, calculate representative temperatures and densities, compute ionic abundances from both collisionally excited lines and recombination lines, and finally to estimate total elemental abundances using an ionization correction scheme. NEAT uses a Monte Carlo technique to robustly propagate uncertainties from line flux measurements through to the derived abundances.

NEBULA performs the radiative transfer of the 3He+ hyperfine transition, radio recombination lines (RRLs), and free-free continuum emission through a model nebula. The model nebula is composed of only H and He within a three-dimension Cartesian grid with arbitrary density, temperature, and ionization structure. The 3He+ line is assumed to be in local thermodynamic equilibrium (LTE), but non-LTE effects and pressure broadening from electron impacts can be included for the RRLs. All spectra are broadened by thermal and microturbulent motions.

NEBULAR synthesizes the spectrum of a mixed hydrogen helium gas in collisional ionization equilibrium. It is not a spectral fitting code, but it can be used to resample a model spectrum onto the wavelength grid of a real observation. It supports a wide range of temperatures and densities. NEBULAR includes free-free, free-bound, two-photon and line emission from HI, HeI and HeII. The code will either return the composite model spectrum, or, if desired, the unrescaled atomic emission coefficients. It is written in C++ and depends on the GNU Scientific Library (GSL).

NeedATool (Needlet Analysis Tool) performs data analysis based on needlets, a wavelet rendition powerful for the analysis of fields defined on a sphere. Needlets have been applied successfully to the treatment of astrophysical and cosmological observations, particularly to the analysis of cosmic microwave background (CMB) data. Wavelets have emerged as a useful tool for CMB data analysis, as they combine most of the advantages of both pixel space, where it is easier to deal with partial sky coverage and experimental noise, and the harmonic domain, in which beam treatment and comparison with theoretical predictions are more effective due in large part to their sharp localization.

NEMESIS (Non-linear optimal Estimator for MultivariatE spectral analySIS) is the general purpose correlated-k/LBL retrieval code developed from the RADTRAN project (ascl:2210.008). Originally based on the correlated-k approximation, NEMESIS also works in line-by-line (LBL) mode. It has been designed to be generally applicable to any planet and with any observing mode and so is suitable for both solar-system studies and also exoplanetary studies.

nemiss calculates neutrino emission from an astrophysical jet. nemiss works as part of the PLUTO-nemiss-rlos pipeline. PLUTO (ascl:1010.045) produces a hydrodynamical jet. Then, nemiss calculates beamed neutrino emission at each eligible cell along a given direction in space. Finally, rlos (ascl:1811.009) produces a synthetic neutrino image of the jet along the given direction, taking into consideration the finite nature of the speed of light.

NEMO is an extendible Stellar Dynamics Toolbox, following an Open-Source Software model. It has various programs to create, integrate, analyze and visualize N-body and SPH like systems, following the pipe and filter architecture. In addition there are various tools to operate on images, tables and orbits, including FITS files to export/import to/from other astronomical data reduction packages. A large growing fraction of NEMO has been contributed by a growing list of authors. The source code consist of a little over 4000 files and a little under 1,000,000 lines of code and documentation, mostly C, and some C++ and Fortran. NEMO development started in 1986 in Princeton (USA) by Barnes, Hut and Teuben. See also ZENO (ascl:1102.027) for the version that Barnes maintains.

Nemo detects millimeter-wave Sunyaev-Zel'dovich galaxy clusters and compact sources. Originally developed for the Atacama Cosmology Telescope project, the code is capable of analyzing the next generation of deep, wide multifrequency millimeter-wave maps that will be produced by experiments such as the Simons Observatory. Nemo provides several modules for analyzing ACT/SO data in addition to the command-line programs provided in the package.

The NEOexchange web portal and Target and Observation Manager ingests solar system objects, including Near-Earth Object (NEO) candidates from the Minor Planet Center, schedules observations on the Las Cumbres Observatory global telescope network and reduces, displays, and analyzes the resulting data. NEOexchange produces calibrated photometry from the imaging data and uses Source Extractor (ascl:1010.064) and SCAMP (ascl:1010.063) to perform object detection and astrometric fitting and calviacat (ascl:2207.015) to perform photometric calibration against photometric catalogs. It also has the ability to perform image registration and subtraction using SWARP (ascl:1010.068) and HOTPANTS (ascl:1504.004) and image stacking, alignment, and faint feature detection using gnuastro (ascl:1801.009).

nessai performs nested sampling for Bayesian Inference and incorporates normalizing flows. It is designed for applications where the Bayesian likelihood is computationally expensive. nessai uses PyTorch and also supports the use of bilby (ascl:1901.011).

