[ascl:1611.005]
Exo-Transmit: Radiative transfer code for calculating exoplanet transmission spectra

Exo-Transmit calculates the transmission spectrum of an exoplanet atmosphere given specified input information about the planetary and stellar radii, the planet's surface gravity, the atmospheric temperature-pressure (T-P) profile, the location (in terms of pressure) of any cloud layers, the composition of the atmosphere, and opacity data for the atoms and molecules that make up the atmosphere. The code solves the equation of radiative transfer for absorption of starlight passing through the planet's atmosphere as it transits, accounting for the oblique path of light through the planetary atmosphere along an Earth-bound observer's line of sight. The fraction of light absorbed (or blocked) by the planet plus its atmosphere is calculated as a function of wavelength to produce the wavelength-dependent transmission spectrum. Functionality is provided to simulate the presence of atmospheric aerosols in two ways: an optically thick (gray) cloud deck can be generated at a user-specified height in the atmosphere, and the nominal Rayleigh scattering can be increased by a specified factor.

[ascl:1611.004]
PRECESSION: Python toolbox for dynamics of spinning black-hole binaries

PRECESSION is a comprehensive toolbox for exploring the dynamics of precessing black-hole binaries in the post-Newtonian regime. It allows study of the evolution of the black-hole spins along their precession cycles, performs gravitational-wave-driven binary inspirals using both orbit-averaged and precession-averaged integrations, and predicts the properties of the merger remnant through fitting formulas obtained from numerical-relativity simulations. PRECESSION can add the black-hole spin dynamics to larger-scale numerical studies such as gravitational-wave parameter estimation codes, population synthesis models to predict gravitational-wave event rates, galaxy merger trees and cosmological simulations of structure formation, and provides fast and reliable integration methods to propagate statistical samples of black-hole binaries from/to large separations where they form to/from small separations where they become detectable, thus linking gravitational-wave observations of spinning black-hole binaries to their astrophysical formation history. The code is also useful for computing initial parameters for numerical-relativity simulations targeting specific precessing systems.

[ascl:1611.003]
MPDAF: MUSE Python Data Analysis Framework

MPDAF, the MUSE Python Data Analysis Framework, provides tools to work with MUSE-specific data (for example, raw data and pixel tables), and with more general data such as spectra, images, and data cubes. Originally written to work with MUSE data, it can also be used for other data, such as that from the Hubble Space Telescope. MPDAF also provides MUSELET, a SExtractor-based tool to detect emission lines in a data cube, and a format to gather all the information on a source in one FITS file. MPDAF was developed and is maintained by CRAL (Centre de Recherche Astrophysique de Lyon).

[ascl:1611.002]
tf_unet: Generic convolutional neural network U-Net implementation in Tensorflow

tf_unet mitigates radio frequency interference (RFI) signals in radio data using a special type of Convolutional Neural Network, the U-Net, that enables the classification of clean signal and RFI signatures in 2D time-ordered data acquired from a radio telescope. The code is not tied to a specific segmentation and can be used, for example, to detect radio frequency interference (RFI) in radio astronomy or galaxies and stars in widefield imaging data. This U-Net implementation can outperform classical RFI mitigation algorithms.

[ascl:1611.001]
UltraNest: Pythonic Nested Sampling Development Framework and UltraNest

This three-component package provides a Pythonic implementation of the Nested Sampling integration algorithm for Bayesian model comparison and parameter estimation. It offers multiple implementations for constrained drawing functions and a test suite to evaluate the correctness, accuracy and efficiency of various implementations. The three components are:

- a modular framework for nested sampling algorithms (nested_sampling) and their development;
- a test framework to evaluate the performance and accuracy of algorithms (testsuite); and
- UltraNest, a fast C implementation of a mixed RadFriends/MCMC nested sampling algorithm.

[ascl:1610.016]
PyMC3: Python probabilistic programming framework

PyMC3 performs Bayesian statistical modeling and model fitting focused on advanced Markov chain Monte Carlo and variational fitting algorithms. It offers powerful sampling algorithms, such as the No U-Turn Sampler, allowing complex models with thousands of parameters with little specialized knowledge of fitting algorithms, intuitive model specification syntax, and optimization for finding the maximum a posteriori (MAP) point. PyMC3 uses Theano to compute gradients via automatic differentiation as well as compile probabilistic programs on-the-fly to C for increased speed.

[ascl:1610.015]
NuPyCEE: NuGrid Python Chemical Evolution Environment

The NuGrid Python Chemical Evolution Environment (NuPyCEE) simulates the chemical enrichment and stellar feedback of stellar populations. It contains three modules. The Stellar Yields for Galactic Modeling Applications module (SYGMA) models the enrichment and feedback of simple stellar populations which can be included in hydrodynamic simulations and semi-analytic models of galaxies. It is the basic building block of the One-zone Model for the Evolution of GAlaxies (OMEGA) module which allows the modelling of the chemical evolution of galaxies such as the Milky Way and its dwarf satellites. The STELLAB (STELLar ABundances) module provides a library of observed stellar abundances useful for comparing predictions of SYGMA and OMEGA.

[ascl:1610.014]
Freddi: Fast Rise Exponential Decay accretion Disk model Implementation

Freddi (Fast Rise Exponential Decay: accretion Disk model Implementation) solves 1-D evolution equations of the Shakura-Sunyaev accretion disk. It simulates fast rise exponential decay (FRED) light curves of low mass X-ray binaries (LMXBs). The basic equation of the viscous evolution relates the surface density and viscous stresses and is of diffusion type; evolution of the accretion rate can be found on solving the equation. The distribution of viscous stresses defines the emission from the source. The standard model for the accretion disk is implied; the inner boundary of the disk is at the ISCO or can be explicitely set. The boundary conditions in the disk are the zero stress at the inner boundary and the zero accretion rate at the outer boundary. The conditions are suitable during the outbursts in X-ray binary transients with black holes. In a binary system, the accretion disk is radially confined. In Freddi, the outer radius of the disk can be set explicitely or calculated as the position of the tidal truncation radius.

