DiskJockey derives dynamical masses for T Tauri stars using the Keplerian motion of their circumstellar disks, applied to radio interferometric data from the Atacama Large Millimeter Array (ALMA) and the Submillimeter Array (SMA). The package relies on RADMC-3D (ascl:1202.015) to perform the radiative transfer of the disk model. DiskJockey is designed to work in a parallel environment where the calculations for each frequency channel can be distributed to independent processors. Due to the computationally expensive nature of the radiative synthesis, fitting sizable datasets (e.g., SMA and ALMA) will require a substantial amount of CPU cores to explore a posterior distribution in a reasonable timeframe.

DiskMINT (Disk Model for INdividual Targets) models individual disks and derives robust disk mass estimates. Built on RADMC-3D (ascl:1202.015) for continuum (and gas line) radiative transfer, the code includes a reduced chemical network to determine the C18O emission. DiskMINT has a Python3 module that generates a self-consistent 2D disk structure to satisfy VHSE (Vertical Hydrostatic Equilibrium). It also contains a Fortran code of the reduced chemical network that contains the main chemical processes necessary for C18O modeling: the isotopologue-selective photodissociation, and the grain-surface chemistry where the CO converting to CO2 ice is the main reaction.

DISKMODs provides radial structure models of accretion disk solutions. The following models are included: Novikov-Thorne thin disk model and Sadowski polytropic slim disk model. Each model implements a common interface that gives the radial dependence of selected geometrical, physical and thermodynamic quantities of the accretion flow. The model interpolates through a set of tabulated numerical solutions. These solutions are computed for a reference mass M=10 M_sun. The model can rescale the disk structure to any mass, with masses in the range of 5-20 M_sun giving reasonably good results.

DiskSim is a source-code distribution of the SPH accretion disk modeling code previously released in a Windows executable form as FITDisk (ascl:1305.011). The code released now is the full research code in Fortran and can be modified as needed by the user.

DISKSTRUCT is a simple 1+1-D code for modeling protoplanetary disks. It is not based on multidimensional radiative transfer! Instead, a flaring-angle recipe is used to compute the irradiation of the disk, while the disk vertical structure at each cylindrical radius is computed in a 1-D fashion; the models computed with this code are therefore approximate. Moreover, this model cannot deal with the dust inner rim.

In spite of these simplifications and drawbacks, the code can still be very useful for disk studies, for the following reasons:

• It allows the disk structure to be studied in a 1-D vertical fashion (one radial cylinder at a time). For understanding the structure of disks, and also for using it as a basis of other models, this can be a great advantage.
• For very optically thick disks this code is likely to be much faster than the RADMC full disk model.
• Viscous internal heating of the disk is implemented and converges quickly, whereas the RADMC code is still having difficulty to deal with high optical depth combined with viscously generated internal heat.

disksurf measures the height of optically thick emission or photosphere in moderately inclined protoplanetary disks. The package is dependent on AstroPy (ascl:1304.002) and uses GoFish (ascl:2011.016) to retrieve data from FITS data cubes and user-specified parameters to return a surface object containing the disk-centric coordinates of the surface and the gas temperature and rotation velocity at those locations. disksurf provides clipping, smoothing, and diagnostic functions as well.

disnht computes the absorption spectrum for a user-defined distribution of column densities. The input is a file including the array of column density values; a python routine is provided that can make logarithmic distribution of column density that can be used as an input. Other optional inputs are a cross-section file that includes the 2-d array [energy, cross-section]; a script is provided for computing cross sections for different abundance model for the interstellar medium (solar values). Other boolean flags can be used for input and output description, rebin, plot or save.

DISORT (DIScrete Ordinate Radiative Transfer) solves the problem of 1D scalar radiative transfer in a single optical medium, such as a planetary atmosphere. The code correctly accounts for multiple scattering by an isotropic or plane-parallel beam source, internal Planck sources, and reflection from a lower boundary. Provided that polarization effects can be neglected, DISORT efficiently calculates accurate fluxes and intensities at any user-specified angle and location within the user-specified medium.

