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[ascl:1102.001]
N-MODY: A Code for Collisionless N-body Simulations in Modified Newtonian Dynamics

N-MODY is a parallel particle-mesh code for collisionless N-body simulations in modified Newtonian dynamics (MOND). N-MODY is based on a numerical potential solver in spherical coordinates that solves the non-linear MOND field equation, and is ideally suited to simulate isolated stellar systems. N-MODY can be used also to compute the MOND potential of arbitrary static density distributions. A few applications of N-MODY indicate that some astrophysically relevant dynamical processes are profoundly different in MOND and in Newtonian gravity with dark matter.

[ascl:1109.023]
MOKA: A New Tool for Strong Lensing Studies

MOKA simulates the gravitational lensing signal from cluster-sized haloes. This algorithm implements recent results from numerical simulations to create realistic lenses with properties independent of numerical resolution and can be used for studies of the strong lensing cross section in dependence of halo structure.

[ascl:1208.005]
PSM: Planck Sky Model

Ashdown, Mark; Aumont, Jonathan; Baccigalupi, Carlo; Banday, Anthony; Basak, Soumen; Bernard, Jean-Philippe; Betoule, Marc; Bouchet, François; Castex, Guillaume; Clements, Dave; Da Silva, Antonio; De Zotti, Gianfranco; Delabrouille, Jacques; Dickinson, Clive; Dodu, Fabrice; Dolag, Klaus; Elsner, Franz; Fauvet, Lauranne; Faÿ, Gilles; Giardino, Giovanna; Gonzalez-Nuevo, Joaquin; le Jeune, Maude; Leach, Samuel; Lesgourgues, Julien; Liguori, Michele; Macias, Juan; Massardi, Marcella; Matarrese, Sabino; Mazzotta, Pasquale; Melin, Jean-Baptiste; Miville-Deschênes, Marc-Antoine; Montier, Ludovic; Mottet, Sylvain; Paladini, Roberta; Partridge, Bruce; Piffaretti, Rocco; Prézeau, Gary; Prunet, Simon; Ricciardi, Sara; Roman, Matthieu; Schaefer, Bjorn; Toffolatti, Luigi

The Planck Sky Model (PSM) is a global representation of the multi-component sky at frequencies ranging from a few GHz to a few THz. It summarizes in a synthetic way as much of our present knowledge as possible of the GHz sky. PSM is a complete and versatile set of programs and data that can be used for the simulation or the prediction of sky emission in the frequency range of typical CMB experiments, and in particular of the Planck sky mission. It was originally developed as part of the activities of Planck component separation Working Group (or "Working Group 2" - WG2), and of the ADAMIS team at APC.

PSM gives users the opportunity to investigate the model in some depth: look at its parameters, visualize its predictions for all individual components in various formats, simulate sky emission compatible with a given parameter set, and observe the modeled sky with a synthetic instrument. In particular, it makes possible the simulation of sky emission maps as could be plausibly observed by Planck or other CMB experiments that can be used as inputs for the development and testing of data processing and analysis techniques.

[ascl:1302.013]
NIFTY: A versatile Python library for signal inference

Selig, Marco; Bell, Michael R.; Junklewitz, Henrik; Oppermann, Niels; Reinecke, Martin; Greiner, Maksim; Pachajoa, Carlos; Ensslin, Torsten A.

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.

[ascl:1408.003]
PIA: ISOPHOT Interactive Analysis

Gabriel, Carlos; Acosta, Jose; Heinrichsen, Ingolf; Skaley, Detlef; Tai, Wai Ming; Morris, Huw; Merluzzi, Paola

ISOPHOT is one of the instruments on board the Infrared Space Observatory (ISO). ISOPHOT Interactive Analysis (PIA) is a scientific and calibration interactive data analysis tool for ISOPHOT data reduction. Written in IDL under Xwindows, PIA offers a full context sensitive graphical interface for retrieving, accessing and analyzing ISOPHOT data. It is available in two nearly identical versions; a general observers version omits the calibration sequences.

[ascl:1510.007]
ccdproc: CCD data reduction software

Craig, M. W.; Crawford, S. M.; Deil, Christoph; Gomez, Carlos; Günther, Hans Moritz; Heidt, Nathan; Horton, Anthony; Karr, Jennifer; Nelson, Stefan; Ninan, Joe Phillip; Pattnaik, Punyaslok; Rol, Evert; Schoenell, William; Seifert, Michael; Singh, Sourav; Sipocz, Brigitta; Stotts, Connor; Streicher, Ole; Tollerud, Erik; Walker, Nathan; ccdproc contributors

Ccdproc is an affiliated package for the AstroPy package for basic data reductions of CCD images. The ccdproc package provides many of the necessary tools for processing of ccd images built on a framework to provide error propagation and bad pixel tracking throughout the reduction process.

[ascl:1505.023]
SNooPy: TypeIa supernovae analysis tools

Burns, Christopher R.; Stritzinger, Maximilian; Phillips, M. M.; Kattner, ShiAnne; Persson, S. E.; Madore, Barry F.; Freedman, Wendy L.; Boldt, Luis; Campillay, Abdo; Contreras, Carlos; Folatelli, Gaston; Gonzalez, Sergio; Krzeminski, Wojtek; Morrell, Nidia; Salgado, Francisco; Suntzeff, Nicholas B.

The SNooPy package (also known as SNpy), written in Python, contains tools for the analysis of TypeIa supernovae. It offers interactive plotting of light-curve data and models (and spectra), computation of reddening laws and K-corrections, LM non-linear least-squares fitting of light-curve data, and various types of spline fitting, including Diercx and tension. The package also includes a SNIa lightcurve template generator in the CSP passbands, estimates of Milky-Way Extinction, and a module for dealing with filters and spectra.