[ascl:1610.013]
MC^{3}: Multi-core Markov-chain Monte Carlo code

Cubillos, Patricio; Harrington, Joseph; Lust, Nate; Foster, AJ; Stemm, Madison; Loredo, Tom; Stevenson, Kevin; Campo, Chris; Hardin, Matt; Hardy, Ryan

MC^{3} (Multi-core Markov-chain Monte Carlo) is a Bayesian statistics tool that can be executed from the shell prompt or interactively through the Python interpreter with single- or multiple-CPU parallel computing. It offers Markov-chain Monte Carlo (MCMC) posterior-distribution sampling for several algorithms, Levenberg-Marquardt least-squares optimization, and uniform non-informative, Jeffreys non-informative, or Gaussian-informative priors. MC^{3} can share the same value among multiple parameters and fix the value of parameters to constant values, and offers Gelman-Rubin convergence testing and correlated-noise estimation with time-averaging or wavelet-based likelihood estimation methods.

[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:1610.011]
BXA: Bayesian X-ray Analysis

BXA connects the nested sampling algorithm MultiNest (ascl:1109.006) to the X-ray spectral analysis environments Xspec/Sherpa for Bayesian parameter estimation and model comparison. It provides parameter estimation in arbitrary dimensions and plotting of spectral model vs. the data for best fit, posterior samples, or each component. BXA allows for model selection; it computes the evidence for the considered model, ready for use in computing Bayes factors and is not limited to nested models. It also visualizes deviations between model and data with Quantile-Quantile (QQ) plots, which do not require binning and are more comprehensive than residuals.

[ascl:1610.010]
BurnMan: Lower mantle mineral physics toolkit

BurnMan determines seismic velocities for the lower mantle. Written in Python, BurnMan calculates the isotropic thermoelastic moduli by solving the equations-of-state for a mixture of minerals defined by the user. The user may select from a list of minerals applicable to the lower mantle included or can define one. BurnMan provides choices in methodology, both for the EoS and for the multiphase averaging scheme and the results can be visually or quantitatively compared to observed seismic models.

[ascl:1610.009]
velbin: radial velocity corrected for binary orbital motions

Velbin convolves the radial velocity offsets due to binary orbital motions with a Gaussian to model an observed velocity distribution. This can be used to measure the mean velocity and velocity dispersion from an observed radial velocity distribution, corrected for binary orbital motions. Velbin fits single- or multi-epoch data with any arbitrary binary orbital parameter distribution (as long as it can be sampled properly), however it always assumes that the intrinsic velocity distribution (i.e. corrected for binary orbital motions) is a Gaussian. Velbin samples (and edits) a binary orbital parameter distribution, fits an observed radial velocity distribution, and creates a mock radial velocity distribution that can be used to provide the fitted radial velocities in the single_epoch or multi_epoch methods.

[ascl:1610.008]
cluster-in-a-box: Statistical model of sub-millimeter emission from embedded protostellar clusters

Cluster-in-a-box provides a statistical model of sub-millimeter emission from embedded protostellar clusters and consists of three modules grouped in two scripts. The first (cluster_distribution) generates the cluster based on the number of stars, input initial mass function, spatial distribution and age distribution. The second (cluster_emission) takes an input file of observations, determines the mass-intensity correlation and generates outflow emission for all low-mass Class 0 and I sources. The output is stored as a FITS image where the flux density is determined by the desired resolution, pixel scale and cluster distance.

[ascl:1610.007]
gatspy: General tools for Astronomical Time Series in Python

Gatspy contains efficient, well-documented implementations of several common routines for Astronomical time series analysis, including the Lomb-Scargle periodogram, the Supersmoother method, and others.

[ascl:1610.006]
C^{3}: Command-line Catalogue Crossmatch for modern astronomical surveys

The Command-line Catalogue Cross-matching (C^{3}) software efficiently performs the positional cross-match between massive catalogues from modern astronomical surveys, whose size have rapidly increased in the current data-driven science era. Based on a multi-core parallel processing paradigm, it is executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline. C^{3} provides its users with flexibility in portability, parameter configuration, catalogue formats, angular resolution, region shapes, coordinate units and cross-matching types.

[ascl:1610.005]
GSGS: In-Focus Phase Retrieval Using Non-Redundant Mask Data

GSGS does phase retrieval on images given an estimate of the pupil phase (from a non-redundant mask or other interferometric approach), the pupil geometry, and the in-focus image. The code uses a modified Gerchberg-Saxton algorithm that iterates between pupil plane and image plane to measure the pupil phase.

[ascl:1610.004]
MUSE-DRP: MUSE Data Reduction Pipeline

The MUSE pipeline turns the complex raw data of the MUSE integral field spectrograph into a ready-to-use datacube for scientific analysis.

[submitted]
centerRadon: Center Determination Code in Stellar Images

centerRadon finds the center of stars based on Radon Transform (Pueyo et al., 2015) to sub-pixel precision. For a coronagraphic image of a star, it starts from a given location, then for each sub-pixel position, it interpolates the image and sums the pixels along different angles, creating a cost function. The center of the star is expected to correspond with where the cost function maximizes. The default values are set for the STIS coronagraphic images of the Hubble Space Telescope by summing over the diagonals (i.e., 45° and 135°), but it can be generally applied to other high-contrast imaging instruments with or without Adaptive Optics systems such as HST-NICMOS, P1640, or GPI.

[ascl:1610.003]
DSDEPROJ: Direct Spectral Deprojection

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.

[ascl:1610.002]
CERES: Collection of Extraction Routines for Echelle Spectra

The Collection of Extraction Routines for Echelle Spectra (CERES) constructs automated pipelines for the reduction, extraction, and analysis of echelle spectrograph data. This modular code includes tools for handling the different steps of the processing: CCD reductions, tracing of the echelle orders, optimal and simple extraction, computation of the wave-length solution, estimation of radial velocities, and rough and fast estimation of the atmospheric parameters. The standard output of pipelines constructed with CERES is a FITS cube with the optimally extracted, wavelength calibrated and instrumental drift-corrected spectrum for each of the science images. Additionally, CERES includes routines for the computation of precise radial velocities and bisector spans via the cross-correlation method, and an automated algorithm to obtain an estimate of the atmospheric parameters of the observed star.

[ascl:1610.001]
Piccard: Pulsar timing data analysis package

Piccard is a Bayesian-inference pipeline for Pulsar Timing Array (PTA) data and interacts with Tempo2 (ascl:1210.015) through libstempo. The code is use mainly for single-pulsar analysis and gravitational-wave detection purposes of full Pulsar Timing Array datasets. Modeling of the data can include correlated signals per frequency or modeled spectrum, with uniform, dipolar, quadrupolar, or anisotropic correlations; multiple error bars and EFACs per pulsar; and white and red noise. Timing models can be numerically included, either by using the design matrix (linear timing model), or by calling libstempo for the full non-linear timing model. Many types of samplers are included. For common-mode mitigation, the signals can be reconstructed mitigating arbitrary signals simultaneously.