DisPerSE is open source software for the identification of persistent topological features such as peaks, voids, walls and in particular filamentary structures within noisy sampled distributions in 2D, 3D. Using DisPerSE, structure identification can be achieved through the computation of the discrete Morse-Smale complex. The software can deal directly with noisy datasets via the concept of persistence (a measure of the robustness of topological features). Although developed for the study of the properties of filamentary structures in the cosmic web of galaxy distribution over large scales in the Universe, the present version is quite versatile and should be useful for any application where a robust structure identification is required, such as for segmentation or for studying the topology of sampled functions (for example, computing persistent Betti numbers). Currently, it can be applied can work indifferently on many kinds of cell complex (such as structured and unstructured grids, 2D manifolds embedded within a 3D space, discrete point samples using delaunay tesselation, and Healpix tesselations of the sphere). The only constraint is that the distribution must be defined over a manifold, possibly with boundaries.

distance-omnibus computes posterior DPDFs for catalog sources using the Bayesian application of kinematic distance likelihoods derived from a Galactic rotation curve with prior Distance Probability Density Functions (DPDFs) derived from ancillary data. The methodology and code base are generalized for use with any (sub-)millimeter survey of the Galactic plane.

DistClassiPy uses different distance metrics to classify objects such as light curves. It provides state-of-the-art performance for time-domain astronomy, and offers lower computational requirements and improved interpretability over traditional methods such as Random Forests, making it suitable for large datasets. DistClassiPy allows fine-tuning based on scientific objectives by selecting appropriate distance metrics and features, which enhances its performance and improves classification interpretability.

distlink computes the minimum orbital intersection distance (MOID), or global minimum of the distance between the points lying on two Keplerian ellipses by finding all stationary points of the distance function, based on solving an algebraic polynomial equation of 16th degree. The program tracks numerical errors and carefully treats nearly degenerate cases, including practical cases with almost circular and almost coplanar orbits. Benchmarks confirm its high numeric reliability and accuracy, and even with its error-controlling overheads, this algorithm is a fast MOID computation method that may be useful in processing large catalogs. Written in C++, the library also includes auxiliary functions.

DM_phase maximizes the coherent power of a radio signal instead of its intensity to calculate the best dispersion measure (DM) for a burst such as those emitted by pulsars and fast radio bursts (FRBs). It is robust to complex burst structures and interference, thus mitigating the limitations of traditional methods that search for the best DM value of a source by maximizing the signal-to-noise ratio (S/N) of the detected signal.

DM_statistics calculates the free-electron power spectrum and the cosmological dispersion measure (DM) statistics (such as its mean and variance, angular power spectrum and correlation function). The default cosmological parameters are consistent with the Planck 2015 LambdaCDM model; the cosmological model can be easily changed by editing a few lines of the C code.

DMATIS (Dark Matter ATtenuation Importance Sampling) calculates the trajectories of DM particles that propagate in the Earth's crust and the lead shield to reach the DAMIC detector using an importance sampling Monte-Carlo simulation. A detailed Monte-Carlo simulation avoids the deficiencies of the SGED/KS method that uses a mean energy loss description to calculate the lower bound on the DM-proton cross section. The code implementing the importance sampling technique makes the brute-force Monte-Carlo simulation of moderately strongly interacting DM with nucleons computationally feasible. DMATIS is written in Python 3 and MATHEMATICA.

The dmdd package enables simple simulation and Bayesian posterior analysis of recoil-event data from dark-matter direct-detection experiments under a wide variety of scattering theories. It enables calculation of the nuclear-recoil rates for a wide range of non-relativistic and relativistic scattering operators, including non-standard momentum-, velocity-, and spin-dependent rates. It also accounts for the correct nuclear response functions for each scattering operator and takes into account the natural abundances of isotopes for a variety of experimental target elements.

DMRadon calculates the Radon Transform for use in the analysis of Directional Dark Matter Direct Detection. The code can calculate speed distributions, velocity distribution, velocity integral (eta) and Radon Transforms or a standard Maxwell-Boltzmann distribution. DMRadon also calculates the velocity distribution averaged over different angular bins.

This code is a general Monte Carlo method based on Nested Sampling (NS) for sampling complex probability distributions and estimating the normalising constant. The method uses one or more particles, which explore a mixture of nested probability distributions, each successive distribution occupying ~e^-1 times the enclosed prior mass of the previous distribution. While NS technically requires independent generation of particles, Markov Chain Monte Carlo (MCMC) exploration fits naturally into this technique. This method can achieve four times the accuracy of classic MCMC-based Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup. An additional benefit is that more samples and a more accurate evidence value can be obtained simply by continuing the run for longer, as in standard MCMC.

