The Astrophysics Source Code Library (ASCL) is a free online registry for source codes of interest to astronomers and astrophysicists and lists codes that have been used in research that has appeared in, or been submitted to, peer-reviewed publications. The ASCL is indexed by the SAO/NASA Astrophysics Data System (ADS) and is citable by using the unique ascl ID assigned to each code. The ascl ID can be used to link to the code entry by prefacing the number with ascl.net (*i.e.*, ascl.net/1201.001).

[submitted]
CoSMoMVPA MultiVariate Pattern Analysis in Matlab / GNU Octave

CoSMoMVPA provides univariate and multivariate analyses for large datasets in the Matlab / GNU Octave language. It supports a uniform data structure with support for cross validation, classification, similarity measures, data-driven information mapping, and chance capitalization correction.

[submitted]
filltex: Automatic queries to ADS and INSPIRE databases to fill LaTex bibliography

filltex speeds up the latex scientific writing workflow by automatically filling reference lists with records from the ADS and INSPIRE databases. ADS and INSPIRE are the most common databases used among the astronomy and theoretical physics communities, respectively. filltex automatically looks for all citation labels present in a tex document and, by means of web-scraping, downloads all the required citation records from either of the two databases. All required actions (compile the tex file, fill the bibliography, compile the bibliography, compile the tex file again) are automated in a single command. We also provide an integration of filltex for the macOS latex editor TexShop.

[submitted]
CCFpams: Atmospheric Stellar Parameters from Cross-Correlation Functions

CCFpams is a novel approach that allows the measurement of stellar temperature, metallicity and gravity within a few seconds and in a completely automated fashion. Rather than performing comparisons with spectral libraries, our technique is based on the determination of several cross-correlation functions (CCFs) obtained by including spectral features with different sensitivity to the photospheric parameters. We use literature stellar parameters of high signal-to-noise (SNR), high-resolution HARPS spectra of FGK Main Sequence stars to calibrate the stellar parameters as a function of CCF areas. For FGK stars we achieve a precision of 50K in temperature, 0.09 dex in gravity and 0.035 dex in metallicity at SNR=50 while the precision for observation with SNR>100 and the overall accuracy are constrained by the literature values used to calibrate the CCFs.

[submitted]
pyaneti

Pyaneti is a multi-planet Radial Velocity and Transit fit software. The code uses Marcov chain Monte Carlo (MCMC) methods with a Bayesian approach. It uses a parallelized ensemble sampler algorithm in Fortran which makes the code fast. Pyaneti is a free and fast code with the robustness of Fortran and the versatility of Python.

[submitted]
Deep Convolutional Mixture Density Network (DCMDN): Photometric redshift estimation via deep learning

The need to analyze the available large synoptic multi-band surveys drives the development of new data-analysis methods. Photometric redshift estimation is one field of application where such new methods improved the results, substantially. Up to now, the vast majority of applied redshift estimation methods utilize photometric features.

We propose a method to derive probabilistic photometric redshift directly from multi-band imaging data, rendering pre-classification of objects and feature extraction obsolete.

A modified version of a deep convolutional network is combined with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. We adopt a feature based random forest and a plain mixture density network to compare performances on experiments with data from SDSS(DR9).

We show that the proposed method is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars. Thereby the prediction performance is better than both presented reference methods and is comparable to results from the literature.

The presented method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, e.g. estimating metallicity or star formation rate of galaxies. This kind of methodology is tremendously important for the next generation of surveys.

[ascl:1707.001]
HRM: HII Region Models

HII Region Models fits HII region models to observed radio recombination line and radio continuum data. The algorithm includes the calculations of departure coefficients to correct for non-LTE effects. HII Region Models has been used to model star formation in the nucleus of IC 342.

[ascl:1706.012]
KeplerSolver: Kepler equation solver

KeplerSolver solves Kepler's equation for arbitrary epoch and eccentricity, using continued fractions. It is written in C and its speed is nearly the same as the SWIFT routines, while achieving machine precision. It comes with a test program to demonstrate usage.

[ascl:1706.011]
PyPulse: PSRFITS handler

PyPulse handles PSRFITS files and performs subsequent analyses on pulse profiles.

[ascl:1706.010]
EXOSIMS: Exoplanet Open-Source Imaging Mission Simulator

EXOSIMS generates and analyzes end-to-end simulations of space-based exoplanet imaging missions. The software is built up of interconnecting modules describing different aspects of the mission, including the observatory, optical system, and scheduler (encoding mission rules) as well as the physical universe, including the assumed distribution of exoplanets and their physical and orbital properties. Each module has a prototype implementation that is inherited by specific implementations for different missions concepts, allowing for the simulation of widely variable missions.

[submitted]
SASRST: Semi-Analytic Solutions for 1-D Radiative Shock Tubes

This small collection of Python scripts attempts to reproduce the semi-analytical one-dimensional equilibrium and non-equilibrium radiative shock tube solutions of Lowrie & Rauenzahn (2007, Shock Waves, 16, 445-453) and Lowrie & Edwards (2008, Shock Waves, 18, 129-143), respectively. The included code not only calculates the solution for a given set of input parameters, but also plots the results (using Matplotlib). This software was written to provide validation for numerical radiative shock tube solutions produced by a radiation hydrodynamics code, as exemplified in Ramsey & Dullemond (2015, A&A, 574, A81).