[ascl:1609.025]
PYESSENCE: Generalized Coupled Quintessence Linear Perturbation Python Code

PYESSENCE evolves linearly perturbed coupled quintessence models with multiple (cold dark matter) CDM fluid species and multiple DE (dark energy) scalar fields, and can be used to generate quantities such as the growth factor of large scale structure for any coupled quintessence model with an arbitrary number of fields and fluids and arbitrary couplings.

[ascl:1609.024]
AdaptiveBin: Adaptive Binning

AdaptiveBin takes one or more images and adaptively bins them. If one image is supplied, then the pixels are binned by fractional error on the intensity. If two or more images are supplied, then the pixels are fractional binned by error on the combined color.

[ascl:1609.023]
contbin: Contour binning and accumulative smoothing

Contbin bins X-ray data using contours on an adaptively smoothed map. The generated bins closely follow the surface brightness, and are ideal where the surface brightness distribution is not smooth, or the spectral properties are expected to follow surface brightness. Color maps can be used instead of surface brightness maps.

[ascl:1609.022]
PyPHER: Python-based PSF Homogenization kERnels

Boucaud, Alexandre; Bocchio, Marco; Abergel, Alain; Orieux, François; Dole, Hervé; Amine Hadj-Youcef, Mohamed

PyPHER (Python-based PSF Homogenization kERnels) computes an homogenization kernel between two PSFs; the code is well-suited for PSF matching applications in both an astronomical or microscopy context. It can warp (rotation + resampling) the PSF images (if necessary), filter images in Fourier space using a regularized Wiener filter, and produce a homogenization kernel. PyPHER requires the pixel scale information to be present in the FITS files, which can if necessary be added by using the provided ADDPIXSCL method.

[ascl:1609.021]
TIDEV: Tidal Evolution package

TIDEV (Tidal Evolution package) calculates the evolution of rotation for tidally interacting bodies using Efroimsky-Makarov-Williams (EMW) formalism. The package integrates tidal evolution equations and computes the rotational and dynamical evolution of a planet under tidal and triaxial torques. TIDEV accounts for the perturbative effects due to the presence of the other planets in the system, especially the secular variations of the eccentricity. Bulk parameters include the mass and radius of the planet (and those of the other planets involved in the integration), the size and mass of the host star, the Maxwell time and Andrade's parameter. TIDEV also calculates the time scale that a planet takes to be tidally locked as well as the periods of rotation reached at the end of the spin-orbit evolution.

[ascl:1609.020]
Askaryan Module: Askaryan electric fields predictor

The Askaryan Module is a C++ class that predicts the electric fields that Askaryan-based detectors detect; it is computationally efficient and accurate, performing fully analytic calculations requiring no *a priori* MC analysis to compute the entire field, for any frequencies, times, or viewing angles chosen by the user.

[ascl:1609.019]
SuperBoL: Module for calculating the bolometric luminosities of supernovae

SuperBoL calculates the bolometric lightcurves of Type II supernovae using observed photometry; it includes three different methods for calculating the bolometric luminosity: quasi-bolometric, direct, and bolometric correction. SuperBoL propagates uncertainties in the input data through the calculations made by the code, allowing for error bars to be included in plots of the lightcurve.

[ascl:1609.018]
SIP: Systematics-Insensitive Periodograms

SIP (Systematics-Insensitive Periodograms) extends the generative model used to create traditional sine-fitting periodograms for finding the frequency of a sinusoid by including systematic trends based on a set of eigen light curves in the generative model in addition to using a sum of sine and cosine functions over a grid of frequencies, producing periodograms with vastly reduced systematic features. Acoustic oscillations in giant stars and measurement of stellar rotation periods can be recovered from the SIP periodograms without detrending. The code can also be applied to detection other periodic phenomena, including eclipsing binaries and short-period exoplanet candidates.

[ascl:1609.017]
spectral-cube: Read and analyze astrophysical spectral data cubes

Spectral-cube provides an easy way to read, manipulate, analyze, and write data cubes with two positional dimensions and one spectral dimension, optionally with Stokes parameters. It is a versatile data container for building custom analysis routines. It provides a uniform interface to spectral cubes, robust to the wide range of conventions of axis order, spatial projections, and spectral units that exist in the wild, and allows easy extraction of cube sub-regions using physical coordinates. It has the ability to create, combine, and apply masks to datasets and is designed to work with datasets too large to load into memory, and provide basic summary statistic methods like moments and array aggregates.

[ascl:1609.016]
PKDGRAV3: Parallel gravity code

Pkdgrav3 is an 𝒪(*N*) gravity calculation method; it uses a binary tree algorithm with fifth order fast multipole expansion of the gravitational potential, using cell-cell interactions. Periodic boundaries conditions require very little data movement and allow a high degree of parallelism; the code includes GPU acceleration for all force calculations, leading to a significant speed-up with respect to previous versions (ascl:1305.005). Pkdgrav3 also has a sophisticated time-stepping criterion based on an estimation of the local dynamical time.

[ascl:1609.015]
FIT3D: Fitting optical spectra

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

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

[ascl:1609.014]
Sky3D: Time-dependent Hartree-Fock equation solver

Written in Fortran 90, Sky3D solves the static or dynamic equations on a three-dimensional Cartesian mesh with isolated or periodic boundary conditions and no further symmetry assumptions. Pairing can be included in the BCS approximation for the static case. The code can be easily modified to include additional physics or special analysis of the results and requires LAPACK and FFTW3.

[ascl:1609.013]
21cmSense: Calculating the sensitivity of 21cm experiments to the EoR power spectrum

21cmSense calculates the expected sensitivities of 21cm experiments to the Epoch of Reionization power spectrum. Written in Python, it requires NumPy, SciPy, and AIPY (ascl:1609.012).

[ascl:1609.012]
AIPY: Astronomical Interferometry in PYthon

AIPY collects together tools for radio astronomical interferometry. In addition to pure-python phasing, calibration, imaging, and deconvolution code, this package includes interfaces to MIRIAD (ascl:1106.007) and HEALPix (ascl:1107.018), and math/fitting routines from SciPy.