DNest3 is a C++ implementation of Diffusive Nested Sampling (ascl:1010.029), a Markov Chain Monte Carlo (MCMC) algorithm for Bayesian Inference and Statistical Mechanics. Relative to older DNest versions, DNest3 has improved performance (in terms of the sampling overhead, likelihood evaluations still dominate in general) and is cleaner code: implementing new models should be easier than it was before. In addition, DNest3 is multi-threaded, so one can run multiple MCMC walkers at the same time, and the results will be combined together.

Dolphin uniformly models large lens samples. It is a wrapper for Lenstronomy (ascl:1804.012), and features semi-automated modeling of a large sample of quasar and galaxy-galaxy lenses. Dolphin, written in Python, provides easy portability between local and MPI environments.

DOLPHOT is a stellar photometry package that was adapted from HSTphot for general use. It supports two modes; the first is a generic PSF-fitting package, which uses analytic PSF models and can be used for any camera. The second mode uses ACS PSFs and calibrations, and is effectively an ACS adaptation of HSTphot. A number of utility programs are also included with the DOLPHOT distribution, including basic image reduction routines.

The DAOSPEC Output Optimizer pipeline (DOOp) runs efficient and convenient equivalent widths measurements in batches of hundreds of spectra. It uses a series of BASH scripts to work as a wrapper for the FORTRAN code DAOSPEC (ascl:1011.002) and uses IRAF (ascl:9911.002) to automatically fix some of the parameters that are usually set by hand when using DAOSPEC. This allows batch-processing of quantities of spectra that would be impossible to deal with by hand. DOOp was originally built for the large quantity of UVES and GIRAFFE spectra produced by the Gaia-ESO Survey, but just like DAOSPEC, it can be used on any high resolution and high signal-to-noise ratio spectrum binned on a linear wavelength scale.

dopmap constructs Doppler maps from the orbital variation of line profiles of (mass transferring) binaries. It uses an algorithm related to Richardson-Lucy iteration and includes an IDL-based set of routines for manipulating and plotting the input and output data.

The parameters of the mutual orbit of eclipsing binaries that are physically connected can be obtained by precision timing of minima over time through light travel time effect, apsidal motion or orbital precession. This, however, requires joint analysis of data from different sources obtained through various techniques and with insufficiently quantified uncertainties. In particular, photometric uncertainties are often underestimated, which yields too small uncertainties in minima timings if determined through analysis of a χ2 surface. The task is even more difficult for double eclipsing binaries, especially those with periods close to a resonance such as CzeV344, where minima get often blended with each other.

This code solves the double binary parameters simultaneously and then uses these parameters to determine minima timings (or more specifically O-C values) for individual datasets. In both cases, the uncertainties (or more precisely confidence intervals) are determined through bootstrap resampling of the original data. This procedure to a large extent alleviates the common problem with underestimated photometric uncertainties and provides a check on possible degeneracies in the parameters and the stability of the results. While there are shortcomings to this method as well when compared to Markov Chain Monte Carlo methods, the ease of the implementation of bootstrapping is a significant advantage.

DP3 (the Default Preprocessing Pipeline) is the LOFAR data pipeline processing program and is the successor to DPPP (ascl:1804.003). It performs many kinds of operations on the data in a pipelined way so the data are read and written only once. DP3 preprocesses the data of a LOFAR observation by executing steps such as flagging or averaging. Such steps can be used for the raw data as well as the calibrated data by defining the data column to use. One or more of the following steps can be defined as a pipeline. DP3 has an implicit input and output step. It is also possible to have intermediate output steps. DP3 comes with predefined steps, but also allows the user to plug in arbitrary steps implemented in either C++ or Python.

DPI is a FORTRAN77 library that supplies the symplectic mapping method for binary star systems for the Mercury N-Body software package (ascl:1201.008). The binary symplectic mapping is implemented as a hybrid symplectic method that allows close encounters and collisions between massive bodies and is therefore suitable for planetary accretion simulations.

DPPP (Default Pre-Processing Pipeline, also referred to as NDPPP) reads and writes radio-interferometric data in the form of Measurement Sets, mainly those that are created by the LOFAR telescope. It goes through visibilities in time order and contains standard operations like averaging, phase-shifting and flagging bad stations. Between the steps in a pipeline, the data is not written to disk, making this tool suitable for operations where I/O dominates. More advanced procedures such as gain calibration are also included. Other computing steps can be provided by loading a shared library; currently supported external steps are the AOFlagger (ascl:1010.017) and a bridge that enables loading python steps.