NEST (Noble Element Simulation Technique) offers comprehensive, accurate, and precise simulation of the excitation, ionization, and corresponding scintillation and electroluminescence processes in liquid noble elements, useful for direct dark matter detectors, double beta decay searches, PET scans, and general radiation detection technology. Written in C++, NEST is an add-on module for the Geant4 simulation package that incorporates more detailed physics than is currently available into the simulation of scintillation. NEST is of particular use for low-energy nuclear recoils. All available liquid xenon data on nuclear recoils and electron recoils to date have been taken into consideration in arriving at the current models. NEST also handles the magnitude of the light and charge yields of nuclear recoils, including their electric field dependence, thereby shedding light on the possibility of detection or exclusion of a low-mass dark matter WIMP by liquid xenon detectors.

Nestcheck analyzes nested sampling runs and estimates numerical uncertainties on calculations using them. The package can load results from a number of nested sampling software packages, including MultiNest (ascl:1109.006), PolyChord (ascl:1502.011), dynesty (ascl:1809.013) and perfectns (ascl:1809.005), and offers the flexibility to add input functions for other nested sampling software packages. Nestcheck utilities include error analysis, diagnostic tests, and plots for nested sampling calculations.

nestle is a pure Python implementation of nested sampling algorithms for evaluating Bayesian evidence. Nested sampling integrates posterior probability in order to compare models in Bayesian statistics. It is similar to Markov Chain Monte Carlo (MCMC) in that it generates samples that can be used to estimate the posterior probability distribution. Unlike MCMC, the nature of the sampling also allows one to calculate the integral of the distribution. It is also a pretty good method for robustly finding global maxima.

We have developed a method to efficiently simulate the dynamics of the magnetic flux in the solar network. We call this method Network Flux Transport (NFT). Implemented using a Spherical Centroidal Voronoi Tessellation (SCVT) based network model, magnetic flux is advected by photospheric plasma velocity fields according to the geometry of the SCVT model. We test NFT by simulating the magnetism of the Solar poles. The poles of the sun above 55 deg latitude are free from flux emergence from active regions or ephemeral regions. As such, they are ideal targets for a simplified simulation that relies on the strengths of the NFT model. This simulation method reproduces the magnetic and spatial distributions for the solar poles over two full solar cycles.

The first step in a science project is the acquisition and understanding of the relevant data. The tools range from simple data transfer methods to more complex browser-emulating scripts. When integrated with a defined sample or catalog, these scripts provide seamless techniques to retrieve and store data of varying types. These tools can be used to leapfrog from website to website to acquire multi-wavelength datasets. This project demonstrates the capability to use multiple data websites, in conjunction, to perform the type of calculations once reserved for on-site datasets.

NeutrinoFog calculates the neutrino floor based on the derivative of a hypothetical experimental discovery limit as a function of exposure, and leads to a neutrino floor that is only influenced by the systematic uncertainties on the neutrino flux normalizations.

Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages. Its fluent DSL simplifies the implementation and the deployment of complex parallel and reactive workflows on clouds and clusters. Nextflow supports deploying workflows on a variety of execution platforms including local, HPC schedulers, AWS Batch, Google Cloud Life Sciences, and Kubernetes. Additionally, it provides support for workflow dependencies through built-in support for, for example, Conda, Spack, Docker, Podman, Singularity, and Modules.

nfield uses a stochastic formalism to compute the IR correlation functions of quantum fields during cosmic inflation in n-field dimensions. This is a necessary 1-loop resummation of the correlation functions to render them finite. The code supports the implementation of n-numbers of coupled test fields (energetically sub-dominant) as well as non-test fields.

nFITSview is a simple, user-friendly and open-source FITS image viewer available for Linux and Windows. One of the main concepts of nFITSview is to provide an intuitive user interface which may be helpful both for scientists and for amateur astronomers. nFITSview has different color mapping and manipulation schemes, supports different formats of FITS data files as well as exporting them to different popular image formats. It also supports command-line exporting (with some restrictions) of FITS files to other image formats.
The application is written in C++/Qt for achieving better performance, and with every next version the performance aspect is taken into account.
nFITSview uses its own libnfits library (can be used separately as well) for parsing the FITS files.

Available in R and Python, the simple analytic scheme NFWdist performs highly efficient and exact sampling of the Navarro, Frenk & White (NFW) profile as a true probability distribution function, with the only variable being the concentration.