[ascl:1609.011]
Photutils: Photometry tools

Bradley, Larry; Sipocz, Brigitta; Robitaille, Thomas; Tollerud, Erik; Deil, Christoph; Vinícius, Zè; Barbary, Kyle; Günther, Hans Moritz; Bostroem, Azalee; Droettboom, Michael; Bray, Erik; Bratholm, Lars Andersen; Pickering, T. E.; Craig, Matt; Pascual, Sergio; Greco, Johnny; Donath, Axel; Kerzendorf, Wolfgang; Littlefair, Stuart; Barentsen, Geert; D'Eugenio, Francesco; Weaver, Benjamin Alan

Photutils provides tools for detecting and performing photometry of astronomical sources. It can estimate the background and background rms in astronomical images, detect sources in astronomical images, estimate morphological parameters of those sources (e.g., centroid and shape parameters), and perform aperture and PSF photometry. Written in Python, it is an affiliated package of Astropy (ascl:1304.002).

[ascl:1609.010]
CuBANz: Photometric redshift estimator

CuBAN*z* is a photometric redshift estimator code for high redshift galaxies that uses the back propagation neural network along with clustering of the training set, making it very efficient. The training set is divided into several self learning clusters with galaxies having similar photometric properties and spectroscopic redshifts within a given span. The clustering algorithm uses the color information (i.e. u-g, g-r etc.) rather than the apparent magnitudes at various photometric bands, as the photometric redshift is more sensitive to the flux differences between different bands rather than the actual values. The clustering method enables accurate determination of the redshifts. CuBAN*z* considers uncertainty in the photometric measurements as well as uncertainty in the neural network training. The code is written in C.

[ascl:1609.009]
NSCool: Neutron star cooling code

NSCool is a 1D (i.e., spherically symmetric) neutron star cooling code written in Fortran 77. The package also contains a series of EOSs (equation of state) to build stars, a series of pre-built stars, and a TOV (Tolman- Oppenheimer-Volkoff) integrator to build stars from an EOS. It can also handle “strange stars” that have a huge density discontinuity between the quark matter and the covering thin baryonic crust. NSCool solves the heat transport and energy balance equations in whole GR, resulting in a time sequence of temperature profiles (and, in particular, a Teff - age curve). Several heating processes are included, and more can easily be incorporated. In particular it can evolve a star undergoing accretion with the resulting deep crustal heating, under a steady or time-variable accretion rate. NSCool is robust, very fast, and highly modular, making it easy to add new subroutines for new processes.

[ascl:1609.008]
GRASP: General-purpose Relativistic Atomic Structure Package

GRASP (General-purpose Relativistic Atomic Structure Package) calculates atomic structure, including energy levels, radiative rates (A-values) and lifetimes; it is a fully relativistic code based on the *jj* coupling scheme. This code has been superseded by GRASP2K (ascl:1611.007).

[ascl:1609.007]
Weighted EMPCA: Weighted Expectation Maximization Principal Component Analysis

Weighted EMPCA performs principal component analysis (PCA) on noisy datasets with missing values. Estimates of the measurement error are used to weight the input data such that the resulting eigenvectors, when compared to classic PCA, are more sensitive to the true underlying signal variations rather than being pulled by heteroskedastic measurement noise. Missing data are simply limiting cases of weight = 0. The underlying algorithm is a noise weighted expectation maximization (EM) PCA, which has additional benefits of implementation speed and flexibility for smoothing eigenvectors to reduce the noise contribution.

[ascl:1609.006]
SCIMES: Spectral Clustering for Interstellar Molecular Emission Segmentation

SCIMES identifies relevant molecular gas structures within dendrograms of emission using the spectral clustering paradigm. It is useful for decomposing objects in complex environments imaged at high resolution.

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

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

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

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

[ascl:1609.003]
Kranc: Cactus modules from Mathematica equations

Kranc turns a tensorial description of a time dependent partial differential equation into a module for the Cactus Computational Toolkit (ascl:1102.013). This Mathematica application takes a simple continuum description of a problem and generates highly efficient and portable code, and can be used both for rapid prototyping of evolution systems and for high performance supercomputing.

[submitted]
pyLIMA: An Open Source Package for Microlensing Modeling

pyLIMA is an open source software for microlensing modeling. Based on Python, the goal is to offer to users an efficient and user friendly package to analyze their data. The code is written and tested with professional standards, such as PEP8 or unit testing.

[ascl:1609.002]
StarPy: Quenched star formation history parameters of a galaxy using MCMC

Smethurst, R. J.; Lintott, C. J.; Simmons, B. D.; Schawinski, K.; Marshall, P. J.; Bamford, S.; Fortson, L.; Kaviraj, S.; Masters, K. L.; Melvin, T.; Nichol, R. C.; Skibba, R. A.; Willett, K. W.

StarPy derives the quenching star formation history (SFH) of a single galaxy through the Bayesian Markov Chain Monte Carlo method code *emcee* (ascl:1303.002). The sample function implements the emcee EnsembleSampler function for the galaxy colors input. Burn-in is run and calculated for the length specified before the sampler is reset and then run for the length of steps specified. StarPy provides the ability to use the look-up tables provided or creating your own.

[ascl:1609.001]
T-PHOT: PSF-matched, prior-based, multiwavelength extragalactic deconfusion photometry

Merlin, E.; Fontana, A.; Ferguson, H. C.; Dunlop, J. S.; Elbaz, D.; Bourne, N.; Bruce, V. A.; Buitrago, F.; Castellano, M.; Schreiber, C.; Grazian, A.; McLure, R. J.; Okumura, K.; Shu, X.; Wang, T.; Amorín, R.; Boutsia, K.; Cappelluti, N.; Comastri, A.; Derriere, S.; Faber, S. M.; Santini, P.

T-PHOT extracts accurate photometry from low-resolution images of extragalactic fields, where the blending of sources can be a serious problem for accurate and unbiased measurement of fluxes and colors. It gathers data from a high-resolution image of a region of the sky and uses the source positions and morphologies to obtain priors for the photometric analysis of the lower resolution image of the same field. T-PHOT handles different types of datasets as input priors, including a list of objects that will be used to obtain cutouts from the real high-resolution image, a set of analytical models (as .fits stamps), and a list of unresolved, point-like sources, useful for example for far-infrared wavelength domains. T-PHOT yields accurate estimations of fluxes within the intrinsic uncertainties of the method when systematic errors are taken into account (which can be done using a flagging code given in the output), and handles multiwavelength optical to far-infrared image photometry. T-PHOT was developed as part of the ASTRODEEP project (www.astrodeep.eu).