DPUSER is an interactive language capable of handling numbers (both real and complex), strings, and matrices. Its main aim is to do astronomical image analysis, for which it provides a comprehensive set of functions, but it can also be used for many other applications.

draco analyzes transit radio data with the m-mode formalism. It is telescope agnostic, and is used as part of the analysis and simulation pipeline for the CHIME (Canadian Hydrogen Intensity Mapping Experiment) telescope. It can simulate time stream data from maps of the sky (using the m-mode formalism) and add gain fluctuations and correctly correlated instrumental noise (i.e. Wishart distributed). Further, it can perform various cuts on the data and make maps of the sky from data using the m-mode formalism.

DRACULA classifies objects using dimensionality reduction and clustering. The code has an easy interface and can be applied to separate several types of objects. It is based on tools developed in scikit-learn, with some usage requiring also the H2O package.

A Monte Carlo generator of the final state of hadrons emitted from an ultrarelativistic nuclear collision is introduced. An important feature of the generator is a possible fragmentation of the fireball and emission of the hadrons from fragments. Phase space distribution of the fragments is based on the blast wave model extended to azimuthally non-symmetric fireballs. Parameters of the model can be tuned and this allows to generate final states from various kinds of fireballs. A facultative output in the OSCAR1999A format allows for a comprehensive analysis of phase-space distributions and/or use as an input for an afterburner. DRAGON's purpose is to produce artificial data sets which resemble those coming from real nuclear collisions provided fragmentation occurs at hadronisation and hadrons are emitted from fragments without any further scattering. Its name, DRAGON, stands for DRoplet and hAdron GeneratOr for Nuclear collisions. In a way, the model is similar to THERMINATOR, with the crucial difference that emission from fragments is included.

DRAGON adopts a second-order Cranck-Nicholson scheme with Operator Splitting and time overrelaxation to solve the diffusion equation. This provides a fast solution that is accurate enough for the average user. Occasionally, users may want to have very accurate solutions to their problem. To enable this feature, users may get close to the accurate solution by using the fast method, and then switch to a more accurate solution scheme featuring the Alternating-Direction-Implicit (ADI) Cranck-Nicholson scheme.

DRAGONS (Data Reduction for Astronomy from Gemini Observatory North and South) is Gemini's Python-based data reduction platform. DRAGONS offers an automation system that allows for hands-off pipeline reduction of Gemini data, or of any other astronomical data once configured. The platform also allows researchers to control input parameters and in some cases will offer to interactively optimize some data reduction steps, e.g. change the order of fit and visualize the new solution.

DRAGraces (Data Reduction and Analysis for GRACES) reduces GRACES spectra taken with the Gemini North high-resolution spectrograph. It finds GRACES frames in a given directory, determines the list of bias, flat, arc and science frames, and performs the reduction and extraction. Written in IDL, DRAGraces is straightforward and easy to use.

DRAKE (Dark matter Relic Abundance beyond Kinetic Equilibrium) predicts the dark matter relic abundance in situations where the standard assumption of kinetic equilibrium during the freeze-out process may not be satisfied. The code comes with a set of three dedicated Boltzmann equation solvers that implement, respectively, the traditionally adopted equation for the dark matter number density, fluid-like equations that couple the evolution of number density and velocity dispersion, and a full numerical evolution of the phase-space distribution.

DRAMA is a fast, distributed environment for writing instrumentation control systems. It allows low level instrumentation software to be controlled from user interfaces running on UNIX, MS Windows or VMS machines in a consistent manner. Such instrumentation tasks can run either on these machines or on real time systems such as VxWorks. DRAMA uses techniques developed by the AAO while using the Starlink-ADAM environment, but is optimized for the requirements of instrumentation control, portability, embedded systems and speed. A special program is provided which allows seamless communication between ADAM and DRAMA tasks.

