muLAn is a Python modeling software developed to analyze and fit light curves of gravitational microlensing events. The software has been designed to be easy to use even for the newcomer in microlensing, thanks to external, synthetic and self-explanatory setup files containing all important commands and option settings. The user may choose to launch the code through command line instructions, or to import muLAn within another Python project like any standard Python package. It also comes with many useful routines (export publication-quality figures, data formatting and cleaning) and state-of-the-art statistical tools. The result of the modeling can be interpreted using an interactive html page which contains all information about the light curve model, caustics, source trajectory, best-fit parameters and chi-square. Parameters uncertainties and statistical properties (such as multi-modal features of the posterior density) can be assessed from correlation plots. The code includes all classical microlensing models (single and binary microlenses, ground- and space-based parallax effects, orbital motion, finite-source effects, limb-darkening, etc.) which can be combined into several time intervals of the analyzed light curve. Minimization methods include an Affine-Invariant Ensemble Sampler to generate a multivariate proposal function while running several Markov Chain Monte Carlo (MCMC) chains, for the set of parameters which is chosen to be fit; non-fitting parameters can be either kept fixed or set on a grid defined by the user. Furthermore, the software offers a model-free option to align all data sets together and allows to inspect the light curve before any modeling work. The code is modular: users can add their own model's computation or minimization routines by directly adding their Python files without modifying the main code. This flexibility aims to offer a valuable framework to develop automated open-source microlensing modeling codes.

VISIBLE applies approximated matched filters to interferometric data, allowing line detection directly in visibility space. The filter can be created from a FITS image or RADMC3D output image, and the weak line data can be a CASA MS or uvfits file. The filter response spectrum can be output either to a .npy file or returned back to the user for scripting.

Verne calculates the Earth-stopping effect for super-heavy Dark Matter (DM). The code allows you to calculate the speed distribution (and DM signal rate) at an arbitrary detector location on the Earth. The calculation takes into account the full anisotropic DM velocity distribution and the full velocity dependence of the DM-nucleus cross section. Results can be obtained for any DM mass and cross section, though the results are most reliable for very heavy DM particles.

The Opik method gives the mean probability of collision of a small body with a given planet. It is a statistical value valid for an orbit with given (a,e,i) and undefined argument of perihelion. In some cases, the planet can eject the small body from the solar system; in these cases, the program estimates the mean time for the ejection. The Opik method does not take into account other perturbers than the planet considered, so it only provides an idea of the timescales involved.

Above a critical dark matter-nucleus scattering cross section any terrestrial direct detection experiment loses sensitivity to dark matter, since the Earth crust, atmosphere, and potential shielding layers start to block off the dark matter particles. This critical cross section is commonly determined by describing the average energy loss of the dark matter particles analytically. However, this treatment overestimates the stopping power of the Earth crust. Therefore the obtained bounds should be considered as conservative.
This tool allows to determine the critical cross-section for strongly interacting DM for various direct detection experiments systematically and precisely using Monte Carlo simulations of DM trajectories inside the Earth crust/atmosphere/any kind of shielding.

The Automated Radio Telescope Image Processing Pipeline (ARTIP) automates the entire process of flagging, calibrating, and imaging for radio-interferometric data. ARTIP starts with raw data, i.e. a measurement set and goes through multiple stages, such as flux calibration, bandpass calibration, phase calibration, and imaging to generate continuum and spectral line images. Each stage can also be run independently. The pipeline provides continuous feedback to the user through various messages, charts and logs. It is written using standard python libraries and the CASA package. The pipeline can deal with datasets with multiple spectral windows and also multiple target sources which may have arbitrary combinations of flux/bandpass/phase calibrators.

CMacIonize simulates the self-consistent evolution of HII regions surrounding young O and B stars, or other sources of ionizing radiation. The code combines a Monte Carlo photoionization algorithm that uses a complex mix of hydrogen, helium and several coolants in order to self-consistently solve for the ionization and temperature balance at any given time, with a standard first order hydrodynamics scheme. The code can be run as a post-processing tool to get the line emission from an existing simulation snapshot, but can also be used to run full radiation hydrodynamical simulations. Both the radiation transfer and the hydrodynamics are implemented in a general way that is independent of the grid structure that is used to discretize the system, allowing it to be run both as a standard fixed grid code and also as a moving-mesh code.

venice reads a mask file (DS9 or fits type) and a catalogue of objects (ascii or fits type) to create a pixelized mask, find objects inside/outside a mask, or generate a random catalogue of objects inside/outside a mask. The program reads the mask file and checks if a point, giving its coordinates, is inside or outside the mask, i.e. inside or outside at least one polygon of the mask.

This code is meant to emulate a fortran program written by B. Peterson for use with reverberation mapping.
The code cross correlates two light curves that are unevenly sampled using linear interpolation and measures the peak and centroid of the cross-correlation function. In addition, it is possible to run Monteo Carlo iterations using flux randomization and random subset selection (RSS) to produce cross-correlation centroid distributions to estimate the uncertainties in the cross correlation results. The ideas behind the methodology that this code implements are described in detail by Peterson et al. (1998): http://arxiv.org/abs/astro-ph/9802103

FAC calculates various atomic radiative and collisional processes, including radiative transition rates, collisional excitation and ionization by electron impact, energy levels, photoionization, and autoionization, and their inverse processes radiative recombination and dielectronic capture. The package also includes a collisional radiative model to construct synthetic spectra for plasmas under different physical conditions.