[ascl:1608.020]
SPIDERz: SuPport vector classification for IDEntifying Redshifts

SPIDERz (SuPport vector classification for IDEntifying Redshifts) applies powerful support vector machine (SVM) optimization and statistical learning techniques to custom data sets to obtain accurate photometric redshift (photo-z) estimations. It is written for the IDL environment and can be applied to traditional data sets consisting of photometric band magnitudes, or alternatively to data sets with additional galaxy parameters (such as shape information) to investigate potential correlations between the extra galaxy parameters and redshift.

[ascl:1608.019]
NEBULAR: Spectrum synthesis for mixed hydrogen-helium gas in ionization equilibrium

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).

[ascl:1608.018]
LORENE: Spectral methods differential equations solver

LORENE (Langage Objet pour la RElativité NumériquE) solves various problems arising in numerical relativity, and more generally in computational astrophysics. It is a set of C++ classes and provides tools to solve partial differential equations by means of multi-domain spectral methods. LORENE classes implement basic structures such as arrays and matrices, but also abstract mathematical objects, such as tensors, and astrophysical objects, such as stars and black holes.

[ascl:1608.017]
21CMMC: Parallelized Monte Carlo Markov Chain analysis tool for the epoch of reionization (EoR)

21CMMC is an efficient Python sampler of the semi-numerical reionization simulation code 21cmFAST (ascl:1102.023). It can recover constraints on astrophysical parameters from current or future 21 cm EoR experiments, accommodating a variety of EoR models, as well as priors on individual model parameters and the reionization history. By studying the resulting impact on the EoR astrophysical constraints, 21CMMC can be used to optimize foreground cleaning algorithms; interferometer designs; observing strategies; alternate statistics characterizing the 21cm signal; and synergies with other observational programs.

[ascl:1608.016]
NICIL: Non-Ideal magnetohydrodynamics Coefficients and Ionisation Library

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.

[ascl:1608.015]
2DFFT: Measuring Galactic Spiral Arm Pitch Angle

Davis, Benjamin L.; Berrier, Joel C.; Shields, Douglas W.; Kennefick, Julia; Kennefick, Daniel; Seigar, Marc S.; Lacy, Claud H. S.; Puerari, Ivânio

2DFFT utilizes two-dimensional fast Fourier transformations of images of spiral galaxies to isolate and measure the pitch angles of their spiral arms; this provides a quantitative way to measure this morphological feature and allows comparison of spiral galaxy pitch angle to other galactic parameters and test spiral arm genesis theories. 2DFFT requires fourn.c from *Numerical Recipes in C* (Press et al. 1989).

[ascl:1608.014]
gevolution: General Relativity Cosmological N-body code for evolution of large scale structures

The N-body code *gevolution* complies with general relativity principles at every step; it calculates all six metric degrees of freedom in Poisson gauge. N-body particles are evolved by solving the geodesic equation written in terms of a canonical momentum to remain valid for relativistic particles. *gevolution* can be extended to include different kinds of dark energy or modified gravity models, going beyond the usually adopted quasi-static approximation. A weak field expansion is the central element of *gevolution*; this permits the code to treat settings in which no strong gravitational fields appear, including arbitrary scenarios with relativistic sources as long as gravitational fields are not very strong. The framework is well suited for cosmology, but may also be useful for astrophysical applications with moderate gravitational fields where a Newtonian treatment is insufficient.

[ascl:1608.013]
DOLPHOT: Stellar photometry

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.

[submitted]
ExoPlanet

ExoPlanet provides a graphical interface for the construction, evaluation and application of a machine learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, ExoPlanet couples fast and well tested algorithms, a UI designed over the PyQt framework, and graphs rendered using Matplotlib. This serves to provide the user with a rich interface, rapid analytics and interactive visuals.

ExoPlanet is designed to have a minimal learning curve to allow researchers to focus more on the applicative aspect of machine learning algorithms rather than their implementation details and supports both methods of learning, providing algorithms for unsupervised and supervised training, which may be done with continuous or discrete labels. The parameters of each algorithms can be adjusted to ensure the best fit for the data. Training data is read from a CSV file, and after training is complete, ExoPlanet automates the building of the visual representations for the trained model. Once training and evaluation yield satisfactory results, the model may be used to make data based predictions on a new data set.

[ascl:1608.012]
OBERON: OBliquity and Energy balance Run on N-body systems

OBERON (OBliquity and Energy balance Run on N-body systems) models the climate of Earthlike planets under the effects of an arbitrary number and arrangement of other bodies, such as stars, planets and moons. The code, written in C++, simultaneously computes N body motions using a 4th order Hermite integrator, simulates climates using a 1D latitudinal energy balance model, and evolves the orbital spin of bodies using the equations of Laskar (1986a,b).

[ascl:1608.011]
PROFFIT: Analysis of X-ray surface-brightness profiles

PROFFIT analyzes X-ray surface-brightness profiles for data from any X-ray instrument. It can extract surface-brightness profiles in circular or elliptical annuli, using constant or logarithmic bin size, from the image centroid, the surface-brightness peak, or any user-given center, and provides surface-brightness profiles in any circular or elliptical sectors. It offers background map support to extract background profiles, can excise areas using SAO DS9-compatible (ascl:0003.002) region files to exclude point sources, provides fitting with a number of built-in models, including the popular beta model, double beta, cusp beta, power law, and projected broken power law, uses chi-squared or C statistic, and can fit on the surface-brightness or counts data. It has a command-line interface similar to HEASOFT’s XSPEC (ascl:9910.005) package, provides interactive help with a description of all the commands, and results can be saved in FITS, ROOT or TXT format.

[ascl:1608.010]
pvextractor: Position-Velocity Diagram Extractor

Given a path defined in sky coordinates and a spectral cube, pvextractor extracts a slice of the cube along that path and along the spectral axis to produce a position-velocity or position-frequency slice. The path can be defined programmatically in pixel or world coordinates, and can also be drawn interactively using a simple GUI. Pvextractor is the main function, but also includes a few utilities related to header trimming and parsing.