Driftscan simulates and analyzes transit radio interferometers, with a particular focus on 21cm cosmology. Given a design of a telescope, it generates a set of products used to analyze data from it and simulate timestreams. Driftscan also constructs a filter to extract cosmological 21 cm emission from astrophysical foregrounds, such as our galaxy and radio point sources, and estimates the 21cm power spectrum using an optimal quadratic estimator.

drive-casa provides a Python interface for scripting of CASA (ascl:1107.013) subroutines from a separate Python process, allowing for utilization alongside other Python packages which may not easily be installed into the CASA environment. This is particularly useful for embedding use of CASA subroutines within a larger pipeline. drive-casa runs plain-text casapy scripts directly; alternatively, the package includes a set of convenience routines which try to adhere to a consistent style and make it easy to chain together successive CASA reduction commands to generate a command-script programmatically.

DrizzlePac allows users to easily and accurately align and combine HST images taken at multiple epochs, and even with different instruments. It is a suite of supporting tasks for AstroDrizzle which includes:

- astrodrizzle to align and combine images
- tweakreg and tweakback for aligning images in different visits
- pixtopix transforms an X,Y pixel position to its pixel position after distortion corrections
- skytopix transforms sky coordinates to X,Y pixel positions. A reverse transformation can be done using the task pixtosky.

Deprojection of X-ray data by methods such as PROJCT, which are model dependent, can produce large and unphysical oscillating temperature profiles. Direct Spectral Deprojection (DSDEPROJ) solves some of the issues inherent to model-dependent deprojection routines. DSDEPROJ is a model-independent approach, assuming only spherical symmetry, which subtracts projected spectra from each successive annulus to produce a set of deprojected spectra.

dsigma analyzes galaxy-galaxy lensing. Written in Python, it has a broadly applicable API and is optimized for computational efficiency. While originally intended to be used with the shape catalog of the Hyper-Suprime Cam (HSC) survey, it should work for other surveys, most prominently the Dark Energy Survey (DES) and the Kilo-Degree Survey (KiDS).

DSPS synthesizes stellar populations, leading to fully-differentiable predictions for galaxy photometry and spectroscopy. The code implements an empirical model for stellar metallicity, and it also supports the Diffstar (ascl:2302.012) model of star formation and dark matter halo history. DSPS rapidly generates and simulates galaxy-halo histories on both CPU and GPU hardware.

DSPSR, written primarily in C++, is an open-source, object-oriented, digital signal processing software library and application suite for use in radio pulsar astronomy. The library implements an extensive range of modular algorithms for use in coherent dedispersion, filterbank formation, pulse folding, and other tasks. The software is installed and compiled using the standard GNU configure and make system, and is able to read astronomical data in 18 different file formats, including FITS, S2, CPSR, CPSR2, PuMa, PuMa2, WAPP, ASP, and Mark5.

Dst is a fully parallel Python destriping code for polarimeter data; destriping is a well-established technique for removing low-frequency correlated noise from Cosmic Microwave Background (CMB) survey data. The software destripes correctly formatted HDF5 datasets and outputs hitmaps, binned maps, destriped maps and baseline arrays.

dStar is a collection of modules for computing neutron star structure and evolution, and uses the numerical, utility, and equation of state libraries of MESA (ascl:1010.083).

DUCC (Distinctly Useful Code Collection) provides basic programming tools for numerical computation, including Fast Fourier Transforms, Spherical Harmonic Transforms, non-equispaced Fourier transforms, as well as some concrete applications like 4pi convolution on the sphere and gridding/degridding of radio interferometry data. The code is written in C++17 and provides a simple and comprehensive Python
interface.

Duchamp is software designed to find and describe sources in 3-dimensional, spectral-line data cubes. Duchamp has been developed with HI (neutral hydrogen) observations in mind, but is widely applicable to many types of astronomical images. It features efficient source detection and handling methods, noise suppression via smoothing or multi-resolution wavelet reconstruction, and a range of graphical and text-based outputs to allow the user to understand the detections.

Duo computes rotational, rovibrational and rovibronic spectra of diatomic molecules. The software, written in Fortran 2003, solves the Schrödinger equation for the motion of the nuclei for the simple case of uncoupled, isolated electronic states and also for the general case of an arbitrary number and type of couplings between electronic states. Possible couplings include spin–orbit, angular momenta, spin-rotational and spin–spin. Introducing the relevant couplings using so-called Born–Oppenheimer breakdown curves can correct non-adiabatic effects.

Written in Python, dust calculates X-ray dust scattering and extinction in the intergalactic and local interstellar media.

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