NGMIX implements Gaussian mixture models for 2D images. Both the PSF profile and the galaxy are modeled using mixtures of Gaussians. Convolutions are thus performed analytically, resulting in fast model generation as compared to methods that perform the convolution in Fourier space. For the galaxy model, NGMIX supports exponential disks and de Vaucouleurs and Sérsic profiles; these are implemented approximately as a sum of Gaussians using the fits from Hogg & Lang (2013). Additionally, any number of Gaussians can be fit, either completely free or constrained to be cocentric and co-elliptical.

nicaea calculates cosmology and weak-lensing quantities and functions from theoretical models of the large-scale structure. Written in C, it can compute the Hubble parameter, distances, and geometry for background cosmology, and linear perturbations, including growth factor, transfer function, cluster mass function, and linear 3D power spectra. It also calculates fitting formulae for non-linear power spectra, emulators, and halo model for Non-linear evolution, and the HOD model for galaxy clustering. In addition, nicaea can compute quantities for cosmic shear such as the convergence power spectrum, second-order correlation functions and derived second-order quantities, and third-order aperture mass moment; it can also calculate CMB anisotropies via CAMB (ascl:1102.026).

NICIL (Non-Ideal magnetohydrodynamics Coefficients and Ionisation Library) calculates the ionization values and the coefficients of the non-ideal magnetohydrodynamics terms of Ohmic resistivity, the Hall effect, and ambipolar diffusion. Written as a standalone Fortran90 module that can be implemented in existing codes, NICIL is fully parameterizable, allowing the user to choose which processes to include and decide the values of the free parameters. The module includes both cosmic ray and thermal ionization; the former includes two ion species and three species of dust grains (positively charged, negatively charged and neutral), and the latter includes five elements which can be doubly ionized.

NICOLE, written in Fortran 90, seeks the model atmosphere that provides the best fit to the Stokes profiles (in a least-squares sense) of an arbitrary number of simultaneously-observed spectral lines from solar/stellar atmospheres. The inversion core used for the development of NICOLE is the LORIEN engine (the Lovely Reusable Inversion ENgine), which combines the SVD technique with the Levenberg-Marquardt minimization method to solve the inverse problem.

NIFTY (Numerical Information Field TheorY) is a versatile library enables the development of signal inference algorithms that operate regardless of the underlying spatial grid and its resolution. Its object-oriented framework is written in Python, although it accesses libraries written in Cython, C++, and C for efficiency. NIFTY offers a toolkit that abstracts discretized representations of continuous spaces, fields in these spaces, and operators acting on fields into classes. Thereby, the correct normalization of operations on fields is taken care of automatically. This allows for an abstract formulation and programming of inference algorithms, including those derived within information field theory. Thus, NIFTY permits rapid prototyping of algorithms in 1D and then the application of the developed code in higher-dimensional settings of real world problems. NIFTY operates on point sets, n-dimensional regular grids, spherical spaces, their harmonic counterparts, and product spaces constructed as combinations of those.

NIFTy (Numerical Information Field Theory) facilitates the construction of Bayesian field reconstruction algorithms for fields being defined over multidimensional domains. A NIFTy algorithm can be developed for 1D field inference and then be used in 2D or 3D, on the sphere, or on product spaces thereof. NIFTy5 is a complete redesign of the previous framework (ascl:1302.013), and requires only the specification of a probabilistic generative model for all involved fields and the data in order to be able to recover the former from the latter. This is achieved via Metric Gaussian Variational Inference, which also provides posterior samples for all unknown quantities jointly.

Nightfall is an astronomy application for fun, education, and science. It can produce animated views of eclipsing binary stars, calculate synthetic lightcurves and radial velocity curves, and eventually determine the best-fit model for a given set of observational data of an eclipsing binary star system.

Nightfall comes with a user guide and a set of observational data for several eclipsing binary star systems.

NIGO (Numerical Integrator of Galactic Orbits) predicts the orbital evolution of test particles moving within a fully-analytical gravitational potential generated by a multi-component galaxy. The code can simulate the orbits of stars in elliptical and disc galaxies, including non-axisymmetric components represented by a spiral pattern and/or rotating bar(s).

Nigraha identifies and evaluates planet candidates from TESS light curves. Using a combination of high signal to noise ratio (SNR) shallow transits, supervised machine learning, and detailed vetting, the neural network-based pipeline identifies planet candidates missed by prior searches. The pipeline runs in four stages. It first performs period finding using the Transit Least Squares (TLS) package and runs sector by sector to build a per-sector catalog. It then transforms the flux values in .fits lightcurve files to global/local views and write out the output in .tfRecords files, builds a model on training data, and saves a checkpoint. Finally, it loads a previously saved model to generate predictions for new sectors. Nigraha provides helper scripts to generate candidates in new sectors, thus allowing others to perform their own analyses.