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

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

[ascl:1608.008]
Cuba: Multidimensional numerical integration library

The Cuba library offers four independent routines for multidimensional numerical integration: Vegas, Suave, Divonne, and Cuhre. The four algorithms work by very different methods, and can integrate vector integrands and have very similar Fortran, C/C++, and Mathematica interfaces. Their invocation is very similar, making it easy to cross-check by substituting one method by another. For further safeguarding, the output is supplemented by a chi-square probability which quantifies the reliability of the error estimate.

[ascl:1608.007]
BASE-9: Bayesian Analysis for Stellar Evolution with nine variables

Robinson, Elliot; von Hippel, Ted; Stein, Nathan; Stenning, David; Wagner-Kaiser, Rachel; Si, Shijing; van Dyk, David

The BASE-9 (Bayesian Analysis for Stellar Evolution with nine variables) software suite recovers star cluster and stellar parameters from photometry and is useful for analyzing single-age, single-metallicity star clusters, binaries, or single stars, and for simulating such systems. BASE-9 uses a Markov chain Monte Carlo (MCMC) technique along with brute force numerical integration to estimate the posterior probability distribution for the age, metallicity, helium abundance, distance modulus, line-of-sight absorption, and parameters of the initial-final mass relation (IFMR) for a cluster, and for the primary mass, secondary mass (if a binary), and cluster probability for every potential cluster member. The MCMC technique is used for the cluster quantities (the first six items listed above) and numerical integration is used for the stellar quantities (the last three items in the above list).

[ascl:1608.006]
Gemini IRAF: Data reduction software for the Gemini telescopes

The Gemini IRAF package processes observational data obtained with the Gemini telescopes. It is an external package layered upon IRAF and supports data from numerous instruments, including FLAMINGOS-2, GMOS-N, GMOS-S, GNIRS, GSAOI, NIFS, and NIRI. The Gemini IRAF package is organized into sub-packages; it contains a generic tools package, "gemtools", along with instrument-specific packages. The raw data from the Gemini facility instruments are stored as Multi-Extension FITS (MEF) files. Therefore, all the tasks in the Gemini IRAF package, intended for processing data from the Gemini facility instruments, are capable of handling MEF files.

[ascl:1608.005]
AstroVis: Visualizing astronomical data cubes

AstroVis enables rapid visualization of large data files on platforms supporting the OpenGL rendering library. Radio astronomical observations are typically three dimensional and stored as data cubes. AstroVis implements a scalable approach to accessing these files using three components: a File Access Component (FAC) that reduces the impact of reading time, which speeds up access to the data; the Image Processing Component (IPC), which breaks up the data cube into smaller pieces that can be processed locally and gives a representation of the whole file; and Data Visualization, which implements an approach of Overview + Detail to reduces the dimensions of the data being worked with and the amount of memory required to store it. The result is a 3D display paired with a 2D detail display that contains a small subsection of the original file in full resolution without reducing the data in any way.

[ascl:1608.004]
BART: Bayesian Atmospheric Radiative Transfer fitting code

Cubillos, Patricio; Blecic, Jasmina; Harrington, Joseph; Rojo, Patricio; Lust, Nate; Bowman, Oliver; Stemm, Madison; Foster, Andrew; Loredo, Thomas J.; Fortney, Jonathan; Madhusudhan, Nikku

BART implements a Bayesian, Monte Carlo-driven, radiative-transfer scheme for extracting parameters from spectra of planetary atmospheres. BART combines a thermochemical-equilibrium code, a one-dimensional line-by-line radiative-transfer code, and the Multi-core Markov-chain Monte Carlo statistical module to constrain the atmospheric temperature and chemical-abundance profiles of exoplanets.

[ascl:1608.003]
appaloosa: Python-based flare finding code for Kepler light curves

The appaloosa suite automates flare-finding in every Kepler light curves. It builds quiescent light curve models that include long- and short-cadence data through iterative de-trending and includes completeness estimates via artificial flare injection and recovery tests.

[ascl:1608.002]
pyXSIM: Synthetic X-ray observations generator

pyXSIM simulates X-ray observations from astrophysical sources. X-rays probe the high-energy universe, from hot galaxy clusters to compact objects such as neutron stars and black holes and many interesting sources in between. pyXSIM generates synthetic X-ray observations of these sources from a wide variety of models, whether from grid-based simulation codes such as FLASH (ascl:1010.082), Enzo (ascl:1010.072), and Athena (ascl:1010.014), to particle-based codes such as Gadget (ascl:0003.001) and AREPO, and even from datasets that have been created “by hand”, such as from NumPy arrays. pyXSIM can also manipulate the synthetic observations it produces in various ways and export the simulated X-ray events to other software packages to simulate the end products of specific X-ray observatories. pyXSIM is an implementation of the PHOX (ascl:1112.004) algorithm and was initially the photon_simulator analysis module in yt (ascl:1011.022); it is dependent on yt.

[ascl:1608.001]
Stingray: Spectral-timing software

Stingray is a spectral-timing software package for astrophysical X-ray (and more) data. The package merges existing efforts for a (spectral-)timing package in Python and is composed of a library of time series methods (including power spectra, cross spectra, covariance spectra, and lags); scripts to load FITS data files from different missions; a simulator of light curves and event lists that includes different kinds of variability and more complicated phenomena based on the impulse response of given physical events (e.g. reverberation); and a GUI to ease the learning curve for new users.

[ascl:1607.020]
SEEK: Signal Extraction and Emission Kartographer

Akeret, Joel; Seehars, Sebastian; Chang, Chihway; Monstein, Christian; Amara, Adam; Refregier, Alexandre

SEEK (Signal Extraction and Emission Kartographer) processes time-ordered-data from single dish radio telescopes or from the simulation pipline HIDE (ascl:1607.019), removes artifacts from Radio Frequency Interference (RFI), automatically applies flux calibration, and recovers the astronomical radio signal. With its companion code HIDE (ascl:1607.019), it provides end-to-end simulation and processing of radio survey data.

[ascl:1607.019]
HIDE: HI Data Emulator

Akeret, Joel; Seehars, Sebastian; Chang, Chihway; Monstein, Christian; Amara, Adam; Refregier, Alexandre

HIDE (HI Data Emulator) forward-models the process of collecting astronomical radio signals in a single dish radio telescope instrument and outputs pixel-level time-ordered-data. Written in Python, HIDE models the noise and RFI modeling of the data and with its companion code SEEK (ascl:1607.020) provides end-to-end simulation and processing of radio survey data.