[ascl:1510.005]
GALFORM: Galactic modeling

GALFORM is a semi-analytic model for calculating the formation and evolution of galaxies in hierarchical clustering cosmologies. Using a Monte Carlo algorithm to follow the merging evolution of dark matter haloes with arbitrary mass resolution, it incorporates realistic descriptions of the density profiles of dark matter haloes and the gas they contain. It follows the chemical evolution of gas and stars, and the associated production of dust and includes a detailed calculation of the sizes of discs and spheroids.

[ascl:1603.003]
VIP: Vortex Image Processing pipeline for high-contrast direct imaging of exoplanets

Gomez Gonzalez, Carlos Alberto; Wertz, Olivier; Christiaens, Valentin; Absil, Olivier; Mawet, Dimitri

VIP (Vortex Image Processing pipeline) provides pre- and post-processing algorithms for high-contrast direct imaging of exoplanets. Written in Python, VIP provides a very flexible framework for data exploration and image processing and supports high-contrast imaging observational techniques, including angular, reference-star and multi-spectral differential imaging. Several post-processing algorithms for PSF subtraction based on principal component analysis are available as well as the LLSG (Local Low-rank plus Sparse plus Gaussian-noise decomposition) algorithm for angular differential imaging. VIP also implements the negative fake companion technique coupled with MCMC sampling for rigorous estimation of the flux and position of potential companions.

[ascl:1708.005]
STools: IDL Tools for Spectroscopic Analysis

STools contains a variety of simple tools for spectroscopy, such as reading an IRAF-formatted (multispec) echelle spectrum in FITS, measuring the wavelength of the center of a line, Gaussian convolution, deriving synthetic photometry from an input spectrum, and extracting and interpolating a MARCS model atmosphere (standard composition).

[ascl:1802.008]
AntiparticleDM: Discriminating between Majorana and Dirac Dark Matter

AntiparticleDM calculates the prospects of future direct detection experiments to discriminate between Majorana and Dirac Dark Matter (*i.e.*, to determine whether Dark Matter is its own antiparticle). Direct detection event rates and mock data generation are dealt with by a variation of the WIMpy code.

[ascl:1806.006]
QE: Quantum opEn-Source Package for Research in Electronic Structure, Simulation, and Optimization

Giannozzi, P.; Andreussi, O.; Baroni, S.; Bonini, Nicola; Brumme, T.; Bunau, O.; Buongiorno Nardelli, M.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, Guido L.; Cococcioni, M.; Colonna, N.; Carnimeo, I.; Dabo, Ismaila; Dal Corso, A.; de Gironcoli, S.; Delugas, P.; DiStasio, R. A., Jr.; Fabris, Stefano; Ferretti, A.; Floris, A.; Fratesi, G.; Fugallo, G.; Gebauer, R.; Gerstmann, U.; Giustino, F.; Gorni, T.; Gougoussis, Christos; Jia, J.; Kawamura, M.; Ko, H.-Y.; Kokalj, A.; Küçükbenli, E.; Lazzeri, M.; Marsili, M.; Martin-Samos, Layla; Marzari, N.; Mauri, F.; Mazzarello, Riccardo; Nguyen, N. L.; Nguyen, H.-V.; Otero-de-la-Roza, A.; Paolini, Stefano; Pasquarello, Alfredo; Paulatto, L.; Poncé, S.; Rocca, D.; Sabatini, R.; Santra, B.; Sbraccia, Scandolo, Sandro; Carlo; Schlipf, M.; Sclauzero, Gabriele; Seitsonen, A. P.; Smogunov, A.; Timrov, I.; Thonhauser, T.; Umari, P.; Vast, N.; Wentzcovitch, Renata M.; Wu, X.

Quantum ESPRESSO (opEn-Source Package for Research in Electronic Structure, Simulation, and Optimization) is an integrated suite of codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. QE performs ground-state calculations such as self-consistent total energies, forces, stresses and Kohn-Sham orbitals, Car-Parrinello and Born-Oppenheimer molecular dynamics, and quantum transport such as ballistic transport, coherent transport from maximally localized Wannier functions, and Kubo-Greenwood electrical conductivity. It can also determine spectroscopic properties and examine time-dependent density functional perturbations and electronic excitations, and has a wide range of other functions.

[ascl:1907.019]
GaussPy: Python implementation of the Autonomous Gaussian Decomposition algorithm

GaussPy implements the Autonomous Gaussian Decomposition (AGD) algorithm, which uses computer vision and machine learning techniques to provide optimized initial guesses for the parameters of a multi-component Gaussian model automatically and efficiently. The speed and adaptability of AGD allow it to interpret large volumes of spectral data efficiently. Although it was initially designed for applications in radio astrophysics, AGD can be used to search for one-dimensional Gaussian (or any other single-peaked spectral profile)-shaped components in any data set. To determine how many Gaussian functions to include in a model and what their parameters are, AGD uses a technique called derivative spectroscopy. The derivatives of a spectrum can efficiently identify shapes within that spectrum corresponding to the underlying model, including gradients, curvature and edges.

[ascl:2004.011]
FUNDPAR: Deriving FUNDamental PARameters from equivalent widths

FUNDPAR determines fundamental parameters of solar-type stars, by using as input the Equivalent Widths of Fe I,II lines. The code uses solar-scaled ATLAS9 model atmospheres with NEWODF opacities, together with the 2009 version of the MOOG (ascl:1202.009) program. Parameter files control different details, such as the mixing-length parameter, the overshooting, and the damping of the lines. FUNDPAR also derives the uncertainties of the parameters.