Nii implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) framework for sampling multidimensional posterior distributions and provides an observation simulation platform for the differential astrometric measurement of exoplanets. Although this code specifically focuses on the orbital parameter retrieval problem of differential astrometry, Nii can be applied to other scientific problems with different posterior distributions and offers many control parameters in the APT part to facilitate the adjustment of the MCMC sampling strategy; these include the number of parallel chains, the β values of different chains, the dynamic range of the sampling step sizes, and frequency of adjusting the step sizes.

NIMBLE (Non-parametrIc jeans Modeling with B-spLinEs) inferrs the cumulative mass distribution of a gravitating system from full 6D phase space coordinates of its tracers via spherical Jeans modeling. It models the Milky Way's dark matter halo using Gaia and Dark Energy Spectroscopic Instrument Milky Way Survey (DESI MWS) data. NIMBLE includes a basic inverse modeling Jeans routine that assumes perfect and complete data is available and a more complex forward modeling Jeans routine that deconvolves observational effects (uncertainties and limited survey volume) characteristic of Gaia and the DESI-MWS. It also includes tools for generating simple equilibrium model galaxies using Agama (ascl:1805.008) and imposing mock Gaia+DESI errors on 6D phase space input data.

nimbus is a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on non-detections. This framework makes use of GW 3-D distance information and electromagnetic upper limits from a given survey for multiple events, and self-consistently accounts for finite sky-coverage and probability of astrophysical origin.

NIRDust uses K-band (2.2 micrometers) spectra to measure the temperature of the dust heated by an Active Galactic Nuclei (AGN) accretion disk. The package provides several functionalities to pre-process spectra and fit the hot dust component of a AGN K-band spectrum with a blackbody function. NIRDust needs a minimum of two spectra to run: a target spectrum, where the dust temperature will be estimated, and a reference spectrum, where the emission is considered to be purely stellar. The reference spectrum will be used by NIRDust to model the stellar emission from the target spectrum.

The NIRVANA code is capable of the simulation of multi-scale self-gravitational magnetohydrodynamics problems in three space dimensions employing the technique of adaptive mesh refinement. The building blocks of NIRVANA are (i) a fully conservative, divergence-free Godunov-type central scheme for the solution of the equations of magnetohydrodynamics; (ii) a block-structured mesh refinement algorithm which automatically adds and removes elementary grid blocks whenever necessary to achieve adequate resolution and; (iii) an adaptive mesh Poisson solver based on multigrid philosophy which incorporates the so-called elliptic matching condition to keep the gradient of the gravitational potential continous at fine/coarse mesh interfaces.

The library NLopt performs nonlinear local and global optimization for functions with and without gradient information. It provides a simple, unified interface and wraps many algorithms for global and local, constrained or unconstrained, optimization, and provides interfaces for many other languages, including C++, Fortran, Python, Matlab or GNU Octave, OCaml, GNU Guile, GNU R, Lua, Rust, and Julia.

NMMA probes nuclear physics and cosmology with multimessenger analysis. This fully featured, Bayesian multi-messenger pipeline targets joint analyses of gravitational-wave and electromagnetic data (focusing on the optical). Using bilby (ascl:1901.011) as the back-end, the software uses a variety of samplers to sampling these data sets. NMMA uses chiral effective field theory based neutron star equation of states when performing inference, and is also capable of estimating the Hubble Constant.

NNKCDE is a simple and easily interpretable Conditional Density Estimation (CDE) method. It computes a kernel density estimate of y using the k nearest neighbors of the evaluation point x. The model has only two tuning parameters: the number of nearest neighbors k and the bandwidth h of the smoothing kernel in y-space. Both tuning parameters are chosen in a principled way by minimizing the CDE loss on validation data.

NOD3 processes and analyzes maps from single-dish observations affected by scanning effects from clouds, receiver instabilities, or radio-frequency interference. Its “basket-weaving” tool combines orthogonally scanned maps into a final map that is almost free of scanning effects. A restoration tool for dual-beam observations reduces the noise by a factor of about two compared to the NOD2 version. Combining single-dish with interferometer data in the map plane ensures the full recovery of the total flux density.