[ascl:1607.018]
LZIFU: IDL emission line fitting pipeline for integral field spectroscopy data

LZIFU (LaZy-IFU) is an emission line fitting pipeline for integral field spectroscopy (IFS) data. Written in IDL, the pipeline turns IFS data to 2D emission line flux and kinematic maps for further analysis. LZIFU has been applied and tested extensively to various IFS data, including the SAMI Galaxy Survey, the Wide-Field Spectrograph (WiFeS), the CALIFA survey, the S7 survey and the MUSE instrument on the VLT.

[ascl:1607.017]
BoxRemap: Volume and local structure preserving mapping of periodic boxes

BoxRemap remaps the cubical domain of a cosmological simulation into simple non-cubical shapes. It can be used for on-the-fly remappings of the simulation geometry and is volume-preserving; remapped geometry has the same volume V = L3 as the original simulation box. The remappings are structure-preserving (local neighboring structures are mapped to neighboring places) and one-to-one, with every particle/halo/galaxy/etc. appearing once and only once in the remapped volume.

[ascl:1607.016]
astLib: Tools for research astronomers

astLib is a set of Python modules for performing astronomical plots, some statistics, common calculations, coordinate conversions, and manipulating FITS images with World Coordinate System (WCS) information through PyWCSTools, a simple wrapping of WCSTools (ascl:1109.015).

[ascl:1607.015]
RT1D: 1D code for Rayleigh-Taylor instability

The parallel one-dimensional moving-mesh hydrodynamics code RT1D reproduces the multidimensional dynamics from Rayleigh-Taylor instability in supernova remnants.

[ascl:1607.014]
SOPIE: Sequential Off-Pulse Interval Estimation

SOPIE (Sequential Off-Pulse Interval Estimation) provides functions to non-parametrically estimate the off-pulse interval of a source function originating from a pulsar. The technique is based on a sequential application of P-values obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov, Cramér-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.

[ascl:1607.013]
Kālī: Time series data modeler

The fully parallelized and vectorized software package Kālī models time series data using various stochastic processes such as continuous-time ARMA (C-ARMA) processes and uses Bayesian Markov Chain Monte-Carlo (MCMC) for inferencing a stochastic light curve. Kālī is written in c++ with Python language bindings for ease of use. Kālī is named jointly after the Hindu goddess of time, change, and power and also as an acronym for KArma LIbrary.

[ascl:1607.012]
ZASPE: Zonal Atmospheric Stellar Parameters Estimator

ZASPE (Zonal Atmospheric Stellar Parameters Estimator) computes the atmospheric stellar parameters (Teff, log(g), [Fe/H] and vsin(i)) from echelle spectra via least squares minimization with a pre-computed library of synthetic spectra. The minimization is performed only in the most sensitive spectral zones to changes in the atmospheric parameters. The uncertainities and covariances computed by ZASPE assume that the principal source of error is the systematic missmatch between the observed spectrum and the sythetic one that produces the best fit. ZASPE requires a grid of synthetic spectra and can use any pre-computed library minor modifications.

[ascl:1607.011]
HfS: Hyperfine Structure fitting tool

HfS fits the hyperfine structure of spectral lines, with multiple velocity components. The HfS_nh3 procedures included in HfS fit simultaneously the hyperfine structure of the NH_{3} (J,K)= (1,1) and (2,2) inversion transitions, and perform a standard analysis to derive the NH_{3} column density, rotational temperature T_{rot}, and kinetic temperature Tk. HfS uses a Monte Carlo approach for fitting the line parameters, with special attention to the derivation of the parameter uncertainties. HfS includes procedures that make use of parallel computing for fitting spectra from a data cube.

[ascl:1607.009]
PICsar: Particle in cell pulsar magnetosphere simulator

PICsar simulates the magnetosphere of an aligned axisymmetric pulsar and can be used to simulate other arbitrary electromagnetics problems in axisymmetry. Written in Fortran, this special relativistic, electromagnetic, charge conservative particle in cell code features stretchable body-fitted coordinates that follow the surface of a sphere, simplifying the application of boundary conditions in the case of the aligned pulsar; a radiation absorbing outer boundary, which allows a steady state to be set up dynamically and maintained indefinitely from transient initial conditions; and algorithms for injection of charged particles into the simulation domain. PICsar is parallelized using MPI and has been used on research problems with ~1000 CPUs.

[ascl:1607.010]
K2PS: K2 Planet search

K2PS is an Oxford K2 planet search pipeline. Written in Python, it searches for transit-like signals from the k2sc-detrended light curves.

[ascl:1607.008]
BLS: Box-fitting Least Squares

BLS (Box-fitting Least Squares) is a box-fitting algorithm that analyzes stellar photometric time series to search for periodic transits of extrasolar planets. It searches for signals characterized by a periodic alternation between two discrete levels, with much less time spent at the lower level.

[ascl:1607.007]
JUDE: An Utraviolet Imaging Telescope pipeline

JUDE (Jayant's UVIT Data Explorer) converts the Level 1 data (FITS binary table) from the Ultraviolet Imaging Telescope (UVIT) on ASTROSAT into three output files: a photon event list as a function of frame number (FITS binary table); a FITS image file with two extensions; and a PNG file created from the FITS image file with an automated scaling.

[ascl:1607.006]
Cholla: 3D GPU-based hydrodynamics code for astrophysical simulation

Cholla (Computational Hydrodynamics On ParaLLel Architectures) models the Euler equations on a static mesh and evolves the fluid properties of thousands of cells simultaneously using GPUs. It can update over ten million cells per GPU-second while using an exact Riemann solver and PPM reconstruction, allowing computation of astrophysical simulations with physically interesting grid resolutions (>256^3) on a single device; calculations can be extended onto multiple devices with nearly ideal scaling beyond 64 GPUs.

[ascl:1607.005]
Planetary3br: Three massive body resonance calculator

Given two planets P1 and P2 with arbitrary orbits, planetary3br calculates all possible semimajor axes that a third planet P0 can have in order for the system to be in a three body resonance; these are identified by the combination k0*P0 + k1*P1 + k2*P2. P1 and P2 are assumed to be not in an exact two-body resonance. The program also calculates three "strengths" of the resonance, one for each planet, which are only indicators of the dynamical relevance of the resonance on each planet. Sample input data are available along with the Fortran77 source code.

[ascl:1607.004]
Atlas3bgeneral: Three-body resonance calculator

For a massless test particle and given a planetary system, atlas3bgeneral calculates all three body resonances in a given range of semimajor axes with all the planets taken by pairs. Planets are assumed in fixed circular and coplanar orbits and the test particle with arbitrary orbit. A sample input data file to calculate the three-body resonances is available for use with the Fortran77 source code.

[ascl:1607.003]
Atlas2bgeneral: Two-body resonance calculator

For a massless test particle and given a planetary system, Atlas2bgeneral calculates all resonances in a given range of semimajor axes with all the planets taken one by one. Planets are assumed in fixed circular and coplanar orbits and the test particle with arbitrary orbit. A sample input data file to calculate the two-body resonances is available for use with the Fortran77 source code.

[ascl:1607.002]
DICE: Disk Initial Conditions Environment

DICE models initial conditions of idealized galaxies to study their secular evolution or their more complex interactions such as mergers or compact groups using N-Body/hydro codes. The code can set up a large number of components modeling distinct parts of the galaxy, and creates 3D distributions of particles using a N-try MCMC algorithm which does not require a prior knowledge of the distribution function. The gravitational potential is then computed on a multi-level Cartesian mesh by solving the Poisson equation in the Fourier space. Finally, the dynamical equilibrium of each component is computed by integrating the Jeans equations for each particles. Several galaxies can be generated in a row and be placed on Keplerian orbits to model interactions. DICE writes the initial conditions in the Gadget1 or Gadget2 (ascl:0003.001) format and is fully compatible with Ramses (ascl:1011.007).

[ascl:1607.001]
AGNfitter: SED-fitting code for AGN and galaxies from a MCMC approach

AGNfitter is a fully Bayesian MCMC method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGN) and galaxies from the sub-mm to the UV; it enables robust disentanglement of the physical processes responsible for the emission of sources. Written in Python, AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGN with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star formation rates.

[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:1606.014]
Lmfit: Non-Linear Least-Square Minimization and Curve-Fitting for Python

Newville, Matthew; Stensitzki, Till; Allen, Daniel B; Rawlik, Michal; Ingargiola, Antonino; Nelson, Andrew

Lmfit provides a high-level interface to non-linear optimization and curve fitting problems for Python. Lmfit builds on and extends many of the optimization algorithm of scipy.optimize, especially the Levenberg-Marquardt method from optimize.leastsq. Its enhancements to optimization and data fitting problems include using Parameter objects instead of plain floats as variables, the ability to easily change fitting algorithms, and improved estimation of confidence intervals and curve-fitting with the Model class. Lmfit includes many pre-built models for common lineshapes.

[ascl:1606.013]
Pulse Portraiture: Pulsar timing

Pulse Portraiture is a wideband pulsar timing code written in python. It uses an extension of the FFTFIT algorithm (Taylor 1992) to simultaneously measure a phase (TOA) and dispersion measure (DM). The code includes a Gaussian-component-based portrait modeling routine. The code uses the python interface to the pulsar data analysis package PSRCHIVE (ascl:1105.014) and also requires the non-linear least-squares minimization package lmfit (ascl:1606.014).

[ascl:1606.012]
KMDWARFPARAM: Parameters estimator for K and M dwarf stars

KMDWARFPARAM estimates the physical parameters of a star with mass M < 0.8 M_sun given one or more observational constraints. The code runs a Markov-Chain Monte Carlo procedure to estimate the parameter values and their uncertainties.

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

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

[ascl:1606.010]
SimpLens: Interactive gravitational lensing simulator

SimpLens illustrates some of the theoretical ideas important in gravitational lensing in an interactive way. After setting parameters for elliptical mass distribution and external mass, SimpLens displays the mass profile and source position, the lens potential and image locations, and indicate the image magnifications and contours of virtual light-travel time. A lens profile can be made shallower or steeper with little change in the image positions and with only total magnification affected.

[ascl:1606.009]
Companion-Finder: Planets and binary companions in time series spectra

Companion-Finder looks for planets and binary companions in time series spectra by searching for the spectral lines of stellar companions to other stars observed with high-precision radial-velocity surveys.

[ascl:1606.008]
s2: Object oriented wrapper for functions on the sphere

The s2 package can represent any arbitrary function defined on the sphere. Both real space map and harmonic space spherical harmonic representations are supported. Basic sky representations have been extended to simulate full sky noise distributions and Gaussian cosmic microwave background realisations. Support for the representation and convolution of beams is also provided. The code requires HEALPix (ascl:1107.018) and CFITSIO (ascl:1010.001).

[ascl:1606.007]
COMB: Compact embedded object simulations

COMB supports the simulation on the sphere of compact objects embedded in a stochastic background process of specified power spectrum. Support is provided to add additional white noise and convolve with beam functions. Functionality to support functions defined on the sphere is provided by the S2 code (ascl:1606.008); HEALPix (ascl:1107.018) and CFITSIO (ascl:1010.001) are also required.

[ascl:1606.006]
uvmcmcfit: Parametric models to interferometric data fitter

Uvmcmcfit fits parametric models to interferometric data. It is ideally suited to extract the maximum amount of information from marginally resolved observations with interferometers like the Atacama Large Millimeter Array (ALMA), Submillimeter Array (SMA), and Plateau de Bure Interferometer (PdBI). uvmcmcfit uses emcee (ascl:1303.002) to do Markov Chain Monte Carlo (MCMC) and can measure the goodness of fit from visibilities rather than deconvolved images, an advantage when there is strong gravitational lensing and in other situations. uvmcmcfit includes a pure-Python adaptation of Miriad’s (ascl:1106.007) uvmodel task to generate simulated visibilities given observed visibilities and a model image and a simple ray-tracing routine that allows it to account for both strongly lensed systems (where multiple images of the lensed galaxy are detected) and weakly lensed systems (where only a single image of the lensed galaxy is detected).

[ascl:1606.005]
PyMultiNest: Python interface for MultiNest

PyMultiNest provides programmatic access to MultiNest (ascl:1109.006) and PyCuba, integration existing Python code (numpy, scipy), and enables writing Prior & LogLikelihood functions in Python. PyMultiNest can plot and visualize MultiNest's progress and allows easy plotting, visualization and summarization of MultiNest results. The plotting can be run on existing MultiNest output, and when not using PyMultiNest for running MultiNest.

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