Non-Gaussian Realisations provides code based on a spectral distortion/quantile transformation that generates a realization of a field on a cubic grid that has a specified probability distribution function and a specified power spectrum.

We have constructed a grid of non-LTE disk models for a wide range of black hole mass and mass accretion rate, for several values of viscosity parameter alpha, and for two extreme values of the black hole spin: the maximum-rotation Kerr black hole, and the Schwarzschild (non-rotating) black hole. Our procedure calculates self-consistently the vertical structure of all disk annuli together with the radiation field, without any approximations imposed on the optical thickness of the disk, and without any ad hoc approximations to the behavior of the radiation intensity. The total spectrum of a disk is computed by summing the spectra of the individual annuli, taking into account the general relativistic transfer function. The grid covers nine values of the black hole mass between M = 1/8 and 32 billion solar masses with a two-fold increase of mass for each subsequent value; and eleven values of the mass accretion rate, each a power of 2 times 1 solar mass/year. The highest value of the accretion rate corresponds to 0.3 Eddington. We show the vertical structure of individual annuli within the set of accretion disk models, along with their local emergent flux, and discuss the internal physical self-consistency of the models. We then present the full disk-integrated spectra, and discuss a number of observationally interesting properties of the models, such as optical/ultraviolet colors, the behavior of the hydrogen Lyman limit region, polarization, and number of ionizing photons. Our calculations are far from definitive in terms of the input physics, but generally we find that our models exhibit rather red optical/UV colors. Flux discontinuities in the region of the hydrogen Lyman limit are only present in cool, low luminosity models, while hotter models exhibit blueshifted changes in spectral slope.

NonnegMFPy solves nonnegative matrix factorization (NMF) given a dataset with heteroscedastic uncertainties and missing data with a vectorized multiplicative update rule; this can be used create a mask and iterate the process to exclude certain new data by updating the mask. The code can work on multi-dimensional data, such as images, if the data are first flattened to 1D.

NOVAS is an integrated package of subroutines and functions for computing various commonly needed quantities in positional astronomy. The package can provide, in one or two subroutine or function calls, the instantaneous coordinates of any star or planet in a variety of coordinate systems. At a lower level, NOVAS also supplies astrometric utility transformations, such as those for precession, nutation, aberration, parallax, and the gravitational deflection of light. The computations are accurate to better than one milliarcsecond. The NOVAS package is an easy-to-use facility that can be incorporated into data reduction programs, telescope control systems, and simulations. The U.S. parts of The Astronomical Almanac are prepared using NOVAS. Three editions of NOVAS are available: Fortran, C, and Python.

nProFit analyzes surface brightness profiles. It obtains the best-fit structural, scale, and shape parameters of star clusters in Hubble Space Telescope images of nearby galaxies. The code fits dynamical models and can derive physically-relevant parameters. Among these are central volume and luminosity densities, total masses and luminosities, central velocity dispersions, core radius, half-light radius, tidal radius, and binding energy.

NPTFit is a specialized Python/Cython package that implements Non-Poissonian Template Fitting (NPTF), originally developed for characterizing populations of unresolved point sources. It offers fast evaluation of likelihoods for NPTF analyses and has an easy-to-use interface for performing non-Poissonian (as well as standard Poissonian) template fits using MultiNest (ascl:1109.006) or other inference tools. It allows inclusion of an arbitrary number of point source templates, with an arbitrary number of degrees of freedom in the modeled flux distribution, and has modules for analyzing and plotting the results of an NPTF.

NR-code applies nonlinear reconstruction to the dark matter density field in redshift space and solves for the nonlinear mapping from the initial Lagrangian positions to the final redshift space positions; this reverses the large-scale bulk flows and improves the precision measurement of the baryon acoustic oscillations (BAO) scale.

The NRDD_constraints tool provides simple interpolating functions written in Python that return the most constraining limit on the dark matter-nucleon scattering cross section for a list of non-relativistic effective operators. The package contains four files: the main code, NRDD_constraints.py; a simple driver, NRDD_constraints-example.py; and two data files, NRDD_data1.npy and NRDD_data2.npy

NRPy+ (Python-based Code generation for Numerical Relativity and Beyond) generates highly-optimized C code from complex tensorial expressions input in Einstein-like notation. NRPy+ uses SymPy as its computer algebra system backend. It is part of the NRPy+/SENR numerical relativity code package for solving Einstein's equations of general relativity to model compact objects at about 1/100 the cost in memory of more traditional, AMR-based numerical relativity codes, thus allowing desktop computers to be used for gravitational wave astrophysics.